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Title: Advanced RNA-Seq and Chip-Seq Data Analysis
Date: 1-4 May 2012
Venue: EMBL-EBI, Hinxton, Nr Cambridge, CB10 1SD, UK
Organisers: Gabriella Rustici
Admin Support: Johanna Langrish

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By identifying changes in coral gene expression that are triggered by particular environmental stressors, we can begin to characterize coral stress responses at the molecular level, which should lead to the development of more powerful diagnostic tools for evaluating the health of corals in the field.

With the goal of identifying genetic variants that are more or less resilient in the face of particular stressors, a team led by researchers at  Boston & Stanford Universities performed deep mRNA sequencing of the cauliflower coral, Pocillopora damicornis, a geographically widespread Indo-Pacific species that exhibits a great diversity of colony forms and is able to thrive in habitats subject to a wide range of human impacts. They isolated RNA from colony fragments (“nubbins”) exposed to four environmental stressors (heat, desiccation, peroxide, and hypo-saline conditions) or control conditions. The RNA was pooled and sequenced using the 454 platform. Description. Both the raw reads (n = 1,116,551) and the assembled contigs (n = 70,786; mean length = 836 nucleotides) were deposited in a new publicly available relational database called PocilloporaBase (www.PocilloporaBase.org). Read more

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RNA-SeQC is a java program which computes a series of quality control metrics for RNA-seq data. The input can be one or more BAM files. The output consists of HTML reports and tab delimited files of metrics data. This program can be valuable for comparing sequencing quality across different samples or experiments to evaluate different experimental parameters. It can also be run on individual samples as a means of quality control before continuing with downstream analysis.

Quality Control Metrics Include:

• Read Counts
• Total, unique, duplicate reads
• Mapped reads and mapped unique reads
• rRNA reads
• Transcript-annotated reads (intragenic, intergenic, exonic, intronic)
• Expression profiling efficiency (ratio of exon-derived reads to total reads sequenced)
• Strand specificity
• Coverage
• Mean coverage (reads per base)
• Mean coefficient of variation
• 5′/3′ bias
• Coverage gaps: count, length
• Coverage Plots
• Downsampling
• GC Bias
• Correlation to reference expression profile

Availability: RNA-SeQC can be run online using the GenePattern genomic analysis platform or it can be downloaded and run locally.

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RNA-PET is a paired end tag (PET) sequencing method for full-length mRNA transcripts analysis using the next generation sequencer platforms such as Illumina GA and SOLiD. Unlike RNA-Seq method that sequences randomly sheared shotgun RNA short fragments, RNA-PET captures and sequences the 5′ and 3′ end tags of full-length cDNA fragments of all expressed genes in a biological sample. When mapped to reference genome, RNA-PET sequences can demarcate the boundaries of transcription units genome-wide, in addition to its ability to quantify the transcription level of each expression genes.

• Ruan X, Ruan Y. (2012) Genome Wide Full-Length Transcript Analysis Using 5′ and 3′ Paired-End-Tag Next Generation Sequencing (RNA-PET). Methods Mol Biol 809,535-62. [abstract]

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GenomicTools is a flexible computational platform for the analysis and manipulation of high-throughput sequencing data such as RNA-seq and ChIP-seq. GenomicTools implements a variety of mathematical operations between sets of genomic regions thereby enabling the prototyping of computational pipelines that can address a wide spectrum of tasks from preprocessing and quality control to meta-analyses. More specifically, the user can easily create average read profiles across transcriptional start sites or enhancer sites, quickly prototype customized peak discovery methods for ChIP-seq experiments, perform genome-wide statistical tests such as enrichment analyses, design controls via appropriate randomization schemes, among other applications. Read more

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A team led by researchers at the Austrian Academy of Sciences has developed a protocol that includes column-free RNA preparation, ribosomal RNA depletion, RNA-hydrolysis and ds-cDNA synthesis and is compatible with the all of the major NGS platforms used to date.

They demonstrate that:

• Ribo-depleted RNA-Seq is highly reproducible between different sequencing locations, even when using two different ribosomal RNA depletion strategies.

• Template fragmentation by RNA-hydrolysis produces more homogenous gene coverage than cDNA shearing, and that both fragmentation methods lead to under-representation of 5′ and 3′ UTRs.

• The use of similar template preparation protocols is critical for obtaining a comparable transcriptome.

• RNA populations prepared by ribo-depletion allow RNA-Seq to reliably detect both the non-coding and protein-coding transcriptome, and also to identify biologically relevant gene expression differences in both of these RNA types.

Huang R, Jaritz M, Guenzl P, Vlatkovic I, Sommer A, et al. (2011) An RNA-Seq Strategy to Detect the Complete Coding and Non-Coding Transcriptome Including Full-Length Imprinted Macro ncRNAs. PLoS ONE 6(11), e27288. [article]

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from GenomeWeb

Rearrangements involving genes from two gene families turn up in a significant fraction of breast cancers, according to a study published online in Nature Medicine yesterday.

An international group led by investigators at the University of Michigan found gene fusions in breast cancer when they used transcriptome sequencing to screen dozens of breast cancer cell lines and tissue samples. Though the combinations of genes varied, they found that the fusions found often included genes from the microtubule-associated serine-threonine, or MAST, kinase family or Notch family. From findings so far, they estimate that these sorts of recurrent rearrangements occur in some 5 to 7 percent of breast tumors.

(read more… )

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• study of specific gene families in transcriptome
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Making Sense of RNA-Seq Data

Course Duration
Course will run for four weeks from 02.01.2012 to 29.01.2012.

Course Material
Course material will be delivered online for each week at the start of the week. Register at http://www.labindia-gpod.com or write to uday@labindia-gpod.com

Registration
Send a copy of your biodata along with DD of Rs 5000/- in the name of Labindia Instruments Pvt. Ltd. payable at Thane to Co-ordinator, Labindia-GPOD, Swnand, Jivan Vihar Housing Society, SB Road, Pune or make online payment with debit/credit card using payment gateway.

Course Topics

1. Tools for mapping reads to reference genome or transcriptome
2. De novo assembly of Transcriptome
3. Finding isoforms and novel transcripts
4. Summarising mapped reads
5. Normalization and Differential Expression
6. Gene set and pathway enrichment

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SW4:  Epigenomics: Design, Implementation and Analysis for RNA-seq and Methyl-seq Experiments

Saturday, March 17, 2012
8:00 am – 4:30 pm
Disney’s Contemporary Resort, Orlando, FL

(read more… )

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Scientist III Bioinformatics
Life Technologies (Foster City, CA)
…of the following areas: variant detection (SNPs and Indels), structural variation, RNA – seq , Chip- Seq , cancer genomics, pathway analysis, phenotype prediction …

Associate Director – Bioinformatics
Genentech, Inc. (South San Francisco, CA)
…will use well developed research plans to take advantage of newer technologies such as DNA- seq , ChIP- seq , RNA – seq , proteomics or metabolomics. Areas of … sponsor

Scientific Associate II – DMP
Novartis (Cambridge, MA)
…data processing and interpretation using Excel and Spotfire. Work on generating sample libraries using RNA – seq , shRNA seq and plasmid library seq and perform …

Informatics Scientist, Translational Medicine
OncoMed Pharmaceuticals, Inc. (Redwood City, CA)
…a highly motivated Informatics Scientist to analyze Next Gen Sequencing data (eg, RNA – seq , exome sequencing) from preclinical and clinical studies. The ideal … sponsor

 

Computational Biologist
Broad Institute (Cambridge, MA)
…and execute sequencing and genome characterization projects, including but not limited to RNA – Seq , targeted exon sequencing, and whole genome sequencing…

Bioinformatician III
Harvard University (Boston, MA)
…in several bioinformatics domains such as microarray analysis (expression, methylation), sequencing ( RNA – seq , ChIP- seq , genome assembly, exome/WGS sequencing)…

Programmer Analyst 1
Yale University (CT)
…microarray analysis, gene set enrichment analysis, clustering, promoter analysis, network/pathway building, RNA – Seq , DNA- Seq ) 3. Experience with management of…

Bioinformatics Postdoctoral Scholar
University of California, San Diego (San Diego County, CA)
…biology and cell biology experience in data analysis of high-throughput sequencing data (eg RNA – Seq , Chip- Seq and/or whole genome sequencing ) Knowledge of …

Hevea brasiliensis, the rubber tree, is the most economically important member of the genus Hevea because its sap-like extract, latex, can be collected and is the primary source of natural rubber. The rubber tree initially grew only in the Amazon Rainforest but increasing demand and the discovery of the vulcanization procedure in 1839 led to the rubber boom in that region, enriching the cities of Belém and Manaus.

Now researchers from Mahidol University, Thailand have used RNA-Seq to sequence the transcriptome from the vegetative shoot apex of the rubber tree. Clustering and assembly of the reads produced a total of 113 313 unique sequences, comprising 28 387 isotigs and 84 926 singletons. Also, 17 819 expressed sequence tag (EST)-simple sequence repeats (SSRs) were identified from the data set.

• Triwitayakorn K et al. (2011) Transcriptome Sequencing of Hevea brasiliensis for Development of Microsatellite Markers and Construction of a Genetic Linkage Map. DNA Res [Epub ahead of print]. [article]

Incoming search terms:

• rna seq polyploid
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Xylose is the second most abundant lignocellulosic component besides glucose, but it cannot be fermented by the widely used ethanol-producing yeast Saccharomyces cerevisiae. The yeast Scheffersomyces stipitis, however, is well known for its high native capacity to ferment xylose. Here, researchers at the Chinese Academy of Agricultural Sciences applied next-generation sequencing technology for RNA (RNA-Seq) to generate two high-resolution transcriptional maps of the S. stipitis genome when this yeast was grown using glucose or xylose as the sole carbon source. RNA-Seq revealed that 5,176 of 5,816 annotated open reading frames had a uniform transcription and that 214 open reading frames were differentially transcribed. Read more

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• Scheffersomyces
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POME is a Poisson mixed-effects (or in short, model to characterize base-level read coverage within each transcript. The underlying expression level is included as a key parameter in this model. Because the proposed model is capable of incorporating base-specific variation as well as between-base dependence that affect read coverage profile throughout the transcript, it can lead to improved quantification of the true underlying expression level.

POME can be freely downloaded at http://www.stat.purdue.edu/~yuzhu/pome.html

• Hu M, Zhu Y, Taylor JM, Liu JS, Qin Z. (2011) Using Poisson mixed-effects model to quantify transcript-level gene expression in RNA-Seq. Bioinformatics [Epub ahead of print]. [abstract]

Incoming search terms:

• what is the good on Poisson mixed model on RNA-seq
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• SEQanswers – RNA Sequencing

  • 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
  • Design of expt across multiple lanes May 15, 2013
    Hi, I am performing an RNA-seq experiment to look at differential expression. The design is as follows: 2 populations x 3 biological... […]
    jbono
  • RNA kinds expected in RNA-seq results May 15, 2013
    Hi, We use RNA isolation and library preparation protocols which capture polyadenylated RNA. My question is what kinds of RNA can we expect to... […]
    Kocur

• Biostar – RNA-Seq

  • 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 tag, which is useful for subsequent analyses, such as annotation. However, does this information influence the "mappability" of reads, or is this unaffected? My guess is that the information will be considered for mapping […]
  • 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 […]
  • Cell Type composition in a tissue based on gene marker expression
    I am not sure if the following would even make sense.... Tissues are composed of composite cell types, and often there are studies such as microarray/NGS where we perform a collective sampling of cells from these tissues. Information about the composition (say percentage of cell type) is not taken into consideration. In some case (such as brain/cancer), ther […]
  • Which SNP caller / method to use after aligning RNA-seq with TopHat
    Which SNP caller / method can / should I use after aligning RNA-seq data with TopHat? For genomic data I use GATK, but supposedly it is not just as easy as running GATK on the TopHat RNA-seq data. The team from Broad has no information / documentation on how to use GATK for RNA-seq data. I don't have any variants yet from DNA re-sequencing. […]