Evidence map›Paper›PMID 38567138›Full record

ArticleFrontiers in plant science2024

A comprehensive evaluation of the potential of three next-generation short-read-based plant pan-genome construction strategies for the identification of novel non-reference sequence.

Meiye Jiang, Meili Chen, Jingyao Zeng, Zhenglin Du, Jingfa Xiao

Open access · goldAbstract read
In one paragraph

Article in Frontiers in plant science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.9field-weighted citation impact, top 27% 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

5 citing papers in PubMed, 4 citations in OpenAlex.

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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 at 2 institutions in 1 country.

Meiye JiangNational Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China.
Meili ChenNational Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China.
Jingyao ZengNational Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China.
Zhenglin DuNational Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China.
Jingfa XiaoNational Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China.
Chinese Academy of Sciences · CNBeijing Institute of Genomics · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pan-genome studies are important for understanding plant evolution and guiding the breeding of crops by containing all genomic diversity of a certain species. Three short-read-based strategies for plant pan-genome construction include iterative individual, iteration pooling, and map-to-pan. Their performance is very different under various conditions, while comprehensive evaluations have yet to be conducted nowadays. Here, we evaluate the performance of these three pan-genome construction strategies for plants under different sequencing depths and sample sizes. Also, we indicate the influence of length and repeat content percentage of novel sequences on three pan-genome construction strategies. Besides, we compare the computational resource consumption among the three strategies. Our findings indicate that map-to-pan has the greatest recall but the lowest precision. In contrast, both two iterative strategies have superior precision but lower recall. Factors of sample numbers, novel sequence length, and the percentage of novel sequences' repeat content adversely affect the performance of all three strategies. Increased sequencing depth improves map-to-pan's performance, while not affecting the other two iterative strategies. For computational resource consumption, map-to-pan demands considerably more than the other two iterative strategies. Overall, the iterative strategy, especially the iterative pooling strategy, is optimal when the sequencing depth is less than 20X. Map-to-pan is preferable when the sequencing depth exceeds 20X despite its higher computational resource consumption.

Indexed as

evaluationiterativemap-to-panplant pan-genomeshort-reads based construction strategies

Identifiers

PMID38567138
PMCPMC10986365
OpenAlexW4392952610

What OpenQuestion holds

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LicenceCC BY
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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.