Genome sequencing and assembly of near threatened Clarias dussumieri (Valenciennes, 1840), an endemic catfish of peninsular India.

IF 5.8 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Data Pub Date : 2024-12-19 DOI:10.1038/s41597-024-04272-2

Vindhya Mohindra, Labrechai Mog Chowdhury, Ravi Charan, Valaparambil Saidumohammad Basheer, Joykrushna Jena

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引用次数: 0

Abstract

Clarias dussumieri, a near threatened freshwater catfish, is endemic to peninsular India and has aquaculture potential. Unlike its sister species, C. magur, the male fish needs not be sacrificed during captive breeding. Thus, the generation of genomic information of this species becomes significant for effective genome mining through the bioprospecting of novel genes for important production traits. In this study, the genome assembly was undertaken to address this gap by generating high quality chromosome level genome assembly using PacBio long reads and Hi-C scaffolding. The total assembled genome was found to be 918.72 Mb in size and showed 95.23% completeness. Its characterization exhibited 41.46% repeats, 1,174,725 SSRs and 25,369 predicted genes with functional annotations. The Single copy orthologs analysis placed C. dussumieri in a distinct position with C. magur. The comprehensive genomic information offers resources for comparative genomics with other Clarias species for improvement of economic traits.

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来源期刊

Scientific Data Social Sciences-Education

CiteScore

11.20

自引率

4.10%

发文量

689

审稿时长

16 weeks

期刊介绍： Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.