The Proteins API: accessing key integrated protein and genome information

Nightingale, Andrew; Antunes, Ricardo; Alpi, Emanuele; Bursteinas, Borisas; Gonzales, Leonardo; Liu, Wudong; Luo, Jie; Qi, Guoying; Turner, E. B.; Martin, Maria Jesus

doi:10.1093/nar/gkx237

Cited by 81 publications

(69 citation statements)

References 19 publications

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“…To access the UniProtKB 51 database via RAVEN, we used Protein API. 52 RAVEN sends a query to match gene IDs with their corresponding protein IDs of different databases such as UniProt and NCBI as well as the proteins sequence. The implemented peptide-matching pipeline accesses the MHC-I binding prediction tool provided by the Immune Epitope Database (IEDB) Analysis Resource 32 via a RESTful interface (IEDB-API).…”

Section: Methodsmentioning

confidence: 99%

Systematic identification of cancer-specific MHC-binding peptides with RAVEN

Baldauf¹,

Gerke²,

Kirschner

et al. 2018

OncoImmunology

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Immunotherapy can revolutionize anti-cancer therapy if specific targets are available. Immunogenic peptides encoded by cancer-specific genes (CSGs) may enable targeted immunotherapy, even of oligo-mutated cancers, which lack neo-antigens generated by protein-coding missense mutations. Here, we describe an algorithm and user-friendly software named RAVEN (Rich Analysis of Variable gene Expressions in Numerous tissues) that automatizes the systematic and fast identification of CSG-encoded peptides highly affine to Major Histocompatibility Complexes (MHC) starting from transcriptome data. We applied RAVEN to a dataset assembled from 2,678 simultaneously normalized gene expression microarrays comprising 50 tumor entities, with a focus on oligo-mutated pediatric cancers, and 71 normal tissue types. RAVEN performed a transcriptome-wide scan in each cancer entity for gender-specific CSGs, and identified several established CSGs, but also many novel candidates potentially suitable for targeting multiple cancer types. The specific expression of the most promising CSGs was validated in cancer cell lines and in a comprehensive tissue-microarray. Subsequently, RAVEN identified likely immunogenic CSG-encoded peptides by predicting their affinity to MHCs and excluded sequence identity to abundantly expressed proteins by interrogating the UniProt protein-database. The predicted affinity of selected peptides was validated in T2-cell peptide-binding assays in which many showed binding-kinetics like a very immunogenic influenza control peptide. Collectively, we provide an exquisitely curated catalogue of cancer-specific and highly MHC-affine peptides across 50 cancer types, and a freely available software (https://github.com/JSGerke/RAVENsoftware) to easily apply our algorithm to any gene expression dataset. We anticipate that our peptide libraries and software constitute a rich resource to advance anti-cancer immunotherapy.

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Section: Methodsmentioning

confidence: 99%

Systematic identification of cancer-specific MHC-binding peptides with RAVEN

Baldauf¹,

Gerke²,

Kirschner

et al. 2018

OncoImmunology

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“…The representational state transfer (REST) web services provided by Ensembl (38), UniProt (39), the Proteins API (40) and PubMed (41)…”

Section: Gene Transcript and Protein Annotation And Literature Supportmentioning

confidence: 99%

psichomics: graphical application for alternative splicing quantification and analysis

Saraiva-Agostinho¹,

Barbosa‐Morais²

2018

Preprint

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Motivation: Alternative pre-mRNA splicing generates functionally distinct transcripts from the same gene and is involved in the control of multiple cellular processes, with its dysregulation being associated with a variety of pathologies. The advent of next-generation sequencing has enabled global studies of alternative splicing in different physiological and disease contexts. However, current bioinformatics tools for alternative splicing analysis from RNA-seq data are not user-friendly, disregard available exon-exon junction quantification or have limited downstream analysis features. Results: We have developed psichomics, an R package with an intuitive graphical interface for alternative splicing quantification and downstream dimensionality reduction, differential splicing and gene expression and survival analyses based on The Cancer Genome Atlas, the Genotype-Tissue Expression project and user-provided data. These integrative analyses can also incorporate clinical and molecular sample-associated features. We successfully used psichomics to reveal alternative splicing signatures specific to stage I breast cancer and associated novel putative prognostic factors. Availability and implementation: psichomics is publicly available in Bioconductor at https://bioconductor.org/packages/psichomics, along with graphical and command-line interface tutorials based on the presented case study.

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“…Mapping IMEx mutations to UniProtKB and the genome UniProtKB accessions for human proteins were extracted from the IMEx mutations data set, retaining isoform identifiers, and used to query the EMBL-EBI Protein API 25 .…”

Section: Mutations Re-annotation Effortmentioning

confidence: 99%

Capturing variation impact on molecular interactions: the IMEx Consortium mutations data set

del-Toro

Duesbury

Koch

et al. 2018

Preprint

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The current wealth of genomic variation data identified at the nucleotide level has provided us with the challenge of understanding by which mechanisms amino acid variation affects cellular processes. These effects may manifest as distinct phenotypic differences between individuals or result in the development of disease. Physical interactions between molecules are the linking steps underlying most, if not all, cellular processes. Understanding the effects that amino acid variation of a molecule's sequence has on its molecular interactions is a key step towards connecting a full mechanistic characterization of nonsynonymous variation to cellular phenotype. Herewe present an open access resource created by IMEx database curators over 14 years, featuring 28,000 annotations fully describing the effect of individual point sequence changes on physical protein interactions. We describe how this resource was built, the formats in which the data content is provided and offer a descriptive analysis of the data set. The data set is publicly available through the IntAct website at www.ebi.ac.uk/intact/resources/datasets#mutationDs and is being enhanced with every monthly release. MainCells process information and respond to their environments through dynamic networks of molecular interactions, where the nodes are bio-molecules (e.g. proteins, genes, metabolites, miRNAs) and edges represent functional relationships, including physical protein-protein interactions, transcriptional regulation, genetic interactions and gene/protein modifications. Comprehensive and systematic characterization of these networks is essential to gain a full understanding of complex biological . CC-BY 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint . http://dx.doi.org/10.1101/346833 doi: bioRxiv preprint first posted online Jun. 14, 2018; processes, of how cells behave in response to specific cues, and of how individual components of the network contribute to the whole phenotype, in physiological, pathological or synthetic conditions.Interactions between molecules may be inherently stable and essentially irreversible, resulting in the formation of stable macromolecular complexes, or weak transient interactions characterized by a dissociation constant (KD) in the micromolar range and a lifetime of seconds. A change to a single amino acid in a protein chain can be enough to disrupt a protein binding site and may then alter the composition of a sub-network of transient binders or the formation of a protein complex. A variant leading to the inactivation of a protein kinase molecule may result in widespread disruption of posttranslation phosphorylation events and the rewiring of related signalling networks.Many diseases are caused by specific mutations, and prognosis or response to treatment is frequently mutation-specific. The study of how mutations affect molecular interact...

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The Proteins API: accessing key integrated protein and genome information

Cited by 81 publications

References 19 publications

Systematic identification of cancer-specific MHC-binding peptides with RAVEN

Systematic identification of cancer-specific MHC-binding peptides with RAVEN

psichomics: graphical application for alternative splicing quantification and analysis

Capturing variation impact on molecular interactions: the IMEx Consortium mutations data set

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