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Deep transcriptome sequencing (RNA-Seq) has become a vital tool for studying the state of cells in the context of varying environments, genotypes and other factors. RNA-Seq profiling data enable identification of novel isoforms, quantification of known isoforms and detection of changes in transcriptional or RNA-processing activity. Existing approaches to detect differential isoform abundance between samples either require a complete isoform annotation or fall short in providing statistically robust and calibrated significance estimates.

Now, a team led by researchers from Computational Biology Center, Sloan-Kettering Institute and the Friedrich Miescher Laboratory of the Max-Planck Society have developed a suite of statistical tests to address these open needs that includes:

• A statistical test called rDiff.parametric that based on the gene annotation, detects differential splicing. This test models the counts in a similar way as DESeq and can account for biological variance.
• A statistical test called rDiff.nonparametric that tests for a difference in the read distribution at a genomic locus. Therefore, it only needs the gene start and stop to detect detects differential relative transcript abundance and can be used when the splicing events are not yet annotated. This test is based on the Maximum mean discrepancy test and can also account for biological variance.

The team demonstrates:

1) the statistical calibration of the proposed is better than the one of CuffDiff (although not perfect) .

2) rDiff.parametric outperforms MISO and CuffDiff on a realistic simulated dataset.

3) rDiff.nonparametric has a similar performance as the Miso and CuffDiff without using the gene annotation for testing.

4) the methods perform well on two real datasets (from A.thaliana and D.melanogaster).

Availability – The proposed toolkit is available from http://bioweb.me/rdiff and enables in-depth analyses of transcriptomes, with or without available isoform annotation.

• Drewe P, Stegle O, Hartmann L, Kahles A, Bohnert R, Wachter A, Borgwardt K, Rätsch G. (2013) Accurate detection of differential RNA processing. Nucleic Acids Res [Epub ahead of print]. [article]

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• seecer probailistic for rna sequencing error correction

from figshare – by Eduardo Eyras, Gael P. Alamancos & Eneritz Agirre

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• Methods to study splicing from high-throughput RNA Sequencing data
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Many RNA sequencing studies set out to predict mutations, splice junctions or fusion RNAs. Now a team of researchers in France has developed a method, CRAC, that integrates genomic locations and local coverage to enable such predictions to be made directly from RNA-Seq read analysis. A k-mer profiling approach detects candidate mutations, indels and splice or chimeric junctions in each single read. CRAC increases precision compared with existing tools, reaching 99:5% for splice junctions, without losing sensitivity. Importantly, CRAC predictions improve with read length. In cancer libraries, CRAC recovered 74% of validated fusion RNAs and predicted novel recurrent chimeric junctions.

Availability – CRAC is available at http://crac.gforge.inria.fr.

• Philippe N, Salson M, Commes T, Rivals E. (2013) CRAC: an integrated approach to the analysis of RNA-seq reads. Genome Biology 14, R30. [abstract]

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Novel technologies brought in unprecedented amounts of high-throughput sequencing data along with great challenges in their analysis and interpretation. The percent-spliced-in (PSI, Ψ) metric estimates the incidence of single-exon skipping events and can be computed directly by counting reads that align to known or predicted splice junctions. However, the vast majority of human splicing events are more complex than single-exon skipping.

A team led by scientists at the Centre de Regulació Genòmica, Spain has now developed a framework that generalizes the Ψ metric to arbitrary classes of splicing events. They change the view from exon-centric to intron-centric and split the value of Ψ into two indices, ψ(5) and ψ(3), measuring the rate of splicing at the 5′- and 3′-end of the intron, respectively. The advantage of having two separate indices is that they deconvolute two distinct elementary acts of the splicing reaction. The completeness of splicing index (COSI) is decomposed in a similar way. This framework is implemented as bam2ssj, a BAM-file processing pipeline for strand-specific counting of reads that align to splice junctions or overlap with splice sites. It can be used as a consistent protocol for quantifying splice junctions from RNA-seq data since no such standard procedure currently exists.

AVAILABILITY: The C(++) code of bam2ssj is open-source and is available at https://github.com/pervouchine/bam2ssj CONTACT: dp@crg.eu.

Pervouchine DD, Knowles DG, Guigó R. (2012) Intron-Centric Estimation of Alternative Splicing from RNA-seq data. Bioinformatics [Epub ahead of print]. [article]

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Paired-end whole transcriptome sequencing provides evidence for fusion transcripts. However, due to the repetitiveness of the transcriptome, many reads have multiple high-quality mappings. Previous methods to find gene fusions either ignored these reads or required additional longer single reads. This can obscure up to 30% of fusions and unnecessarily discards much of the data. We present a method for using paired-end reads to find fusion transcripts without requiring unique mappings or additional single read sequencing. Using simulated data and data from tumors and cell lines, we show that our method can find fusions with ambiguously mapping read pairs without generating numerous spurious fusions from the many mapping locations.

Availability: A C++ and Python implementation of the method demonstrated in this paper is available at http://exon.ucsd.edu/ShortFuse

Kinsella M, Harismendy O, Nakano M, Frazer KA, Bafna V. (2012) Sensitive gene fusion detection using ambiguously mapping RNA-Seq read pairs. Bioinformatics 27(8), 1068-75. [abstract]

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JETTA detects alternatively spliced exons based on pre-calculated statistics of exons and junctions in RNA-Seq data sets. It does not support low-level RNA-Seq analysis such as base calling, mapping, alignment, transcript assembly, and expression calculation. For low-level RNA-Seq analysis, please refer to SeqMap, rSeq, and SpliceMap or other tools.

Input Data Files – JETTA requires the following files

• Gene expression file. See an example.
• Exon expression file. See an example.
• Junction expression file (only for junction supports). See an example.
• Alternative splicing structure file (only for junction supports). See an example.

These expression matrixes can be calculated from any RNA-Seq platform, but Illumina RNA-Seq plaforms with GA-II, GA-IIx and HiSeq2000 are suggested. Each expression files should contain samples of two conditions, and at least more than two samples for each condition.

Availability – JETTA source codes and other downloads are available at: http://gluegrant1.stanford.edu/~junhee/JETTA/

SpliceGrapher is a package for creating splice graphs from RNA-Seq data, guided by gene models and EST data (when available).

Features

• Use your favorite read mapping and spliced alignment tools.
• Accurate spliced-alignment filtering using SVM classifiers that recognize splice junction sequence features.
• Generate statistics of alternative splicing.
• Visualization of splice graphs, splice junctions, and read depth
• Use our pipeline or construct your own custom pipeline out of SpliceGrapher modules.

Results obtained using RNA-Seq experiments in Arabidopsis thaliana show that predictions made by SpliceGrapher method are more consistent with current gene models than predictions made by TAU and Cufflinks. Furthermore, analysis of plant and human data indicates that the machine learning approach used by SpliceGrapher is useful for discriminating between real and spurious splice sites, and can improve the reliability of detection of alternative splicing.

SpliceGrapher is available for download at http://SpliceGrapher.sf.net.

• Rogers MF, Thomas J, Reddy AS, Ben-Hur A. (2012) SpliceGrapher: Detecting patterns of alternative splicing from RNA-seq data in the context of gene models and EST data. Genome Biol 13(1), R4. [abstract]

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SpliceTrap is a method to quantify exon inclusion levels using paired-end RNA-seq data. Unlike other tools, which focus on full-length transcript isoforms, SpliceTrap approaches the expression-level estimation of each exon as an independent Bayesian inference problem. In addition, SpliceTrap can identify major classes of alternative splicing events under a single cellular condition, without requiring a background set of reads to estimate relative splicing changes.

Availability SpliceTrap is available for download and installation at: http://rulai.cshl.edu/splicetrap/

• Wu J, Akerman M, Sun S, McCombie WR, Krainer AR, Zhang MQ. (2011) SpliceTrap: a method to quantify alternative splicing under single cellular conditions. Bioinformatics [Epub ahead of print]. [abstract]

Incoming search terms:

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• SEQanswers – RNA Sequencing

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  • a simple question on RNA-Seq terminology
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