Evidence map›Paper›PMID 39524312›Full record

ArticleEcology and evolution2024

What can we infer about mutation calling by using time-series mutation accumulation data and a Bayesian Mutation Finder?

Takahiro Maruki, April Ozere, Jack Freeman, Melania E Cristescu

Abstract read
In one paragraph

Article in Ecology and evolution, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

1 citing paper in PubMed.

  1. 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

4 authors.

Takahiro MarukiDepartment of Biology McGill University Montreal Quebec Canada.ORCID https://orcid.org/0000-0001-9639-4304
April OzereDepartment of Biology McGill University Montreal Quebec Canada.
Jack FreemanDepartment of Biology McGill University Montreal Quebec Canada.
Melania E CristescuDepartment of Biology McGill University Montreal Quebec Canada.ORCID https://orcid.org/0000-0002-0854-4040

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate estimates of mutation rates derived from genome-wide mutation accumulation (MA) data are fundamental to understanding basic evolutionary processes. The rapidly improving high-throughput sequencing technologies provide unprecedented opportunities to identify single nucleotide mutations across genomes. However, such MA derived data are often difficult to analyze and the performance of the available methods of analysis is not well understood. In this study, we used the existing Bayesian Genotype Caller adapted for MA data that we refer to as Bayesian Mutation Finder (BMF) for identifying single nucleotide mutations while considering the characteristics of the data. We compared the performance of BMF with the widely used Genome Analysis Toolkit (GATK) by applying these two methods to time-series MA data as well as simulated data. The time-series data were obtained by propagating

Indexed as

Bayesian Mutation FinderDaphnia pulexmutation ratesingle nucleotide mutationstime‐series mutation accumulation data

Identifiers

PMID39524312
PMCPMC11550904

What OpenQuestion holds

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Registered trials

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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.