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NAVIP: Unraveling the influence of neighboring small sequence variants on functional impact prediction.

作者信息

Baasner Jan-Simon, Rempel Andreas, Howard Dakota, Pucker Boas

机构信息

Genetics and Genomics of Plants, Faculty of Biology & Center for Biotechnology, Bielefeld University, Bielefeld, Germany.

Genome Informatics, Faculty of Technology & Center for Biotechnology, Bielefeld University, Bielefeld, Germany.

出版信息

PLoS Comput Biol. 2025 Feb 18;21(2):e1012732. doi: 10.1371/journal.pcbi.1012732. eCollection 2025 Feb.


DOI:10.1371/journal.pcbi.1012732
PMID:39964984
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11849982/
Abstract

Once a suitable reference sequence has been generated, intra-species variation is often assessed by re-sequencing. Variant calling processes can reveal all differences between strains, accessions, genotypes, or individuals. These variants can be enriched with predictions about their functional implications based on available structural annotations, i.e., gene models. Although these functional impact predictions on a per-variant basis are often accurate, some challenging cases require the simultaneous incorporation of multiple adjacent variants into this prediction process. Examples include neighboring variants which modify each other's functional impact. The Neighborhood-Aware Variant Impact Predictor (NAVIP) considers all variants within a given protein coding sequence when predicting the effect. As a proof of concept, variants between the Arabidopsis thaliana accessions Columbia-0 and Niederzenz-1 were annotated. NAVIP is freely available on GitHub (https://github.com/bpucker/NAVIP) and accessible through a web server (https://pbb-tools.de).

摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/65a5/11849982/e5c2d5986247/pcbi.1012732.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/65a5/11849982/f4e6c89409ba/pcbi.1012732.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/65a5/11849982/e5c2d5986247/pcbi.1012732.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/65a5/11849982/f4e6c89409ba/pcbi.1012732.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/65a5/11849982/e5c2d5986247/pcbi.1012732.g002.jpg

相似文献

[1]
NAVIP: Unraveling the influence of neighboring small sequence variants on functional impact prediction.

PLoS Comput Biol. 2025-2-18

[2]
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[3]
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[4]
Consideration of non-canonical splice sites improves gene prediction on the Arabidopsis thaliana Niederzenz-1 genome sequence.

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[5]
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[6]
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[7]
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[8]
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[9]
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[10]
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本文引用的文献

[1]
Conserved amino acid residues and gene expression patterns associated with the substrate preferences of the competing enzymes FLS and DFR.

PLoS One. 2024

[2]
Publisher Correction: Detection of mosaic and population-level structural variants with Sniffles2.

Nat Biotechnol. 2024-10

[3]
Genomic characterization of a nematode tolerance locus in sugar beet.

BMC Genomics. 2023-12-6

[4]
Long read sequencing to reveal the full complexity of a plant transcriptome by targeting both standard and long workflows.

Plant Methods. 2023-10-21

[5]
Genome-wide prediction of disease variant effects with a deep protein language model.

Nat Genet. 2023-9

[6]
A survey of mapping algorithms in the long-reads era.

Genome Biol. 2023-6-1

[7]
Plant genome sequence assembly in the era of long reads: Progress, challenges and future directions.

Quant Plant Biol. 2022-3-11

[8]
Method of the year: long-read sequencing.

Nat Methods. 2023-1

[9]
NanoSNP: a progressive and haplotype-aware SNP caller on low-coverage nanopore sequencing data.

Bioinformatics. 2023-1-1

[10]
Computational approaches for predicting variant impact: An overview from resources, principles to applications.

Front Genet. 2022-9-29

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