Dataset Information

AlphaMap: an open-source python package for the visual annotation of proteomics data with sequence specific knowledge.

ABSTRACT: Integrating experimental information across proteomic datasets with the wealth of publicly available sequence annotations is a crucial part in many proteomic studies that currently lacks an automated analysis platform. Here we present AlphaMap, a Python package that facilitates the visual exploration of peptide-level proteomics data. Identified peptides and post-translational modifications in proteomic datasets are mapped to their corresponding protein sequence and visualized together with prior knowledge from UniProt and with expected proteolytic cleavage sites. The functionality of AlphaMap can be accessed via an intuitive graphical user interface or-more flexibly-as a Python package that allows its integration into common analysis workflows for data visualization. AlphaMap produces publication-quality illustrations and can easily be customized to address a given research question. AlphaMap is implemented in Python and released under an Apache license. The source code and one-click installers are freely available at https://github.com/MannLabs/alphamap. A detailed user guide for AlphaMap is provided as supplementary data.

SUBMITTER: Voytik E

PROVIDER: S-EPMC8756201 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

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AlphaMap: an open-source Python package for the visual annotation of proteomics data with sequence-specific knowledge.

Voytik Eugenia E Bludau Isabell I Willems Sander S Hansen Fynn M FM Brunner Andreas-David AD Strauss Maximilian T MT Mann Matthias M

Bioinformatics (Oxford, England) 20220101 3

<h4>Summary</h4>Integrating experimental information across proteomic datasets with the wealth of publicly available sequence annotations is a crucial part in many proteomic studies that currently lacks an automated analysis platform. Here, we present AlphaMap, a Python package that facilitates the visual exploration of peptide-level proteomics data. Identified peptides and post-translational modifications in proteomic datasets are mapped to their corresponding protein sequence and visualized to ...[more]

PMID: 34586352

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Project description:IntroductionIn smoking cessation clinical trials, timeline followback (TLFB) interviews are widely used to track daily cigarette consumption. However, there are no standard tools for calculating abstinence based on TLFB data. Individual research groups have to develop their own calculation tools, which is not only time- and resource-consuming but might also lead to variability in the data processing and calculation procedures.Aims and methodsTo address these issues, we developed a novel open-source Python package named abstcal to calculate abstinence using TLFB data. This package provides data verification, duplicate and outlier detection, missing-data imputation, integration of biochemical verification data, and calculation of a variety of definitions of abstinence, including continuous, point-prevalence, and prolonged abstinence.ResultsWe verified the accuracy of the calculator using data derived from a clinical smoking cessation study. To improve the package's accessibility, we have made it available as a free web app.ConclusionsThe abstcal package is a reliable abstinence calculator with open-source access, providing a shared validated online tool to the addiction research field. We expect that this open-source abstinence calculation tool will improve the rigor and reproducibility of smoking and addiction research by standardizing TLFB-based abstinence calculation.ImplicationsAbstinence calculation is an essential task in any smoking intervention study. However, there have not been standard open-source tools available to the researchers. This commentary describes a Python-based package called abstcal that can calculate abstinence from TLFB data, a common methodology to collect smoking consumption data in research settings. The package supports the calculation of point-prevalence, prolonged, and continuous abstinence. Importantly, the package has a web app interface that allows researchers to use the tool without any coding experience. This tool will facilitate smoking research by providing a standardized and easy-to-use abstinence calculation tool.

Dataset Information

AlphaMap: an open-source python package for the visual annotation of proteomics data with sequence specific knowledge.

Publications

AlphaMap: an open-source Python package for the visual annotation of proteomics data with sequence-specific knowledge.

Similar Datasets

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