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EX0-113 - TMap Next Test Engineer - Dump Information

Vendor : Exin
Exam Code : EX0-113
Exam Name : TMap Next Test Engineer
Questions and Answers : 60 Q & A
Updated On : April 19, 2019
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EX0-113 TMap Next Test Engineer

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EX0-113 exam Dumps Source : TMap Next Test Engineer

Test Code : EX0-113
Test Name : TMap Next Test Engineer
Vendor Name : Exin
Q&A : 60 Real Questions

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TMap subsequent® trying out Clouds: First Cloud e-publication to be launched by using Sogeti | killexams.com Real Questions and Pass4sure dumps

ti, kesä 21, 2011 08:30 CET

a brand new book in Sogeti’s TMap® series; TMap subsequent® testing Clouds is launched.

twenty first June, Paris/Helsinki: Sogeti, a number one issuer of knowledgeable technology capabilities, has launched a new booklet in its TMap® collection, at IBM Innovate 2011 in Orlando u . s . a .. TMap next® checking out Clouds is a complete overview of how checking out clouds can carry cost for the early adopter. The writer, Ewald Roodenrijs, describes the cloud company mannequin for checking out, the evolution from assistance know-how (IT) to the idea of enterprise expertise (BT), and the steps that need to be taken in implementing cloud projects.

Cloud computing is at the moment considered as one of the crucial top three IT technologies for the ahead pondering CIO. youngsters the cloud is still at an early stage of building, we are beginning to see mighty increase in cloud-based computing, which is outstripping even probably the most positive predictions. it's increasingly clear that the cloud model will supplement, if not wholly substitute, mainframe and customer/server installations in the years to come. here is based on a compelling cost proposition: pace to market, agility to deliver forward or retire carrier, and the opportunity to move expenditure from Capital Expenditure (CapEx) to Operational Expenditure (OpEx).

As cloud provider adoption becomes ever greater wide-ranging, a brand new world infrastructure is being created that creates gigantic new opportunities for software great assurance and trying out. This new book TMap next® testing Clouds describes in certain two features of the cloud: the business model and the cloud platform, each of which need to be demonstrated. This carries a few features; testing the infrastructure, cloud-enabled applications, and the skill to have fast deployable look at various infrastructure.

Ewald Roodenrijs comments: “This all has an have an effect on on the way we do testing sooner or later. trying out functions on the cloud is an identical as trying out applications on a normal infrastructure. best what's confirmed is different”.

TMap subsequent® testing Clouds is a building from its TMap® predecessors. It outlines how structured checking out, the use of TMap®, will also be leveraged within a cloud atmosphere, it is not a step-via-step instruction manual, however stories in aspect the framework and magnitude of checking out on the cloud, testing cloud approach (in, on or inside the cloud) and the risks involved for corporations in view that migrating applications onto the cloud, corresponding to safety, information integrity, privateness concerns, records restoration and efficiency.

It also can even be read as a associate or comply with-on booklet to seize the Cloud – A supervisor’s book to Success with Cloud Computing[1], exploring in additional aspect the checking out points of the cloud.

TMap subsequent® checking out Clouds is purchasable as an e-booklet in each epub and pdf layout and can be downloaded without charge from the TMap trying out web site or the Sogeti.com web page. Sogeti is more and more producing e-books as an effective skill of well timed distribution as well as enabling the enterprise to reduce its carbon footprint. a published version is available by using Printing on Demand (POD) by way of on-line bookstores or www.tmapbooks.com.

in keeping with Nijs Blokland, international checking out neighborhood Lead and Head of Sogeti NL R&D; “Most of us are privy to the merits of cloud computing, however have much less knowing of the practicalities of correctly and securely implementing cloud functions and using the cloud as a cost-valuable supply of infrastructure and look at various tools. This book TMap subsequent® trying out Clouds gives early perception and counsel on these issues, and is additional proof of Sogeti’s inventive pondering in bringing the merits of both cloud and structured checking out together”.

considering that launching their utility testing as a carrier (STaaS) globally in 2008, Sogeti has been pioneering the use of cloud-primarily based options. As a right away construction of this, Sogeti Netherlands can be launching their first testing Cloud solution, a finished portal with the intention to give a full menu of functions, direct access to quick automatic pricing and the capacity to add trying out artifacts and belongings for cloud-based execution.

For the Dutch enterprise’s shoppers, here's an awful lot more than a entrance-conclusion web page. This portal will deliver full readability of the provider, along side a hard and fast turnaround time and value for each and every of the 22 services, including the advent of a master verify Plan, mannequin based trying out, device preference, efficiency trying out, verify Automation, net Accessibility and security checking out.

Notes to Editors

concerning the writer

Ewald Roodenrijs, writer of TMap next testing Clouds, is the world Lead of the Cloud trying out capabilities in the Sogeti neighborhood and a member of the testing R&D crew of Sogeti Netherlands. he's also the co-writer of the ebook TMap subsequent® – BDTM, contributor to seize the Cloud, a regular contributor to countrywide and overseas technical/skilled publications, a speaker at a lot of foreign conferences, including most these days at IBM Innovate June 2011, a eager blogger on the Cloud and trying out and enthusiastic trainer. he is currently working along with IBM Rational to additional develop this cloud offering and these days got the Capgemini-Sogeti trying out services Innovation Award (particular person) 2011 for his work on Cloud testing.

in regards to the publication

The e-book can also be ordered at foremost online books save, including Amazon, or at once from the writer by means of www.tmapbooks.com or by way of www.sogeti.com. a published copy can also be ordered at www.tmapbooks.com.

Introduction, Chapter 1: An introduction to TMap subsequent® trying out Clouds and Chapter 2 Framework and magnitude of testing; even within the cloud; of pastime to all readers.

Chapter 3: The business in cost of IT; the Cloud and Chapter 4 whatever as a service; cloud-enabled utility trying out as a service of interest to check managers, software managers and company managers and IT branch managers.

Chapter 5 ‘trying out Cloud approach; a movement to 3D’’, Chapter 6 ‘trying out the Cloud; in, on or with...’ and Chapter 7 ‘Cloud hazards; price trying out for…’ are exceptionally interesting for the goal businesses of test managers, verify coordinators, infrastructure testers and testers.

in regards to the Contributors

the following contributors helped within the creation of the content material of this publication from Sogeti, Capgemini and additionally IBM Rational: Andréas Prins, Mark Buenen, Nick Lloyd, Ramanathan Iyer, Alfonso López de Arenosa, Rob Baarda, Richard Ammerlaan, Erik Smit, Flavien Bouche, Kanchan Apt, Michiel Boreel, Pierre Bedard, Michiel Rigterink, Karl Snider, John Bloedjes, Dan Hannigan, Dirkjan Kaper, plus additional assist from Leo van der Aalst, Nicolas Claudon and Clare Argent.

For more suggestions please contact:

Ewald Roodenrijs, international Lead for Cloud trying out, SogetiTel: +31 (0)6 52 32 seventy seven 73Email: ewald.roodenrijs@sogeti.nl

Therese Sinter, group corporate Communications Director, Sogeti Tel: +46 70-361 forty six 21 electronic mail: therese.sinter@sogeti.se

About Sogeti

Sogeti is a leading provider of expert know-how features, focusing on application administration, Infrastructure management, excessive‐Tech Engineering and trying out. Working intently with its valued clientele, Sogeti allows them to leverage technological innovation and achieve maximum outcomes. Sogeti brings collectively more than 20,000 gurus in 15 international locations and is latest in over 100 locations in Europe, the USA and India. Sogeti is a wholly‐owned subsidiary of Cap Gemini S.A., listed on the Paris inventory alternate.

together, Sogeti and Capgemini have developed imaginative, business-driven exceptional assurance (QA) and checking out functions, combining optimal-in-breed testing methodologies (TMap® and TPI®) and the international birth mannequin, Rightshore®, to assist groups obtain their testing and QA dreams. Sogeti and Capgemini have created one of the most biggest committed testing practices on this planet, with over eight,200 verify specialists and a further 12,500 application specialists, principally via a standard core of excellence with testing consultants developed in India.

In Finland Sogeti is focused solely on presenting checking out services on the native market.

For extra guidance please consult with www.sogeti.com and www.sogeti.fi.

[1] Written by means of Erik van Ommeren, Martin van den Berg, Sogeti and published with the aid of Sogeti March 2010. trap the Cloud is a guide through the company and business architecture elements of Cloud Computing, including 11 cases in which main companies share insights won from their adventure with Cloud.

Yritysesittely Sogeti Finland OySogeti on erikoistunut korkealuokkaisten IT-palvelujen toimittamiseen Suomen markkinoille. Keskitämme Suomessa palvelumme ohjelmistojen testaukseen ja laadunvarmistukseen. Sogeti-konsernin pääkonttori on Pariisissa Ranskassa. Konsernilla on palveluksessaan yhteensä noin 20 000 konsulttia 15 maassa: Belgia, Espanja, Hollanti, Intia, Irlanti, Iso-Britannia, Luxemburg, Norja, Ranska, Saksa, Suomi, Sveitsi, Ruotsi, Tanska ja Yhdysvallat. Pohjoismaissa konsultteja on Suomessa, Ruotsissa, Tanskassa ja Norjassa yhteensä yli 1 one hundred. Sogeti-konsernin yhtiöt ovat Pariisin pörssissä listatun Cap Gemini S.A.:n kokonaan omistamia tytäryhtiöitä.

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Characterization of RNA in exosomes secreted by way of human breast cancer mobile strains the use of next-era sequencing | killexams.com Real Questions and Pass4sure dumps

Introduction

Exosomes are nanosized membrane vesicles secreted by way of multiple cellphone kinds (Raposo & Stoorvogel, 2013). The term “exosomes” turned into introduced in 1987 with the aid of Johnstone et al. (1987) to describe the “trash” vesicles launched by using differentiating reticulocytes to dispose unwanted membrane proteins that are common to reduce all through maturation of reticulocytes to erythrocytes. Later exosomes have been implicated in additional advanced capabilities. Raposo et al. (1996) have validated that exosomes derived from B lymphocytes activate T lymphocytes, suggesting a job for exosomes in antigen presentation in vivo. In early experiences, secreted microvesicles were named in response to their mobile origins, e.g., archaeosomes, argosomes, dexosomes, epididymosomes, prostasomes, oncosomes, and many others. (Raposo & Stoorvogel, 2013). greater currently it grew to be obvious that vesicles released by way of the identical cellphone category are heterogeneous and can be labeled into as a minimum three classes based on their mode of biogenesis: (1) exosomes (30–130 nm in diameter), which originate from multivesicular endosome (MVE); (2) microvesicles (a hundred–a thousand nm in diameter), that are shed from the plasma membrane; (3) apoptotic our bodies (1–four µm in diameter), that are launched from fragmented apoptotic cells during late stages of mobile dying (Raposo & Stoorvogel, 2013). numerous purification strategies including sequential centrifugation protocols were proposed to separate these vesicles for extra analysis. Biochemical and proteomic analyses confirmed that exosomes comprise specific protein set reflecting their intracellular website of formation. Exosomes from distinctive cell types comprise endosome-linked proteins, e.g., tetraspanins (CD9, CD63, CD81, CD82), annexins, Rab GTPases and flotillin. Exosomes are additionally enriched in proteins worried in MVE formation (Tsg101 and Alix), chaperones (Hsc73 and Hsc90) and cytoskeletal proteins (Raimondo et al., 2011). in addition, exosomes contain proteins which are particular to the cells from which they are derived (Choi et al., 2012; Sandvig & Llorente, 2012).

The analysis within the exosome container has exploded abruptly after the invention that these microvesicles transport a huge number of mRNA and miRNA (Valadi et al., 2007) and that exosomal mRNAs may be translated into proteins by recipient cells (Ratajczak et al., 2006; Valadi et al., 2007) and exosomal miRNAs are able to modulate gene expression in recipient cells (Mittelbrunn et al., 2011). apparently, certain mRNAs and miRNAs were identified as highly enriched in exosomes compared to that of the host cells indicating the existence of selective sorting mechanism controlling incorporation of RNA into exosomes (Skog et al., 2008; Valadi et al., 2007). up to now, we proven that exosomal RNAs share certain sequence motifs that can also doubtlessly characteristic as cis-acting aspects for focused on to exosomes (Batagov, Kuznetsov & Kurochkin, 2011).

Given the undeniable fact that exosomes lift complex organic tips along with proteins, lipids and RNAs, it isn't impressive to locate that they have got been implicated in plenty of physiological and pathological situations. Skokos et al. (2001) pronounced, for instance, that mast cells talk with different cells of the immune system by way of exosomes promoting mitogenic pastime in B and T lymphocytes. a few reports have verified the position of exosomes within the building of the apprehensive system, synaptic undertaking, neuronal regeneration, neuron-glia communique and insurance policy against harm (Lai & Breakefield, 2012). furthermore, exosomes may also be worried in the pathogenesis of cancer and degenerative ailments. The undeniable fact that tumor cells liberate a huge amount of exosomes changed into at the beginning proven in ovarian cancer sufferers (Taylor, Homesley & Doellgast, 1983). Exosomes have been shown to be secreted by using a number of tumor cells together with those derived from breast (King, Michael & Gleadle, 2012), colorectum (Silva et al., 2012), mind (Graner et al., 2009), ovarian (Escrevente et al., 2011), prostate (Mitchell et al., 2009; Nilsson et al., 2009), lung (Rabinowits et al., 2009), and bladder (Welton et al., 2010) cancer. A vastly greater amount of exosomes turned into found in plasma from lung cancer patients compared to that of handle people (Rabinowits et al., 2009). In colorectal cancer patients, the amount of plasma circulating exosomes changed into constitutively bigger than in normal fit people showing the direct correlation between exosomes quantity and malignancy (Silva et al., 2012).

Manipulating tumor cells to reduce the unencumber of exosomes followed via their injection into immunocompetent mice led to a drastically slower tumor growth in comparison to that of unperturbed cells (Bobrie et al., 2012). It has been shown that exosomes which can be released from tumor cells are in a position to transfer a whole lot of molecules, together with melanoma-particular, to other cells (Al-Nedawi, Meehan & Rak, 2009; Muralidharan-Chari et al., 2009) to govern their environment, making it more favorable for tumor boom and invasion. Glioblastoma-derived exosomes have been found to be enriched in angiogenic proteins that allowed them to stimulate angiogenesis in endothelial cells (Skog et al., 2008). Melanoma exosomes have been proven to be instrumental in melanoma mobilephone dissemination by way of transformations in the angiogenic microenvironment. Hood et al. established that metastatic elements liable for the recruitment of melanoma cells to sentinel lymph nodes are upregulated through melanoma exosomes themselves (Muralidharan-Chari et al., 2009). Exosomes shed with the aid of human MDA-MB-231 breast carcinoma cells and U87 glioma cells were capable of conferring the changed traits of melanoma cells onto ordinary fibroblasts and epithelial cells partly due to transferring tissue transglutaminase and fibronectin (Hood, San & Wickline, 2011).

on account of their small dimension exosomes have an potential to penetrate intercellular contacts to reach far-off parts of the body with the support of the blood movement and other body fluids. Exosomes were purified from human plasma, serum, bronchoalveolar fluid, urine, tumoral effusions, epididymal fluid, amniotic fluid and breast milk (Raposo & Stoorvogel, 2013). since exosomes possess characteristic protein and RNA signatures of their host cells, evaluation of exosomes in a lot of physique fluids can also be potentially utilized for non-invasive diagnostics of cancer and other disorders. for example, aggressive human gliomas often specific a truncated and oncogenic kind of the epidermal boom factor receptor, called EGFRvIII. The tumour-selected EGFRvIII changed into detected in serum microvesicles from glioblastoma patients (Skog et al., 2008). high stability of exosomal RNA (Skog et al., 2008; Valadi et al., 2007) and ease of RNA detection by using tremendously delicate PCR makes detection of exosomal RNA a good looking method for the discovery of biomarkers. indeed, mRNA editions and miRNAs characteristic of gliomas may well be detected in serum microvesicles of glioblastoma sufferers (Skog et al., 2008). Expression profiles of serum microvesicle mRNA by means of microarrays correctly separated people with glioblastoma from general controls (Noerholm et al., 2012). Of all RNA species, secreted miRNAs had been most commonly utilized toward discovery of body fluid-primarily based biomarkers possibly as a result of miRNA expression profiles are extra informative than mRNA expression profiles in a couple of diseases (Grady & Tewari, 2010). in one analyze, analysis of plasma- and serum-derived microvesicles printed 12 miRNAs differently expressed in prostate melanoma sufferers compared to that of in shape controls and 11 miRNAs upregulated in sufferers with metastases in comparison to that of sufferers without metastases (Bryant et al., 2012). apparently, exosomes released by using breast melanoma cells will also be separated into different classes reckoning on their miRNAs content (Palma et al., 2012). Cells undergoing malignant transformation produced exosomes containing selective miRNAs, whose release is increased with the aid of malignant transformation, in distinction to cells that aren't suffering from malignancy, whose exosomes are full of impartial miRNAs (Palma et al., 2012). The alterations in exosomal miRNA cargo may supply a signature of the presence of malignant cells within the body.

the vast majority of reported so far exosomal miRNA and mRNA profiles were generated the usage of microarray tactics that suffer from a number of obstacles. Microarrays are biased for investigation of already found out transcripts. additionally, there's abilities for move-hybridization of RNAs that are enormously related in sequence. recently developed subsequent generation RNA sequencing technology (RNA-Seq) makes it possible for detection of all RNA subtypes as well as of unannotated transcripts and has a excessive sensitivity toward identification of low-abundance RNAs. In case of exosomes, this method changed into applied most effective for the analysis of small (20–70 nt) RNAs (Bellingham, Coleman & Hill, 2012; Nolte-’t Hoen et al., 2012).

during this analyze, we utilized the RNA-Seq approach to symbolize the transcriptomes of exosomes secreted by way of two metastatic human breast melanoma mobile strains. We describe optimized computational workflow to analyze facts generated through the Ion Torrent semiconductor chip-based mostly technology. we have recognized and profiled RNA species current in exosomes and host cells and discuss the utility of exosomal RNA as capabilities breast cancer-certain biomarkers.

materials and techniques cellphone subculture

Human breast melanoma phone traces MDA-MB-436 (ATCC® HTB-one hundred thirty™) and MDA-MB-231 (ATCC® HTB-26™) had been maintained at 37°C in 5% CO2 and cultured in DMEM/F12 supplemented with 10% FBS. 48 h just before exosome assortment, cells have been washed 3 times with PBS and the medium became changed to serum-free CCM5 medium (Thermo Scientific).

Exosome extraction

Exosomes have been remoted and purified from the media of MDA-MB-436 and MDA-MB-231 phone cultures the use of sequential centrifugation protocol. briefly, media was collected and cellular particles turned into removed through centrifugation at 3,000×g for 10 min. The supernatant was centrifuged at 17,000×g for 30 min at 4°C. The supernatant was gathered and centrifuged at a hundred,000×g for two h to pellet exosomes. Exosomes pellets have been then washed in filtered PBS and re-centrifuged at 100,000×g, the supernatant became eliminated and the last exosomal pellet was re-suspended in one hundred µl PBS.

Transmission electron microscopy

A 50 µl aliquot of exosomes become absorbed onto formvar carbon covered nickel grid for 1 h. The grid became placed with the coating facet facing the drop containing exosomes. Then the grids have been washed through sequentially positioning them on accurate of the droplets of 0.1 M sodium cacodylate, pH 7.6 after which fixed in 2% paraformaldehyde and a couple of.5% glutaraldehyde in 0.1 M sodium cacodylate, pH 7.6 for 10 min. Then grids were washed again with 0.1 M sodium cacodylate, pH 7.6 and contrasted with 2% uranyl acetate in 0.1 M sodium cacodylate, pH 7.6 for 15 min. After washing, the grids have been incubated on precise of the drop of 0.13% methyl cellulose and negatively stained with 0.four% uranyl acetate for 10 min, air dried for 5 min and examined with a JEM-2200FS transmission electron microscope operated at 100 kV.

Nanoparticle tracking analysis

Supernatants containing vesicles have been analyzed using a Nano-Sight LM10 instrument equipped with a 405 nm laser (NanoSight, Amesbury, UK) at 25°C. Particle stream became tracked by means of NTA utility (edition 2.2, NanoSight) with low refractive index comparable to telephone-derived vesicles. each and every music changed into then analyzed to get the imply, mode, and median vesicle measurement together with the vesicle concentration (in millions) for each measurement.

RNA isolation and analysis

total RNA from exosomes (MDA-MB-436 and 231) and cultured cells (MDA-MB-231) were isolated the use of the TRIzol reagent (Invitrogen). RNA quality and attention have been assessed with the Agilent 2100 Bioanalyzer (Thermo Scientific). mobile RNA was analyzed the use of RNA 6000 Nano package (Agilent) and exosomal RNA became analyzed the use of RNA 6000 Pico package (Agilent).

RNA-seq with ion torrent personalized genome machine (PGM)

Two a whole bunch ng of exosomal RNA and three µg of total cell RNA changed into used as the starting enter for RNA-Seq library education. Sequencing was carried out via AITbiotech enterprise (Singapore). briefly, complete mobilephone RNA became treated with RiboMinus Eukaryote kit (lifestyles applied sciences) to eliminate rRNA. Then, exosomal and rRNA-depleted cellular RNAs had been fragmented using RNaseIII. entire transcriptome library was developed using the Ion complete-RNA Seq package v2 (existence applied sciences). Bar-coded libraries were quantified with qRT–PCR. each library template turned into clonally amplified on Ion Sphere Particles (life technologies) the usage of Ion One touch 200 Template kit v2 (existence applied sciences). Preparations containing bar-coded libraries have been loaded into 318 Chips and sequenced on the PGM (life applied sciences).

cDNA synthesis and quantitative actual-time PCR (RT-PCR)

Two-step RT-PCR changed into carried out the usage of the QuantiTect Reverse Transcription equipment (QIAGEN GmbH, Hilden, Germany) in line with brand’s protocol. RT-PCR became carried out in a Rotor-Gene (Qiagen) the usage of a SYBR green PCR grasp combine. Primer sequences are offered in table S3. All reactions with template and without template (negative controls) had been run in duplicate and averaged. GAPDH was used as internal manage for mRNA. Ct cost changed into detected for every gene that means the cycle quantity at which the amount of amplified gene of pastime reaches a hard and fast threshold. Relative quantification (fold alternate) turned into decided for the host cells and exosomal genes expression and normalized to an endogenous control GAPDH relative to a calibrator as 2−ΔΔCt (the place ΔC = (Ct of gene of activity) – (Ct of endogenous handle gene (GAPDH) and ΔΔCt = (ΔCt of samples for gene of activity) – (ΔCt of calibrator for the gene of pastime). Melting curves of every amplified products had been analyzed to make sure uniform amplification of the PCR products.

Bioinformatics analysis raw reads filtering

raw reads generated by means of sequencing had been subjected to a few best tests. The low satisfactory reads were eliminated through read trimming and read filtering. examine trimming protected elimination of adapter sequences, elimination of the three′ ends with low excellent rankings and trimming in line with high-Residual Ionogram Values. Filtering of entire reads covered elimination of brief reads, adapter dimers, reads lacking sequencing key, reads with off-scale sign and polyclonal reads. Subsequent analysis became carried out with excessive exceptional reads which handed throughout the described above filtering steps.

Reads mapping

Bowtie 2 edition 2.1.0 become used to align all extraordinary reads to rRNA sequences together with 28S (NR_003287.2), 18S (NR_003286.2), 5S (NR_023379.1), and 5.8S (NR_003285.2) rRNA. Reads mapped to rRNA sequences have been filtered out whereas the rest of the reads have been mapped to the human genome. The high-quality reads had been mapped to hg19 build of the human genome from tuition of California Santa Cruz (america) genome browser database (Meyer et al., 2013) the use of TopHat version 2.0.6 with the aligner Bowtie 2.0.5 (Kim et al., 2013; Langmead & Salzberg, 2012) with their default parameters in end-to-end mode (-b2-sensitive) and defining splice-junctions according to usual splice-junctions (-G). to categorise the reads into conventional and unknown genes, the BAM file generated by way of Tophat2 was intersected to typical gene (RefGene and GENCODE built V14 from america database) using BEDtools (Quinlan & hall, 2010) and changed into used to count number the number of reads via SAMtools (Li et al., 2009).

put up-processing of the aligned reads

The mapped reads were extra manipulated through removing the reads that mapped to varied areas. In particular, the short aligned reads with the size of <20 nucleotides were eradicated to prevent the alignment error corresponding to mapping to distinctive genomic places. further filtering included the elimination of the low great reads which fall under the mapping satisfactory score of 10 (-q 10) the usage of SAMtools. For the coverage search, the BAM file generated via Tophat2 became converted to bed layout with option (-split) using BEDtools. The bed file become converted once again to BAM format the usage of BEDtools. We then developed python script (the usage of pysam as a part of the scripts) to calculate the variety of reads and read coverage in exons and protein-coding sequence (CDS) areas consecutively.

RNA abundance calculation

RNA abundance was estimated with the help of Partek Genomics Suite application (Partek Inc., St. Lous, MO) the use of Reference Sequence Gene (RefSeq Gene) and GENCODE annotation developed version 14 of those now not overlapped with RefSeq Gene from usa genome browser. The Expectation-Maximization (E/M) Algorithm (Xing et al., 2006) was used to estimate the undoubtedly relative expression stages of each gene isoform. Partek’s algorithm was used to quantify the gene isoform expression stage as reads per kilo base per million mapped reads (RPKM).

practical evaluation of genes

Database for Annotation, Visualization, and integrated Discovery (DAVID) (version 6.7) become used to identify gene purposeful annotation terms which are drastically enriched in selected gene lists with the entire Human genes because the historical past (Huang, Sherman & Lempicki, 2009). a listing of gene symbols turned into generated for each dataset and changed into used as input into DAVID. DAVID calculates a modified Fishers actual p-cost to demonstrate Gene ontology (GO) and KEGG molecular pathway enrichment, the place p-values less than 0.05 after Benjamini distinctive check correction are considered to be strongly enriched in the annotation category. We additionally used a count number threshold of 5 and the default price of 0.1 for the ease (enrichment) score settings. We used extra specific GO term classes provided by way of DAVID, referred to as GO fat, to lower the redundancy of frequent GO phrases within the evaluation to boost the specificity of the terms.

outcomes Characterization of exosomes launched with the aid of breast melanoma cells

Exosomes had been remoted from two breast cancer mobile strains, MDA-MB-436 and MDA-MB-231 using classical ultracentrifugation protocol. The dimension distribution and amount of exosomes have been analyzed the use of NanoSight LM10 nanoparticle tracking evaluation (NTA). NTA showed that MDA-MB-231 cells released 4 × 106 vesicles per cm2 of boom area per forty eight h that were predominantly one hundred fifteen nm in size. MDA-MB-436 cells launched 1.04 × 107 vesicles per cm2 of boom enviornment per forty eight h that were predominantly 91 nm in size (Fig. 1). The size of exosomes launched from both cell traces ranged from ∼70 nm to ∼300 nm. An examination of the purified vesicles the usage of transmission electron microscopy printed that they had the size (∼50–100 nm) and morphology (Fig. 2) ordinary of that of exosomes.

determine 1: evaluation of exosomes produced by breast melanoma mobile traces, MDA-MB-436 and MDA-MB-231, with Nanosight LM10-HS instrument. Characterization of exosomal RNA

RNA become isolated from exosomes launched through each breast cancer telephone strains. complete RNA changed into also extracted from MDA-MB-231 telephone line as a manage host phone line that produced exosomes. Bioanalyzer records published that exosomes contain a large latitude of RNA sizes (30–500 nt) and have very small quantity of intact rRNA (5.2% in MDA-MB-231 exosomes and 5.6% in MDA-MB-436 exosomes) (Fig. 3) in line with old reviews on exosomal RNA (Rabinowits et al., 2009; Valadi et al., 2007).

determine 2: TEM graphic of the exosomes produces by way of MDA-MB-436 mobile line. Electron microscopy allowed visualizing membrane-bound nanovesicles sized ∼a hundred nm. White arrowheads pointing to the exosomes. Scale bar = one hundred nm. figure three: analysis of RNA from cells and exosomes via Bioanalyzer. Exosomal and total mobile RNA turned into analyzed with PicoChip and NanoChip, respectively. Exosomal RNA deep sequencing the use of Ion Torrent technology

We utilized the Ion Torrent sequencing expertise to profile exosomal RNA produced by means of MDA-MB-436 and MDA-MB-231 cell strains, as well as RNA received from host telephone line MDA-MB-231. in keeping with RNA high-quality evaluation the usage of Bioanalyzer profiling (Fig. 3) we carried out rRNA depletion for mobile RNA however not for exosomal RNA.

Identification of acceptable alignment tool for effective seize of splice-junction reads produced with the aid of the Ion Torrent technology

RNA-Seq computational analysis workflow is offered in Fig. 4. the only-end RNA reads generated from sequencing of the exosomal and host telephone libraries have been trimmed to eliminate adapter sequences after which filtered. After filtering out low-first-rate reads, the amazing reads had a varying length ranging from 6 bp to >300 bp. These preceded splendid reads were regarded for additional evaluation. the full quantity of reads for RNA from MDA-MB-231 cells become about 4.eight M. RNA-Seq resulted in ∼3.5 M and ∼3.2 M reads for RNA from MDA-MB-436 and MDA-MB-231 cells derived exosomes, respectively. at the beginning, the reads had been aligned to rRNA sequences together with 28S, 18S, 5S, and 5.8S rRNA (see methods). 24% of the reads in mobile and more than 80% of the reads of each exosome samples were mapped to rRNA sequences (Fig. S2). The rest of the reads have been mapped to the human genome (united states of america; hg19 built) with the assist of TMAP aligner application in keeping with the Ion Torrent recommended pipeline (applied sciences) (statistics not proven). besides the fact that children, the use of TMAP resulted in misalignment of the splice-junction reads or reads containing distinctive exons to the genome (Fig. S1). hence, we used alternative aligner Tophat 2.0.6 together with the Bowtie 2.0.5 for the read mapping.The mapped effects are shown in Fig. S2. In complete, this alignment produced greater than four.three M (∼90%) reads of MDA-MB-231 cellular RNA that may be mapped to rRNA sequences and the human genome. For the MDA-MB-231 and MDA-MB-436 telephone-derived exosomes, these alignments resulted in ∼2.8 M (∼90%) and ∼three.2 M (∼ninety one%) of mapped reads, respectively (Fig. S2). greater than 80% of mapped reads from exosomal samples were discovered to be mapped to rRNA sequences. We additional investigated the reads of each exosomal RNA samples, which mapped to rRNA sequences. These reads were counted and plotted as study density over each rRNA sequence (proven in Fig. S3A). The mapped reads covered complete size of all rRNA sequences. The important fractions of mapped reads have been 28S and 18S rRNA (Fig. S3B).

figure 4: Flowchart of RNA-seq statistics analysis. The raw reads are uncovered to pre-alignment fine exams including the removing of adaptor sequences and low first-class reads. The high quality reads are then mapped to rRNA sequences the use of Bowtie2 edition 2.1.0. The non-mapped rRNA reads were mapped to human genome hg19 build of the human genome the usage of TopHat edition 2.0.6 with the aligner Bowtie 2.0.5. After mapping, low mapping first-class reads under 10; brief reads with size under 20 base pairs and multi- reads were eliminated. Estimation of study counts and browse insurance on mapped reads where over ninety% of exon (non-coding) and CDs (for coding) in transcript isoforms of RefGene and/ or GENCODE v14 gene models. The EM algorithm together with GENCODE v14 annotations become used to estimate the examine count number and reads per kilo base per million mapped reads (RPKM) on mapped transcripts.

Reads mapped to numerous places in the genome were eradicated to achieve uniquely mapped reads. Subsequent downstream evaluation was performed with the high high-quality reads which had the read length superior than 20 bps and mapping excellent ranking of above 10 (see methods). consequently, about 81, seventy six, and 70% out of all mapped reads have been regarded as high great mapped reads for MDA-MB-231 and MDA-MB-436 exosomal RNA, and MDA-MB- 231 mobile RNA, respectively.

about 97% (∼2 M reads) of reads from both exosomal RNA samples had been mapped to rRNA sequences (Fig. 5) and ∼2% of the reads had been mapped to general genes (RefSeq and/or GENCODE gene fashions). at the identical time, for the MDA-MB-231 mobile RNA, for which rRNA depletion step turned into performed, the majority of the reads (∼fifty eight%) become mapped to favourite genes (Fig. 5). To raise the number of reads for different RNAs we attempted to fritter away rRNA with the RiboMinus™Eukaryote package counseled by using Ion total RNA-Seq equipment protocol. As anticipated, this protocol correctly pulled down rRNA from cellular RNA (>60%) but had little impact on removing of rRNA from exosomal RNA (Fig. S4). The failure to fritter away exosomal fragmented rRNA can also be explained by means of the design of the RiboMinus™Probe. It contains 2 probes each for 5S, 5.8S, 18S and 28S RNA. since the probe dimension is 22–25 nucleotides, many fragments of rRNA are not centered.

determine 5: Distribution of uniquely mapped RNA-seq reads among transcriptome. Reads which overlapped with annotated gene fashions (RefSeq and/or GENCODE) are termed as “normal genes”. Reads that positioned outdoor of annotated gene models are termed as “unknown”. Reads which can be mapped to rRNA sequences including 5S, 5.8S, 18S, and 28S rRNA are named as “rRNA”. content of cellular and exosomal transcriptomes

In total, 16,086 transcripts (eleven,657 genes) have been detected with a normalized RPKM value of better than 1.0 at the least in a single pattern. We used Integrative Genomics Viewer (IGV) edition 2.1.21 (Thorvaldsdottir, Robinson & Mesirov, 2013) to visualize mapped reads and to determine the insurance throughout human transcriptome. We accompanied generally misannotated transcripts in exosome samples with excessive RPKM value in those circumstances when best small constituents of the transcripts had been lined with the aid of the reads (Fig. 6). This become the impact of low depth sequencing led to by way of the presence of a big quantity of reads representing rRNA. for this reason, we carried out more stringent standards to attain full-size expressed genes via filtering genes in line with the RNA reads coverage. Reads which cover over 90% of protein-coding sequence (CDS) of protein-coding genes and over ninety% of exons in non-coding sequence of non-coding genes have been considered for further evaluation. To determine protein-coding genes in exosomal samples, the mapped reads of exosomal RNA were pooled with estimated CDS insurance of >90% for each exosomal reads. The pooled mapped reads have been used to calculate CDS in each exosomal pattern as >50% of coverage.

figure 6: illustration of low insurance transcript however very high RPKM in AURKAIP1 and ATPIF1 genes. (A) AURKAIP1 gene from chromosome position chr1:1,309,009–1,310,847 is proven the use of Integrative Genomic Viewer. among the three versions, the optimum price of protein-coding sequence (CDS) insurance, read count and RPKM is proven within the correct panel of read mapping. each the exosomes shows very low coverage (7–22%) with study counts of 4, whereas the RPKM cost is sixty five.44 and 80.seventy eight RPKM for exosomes of MDA-MB-231 and MDA-MB-436, respectively. (B) ATPIF1 gene from chromosome position chr1:28,562,494–28,564,655 is visualized. The MDA-MB-231 exosomes display high CDS coverage (91%) with an exon count number of four.

using these criteria, we received lower number of annotated transcripts (6,187 transcripts or three,437 genes) in comparison to that after RPKM values had been regarded (Fig. S5 and desk S1). in consequence, some transcripts showed excessive coverage with CDSs standards (as an example, ATPIF1) in exosomal RNA from MDA-MB-231 cells, even when the variety of reads was small (below 5) (Fig. 6B). Such genes have been also taken into consideration in our analysis. In total, 5821 (3115 genes) and 187 (a hundred and fifteen genes) protein-coding transcripts had been detected according to the RNA reads insurance in cellular and exosomal samples, respectively. For non-coding genes, 360 (317 genes) and 131 (131 genes) transcripts had been detected in line with the RNA reads insurance for mobile and exosomal samples, respectively. analysis of these transcripts published that they represented 90.eight% of protein-coding genes and 9.2% of non-coding genes for host mobile sample; while exosomal RNA samples represented 50.4% and 49.6% of protein-coding and 47.6% and 52.four% of non-coding genes in MDA-MB-231 and MDA-MB-436 cells derived exosomes, respectively (table 1). We found that ninety eight.3% of protein-coding and 97.7% of non-coding exosomal transcripts have been latest in host cells (Fig. 7).

desk 1:

volume of genes in cellular and exosomal RNA in accordance with 90% coverage over protein-coding sequence of genes and exons of non-coding genes.

notice the tremendous proportion of non-coding transcripts in exosomal RNA. Transcript class MDA-MB-231cellulargenes (%) MDA-MB-231exosomalgenes (%) MDA-MB-436exosomalgenes (%) Protein-coding ninety.8 50.four forty seven.6 Non-coding 9.2 49.6 52.four determine 7: Venn diagram presents overlap among protein-coding and non-coding gene symbols in exosomes and cells. practically all of the genes in both exosomal RNA are the subset of mobile genes. Exosomes are enriched in mRNAs functioning in protein translation and rRNA processing

We performed Gene Ontology (GO) enrichment analysis the usage of the DAVID bioinformatics resource, which employs a Fisher’s actual check with Benjamini–Hochberg correction. a total of 377 enriched GO classes have been derived using a P-cost reduce-off of p < 0.05 for 3115 host MDA-MB-231 mobile genes: 286 biological technique (BP) classes and 91 Molecular feature (MF) classes (desk S2). In complete, 18 GO classes including 11 BP and seven MF have been derived from one hundred fifteen exosomal genes from both telephone strains. figure 8 shows suitable 20 BP categories of the host cellular genes which consist of translation manner, mobile cycle, RNA processing, and many others. (Fig. 8A and desk S2B). at the same time exosomal genes printed organic approaches in translation, ribosome biogenesis, rRNA and ncRNA processing GO classes (Fig. 8B and table S2C). considering the fact that the predominant fraction of exosomal samples were rRNA species, drastically reduce number of mRNA may well be detected in exosomal samples. We hypothesized that the genes detected from exosomal samples should be enormously expressed in the cells. To examine the hypothesis, we carried out GO enrichment evaluation for a hundred and fifteen proper expressed genes from MDA-MB-231 cellular pattern. The accurate 10 GO phrases (Fig. 8C and table S2D) of these desirable expressed genes are the identical as in exosomal fraction (Fig. 8B and desk S2C). We additional created box plot of 115 exosomal genes in MDA-MB-231 mobile sample the use of expression values (RPKM) (Fig. 9). These records certainly showed that exosomes are enriched in genes that are incredibly expressed in the host cells.

determine eight: Gene Ontology (GO) enrichment analysis of genes detected in cellular and exosomal RNA from breast melanoma phone lines. The large GO phrases become described as described in substances and strategies. (A) precise 20 huge GO terms found in MDA-MB-231 cellular genes (3115 genes). (B) huge GO terms found in exosomal genes from both mobilephone-traces (MDA-MB-231 and MDA-MB-436). (C) appropriate 20 massive GO terms found in probably the most expressed 115 genes from MDA-MB-231 cellular genes. The asterisks (*) indicate GO terms that present in exosomal genes. determine 9: Expressed genes in exosomes found to be extremely expressed in the host cells. The container plot shows expression stage of all genes in cellular samples as compared to that of genes which were discovered to be categorical in exosomes. Wilcoxon rank sum verify represents large difference in expression degree of both sets.

Non-coding transcripts can be categorized into 13 categories (see table 2). each exosomal and mobile samples contained small nucleolar RNA (snoRNA) as foremost species. The 2d most plentiful classification of non-coding transcripts based on GENCODE annotation was “non-coding RNA” in cellular pattern and small nuclear RNA (snRNA) in exosomal samples. typical, the right five RNA categories represented about ninety% of all noncoding genes in both exosomal and cellular RNA.

desk 2:

quantity of non-coding gene symbol in cellular and exosomal RNA based on 90% coverage over exons of non-coding transcripts.

In exosomes, the properly 5 non-coding gene forms together with small nucleolar RNA, small nuclear RNA, Mt_tRNA, microRNA, and non-coding RNA represents about 90% of non-coding genes in each exosome samples. Gene classification MDA-MB-231cellular(gene symbols) MDA-MB-231exosomal(gene symbols) MDA-MB-436 exosomal(gene symbols) small nucleolar RNA 214 eighty three 51 small nuclear RNA 23 11 10 Mt_tRNA 13 7 four microRNA 34 6 2 non-coding RNA 42 1 0 e-book RNA 20 0 1 vault RNA 3 0 three rRNA 1 1 2 RNase MRP RNA 1 1 1 RNase P RNA 1 1 1 Mt_rRNA 1 1 0 lincRNA 1 0 0 telomerase RNA 1 0 0 Validation evaluation of RNA-seq facts with the aid of qRT-PCR

in keeping with RNA-Seq records we evaluated presence and enrichment of several mRNA transcripts in exosomal RNA - RAB13, RPPH1, EEF1A1, FTH1, FTL and RPL28. qRT-PCR evaluation showed presence of all selected transcripts in exosomal samples (Fig. 10A). figure 10B demonstrates that the fold-trade of qRT-PCR effects are per the fold-exchange of RNA-seq facts.

determine 10: Validation of RNA-seq records with the aid of qRT-PCR. (A) Ct values for six mRNA transcripts which have been detected in exosomal samples by RNA-seq are proven. (B) assessment of distinct expression values (RPKM; MDA-MB-436/RPKM; MDA-MB-231) detected with the aid of RNA-Seq (darkish-grey columns) and qRT-PCR (gentle-gray columns) for six otherwise expressed genes. discussion

unless currently, the alterations in gene expression all through a variety of organic approaches had been analyzed the usage of microarray techniques that center of attention mostly on the conduct of protein-coding transcripts. because microarrays are based on hybridization, they have excessive heritage as a result of move-hybridization, they've a confined dynamic latitude of detection and they count upon known constructions of genes. building of RNA-Seq know-how accredited complete analysis of complete transcriptomes with the only nucleotide resolution enabling quantification of most RNA molecules expressed within the mobile or tissue (Mortazavi et al., 2008). during this look at, we used the Ion Torrent platform to interrogate transcriptomes of exosomes launched from two metastatic breast melanoma cell strains. on the time of conducting our analysis this expertise produced relatively low number of reads, yet we selected it because it offered the longest reads than another sequencing platform. This feature of the Ion Torrent technology was basic as we dealt with RNA isolated from exosomes whose nature and composition are still no longer neatly established. RNA-seq facts evaluation is complicated by means of the intricacy of dealing with enormous datasets, reads excellent control, alignment system etc. distinctive workflows and several algorithms have been proposed to map reads to the reference genome and to operate facts evaluation (Chen, Wang & Shi, 2011; Mortazavi et al., 2008). evaluation of expression stages throughout distinctive samples and experiments is frequently tricky and requires complicated normalization methods and these are still below active building. The circumstance is even more complex in case of exosomal transcriptomes that fluctuate enormously from mobile transcriptomes.To handle this issue, we developed in this look at custom-made bioinformatics workflow and established its utility for evaluation of exosomal RNA. since the Ion Torrent platform produces reads with distinct length the committed algorithm for their alignment to the genome known as TMAP became recommended. We discovered, besides the fact that children, that this device doesn't permit satisfactory mapping of reads that comprise splice-junctions or span introns. therefore, we choose choice aligning tool TopHat2 (with Bowtie2) which might deal with reads of varying length and identify splice-junctions according to conventional splice-junctions in addition to allowed the invention of recent splice-junctions (Kim et al., 2013; Langmead & Salzberg, 2012).

We observed a huge proportion of reads mapped to rRNA regions in exosomal samples. This was unbelievable given the fact that intact 18S and 28S rRNA peaks had been just about undetectable in exosomal RNA (Fig. 3). This remark cautioned that most of exosomal rRNA is fragmented. Exosomal rRNA fragments could be mapped over complete length of rRNA (Fig. S3). Fragmented 28S and 18S rRNA were predominant rRNA species current in exosomes. The reads mapped to 28S and 18S rRNA have been disbursed practically equally in exosomal and mobile RNA samples. what's the feasible explanation for technology of exosomal rRNA fragments? RNases latest in mobilephone culture conditioned medium are unlikely to make contributions to rRNA fragmentation due to the fact exosomal membranes give insurance plan in opposition t RNase attack. certainly, medication of the exosomal preparations with RNase A did not result in big change between handled and manage samples in RNA dimension distribution (information no longer proven). within the examine of Skog et al. (2008) RNase remedy of the glioblastoma exosomes resulted in a really insignificant (below 7%) lower in RNA suggesting that exosomal RNA is inaccessible for RNase from outside the vesicles. A chance exists that the rRNA fragments are generated after secretion through RNases originated from the host cells and integrated into exosome vesicles. however, rRNA fragments may be generated inner cells in advance of their unencumber to exosomes. an extra classification of RNA, tRNA is represented in exosomes mainly by means of its fragments (Nolte-’t Hoen et al., 2012). the most considerable tRNA hits in exosomal RNA are all found at the 5′ end of mature tRNAs (Nolte-’t Hoen et al., 2012).

Regardless the biogenesis of rRNA fragments, it's really useful to function rRNA depletion step even in the absence of seen rRNA peaks on RNA profiles. This method would allow acquiring plenty better sequencing depth for different RNA species. Our attempt to dissipate fragmented rRNA with the common RiboMinus™Eukaryote kit failed on account of the design of the probes. because the probes measurement is brief, many fragments of rRNA don't seem to be focused. using bigger variety of longer probes is expected to provide a more effective manner of pulling-down fragmented rRNA. This technical point of working with exosomal RNA samples should be definitely considered in the future reviews.

on account of colossal rRNA presence in exosomal samples we observed handiest 2% of mapped reads to generic transcripts the usage of RefSeq and GENECODE gene fashions. moreover, with RPKM value >1 we followed a large quantity of misannotation because of negative insurance of the reads over transcripts. therefore, we counseled an additional method, particularly filtering genes in keeping with reads coverage over protein coding sequence for mRNA or exons for non-coding RNA. This manner allowed us to obtain greater than 90% insurance for protein-coding and non-coding regions which we regarded as extremely respectable for purposeful classification. This approach became helpful to exhibit particularly expressed genes in exosomes which can be probably used as noninvasive breast melanoma markers.

We record that exosomes are carrying mRNAs that are enormously expressed within the host breast melanoma cells (Fig. 9). thus exosomal transcriptomes are representative of their cells of starting place and may provide a platform for detection of tumor selected markers. GO analysis published that leading biological and molecular features of both mobile and exosomal transcripts are enriched in proteins worried in ribosome biogenesis, translational elongation, ribosomal subunit assembly and rRNA processing. What may well be the significance of these capabilities in exosomal transcriptome? Exosome-associated mRNAs have been shown to be translated into proteins by recipient cells (Ratajczak et al., 2006; Valadi et al., 2007). We hypothesize that upon arrival to the recipient cells exosomal mRNAs are translated into proteins supporting ribosomal features to be certain productive translation of other exosomal mRNAs within a cellular compartment the place exosome content is launched. Valadi et al. (2007) also described presence of mRNAs for many ribosomal proteins in exosomes secreted with the aid of mouse mast phone line. interestingly, Graner et al. (2009) established the presence of elongation and translation components in exosomes derived from brain tumor.

In conclusion, here we tested for the primary time that fragmented rRNA is an enormous species of exosomal RNA. Proposed right here customized bioinformatics workflow allowed us to reliably notice other, non-ribosomal RNAs beneath circumstances of constrained study numbers. Classification and quantification of the RNA-Seq facts published that exosomal transcripts are consultant of their cells of origin and therefore may kind basis for detection of tumor selected markers. This assistance can also be used for getting insights in the molecular underpinnings of organic outcomes produced by using these microvesicles. discovering that exosomes endure mRNAs encoding the critical add-ons to build-on-web site ribosomes offers a effective insight into biological characteristic of those vesicles.

Supplemental advice assessment of mapping satisfactory between the alignment equipment TopHat2.0.6 and TMAP 0.3.7

FTL gene (chromosome place chr19:49,468,467-49,470,296) is selected as an instance of alignment assessment. TMAP alignment resulted in bad reads mapping and absence of junctions over exon-exon area. as the identical time, TopHat identifies the exon-exon splice junctions and connects the exons via a linker.

Distribution of all mapped and unmapped RNA-seq reads amongst genomic compartments

rRNA defined as 5S, 5.8S, 18S, and 28S rRNA sequences. Reads which overlapped with annotated gene models (RefSeq and/or GENCODE) are termed as “known genes”. Reads that placed backyard of annotated gene models are termed as “unkown”.

Fragments of rRNA in exosomes signify full-length of rRNA sequence

(A) RNA examine density plot represents RNA fragments which wholly covers of 5S, 5.8S, 18S, and 28S rRNA sequences from exosomal RNA. (B) 18S and 28S rRNA were essential fractions of rRNA species.

analysis of rRNA depletion from MDA-MB-231 cellular and exosomal RNA the use of RiboMinus™ Eukaryote package for RNASeq

RNA become detected with PicoChip using Bioanalyzer. The depletion process has been carried out in accordance with the company’s protocol. manage samples (crimson) had been handled exactly as experimental samples (blue) except they did not contain RiboMinus™ Probe.

Venn diagram of genes generated by means of RPKM and browse insurance methods

The detection standards is that gene has more than 1 RPKM in as a minimum one pattern, while a different strategy is that gene has more than ninety% insurance over protein-coding or non-coding sequence.

Two strategies of gene transcripts selection using RPKM or examine coverage

(A) The Partek genomic suite output displaying transcripts with RPKM >1 in as a minimum one sample. The examine counts from the transcript isoforms were estimated using EM algorithm from Partek genomic suite on RefGene and/or GENCODE v14 gene models. (B) Estimation of study insurance (in percentage) and skim count of transcript. The transcripts with >90% insurance for protein-coding sequence and exonic sequence (in case of non-coding transcript) of transcript isoforms on RefGene and/or GENCODE v14 gene models are shown.

Gene Ontology (GO) enrichment analysis the use of the DAVID bioinformatics aid of Genes present in mobile and exosomal samples

(A) Gene lists for GO enrichment evaluation. (B) GO enrichment of cellular genes. (C) GO enrichment of exosomal genes. (D) GO enrichment of properly one hundred fifteen expressed cellular genes.

checklist of primers used for qRT-PCR

Sogeti Launches Free iOS and Android software testing life Cycle App: TMap life Cycle | killexams.com Real Questions and Pass4sure dumps

Sogeti, a number one issuer of expert technology services, that specialize in utility administration, Infrastructure management, excessive-Tech Engineering and trying out, has launched a free app for iOS and Android: TMap existence Cycle.

Sogeti’s TMap is the realm-main methodology for structured possibility-primarily based software checking out. An standard element of TMap is the lifecycle, covering the key steps in trying out method and execution, in the conclusion-to-end procedure of reaching amazing company-critical functions.

Sogeti’s TMap life Cycle app gives this cell-based framework, guiding application testers during the utility checking out lifecycle, from planning and infrastructure to handle and all levels of a important course. using TMap’s structured framework for end-to-conclusion look at various technique, this app helps checking out authorities song the progress of their initiatives. It additionally makes it possible for the previous identification of defects ensuing within the consistent reduction of timelines by way of as a minimum 30%, lowering normal charges.

the brand new app is attainable now within the Android industry and the App shop. it's appropriate with Android cellular instruments as well because the Apple iPhone, iPod contact and iPads. It offers clients with free downloads to guide the TMap process, including checklists and templates. The app also features video clips explaining check design options, product possibility analysis and strategies to check look at various strategy, in addition to hyperlinks to application checking out materials similar to eBooks and whitepapers.

“TMap is a complicated, proven and trusted application test administration methodology, relied upon by using tens of thousands of application testers across the globe for structured possibility-based mostly trying out,” referred to Dan Hannigan, national vice president of the Managed trying out practice for Sogeti u . s .. “Releasing this free app is only a different extension of our simple aim, which is making structured utility testing and great assurance methods quite simply obtainable to professional testers.”

To download this free app, go to the Android market or App save, and seek “TMap lifestyles Cycle.”

About TMapTMap (check management approach) is Sogeti’s business-pushed, chance-primarily based methodology for structured utility testing, significant to organizations of all sizes and vertical markets. An adaptive system, appropriate for all check situations in building environments, together with new construction, preservation, waterfall/iterative/agile building, personalized or packaged utility, TMap addresses the key considerations of satisfactory, time and price throughout the development lifecycle. The app describes distinctive phases of the TMap lifecycle.


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TMap Next Test Engineer

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TMap NEXT® Testing Clouds: First Cloud e-book to be launched by Sogeti | killexams.com real questions and Pass4sure dumps

ti, kesä 21, 2011 08:30 CET

A new publication in Sogeti’s TMap® series; TMap NEXT® Testing Clouds is launched.

21st June, Paris/Helsinki: Sogeti, a leading provider of professional technology services, has launched a new publication in its TMap® series, at IBM Innovate 2011 in Orlando USA. TMap NEXT® Testing Clouds is a comprehensive overview of how testing clouds can bring value for the early adopter. The author, Ewald Roodenrijs, describes the cloud business model for testing, the evolution from Information Technology (IT) to the concept of Business Technology (BT), and the steps that need to be taken in implementing cloud projects.

Cloud computing is currently regarded as one of the top three IT technologies for the forward thinking CIO. Although the cloud is still at an early stage of development, we are starting to see strong growth in cloud-based computing, which is outstripping even the most optimistic predictions. It is increasingly clear that the cloud model will supplement, if not entirely replace, mainframe and client/server installations in the years to come. This is based on a compelling value proposition: speed to market, agility to bring forward or retire service, and the chance to move expenditure from Capital Expenditure (CapEx) to Operational Expenditure (OpEx).

As cloud service adoption becomes ever more wide-ranging, a new global infrastructure is being created that creates significant new opportunities for application quality assurance and testing. This new book TMap NEXT® Testing Clouds describes in particular two aspects of the cloud: the business model and the cloud platform, both of which must be tested. This consists of several aspects; testing the infrastructure, cloud-enabled applications, and the ability to have instant deployable test infrastructure.

Ewald Roodenrijs comments: “This all has an impact on the way we do testing in the future. Testing applications on the cloud is the same as testing applications on a traditional infrastructure. Only what is tested is different”.

TMap NEXT® Testing Clouds is a development from its TMap® predecessors. It outlines how structured testing, using TMap®, can be leveraged within a cloud environment, it is not a step-by-step handbook, but reviews in detail the framework and importance of testing on the cloud, testing cloud strategy (in, on or within the cloud) and the risks involved for companies considering migrating applications onto the cloud, such as security, data integrity, privacy issues, data recovery and performance.

It also can also be read as a companion or follow-on publication to Seize the Cloud – A Manager’s Guide to Success with Cloud Computing[1], exploring in more detail the testing aspects of the cloud.

TMap NEXT® Testing Clouds is available as an e-book in both epub and pdf format and can be downloaded for free from the TMap testing website or the Sogeti.com website. Sogeti is increasingly producing e-books as an efficient means of timely distribution as well as enabling the company to reduce its carbon footprint. A printed version is available by using Printing on Demand (POD) via online bookstores or www.tmapbooks.com.

According to Nijs Blokland, Global Testing Community Lead and Head of Sogeti NL R&D; “Most of us are aware of the benefits of cloud computing, but have less understanding of the practicalities of successfully and securely implementing cloud applications and using the cloud as a cost-effective source of infrastructure and test tools. This book TMap NEXT® Testing Clouds provides early insight and guidance on these issues, and is further evidence of Sogeti’s innovative thinking in bringing the advantages of both cloud and structured testing together”.

Since launching their Software Testing as a Service (STaaS) globally in 2008, Sogeti has been pioneering the use of cloud-based solutions. As a direct development of this, Sogeti Netherlands will be launching their first Testing Cloud Solution, a comprehensive portal which will provide a full menu of services, direct access to fast automated pricing and the ability to upload testing artifacts and assets for cloud-based execution.

For the Dutch company’s clients, this is much more than a front-end website. This portal will provide full clarity of the service, together with a fixed turnaround time and price for each of the 22 services, including the creation of a Master Test Plan, Model Based Testing, Tool selection, Performance testing, Test Automation, Web Accessibility and Security Testing.

Notes to Editors

About the Author

Ewald Roodenrijs, author of TMap NEXT Testing Clouds, is the Global Lead of the Cloud Testing services within the Sogeti Group and a member of the testing R&D team of Sogeti Netherlands. He is also the co-author of the book TMap NEXT® – BDTM, contributor to Seize the Cloud, a regular contributor to national and international technical/expert publications, a speaker at numerous international conferences, including most recently at IBM Innovate June 2011, a keen blogger on the Cloud and testing and enthusiastic trainer. He is currently working together with IBM Rational to further develop this cloud offering and recently received the Capgemini-Sogeti Testing Services Innovation Award (Individual) 2011 for his work on Cloud Testing.

About the Book

The book can be ordered at major online books store, including Amazon, or directly from the publisher via www.tmapbooks.com or via www.sogeti.com. A printed copy can be ordered at www.tmapbooks.com.

Introduction, Chapter 1: An introduction to TMap NEXT® Testing Clouds and Chapter 2 Framework and importance of testing; even in the cloud; of interest to all readers.

Chapter 3: The Business in charge of IT; the Cloud and Chapter 4 Whatever as a Service; cloud-enabled Software Testing as a Service of interest to test managers, program managers and business managers and IT department managers.

Chapter 5 ‘Testing Cloud Strategy; a move to 3D’’, Chapter 6 ‘Testing the Cloud; in, on or with...’ and Chapter 7 ‘Cloud risks; worth testing for…’ are mainly interesting for the target groups of test managers, test coordinators, infrastructure testers and testers.

About the Contributors

The following contributors helped in the creation of the content of this book from Sogeti, Capgemini and also IBM Rational: Andréas Prins, Mark Buenen, Nick Lloyd, Ramanathan Iyer, Alfonso López de Arenosa, Rob Baarda, Richard Ammerlaan, Erik Smit, Flavien Bouche, Kanchan Apt, Michiel Boreel, Pierre Bedard, Michiel Rigterink, Karl Snider, John Bloedjes, Dan Hannigan, Dirkjan Kaper, plus additional support from Leo van der Aalst, Nicolas Claudon and Clare Argent.

For more information please contact:

Ewald Roodenrijs, Global Lead for Cloud Testing, SogetiTel: +31 (0)6 52 32 77 73Email: ewald.roodenrijs@sogeti.nl

Therese Sinter, Group Corporate Communications Director, Sogeti Tel: +46 70-361 46 21 Email: therese.sinter@sogeti.se

About Sogeti

Sogeti is a leading provider of professional technology services, specializing in Application Management, Infrastructure Management, High‐Tech Engineering and Testing. Working closely with its clients, Sogeti enables them to leverage technological innovation and achieve maximum results. Sogeti brings together more than 20,000 professionals in 15 countries and is present in over 100 locations in Europe, the US and India. Sogeti is a wholly‐owned subsidiary of Cap Gemini S.A., listed on the Paris Stock Exchange.

Together, Sogeti and Capgemini have developed innovative, business-driven quality assurance (QA) and testing services, combining best-in-breed testing methodologies (TMap® and TPI®) and the global delivery model, Rightshore®, to help organizations achieve their testing and QA goals. Sogeti and Capgemini have created one of the largest dedicated testing practices in the world, with over 8,200 test professionals and a further 12,500 application specialists, notably through a common center of excellence with testing specialists developed in India.

In Finland Sogeti is focused solely on providing testing services on the local market.

For more information please visit www.sogeti.com and www.sogeti.fi.

[1] Written by Erik van Ommeren, Martin van den Berg, Sogeti and published by Sogeti March 2010. Seize the Cloud is a guide through the business and enterprise architecture aspects of Cloud Computing, including 11 cases in which leading organizations share insights gained from their experience with Cloud.

Yritysesittely Sogeti Finland OySogeti on erikoistunut korkealuokkaisten IT-palvelujen toimittamiseen Suomen markkinoille. Keskitämme Suomessa palvelumme ohjelmistojen testaukseen ja laadunvarmistukseen. Sogeti-konsernin pääkonttori on Pariisissa Ranskassa. Konsernilla on palveluksessaan yhteensä noin 20 000 konsulttia 15 maassa: Belgia, Espanja, Hollanti, Intia, Irlanti, Iso-Britannia, Luxemburg, Norja, Ranska, Saksa, Suomi, Sveitsi, Ruotsi, Tanska ja Yhdysvallat. Pohjoismaissa konsultteja on Suomessa, Ruotsissa, Tanskassa ja Norjassa yhteensä yli 1 100. Sogeti-konsernin yhtiöt ovat Pariisin pörssissä listatun Cap Gemini S.A.:n kokonaan omistamia tytäryhtiöitä.

Avainsanat:


Characterization of RNA in exosomes secreted by human breast cancer cell lines using next-generation sequencing | killexams.com real questions and Pass4sure dumps

Introduction

Exosomes are nanosized membrane vesicles secreted by multiple cell types (Raposo & Stoorvogel, 2013). The term “exosomes” was introduced in 1987 by Johnstone et al. (1987) to describe the “trash” vesicles released by differentiating reticulocytes to dispose unwanted membrane proteins which are known to diminish during maturation of reticulocytes to erythrocytes. Later exosomes have been implicated in more complex functions. Raposo et al. (1996) have demonstrated that exosomes derived from B lymphocytes activate T lymphocytes, suggesting a role for exosomes in antigen presentation in vivo. In early studies, secreted microvesicles were named based on their cellular origins, e.g., archaeosomes, argosomes, dexosomes, epididymosomes, prostasomes, oncosomes, etc. (Raposo & Stoorvogel, 2013). More lately it became apparent that vesicles released by the same cell type are heterogeneous and can be classified into at least three classes based on their mode of biogenesis: (1) exosomes (30–130 nm in diameter), which originate from multivesicular endosome (MVE); (2) microvesicles (100–1000 nm in diameter), which are shed from the plasma membrane; (3) apoptotic bodies (1–4 µm in diameter), which are released from fragmented apoptotic cells during late stages of cell death (Raposo & Stoorvogel, 2013). Various purification procedures including sequential centrifugation protocols have been proposed to separate these vesicles for further analysis. Biochemical and proteomic analyses showed that exosomes contain specific protein set reflecting their intracellular site of formation. Exosomes from different cell types contain endosome-associated proteins, e.g., tetraspanins (CD9, CD63, CD81, CD82), annexins, Rab GTPases and flotillin. Exosomes are also enriched in proteins involved in MVE formation (Tsg101 and Alix), chaperones (Hsc73 and Hsc90) and cytoskeletal proteins (Raimondo et al., 2011). Furthermore, exosomes contain proteins that are specific to the cells from which they are derived (Choi et al., 2012; Sandvig & Llorente, 2012).

The research in the exosome field has exploded rapidly after the discovery that these microvesicles transport a large number of mRNA and miRNA (Valadi et al., 2007) and that exosomal mRNAs could be translated into proteins by recipient cells (Ratajczak et al., 2006; Valadi et al., 2007) and exosomal miRNAs are able to modulate gene expression in recipient cells (Mittelbrunn et al., 2011). Interestingly, certain mRNAs and miRNAs were identified as highly enriched in exosomes compared to that of the host cells indicating the existence of selective sorting mechanism controlling incorporation of RNA into exosomes (Skog et al., 2008; Valadi et al., 2007). Previously, we demonstrated that exosomal RNAs share specific sequence motifs that may potentially function as cis-acting elements for targeting to exosomes (Batagov, Kuznetsov & Kurochkin, 2011).

Given the fact that exosomes carry complex biological information consisting of proteins, lipids and RNAs, it is not surprising to find that they have been implicated in a variety of physiological and pathological conditions. Skokos et al. (2001) reported, for example, that mast cells communicate with other cells of the immune system via exosomes promoting mitogenic activity in B and T lymphocytes. A number of studies have demonstrated the role of exosomes in the development of the nervous system, synaptic activity, neuronal regeneration, neuron-glia communication and protection against injury (Lai & Breakefield, 2012). Furthermore, exosomes can be involved in the pathogenesis of cancer and degenerative diseases. The fact that tumor cells release a large amount of exosomes was initially demonstrated in ovarian cancer patients (Taylor, Homesley & Doellgast, 1983). Exosomes were shown to be secreted by various tumor cells including those derived from breast (King, Michael & Gleadle, 2012), colorectum (Silva et al., 2012), brain (Graner et al., 2009), ovarian (Escrevente et al., 2011), prostate (Mitchell et al., 2009; Nilsson et al., 2009), lung (Rabinowits et al., 2009), and bladder (Welton et al., 2010) cancer. A significantly higher amount of exosomes was found in plasma from lung cancer patients compared to that of control individuals (Rabinowits et al., 2009). In colorectal cancer patients, the amount of plasma circulating exosomes was constitutively higher than in normal healthy individuals showing the direct correlation between exosomes quantity and malignancy (Silva et al., 2012).

Manipulating tumor cells to decrease the release of exosomes followed by their injection into immunocompetent mice led to a significantly slower tumor growth compared to that of unperturbed cells (Bobrie et al., 2012). It has been shown that exosomes which are released from tumor cells are able to transfer a variety of molecules, including cancer-specific, to other cells (Al-Nedawi, Meehan & Rak, 2009; Muralidharan-Chari et al., 2009) to manipulate their environment, making it more favorable for tumor growth and invasion. Glioblastoma-derived exosomes were found to be enriched in angiogenic proteins that allowed them to stimulate angiogenesis in endothelial cells (Skog et al., 2008). Melanoma exosomes were shown to be instrumental in melanoma cell dissemination via alterations in the angiogenic microenvironment. Hood et al. demonstrated that metastatic factors responsible for the recruitment of melanoma cells to sentinel lymph nodes are upregulated by melanoma exosomes themselves (Muralidharan-Chari et al., 2009). Exosomes shed by human MDA-MB-231 breast carcinoma cells and U87 glioma cells were capable of conferring the transformed characteristics of cancer cells onto normal fibroblasts and epithelial cells in part due to transferring tissue transglutaminase and fibronectin (Hood, San & Wickline, 2011).

Because of their small size exosomes have an ability to penetrate intercellular contacts to reach distant parts of the body with the help of the blood stream and other body fluids. Exosomes have been purified from human plasma, serum, bronchoalveolar fluid, urine, tumoral effusions, epididymal fluid, amniotic fluid and breast milk (Raposo & Stoorvogel, 2013). Since exosomes possess characteristic protein and RNA signatures of their host cells, analysis of exosomes in various body fluids can be potentially utilized for non-invasive diagnostics of cancer and other disorders. For example, aggressive human gliomas often express a truncated and oncogenic form of the epidermal growth factor receptor, known as EGFRvIII. The tumour-specific EGFRvIII was detected in serum microvesicles from glioblastoma patients (Skog et al., 2008). High stability of exosomal RNA (Skog et al., 2008; Valadi et al., 2007) and ease of RNA detection by highly sensitive PCR makes detection of exosomal RNA an attractive approach for the discovery of biomarkers. Indeed, mRNA variants and miRNAs characteristic of gliomas could be detected in serum microvesicles of glioblastoma patients (Skog et al., 2008). Expression profiles of serum microvesicle mRNA by microarrays correctly separated individuals with glioblastoma from normal controls (Noerholm et al., 2012). Of all RNA species, secreted miRNAs were most frequently utilized toward discovery of body fluid-based biomarkers perhaps because miRNA expression profiles are more informative than mRNA expression profiles in a number of diseases (Grady & Tewari, 2010). In one study, analysis of plasma- and serum-derived microvesicles revealed 12 miRNAs differently expressed in prostate cancer patients compared to that of healthy controls and 11 miRNAs upregulated in patients with metastases compared to that of patients without metastases (Bryant et al., 2012). Interestingly, exosomes released by breast cancer cells can be separated into different classes depending on their miRNAs content (Palma et al., 2012). Cells undergoing malignant transformation produced exosomes containing selective miRNAs, whose release is increased by malignant transformation, in contrast to cells that are not affected by malignancy, whose exosomes are packed with neutral miRNAs (Palma et al., 2012). The changes in exosomal miRNA cargo could provide a signature of the presence of malignant cells in the body.

The majority of reported to date exosomal miRNA and mRNA profiles have been generated using microarray approaches that suffer from several limitations. Microarrays are biased for investigation of already discovered transcripts. In addition, there is potential for cross-hybridization of RNAs that are highly related in sequence. Recently developed next generation RNA sequencing technology (RNA-Seq) allows detection of all RNA subtypes as well as of unannotated transcripts and has a high sensitivity toward identification of low-abundance RNAs. In case of exosomes, this approach was applied only for the analysis of small (20–70 nt) RNAs (Bellingham, Coleman & Hill, 2012; Nolte-’t Hoen et al., 2012).

In this study, we utilized the RNA-Seq approach to characterize the transcriptomes of exosomes secreted by two metastatic human breast cancer cell lines. We describe optimized computational workflow to analyze data generated by the Ion Torrent semiconductor chip-based technology. We have identified and profiled RNA species present in exosomes and host cells and discuss the utility of exosomal RNA as potential breast cancer-specific biomarkers.

Materials and Methods Cell culture

Human breast cancer cell lines MDA-MB-436 (ATCC® HTB-130™) and MDA-MB-231 (ATCC® HTB-26™) were maintained at 37°C in 5% CO2 and cultured in DMEM/F12 supplemented with 10% FBS. 48 h prior to exosome collection, cells were washed 3 times with PBS and the medium was changed to serum-free CCM5 medium (Thermo Scientific).

Exosome extraction

Exosomes were isolated and purified from the media of MDA-MB-436 and MDA-MB-231 cell cultures using sequential centrifugation protocol. Briefly, media was collected and cellular debris was removed by centrifugation at 3,000×g for 10 min. The supernatant was centrifuged at 17,000×g for 30 min at 4°C. The supernatant was collected and centrifuged at 100,000×g for 2 h to pellet exosomes. Exosomes pellets were then washed in filtered PBS and re-centrifuged at 100,000×g, the supernatant was removed and the final exosomal pellet was re-suspended in 100 µl PBS.

Transmission electron microscopy

A 50 µl aliquot of exosomes was absorbed onto formvar carbon coated nickel grid for 1 h. The grid was positioned with the coating side facing the drop containing exosomes. Then the grids were washed by sequentially positioning them on top of the droplets of 0.1 M sodium cacodylate, pH 7.6 and then fixed in 2% paraformaldehyde and 2.5% glutaraldehyde in 0.1 M sodium cacodylate, pH 7.6 for 10 min. Then grids were washed again with 0.1 M sodium cacodylate, pH 7.6 and contrasted with 2% uranyl acetate in 0.1 M sodium cacodylate, pH 7.6 for 15 min. After washing, the grids were incubated on top of the drop of 0.13% methyl cellulose and negatively stained with 0.4% uranyl acetate for 10 min, air dried for 5 min and examined with a JEM-2200FS transmission electron microscope operated at 100 kV.

Nanoparticle tracking analysis

Supernatants containing vesicles were analyzed using a Nano-Sight LM10 instrument equipped with a 405 nm laser (NanoSight, Amesbury, UK) at 25°C. Particle movement was tracked by NTA software (version 2.2, NanoSight) with low refractive index corresponding to cell-derived vesicles. Each track was then analyzed to get the mean, mode, and median vesicle size together with the vesicle concentration (in millions) for each size.

RNA isolation and analysis

Total RNA from exosomes (MDA-MB-436 and 231) and cultured cells (MDA-MB-231) were isolated using the TRIzol reagent (Invitrogen). RNA quality and concentration were assessed with the Agilent 2100 Bioanalyzer (Thermo Scientific). Cellular RNA was analyzed using RNA 6000 Nano Kit (Agilent) and exosomal RNA was analyzed using RNA 6000 Pico kit (Agilent).

RNA-seq with ion torrent personalized genome machine (PGM)

Two hundreds ng of exosomal RNA and 3 µg of total cell RNA was used as the starting input for RNA-Seq library preparation. Sequencing was performed by AITbiotech company (Singapore). Briefly, total cell RNA was treated with RiboMinus Eukaryote kit (Life Technologies) to remove rRNA. Then, exosomal and rRNA-depleted cellular RNAs were fragmented using RNaseIII. Whole transcriptome library was constructed using the Ion Total-RNA Seq Kit v2 (Life Technologies). Bar-coded libraries were quantified with qRT–PCR. Each library template was clonally amplified on Ion Sphere Particles (Life Technologies) using Ion One Touch 200 Template Kit v2 (Life Technologies). Preparations containing bar-coded libraries were loaded into 318 Chips and sequenced on the PGM (Life Technologies).

cDNA synthesis and quantitative real-time PCR (RT-PCR)

Two-step RT-PCR was performed using the QuantiTect Reverse Transcription Kit (QIAGEN GmbH, Hilden, Germany) according to manufacturer’s protocol. RT-PCR was performed in a Rotor-Gene (Qiagen) using a SYBR Green PCR Master Mix. Primer sequences are provided in Table S3. All reactions with template and without template (negative controls) were run in duplicate and averaged. GAPDH was used as internal control for mRNA. Ct value was detected for each gene meaning the cycle number at which the amount of amplified gene of interest reaches a fixed threshold. Relative quantification (fold change) was determined for the host cells and exosomal genes expression and normalized to an endogenous control GAPDH relative to a calibrator as 2−ΔΔCt (where ΔC = (Ct of gene of interest) – (Ct of endogenous control gene (GAPDH) and ΔΔCt = (ΔCt of samples for gene of interest) – (ΔCt of calibrator for the gene of interest). Melting curves of each amplified products were analyzed to ensure uniform amplification of the PCR products.

Bioinformatics analysis Raw reads filtering

Raw reads generated by sequencing were subjected to several quality checks. The low quality reads were removed by read trimming and read filtering. Read trimming included removal of adapter sequences, removal of the 3′ ends with low quality scores and trimming based on High-Residual Ionogram Values. Filtering of entire reads included removal of short reads, adapter dimers, reads lacking sequencing key, reads with off-scale signal and polyclonal reads. Subsequent analysis was performed with high quality reads which passed through the described above filtering steps.

Reads mapping

Bowtie 2 version 2.1.0 was used to align all high-quality reads to rRNA sequences including 28S (NR_003287.2), 18S (NR_003286.2), 5S (NR_023379.1), and 5.8S (NR_003285.2) rRNA. Reads mapped to rRNA sequences were filtered out while the rest of the reads were mapped to the human genome. The high-quality reads were mapped to hg19 build of the human genome from University of California Santa Cruz (UCSC) genome browser database (Meyer et al., 2013) using TopHat version 2.0.6 with the aligner Bowtie 2.0.5 (Kim et al., 2013; Langmead & Salzberg, 2012) with their default parameters in end-to-end mode (-b2-sensitive) and defining splice-junctions based on known splice-junctions (-G). To classify the reads into known and unknown genes, the BAM file generated by Tophat2 was intersected to known gene (RefGene and GENCODE built V14 from UCSC database) using BEDtools (Quinlan & Hall, 2010) and was used to count the number of reads by SAMtools (Li et al., 2009).

Post-processing of the aligned reads

The mapped reads were further manipulated by removing the reads that mapped to multiple locations. In particular, the short aligned reads with the length of <20 nucleotides were eliminated to avoid the alignment errors such as mapping to multiple genomic locations. Further filtering included the removal of the low quality reads which fall below the mapping quality score of 10 (-q 10) using SAMtools. For the coverage search, the BAM file generated by Tophat2 was converted to BED format with option (-split) using BEDtools. The BED file was converted again to BAM format using BEDtools. We then developed python script (using pysam as part of the scripts) to calculate the number of reads and read coverage in exons and protein-coding sequence (CDS) regions consecutively.

RNA abundance calculation

RNA abundance was estimated with the help of Partek Genomics Suite software (Partek Inc., St. Lous, MO) using Reference Sequence Gene (RefSeq Gene) and GENCODE annotation built version 14 of those not overlapped with RefSeq Gene from UCSC genome browser. The Expectation-Maximization (E/M) Algorithm (Xing et al., 2006) was used to estimate the most likely relative expression levels of each gene isoform. Partek’s algorithm was used to quantify the gene isoform expression level as reads per kilo base per million mapped reads (RPKM).

Functional analysis of genes

Database for Annotation, Visualization, and Integrated Discovery (DAVID) (version 6.7) was used to identify gene functional annotation terms that are significantly enriched in particular gene lists with the whole Human genes as the background (Huang, Sherman & Lempicki, 2009). A list of gene symbols was generated for each dataset and was used as input into DAVID. DAVID calculates a modified Fishers Exact p-value to demonstrate Gene ontology (GO) and KEGG molecular pathway enrichment, where p-values less than 0.05 after Benjamini multiple test correction are considered to be strongly enriched in the annotation category. We also used a count threshold of 5 and the default value of 0.1 for the EASE (enrichment) score settings. We used more specific GO term categories provided by DAVID, called GO FAT, to minimize the redundancy of general GO terms in the analysis to increase the specificity of the terms.

Results Characterization of exosomes released by breast cancer cells

Exosomes were isolated from two breast cancer cell lines, MDA-MB-436 and MDA-MB-231 using classical ultracentrifugation protocol. The size distribution and amount of exosomes were analyzed using NanoSight LM10 nanoparticle tracking analysis (NTA). NTA showed that MDA-MB-231 cells released 4 × 106 vesicles per cm2 of growth area per 48 h that were predominantly 115 nm in size. MDA-MB-436 cells released 1.04 × 107 vesicles per cm2 of growth area per 48 h that were predominantly 91 nm in size (Fig. 1). The size of exosomes released from both cell lines ranged from ∼70 nm to ∼300 nm. An examination of the purified vesicles using transmission electron microscopy revealed that they had the size (∼50–100 nm) and morphology (Fig. 2) typical of that of exosomes.

Figure 1: Analysis of exosomes produced by breast cancer cell lines, MDA-MB-436 and MDA-MB-231, with Nanosight LM10-HS instrument. Characterization of exosomal RNA

RNA was isolated from exosomes released by both breast cancer cell lines. Total RNA was also extracted from MDA-MB-231 cell line as a control host cell line that produced exosomes. Bioanalyzer data revealed that exosomes contain a broad range of RNA sizes (30–500 nt) and have very small amount of intact rRNA (5.2% in MDA-MB-231 exosomes and 5.6% in MDA-MB-436 exosomes) (Fig. 3) consistent with previous reports on exosomal RNA (Rabinowits et al., 2009; Valadi et al., 2007).

Figure 2: TEM image of the exosomes produces by MDA-MB-436 cell line. Electron microscopy allowed visualizing membrane-bound nanovesicles sized ∼100 nm. White arrowheads pointing to the exosomes. Scale bar = 100 nm. Figure 3: Analysis of RNA from cells and exosomes by Bioanalyzer. Exosomal and total cell RNA was analyzed with PicoChip and NanoChip, respectively. Exosomal RNA deep sequencing using Ion Torrent technology

We utilized the Ion Torrent sequencing technology to profile exosomal RNA produced by MDA-MB-436 and MDA-MB-231 cell lines, as well as RNA obtained from host cell line MDA-MB-231. Based on RNA quality assessment using Bioanalyzer profiling (Fig. 3) we performed rRNA depletion for cellular RNA but not for exosomal RNA.

Identification of appropriate alignment tool for efficient capture of splice-junction reads produced by the Ion Torrent technology

RNA-Seq computational analysis workflow is presented in Fig. 4. The single-end RNA reads generated from sequencing of the exosomal and host cell libraries were trimmed to remove adapter sequences and then filtered. After filtering out low-quality reads, the high-quality reads had a varying length ranging from 6 bp to >300 bp. These preceded high-quality reads were considered for further analysis. The total amount of reads for RNA from MDA-MB-231 cells was about 4.8 M. RNA-Seq resulted in ∼3.5 M and ∼3.2 M reads for RNA from MDA-MB-436 and MDA-MB-231 cells derived exosomes, respectively. At first, the reads were aligned to rRNA sequences including 28S, 18S, 5S, and 5.8S rRNA (see Methods). 24% of the reads in cellular and more than 80% of the reads of both exosome samples were mapped to rRNA sequences (Fig. S2). The rest of the reads were mapped to the human genome (UCSC; hg19 built) with the help of TMAP aligner program in accordance with the Ion Torrent recommended pipeline (Technologies) (data not shown). However, the use of TMAP resulted in misalignment of the splice-junction reads or reads containing multiple exons to the genome (Fig. S1). Therefore, we used alternative aligner Tophat 2.0.6 along with the Bowtie 2.0.5 for the read mapping.The mapped results are shown in Fig. S2. In total, this alignment produced more than 4.3 M (∼90%) reads of MDA-MB-231 cellular RNA that could be mapped to rRNA sequences and the human genome. For the MDA-MB-231 and MDA-MB-436 cell-derived exosomes, these alignments resulted in ∼2.8 M (∼90%) and ∼3.2 M (∼91%) of mapped reads, respectively (Fig. S2). More than 80% of mapped reads from exosomal samples were found to be mapped to rRNA sequences. We further investigated the reads of both exosomal RNA samples, which mapped to rRNA sequences. These reads were counted and plotted as read density over each rRNA sequence (shown in Fig. S3A). The mapped reads covered entire length of all rRNA sequences. The major fractions of mapped reads were 28S and 18S rRNA (Fig. S3B).

Figure 4: Flowchart of RNA-seq data analysis. The raw reads are exposed to pre-alignment quality checks including the removal of adaptor sequences and low quality reads. The high quality reads are then mapped to rRNA sequences using Bowtie2 version 2.1.0. The non-mapped rRNA reads were mapped to human genome hg19 build of the human genome using TopHat version 2.0.6 with the aligner Bowtie 2.0.5. After mapping, low mapping quality reads less than 10; short reads with length less than 20 base pairs and multi- reads were removed. Estimation of read counts and read coverage on mapped reads where over 90% of exon (non-coding) and CDs (for coding) in transcript isoforms of RefGene and/ or GENCODE v14 gene models. The EM algorithm along with GENCODE v14 annotations was used to estimate the read count and reads per kilo base per million mapped reads (RPKM) on mapped transcripts.

Reads mapped to multiple locations in the genome have been eliminated to obtain uniquely mapped reads. Subsequent downstream analysis was performed with the high quality reads which had the read length greater than 20 bps and mapping quality score of above 10 (see Methods). As a result, about 81, 76, and 70% out of all mapped reads were considered as high quality mapped reads for MDA-MB-231 and MDA-MB-436 exosomal RNA, and MDA-MB- 231 cellular RNA, respectively.

Approximately 97% (∼2 M reads) of reads from both exosomal RNA samples were mapped to rRNA sequences (Fig. 5) and ∼2% of the reads were mapped to known genes (RefSeq and/or GENCODE gene models). At the same time, for the MDA-MB-231 cellular RNA, for which rRNA depletion step was performed, the majority of the reads (∼58%) was mapped to known genes (Fig. 5). To increase the number of reads for other RNAs we attempted to deplete rRNA with the RiboMinus™Eukaryote Kit recommended by Ion Total RNA-Seq Kit protocol. As expected, this protocol efficiently pulled down rRNA from cellular RNA (>60%) but had little effect on removal of rRNA from exosomal RNA (Fig. S4). The failure to deplete exosomal fragmented rRNA can be explained by the design of the RiboMinus™Probe. It consists of 2 probes each for 5S, 5.8S, 18S and 28S RNA. Because the probe size is 22–25 nucleotides, many fragments of rRNA are not targeted.

Figure 5: Distribution of uniquely mapped RNA-seq reads among transcriptome. Reads which overlapped with annotated gene models (RefSeq and/or GENCODE) are termed as “known genes”. Reads that placed outside of annotated gene models are termed as “unknown”. Reads which are mapped to rRNA sequences including 5S, 5.8S, 18S, and 28S rRNA are named as “rRNA”. Content of cellular and exosomal transcriptomes

In total, 16,086 transcripts (11,657 genes) were detected with a normalized RPKM value of greater than 1.0 at least in one sample. We used Integrative Genomics Viewer (IGV) version 2.1.21 (Thorvaldsdottir, Robinson & Mesirov, 2013) to visualize mapped reads and to check the coverage across human transcriptome. We observed frequently misannotated transcripts in exosome samples with high RPKM value in those cases when only small parts of the transcripts were covered by the reads (Fig. 6). This was the effect of low depth sequencing caused by the presence of a large amount of reads representing rRNA. Therefore, we implemented more stringent criteria to obtain full-length expressed genes by filtering genes based on the RNA reads coverage. Reads which cover over 90% of protein-coding sequence (CDS) of protein-coding genes and over 90% of exons in non-coding sequence of non-coding genes were considered for further analysis. To identify protein-coding genes in exosomal samples, the mapped reads of exosomal RNA were pooled with estimated CDS coverage of >90% for both exosomal reads. The pooled mapped reads were used to calculate CDS in each exosomal sample as >50% of coverage.

Figure 6: Example of low coverage transcript but very high RPKM in AURKAIP1 and ATPIF1 genes. (A) AURKAIP1 gene from chromosome position chr1:1,309,009–1,310,847 is shown using Integrative Genomic Viewer. Among the three variants, the maximum value of protein-coding sequence (CDS) coverage, read count and RPKM is shown in the right panel of read mapping. Both the exosomes shows very low coverage (7–22%) with read counts of 4, whereas the RPKM value is 65.44 and 80.78 RPKM for exosomes of MDA-MB-231 and MDA-MB-436, respectively. (B) ATPIF1 gene from chromosome position chr1:28,562,494–28,564,655 is visualized. The MDA-MB-231 exosomes exhibit high CDS coverage (91%) with an exon count of 4.

Using these criteria, we obtained lower number of annotated transcripts (6,187 transcripts or 3,437 genes) compared to that when RPKM values were considered (Fig. S5 and Table S1). As a result, some transcripts showed high coverage with CDSs criteria (for example, ATPIF1) in exosomal RNA from MDA-MB-231 cells, even when the number of reads was small (less than 5) (Fig. 6B). Such genes were also taken into consideration in our analysis. In total, 5821 (3115 genes) and 187 (115 genes) protein-coding transcripts were detected based on the RNA reads coverage in cellular and exosomal samples, respectively. For non-coding genes, 360 (317 genes) and 131 (131 genes) transcripts were detected based on the RNA reads coverage for cellular and exosomal samples, respectively. Analysis of these transcripts revealed that they represented 90.8% of protein-coding genes and 9.2% of non-coding genes for host cell sample; while exosomal RNA samples represented 50.4% and 49.6% of protein-coding and 47.6% and 52.4% of non-coding genes in MDA-MB-231 and MDA-MB-436 cells derived exosomes, respectively (Table 1). We found that 98.3% of protein-coding and 97.7% of non-coding exosomal transcripts were present in host cells (Fig. 7).

Table 1:

Amount of genes in cellular and exosomal RNA based on 90% coverage over protein-coding sequence of genes and exons of non-coding genes.

Note the large proportion of non-coding transcripts in exosomal RNA. Transcript type MDA-MB-231cellulargenes (%) MDA-MB-231exosomalgenes (%) MDA-MB-436exosomalgenes (%) Protein-coding 90.8 50.4 47.6 Non-coding 9.2 49.6 52.4 Figure 7: Venn diagram presents overlap among protein-coding and non-coding gene symbols in exosomes and cells. Almost all the genes in both exosomal RNA are the subset of cellular genes. Exosomes are enriched in mRNAs functioning in protein translation and rRNA processing

We performed Gene Ontology (GO) enrichment analysis using the DAVID bioinformatics resource, which employs a Fisher’s Exact Test with Benjamini–Hochberg correction. A total of 377 enriched GO categories were derived using a P-value cut-off of p < 0.05 for 3115 host MDA-MB-231 cellular genes: 286 Biological Process (BP) categories and 91 Molecular Function (MF) categories (Table S2). In total, 18 GO categories including 11 BP and 7 MF were derived from 115 exosomal genes from both cell lines. Figure 8 shows top 20 BP categories of the host cellular genes which include translation process, cell cycle, RNA processing, etc. (Fig. 8A and Table S2B). At the same time exosomal genes revealed biological processes in translation, ribosome biogenesis, rRNA and ncRNA processing GO categories (Fig. 8B and Table S2C). Since the major fraction of exosomal samples were rRNA species, significantly lower number of mRNA could be detected in exosomal samples. We hypothesized that the genes detected from exosomal samples should be highly expressed in the cells. To test the hypothesis, we performed GO enrichment analysis for 115 top expressed genes from MDA-MB-231 cellular sample. The top 10 GO terms (Fig. 8C and Table S2D) of these top expressed genes are the same as in exosomal fraction (Fig. 8B and Table S2C). We further created box plot of 115 exosomal genes in MDA-MB-231 cellular sample using expression values (RPKM) (Fig. 9). These data clearly showed that exosomes are enriched in genes which are highly expressed in the host cells.

Figure 8: Gene Ontology (GO) enrichment analysis of genes detected in cellular and exosomal RNA from breast cancer cell lines. The significant GO terms was defined as described in Materials and Methods. (A) Top 20 significant GO terms found in MDA-MB-231 cellular genes (3115 genes). (B) Significant GO terms found in exosomal genes from both cell-lines (MDA-MB-231 and MDA-MB-436). (C) Top 20 significant GO terms found in the most expressed 115 genes from MDA-MB-231 cellular genes. The asterisks (*) indicate GO terms that present in exosomal genes. Figure 9: Expressed genes in exosomes found to be highly expressed in the host cells. The box plot indicates expression level of all genes in cellular samples as compared to that of genes which were found to be express in exosomes. Wilcoxon rank sum test represents significant difference in expression level of the two sets.

Non-coding transcripts could be classified into 13 categories (see Table 2). Both exosomal and cellular samples contained small nucleolar RNA (snoRNA) as major species. The second most abundant class of non-coding transcripts according to GENCODE annotation was “non-coding RNA” in cellular sample and small nuclear RNA (snRNA) in exosomal samples. Overall, the top five RNA categories represented about 90% of all noncoding genes in both exosomal and cellular RNA.

Table 2:

Amount of non-coding gene symbol in cellular and exosomal RNA based on 90% coverage over exons of non-coding transcripts.

In exosomes, the top 5 non-coding gene types including small nucleolar RNA, small nuclear RNA, Mt_tRNA, microRNA, and non-coding RNA represents about 90% of non-coding genes in both exosome samples. Gene type MDA-MB-231cellular(gene symbols) MDA-MB-231exosomal(gene symbols) MDA-MB-436 exosomal(gene symbols) small nucleolar RNA 214 83 51 small nuclear RNA 23 11 10 Mt_tRNA 13 7 4 microRNA 34 6 2 non-coding RNA 42 1 0 guide RNA 20 0 1 vault RNA 3 0 3 rRNA 1 1 2 RNase MRP RNA 1 1 1 RNase P RNA 1 1 1 Mt_rRNA 1 1 0 lincRNA 1 0 0 telomerase RNA 1 0 0 Validation analysis of RNA-seq data by qRT-PCR

Based on RNA-Seq data we evaluated presence and enrichment of several mRNA transcripts in exosomal RNA - RAB13, RPPH1, EEF1A1, FTH1, FTL and RPL28. qRT-PCR analysis showed presence of all selected transcripts in exosomal samples (Fig. 10A). Figure 10B demonstrates that the fold-change of qRT-PCR results are consistent with the fold-change of RNA-seq data.

Figure 10: Validation of RNA-seq data by qRT-PCR. (A) Ct values for six mRNA transcripts which were detected in exosomal samples by RNA-seq are shown. (B) Comparison of different expression values (RPKM; MDA-MB-436/RPKM; MDA-MB-231) detected by RNA-Seq (dark-grey columns) and qRT-PCR (light-grey columns) for six differently expressed genes. Discussion

Until recently, the changes in gene expression during various biological processes have been analyzed using microarray approaches that focus largely on the behavior of protein-coding transcripts. Because microarrays are based on hybridization, they have high background owing to cross-hybridization, they have a limited dynamic range of detection and they rely upon known structures of genes. Development of RNA-Seq technology permitted comprehensive analysis of whole transcriptomes with the single nucleotide resolution allowing quantification of most RNA molecules expressed in the cell or tissue (Mortazavi et al., 2008). In this study, we used the Ion Torrent platform to interrogate transcriptomes of exosomes released from two metastatic breast cancer cell lines. At the time of conducting our analysis this technology produced relatively low number of reads, yet we selected it as it provided the longest reads than any other sequencing platform. This feature of the Ion Torrent technology was essential as we dealt with RNA isolated from exosomes whose nature and composition are still not well established. RNA-seq data analysis is complicated by the intricacy of dealing with large datasets, reads quality control, alignment procedure etc. Different workflows and several algorithms have been proposed to map reads to the reference genome and to perform data analysis (Chen, Wang & Shi, 2011; Mortazavi et al., 2008). Comparison of expression levels across different samples and experiments is often difficult and requires complicated normalization methods and these are still under active development. The situation is even more complex in case of exosomal transcriptomes that differ significantly from cellular transcriptomes.To address this issue, we developed in this study customized bioinformatics workflow and demonstrated its utility for analysis of exosomal RNA. Because the Ion Torrent platform produces reads with different length the dedicated algorithm for their alignment to the genome called TMAP was recommended. We found out, however, that this tool does not allow satisfactory mapping of reads that contain splice-junctions or span introns. Therefore, we choose alternative aligning tool TopHat2 (with Bowtie2) which could handle reads of varying length and identify splice-junctions based on known splice-junctions as well as allowed the discovery of new splice-junctions (Kim et al., 2013; Langmead & Salzberg, 2012).

We observed a large proportion of reads mapped to rRNA regions in exosomal samples. This was surprising given the fact that intact 18S and 28S rRNA peaks were almost undetectable in exosomal RNA (Fig. 3). This observation suggested that the majority of exosomal rRNA is fragmented. Exosomal rRNA fragments could be mapped over entire length of rRNA (Fig. S3). Fragmented 28S and 18S rRNA were major rRNA species present in exosomes. The reads mapped to 28S and 18S rRNA were distributed almost equally in exosomal and cellular RNA samples. What is the possible reason for generation of exosomal rRNA fragments? RNases present in cell culture conditioned medium are unlikely to contribute to rRNA fragmentation since exosomal membranes provide protection against RNase attack. Indeed, treatment of the exosomal preparations with RNase A did not lead to significant difference between treated and control samples in RNA size distribution (data not shown). In the study of Skog et al. (2008) RNase treatment of the glioblastoma exosomes led to a very insignificant (less than 7%) decrease in RNA suggesting that exosomal RNA is inaccessible for RNase from outside the vesicles. A possibility exists that the rRNA fragments are generated after secretion by RNases originated from the host cells and incorporated into exosome vesicles. Alternatively, rRNA fragments could be generated inside cells prior to their release to exosomes. Another class of RNA, tRNA is represented in exosomes mainly by its fragments (Nolte-’t Hoen et al., 2012). The most abundant tRNA hits in exosomal RNA are all located at the 5′ end of mature tRNAs (Nolte-’t Hoen et al., 2012).

Regardless the biogenesis of rRNA fragments, it is advisable to perform rRNA depletion step even in the absence of visible rRNA peaks on RNA profiles. This procedure would allow obtaining much higher sequencing depth for other RNA species. Our attempt to deplete fragmented rRNA with the popular RiboMinus™Eukaryote Kit failed because of the design of the probes. Because the probes size is short, many fragments of rRNA are not targeted. The use of larger number of longer probes is expected to produce a more efficient way of pulling-down fragmented rRNA. This technical aspect of working with exosomal RNA samples should be certainly considered in the future studies.

As a result of large rRNA presence in exosomal samples we observed only 2% of mapped reads to known transcripts using RefSeq and GENECODE gene models. Moreover, with RPKM value >1 we observed a large amount of misannotation due to poor coverage of the reads over transcripts. Therefore, we suggested another approach, namely filtering genes based on reads coverage over protein coding sequence for mRNA or exons for non-coding RNA. This procedure allowed us to achieve more than 90% coverage for protein-coding and non-coding regions which we considered as highly reliable for functional classification. This approach was helpful to reveal highly expressed genes in exosomes which could be potentially used as noninvasive breast cancer markers.

We report that exosomes are carrying mRNAs that are highly expressed in the host breast cancer cells (Fig. 9). Thus exosomal transcriptomes are representative of their cells of origin and should provide a platform for detection of tumor specific markers. GO analysis revealed that main biological and molecular functions of both cellular and exosomal transcripts are enriched in proteins involved in ribosome biogenesis, translational elongation, ribosomal subunit assembly and rRNA processing. What could be the significance of these functions in exosomal transcriptome? Exosome-associated mRNAs were shown to be translated into proteins by recipient cells (Ratajczak et al., 2006; Valadi et al., 2007). We hypothesize that upon arrival to the recipient cells exosomal mRNAs are translated into proteins supporting ribosomal functions to ensure efficient translation of other exosomal mRNAs within a cellular compartment where exosome content is released. Valadi et al. (2007) also described presence of mRNAs for many ribosomal proteins in exosomes secreted by mouse mast cell line. Interestingly, Graner et al. (2009) demonstrated the presence of elongation and translation factors in exosomes derived from brain tumor.

In conclusion, here we demonstrated for the first time that fragmented rRNA is a major species of exosomal RNA. Proposed here custom bioinformatics workflow allowed us to reliably detect other, non-ribosomal RNAs under conditions of limited read numbers. Classification and quantification of the RNA-Seq data revealed that exosomal transcripts are representative of their cells of origin and thus could form basis for detection of tumor specific markers. This information can also be used for getting insights in the molecular underpinnings of biological effects produced by these microvesicles. Finding that exosomes bear mRNAs encoding the necessary components to build-on-site ribosomes provides a valuable insight into biological function of these vesicles.

Supplemental Information Comparison of mapping quality between the alignment tools TopHat2.0.6 and TMAP 0.3.7

FTL gene (chromosome position chr19:49,468,467-49,470,296) is selected as an example of alignment comparison. TMAP alignment resulted in poor reads mapping and absence of junctions over exon-exon region. As the same time, TopHat identifies the exon-exon splice junctions and connects the exons through a linker.

Distribution of all mapped and unmapped RNA-seq reads among genomic compartments

rRNA defined as 5S, 5.8S, 18S, and 28S rRNA sequences. Reads which overlapped with annotated gene models (RefSeq and/or GENCODE) are termed as “known genes”. Reads that placed outside of annotated gene models are termed as “unkown”.

Fragments of rRNA in exosomes represent full-length of rRNA sequence

(A) RNA read density plot represents RNA fragments which fully covers of 5S, 5.8S, 18S, and 28S rRNA sequences from exosomal RNA. (B) 18S and 28S rRNA were major fractions of rRNA species.

Analysis of rRNA depletion from MDA-MB-231 cellular and exosomal RNA using RiboMinus™ Eukaryote Kit for RNASeq

RNA was detected with PicoChip using Bioanalyzer. The depletion procedure has been performed according to the manufacturer’s protocol. Control samples (red) were treated exactly as experimental samples (blue) except they did not contain RiboMinus™ Probe.

Venn diagram of genes generated by RPKM and read coverage approaches

The detection criteria is that gene has more than 1 RPKM in at least one sample, while another approach is that gene has more than 90% coverage over protein-coding or non-coding sequence.

Two approaches of gene transcripts selection using RPKM or read coverage

(A) The Partek genomic suite output showing transcripts with RPKM >1 in at least one sample. The read counts from the transcript isoforms were estimated using EM algorithm from Partek genomic suite on RefGene and/or GENCODE v14 gene models. (B) Estimation of read coverage (in percentage) and read count of transcript. The transcripts with >90% coverage for protein-coding sequence and exonic sequence (in case of non-coding transcript) of transcript isoforms on RefGene and/or GENCODE v14 gene models are shown.

Gene Ontology (GO) enrichment analysis using the DAVID bioinformatics resource of Genes found in cellular and exosomal samples

(A) Gene lists for GO enrichment analysis. (B) GO enrichment of cellular genes. (C) GO enrichment of exosomal genes. (D) GO enrichment of top 115 expressed cellular genes.

List of primers used for qRT-PCR

Sogeti Launches Free iOS and Android Software Testing Life Cycle App: TMap Life Cycle | killexams.com real questions and Pass4sure dumps

Sogeti, a leading provider of professional technology services, specializing in Application Management, Infrastructure Management, High-Tech Engineering and Testing, has launched a free app for iOS and Android: TMap Life Cycle.

Sogeti’s TMap is the world-leading methodology for structured risk-based software testing. An essential element of TMap is the lifecycle, covering the key steps in testing strategy and execution, in the end-to-end process of achieving robust business-critical applications.

Sogeti’s TMap Life Cycle app provides this mobile-based framework, guiding software testers through the software testing lifecycle, from planning and infrastructure to control and all stages of a critical path. Using TMap’s structured framework for end-to-end test process, this app helps testing professionals track the progress of their projects. It also enables the earlier identification of defects resulting in the consistent reduction of timelines by at least 30%, lowering overall costs.

The new app is available now in the Android Marketplace and the App Store. It is compatible with Android mobile devices as well as the Apple iPhone, iPod Touch and iPads. It provides users with free downloads to support the TMap process, including checklists and templates. The app also features videos explaining test design techniques, product risk analysis and methods to determine test strategy, as well as links to software testing resources such as eBooks and whitepapers.

“TMap is a sophisticated, proven and trusted software test management methodology, relied upon by tens of thousands of software testers across the globe for structured risk-based testing,” said Dan Hannigan, National Vice President of the Managed Testing Practice for Sogeti USA. “Releasing this free app is just another extension of our primary goal, which is making structured software testing and quality assurance methods easily available to professional testers.”

To download this free app, go to the Android Marketplace or App Store, and search for “TMap Life Cycle.”

About TMapTMap (Test Management Approach) is Sogeti’s business-driven, risk-based methodology for structured software testing, relevant to organizations of all sizes and vertical markets. An adaptive method, suitable for all test situations in development environments, including new development, maintenance, waterfall/iterative/agile development, customized or packaged software, TMap addresses the key issues of quality, time and cost across the development lifecycle. The app describes different phases of the TMap lifecycle.



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Exin EX0-113 Exam (TMap Next Test Engineer) Detailed Information



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