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It has been shown in small RNA sequencing-based studies that some small RNA fragments are specifically processed from known structural non-coding RNAs, either through Dicer-dependent or Dicer-independent pathways. Although these small RNAs are often less abundant compared to microRNAs in normal mammalian tissues, they are always present in all sequenced libraries. In this paper, researchers from the Institut Curie, France use the ncPRO-seq pipeline, to describe different profiles of these small RNA fragments, and to discuss their potential processing pathways and functions. To assess whether more small RNA fragments can be detected in small RNA sequencing datasets, they decided to focus on small nuclear RNAs, abbreviated as snRNAs, which are associated with Sm ribonucleoproteins to form functional RNA-protein complexes. Here, they describe a group of small RNA fragments derived from snRNAs, which are typically highly enriched in regions bound by Sm proteins. Based on this, they propose the existence of a potential novel small RNA family associated with Sm proteins.

• Chen CJ, Heard E. (2013) Small RNAs derived from structural non-coding RNAs. Methods [Epub ahead of print]. [astract]

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• rna-seq adaptation

This tutorial shows the analysis of a simple RNA-Seq experiment containing two samples and goes through the process of quantitating and normalising the data, before going on to perform a simple differential expression analysis.

Incoming search terms:

• www rna-seqblog com analysis-of-a-simple-rna-seq-experiment
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Finding genes that are differentially expressed between conditions is an integral part of understanding the molecular basis of phenotypic variation. In the past decades, DNA microarrays have been used extensively to quantify the abundance of mRNA corresponding to different genes, and more recently high-throughput sequencing of cDNA (RNA-Seq) has emerged as a powerful competitor. As the cost of sequencing decreases, it is conceivable that the use of RNA-Seq for differential expression analysis will increase rapidly. To exploit the possibilities and address the challenges posed by this relatively new type of data, a number of software packages have been developed especially for differential expression analysis of RNA-Seq data.

Scientists at the Swiss Institute of Bioinformatics have conducted an extensive comparison of eleven methods for differential expression analysis of RNA-Seq data. All methods are freely available within the R framework and take as input a matrix of counts, i.e. the number of reads mapping to each genomic feature of interest in each of a number of samples. They evaluated the methods based on both simulated data and real RNA-Seq data.

The found that very small sample sizes, which are still common in RNA-Seq experiments, impose problems for all evaluated methods and any results obtained under such conditions should be interpreted with caution. For larger sample sizes, the methods combining a variance-stabilizing transformation with the ‘limma’ method for differential expression analysis perform well under many different conditions, as does the nonparametric SAMseq method.

Soneson C, Delorenzi M. (2013) A comparison of methods for differential expression analysis of RNA-Seq data. BMC Bioinformatics 14(1), 91. [article]

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Next-generation RNA-sequencing (RNA-Seq) is rapidly outcompeting microarrays as the technology of choice for whole-transcriptome studies. However, the bioinformatics skills required for RNA-Seq data analysis often pose a significant hurdle for many biologists. Here, researchers at Utrecht University, The Netherlands put forward the concepts and considerations that are critical for RNA-Seq data analysis and provide a generic tutorial with example data that outlines the whole pipeline from next-generation sequencing output to quantification of differential gene expression.

Van Verk MC, Hickman R, Pieterse CM, Van Wees SC. (2013) RNA-Seq: revelation of the messengers. Trends Plant Sci [Epub ahead of print]. [abstract]

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GitHub helps people build software together.

yarden/MISO
MISO: Mixture of Isoforms model for RNA-Seq isoform quantitation

jrbustosm/rna-seq
rna-seq analysis utils

drli/RNA-seq
RNA-seq data analysis

andymckenzie/RNA-Seq
algorithms for analyzing rna-seq data

jnhutchinson/ensembl_based_RNA_seq
ensembl_based_RNA_seq

vsbuffalo/rna-seq-example
An analysis of Arabidopsis RNA-seq data (hy5 mutant and wt, two replicates each; SRA accession SRX029582)

fatPerlHacker/rna-seq-analysis-pipeline

sgivan/RNA-Seq-Toolkit
Collection of scripts to facilitate the analysis of RNA-Seq data

gusevfe/RnaSeqAB
Tool for detecting allele bias in Genome vs. RnaSeq data

luwening/RNA-Seq-RP-Pseudogenes

Lots more shared RNA-Seq Code…

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For those who missed it… here are the slides from the recent short training class presented by the Bioinformatics & Research Computing group at the Whitehead Institute.

January 17, 2013 – RNA-seq Analysis in Galaxy

Hands-on 1 Quantification and assay for differential expression of reference annotation

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In recent years RNA-Seq technology has been used not only to quantify differences in gene expression but also to understand the underlying mechanisms that lead to these differences. Nucleotide sequence variation arising through evolution may differentially affect the expression profiles of divergent species. RNA-Seq technology, combined with techniques to differentiate parental alleles and quantify their abundance, have recently become popular methods for allele specific gene expression (ASGE) analyses. However, analysis of gene expression within interspecies hybrids may be difficult when one of the two parental genomes represented in the hybrid does not have robust genomic resources or available transcriptome data. Read more

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• Identification of transcriptome SNPs between Xiphophorus lines and species for assessing allele specific gene expression within F-1 interspecies hybrids

posted by bodhisattvax at Biostar

Hi all I’ve finally put together the results of the survey! First of all, thanks to everyone who participated – the response has been great, with 93 people completing the survey as of today.

The respondents have been a varied bunch, including all levels of academia (pre-docs, grad-students, pot-docs and PIs), core bioinformaticians and bioinformatics managers, as well as many from the industry. The majority of respondents appear to be based in the US and Europe but also in China, Korea and Australia.

I provide below my own summary of the survey’s findings, and I have a document which contains all the results, including all unedited comments. I’m not sure how I can upload this file on this site. If you would like it, please either check my post on seqanswers where I have been able to upload the file, or get in touch with me so I can email it to you. Biostars admins can you help here? Read more

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Next generation sequencing technology provides a powerful tool for measuring gene expression (mRNA) levels in the form of RNA-sequence data. Method development for identifying differentially expressed (DE) genes from RNA-Seq data, which frequently includes many low-count integers and can exhibit severe overdispersion relative to Poisson or binomial distributions, is a popular area of ongoing research.

Here, researchers at National Institute of Standards and Technology present quasi-likelihood methods with shrunken dispersion estimates based on an adaptation of Smyth’s (2004) approach to estimating gene-specific error variances for microarray data. The suggested methods are computationally simple, analogous to ANOVA and compare favorably versus competing methods in detecting DE genes and estimating false discovery rates across a variety of simulations based on real data.

An R package called QuasiSeq, used to implement the suggested methods of this article is available from the CRAN website: http://cran.r-project.org/web/packages/QuasiSeq/index.html

• Lund SP, Nettleton D, McCarthy DJ, Smyth GK. (2012) Detecting Differential Expression in RNA-sequence Data Using Quasi-likelihood with Shrunken Dispersion Estimates. Stat Appl Genet Mol Biol. 11(5). [article]

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UZH/ETH – FGCZ – Functional Genomics Center Zurich

www.fgcz.ch/

The Functional Genomics Center Zurich (FGCZ) is a joint state-of-the-art research and training facility of the ETH Zurich and the University of Zurich. With latest technologies and expert support in genomics, transcriptomics, proteomics, metabolomics, and bioinformatics, the FGCZ carries out research projects and technology development in collaboration with the Zurich Life Science research community.

Incoming search terms:

• splice event RNA-seq

Transcriptomic sequence resources represent invaluable assets for research, in particular for non-model species without a sequenced genome. To date, the Next Generation Sequencing technologies 454/Roche and Illumina have been used to generate transcriptome sequence databases by RNA-Seq for more than fifty different plant species. While some of the databases were successfully used for downstream applications, such as proteomics, the assembly parameters indicate that the assemblies do not yet accurately reflect the actual plant transcriptomes. Two different assembly strategies have been used, overlap consensus based assemblers for long reads and Eulerian path/de Bruijn graph assembler for short reads.

In this review, researchers from the Heinrich Heine University, Germany discuss the challenges and solutions to the transcriptome assembly problem. A list of quality control parameters and the necessary scripts to produce them are provided.

• Schliesky S, Gowik U, Weber AP, Bräutigam A. (2012) RNA-Seq Assembly – Are We There Yet? Front Plant Sci [Epub ahead of print]. [article]

Incoming search terms:

• trans-abyss tutorial
• reference based transcriptome assembly
• merge transcriptome assemblies
• Next-generation transcriptome assembly

This workshop will explore RNA-seq analysis methods in GenePattern (Alignment, Isoform detection, Transcript quantitation, Transcriptome reconstruction) and visualizing RNA-seq data in the Integrative Genomics Viewer (IGV).

 

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• SEQanswers – RNA Sequencing

  • 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
  • cannot away with cuffdiff,incredible May 23, 2013
    Hi,all I have 4(A,B,C,D) sample in 4 times(increasing time),I got diff result in 3 different cuffdiff 1.cuffdiff 3(A,B,C) individual... […]
    upper
  • TopHat extremely low paired mapping rate. PLS HELP! May 22, 2013
    Hey guys, I have some problems with my paried-end RNA seq analysis on Galaxy. As you can see in the bam flagstat output, my tophat alignment rate is... […]
    Felix.Lee
  • Identifying small RNA sequence within whole genome sequence May 21, 2013
    Hi all, I want to know if there are any useful bioinformatic tool to find small RNA sequence within a whole bacteria genome. Thank you in... […]
    Inma
  • standard of clean data May 21, 2013
    Hi all I recently got my prokaryotes RNA-seq data report back. the standard filter steps of the raw data set by our local sequencing center is as... […]
    Pengfei Liu
  • Problem with cummeRbund diffData() May 20, 2013
    Hi all, I'm running Tophat/cufflinks/cuffdiff for differential gene expression and analysis with cummeRbund (v 2.0.0). I'm having an issue with... […]
    Enrique Zudaire

• 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 […]