Dataset Information

PASTA: splice junction identification from RNA-sequencing data.

ABSTRACT:

Background

Next generation transcriptome sequencing (RNA-Seq) is emerging as a powerful experimental tool for the study of alternative splicing and its regulation, but requires ad-hoc analysis methods and tools. PASTA (Patterned Alignments for Splicing and Transcriptome Analysis) is a splice junction detection algorithm specifically designed for RNA-Seq data, relying on a highly accurate alignment strategy and on a combination of heuristic and statistical methods to identify exon-intron junctions with high accuracy.

Results

Comparisons against TopHat and other splice junction prediction software on real and simulated datasets show that PASTA exhibits high specificity and sensitivity, especially at lower coverage levels. Moreover, PASTA is highly configurable and flexible, and can therefore be applied in a wide range of analysis scenarios: it is able to handle both single-end and paired-end reads, it does not rely on the presence of canonical splicing signals, and it uses organism-specific regression models to accurately identify junctions.

Conclusions

PASTA is a highly efficient and sensitive tool to identify splicing junctions from RNA-Seq data. Compared to similar programs, it has the ability to identify a higher number of real splicing junctions, and provides highly annotated output files containing detailed information about their location and characteristics. Accurate junction data in turn facilitates the reconstruction of the splicing isoforms and the analysis of their expression levels, which will be performed by the remaining modules of the PASTA pipeline, still under development. Use of PASTA can therefore enable the large-scale investigation of transcription and alternative splicing.

SUBMITTER: Tang S

PROVIDER: S-EPMC3623791 | biostudies-literature | 2013 Apr

REPOSITORIES: biostudies-literature

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Publications

PASTA: splice junction identification from RNA-sequencing data.

Tang Shaojun S Riva Alberto A

BMC bioinformatics 20130404

<h4>Background</h4>Next generation transcriptome sequencing (RNA-Seq) is emerging as a powerful experimental tool for the study of alternative splicing and its regulation, but requires ad-hoc analysis methods and tools. PASTA (Patterned Alignments for Splicing and Transcriptome Analysis) is a splice junction detection algorithm specifically designed for RNA-Seq data, relying on a highly accurate alignment strategy and on a combination of heuristic and statistical methods to identify exon-intron ...[more]

PMID: 23557086

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Project description:Human proteomic databases required for MS peptide identification are frequently updated and carefully curated, yet are still incomplete because it has been challenging to acquire every protein sequence from the diverse assemblage of proteoforms expressed in every tissue and cell type. In particular, alternative splicing has been shown to be a major source of this cell-specific proteomic variation. Many new alternative splice forms have been detected at the transcript level using next generation sequencing methods, especially RNA-Seq, but it is not known how many of these transcripts are being translated. Leveraging the unprecedented capabilities of next generation sequencing methods, we collected RNA-Seq and proteomics data from the same cell population (Jurkat cells) and created a bioinformatics pipeline that builds customized databases for the discovery of novel splice-junction peptides. Eighty million paired-end Illumina reads and ?500,000 tandem mass spectra were used to identify 12,873 transcripts (19,320 including isoforms) and 6810 proteins. We developed a bioinformatics workflow to retrieve high-confidence, novel splice junction sequences from the RNA data, translate these sequences into the analogous polypeptide sequence, and create a customized splice junction database for MS searching. Based on the RefSeq gene models, we detected 136,123 annotated and 144,818 unannotated transcript junctions. Of those, 24,834 unannotated junctions passed various quality filters (e.g. minimum read depth) and these entries were translated into 33,589 polypeptide sequences and used for database searching. We discovered 57 splice junction peptides not present in the Uniprot-Trembl proteomic database comprising an array of different splicing events, including skipped exons, alternative donors and acceptors, and noncanonical transcriptional start sites. To our knowledge this is the first example of using sample-specific RNA-Seq data to create a splice-junction database and discover new peptides resulting from alternative splicing.

Dataset Information

PASTA: splice junction identification from RNA-sequencing data.

Background

Results

Conclusions

Publications

PASTA: splice junction identification from RNA-sequencing data.

Similar Datasets

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