• Subscribe to the RNA-Seq Blog

  
  

  
  Or subscribe by RSS Reader
  
• 6,839 feed subscribers
• 
  

• Featured Events

  
  

• Media Partners

  
  

• 

• Polls

  What is the greatest immediate need facing the RNA Sequencing community? (Choose Only One)

  • Further advancement in sequencing technology capabilities (throughput, depth, accuracy, etc)
  • Better (more uniform, less bias, simpler, faster, easier, etc) library preparation protocols
  • Continued reduction in cost of sequencing reagents/services
  • A standardized data analysis pipeline
  • Skilled bioinformatics specialists
  • More sequencing capacity

  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 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 nih 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 Data Visualization 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

  • Anthony on A Review of Computational Tools in microRNA Discovery
  • Nicolas on Computational approaches to the analysis of RNA-seq data
  • Ernesto on Novel amplification based RNA-seq methodology generates highly reproducible sequencing libraries from as low as 25 picograms of mRNA
  • Arrayers on RNA-Seq Data Analysis Tools
  • Brian Haas on Interview – Brian Haas Manager of Genome Annotation, Outreach, Bioinformatics and Analysis Broad Institute

• Popular Search Terms

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

• Recent Search Terms

  • principle of rna-seq
  • bioconductor de novo sequencing
  • rnaseq without reference genome
  • illumina rna seq grant finals 2012
  • bioinformatica rna
  • gene set enrichment analysis rna seq
  • visualizing rnaseq data
  • CaptureSeq
  • rna-seq analysis pipeline
  • rna seq vs microarray

• Archives

  • June 2013
  • 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

Breast cancer transcriptome acquires a myriad of regulation changes, and splicing is critical for the cell to “tailor-make” specific functional transcripts. Researchers at The George Washington University systematically revealed splicing signatures of the three most common types of breast tumors using RNA sequencing: TNBC, non-TNBC and HER2-positive breast cancer.

1. Discovered subtype specific differentially spliced genes and splice isoforms not previously recognized in human transcriptome.
2. Demonstrated that exon skip and intron retention are predominant splice events in breast cancer.
3. Found that differential expression of primary transcripts and promoter switching are significantly deregulated in breast cancer compared to normal breast.
4. Validated the presence of novel hybrid isoforms of critical molecules like CDK4, LARP1, ADD3, and PHLPP2.

This study provides the first comprehensive portrait of transcriptional and splicing signatures specific to breast cancer sub-types, as well as previously unknown transcripts that prompt the need for complete annotation of tissue and disease specific transcriptome.

• Eswaran J, Horvath A, Godbole S, Reddy SD, Mudvari P, Ohshiro K, Cyanam D, Nair S, Fuqua SA, Polyak K, Florea LD, Kumar R. (2013) RNA sequencing of cancer reveals novel splicing alterations. Sci Rep 3, 1689. [article]

Incoming search terms:

• www paenms seq gob mx
• tcga rsem parameter
• RNA sequencing of cancer reveals novel splicing alterations
• splicing analysis software
• rna seq breast cancer
• transcriptome breast cancer cell line
• phd position transcriptome rna-seq 2013
• splice seq
• splice junctions length statistics
• seqs of breast cancer

Resistance to tamoxifen (Tam), a widely used antagonist of the estrogen receptor (ER), is a common obstacle to successful breast cancer treatment. While adjuvant therapy with Tam has been shown to significantly decrease the rate of disease recurrence and mortality, recurrent disease occurs in one third of patients treated with Tam within 5 years of therapy. A better understanding of gene expression alterations associated with Tam resistance will facilitate circumventing this problem.

Using an RNA-Seq approach and a new bioinformatics model, a team led by researchers at Penn State University compared the transcriptomes of Tam-sensitive and Tam-resistant breast cancer cells for identification of genes involved in the development of Tam resistance. They identified differential expression of 1215 mRNA and 513 small RNA transcripts clustered into ERα functions, cell cycle regulation, transcription/translation, and mitochondrial dysfunction. The extent of alterations found at multiple levels of gene regulation highlights the ability of the Tam-resistant cells to modulate global gene expression.

Alterations of small nucleolar RNA, oxidative phosphorylation, and proliferation processes in Tam-resistant cells present areas for diagnostic and therapeutic tool development for combating resistance to this anti-estrogen agent.

• Huber-Keener KJ, Liu X, Wang Z, Wang Y, Freeman W, Wu S, Planas-Silva MD, Ren X, Cheng Y, Zhang Y, Vrana K, Liu CG, Yang JM, Wu R.(2012) Differential Gene Expression in Tamoxifen-Resistant Breast Cancer Cells Revealed by a New Analytical Model of RNA-Seq Data. PLoS One 7(7), e41333. [article]

Incoming search terms:

• microRNA ppt
• breast cancer RNA-seq
• tcga rna-seq protocol
• breast cancer rna seq
• raw data that is available from published data sets (except TCGA) for exome seq/whole gen seq/rna seq of CRC
• rna-seq estrogen
• rna-seq human cancer
• rna-seq network r
• TCGA breast cancer splicing
• tcga rna-seq analysis rsem

The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups

The elucidation of breast cancer subgroups and their molecular drivers requires integrated views of the genome and transcriptome from representative numbers of patients.

A team led by researchers at the University of Cambridge, UK performed an integrated analysis of copy number and gene expression in a discovery and validation set of 997 and 995 primary breast tumours, respectively, with long-term clinical follow-up. Inherited variants (copy number variants and single nucleotide polymorphisms) and acquired somatic copy number aberrations (CNAs) were associated with expression in ~40% of genes, with the landscape dominated by cis- and trans-acting CNAs. (read more…)

• Curtis C et al. (2012) The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature [Epub ahead of print]. [article]

Incoming search terms:

• ion torrent
• breast cancer landscape
• the breast cancer landscape

A team led by researchers at the George Washington University has published a study that is the first of its kind to use mRNA sequencing to look at the expression of genome, at a unprecedented resolution at the current time, in three types of breast cancer. The study titled, “Transcriptomic landscape of breast cancer through mRNA sequencing,” is published in the Feb. 14 edition of the journal, Scientific Reports, a new open access Nature journal for large volume data.

Breast cancer is a heterogeneous disease with a poorly defined genetic landscape, which poses a major challenge in diagnosis and treatment. By massively parallel mRNA sequencing, the team obtained 1.2 billion reads from 17 individual human tissues belonging to TNBC, Non-TNBC, and HER2-positive breast cancers and defined their comprehensive digital transcriptome for the first time. Surprisingly, they identified a high number of novel and unannotated transcripts, revealing the global breast cancer transcriptomic adaptations. Comparative transcriptomic analyses elucidated differentially expressed transcripts between the three breast cancer groups, identifying several new modulators of breast cancer. The study also identified common transcriptional regulatory elements, such as highly abundant primary transcripts, including osteonectin, RACK1, calnexin, calreticulin, FTL, and B2M, and “genomic hotspots” enriched in primary transcripts between the three groups. Thus, this study opens previously unexplored niches that could enable a better understanding of the disease and the development of potential intervention strategies.

(read more… )

• Eswaran J, Cyanam D, Mudvari P, Reddy SDNR, Pakala SB, Nair SS, Florea L, Fuqua SAW, Godbole S, Kumar R. (2012) Transcriptomic landscape of breast cancers through mRNA sequencing. Scientific Reports [Epub ahead of print]. [abstract]

Incoming search terms:

• RNA-seq cancer
• breast cancer rna-seq sample
• mRNA sequencing based transcriptomic analysis and cancer
• types breast cancer

Despite the development of molecular classifications, and prognostic and predictive gene-expression signatures, microarray-based studies have not yielded definitive answers to many of the questions that remain germane for the successful implementation of personalized medicine. Similar challenges are likely to be encountered in translating next-generation sequencing data into clinically useful information.

Needs

• The development of novel high-throughput functional assays that could interrogate the consequences of nucleic acid variations in a large number of loci and in many different genes at the same time are urgently needed in the laboratory.
• The functional significance of mutations affecting non-protein-coding genes will require the development of imaginative new functional assays.
• Training of translational and clinical researchers to understand the intricate complexity of interpreting massively parallel sequencing results and its derivatives, in conjunction with their functional consequences.

Challenges

• The current methods to determine the biological significance of novel nucleic acid variations are tedious and low throughput in the laboratory.
• The bioinformatics tools to distinguish passenger from driver mutations are still nascent.
• These tools’ sensitivity, and specificity can vary according to context, stage of tumor development, and clinical scenario.
• The functional consequences of somatic mutations in tumors may also be amplified or diminished by the genomic background of the host.
• New prognostic signatures or markers could be useful, but those currently on the horizon are unlikely to be sufficiently accurate to safely justify withholding adjuvant chemotherapy.
• No single investigator can fully comprehend and control each of the multitude of analytical steps.
• Translational medicine is a multidisciplinary endeavor; contribution from individual’s with expertise in various fields will be required.

Weigelt B, Pusztai L, Ashworth A, Reis-Filho JS. Challenges translating breast cancer gene signatures into the clinic. Nat Rev Clin Oncol [Epub ahead of print]. [abstract]

Incoming search terms:

• rna seq cancer
• cancer rna-seq
• rna seq chemotherapy
• rnaseq cancer
• rna-seq tumor
• rna-seq cancer nature
• rna-seq and cancer
• rna sequencing and caner
• rna seq in cancer
• what to do with rna-seq and cancer

To identify potential tumor suppressor genes, genome-wide data from exome and transcriptome sequencing were combined to search for genes with loss of heterozygosity and allele-specific expression. The analysis was conducted on the breast cancer cell line HCC1954, and a lymphoblast cell line from the same individual, HCC1954BL.

By comparing exome sequences from the two cell lines, researchers identified loss of heterozygosity events at 403 genes in HCC1954 and at one gene in HCC1954BL. The combination of exome and transcriptome sequence data also revealed 86 and 50 genes with allele specific expression events in HCC1954 and HCC1954BL, which comprise 5.4% and 2.6% of genes surveyed, respectively. Many of these genes identified by loss of heterozygosity and allele-specific expression are known or putative tumor suppressor genes, such as BRCA1, MSH3 and SETX, which participate in DNA repair pathways.

These results demonstrate that the combined application of high throughput sequencing to exome and allele-specific transcriptome analysis can reveal genes with known tumor suppressor characteristics, and a shortlist of novel candidates for the study of tumor suppressor activities.

Zhao Q, Kirkness EF, Caballero OL, Galante PA, Parmigiani RB, Edsall L, Kuan S, Ye Z, Levy S, Vasconcelos AT, Ren B, de Souza SJ, Camargo AA, Simpson AJ, Strausberg RL. (2011) Systematic detection of putative tumor suppressor genes through the combined use of exome and transcriptome sequencing. Genome Biol 11(11), R114. [article]

Incoming search terms:

• exome sequencing and rna-seq
• exome vs transcriptome
• exome rna seq
• RNA seq exome seq
• exome AND transcriptome sequencing
• exome rnaseq
• exome sequencing gene expression
• exome rna sequencing
• rna exome sequencing
• rna seq vs exome cancer

Until recently, chromosomal translocations and fusion genes have been an underappreciated class of mutations in solid tumors. Next-generation sequencing technologies provide an opportunity for systematic characterization of cancer cell transcriptomes, including the discovery of expressed fusion genes resulting from underlying genomic rearrangements.

Using paired-end RNA-seq and improved bioinformatic stratification, researchers at the Institute for Molecular Medicine Finland have discovered a number of novel fusion genes in breast cancer, and identified VAPB-IKZF3 as a potential fusion gene with importance for the growth and survival of breast cancer cells¹.

Researchers at Genentech performed single-end RNA-Seq on human prostate adenocarcinoma samples and their corresponding normal tissues, and developed bioinformatics methods to specifically identify transcription-induced chimeras (TICs), a type of gene fusion². Both prostate and reference samples exhibit a wide range of TIC events and some TIC events, such as MSMB-NCOA4, may play functional roles in cancer.

Deep transcriptional analysis with either single-end or paired-end RNA sequencing can effectively identify gene fusions across the genome.

1.     Edgren H, Murumaegi A, Kangaspeska S, Nicorici D, Hongisto V, Kleivi K, Rye IH, Nyberg S, Wolf M, Boerresen-Dale AL, Kallioniemi O. (2011) Identification of fusion genes in breast cancer by paired-end RNA-sequencing. Genome Biol 12(1), R6. [abstract]

2.     Nacu S, Yuan W, Kan Z, Bhatt D, Rivers CS, Stinson J, Peters BA, Modrusan Z, Jung K, Seshagiri S, Wu TD.  (2011) Deep RNA sequencing analysis of readthrough gene fusions in human prostate adenocarcinoma and reference samples. BMC Med Genomics 4(1), 11. [abstract]

Incoming search terms:

• gene fusion rna-seq
• rna-seq gene fusion
• rna-seq fusion gene
• rna seq gene fusion
• how to identify fusion genes
• gene fusion rnaseq
• rna-seq fusion review
• fusion gene rnaseq review
• rna seq gene discovery
• rna seq application in gene discovery

• 

• 

• 

• 

• Social Networking Pages

  

• 

• SEQanswers – RNA Sequencing

  • RNAseq (SOLiD) from 18 - 200 nt June 18, 2013
    We are interested in small non-coding RNAs. Whomever you ask about the size range of small RNAs, you get a different answer. ;) Lets assume, small... […]
    GenomicIBK
  • Unmapped ratio very high on mouse genome June 17, 2013
    Hi, My problem regards RNA-Seq data. I've downloaded public data (SAGE libs w/ 6 different samples from mouse liver ) to analyse using ArrayStudio.... […]
    le.nono
  • RNASeq: Read length different from expected June 17, 2013
    Hello all, I have received paired-end reads for 40 samples. The reads are supposed to be 100bp per end. Instead, 20 of my samples are 101bp per... […]
    gogodidi
  • How to install xgawk June 16, 2013
    Hi, This is Shrujan, i have a problem while running RNA Sequencing QC. It shows an error that xgawk is not found. So please help me installing... […]
    shrujan
  • RNA Sequencing QC Error while using with Sequence_QC.sh file June 15, 2013
    Hi, This is Shrujan kumar Madadha, I had an error while running QC for Drosophila Yukuba fastq RNA file using Sequence_QC.sh file of FASTX... […]
    shrujan
  • Cuffmerge related query June 12, 2013
    I have a query regarding what samples should be merged using cuffmerge, when you have multiple phenotypes (each with replicates). Lets say my mouse... […]
    ParthavJailwala

• Biostar – RNA-Seq

  • edgeR: very low p-value and very high variance within the group of replicates. What's my problem??
    I'm using edgeR in order to perform differential expression analysis from RNA-seq experiment. I have 6 samples of tumor cell, same tumor and same treatment: 3 patient with good prognosis and 3 patient with bad prognosis. I want to compare the gene expression among the two groups. I ran the edgeR pakage like follow: x […]
  • Normalising tag count to RPKM
    Hi! I was wondering if their is a way to normalise the number of reads in a region and the RPKM of the nearest gene to that region, so that a correlation could be computed. Like the following data shows number of tags in first column and RPKM in second column Tags RPKM 15 0.14619 11 0 203 0.2259 129 10.701 300 7.0772 122 2.3234 346 10.666 77 3.117 201 16.749 […]
  • a simple question on RNA-Seq terminology
    This question may be very simple and basic, but I just need to confirm that I understand the differences among those terminologies in the RNA-Seq context. Suppose I have a sample called SLR, and it is sequenced on 5 lanes, so I have (among other output files) BAM files like L1_SLR, L2_SLR, L3_SLR, L5_SLR and L7_SLR.bam. Here, the letter "L" denotes […]
  • FInding regions of interest with minimum coverage
    Hi, I have a bam file of all my accepted hits (tophat output) and an gtf file with my genes of interest for which I am trying to find potential antisense transcripts. I would like to create a list - preferably one that can be visualized in a genome browser - that shows all genes that have antisense reads in the accepted hits.bam file provided that there are […]
  • How to remove the intronic reads before counting
    I got RNASeq data in several samples. I checked the FastQC, seems the read quality are good (Hiseq 2000). But the problem is many reads are mapped to intronic region, and the regions have no any reference exons there (Refseq, ensembl, gencode). We don't know what they are. We guess the problem happend in library preparation, the concentration was low. N […]
  • Which strand of the mRNA molecule does the sequencer output as a "read"?
    In Illumina Stranded RNA-Seq (using the dUTP method), do the final reads in the fastq files correspond to the initial molecule (that was transcribed), or to the reverse complement of the molecule? C […]