Evidence map›Paper›PMID 32650610›Full record

ArticleProteomes2020

Precursor Intensity-Based Label-Free Quantification Software Tools for Proteomic and Multi-Omic Analysis within the Galaxy Platform.

Subina Mehta, Caleb W Easterly, Ray Sajulga, Robert J Millikin, Andrea Argentini, Ignacio Eguinoa, Lennart Martens, Michael R Shortreed, Lloyd M Smith, Thomas McGowan and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Proteomes, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
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

8 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
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

14 authors.

Subina MehtaDepartment of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA.ORCID 0000-0001-9818-0537
Caleb W EasterlyDepartment of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA.ORCID 0000-0001-7853-377X
Ray SajulgaDepartment of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA.
Robert J MillikinDepartment of Chemistry, University of Wisconsin, Madison, WI 53706, USA.
Andrea ArgentiniVIB-UGent Center for Medical Biotechnology, VIB, Ghent University, 9000 Ghent, Belgium.
Ignacio EguinoaVIB-UGent Center for Medical Biotechnology, VIB, Ghent University, 9000 Ghent, Belgium.
Lennart MartensVIB-UGent Center for Medical Biotechnology, VIB, Ghent University, 9000 Ghent, Belgium.
Michael R ShortreedDepartment of Chemistry, University of Wisconsin, Madison, WI 53706, USA.
Lloyd M SmithDepartment of Chemistry, University of Wisconsin, Madison, WI 53706, USA.
Thomas McGowanMinnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN 55455, USA.
Praveen KumarDepartment of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA.ORCID 0000-0002-7818-671X
James E JohnsonMinnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN 55455, USA.
Timothy J GriffinDepartment of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA.ORCID 0000-0001-6801-2559
Pratik D JagtapDepartment of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA.ORCID 0000-0003-0984-0973

Funding

Institutional Training in the Genomic SciencesT32HG002760 · NHGRI · UNIVERSITY OF WISCONSIN-MADISON · PI Qiongshi Lu · 2003 to 2026
$17.7M
Recurrent Tumor-Specific Alternately Processed Transcripts as a Source of Neoantigens for NF1-associated Malignant Peripheral Nerve Sheath Tumor ImmunopreventionUG3CA244687 · NCI · UNIVERSITY OF MINNESOTA · PI LARGAESPADA, DAVID ANDREW · 2019 to 2020
$1.5M
NCI NIH HHS UG3 CA244687NHGRI NIH HHS T32 HG002760
6 · The paper itself

Abstract

For mass spectrometry-based peptide and protein quantification, label-free quantification (LFQ) based on precursor mass peak (MS1) intensities is considered reliable due to its dynamic range, reproducibility, and accuracy. LFQ enables peptide-level quantitation, which is useful in proteomics (analyzing peptides carrying post-translational modifications) and multi-omics studies such as metaproteomics (analyzing taxon-specific microbial peptides) and proteogenomics (analyzing non-canonical sequences). Bioinformatics workflows accessible via the Galaxy platform have proven useful for analysis of such complex multi-omic studies. However, workflows within the Galaxy platform have lacked well-tested LFQ tools. In this study, we have evaluated moFF and FlashLFQ, two open-source LFQ tools, and implemented them within the Galaxy platform to offer access and use via established workflows. Through rigorous testing and communication with the tool developers, we have optimized the performance of each tool. Software features evaluated include: (a) match-between-runs (MBR); (b) using multiple file-formats as input for improved quantification; (c) use of containers and/or conda packages; (d) parameters needed for analyzing large datasets; and (e) optimization and validation of software performance. This work establishes a process for software implementation, optimization, and validation, and offers access to two robust software tools for LFQ-based analysis within the Galaxy platform.

Indexed as

galaxy frameworklabel-free quantificationproteomicsworkflows

Identifiers

PMID32650610
PMCPMC7563855

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.