Evidence map›Paper›PMID 37335470›Full record

ReviewMethods in molecular biology (Clifton, N.J.)2023

K-Mer-Based Genome Size Estimation in Theory and Practice.

Uljana Hesse

Abstract readReview
PubMed Publisher
In one paragraph

Review in Methods in molecular biology (Clifton, N.J.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing 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

18 citing papers in PubMed.

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  3. Nuclear genome profiling of two species ofNAR genomics and bioinformatics · 2026
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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

1 author.

Uljana HesseDepartment of Biotechnology, University of the Western Cape, Bellville, South Africa. uhesse@uwc.ac.za.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent advances in sequencing technologies have made genome sequencing of non-model organisms with very large and complex genomes possible. The data can be used to estimate diverse genome characteristics, including genome size, repeat content, and levels of heterozygosity. K-mer analysis is a powerful biocomputational approach with a wide range of applications, including estimation of genome sizes. However, interpretation of the results is not always straightforward. Here, I review k-mer-based genome size estimation, focusing specifically on k-mer theory and peak calling in k-mer frequency histograms. I highlight common pitfalls in data analysis and result interpretation, and provide a comprehensive overview on current methods and programs developed to conduct these analyses.

Indexed as

AlgorithmsSoftwareBase SequenceChromosome MappingGenome SizeSequence Analysis, DNABB-toolsCovESTFindGSEGCEGenomeScopeJellyfishKmergenieKSARESPECT

Identifiers

PMID37335470

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.