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Good day to all our RNA-Seq Blog subscribers!

We have some important information regarding our news blog feed.  Google has decided to shut down Feedburner.  This is the service we were using to deliver our news posts and manage our email subscriptions.  They will discontinue this service as of Oct 20th, 2012 so we have changed over to WordPress RSS feeds and Mailchimp email subscriptions.

Unfortunately, if you had subscribed to the RNA-Seq Blog in an RSS reader, you will need to update your subscription from:

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We apologize for this most annoying inconvenience.

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Sincerely,

The RNA-Seq Blog Team

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Bioinformatics Analyst  – Sanofi Group (Cambridge, MA)
…quality control pipelines for a variety of Next Generation Sequencing (NGS) applications such as DNA- seq , ChIP- seq , and RNA – seq . Develop novel statistical …
Sanofi Group (09/26/12)

Bioinformatics Specialist  – Rockefeller University (New York, NY)
…genome databases. Must have experience analyzing large Next Gen sequencing data sets (both ChIP- seq and RNA – seq ). Must be well versed in graphics/visualization …
Rockefeller University (09/26/12)

Staff Scientist Bioinformatics  – Life Technologies (Foster City, CA)
…to solve problems in one of more of mapping, assembly, variant detection, RNA – seq , structural variation, CNV, etc. is highly desirable. Responsibilities: to …
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Bioinformatics Scientist – Integrative Data…  – Eli Lilly (Indianapolis, IN)
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iHireJobNetwork (09/21/12)

Bioinformatics NGS Analyst, Biomarker Development  – Novartis Institutes for BioMedical Research (Cambridge, MA)
…implement, and apply cutting edge computational methods to analyze multiple types of NGS data (DNA- Seq , RNA – Seq , and microRNA- Seq ) * Analyze NGS data …
BioSpace.com (08/06/12)

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Gene expression is a stochastic process so biological replicates in the same treatment group do not share identical expression levels. The presence of biological variation leads to the “over-dispersion” problem, e.g. the read counts show variation greater than expected from Poisson random variables.  The authors evaluate several large public RNA-seq datasets and find that the estimated dispersion in existing methods does not adequately capture the heterogeneity of biological variance among samples.

They present Dispersion Shrinkage for Sequencing (DSS), a new empirical Bayes shrinkage estimate of the dispersion parameters that overcomes the over-dispersion problem.

The new method is implemented in an R package which is available from Bioconductor: http://www.bioconductor.org/packages/devel/bioc/html/DSS.html

• Wu H, Wang C, Wu Z. (2012) A new shrinkage estimator for dispersion improves differential expression detection in RNA-seq data. Biostatistics. [Epub ahead of print]. [article]

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Researchers at Uppsala University, Sweden have used a comprehensive simulation approach to explore how various features of the transcriptome (complexity, degree of polymorphism π, alternative splicing), technological processing (sequencing error ε, library normalization) and bioinformatic workflow (de novo vs. mapping assembly, reference genome quality) impact transcriptome quality and inference of differential gene expression (DE).

They found:

•  That transcriptome assembly and gene expression profiling (EdgeR vs. BaySeq software) works well even in the absence of a reference genome and is robust across a broad range of parameters.
•  They advise against library normalization and in most situations advocate mapping assemblies to an annotated genome of a divergent sister clade, which generally outperformed de novo assembly (Trans-Abyss, Trinity, Soapdenovo-Trans).
•  That transcriptome complexity (size, paralogs, alternative splicing isoforms) negatively affected the assembly and DE profiling, whereas the effects of sequencing error and polymorphism were almost negligible.
•  Both mapping strategies and the quality of reference genomes are very important.

Transcriptome Shotgun Sequencing (RNA-seq) has been readily embraced by geneticists and molecular ecologists alike. As with all high-throughput technologies, it is critical to understand which analytic strategies are best suited and which parameters may bias the interpretation of the data.

For more on the effects of methods on differential expression results, see http://www.rna-seqblog.com/publications/effects-of-the-method-on-estimation-of-dge/

• Vijay N, Poelstra JW, Künstner A, Wolf JB. (2012) Challenges and strategies in transcriptome assembly and differential gene expression quantification. A comprehensive in silico assessment of RNA-seq experiments. Mol Ecol [Epub ahead of print]. [abstract]

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A team of researchers at USDA and Cornell University provide an efficient and low-cost method for small RNA sequencing library preparation for plants, which takes two days to complete and costs around $20 per library.

Current small RNA sequencing and directional mRNA sequencing library construction protocols require two adapters to be sequentially attached to the RNA molecule to create the priming sites for subsequent PCR amplification. These adapters are modified, adenylated DNA oligos which are very costly to synthesize. We demonstrate that DNA oligos with 5′ phosphate and 3′ amine groups can be enzymatically adenylated by T4 RNA ligase 1 to generate customized pre-adenylated adapters, thus eliminating expensive synthesis requirements.

This protocol has been tested in several plant species for small RNA sequencing including sweet potato, pepper, watermelon, and cowpea, and could be readily applied to any RNA samples.

• Chen YR, Zheng Y, Liu B, Zhong S, Giovannoni J, Fei Z. (2012) A cost-effective method for Illumina small RNA-Seq library preparation using T4 RNA ligase 1 adenylated adapters. Plant Methods 8(1), 41. [article]

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During the last 3 years, a number of approaches for the normalization of RNA sequencing data have emerged in the literature, differing both in the type of bias adjustment and in the statistical strategy adopted. However, as data continue to accumulate, there has been no clear consensus on the appropriate normalization method to be used or the impact of a chosen method on the downstream analysis.

Members of the The French StatOmique Consortium have now used a varied group of real and simulated datasets involving different species and experimental designs to perform a comprehensive comparison of seven recently proposed normalization methods for the differential analysis of RNA-seq data

Based on this comparison study, they propose practical recommendations on the appropriate normalization method to be used and its impact on the differential analysis of RNA-seq data. Read more

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This work quantitatively explores comparisons between contemporary analysis tools and experimental design choices for the detection of differential expression using RNA-Seq. The authors found that the DESeq algorithm performs more conservatively than edgeR and NBPSeq. With regard to testing of various experimental designs, this work strongly suggests that greater power is gained through the use of biological replicates relative to library (technical) replicates and sequencing depth. Strikingly, sequencing depth could be reduced as low as 15% without substantial impacts on false positive or true positive rates.

• Robles JA, Qureshi SE, Stephen SJ, Wilson SR, Burden CJ, Taylor JM. (2012) Efficient experimental design and analysis strategies for the detection of differential expression using RNA-Sequencing. BMC Genomics 13(1), 484. [article]

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The authors provide a step-by-step guide and outline a strategy using currently available statistical tools that results in a conservative list of differentially expressed genes. We also discuss potential sources of error in RNA-Seq analysis that could alter interpretation of global changes in gene expression.

When comparing statistical tools, the negative binomial distribution-based methods, edgeR and DESeq, several limitations of these analytic tools were revealed, including evidence for overly stringent parameters for determining statistical significance of differentially expressed genes as well as increased type II error for high abundance transcripts.

Because of the high variability between methods for determining differential expression of RNA-Seq data, the authors suggest using several bioinformatics tools, as outlined here, to ensure that a conservative list of differentially expressed genes is obtained. They also conclude that despite these analytical limitations, RNA-Seq provides highly accurate transcript abundance quantification that is comparable to qRT-PCR.

• Yendrek CR, Ainsworth EA, Thimmapuram J. (2012) The bench scientist’s guide to statistical analysis of RNA-Seq data. BMC Res Notes 5(1), 506. [article]

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The study of regulatory RNAs in the control of prokaryotic genomes has become a very active and rapidly growing field. New small and large noncoding RNA molecules continue to be discovered at a staggering rate in bacterial model organisms as well as in the transcriptomes of bacterial communities. Newly discovered structural and functional aspects of such RNAs have reached a degree of breadth that requires a meeting with a strong focus on bacterial RNA research to fully address the diversity of these new regulators of gene expression and bring together the scientists involved in these studies. Regulating with RNA in Bacteria was the first conference dedicated to this topic and premiered a forum for the presentation of cutting-edge advances and the latest perspectives in the areas of discovery, mechanisms and structure of bacterial riboregulators. Read more

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Date:	Tuesday, October 16, 2012,
Time:	01:00 pm – 04:00 pm

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Researchers at Chalmers University of Technology, Sweden set out to assess the contribution of the different analytical steps involved in the analysis of RNA-seq data generated with the Illumina platform, and to perform a cross-platform comparison based on the results obtained through Affymetrix microarray. They investigated: the use of three different aligners for read-mapping (Gsnap, Stampy and TopHat) on the genome, the capabilities of five different statistical methods to detect differential gene expression (baySeq, Cuffdiff, DESeq, edgeR and NOISeq) and they explored the consistency between RNA-seq analysis using reference genome and de novo assembly approach.

Results derived from different statistical methods of RNA-seq gave similar biological interpretations as is demonstrated by GO enrichment analysis. Their results strongly supports the robustness and reliability of different processing and analysis of RNA-seq data. Furthermore, we identified high consistency between microarray and RNA-seq platforms, thus encouraging the continual use of microarray as a versatile tool for differential gene expression analysis.

• Nookaew I, Papini M, Pornputtpong N, Scalcinati G, Fagerberg L, Uhlén M, Nielsen J. (2012) A comprehensive comparison of RNA-Seq-based transcriptome analysis from reads to differential gene expression and cross-comparison with microarrays: a case study in Saccharomyces cerevisiae. Nucleic Acids Res [Epub ahead of print]. [article]

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by Julia Salzman – Pat Brown’s Lab

Department of Biochemistry, Stanford University School of Medicine Howard Hughes Medical Institute

RNA-Seq as a Discovery Tool.pptx  

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• SEQanswers – RNA Sequencing

  • DESeq; can I omit timepoints during dispersal estimation? May 24, 2013
    I have a bacterial timecourse with 2 biological replicates per timepoint. There is a fair bit of variance between my replicates. I have spent the... […]
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  • reason for low mapping rate?? May 23, 2013
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  • cross-species data - questions about normalization May 23, 2013
    Hi, I have some data form various samples (cell types) in different species. I want to compare and analyze gene expression variability across the... […]
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  • CuffDiff strange output May 23, 2013
    Hi, I hope that someone can be so gentle to help me. I'm analizing some data from RNA-Seq with TopHat and Cufflinks and I focus my attention on... […]
    Pruexel

• Biostar – RNA-Seq

  • Why am I getting so many unmapped reads in STAR, classified as "too short"?
    I am currently using STAR to map several Hi-SEQ mRNA runs. I'm having trouble getting a decent amount of reads to map, but I don't really understand why. I'm hoping you can shed some light :) In the final log, only about 50% (or less) of the reads map to the reference. I'm using a GTF in addition to the genome. The unmapped bin that most […]
  • What are the best practices for SNP identification in RNA seq transcriptome data
    I have 20 RICE RNA seq tranascriptome data hiseq 2000 platform paired end reads. I aligned fasta reads with BWA and remove PCR duplicates with PICARD. Later I call SNP with samtools using various parameters. I would like to clarify what parameters should I used while alinging to reference rice genome for looking SNP location 100 bp upstream and 250 bp downst […]
  • How do TopHat options -g , --supress-hits, and Bowtie options interplay?
    Hi, I am currently using TopHat2 to map RNA-seq runs. I think there have been some changes pertaining the -g option. Does anyone know how it works now? I used to think that setting -g would look for n alignments for a given read, report them [if top-scoring] and discard those reads that had more than g [top scoring] alignments. Now, the description sounds mo […]
  • What happened to -k in TopHat for multiple-mapping reads?
    Selecting -g n in tophat does not discard reads mapping more than n, but instead only reports n alignments for those out all all their TOP scoring alignments. I think there used to be an option -k that would allow one to discard reads that topped x alignments -- whatever happened to that? I only see -g in the tophat 2 manual, no reporting options like before […]
  • Does tophat use the library-type information for mapping, or just for the XS flag?
    When I specify library-type to TopHat, i.e., first-strand, second-strand, unstranded, TopHat appends a value + or - to the XS:A flag, which is useful for subsequent analyses, such as annotation. However, does this information actually influence the "mappability" of reads, or is this unaffected? My thinking is that the information would be considere […]
  • Purpose of Y-shaped adapters in Illumina Sequencing?
    Hi all, Y adapters different sequences to be annealed to the 5' and 3' ends of each molecule in a library. The arms of the Y are unique, and the middle part, connected to the DNA fragment, is complementary. What are the advantages of this? My take of this over having fully-complementary adapters (ADAPTER1 - - - - - ADAPTER1) is that: -Upon primer a […]