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Joint Statistical Meeting July 30^th-August 4^th, 2011
Miami Beach, Fl

Sharing Information Across Genes to Estimate Overdispersion in RNA-seq Data
Steven Peder Lund, Iowa State University; Dan Nettleton, Iowa State University31/2011

Differential expression analysis for paired RNA-seq data
Lisa M Chung, Department of Epidemiology and Public Health, Yale University ; John Ferguson, Department of Epidemiology and Public Health, Yale University ; Hongyu Zhao, Yale University

How to characterize dynamic bayesian networks across multiple species from time series mRNA-Seq count gene expression profiles:An intelligent Dynamic Bayesian Networks (IDBNs)
sunghee OH, Yale University; Hongyu Zhao, Yale University; James P. Noonan, Yale University

Statistical methods for the analysis of next-generation sequencing data
Karthik Devarajan, Fox Chase Cancer Center

MEAN-VARIANCE MODELING OF RNA-SEQ TRANSCRIPTIONAL COUNT DATA
Yihui Zhou, Univ North Carolina; Fred Andrew Wright, Univ North Carolina

On Differential Gene Expression Using RNA-Seq Data
Ju Hee Lee, University of Texas, MD Anderson Cancer Center; Yuan Ji, MD Anderson Cancer Centre – University of Texas; Shoudan Liang, University of Texas, MD Anderson Cancer Center; Guoshuai Cai, University of Texas, MD Anderson Cancer Center; Peter Mueller, MD Anderson Cancer Center

A Bayesian nonparametric method for differential expression analysis of RNA-seq data
Yiyi Wang, Department of Statistics, Texas A&M University; David B. Dahl, Department of Statistics, Texas A&M University

Statistical strategy for eQTL mapping using RNA-seq data
Wei Sun, University of North Carolina, Chapel Hill

Joint analyses of high-throughput DNA and RNA-seq data from cancer samples
Su Yeon Kim, University of California, Berkeley; Terence Speed, University of California, Berkeley

Significance Analysis of time-series gene expression profiles :via differential/trajectory models in temporal mRNA-Seq data
sunghee OH, Yale University; Hongyu Zhao, Yale University; James P. Noonan, Yale University

Model-Based Clustering for RNA-Seq Data
Yaqing Si, Iowa State University; Peng Liu, Iowa State University

An integrative approach to comparing and normalizing gene expression data generated from RNA-seq, microarray, and RT-PCR technologies
Zhaonan Sun, Department of Statistics, Purdue University; Yu Zhu, Department of Statistics, Purdue University

Normalization, testing, and false discovery rate estimation for RNA-sequencing data
Jun Li, Department of Statistics, Stanford University; Daniela Witten, University of Washington; Iain M Johnstone, Stanford University; Robert Tibshirani, Dept of Health Research and Policy, & Statistics, Stanford University

Sea urchins in the genus Strongylocentrotus are important research models in many areas of bioscience including developmental and cell biology, reproductive biology and evolutionary biology. The purple urchin, specifically S.nudus is one of the most economically important marine animals as the gonads of S. nudus are extensively eaten as a delicacy in China, Korea and Japan. In China, however, natural stocks of S. nudus have declined dramatically due to overfishing and damage of natural habitat. The economic and biomedical importance of the urchin has lead to significant efforts to decode the urchin genome and its genetics. However, only 45 microRNAs are currently listed in miRBase version 16.0, laging behind other deuterostomia species.

Recently, a collaboration of researchers from a group of Life Science universities in China set out to understand the miRNA-based regulatory system of this urchin species as well as basel deuterostomia lineages. First, high throughput sequencing analysis of miRNAs was performed on a small RNA library isolated from five tissues of S. nudus. with the Illumina sequencing platform. Quality reads were filtered for miRNA prediction with the ACGT101-miR-v3.5 package. Reads that matched to rRNA, tRNA, snRNA, snoRNA, repeat sequences, and other ncRNAs deposited in Rfam 8.0, as well as to the sequences containing polyA tails, were discarded. The retained 18-26 nt reads were mapped onto the Strongylocentrotus purpuratus genome and to all deuterostoma known mature miRNA sequences in miRBase Version 16.0. The bioinformatics analysis yielded 415 unique microRNAs including 345 deuterostoma conserved and 70 urchin specific microRNAs, as well as 5 microRNA* sequences.

Next, a miRNA microarray assay was used to confirm the expression of miRNAs in female urchin gonad. A custom microarray consisting of 460 probes for miRNAs corresponding to 415 identified in this research and 45 known urchin miRNAs were designed and in situ synthesized using the µParaflo platform. One hundred miRNAs were confirmed to express at different signal values, 68 of which were identified first time in this research and others were known S.purpuratus miRNAs.

Wei Z, Liu X, Feng T, Chang Y. (2011) Novel and Conserved Micrornas in Dalian Purple Urchin (Strongylocentrotus Nudus) Identified by Next Generation Sequencing. Int J Biol Sci 7, 180-192.[article]

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from GenomeWeb

NEW YORK (GenomeWeb News) – Pacific Biosciences reported after the close of the market Tuesday an increase in its grant revenues for the fourth quarter and full-year 2010, and a $140.2 million loss for the year as it prepares for the commercial launch of its PacBio RS system.

The Menlo Park, Calif.-based firm reported fourth-quarter revenues of $280,000, up from $220,000 for the fourth quarter of 2009. All of its Q4 and FY 2010 revenues are from government grants.

PacBio anticipates commercial launch of its first product — the real-time, single-molecule PacBio RS sequencing system — in the second quarter of this year.

(read the entire story… )

02/03/2011 – 09:01 AM
Now Supporting Paired-End Data For RNA-seq With New Version of Premier Desktop Software Package From CLC bio

02/03/2011 – 08:30 AM
Partek and Ion Torrent Announce Partnership to Deliver Scalable Software Solutions for Ion Torrent Sequencing

02/02/2011 – 11:00 AM
Agilent Technologies Debuts RNA Target Enrichment, Streamlining Transcriptome Profiling Studies to Advance Cancer and Stem Cell Research

01/26/2011 – 05:00 AM
NuGEN Advances Microbial Transcriptome Analysis with the Ovation® Prokaryotic RNA-Seq System

Workshop Presentations

(W14-1) Tools for Next Generation Sequencing Data Analysis
K. Bodi
Tufts University School of Medicine, Tufts University Core Facility, Boston, MA, United States
As NGS technology continues to improve, the amount of data generated per run grows exponentially. Unfortunately, the primary bottleneck in NGS studies is still bioinformatics analysis. There are a variety of workflows for NGS analyses that use open-source tools. This includes peak calling analyses for ChIP-Seq (MACS, GeneTrack indexer, Peak predictor), RNA-Seq (Tophat, Cufflinks), and finding small insertions, deletions, and SNPs using SAMtools.

Research Group Presentations

(R5b) Microarray Research Group Projects, 2010-11
D. Baldwin¹, N.G. Reyero-Vinas², N. Jafari^3
1Penn Molecular Profiling Facility, University of Pennsylvania, Philadelphia, PA, United States; ²Jackson State University, Jackson, MS, United States; ³Northwestern University, Evanston, IL, United States
Members of the MARG will discuss our research projects: Comparison of microarray and deep sequencing platforms for microRNA profiling, Performance of a synthetic human microRNA reference panel, Participation in the SEQC Sequencing Quality Control consortium, and RNA-Seq profiling of environmental samples exposed to the Gulf oil spill.

Poster Presentations

120 Genome Technology Center at the NYU Langone Medical Center: New Support for Clinical and Translational Science
J. Zavadil, S. Mische
New York University Langone Medical Center, New York, NY, United States
To significantly enhance support for clinical and translational research within the framework of its CTSI, the NYU Langone Medical Center consolidated the Microarray and DNA Sequencing Cores into a new Genome Technology Center, a shared resource overseen by the Office for Collaborative Science.

128 NYULMC Office of Collaborative Science Cores – Enabling Personalized Medicine through Translational Research
C. Curchoe, T. Winner, J. Salcedo, S. Mische, D. Levy
New York University, Langone Medical Center, New York, NY, United States
The New York University Langone Medical Center (NYULMC) has committed $15 million to ensure that researchers have access to cutting edge enabling technologies. The Genome Technology Centers sequencing and microarray units are dedicated to RNA-seq of cancer patient transciptomes, array expression profiling and SNP genotyping, enabling translation research as well as the preparation of nucleic acids for molecular profiling, mutational analysis and microRNA screens. Read more

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Computational prediction of microRNA targets remains a challenging problem. The existing rule-based, data-driven and expression profiling approaches to target prediction are mostly approached from the gene-level. The advent of next-generation sequencing technologies provides new opportunities to profile transcriptomes and microRNA targetomes at base-wise resolution and provides a new perspective for microRNA target prediction on the isoform-level.  We hypothesize that the splicing isoform is the ultimate effector in microRNA targeting and that with the use of gene-structure information derived from the RNA-seq data to assess isoform specific microRNA regulation it is possible to predict non-dominant isoform targets as well as their targeting regions that are otherwise invisible to many existing approaches.

Deng N, Puetter A, Zhang K, Johnson K, Zhao Z, Taylor C, Flemington EK, Zhu D. (2011) Isoform-level microRNA-155 target prediction using RNA-seq. Nucleic Acids Res {Epub ahead of print]. [article]

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MicroRNAs (miRNAs) are key regulators of gene expression and contribute to a variety of biological processes including cell growth, differentiation, and development. Abnormal microRNA expression has been reported in various diseases including various cancers, cardiovascular disease, and neurological disorders. Therefore microRNAs are considered to be promising diagnostic and therapeutic candidates for the treatment of human disease.

The miRBase sequence database, is the public repository for all known microRNAs. Newly discovered microRNAs are routinely added and it has grown rapidly with approximately >10,000 entries to date. Despite this rapid growth, many miRNAs have not yet been validated, and many believe there are numerous microRNAs yet to be identified. A lack of a full complement of miRNAs has imposed limitations on recognizing their important roles in development and disease.

Now researchers are using the latest in deep sequencing technology along with advanced bioinformatics packages to identify novel microRNAs in various tissue types and species.

• Ryu S, Joshi N, McDonnell K, Woo J, Choi H, et al. (2011) Discovery of Novel Human Breast Cancer MicroRNAs from Deep Sequencing Data by Analysis of Pri-MicroRNA Secondary Structures. PLoS ONE 6(2), e16403. [article]
• Xie SS, Li XY, Liu T, Cao JH, Zhong Q, Zhao SH. (2011) Discovery of Porcine microRNAs in Multiple Tissues by a Solexa Deep Sequencing Approach. PLoS One 6(1), e16235. [article]
• Creighton CJ, Benham AL, Zhu H, Khan MF, Reid JG, Nagaraja AK, Fountain MD, Dziadek O, Han D, Ma L, Kim J, Hawkins SM, Anderson ML, Matzuk MM, Gunaratne PH. (2010) Discovery of novel microRNAs in female reproductive tract using next generation sequencing. PLoS One 5(3), e9637. [article]
• Huang QX, Cheng XY, Mao ZC, Wang YS, Zhao LL, Yan X, Ferris VR, Xu RM, Xie BY. (2010) MicroRNA discovery and analysis of pinewood nematode (Bursaphelenchus xylophilus) by deep sequencing. PLoS One 5(10), e13271. [article]
• Song C, Wang C, Zhang C, Korir NK, Yu H, Ma Z, Fang J. (2010) Deep sequencing discovery of novel and conserved microRNAs in trifoliate orange (Citrus trifoliata). BMC Genomics 11, 431. [article]
• Zhao CZ, Xia H, Frazier TP, Yao YY, Bi YP, Li AQ, Li MJ, Li CS, Zhang BH, Wang XJ. (2010) Deep sequencing identifies novel and conserved microRNAs in peanuts (Arachis hypogaea L.). BMC Plant Biol 10, 3. [article]

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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]

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Recent studies strongly indicate that aberrations in the control of gene expression might contribute to the initiation and progression of Alzheimer’s disease (AD). In particular, alternative splicing has been suggested to play a role in spontaneous cases of AD.

This study provides, for the first time, transcriptomic analysis for distinct regions of the Alzheimer’s disease (AD) brain using RNA-Seq next-generation sequencing technology. Illumina RNA-Seq analysis was used to survey transcriptome profiles from total brain, frontal and temporal lobe of healthy and AD post-mortem tissue. Gene expression levels, splicing isoforms and alternative transcript start sites were quantified.

Gene Ontology term enrichment analysis revealed an overrepresentation of genes associated with a neuron’s cytological structure and synapse function in AD brain samples. Analysis of the temporal lobe with the Cufflinks tool revealed that transcriptional isoforms of the apolipoprotein E gene, APOE-001, -002 and -005, are under the control of different promoters in normal and AD brain tissue. These results indicate that alternative splicing and promoter usage of the APOE gene in AD brain tissue might reflect the progression of neurodegeneration.

Twine NA, Janitz K, Wilkins MR, Janitz M. (2011) Whole transcriptome sequencing reveals gene expression and splicing differences in brain regions affected by Alzheimer’s disease. PLoS One 6(1), e16266. [article]

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A crucial step in the analysis of deep sequencing data is mapping to a reference genome. Accurate and complete mapping greatly enhances the potential discovery of biological results and improves statistical predictions while inaccurate or incomplete mapping may lead to noise and reduced coverage. Read more

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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]

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• 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 […]