Evidence map›Paper›PMID 37601316›Full record

ArticleApplications in plant sciences

Target capture and genome skimming for plant diversity studies.

Flávia Fonseca Pezzini, Giada Ferrari, Laura L Forrest, Michelle L Hart, Kanae Nishii, Catherine A Kidner

Abstract read
In one paragraph

Article in Applications in plant sciences. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 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

6 authors.

Flávia Fonseca PezziniRoyal Botanic Garden Edinburgh Edinburgh United Kingdom.ORCID 0000-0001-5988-7361
Giada FerrariRoyal Botanic Garden Edinburgh Edinburgh United Kingdom.ORCID 0000-0002-0850-1518
Laura L ForrestRoyal Botanic Garden Edinburgh Edinburgh United Kingdom.
Michelle L HartRoyal Botanic Garden Edinburgh Edinburgh United Kingdom.
Kanae NishiiRoyal Botanic Garden Edinburgh Edinburgh United Kingdom.
Catherine A KidnerRoyal Botanic Garden Edinburgh Edinburgh United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent technological advances in long-read high-throughput sequencing and assembly methods have facilitated the generation of annotated chromosome-scale whole-genome sequence data for evolutionary studies; however, generating such data can still be difficult for many plant species. For example, obtaining high-molecular-weight DNA is typically impossible for samples in historical herbarium collections, which often have degraded DNA. The need to fast-freeze newly collected living samples to conserve high-quality DNA can be complicated when plants are only found in remote areas. Therefore, short-read reduced-genome representations, such as target capture and genome skimming, remain important for evolutionary studies. Here, we review the pros and cons of each technique for non-model plant taxa. We provide guidance related to logistics, budget, the genomic resources previously available for the target clade, and the nature of the study. Furthermore, we assess the available bioinformatic analyses, detailing best practices and pitfalls, and suggest pathways to combine newly generated data with legacy data. Finally, we explore the possible downstream analyses allowed by the type of data generated using each technique. We provide a practical guide to help researchers make the best-informed choice regarding reduced genome representation for evolutionary studies of non-model plants in cases where whole-genome sequencing remains impractical.

Indexed as

barcodingcoalescent analysisherbarianon‐model plantsshort‐read sequencing

Identifiers

PMID37601316
PMCPMC10439825

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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.