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Researchers at the USDA have generated and characterized a reference transcriptome for rainbow trout that represents multiple tissues responding to multiple stressors common to aquaculture production environments. This resource compliments existing public transcriptome data and will facilitate approaches aiming to evaluate gene expression associated with stress in this species.

Sequencing of a pooled normalized transcriptome library created from gill, brain, liver, spleen, kidney and muscle RNA of control and stressedfish produced 3,160,306 expressed sequence tags which were assembled and annotated. SNP discovery resulted in identification of ~58,000 putative single nucleotide polymorphisms including 24,479 which were predicted to fall within exons.

• C Sanchez CC, Weber GM, Gao G, Cleveland BM, Yao J, Rexroad CE. (2011) Generation of a reference transcriptome for evaluating rainbow trout responses to various stressors. BMC Genomics 12, 626. [article]

Incoming search terms:

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RNA-Seq – The Use of Short Read Illumina Data for Transcriptome Annotation and Quantification.

Monday, January 2, 2012 at 09:30-12:00.

Location: Harry Levine Family Building, Computer Classroom, Weizmann Institute, Rehovot.

Lecturer:  Dr. Dena Leshkowitz Bioinformatics Unit,

Sponsor: Life Sciences Bioinformatics & Biological Computing Unit.

Topics: This workshop will start with an hour lecture on the various computational approaches and tools to analyze the data, including:

• Read mapping
• Transcriptome reconstruction
• Expression quantification
• Detecting differential genes and transcripts

A hands-on session will follow the lecture. In this session we will practice RNA-Seq workflows provided in Galaxy (Tophat, cufflinks and cuffdiff) and the Partek Genomics Suite software.

More info

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To detect differentially expressed genes under two conditions, statistical methods such as Poisson distributions are often used. However, to accurately detect differential expression of gene with low expression levels, more powerful statistical methods are desirable. In statistical literature, several methods have been proposed to compare two Poisson means (rates).

Through simulation study and real data analysis, the authors find that the Wald test with the data being log transformed is more powerful than other methods, including the likelihood ratio test, the variance stabilizing transformation test, the conditional exact test and the Fisher exact test.

When the count data in RNA-seq can be reasonably modelled as Poisson distribution, the Wald-Log test is more powerful and should be used to detect the differentially expressed genes.

• Chen et al. (2011) Statistical methods on detecting differentially expressed genes for RNA-seq data. BMC Systems Biology 5(Suppl 3), S1. [article]

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The transcriptome of embryos of the green anole, Anolis carolinensis

Illumina HiSeq2000 RNA-Seq data sets for A. carolinensis whole embryo (28 and 38 somite-pair stages, approximately at egg laying and 1 day after egg laying) have been uploaded to the NIH NCBI NIH-GEO repository. Links to the data can be found at http://anolisgenome.org/

• Eckalbar WL, Lasku E, Infante CR, Elsey RM, Markov GJ, Allen AN, Corneveaux JJ, Losos JB, Denardo DF, Huentelman MJ, Wilson-Rawls J, Rawls A, Kusumi K. (2011) Somitogenesis in the anole lizard and alligator reveals evolutionary convergence and divergence in the amniote segmentation clock. Dev Biol [Epub ahead of print]. [abstract]

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Transcriptome sequencing (RNA-Seq) has become the assay of choice for high-throughput studies of gene expression. However, as is the case with microarrays, major technology-related artifacts and biases affect the resulting expression measures. Normalization is therefore essential to ensure accurate inference of expression levels and subsequent analyses thereof.

A group led by researchers at University of Padua focused on biases related to GC-content and demonstrate the existence of strong sample-specific GC-content effects on RNA-Seq read counts, which can substantially bias differential expression analysis. They propose three simple within-lane gene-level GC-content normalization approaches and assess their performance on two different RNA-Seq datasets, involving different species and experimental designs. The methods are compared to state-of-the-art normalization procedures in terms of bias and mean squared error for expression fold-change estimation and in terms of Type I error and p-value distributions for tests of differential expression. The exploratory data analysis and normalization methods proposed in this article are implemented in the open-source Bioconductor R package EDASeq.

These within-lane normalization procedures, followed by between-lane normalization, reduce GC-content bias and lead to more accurate estimates of expression fold-changes and tests of differential expression. Such results are crucial for the biological interpretation of RNA-Seq experiments, where downstream analyses can be sensitive to the supplied lists of genes.

• Risso D, Schwartz K, Sherlock G,Dudoit S. (2011) GC-Content Normalization for RNA-Seq Data. BMC Bioinformatics [Epub ahead of print]. [abstract]

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EMBO Practical Course on Analysis of High-Throughput Sequencing Data

In this lecture, Wolfgang Huber, Group Leader in the Genome Biology Unit at EMBL Heidelberg, gives an introduction toRNA-Seq data as well as the statistical and computational methods used to analyse such data.

This lecture was followed by a practical that focused on the use of the Bioconductor DESeq package for the analysis of differential expression and RNA-seq style analysis. The practical is the vignette of the DESeq package and can be downloaded here, together with the code.

In order to follow this practical you will need to install the following Bioconductor packages in your R session:

• DESeq
• pasilla

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from GenomeWeb – In Sequence

In a head-to-head comparison of the Roche 454 GS FLX and Illumina GAII sequencing platforms, fish population researchers found that the two systems were equally capable of finding SNPs by sequencing the transcriptomes of fish from a species lacking a reference genome, though versions of the Illumina system introduced since the study was conducted appear to have a cost advantage over newer 454…

(Read more… )

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RNA-Seq transcriptome analysis for gene identification, polymorphism detection and transcript profiling in two perennial ryegrass genotypes with divergent vernalization requirement

By senior scientist Torben Asp, Department of Molecular Biology and Genetics, Aarhus University

Thursday, 15 December 2011
at 10:00-11:00

Center for LIFE Applied Bioinformatics and Department of Plant Biology and Biotechnology hereby invites you to attend the seminar:

Meeting room M117-1 + K117-2,
Thorvaldsensvej 40, 1.floor (map)
1871 Frederiksberg

(Read more … )

Despite accumulating data on animal and plant microRNAs and their functions, existing public miRNA resources usually collect miRNAs from a very limited number of species. A lot of microRNAs, including those from model organisms, remain undiscovered. As a result there is a continuous need to search for new microRNAs.

Izabela Makałowska’s Laboratory of Evolutionary Genomics at The Adam Mickiewicz University in Poznan just published miRNEST, a comprehensive database of animal, plant and virus microRNAs. The core part of the database is built from the author’s miRNA predictions conducted on Expressed Sequence Tags of 225 animal and 202 plant species. The miRNA search was performed based on sequence similarity and as many as 10 004 miRNA candidates in 221 animal and 199 plant species were discovered. Out of them only 299 have already been deposited in miRBase. Additionally, miRNEST has been integrated with external miRNA data from literature and 13 databases, which includes miRNA sequences, small RNA sequencing data, expression, polymorphisms and targets data as well as links to external miRNA resources, whenever applicable. All this makes miRNEST a considerable miRNA resource in a sense of number of species (544) that integrates a scattered miRNA data into a uniform format with a user-friendly web interface.

miRNEST: http://mirnest.amu.edu.pl

Szczesniak MW, Deorowicz S, Gapski J, Kaczynski L, Makalowska I. (2011) miRNEST database: an integrative approach in microRNA search and annotation. Nucleic Acids Res [Epub ahead of print]. [article]

Practical Genomics: Next Generation Sequencing technologies & RNA-Seq hands-on Illumina data

at the Biomedical Research Foundation of the Academy of Athens

BRFAA Soranou Ephessiou 4 Papagos 115 27 Athens, GR, Teleconference Lab (Building E4, 1stFloor) – Athens, GR

Monday 20 – Tuesday 21, February 2012

Incoming search terms:

• rna seq hands on

Sashimi-plot is a utility for automatically producing publication-quality plots for RNA-Seq analyses of isoform expression. It is part of the MISO framework. In particular, sashimi-plot can: (1) plot raw RNA-Seq densities along exons and junctions for multiple samples, while simultaneously visualizing the gene model/isoforms to which reads map, and (2) plot MISO output alongside the raw data or separately.

Katz Y, Wang ET, Airoldi EM, Burge CB. (2010) Analysis and design of RNA sequencing experiments for identifying isoform regulation. Nature Methods 7, 1009-1015. [abstract]

Incoming search terms:

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• Sashimi plots: Quantitative visualization of RNA sequencing read alignments
• sashimi map

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