Dataset Information

Neopeptide Analyser: A software tool for neopeptide discovery in proteomics data.

ABSTRACT: Experiments involving mass spectrometry (MS)-based proteomics are widely used for analyses of connective tissues. Common examples include the use of relative quantification to identify differentially expressed peptides and proteins in cartilage and tendon. We are working on characterising so-called 'neopeptides', i.e. peptides formed due to native cleavage of proteins, for example under pathological conditions. Unlike peptides typically quantified in MS workflows due to the in vitro use of an enzyme such as trypsin, a neopeptide has at least one terminus that was not due to the use of trypsin in the workflow. The identification of neopeptides within these datasets is important in understanding disease pathology, and the development of antibodies that could be utilised as diagnostic biomarkers for diseases, such as osteoarthritis, and targets for novel treatments. Our previously described neopeptide data analysis workflow was laborious and was not amenable to robust statistical analysis, which reduced confidence in the neopeptides identified. To overcome this, we developed 'Neopeptide Analyser', a user friendly neopeptide analysis tool used in conjunction with label-free MS quantification tool Progenesis QIP for proteomics. Neopeptide Analyser filters data sourced from Progenesis QIP output to identify neopeptide sequences, as well as give the residues that are adjacent to the peptide in its corresponding protein sequence. It also produces normalised values for the neopeptide quantification values and uses these to perform statistical tests, which are also included in the output. Neopeptide Analyser is available as a Java application for Mac, Windows and Linux. The analysis features and ease of use encourages data exploration, which could aid the discovery of novel pathways in extracellular matrix degradation, the identification of potential biomarkers and as a tool to investigate matrix turnover. Neopeptide Analyser is available from https://github.com/PGB-LIV/neo-pep-tool/releases/.

SUBMITTER: Peffers M

PROVIDER: S-EPMC5428739 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

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Neopeptide Analyser: A software tool for neopeptide discovery in proteomics data.

Peffers Mandy M Jones Andrew R AR McCabe Antony A Anderson James J

Wellcome open research 20170407

Experiments involving mass spectrometry (MS)-based proteomics are widely used for analyses of connective tissues. Common examples include the use of relative quantification to identify differentially expressed peptides and proteins in cartilage and tendon. We are working on characterising so-called 'neopeptides', i.e. peptides formed due to native cleavage of proteins, for example under pathological conditions. Unlike peptides typically quantified in MS workflows due to the <i>in vitro</i> use o ...[more]

PMID: 28503667

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Project description:BackgroundProtein inference from peptide identifications in shotgun proteomics must deal with ambiguities that arise due to the presence of peptides shared between different proteins, which is common in higher eukaryotes. Recently data independent acquisition (DIA) approaches have emerged as an alternative to the traditional data dependent acquisition (DDA) in shotgun proteomics experiments. MSE is the term used to name one of the DIA approaches used in QTOF instruments. MSE data require specialized software to process acquired spectra and to perform peptide and protein identifications. However the software available at the moment does not group the identified proteins in a transparent way by taking into account peptide evidence categories. Furthermore the inspection, comparison and report of the obtained results require tedious manual intervention. Here we report a software tool to address these limitations for MSE data.ResultsIn this paper we present PAnalyzer, a software tool focused on the protein inference process of shotgun proteomics. Our approach considers all the identified proteins and groups them when necessary indicating their confidence using different evidence categories. PAnalyzer can read protein identification files in the XML output format of the ProteinLynx Global Server (PLGS) software provided by Waters Corporation for their MSE data, and also in the mzIdentML format recently standardized by HUPO-PSI. Multiple files can also be read simultaneously and are considered as technical replicates. Results are saved to CSV, HTML and mzIdentML (in the case of a single mzIdentML input file) files. An MSE analysis of a real sample is presented to compare the results of PAnalyzer and ProteinLynx Global Server.ConclusionsWe present a software tool to deal with the ambiguities that arise in the protein inference process. Key contributions are support for MSE data analysis by ProteinLynx Global Server and technical replicates integration. PAnalyzer is an easy to use multiplatform and free software tool.

Project description:In order that biological meaning may be derived and testable hypotheses may be built from proteomics experiments, assignments of proteins identified by mass spectrometry or other techniques must be supplemented with additional notation, such as information on known protein functions, protein-protein interactions, or biological pathway associations. Collecting, organizing, and interpreting this data often requires the input of experts in the biological field of study, in addition to the time-consuming search for and compilation of information from online protein databases. Furthermore, visualizing this bulk of information can be challenging due to the limited availability of easy-to-use and freely available tools for this process. In response to these constraints, we have undertaken the design of software to automate annotation and visualization of proteomics data in order to accelerate the pace of research. Here we present the Software Tool for Researching Annotations of Proteins (STRAP), a user-friendly, open-source C# application. STRAP automatically obtains gene ontology (GO) terms associated with proteins in a proteomics results ID list using the freely accessible UniProtKB and EBI GOA databases. Summarized in an easy-to-navigate tabular format, STRAP results include meta-information on the protein in addition to complementary GO terminology. Additionally, this information can be edited by the user so that in-house expertise on particular proteins may be integrated into the larger data set. STRAP provides a sortable tabular view for all terms, as well as graphical representations of GO-term association data in pie charts (biological process, cellular component, and molecular function) and bar charts (cross comparison of sample sets) to aid in the interpretation of large data sets and differential analyses experiments. Furthermore, proteins of interest may be exported as a unique FASTA-formatted file to allow for customizable re-searching of mass spectrometry data, and gene names corresponding to the proteins in the lists may be encoded in the Gaggle microformat for further characterization, including pathway analysis. STRAP, a tutorial, and the C# source code are freely available from http://cpctools.sourceforge.net.

Project description:Genotyping by re-sequencing has become a standard approach to estimate single nucleotide polymorphism (SNP) diversity, haplotype structure and the biodiversity and has been defined as an efficient approach to address geographical population genomics of several model species. To access core SNPs and insertion/deletion polymorphisms (indels), and to infer the phyletic patterns of speciation, most such approaches map short reads to the reference genome. Variant calling is important to establish patterns of genome-wide association studies (GWAS) for quantitative trait loci (QTLs), and to determine the population and haplotype structure based on SNPs, thus allowing content-dependent trait and evolutionary analysis. Several tools have been developed to investigate such polymorphisms as well as more complex genomic rearrangements such as copy number variations, presence/absence variations and large deletions. The programs available for this purpose have different strengths (e.g. accuracy, sensitivity and specificity) and weaknesses (e.g. low computation speed, complex installation procedure and absence of a user-friendly interface). Here we introduce Altools, a software package that is easy to install and use, which allows the precise detection of polymorphisms and structural variations.Altools uses the BWA/SAMtools/VarScan pipeline to call SNPs and indels, and the dnaCopy algorithm to achieve genome segmentation according to local coverage differences in order to identify copy number variations. It also uses insert size information from the alignment of paired-end reads and detects potential large deletions. A double mapping approach (BWA/BLASTn) identifies precise breakpoints while ensuring rapid elaboration. Finally, Altools implements several processes that yield deeper insight into the genes affected by the detected polymorphisms. Altools was used to analyse both simulated and real next-generation sequencing (NGS) data and performed satisfactorily in terms of positive predictive values, sensitivity, the identification of large deletion breakpoints and copy number detection.Altools is fast, reliable and easy to use for the mining of NGS data. The software package also attempts to link identified polymorphisms and structural variants to their biological functions thus providing more valuable information than similar tools.

Dataset Information

Neopeptide Analyser: A software tool for neopeptide discovery in proteomics data.

Publications

Neopeptide Analyser: A software tool for neopeptide discovery in proteomics data.

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