Evidence map›Paper›PMID 39198030›Full record

ArticleGenomics, proteomics & bioinformatics2024

APIR: Aggregating Universal Proteomics Database Search Algorithms for Peptide Identification with FDR Control.

Yiling Elaine Chen, Xinzhou Ge, Kyla Woyshner, MeiLu McDermott, Antigoni Manousopoulou, Scott B Ficarro, Jarrod A Marto, Kexin Li, Leo David Wang, Jingyi Jessica Li

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Genomics, proteomics & bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

10 authors.

Yiling Elaine ChenDepartment of Statistics and Data Science, University of California, Los Angeles, CA 90095, USA.ORCID 0000-0001-7259-2787
Xinzhou GeDepartment of Statistics and Data Science, University of California, Los Angeles, CA 90095, USA.ORCID 0000-0002-8229-5716
Kyla WoyshnerDepartment of Immuno-Oncology, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA 91010, USA.ORCID 0000-0002-1005-9896
MeiLu McDermottDepartment of Immuno-Oncology, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA 91010, USA.ORCID 0000-0002-0866-0738
Antigoni ManousopoulouDepartment of Immuno-Oncology, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA 91010, USA.ORCID 0000-0002-5445-3561
Scott B FicarroDepartment of Cancer Biology and Blais Proteomics Center, Dana-Farber Cancer Institute, Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02215, USA.ORCID 0000-0002-1521-7996
Jarrod A MartoDepartment of Cancer Biology and Blais Proteomics Center, Dana-Farber Cancer Institute, Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02215, USA.ORCID 0000-0003-2086-1134
Kexin LiDepartment of Statistics and Data Science, University of California, Los Angeles, CA 90095, USA.ORCID 0000-0002-4955-3832
Leo David WangDepartment of Immuno-Oncology, Beckman Research Institute, City of Hope National Medical Center, Duarte, CA 91010, USA.ORCID 0000-0002-2945-9005
Jingyi Jessica LiDepartment of Statistics and Data Science, University of California, Los Angeles, CA 90095, USA.ORCID 0000-0002-9288-5648

Funding

Transgenic Mouse FacilityP30CA033572 · NCI · CITY OF HOPE/BECKMAN RESEARCH INSTITUTE · PI John Charles Williams · 1985 to 2026
$86.3M
Statistical Methods for Elucidating Regulatory Mechanisms and Functional Impacts of Transcriptome Variation at Population and Single-Cell ScalesR35GM140888 · NIGMS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI LI, JINGYI JESSICA · 2021 to 2025
$2.0M
Robust Identification and accurate quantification of RNA transcripts on a system wide scaleR01GM120507 · NIGMS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI LI, JINGYI JESSICA · 2016 to 2021
$1.7M
Biomedical Big Data Training GrantT32LM012424 · NLM · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI PELLEGRINI, MATTEO · 2016 to 2019
$1.0M
Targeting the ARHGAP25 pathway in acute myelogenous leukemia stem cellsK08CA201591 · NCI · BECKMAN RESEARCH INSTITUTE/CITY OF HOPE · PI WANG, LEO D. · 2017 to 2021
$848k
National Cancer Institute under Cancer Center P30CA033572National Cancer Institute, USA K08CA201591NCI NIH HHS K08 CA201591NCI NIH HHS P30 CA033572NIGMS NIH HHS R01 GM120507NIGMS NIH HHS R35 GM140888NLM NIH HHS T32 LM012424NSF GRANT NOS. DBI-1846216 AND DMS-2113754
6 · The paper itself

Abstract

Advances in mass spectrometry (MS) have enabled high-throughput analysis of proteomes in biological systems. The state-of-the-art MS data analysis relies on database search algorithms to quantify proteins by identifying peptide-spectrum matches (PSMs), which convert mass spectra to peptide sequences. Different database search algorithms use distinct search strategies and thus may identify unique PSMs. However, no existing approaches can aggregate all user-specified database search algorithms with a guaranteed increase in the number of identified peptides and a control on the false discovery rate (FDR). To fill in this gap, we proposed a statistical framework, Aggregation of Peptide Identification Results (APIR), that is universally compatible with all database search algorithms. Notably, under an FDR threshold, APIR is guaranteed to identify at least as many, if not more, peptides as individual database search algorithms do. Evaluation of APIR on a complex proteomics standard dataset showed that APIR outpowers individual database search algorithms and empirically controls the FDR. Real data studies showed that APIR can identify disease-related proteins and post-translational modifications missed by some individual database search algorithms. The APIR framework is easily extendable to aggregating discoveries made by multiple algorithms in other high-throughput biomedical data analysis, e.g., differential gene expression analysis on RNA sequencing data. The APIR R package is available at https://github.com/yiling0210/APIR.

Indexed as

AlgorithmsDatabases, ProteinPeptidesProteomicsHumansSoftwarePeptidesAggregation of listsFDR controlPeptide identificationPeptide–spectrum matchShotgun proteomics

Identifiers

PMID39198030
PMCPMC12536914

What Socratic 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 Socratic graph.