Evidence map›Paper›PMID 34404349›Full record

ArticleBMC bioinformatics2021

Immunopeptidomics toolkit library (IPTK): a python-based modular toolbox for analyzing immunopeptidomics data.

Hesham ElAbd, Frauke Degenhardt, Tomas Koudelka, Ann-Kristin Kamps, Andreas Tholey, Petra Bacher, Tobias L Lenz, Andre Franke, Mareike Wendorff

Open access · goldAbstract read
In one paragraph

Article in BMC bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.3field-weighted citation impact, top 44% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 5 citations in OpenAlex.

  1. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 2 institutions in 1 country.

Hesham ElAbdInstitute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany.
Frauke DegenhardtInstitute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany.
Tomas KoudelkaProteomics and Bioanalytics, Institute for Experimental Medicine, Christian-Albrechts-University of Kiel, Kiel, Germany.
Ann-Kristin KampsInstitute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany.
Andreas TholeyProteomics and Bioanalytics, Institute for Experimental Medicine, Christian-Albrechts-University of Kiel, Kiel, Germany.
Petra BacherInstitute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany.
Tobias L LenzResearch Unit for Evolutionary Immunogenomics, Department of Biology, University of Hamburg, Hamburg, Germany.
Andre Franke *Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany. a.franke@mucosa.de.ORCID http://orcid.org/0000-0003-1530-5811
Mareike Wendorff *Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany.
Christian-Albrechts-Universität zu Kiel · DEUniversität Hamburg · DE

Funding

Deutsche Forschungsgemeinschaft EXC 2167-390884018Deutsche Forschungsgemeinschaft Research Training Group 1743, 'Genes, Environment and Inflammation'Deutsche Forschungsgemeinschaft (DE) Research Training Group 1743, 'Genes, Environment and Inflammation'
6 · The paper itself

Abstract

backgroundThe human leukocyte antigen (HLA) proteins play a fundamental role in the adaptive immune system as they present peptides to T cells. Mass-spectrometry-based immunopeptidomics is a promising and powerful tool for characterizing the immunopeptidomic landscape of HLA proteins, that is the peptides presented on HLA proteins. Despite the growing interest in the technology, and the recent rise of immunopeptidomics-specific identification pipelines, there is still a gap in data-analysis and software tools that are specialized in analyzing and visualizing immunopeptidomics data.

resultsWe present the IPTK library which is an open-source Python-based library for analyzing, visualizing, comparing, and integrating different omics layers with the identified peptides for an in-depth characterization of the immunopeptidome. Using different datasets, we illustrate the ability of the library to enrich the result of the identified peptidomes. Also, we demonstrate the utility of the library in developing other software and tools by developing an easy-to-use dashboard that can be used for the interactive analysis of the results.

conclusionIPTK provides a modular and extendable framework for analyzing and integrating immunopeptidomes with different omics layers. The library is deployed into PyPI at https://pypi.org/project/IPTKL/ and into Bioconda at https://anaconda.org/bioconda/iptkl , while the source code of the library and the dashboard, along with the online tutorials are available at https://github.com/ikmb/iptoolkit .

Indexed as

Data AnalysisSoftwareHistocompatibility Antigens Class IHumansMass SpectrometryPeptidesHistocompatibility Antigens Class IPeptidesAntigen processing and presentationComputational immunologyHLAImmunopeptidomicsInteractive data analysis

Identifiers

PMID34404349
PMCPMC8369717
OpenAlexW3195566912

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.