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Two-pass alignment improves novel splice junction quantification.

ABSTRACT: Discovery of novel splicing from RNA sequence data remains a critical and exciting focus of transcriptomics, but reduced alignment power impedes expression quantification of novel splice junctions.Here, we profile performance characteristics of two-pass alignment, which separates splice junction discovery from quantification. Per sample, across a variety of transcriptome sequencing datasets, two-pass alignment improved quantification of at least 94% of simulated novel splice junctions, and provided as much as 1.7-fold deeper median read depth over those splice junctions. We further demonstrate that two-pass alignment works by increasing alignment of reads to splice junctions by short lengths, and that potential alignment errors are readily identifiable by simple classification. Taken together, two-pass alignment promises to advance quantification and discovery of novel splicing events.arul@med.umich.edu, nesvi@med.umich.eduTwo-pass alignment was implemented here as sequential alignment, genome indexing, and re-alignment steps with STAR. Full parameters are provided in Supplementary Table 2.Supplementary data are available at Bioinformatics online.

SUBMITTER: Veeneman BA

PROVIDER: S-EPMC5006238 | biostudies-other | 2016 Jan

REPOSITORIES: biostudies-other

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Two-pass alignment improves novel splice junction quantification.

Veeneman Brendan A BA Shukla Sudhanshu S Dhanasekaran Saravana M SM Chinnaiyan Arul M AM Nesvizhskii Alexey I AI

Bioinformatics (Oxford, England) 20151030 1

<h4>Motivation</h4>Discovery of novel splicing from RNA sequence data remains a critical and exciting focus of transcriptomics, but reduced alignment power impedes expression quantification of novel splice junctions.<h4>Results</h4>Here, we profile performance characteristics of two-pass alignment, which separates splice junction discovery from quantification. Per sample, across a variety of transcriptome sequencing datasets, two-pass alignment improved quantification of at least 94% of simulate ...[more]

PMID: 26519505

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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.

Project description:BACKGROUND:Myocardial perfusion with cardiovascular magnetic resonance (CMR) imaging is an established diagnostic test for evaluation of myocardial ischaemia. For quantification purposes, the 16 segment American Heart Association (AHA) model poses limitations in terms of extracting relevant information on the extent/severity of ischaemia as perfusion deficits will not always fall within an individual segment, which reduces its diagnostic value, and makes an accurate assessment of outcome data or a result comparison across various studies difficult. We hypothesised that division of the myocardial segments into epi- and endocardial layers and a further circumferential subdivision, resulting in a total of 96 segments, would improve the accuracy of detecting myocardial hypoperfusion. Higher (sub-)subsegmental recording of perfusion abnormalities, which are defined relatively to the normal reference using the subsegment with the highest value, may improve the spatial encoding of myocardial blood flow, based on a single stress perfusion acquisition. OBJECTIVE:A proof of concept comparison study of subsegmentation approaches based on transmural segments (16 AHA and 48 segments) vs. subdivision into epi- and endocardial (32) subsegments vs. further circumferential subdivision into 96 (sub-)subsegments for diagnostic accuracy against invasively defined obstructive coronary artery disease (CAD). METHODS:Thirty patients with obstructive CAD and 20 healthy controls underwent perfusion stress CMR imaging at 3 T during maximal adenosine vasodilation and a dual bolus injection of 0.1 mmol/kg gadobutrol. Using Fermi deconvolution for blood flow estimation, (sub-)subsegmental values were expressed relative to the (sub-)subsegment with the highest flow. In addition, endo-/epicardial flow ratios were calculated based on 32 and 96 (sub-)subsegments. A receiver operating characteristics (ROC) curve analysis was performed to compare the diagnostic performance of discrimination between patients with CAD and healthy controls. Observer reproducibility was assessed using Bland-Altman approaches. RESULTS:Subdivision into more and smaller segments revealed greater accuracy for #32, #48 and # 96 compared to the standard #16 approach (area under the curve (AUC): 0.937, 0.973 and 0.993 vs 0.820, p < 0.05). The #96-based endo-/epicardial ratio was superior to the #32 endo-/epicardial ratio (AUC 0.979, vs. 0.932, p < 0.05). Measurements for the #16 model showed marginally better reproducibility compared to #32, #48 and #96 (mean difference ± standard deviation: 2.0 ± 3.6 vs. 2.3 ± 4.0 vs 2.5 ± 4.4 vs. 4.1 ± 5.6). CONCLUSIONS:Subsegmentation of the myocardium improves diagnostic accuracy and facilitates an objective cut-off-based description of hypoperfusion, and facilitates an objective description of hypoperfusion, including the extent and severity of myocardial ischaemia. Quantification based on a single (stress-only) pass reduces the overall amount of gadolinium contrast agent required and the length of the overall diagnostic study.

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Two-pass alignment improves novel splice junction quantification.

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Two-pass alignment improves novel splice junction quantification.

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