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Next generation sequencing (NGS) technologies allow us to explore virus interactions with host genomes that lead to carcinogenesis or other diseases; however, this effort is largely hindered by the dearth of efficient computational tools.

Now, researchers at Vanderbilt University School of Medicine have developed  a new tool, VirusFinder, for the identification of viruses and their integration sites in host genomes using NGS data, including whole transcriptome sequencing (RNA-Seq), whole genome sequencing (WGS), and targeted sequencing data. VirusFinder’s unique features include the characterization of insertion loci of virus of arbitrary type in the host genome and high accuracy and computational efficiency as a result of its well-designed pipeline.

Availability: The source code as well as additional data of VirusFinder is publicly available at http://bioinfo.mc.vanderbilt.edu/VirusFinder/.

Wang Q, Jia P, Zhao Z. (2013) VirusFinder: Software for Efficient and Accurate Detection of Viruses and Their Integration Sites in Host Genomes through Next Generation Sequencing Data. PLoS One 2013 May 24;8(5), e64465. [article]

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Epidermal Growth Factor (EGF) plays an important function in the regulation of cell growth, proliferation, and differentiation by binding to its receptor (EGFR) and providing cancer cells with increased survival responsiveness. Signal transduction carried out by EGF has been extensively studied at both transcriptional and post-transcriptional levels. Little is known about the involvement of microRNAs (miRNAs) in the EGF signaling pathway. miRNAs have emerged as major players in the complex networks of gene regulation, and cancer miRNA expression studies have evidenced a direct involvement of miRNAs in cancer progression.

In this study, a team led by researchers at the Centre for Genomic Regulation (CRG), Barcelona, Spain have used an integrative high content analysis approach to identify the specific miRNAs implicated in EGF signaling in HeLa cells as potential mediators of cancer mediated functions. They used microarray and deep-sequencing technologies in order to obtain a global view of the EGF miRNA transcriptome with a robust experimental cross-validation. By applying a procedure based on Rankprod tests, they delimited a solid set of EGF-regulated miRNAs. After validating regulated miRNAs by reverse transcription quantitative PCR, they derived protein networks and biological functions from the predicted targets of the regulated miRNAs to gain insight into the potential role of miRNAs in EGF-treated cells. In addition, they analyzed sequence heterogeneity due to editing relative to the reference sequence (isomiRs) among regulated miRNAs.

The researchers propose that the use of global genomic miRNA cross-validation derived from high throughput technologies can be used to generate more reliable datasets inferring more robust networks of co-regulated predicted miRNA target genes.

• Llorens F, Hummel M, Pantano L, Pastor X, Vivancos A, Castillo E, Matllin H, Ferrer A, Ingham M, Noguera M, Kofler R, Dohm JC, Pluvinet R, Bayés M, Himmelbauer H, Del Rio JA, Martí E, Sumoy L. (2013) Microarray and deep sequencing cross-platform analysis of the mirRNome and isomiR variation in response to epidermal growth factor. BMC Genomics 14(1), 371. [Epub ahead of print]. [abstract]

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The surprising observation that virtually the entire human genome is transcribed means we know little about the function of many emerging classes of RNAs, except their astounding diversities. Traditional RNA function prediction methods rely on sequence or alignment information, which are limited in their abilities to classify the various collections of non-coding RNAs (ncRNAs). To address this, researchers from the University of Pennsylvania developed Classification of RNAs by Analysis of Length (CoRAL), a machine learning-based approach for classification of RNA molecules. CoRAL uses biologically interpretable features including fragment length and cleavage specificity to distinguish between different ncRNA populations. They evaluated CoRAL using genome-wide small RNA sequencing data sets from four human tissue types and were able to classify six different types of RNAs with ∼80% cross-validation accuracy. Analysis by CoRAL revealed that microRNAs, small nucleolar and transposon-derived RNAs are highly discernible and consistent across all human tissue types assessed, whereas long intergenic ncRNAs, small cytoplasmic RNAs and small nuclear RNAs show less consistent patterns. The ability to reliably annotate loci across tissue types demonstrates the potential of CoRAL to characterize ncRNAs using small RNA sequencing data in less well-characterized organisms.

Availability – The CoRAL source code, required genome annotation files, and prediction results are available at http://wanglab.pcbi.upenn.edu/coral.

• Leung YY, Ryvkin P, Ungar LH, Gregory BD, Wang LS. (2013) CoRAL: predicting non-coding RNAs from small RNA-sequencing data. Nucleic Acids Res [Epub ahead of print]. [article]

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Many species have evolved into diverse strains with phenotypic and genotypic variations that facilitate adaptation to different ecological niches and, in the case of pathogens, to different hosts. Whereas comparison of genome sequences reveals differences and similarities among strains, the consequences of genomic variations can be tracked by studying the functional output from the genome. RNA sequencing has been revolutionizing transcriptome analyses of both pro- and eukaryotes. However, the bioinformatics-based analysis is still lagging behind, and transcriptome features are often manually annotated, which is laborious and time-consuming. This is even more compounded for the analyses of multiple strains.

Here, a team led by researchers at the University of Würzburg and the University of Tübingen, Germany compared the primary transcriptomes of four isolates of Campylobacter jejuni, the leading cause of bacterial gastroenteritis in humans, and provide genome-wide transcriptional start site (TSS) maps using a novel automated annotation method. Their comparative RNA–seq showed that most TSS are conserved in multiple strains, but they also observed SNP–dependent promoter usage. Furthermore, the researchers identified a novel minimal RNA–based CRISPR immune system as well as strain-specific small RNA repertoires. This automated, comparative TSS annotation will facilitate and improve transcriptome annotation for a wider range of organisms and provides insights into the contribution of transcriptome differences to phenotypic variation among closely related species.

• Dugar G, Herbig A, Förstner KU, Heidrich N, Reinhardt R, et al. (2013) High-Resolution Transcriptome Maps Reveal Strain-Specific Regulatory Features of Multiple Campylobacter jejuni Isolates. PLoS Genet 9(5), e1003495. [article]

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Exosomes, endosome-derived membrane microvesicles, contain specific RNA transcripts that are thought to be involved in cell-cell communication. These RNA transcripts have great potential as disease biomarkers. To characterize exosomal RNA profiles systemically, a team led by researchers at the Medical College of Wisconsin performed RNA sequencing analysis using three human plasma samples and evaluated the efficacies of small RNA library preparation protocols from three manufacturers. In all they evaluated 14 libraries (7 replicates).

From the 14 size-selected sequencing libraries, the researchers obtained a total of 101.8 million raw single-end reads, an average of about 7.27 million reads per library. Sequence analysis showed that there was a diverse collection of the exosomal RNA species among which microRNAs (miRNAs) were the most abundant, making up over 42.32% of all raw reads and 76.20% of all mappable reads. At the current read depth, 593 miRNAs were detectable. The five most common miRNAs (miR-99a-5p, miR-128, miR-124-3p, miR-22-3p, and miR-99b-5p) collectively accounted for 48.99% of all mappable miRNA sequences. MiRNA target gene enrichment analysis suggested that the highly abundant miRNAs may play an important role in biological functions such as protein phosphorylation, RNA splicing, chromosomal abnormality, and angiogenesis. From the unknown RNA sequences, they predicted 185 potential miRNA candidates. Furthermore, they detected significant fractions of other RNA species including ribosomal RNA (9.16% of all mappable counts), long non-coding RNA (3.36%), piwi-interacting RNA (1.31%), transfer RNA (1.24%), small nuclear RNA (0.18%), and small nucleolar RNA (0.01%); fragments of coding sequence (1.36%), 5’ untranslated region (0.21%), and 3’ untranslated region (0.54%) were also present. In addition to the RNA composition of the libraries, they found that the three tested commercial kits generated a sufficient number of DNA fragments for sequencing but each had significant bias toward capturing specific RNAs. Read more

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Transcriptome analysis is a valuable tool for identification and characterization of genes and pathways underlying plant growth and development. A team led by scientists at University of Wisconsin previously published a microarray-based maize gene atlas from the analysis of 60 unique spatially and temporally separated tissues from 11 maize organs. To enhance the coverage and resolution of the maize gene atlas, they have analyzed 18 selected tissues representing five organs using RNA sequencing (RNA-Seq). For a direct comparison of the two methodologies, the same RNA samples originally used for our microarray-based atlas were evaluated using RNA-Seq. Both technologies produced similar transcriptome profiles as evident from high Pearson’s correlation statistics ranging from 0.70 to 0.83, and from nearly identical clustering of the tissues.

RNA-Seq provided enhanced coverage of the transcriptome, with 82.1% of the filtered maize genes detected as expressed in at least one tissue by RNA-Seq compared to only 56.5% detected by microarrays. Further, from the set of 465 maize genes that have been historically well characterized by mutant analysis, 427 show significant expression in at least one tissue by RNA-Seq compared to 390 by microarray analysis. RNA-Seq provided higher resolution for identifying tissue-specific expression as well as for distinguishing the expression profiles of closely related paralogs as compared to microarray-derived profiles. Co-expression analysis derived from the microarray and RNA-Seq data revealed that broadly similar networks result from both platforms, and that co-expression estimates are stable even when constructed from mixed data including both RNA-Seq and microarray expression data. The RNA-Seq information provides a useful complement to the microarray-based maize gene atlas and helps to further understand the dynamics of transcription during maize development.

• Sekhon RS, Briskine R, Hirsch CN, Myers CL, Springer NM, et al. (2013) Maize Gene Atlas Developed by RNA Sequencing and Comparative Evaluation of Transcriptomes Based on RNA Sequencing and Microarrays. PLoS ONE 8(4), e61005. [article]

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Please feel free to contact me at bhamory@theranos.com

Theranos is actively building a world-class team. Ideal candidates are currently employed or have been employed in similar types of positions and want to be part of a paradigm-shifting company doing innovative work. Candidates must be hard-working with an unfaltering determination to excel in an intense start-up environment and a desire to gain tremendous personal and career growth. Read more

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Genome-wide analyses and high-throughput screening was long reserved for biomedical applications and genetic model organisms. With the rapid development of massively parallel sequencing nanotechnology (or next-generation sequencing) and simultaneous maturation of bioinformatic tools, this situation has dramatically changed. Many in the eco-evolutionary sciences will be working with large-scale genomic data sets, and a basic understanding of the concepts and underlying methods is necessary to judge the work of others.

Here, the author briefly introduces next-generation sequencing and then focuses on transcriptome shotgun sequencing (RNA-Seq). This article gives a broad overview and provides practical guidance for the many steps involved in a typical RNA-Seq work flow from sampling, to RNA extraction, library preparation and data analysis. He focuses on principles, presents useful tools where appropriate and point out where caution is needed or progress to be expected. This tutorial is mostly targeted at beginners, but also contains potentially useful reflections for the more experienced.

• Wolf JB. (2013) Principles of transcriptome analysis and gene expression quantification: an RNA-seq tutorial. Mol Ecol Resour [Epub ahead of print]. [article]

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MicroRNAs (miRNAs) are a class of small RNAs that post-transcriptionally regulate gene expression in animals and plants. The recent rapid advancement in miRNA biology, including high-throughput sequencing of small RNA libraries, inspired the development of a bioinformatics software, miRAuto, which predicts putative miRNAs in model plant genomes computationally. Furthermore, miRAuto enables users to identify miRNAs in non-model plant species whose genomes have yet to be fully sequenced. miRAuto analyzes the expression of the 5′-end position of mapped small RNAs in reference sequences to prevent the possibility of mRNA fragments being included as candidate miRNAs.

Researchers at Seoul National University validated the utility of miRAuto on a small RNA dataset, and the results were compared to other publicly available miRNA prediction programs. In conclusion, miRAuto is a fully automated user-friendly tool for predicting miRNAs from small RNA sequencing data in both model and non-model plant species.

Availability – miRAuto is available at http://nature.snu.ac.kr/software/miRAuto.htm .

• Lee J, Kim DI, Park JH, Choi IY, Shin C. (2013) miRAuto: An automated user-friendly MicroRNA prediction tool utilizing plant small RNA sequencing data. Mol Cells 35(4), 342-7. [abstract]

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TopHat, a popular spliced aligner for RNA-seq experiments has now been succeeded by TopHat2, which incorporates many significant enhancements to TopHat. TopHat2 can align reads of various lengths produced by the latest sequencing technologies, while allowing for variable-length indels with respect to the reference genome. In addition to de novo spliced alignment, TopHat2 can align reads across fusion breaks, which occur after genomic translocations. TopHat2 combines the ability to discover novel splice sites with direct mapping to known transcripts, producing sensitive and accurate alignments, even for highly repetitive genomes or in the presence of pseudogenes.

Availability: TopHat2 is available at http://ccb.jhu.edu/software/tophat.

• Kim D, Pertea G, Trapnell C, Pimentel H, Kelley R, Salzberg SL. (2013) TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol 14(4), R36. [Epub ahead of print]. [abstract]

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The methanogenic archaeon Methanopyrus kandleri grows near the upper temperature limit for life. Genome analyses revealed strategies to adapt to these harsh conditions and elucidated a unique transfer RNA (tRNA) C-to-U editing mechanism at base 8 for 30 different tRNA species.

Here, RNA-Seq deep sequencing methodology was combined with computational analyses to characterize the small RNome of this hyperthermophilic organism and to obtain insights into the RNA metabolism at extreme temperatures. A large number of 132 small RNAs were identified that guide RNA modifications, which are expected to stabilize structured RNA molecules. The C/D box guide RNAs were shown to exist as circular RNA molecules. In addition, clustered regularly interspaced short palindromic repeats RNA processing and potential regulatory RNAs were identified. Finally, the identification of tRNA precursors before and after the unique C8-to-U8 editing activity enabled the determination of the order of tRNA processing events with termini truncation preceding intron removal. This order of tRNA maturation follows the compartmentalized tRNA processing order found in Eukaryotes and suggests its conservation during evolution.

• Su AAH, Tripp V, Randau L. (2013) RNA-Seq analyses reveal the order of tRNA processing events and the maturation of C/D box and CRISPR RNAs in the hyperthermophile Methanopyrus kandleri NAR [Epub ahead of print]. [article]

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Small RNA sequencing allows genome-wide discovery, categorization, and quantification of genes producing regulatory small RNAs. Many tools have been described for annotation and quantification of microRNA loci (MIRNAs) from small RNA-seq data. However, in many organisms and tissue types, MIRNA genes comprise only a small fraction of all small RNA-producing genes.

ShortStack is a stand-alone application that analyzes reference-aligned small RNA-seq data and performs comprehensive de novo annotation and quantification of the inferred small RNA genes. ShortStack’s output reports multiple parameters of direct relevance to small RNA gene annotation, including RNA size distributions, repetitiveness, strandedness, hairpin-association, MIRNA annotation, and phasing. In this study, ShortStack is demonstrated to perform accurate annotations and useful descriptions of diverse small RNA genes from four plants (Arabidopsis, tomato, rice, and maize) and three animals (Drosophila, mice, and humans). ShortStack efficiently processes very large small RNA-Seq data sets using modest computational resources, and its performance compares favorably to previously described tools. Annotation of MIRNA loci by ShortStack is highly specific in both plants and animals.

Availability: ShortStack is freely available under a GNU General Public License – ShortStack – Axtell Lab @ Penn State

Axtell MJ. (2013) ShortStack: Comprehensive annotation and quantification of small RNA genes. RNA [Epub ahead of print]. [abstract]

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Measures aim to ensure sustained success in a fast-changing market environment

Roche today announced changes to the management and set-up of its life-science business. The Applied Science Business Area will be dissolved and its portfolio of products integrated within Roche’s other Diagnostics Business Areas.

Following assessment of its sequencing R&D portfolio, Roche has decided to:

1. Return the ISFET project for the development of a semiconductor-based sequencing system to DNA Electronics. The project was a partnership with 454 Life Sciences.  Roche believes that it will be unable to disrupt the market with the product at launch.
2. End its partnership with IBM for the development of a nanopore-based sequencing platform due to high technical risks involved.
3. Establish a dedicated unit to focus solely on sequencing. This unit will be tasked with implementing a sequencing strategy from life-science research to clinical diagnostics, explore internal and external opportunities that can provide customers with differentiated products, and will also manage Roche’s existing sequencing business.

The price pressure and funding cuts in life-science research that have been features of the market environment for some time now are expected to continue. The organisational arrangements Roche is introducing are designed to further improve productivity and enhance the market responsiveness of its life-science business, which currently accounts for about 7 % of total Diagnostics sales.

As a consequence, the Applied Science business area will be dissolved as of the end of 2013.

Changes expected to affect approximately 110 positions in Penzberg (Germany) and 60 positions in Branford (Connecticut, USA), the home of its 454 Life Sciences business.

(read the entire press release…)

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