• Subscribe to the RNA-Seq Blog

  
  

  
  Or subscribe by RSS Reader
  
• 6,038 feed subscribers
• 
  

• Featured Events

  

• 

• 

• Media Partners

  
  

• 

• Polls

  How long have you been involved with RNA Sequencing?

  • Just now learning about RNA-Seq
  • less 1 year
  • more than 1 year
  • more than 2 years
  • more than 3 years
  • more than 4 years
  • more than 5 years

  View Results

   Loading ...
• Search for:

• Data Analysis Tools

  Transcriptome Assembly Tools

  Expression & Quantification

  Unpliced Mapping Tools

  Splicing and Junction Mapping

  Analysis Pipelines

  Clouding Platforms

  Databases

  Other Tools

• Tag Cloud

  allele-specific expression alternative splicing bioconductor bioinformatics breast cancer broad institute chip-seq cloud computing cufflinks Data Analysis data analysis pipeline deep sequencing de novo assembly differential expression encode ensembl expression analysis galaxy gene expression high-throughput sequencing illumina ion torrent junction mapping library preparation life technologies lncrna microarray microarrays microrna mirna next-generation sequencing next-gen sequencing pacific biosciences poll results RNA-Seq rnaseq rna sequencing roche tophat transcriptome transcriptome analysis transcriptome assembly transcriptomes transcriptome sequencing transcriptomics

  WP Cumulus Flash tag cloud by Roy Tanck requires Flash Player 9 or better.

• Categories

  Select Category Advertisers Analysis Pipelines Annotation Clouding Platforms Controls Data Analysis Data Analysis Data Sets Databases Events Expression and Quantification Grants / Funding Industry News Information Jobs Library Preparation Methods News Other Tools Patents Pathway Analysis Poll Results Presentations Press Release Publications Reader Conributions Request for RNA-Seq Services Review Sequencing Protocols SNP Detection Splicing and Junction Mapping Statistical Analysis Transcriptome Assembly Tools Transcriptome Sequenced Uncategorized Unspliced Mapping Tools Web Tools Workflow
• 
  
  

• Recent Comments

  • Brian Haas on Interview – Brian Haas Manager of Genome Annotation, Outreach, Bioinformatics and Analysis Broad Institute
  • Bret on The Magic of RNA-Seq
  • Bob Copeman on RobiNA – a user-friendly, integrated software solution for RNA-Seq-based transcriptomics
  • Oz Solomon on Expression Analysis Announces 2013 Grant Program
  • Dr. Jayakishan Meher on Bioinformatics postdoctoral research fellowship available

• Popular Search Terms

  • grape
  • tirthadipa pradhan
  • rna seq analysis
  • rna-seq analysis
  • next generation sequencing ppt
  • rna seq data analysis
  • rna seq vs microarray
  • wine grapes
  • RSEM
  • mangrove

• Recent Search Terms

  • alternative splicing tool
  • rna-seq tutorial
  • integrating chipseq rnaseq
  • rna seq drug target
  • smart seq
  • lhufwwzwx
  • tophat rum
  • rna seq quantitative
  • lcm to next gen sequening
  • سكس مانيلا

• Archives

  • May 2013
  • April 2013
  • March 2013
  • February 2013
  • January 2013
  • December 2012
  • November 2012
  • October 2012
  • September 2012
  • August 2012
  • July 2012
  • June 2012
  • May 2012
  • April 2012
  • March 2012
  • February 2012
  • January 2012
  • December 2011
  • November 2011
  • October 2011
  • September 2011
  • August 2011
  • July 2011
  • June 2011
  • May 2011
  • April 2011
  • March 2011
  • February 2011
  • January 2011
  • December 2010
  • November 2010
  • October 2010
  • September 2010
  • August 2010
  • July 2010
  • June 2010
  • May 2010
  • April 2010
  • February 2010
  • January 2010
  • January 2009

RNA-Seq technology measures the transcript abundance by generating sequence reads and counting their frequencies across different biological conditions. To identify differentially expressed genes between two conditions, it is important to consider the experimental design as well as the distributional property of the data. In many RNA-Seq studies, the expression data are obtained as multiple pairs, e.g., pre- vs. post-treatment samples from the same individual. We seek to incorporate paired structure into analysis.

Now, a team led by researchers at Yale University have developed a Bayesian hierarchical mixture model for RNA-Seq data to separately account for the variability within and between individuals from a paired data structure. The method assumes a Poisson distribution for the data mixed with a gamma distribution to account variability between pairs. The effect of differential expression is modeled by two-component mixture model. The performance of this approach is examined by simulated and real data.

In this setting, the proposed model provides higher sensitivity than existing methods to detect differential expression. Application to real RNA-Seq data demonstrates the usefulness of this method for detecting expression alteration for genes with low average expression levels or shorter transcript length.

Availability: The method was implemented in R and is available at http://bioinformatics.med.yale.edu

• Chung LM, Ferguson JP, Zheng W, Qian F,Bruno V, Montgomery RR, Zhao H(2013) Differential expression analysis for paired RNA-seq data. BMC Bioinformatics 14, 110. [abstract]

Incoming search terms:

• cigar
• RNA bioinformatics
• cryptic RNA-seq bioinformatics
• bioinformatics rna-seq analysis
• list of RNA-sequencing service institute and university
• Baylor Research Institute Dallas
• rna seq differential expression comparison master thesis
• rna seq structural variation
• rna sequencing data analysis service
• rna sequencing yale

De novo transcriptome assemblies of RNA-Seq data are important for genomics applications of unsequenced organisms. Due to the complexity and often incomplete representation of transcripts in sequencing libraries, the assembly of high-quality transcriptomes can be challenging. However, with the rapidly growing number of sequenced genomes it is now feasible to improve RNA-Seq assemblies by guiding them with genomic sequences.

This study introduces BRANCH, an algorithm designed for improving de novo transcriptome assemblies by utilizing genomic information that can be partial or complete genome sequences from the same or a related organism. Its input includes assembled RNA reads (transfrags), genomic sequences (e.g. contigs) and the RNA reads themselves. It uses a customized version of BLAT to align the transfrags and RNA reads to the genomic sequences. After identifying exons from the alignments, it defines a directed acyclic graph and maps the transfrags to paths on the graph. It then joins and extends the transfrags by applying an algorithm that solves a combinatorial optimization problem, called the Minimum weight Minimum Path Cover with given Paths (MMPCP). In performance tests on real data from C. elegans and S. cerevisiae, assisted by genomic contigs from the same species, BRANCH improved the sensitivity and precision of transfrags generated by Velvet/Oases or Trinity by 5.1-56.7% and 0.3-10.5%, respectively. These improvements added 3.8-74.1% complete transcripts and 8.3-33.8% proteins to the initial assembly. Similar improvements were achieved when guiding the BRANCH processing of a transcriptome assembly from a more complex organism (mouse) with genomic sequences from a related species (rat).

Availability: The BRANCH software can be downloaded for free from this site: http://manuals.bioinformatics.ucr.edu/home/branch.

Contact: thomas.girke@ucr.edu

• Bao E, Jiang T, Girke T.(2013) BRANCH: boosting RNA-Seq assemblies with partial or related genomic sequences. Bioinformatics [Epub ahead of print]. [abstract]

Incoming search terms:

• disadvantages of using abyss sequence assembly manual
• structural variation rna seq
• rnaseq assembler comparison
• rna-seq splicing graph
• transcript assembly software
• transcriptome and assembly
• combining assemblies from different software
• combined transcriptome assemblies
• rnaseq best assembling
• assembly of rna

The RegulatoryGenomics website posts and updates a comprehensive list of tools for RNA-Seq analysis.

This is their current version.

Spliced-mappers
Method	Reference	Web-site
TopHap	(Trapnell et al. 2009)	http://tophat.cbcb.umd.edu/
MapSplice	(Wang et al. 2010)	http://www.netlab.uky.edu/p/bioinfo/MapSplice
SpliceMap	(Auger et al. 2010)	http://www.stanford.edu/group/wonglab/SpliceMap/
HMMSplicer	(Dimon et al. 2010)	http://derisilab.ucsf.edu/index.php?software=105
TrueSight	(Li et al. 2012b)	http://bioen-compbio.bioen.illinois.edu/TrueSight/
SOAPsplice	(Huang et al. 2011)	http://soap.genomics.org.cn/soapsplice.html
PASSion	(Zhang et al. 2012)	https://trac.nbic.nl/passion
PALMapper	(Jean et al. 2010)	http://galaxy.raetschlab.org/
SplitSeek	(Ameur et al. 2010)	http://solidsoftwaretools.com/gf/project/splitseek
Supersplat	(Bryant et al. 2010)	http://mocklerlab-tools.cgrb.oregonstate.edu/
SeqSaw	(Wang et al. 2011)	http://bioinfo.au.tsinghua.edu.cn/software/seqsaw
MapNext	(Bao et al. 2009)	http://evolution.sysu.edu.cn/english/software/mapnext.htm
STAR	(Dobin et al. 2012)	http://gingeraslab.cshl.edu/STAR/
GSNAP	(Wu et al. 2010)	http://research-pub.gene.com/gmap/
QPALMA	(De Bona et al. 2008)	http://www.raetschlab.org/suppl/qpalma
OSA	(Hu et al. 2012)	http://omicsoft.com/osa/
Read more

Incoming search terms:

• pathyway analysis for rna seq data
• statistical methods for differential pathway activities
• star splice junctions
• solas rna analysis
• scarf file rna
• rna seq alternative splicing method
• alternative splicing expression
• MethodstostudyEvent/IsoformExpressionandAlternativeSplicingfromRNA-Seq|RNA-SeqBlog
• junction map mrna deep sequencing
• juncbase alternative splicing

Research Associate III Baylor Research Institute Dallas Bioinformatics\Computational Biology Weekday
Job Number:	81099147
Company Name:	Baylor Health Care System
Location:	Dallas, TX US
Career Focus:	Healthcare & Medical / Science & Biotech
Apply to Job
Read more

Incoming search terms:

• url shortener
• shorten url
• www rna-seqblog com featured-rna-seq-job-research-associate-iii-baylor-research-institute-dallas-bioinformaticscomputational-biology
• new research areas in bioinformatics RNA
• rna-seq bioinformatic analysis

Title – Analyzing Illumina RNA-­seq Data with the CRI

Instructor - Elizabeth Bartom, PhD and Lei Huang, PhD

Location – Biological Sciences Learning Center – Room 018 (basement)

Date & Time – February 25, 2013 9am-12pm

Register - Register Here

RNA-seq is a revolutionary approach to transcriptome profiling that uses the next-generation sequencing technologies. It provides more accurate measurement of the expression levels of transcripts and their isoforms compared to other methods including microarrays. In this tutorial, we will learn to use the CRI’s RNA-Seq pipeline (available on brdfgate server, BIOS HPC cluster, and CRI Galaxy) to analyze Illumina RNA sequencing data. The analysis will be performed in four steps :

• Step 1: Assess the sequencing data quality using FastQC
• Step 2: Align the short reads to the human genome using Tophat
• Step 3: Analyze expression using Cufflinks
• Step 4: Collate results with in-house scripts

(find out more…)

The Center for Research Informatics (CRI) provides computational resources and expertise in biomedical informatics for researchers in the Biological Sciences Division (BSD) of the University of Chicago. This workshop is part of a series of monthly training events focusing on using University of Chicago’s computational resources to analyze Next-Generation Sequencing and Microarray data.

As a bioinformatics core, we are actively improving our pipelines and expanding pipeline functions. The tutorials will be updated in a timely manner but may not reflect the newest updates of the pipelines. Stay tuned with us for the latest pipeline release.

If you have any questions, comments, or suggestions, feel free to contact our core at bioinformatics@bsd.uchicago.edu or one of our bioinformaticians.

 

Incoming search terms:

• advanced rna-seq analysis topics and trouble- shooting
• bioinformation rna-seq
• Lei Huang RNA-Seq

from Wikipedia, the free encyclopedia

• 1 Introduction
• 2 List of bioinformatics tools associated with RNA-Seq
  • 2.1 Quality control and filtering data
  • 2.2 Short (Unspliced) aligners (see also HTS Mappers)
  • 2.3 Spliced aligners
  • 2.4 Quantitative analysis
  • 2.5 Differential expression
  • 2.6 Statistical analysis
  • 2.7 RNA-Seq simulators
  • 2.8 Trancriptome assemblers (Genome-Guided)
  • 2.9 Trancriptome assemblers (Genome-Independent)
  • 2.10 Visualization tools
  • 2.11 Workbench (analysis pipeline)
• 3 References

(read more…)

Incoming search terms:

• rna-seq gene regulatory network
• RNA-seq plant stress experimental design
• bioinformatics rna seq
• bioinformatics tools rna-seq
• rna-seq gene regulatory network clustering
• chip-seq course
• bioinformatics blog list
• rna-seq classes usa
• rna seq blog list tools
• List of RNA-Seq bioinformatics tools

Next generation sequencing is rapidly becoming the approach of choice for transcriptional analysis experiments. Substantial advances have been achieved in computational approaches to support these technologies. These approaches typically rely on existing transcript annotations, introducing a bias towards known genes, require specific experimental design and computational resources, or focus only on identification of splice variants (ignoring other biologically relevant transcribed features contained within the data that may be important for downstream analysis). Biologically relevant transcribed features also include large and small non-coding RNA, new transcription start sites, alternative promoters, RNA editing and processing of coding transcripts. Also, many existing solutions lack accessible interfaces required for wide scale adoption.

Researchers at the Monash Institute of Medical Research, Monash University, Australia have developed a user-friendly, rapid and computation-efficient feature annotation framework (RNA-eXpress) that enables identification of transcripts and other genomic and transcriptional features independently of current annotations. RNA-eXpress accepts mapped reads in the standard binary alignment (BAM) format and produces a study-specific feature annotation in GTF format, comparison statistics, sequence extraction and feature counts. The framework is designed to be easily accessible while allowing advanced users to integrate new feature-identification algorithms through simple class extension, thus facilitating expansion to novel feature types or identification of study specific feature types.

Availability and Implementation: RNA-eXpress software, source code, user manuals, supporting tutorials, developer guides and example data are available at http://www.rnaexpress.org.

Contact: paul.hertzog@monash.edu

• Forster S, Finkel A, Gould J, Hertzog P. (2013) RNA-eXpress annotates novel transcript features in RNA-seq data Bioinformatics [Epub ahead of print]. [abstract]

Incoming search terms:

• novel transcripts
• rna express transcriptome assembly
• tophat unmapped bam noval transcripts
• eXpress bioconductor RNA reads
• find novel transcripts as existing genes in other
• galaxy genome express
• INRA RNA seq DATABASE
• miRNA mediated translation regulation in plants
• rna pea galacy

Belgian Nuclear Research Center
Mol, Belgium

Incoming search terms:

• rna seq data analysis thesis
• Deep Sequencing Thesis Review
• m sc project thesis in rna seq assembly pdf
• bioinformatics analysis thesis pdf
• master thesis using glm
• master thesis bioinformatik
• M sc bioinformatics project thesis in genomics in pdf
• ion torrent sequencing results thesis pdf
• bioinformatics thesis proposal
• bioinformatics thesis pdf

Mapping, Visualization, Basic Analyses

May 13th – 14th 2013, Leipzig, Germany

Scope and Topics

The purpose of this workshop is to get a deeper understanding in High-Throughput Sequencing (HTS) with a special focus on bioinformatics issues. Advantages and disadvantages of current sequencing machines and their implications on data analysis will be discovered. The participants will be trained on understanding their own HTS data, finding potential problems/errors and finally start writing their own pipelines. In the course we will use a real-life RNA-seq dataset from the current market leader Illumina.

All analyses will be performed using cloud services. By saving their final cloud-images, the participants will be able to reuse all tools/pipelines and to continue their analyses after the workshop(platform independently: Windows, Mac OS, Linux).

This course will be limited to 15 participants, ideally with similar knowledge base, to allow personal assistance and efficient learning. After registration, participants will be selected on the basis of their background and in order of incoming registrations.

(find out more…)

Postdoctoral Bioinformatician

Nuffield Department of Medicine (NDM), Ludwig Institute for Cancer Research, Old Road Campus Research Building, Roosevelt Drive, Headington, Oxford

The Oxford Branch of the Ludwig Institute for Cancer Research, in association with the Wellcome Trust Centre for Human Genetics, is seeking to appoint a Postdoctoral Bioinformatician. The successful candidate will apply high-level bioinformatics to ChIP-seq and RNA-seq data sets, as well as gene array data, generated through a number of cancer and gene regulation projects currently being undertaken at the Ludwig Institute for Cancer Research. Read more

Incoming search terms:

• rna-seq and bioinformatics and review
• rnaseq bioinformatics
• abi rna-seq
• rna-postdoctoral position-2013
• RNA postdoc position
• postdoc position lncRNA
• post doctoral position in rna -protein
• nuffield department of medicine
• microrna postdoctoral positions uk
• abi solid tophat

Job Number:                         165654

Category:                               A – Immediate/Urgent Need

Description: 

Responsibilities
-Run existing sequence bioinformatics pipelines in the UNIX environment to support high-throughput research pipelines
-Troubleshoot bioinformatics pipelines independently and in collaboration with senior bioinformatics developers and global IT to ensure continuous data deliver
-Collaborate with leadership to design and execute experiments to evaluate novel bioinformatics algorithms. Report results within the expected timeframe and in a clear and concise fashion.
-Maintain cutting edge pipelines, modifying existing pipelines to utilize novel algorithms and provide new reports to stakeholders. Modifications will be done at the request of leadership.
-Effectively manage multiple projects at the same time and maintain an excellent on time delivery rate.
-Communicate clearly and effectively, in both written and oral forms, with stakeholders Read more

Incoming search terms:

• dr emmert streib news paper 2013
• dr emmert-streib
• percentage of reads which fully overlap exon from rnaseq analysed by partek software
• rnaseq bioinformatics jobs

Biotechnology Training Courses at the National Institutes of Health

This course will introduce students to bioinformatic analysis of next generation sequencing data, particularly for DNA-seq, RNA-seq, CHIP-seq, and epigenomics. The course will be comprised of lectures and hand-on sessions. Lectures will cover background knowledge and survey various software programs. For hand-on sessions, command line tools will be presented and the galaxy web based platform will be used to analyze primary data. Cloud computing, genomic databases, and de novo assembly will be surveyed. Read more

Incoming search terms:

• bioinformatics RNA-seq
• [ppt]next generation sequencing data analysis
• bioinformatics next generation sequencing ppt
• next generation sequencing bioinformatics ppt
• trac 45: bioinformatic analysis of next generation sequencing data
• tophat bioinformatics : ppt
• review bioinformatics tools for NGS data analysis
• ppt for data visualization in bioinformatics
• data visualization in bioinformatics ppt
• data related next generation sequencing (ppt)

Description:

The Greengard laboratory (Rockefeller University, New York, USA) is accepting applications from outstanding individuals for a 2-3 year postdoctoral research fellowship in the field of bio-informatics and/or applied mathematics. The position is available immediately. Read more

Incoming search terms:

• Bioinformatics postdoc
• postdoc bioinformatics
• rna-seq postdoc
• plant postdoctoral position bioinformatics 2013
• bioinformatics postdoctoral position
• postdoc bioinformatics 2013
• postdoc in bioinformatics
• postdoc position bioinformatics
• research fellow bioinformatics
• oferta postdoctoral bioinformatica 2013

Next Page →

• 

• 

• 

• 

• Social Networking Pages

  

• 

• SEQanswers – RNA Sequencing

  • 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
  • How to increase rowsize in heatmap? May 16, 2013
    Hi, 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, 2013
    Hi, 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

• 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 […]