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May
2
Genotype-Tissue Expression Project Community Meeting
Filed Under Events, Reader Conributions | Leave a Comment
The goal of the first GTEx Project Community Meeting is to share information about the project and to showcase advances made during the project’s initial pilot phase. In addition to describing the project’s current status and goals during the upcoming scale up phase, the meeting will highlight the current pilot data set available including demonstrations of how and where to access the data, what data are available, current methodologies used, and what analyses can be done using the project portal.
Most of the meeting will be devoted to scientific presentations highlighting use of the current data set in analyses including estimates of gene expression variability, alternative splicing, and allele specific expression among individuals and tissues, discovery of shared and tissue specific eQTL’s, integration with ENCODE annotation, and network inference.
Meeting Agenda will be announced as oral presentations are finalized.
Registration is free but space is limited, so sign up today!
About the Genotype-Tissue Expression project
The Genotype-Tissue Expression project (GTEx) aims to create a public atlas of human gene expression and its regulation across multiple tissue types, enabling the research community to discover expression quantitative trait loci (eQTL) and help interpret associations with disease. While initial transcript data was generated using both arrays and RNA Seq to benchmark against one another, all data moving forward will be generated solely by RNA sequencing. By the end of the project we expect to have generated RNA sequence data from ~25,000 human tissues.
GTEx pilot data are now available at dbGaP and the GTEx Portal.
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- POSTER PRESENTATION Open AccessEvaluation of bacterial ribosomal RNA (rRNA)depletion methods for sequencing microbialcommunity transcriptomes
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- www rna-seqblog com genotype-tissue-expression-project-community-meeting
May
14
Requisition Number: 790
Job Title: Computational Biologist, RNA-Seq
Area of Interest: Computational Biology
City: Cambridge
State/Province: Massachusetts
Job Description: The role of this researcher will be to develop and apply new and existing computational methods, interpret results within a biological context, integrate best practices from other groups using RNA-Seq data, and refine techniques and metrics appropriate for RNA-Seq analysis pipelines. As a member of the Molecular Biology Research & Development (MBRD) group this researcher will work in close collaboration with laboratory development scientists to collaborate on development of new laboratory methods related to next-generation sequencing of RNA samples. The role involves rapid prototyping and is focused on development of molecular biology applications and sequencing technology. The role also involves direct interaction with MBRD co-workers, Aviv Regev and her group, as well as researchers in other groups within the Broad Institute. Responsibilities include communication of results to the scientific community at Broad and externally through conference presentations, peer-reviewed publications, and project reports. (read more… )
Incoming search terms:
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May
16
Trinity – a method for de novo assembly of full-length transcripts
Filed Under Publications, Transcriptome Assembly Tools | Leave a Comment
Massively parallel sequencing of cDNA has enabled deep and efficient probing of transcriptomes. Current approaches for transcript reconstruction from such data often rely on aligning reads to a reference genome, and are thus unsuitable for samples with a partial or missing reference genome.
Here the authors present Trinity and evaluate it on samples from fission yeast, mouse and whitefly, whose reference genome is not yet available. By efficiently constructing and analyzing sets of de Bruijn graphs, Trinity fully reconstructs a large fraction of transcripts, including alternatively spliced isoforms and transcripts from recently duplicated genes. Compared with other de novo transcriptome assemblers, Trinity recovers more full-length transcripts across a broad range of expression levels, with a sensitivity similar to methods that rely on genome alignments. This approach provides a unified solution for transcriptome reconstruction in any sample, especially in the absence of a reference genome.
Trinity is available at: http://trinityrnaseq.sourceforge.net/
Grabherr, MG et al. (2011) Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nature Biotechnology [Epub ahead of print]. [abstract]
Incoming search terms:
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Mar
7
GenePattern – RNA-Seq Analysis Tools from the Broad Institute
Filed Under Clouding Platforms | Leave a Comment
GenePattern – is a powerful genomic analysis platform that provides access to more than 100 tools for gene expression analysis, proteomics, SNP analysis and common data processing tasks.
GenePattern offers a suite of tools to support a wide variety of RNA-seq analyses, including short-read mapping, identification of splice junctions, transcript and isoform detection, quantitation, and differential expression. The modules have been adapted from widely-used tools. GenePattern also provides pipelines that allow you to perform a number of multi-step RNA-seq analyses automatically. Read more
Incoming search terms:
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Jan
1
Trinity RNA-Seq Assembly – software for the reconstruction of full-length transcripts and alternatively spliced isoforms
Filed Under Transcriptome Assembly Tools | Leave a Comment
Trinity, developed at the Broad Institute, represents a novel method for the efficient and robust de novo reconstruction of transcriptomes from RNA-Seq data. Trinity combines three independent software modules: Inchworm, Chrysalis, and Butterfly, applied sequentially to process large volumes of RNA-Seq reads. Trinity partitions the sequence data into many individual de Bruijn graphs, each representing the transcriptional complexity at at a given gene or locus, and then processes each graph independently to extract full-length splicing isoforms and to tease apart transcripts derived from paralogous genes. Read more
Incoming search terms:
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SEQanswers – RNA Sequencing- TopHat extremely low paired mapping rate. PLS HELP! May 22, 2013Hey 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, 2013Hi 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, 2013Hi 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, 2013Hi 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
- How to increase rowsize in heatmap? May 16, 2013Hi, I am a complete newbie to all things cummeRbund and am currently fighting with generating readable heatmaps. When I use ... […]Mags
- novoalign mapping May 15, 2013Hi, I want to use novoalign to map reads - allowing up to 15 mismatches for 100 bp paired-end reads I am new to novoalign(went through the... […]abh
- TopHat extremely low paired mapping rate. PLS HELP! May 22, 2013
- 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 dataI 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 […]
- Why am I getting so many unmapped reads in STAR, classified as "too short"?