Evidence map›Paper›PMID 34465178›Full record

ArticleAnnual review of biomedical data science2021

Mutational Signatures: From Methods to Mechanisms.

Yoo-Ah Kim, Mark D M Leiserson, Priya Moorjani, Roded Sharan, Damian Wojtowicz, Teresa M Przytycka

Open access · hybridAbstract read
In one paragraph

Article in Annual review of biomedical data science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
3.3field-weighted citation impact, top 6% 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

17 citing papers in PubMed, 40 citations in OpenAlex.

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  12. Review
  13. Cancer driver mutations: predictions and reality.Trends in molecular medicine · 2023
    Review
  14. Article
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  17. Review
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

6 authors at 4 institutions in 2 countries.

Yoo-Ah KimNational Center of Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894, USA; email: przytyck@ncbi.nlm.nih.gov.
Mark D M LeisersonDepartment of Computer Science and Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland 20742, USA.
Priya MoorjaniDepartment of Molecular and Cell Biology and Center for Computational Biology, University of California, Berkeley, California 94720, USA.
Roded SharanBlavatnik School of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel.
Damian WojtowiczNational Center of Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894, USA; email: przytyck@ncbi.nlm.nih.gov.
Teresa M PrzytyckaNational Center of Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894, USA; email: przytyck@ncbi.nlm.nih.gov.
National Center for Biotechnology Information · USTel Aviv University · ILUniversity of California, Berkeley · USUniversity of Maryland, College Park · US

Funding

Combinatorial and graph theoretical approach to systems biology and mol. evo.ZIALM200887 · NLM · NATIONAL LIBRARY OF MEDICINE · PI PRZYTYCKA, TERESA · 2009 to 2025
$24.4M
Intramural NIH HHS ZIA LM200887
6 · The paper itself

Abstract

Mutations are the driving force of evolution, yet they underlie many diseases, in particular, cancer. They are thought to arise from a combination of stochastic errors in DNA processing, naturally occurring DNA damage (e.g., the spontaneous deamination of methylated CpG sites), replication errors, and dysregulation of DNA repair mechanisms. High-throughput sequencing has made it possible to generate large datasets to study mutational processes in health and disease. Since the emergence of the first mutational process studies in 2012, this field is gaining increasing attention and has already accumulated a host of computational approaches and biomedical applications.

Indexed as

NeoplasmsDNA DamageDNA RepairHigh-Throughput Nucleotide SequencingHumansMutationcancercancer evolutioncomputational modelsmutagenic processesmutational signaturespopulation genetics

Identifiers

PMID34465178
PMCPMC12232997
OpenAlexW3163530705

What OpenQuestion holds

Textmetadata
LicenceTDM
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.