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Welcome back to the RNA-Seq Blog.  Due to blog maintenance there was a short outage today, but we are back!

Next-Gen Sequencing: Novel Approaches to Automated Sample Prep and Data Analysis for RNA-Seq

Date:	Wednesday, May 30, 2012
Time:	1 p.m. Eastern, 10 a.m. Pacific, 7 p.m. Central Europe
Duration:	1 hour

Webinar Description:

Next Generation Sequencing of DNA provides direct access to genomic information that has enabled researchers to transition away from more complex systems for the interrogation of this information, to direct sequencing of genomes and exomes. More recently the cost-effectiveness and accessibility of such data through the power of next-generation sequencing is being leveraged for other applications such as RNA-Seq, that allows the characterization of the transcriptome at an unprecedented level.

In this webinar, we will present an automated platform for the high throughput processing of extracted RNA via the preparation of high-quality libraries, and describe an easy-to-use, best-practices solution for the analysis of the overwhelming mass of gene expression data generated from Next Gen Sequencing.

Speakers:

Andrew Barry
Product Marketing Manager
PerkinElmer

Hugh Arnold, PhD
Senior Applications Scientist, Geospiza
PerkinElmer

Register Now

The Nonhuman Primate Reference Transcriptome Resource (NHPRTR) is a project that was initiated in mid 2010. The concept was to develop a NHP reference transcriptome resource consisting of Next Generation Sequence complete transcriptomes from multiple NHP species. A consortium of primate biologists, molecular biologists, and bioinformatists participate in the development and extension of the NHPRTR. The Steering Committee overseeing the collection, processing, sequencing and assemblies within the resource is composed of Michael Katze of the University of Washington (UW) and Chris Mason of Cornell University (CU) along with Gary Schroth of Illumina.

Accurate estimation of expression levels from RNA-Seq data entails precise mapping of the sequence reads to a reference genome. Because the standard reference genome contains only one allele at any given locus, reads overlapping polymorphic loci that carry a non-reference allele are at least one mismatch away from the reference and, hence, are less likely to be mapped. This bias in read mapping leads to inaccurate estimates of allele-specific expression (ASE).

To address this read-mapping bias, researchers at the DoD Biotechnology Software Applications Institute proposed the construction of an enhanced reference genome that includes the alternative alleles at known polymorphic loci. They show that mapping to this enhanced reference reduced the read-mapping biases, leading to more reliable estimates of ASE.

Experiments on simulated data show that the proposed strategy reduced the number of loci with mapping bias by ≥63% when compared with a previous approach that relies on masking the polymorphic loci and by ≥18% when compared with the standard approach that uses an unaltered reference. When this strategy was applied to actual RNA-Seq data, up to 15% more reads were mapped than the previous approaches and many seemingly incorrect inferences were identified.

The executables to construct the enhanced reference genome and the Perl scripts to analyze the mapped reads are available for download from http://www.bhsai.org/downloads/ase/

• Vijaya Satya R, Zavaljevski N, Reifman J. (2012) A new strategy to reduce allelic bias in RNA-Seq readmapping. Nucleic Acids Res [Epub ahead of print]. [article]

The genus Setaria includes natural and cultivated species that demonstrate a wide capacity for adaptation. A team led by researchers at the University of Georgia has generated a high-quality reference genome sequence for foxtail millet (Setaria italica). The ~400-Mb assembly covers ~80% of the genome and >95% of the gene space. The assembly was anchored to a 992-locus genetic map and was annotated by comparison with >1.3 million expressed sequence tag reads.

They produced more than 580 million RNA-Seq reads to facilitate expression analyses. They also sequenced Setaria viridis, the ancestral wild relative of S. italica, and identified regions of differential single-nucleotide polymorphism density, distribution of transposable elements, small RNA content, chromosomal rearrangement and segregation distortion.

• Bennetzen JL et. al. (2012) Reference genome sequence of the model plant Setaria. Nat Biotechnol [Epub ahead of print]. [article]

miRFANs, an online database for Arabidopsis thaliana miRNA function annotations. The creators integrated various type of datasets, including miRNA-target interactions, transcription factor (TF) and their targets, expression profiles, genomic annotations and pathways, into a comprehensive database, and developed various statistical and mining tools.

miRFANs consists of:

1. Comprehensive collection of miRNA targets for Arabidopsis thaliana provides valuable information about the functions of plant miRNAs.
2. Highly informative miRNA-mediated genetic regulatory network is extracted from our integrative database.
3. Set of statistical and mining tools is equipped for analyzing and mining the database.
4. User-friendly web interface is developed to facilitate the browsing and analysis of the collected data.

miRFANs is freely available at: http://www.cassava-genome.cn/mirfans

• Liu H, Jin T, Liao R, Wan L, Xu B, Zhou S, Guan J. (2012) miRFANs: an integrated database for Arabidopsis thaliana microRNA function annotations. BMC Plant Biology [Epub ahead of print]. [abstract]

Requisition Number: 790

Job Title: Computational Biologist, RNA-Seq

Area of Interest: Computational Biology
City: Cambridge
State/Province: Massachusetts

Job Description: The role of this researcher will be to develop and apply new and existing computational methods, interpret results within a biological context, integrate best practices from other groups using RNA-Seq data, and refine techniques and metrics appropriate for RNA-Seq analysis pipelines. As a member of the Molecular Biology Research & Development (MBRD) group this researcher will work in close collaboration with laboratory development scientists to collaborate on development of new laboratory methods related to next-generation sequencing of RNA samples. The role involves rapid prototyping and is focused on development of molecular biology applications and sequencing technology. The role also involves direct interaction with MBRD co-workers, Aviv Regev and her group, as well as researchers in other groups within the Broad Institute. Responsibilities include communication of results to the scientific community at Broad and externally through conference presentations, peer-reviewed publications, and project reports. (read more… )

PhenoLink is an easily accessible web-tool to facilitate identifying relations from large and often noisy phenotype and ~omics datasets.

Visualization of links to phenotypes offered in PhenoLink allows prioritizing links, finding relations between features, finding relations between phenotypes, and identifying outliers in phenotype data. PhenoLink can be used to uncover phenotype links to a multitude of ~omics data, e.g., gene presence/absence (determined by e.g.: CGH or next-generation sequencing), gene expression (determined by e.g.: microarrays or RNA-seq), or metabolite abundance (determined by e.g.: GC-MS).

PhenoLink is available on the web at: http://bamics2.cmbi.ru.nl/websoftware/phenolink/

BreakFusion is a software package, that combines the strength of reference alignment followed by read-pair analysis and de novo assembly to achieve a good balance in sensitivity, specificity, and computational efficiency for identification of gene fusions from next-generation whole transcriptome sequencing (RNA-Seq) data.

Availability: http://bioinformatics.mdanderson.org/main/BreakFusion

• Chen K et. Al. (2012) BreakFusion: Targeted Assembly-based Identification of Gene Fusions in Whole Transcriptome Paired-end Sequencing Data. Bioinformatics [Epub ahead of print]. [abstract]

Bulked segregant analysis (BSA) is an efficient method to map genes responsible for mutant phenotypes. BSR-Seq makes use of RNA-Seq reads to efficiently map genes even in populations for which no polymorphic markers have been previously identified.

BSR-Seq provides not only the map position of a gene responsible for a mutant phenotype but also the effects of such a mutant on global patterns of gene expression. The expression patterns of genes within the mapping interval can be used to prioritize candidate genes based on the fact that the causal gene will often be down-regulated in the mutant pool as compared to the non-mutant pool. In addition, this strategy yields a collection of polymorphic SNPs that are tightly linked to the mutant. These SNPs could be used to fine map the mutant or clone the affected gene via chromosome walking. Hence, BSR-Seq is not only an efficient strategy for mapping genes, but also yields other data that facilitate gene cloning. Read more

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