• Subscribe to the RNA-Seq Blog

  
  

  
  Or subscribe by RSS Reader
  
• 6,157 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 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 small rna 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

  • next generation sequencing ion torrent
  • www rna-seqblog com
  • rna-seq expression profiling
  • star: ultrafast universal rna-seq aligner pdf
  • transcriptome analysis using next-generation sequencing
  • get reads from rpkm
  • more deepness sequencing
  • Exogenous plant MIR168a specifically targets mammalian LDLRAP1: evidence of cross-kingdom regulation by microRNA
  • data visualization in bioinformatics ppt
  • current picture of the transcriptome

• 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

Incoming search terms:

• nat-siRNA mlncRNA
• antisense takeover

We asked – Assuming Human samples, how many biological replicates from each test group are required for good RNA-Seq data?

Total Responses – 168

Thanks to all who participated and check out our new poll in the left-hand sidebar.

Incoming search terms:

• rna seq results
• rna-seq results
• examples of a blog poll

The Human Leukocyte Antigen (HLA) is key to many aspects of human physiology and medicine. All current sequence-based HLA typing methodologies are targeted approaches requiring the amplification of specific HLA gene segments. Whole genome, exome and transcriptome shotgun sequencing can generate prodigious data but due to the complexity of HLA loci these data have not been immediately informative regarding HLA genotype.

Now, two groups have developed new methods for HLA typing from RNA-Seq sequence reads:

Scientists at TRON (Translational Oncology at the University Medical Center of the Johannes Gutenberg University, Germany) have developed a new method, seq2HLA, for obtaining an individual’s HLA class I and II type and expression using standard NGS RNA-Seq data¹. It comprises mapping RNA-Seq reads against a reference database of HLA alleles, determining and reporting HLA type, confidence score and locus-specific expression level.

They have applied seq2HLA to 50 CEU HapMap individuals previously HLA-typed, yielding 100% specificity and 94% sensitivity at a p-value of 0.1 for 2-digit HLA types. They determined HLA-type and expression for the previously un-typed Illumina Body Map tissues and a cohort of Korean lung cancer patients. Because the algorithm uses standard RNA-Seq reads and requires no change to lab protocols, it can be used for both existing datasets and future studies, thus adding a new dimension for HLA typing and biomarker studies.

Availability – Seq2HLA is / is written in python and R and available as stand-alone and Galaxy modules from: http://tron-mainz.de/tronfacilities/computational-medicine/seq2HLA

Scientists at the BC Cancer Agency, Michael Smith Genome Sciences Centre, Canada have developed a new method, – a computational method for identifying HLA alleles directly from shotgun sequence datasets². Their approach circumvents the additional time and cost of generating HLA-specific data and capitalizes on the increasing accessibility and affordability of massively-parallel sequencing.

Availability – HLAminer is available at: http://www.bcgsc.ca/platform/bioinfo/software/hlaminer

• Boegel S, Lower M, Schafer M, Bukur T, de Graaf J, Boisguerin V, Tureci O, Diken M, Castle JC, Sahin U. (2012) HLA typing from RNA-Seq sequence reads. Genome Med 4(12):102. [abstract]
• Warren RL, Choe G, Freeman DJ, Castellarin M, Munro S, Moore R, Holt RA. (2012) Derivation of HLA types from shotgun sequence datasets. Genome Med 4(12):95. [abstract]

Incoming search terms:

• HLA typing
• seq2hla
• HLA typing NGS
• hla genotyping methods
• NGS HLA
• rna-seq hla
• colouring pages of scientist
• sequence based typing methods
• illumina ngs hla
• illumina platform and hla typing

Cis-natural antisense transcripts (cis-NATs) are RNAs transcribed from the antisense strand of a gene locus, and are complementary to the RNA transcribed from the sense strand. Common techniques including microarray approach and analysis of transcriptome databases are the major ways to globally identify cis-NATs in various eukaryotic organisms. Genome-wide in silico analysis has identified a large number of cis-NATs that may generate endogenous short interfering RNAs (nat-siRNAs), which participate in important biogenesis mechanisms for transcriptional and post-transcriptional regulation in rice. However, the transcriptomes are yet to be deeply sequenced to comprehensively investigate cis-NATs. Read more

Incoming search terms:

• cis-nats
• cis-nat-sirna
• cufflinks assembly sirna
• from transcriptome nat-siRNA
• nat siRNA
• nat-siRNA transcriptome
• nature antisense transcript rna-SEQ

Fermentation production of biofuel ethanol consumes agricultural crops, which will compete directly with the food supply. As an alternative, photosynthetic cyanobacteria have been proposed as microbial factories to produce ethanol directly from solar energy and CO2. However, the ethanol productivity from photoautotrophic cyanobacteria is still very low, mostly due to the low tolerance of cyanobacterial systems to ethanol stress.

To build a foundation necessary to engineer robust ethanol-producing cyanobacterial hosts, researchers at Tianjin University, China have applied a quantitative transcriptomics approach with a next-generation sequencing technology, combined with quantitative reverse-transcript PCR (RT-PCR) analysis, to reveal the global metabolic responses to ethanol in model cyanobacterial Synechocystis sp. PCC 6803.

The results showed that ethanol exposure induced genes involved in common stress responses, transporting and cell envelope modification. In addition, the cells can also utilize enhanced polyhydroxyalkanoates (PHA) accumulation and glyoxalase detoxication pathway as means against ethanol stress. The up-regulation of photosynthesis by ethanol was also further confirmed at transcriptional level. Finally, the researchers used gene knockout strains to validate the potential target genes related to ethanol tolerance.

RNA-Seq based global transcriptomic analysis provided a comprehensive view of cellular response to ethanol exposure. The analysis provided a list of gene targets for engineering ethanol tolerance in cyanobacterium Synechocystis.

• Wang J, Chen L, Huang S, Liu J, Ren X, Tian X, Qiao J, Zhang W. (2012) RNA-seq based identification and mutant validation of gene targets related to ethanol resistance in cyanobacterial Synechocystis sp. PCC 6803. Biotechnol Biofuels 5(1):89. [abstract]

Incoming search terms:

• What is global RNA seq
• transcriptomics qRT-PCR
• rnaseq stress cells
• rna-seq analysis a global transcriptional view
• RNA list
• rna chart
• indentify genes related to stress tolerance from rna-seq data
• human genetic engineer charts
• graphs and charts on cons of genetic engineering
• cyanobacteria ppt

Development of post-GWAS (genome-wide association study) methods are greatly needed for characterizing the function of trait-associated SNPs. Strategies integrating various biological data sets with GWAS results will provide insights into the mechanistic role of associated SNPs.

Here, researchers at University of California, Berkeley present a method that integrates RNA sequencing (RNA-seq) and allele-specific expression data with GWAS data to further characterize SNPs associated with follicular lymphoma (FL). They investigated the influence on gene expression of three established FL-associated loci-rs10484561, rs2647012, and rs6457327-by measuring their correlation with human-leukocyte-antigen (HLA) expression levels obtained from publicly available RNA-seq expression data sets from lymphoblastoid cell lines. Their results suggest that SNPs linked to the protective variant rs2647012 exert their effect by a cis-regulatory mechanism involving modulation of HLA-DQB1 expression. In contrast, no effect on HLA expression was observed for the colocalized risk variant rs10484561. The application of integrative methods, such as those presented here, to other post-GWAS investigations will help identify causal disease variants and enhance our understanding of biological disease mechanisms.

• Conde L, Bracci PM, Richardson R, Montgomery SB, Skibola CF. (2012) Integrating GWAS and Expression Data for Functional Characterization of Disease-Associated SNPs: An Application to Follicular Lymphoma. Am J Hum Genet [Epub ahead of print]. [abstract]

Incoming search terms:

• gwas
• rna sequencing GWAS
• scientist coloring pages\
• integrate gwas and chip-seq
• Gene set enrichmen analysis of RNA-seq data: integrating differential expression and splicing
• post-gwas ppt
• RNA sequencing function
• search snp database using rna expression
• snps from RNA-Seq data
• webtool gwas data analysis

A team led by researchers at Georgia State University now propose a novel statistical genome-guided method called “Transcriptome Reconstruction using Integer Programing” (TRIP) that incorporates fragment length distribution into novel transcript reconstruction from paired-end RNA-Seq reads. To reconstruct novel transcripts, they create a splice graph based on inferred exon boundaries and RNA-Seq reads. A splice graph is a directed acyclic graph (DAG), whose vertices represent exons and edges represent splicing events. They enumerate all maximal paths in the splice graph using a depth-first-search (DFS) algorithm. These paths correspond to putative transcripts and are the input for the TRIP algorithm.

To solve the transcriptome reconstruction problem you must select a set of putative transcripts with the highest support from the RNA-Seq reads. They formulate this problem as an integer program. The objective to select the smallest set of putative transcripts that yields a good statistical fit between the fragment length distribution empirically determined during library preparation and fragment lengths implied by mapping read pairs to selected transcripts.

Preliminary experimental results on synthetic datasets generated with various sequencing parameters and distribution assumptions show that TRIP has increased transcriptome reconstruction accuracy compared to previous methods that ignore fragment length distribution information.

• Mangul S, Caciula A, Brinza D, Mandoiu II, Zelikovsky A.  (2012) TRIP: a method for novel transcript reconstruction from paired-end RNA-seq reads. BMC Bioinformatics – part of the supplement: Highlights from the Eighth International Society for Computational Biology (ISCB) Student Council Symposium 2012. [abstract]

Incoming search terms:

• rna-seq novel transcripts
• STAR mapping colorspace
• map reads to transcripts
• mapping reads to transcripts
• Novel Transcription RNA SEQ

seqnews.net: discover and share news on next-generation sequencing, genomics and biological data analysis

Next-generation sequencing rapidly changes the face of biology and medicine, and it’s hard to keep track of all recent developments. This site shall help to discover new findings, techniques and tools.

Main topics:

__________________

Scientists at Tsinghua University, China  have used the negative binomial (NB) distribution to model sequencing reads on exons, and propose a NB-statistic to detect differentially spliced genes between two groups of samples by comparing read counts on all exons. The method opens a new exon-based approach instead of isoform-based approach for the task. It does not require information about isoform composition, nor need the estimation of isoform expression. Experiments on simulated data and real RNA-seq data of human kidney and liver samples illustrated the method’s good performance and applicability. It can also detect previously unknown alternative splicing events, and highlight exons that are most likely differentially spliced between the compared samples.

DSGseq is available at http://bioinfo.au.tsinghua.edu.cn/software/DSGseq.

• Wang W, Qin Z, Feng Z, Wang X, Zhang X. (2012) Identifying differentially spliced genes from two groups of RNA-seq samples. Gene [Epub ahead of print]. [abstract]

Incoming search terms:

• dsgseq
• dsgseq software
• identify differentially spliced regions in RNA-seq
• identifying differentially spliced genes

Bud dormancy is a critical developmental process that allows perennial plants to survive unfavorable environmental conditions. Pear is one of the most important deciduous fruit trees in the world, but the mechanisms regulating bud dormancy in this species are unknown. Because genomic information for pear is currently unavailable, transcriptome and digital gene expression data for this species would be valuable resources to better understand the molecular and biological mechanisms regulating its bud dormancy. Read more

Incoming search terms:

• pear
• fruit tree of the world
• pear dormancy next generation seuqncing china
• Suli pear pyrus pyrifolia white pear group
• transcriptomic analysis of pear

To view the agenda and registration options, visit http://www.xgencongress.com/RNA-Seq

Incoming search terms:

• reminder on deadline for conference registration

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
• percentage of reads which fully overlap exon from rnaseq analysed by partek software
• rnaseq bioinformatics jobs

Study on long non-coding RNAs (lncRNAs) has been promoted by high-throughput RNA sequencing (RNA-Seq). However, it is still not trivial to identify lncRNAs from the RNA-Seq data and it remains a challenge to uncover their functions.

Now, researchers at China University of Mining and Technology, Xuzhou present a computational pipeline for detecting novel lncRNAs from the RNA-Seq data. First, the genome-guided transcriptome reconstruction is used to generate initially assembled transcripts. The possible partial transcripts and artefacts are filtered according to the quantified expression level. After that, novel lncRNAs are detected by further filtering known transcripts and those with high protein coding potential, using a newly developed program called lncRScan.

They applied the pipeline to a mouse Klf1 knockout dataset, and discussed the plausible functions of the novel lncRNAs they detected by differential expression analysis. The researchers identified 308 novel lncRNA candidates, which have shorter transcript length, fewer exons, shorter putative open reading frame, compared with known protein-coding transcripts. Of the lncRNAs, 52 large intergenic ncRNAs (lincRNAs) show lower expression level than the protein-coding ones and 13 lncRNAs represent significant differential expression between the wild-type and Klf1 knockout conditions.

This method can predict a set of novel lncRNAs from the RNA-Seq data. Some of the lncRNAs are showed differentially expressed between the wild-type and Klf1 knockout strains, suggested that those novel lncRNAs can be given high priority in further functional studies.

• Sun L, Zhang Z, Bailey TL, Perkins AC, Tallack MR, Xu Z, Liu H.(2012) Prediction of novel long non-coding RNAs based on RNA-Seq data of mouse Klf1 knockout study. BMC Bioinformatics [Epub ahead of print]. [abstract] [provisional PDF]

Incoming search terms:

• lncRscan
• why lnc RNA not found
• lncrna scan
• high throughput lncrna
• lnc rna assembly
• lncRNA differential expression
• lincRNA and rna-seq ppt
• novel lncRNA
• RNAseq lncRNAs and cancer
• tutorial installing rna-seq pipeline macosx

Next Page →

• 

• 

• 

• 

• Social Networking Pages

  

• 

• 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... […]
    amcloon
  • HT Seq Count stranded options May 24, 2013
    I am very new to bioinformatics, so I would be really grateful for some help! I have been using *HTSeq Count v0.5.3* and I am bit confused about... […]
    qwrissie
  • Tophat 2.0.8b installation error May 24, 2013
    I install tophat-2.0.8b to rerun the mapping. but when i make it, the error appears like this. make[1]: Entering directory... […]
    canhu
  • reason for low mapping rate?? May 23, 2013
    we did RNASeq using HiSeq 2000 100PE. When the data were back, I mapping them to the reference sequence, but got very low mapping rate (30-40%). I... […]
    miaom
  • 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... […]
    trelek2
  • 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 […]