ArticleEvolutionary bioinformatics online2019
Assexon: Assembling Exon Using Gene Capture Data.
Article in Evolutionary bioinformatics online, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Targeted ortholog search in unannotated genome assemblies with fDOG-Assembly.Nucleic acids research · 2026Article
- Chromosome-level genome assembly of Trichiurus japonicus resolves species boundaries and evolutionary history of cutlassfishes in Northwest Pacific.BMC genomics · 2026Article
- Population Patterns and Dynamics ofEvolutionary applications · 2025Article
- Divergence of the freshwater sleeper,ZooKeys · 2024Article
- A high-resolution genome of an euryhaline and eurythermal rhinogoby (Rhinogobius similis Gill 1895).G3 (Bethesda, Md.) · 2022Article
- Cross-species gene enrichment revealed a single population of Hilsa shad (Tenualosa ilisha) with low genetic variation in Bangladesh waters.Scientific reports · 2021Article
- New alignment-based sequence extraction software (ALiBaSeq) and its utility for deep level phylogenetics.PeerJ · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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Abstract
Exon capture across species has been one of the most broadly applied approaches to acquire multi-locus data in phylogenomic studies of non-model organisms. Methods for assembling loci from short-read sequences (eg, Illumina platforms) that rely on mapping reads to a reference genome may not be suitable for studies comprising species across a wide phylogenetic spectrum; thus, de novo assembling methods are more generally applied. Current approaches for assembling targeted exons from short reads are not particularly optimized as they cannot (1) assemble loci with low read depth, (2) handle large files efficiently, and (3) reliably address issues with paralogs. Thus, we present Assexon: a streamlined pipeline that de novo assembles targeted exons and their flanking sequences from raw reads. We tested our method using reads from
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Registered trials
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