Evidence map›Paper›PMID 36173322›Full record

ArticleGenetics2022

Estimation of site frequency spectra from low-coverage sequencing data using stochastic EM reduces overfitting, runtime, and memory usage.

Malthe Sebro Rasmussen, Genís Garcia-Erill, Thorfinn Sand Korneliussen, Carsten Wiuf, Anders Albrechtsen

Abstract read
In one paragraph

Article in Genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing 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

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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

20 citing papers in PubMed.

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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.

Malthe Sebro RasmussenDepartment of Biology, University of Copenhagen, 2200 København N, Denmark.ORCID 0000-0002-2982-6258
Genís Garcia-ErillDepartment of Biology, University of Copenhagen, 2200 København N, Denmark.ORCID 0000-0003-3150-1708
Thorfinn Sand KorneliussenGlobe Institute, University of Copenhagen, 1350 København K, Denmark.ORCID 0000-0001-7576-5380
Carsten WiufDepartment of Mathematical Sciences, University of Copenhagen, 2100 København Ø, Denmark.ORCID 0000-0002-1302-4445
Anders AlbrechtsenDepartment of Biology, University of Copenhagen, 2200 København N, Denmark.ORCID 0000-0001-7306-031X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The site frequency spectrum is an important summary statistic in population genetics used for inference on demographic history and selection. However, estimation of the site frequency spectrum from called genotypes introduces bias when working with low-coverage sequencing data. Methods exist for addressing this issue but sometimes suffer from 2 problems. First, they can have very high computational demands, to the point that it may not be possible to run estimation for genome-scale data. Second, existing methods are prone to overfitting, especially for multidimensional site frequency spectrum estimation. In this article, we present a stochastic expectation-maximization algorithm for inferring the site frequency spectrum from NGS data that address these challenges. We show that this algorithm greatly reduces runtime and enables estimation with constant, trivial RAM usage. Furthermore, the algorithm reduces overfitting and thereby improves downstream inference. An implementation is available at github.com/malthesr/winsfs.

Indexed as

AlgorithmsGenetics, PopulationBiasGenomeGenotypeHigh-Throughput Nucleotide Sequencingdemographic historyexpectation–maximizationgenotype likelihoodslow-coverage datanext-generation sequencingsite frequency spectrum

Identifiers

PMID36173322
PMCPMC9713400

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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.