Dataset Information

An open-source computational and data resource to analyze digital maps of immunopeptidomes.

ABSTRACT: We present a novel mass spectrometry-based high-throughput workflow and an open-source computational and data resource to reproducibly identify and quantify HLA-associated peptides. Collectively, the resources support the generation of HLA allele-specific peptide assay libraries consisting of consensus fragment ion spectra, and the analysis of quantitative digital maps of HLA peptidomes generated from a range of biological sources by SWATH mass spectrometry (MS). This study represents the first community-based effort to develop a robust platform for the reproducible and quantitative measurement of the entire repertoire of peptides presented by HLA molecules, an essential step towards the design of efficient immunotherapies.

SUBMITTER: Caron E

PROVIDER: S-EPMC4507788 | biostudies-literature | 2015 Jul

REPOSITORIES: biostudies-literature

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An open-source computational and data resource to analyze digital maps of immunopeptidomes.

Caron Etienne E Espona Lucia L Kowalewski Daniel J DJ Schuster Heiko H Ternette Nicola N Alpízar Adán A Schittenhelm Ralf B RB Ramarathinam Sri H SH Lindestam Arlehamn Cecilia S CS Chiek Koh Ching C Gillet Ludovic C LC Rabsteyn Armin A Navarro Pedro P Kim Sangtae S Lam Henry H Sturm Theo T Marcilla Miguel M Sette Alessandro A Campbell David S DS Deutsch Eric W EW Moritz Robert L RL Purcell Anthony W AW Rammensee Hans-Georg HG Stevanovic Stefan S Aebersold Ruedi R

eLife 20150708

We present a novel mass spectrometry-based high-throughput workflow and an open-source computational and data resource to reproducibly identify and quantify HLA-associated peptides. Collectively, the resources support the generation of HLA allele-specific peptide assay libraries consisting of consensus fragment ion spectra, and the analysis of quantitative digital maps of HLA peptidomes generated from a range of biological sources by SWATH mass spectrometry (MS). This study represents the first ...[more]

PMID: 26154972

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Project description:Peer Review is at the heart of scholarly communications and the cornerstone of scientific publishing. However, academia often criticizes the peer review system as non-transparent, biased, arbitrary, a flawed process at the heart of science, leading to researchers arguing with its reliability and quality. These problems could also be due to the lack of studies with the peer-review texts for various proprietary and confidentiality clauses. Peer review texts could serve as a rich source of Natural Language Processing (NLP) research on understanding the scholarly communication landscape, and thereby build systems towards mitigating those pertinent problems. In this work, we present a first of its kind multi-layered dataset of 1199 open peer review texts manually annotated at the sentence level (∼ 17k sentences) across the four layers, viz. Paper Section Correspondence, Paper Aspect Category, Review Functionality, and Review Significance. Given a text written by the reviewer, we annotate: to which sections (e.g., Methodology, Experiments, etc.), what aspects (e.g., Originality/Novelty, Empirical/Theoretical Soundness, etc.) of the paper does the review text correspond to, what is the role played by the review text (e.g., appreciation, criticism, summary, etc.), and the importance of the review statement (major, minor, general) within the review. We also annotate the sentiment of the reviewer (positive, negative, neutral) for the first two layers to judge the reviewer's perspective on the different sections and aspects of the paper. We further introduce four novel tasks with this dataset, which could serve as an indicator of the exhaustiveness of a peer review and can be a step towards the automatic judgment of review quality. We also present baseline experiments and results for the different tasks for further investigations. We believe our dataset would provide a benchmark experimental testbed for automated systems to leverage on current NLP state-of-the-art techniques to address different issues with peer review quality, thereby ushering increased transparency and trust on the holy grail of scientific research validation. Our dataset and associated codes are available at https://www.iitp.ac.in/~ai-nlp-ml/resources.html#Peer-Review-Analyze.

Dataset Information

An open-source computational and data resource to analyze digital maps of immunopeptidomes.

Publications

An open-source computational and data resource to analyze digital maps of immunopeptidomes.

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