Evidence map›Paper›PMID 30169737›Full record

ArticleBioinformatics (Oxford, England)2019

PDV: an integrative proteomics data viewer.

Kai Li, Marc Vaudel, Bing Zhang, Yan Ren, Bo Wen

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 80 papers.

0numbers the graph read from it
0cells of the map it votes in
80citing 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

80 citing papers in PubMed.

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20 more citing papers are in PubMed but not listed here.

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

5 authors.

Kai LiBGI-Shenzhen, Shenzhen, China.
Marc VaudelDepartment of Clinical Science, KG Jebsen Center for Diabetes Research, University of Bergen, Norway.
Bing ZhangLester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, USA.
Yan RenBGI-Shenzhen, Shenzhen, China.
Bo WenLester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, USA.

Funding

iPGDAC, An Integrative Proteogenomic Data Analysis Center for CPTACU24CA210954 · NCI · BAYLOR COLLEGE OF MEDICINE · PI ZHANG, BING · 2016 to 2020
$4.9M
NCI NIH HHS U24 CA210954
6 · The paper itself

Abstract

summaryData visualization plays critical roles in proteomics studies, ranging from quality control of MS/MS data to validation of peptide identification results. Herein, we present PDV, an integrative proteomics data viewer that can be used to visualize a wide range of proteomics data, including database search results, de novo sequencing results, proteogenomics files, MS/MS data in mzML/mzXML format and data from public proteomics repositories. PDV is a lightweight visualization tool that enables intuitive and fast exploration of diverse, large-scale proteomics datasets on standard desktop computers in both graphical user interface and command line modes. AVAILABILITY AND IMPLEMENTATION: PDV software and the user manual are freely available at http://pdv.zhang-lab.org. The source code is available at https://github.com/wenbostar/PDV and is released under the GPL-3 license. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Indexed as

ProteomicsPeptidesSoftwareTandem Mass SpectrometryPeptides

Identifiers

PMID30169737
PMCPMC6821182

What OpenQuestion holds

Textmetadata
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.