Motivation: A new protocol for sequencing the messenger RNA in a cell, known as RNA-Seq, generates millions of short sequence fragments in a single run. These fragments, or ‘reads’, can be used to measure levels of gene expression and to identify novel splice variants of genes. However, current software for aligning RNA-Seq data to a genome relies on known splice junctions and cannot identify novel ones. TopHat is an efficient read-mapping algorithm designed to align reads from an RNA-Seq experiment to a reference genome without relying on known splice sites.

Results: We mapped the RNA-Seq reads from a recent mammalian RNA-Seq experiment and recovered more than 72% of the splice junctions reported by the annotation-based software from that study, along with nearly 20 000 previously unreported junctions. The TopHat pipeline is much faster than previous systems, mapping nearly 2.2 million reads per CPU hour, which is sufficient to process an entire RNA-Seq experiment in less than a day on a standard desktop computer. We describe several challenges unique to ab initio splice site discovery from RNA-Seq reads that will require further algorithm development.

Availability: TopHat is free, open-source software available from http://tophat.cbcb.umd.edu

Contact: ude.dmu.sc@eloc

Supplementary information: Supplementary data are available at Bioinformatics online.

/pdf/tophat-discovering-splice-junctions-with-rna-seq-4w2xcvy2p0.pdf

TopHat: discovering splice junctions with RNA-Seq

Motivation: A new protocol for sequencing the messenger RNA in a cell, known as RNA-Seq, generates millions of short sequence fragments in a single run. These fragments, or ‘reads’, can be used to measure levels of gene expression and to identify novel splice variants of genes. However, current software for aligning RNA-Seq data to a genome relies on known splice junctions and cannot identify novel ones. TopHat is an efficient read-mapping algorithm designed to align reads from an RNA-Seq experiment to a reference genome without relying on known splice sites. Results: We mapped the RNA-Seq reads from a recent mammalian RNA-Seq experiment and recovered more than 72% of the splice junctions reported by the annotation-based software from that study, along with nearly 20 000 previously unreported junctions. The TopHat pipeline is much faster than previous systems, mapping nearly 2.2 million reads per CPU hour, which is sufficient to process an entire RNA-Seq experiment in less than a day on a standard desktop computer. We describe several challenges unique to ab initio splice site discovery from RNA-Seq reads that will require further algorithm development. Availability: TopHat is free, open-source software available from http://tophat.cbcb.umd.edu Contact: cole@cs.umd.edu Supplementary information: Supplementary data are available at Bioinformatics online.

The accurate mapping of reads that span splice junctions is a critical component of all analytic techniques that work with RNA-seq data. We introduce a second generation splice detection algorithm, MapSplice, whose focus is high sensitivity and specificity in the detection of splices as well as CPU and memory efficiency. MapSplice can be applied to both short (<75 bp) and long reads (≥ 75 bp). MapSplice is not dependent on splice site features or intron length, consequently it can detect novel canonical as well as non-canonical splices. MapSplice leverages the quality and diversity of read alignments of a given splice to increase accuracy. We demonstrate that MapSplice achieves higher sensitivity and specificity than TopHat and SpliceMap on a set of simulated RNA-seq data. Experimental studies also support the accuracy of the algorithm. Splice junctions derived from eight breast cancer RNA-seq datasets recapitulated the extensiveness of alternative splicing on a global level as well as the differences between molecular subtypes of breast cancer. These combined results indicate that MapSplice is a highly accurate algorithm for the alignment of RNA-seq reads to splice junctions. Software download URL: http://www.netlab.uky.edu/p/bioinfo/MapSplice.

/pdf/mapsplice-accurate-mapping-of-rna-seq-reads-for-splice-2ramxv1fw0.pdf

MapSplice: Accurate mapping of RNA-seq reads for splice junction discovery

We describe Trans-ABySS, a de novo short-read transcriptome assembly and analysis pipeline that addresses variation in local read densities by assembling read substrings with varying stringencies and then merging the resulting contigs before analysis. Analyzing 7.4 gigabases of 50-base-pair paired-end Illumina reads from an adult mouse liver poly(A) RNA library, we identified known, new and alternative structures in expressed transcripts, and achieved high sensitivity and specificity relative to reference-based assembly methods.

De novo assembly and analysis of RNA-seq data

DNA damage, chromosomal abnormalities, oncogene activation, viral infection, substrate detachment and hypoxia can all trigger apoptosis in normal cells. However, cancer cells acquire mutations that allow them to survive these threats that are part and parcel of the transformation process or that may affect the growth and dissemination of the tumor. Eventually, cancer cells accumulate further mutations that make them resistant to apoptosis mediated by standard cytotoxic chemotherapy or radiotherapy. The inhibitor of apoptosis (IAP) family members, defined by the presence of a baculovirus IAP repeat (BIR) protein domain, are key regulators of cytokinesis, apoptosis and signal transduction. Specific IAPs regulate either cell division, caspase activity or survival pathways mediated through binding to their BIR domains, and/or through their ubiquitin-ligase RING domain activity. These protein-protein interactions and post-translational modifications are the subject of intense investigations that shed light on how these proteins contribute to oncogenesis and resistance to therapy. In the past several years, we have seen multiple approaches of IAP antagonism enter the clinic, and the rewards of such strategies are about to reap benefit. Significantly, small molecule pan-IAP antagonists that mimic an endogenous inhibitor of the IAPs, called Smac, have demonstrated an unexpected ability to sensitize cancer cells to tumor necrosis factor-alpha and to promote autocrine or paracrine production of this cytokine by the tumor cell and possibly, other cells too. This review will focus on these and other developmental therapeutics that target the IAPs in cancer.

IAP-targeted therapies for cancer

Alternative splicing is an important regulatory mechanism of mammalian gene expression The alternative splicing database (ASD) consortium is systematically collecting and annotating data on alternative splicing We present the continuation and upgrade of the ASD [T A Thanaraj, S Stamm, F Clark, J J Riethoven, V Le Texier, J Muilu (2004) Nucleic Acids Res 32, D64-D69] that consists of computationally and manually generated data Its largest parts are AltSplice, a value-added database of computationally delineated alternative splicing events Its data include alternatively spliced introns/exons, events, isoform splicing patterns and isoform peptide sequences AltSplice data are generated by examining gene-transcript alignments The data are annotated for various biological features including splicing signals, expression states, (SNP)-mediated splicing and cross-species conservation AEdb forms the manually curated component of ASD It is a literature-based data set containing sequence and properties of alternatively spliced exons, functional enumeration of observed splicing events, characterization of observed splicing regulatory elements, and a collection of experimentally clarified minigene constructs ASD includes a workbench, which is an analysis tool that enables users to carry out splicing related analysis such as characterization of introns for various splicing signals, identification of splicing regulatory elements on a given RNA sequence, prediction of putative exons and prediction of putative translation start codons The different ASD modules are integrated and can be accessed through user-friendly interfaces and visualization tools ASD data has been integrated with Ensembl genome annotation project as a Distributed Annotation System (DAS) resource and can be viewed on Ensembl genome browser The ASD resource is presented at (http://wwwebiacuk/asd)

ASD: a bioinformatics resource on alternative splicing

http://public-files.prbb.org/publicacions/01592c20-0b66-012e-a84a-000c293b26d5.pdf

ASTD: The Alternative Splicing and Transcript Diversity database

The three major mechanisms that regulate transcript formation involve the selection of alternative sites for transcription start (TS), splicing, and polyadenylation. Currently there are efforts that collect data & annotation individually for each of these variants. It is important to take an integrated view of these data sets and to derive a data set of alternate transcripts along with consolidated annotation. We have been developing in the past computational pipelines that generate value-added data at genome-scale on individual variant types; these include AltSplice on splicing and AltPAS on polyadenylation. We now extend these pipelines and integrate the resultant data sets to facilitate an integrated view of the contributions from splicing and polyadenylation in the formation of transcript variants. The AltSplice pipeline examines gene-transcript alignments and delineates alternative splice events and splice patterns; this pipeline is extended as AltTrans to delineate isoform transcript patterns for each of which both introns/exons and 'terminating' polyA site are delineated; EST/mRNA sequences that qualify the transcript pattern confirm both the underlying splicing and polyadenylation. The AltPAS pipeline examines gene-transcript alignments and delineates all potential polyA sites irrespective of underlying splicing patterns. Resultant polyA sites from both AltTrans and AltPAS are merged. The generated database reports data on alternative splicing, alternative polyadenylation and the resultant alternate transcript patterns; the basal data is annotated for various biological features. The data (named as integrated AltTrans data) generated for both the organisms of human and mouse is made available through the Alternate Transcript Diversity web site at http://www.ebi.ac.uk/atd/
 . The reported data set presents alternate transcript patterns that are annotated for both alternative splicing and alternative polyadenylation. Results based on current transcriptome data indicate that the contribution of alternative splicing is larger than that of alternative polyadenylation.

/pdf/alttrans-transcript-pattern-variants-annotated-for-both-2ehipzcofp.pdf

AltTrans: Transcript pattern variants annotated for both alternative splicing and alternative polyadenylation

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ASD: a bioinformatics resource on alternative splicing

ASTD: The Alternative Splicing and Transcript Diversity database

AltTrans: Transcript pattern variants annotated for both alternative splicing and alternative polyadenylation