De novo transcriptome analysis of the Indian squid Uroteuthis duvaucelii (Orbigny, 1848) from the Indian Ocean.

IF 5.8 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Data Pub Date : 2024-11-16 DOI:10.1038/s41597-024-04112-3

Nisha Krishnan, Sandhya Sukumaran, V G Vysakh, Wilson Sebastian, Anjaly Jose, Neenu Raj, A Gopalakrishnan

{"title":"De novo transcriptome analysis of the Indian squid Uroteuthis duvaucelii (Orbigny, 1848) from the Indian Ocean.","authors":"Nisha Krishnan, Sandhya Sukumaran, V G Vysakh, Wilson Sebastian, Anjaly Jose, Neenu Raj, A Gopalakrishnan","doi":"10.1038/s41597-024-04112-3","DOIUrl":null,"url":null,"abstract":"<p><p>Cephalopods have dominated the oceans for hundreds of millions of years and are unquestionably at the peak of molluscan evolution. The development of the large brain and a well-sophisticated sensory system contributed significantly to its success. Therefore, it is considered the best example of convergent evolution and attracted the attention of scientists from various disciplines of biology. The aim of the present study is to construct a reference transcriptome in the Indian squid Uroteuthis duvaucelii to gain insights into cephalopod evolution and enrich the existing cephalopod database. Around 72 million short Illumina reads were generated from five different tissues, including the brain, eye, gill, heart and gonads, and assembled using the Trinity assembler. About 26230 protein-coding sequences were annotated from the assembled transcripts. The BUSCO completeness of the assembly was 71.71% compared to the Mollusca_Odb10 gene set. KEGG and REACTOME pathway analyzes revealed that U. duvaucelii shares many genes and pathways with higher vertebrates.</p>","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"11 1","pages":"1236"},"PeriodicalIF":5.8000,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11569149/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Scientific Data","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1038/s41597-024-04112-3","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}

引用次数: 0

Abstract

Cephalopods have dominated the oceans for hundreds of millions of years and are unquestionably at the peak of molluscan evolution. The development of the large brain and a well-sophisticated sensory system contributed significantly to its success. Therefore, it is considered the best example of convergent evolution and attracted the attention of scientists from various disciplines of biology. The aim of the present study is to construct a reference transcriptome in the Indian squid Uroteuthis duvaucelii to gain insights into cephalopod evolution and enrich the existing cephalopod database. Around 72 million short Illumina reads were generated from five different tissues, including the brain, eye, gill, heart and gonads, and assembled using the Trinity assembler. About 26230 protein-coding sequences were annotated from the assembled transcripts. The BUSCO completeness of the assembly was 71.71% compared to the Mollusca_Odb10 gene set. KEGG and REACTOME pathway analyzes revealed that U. duvaucelii shares many genes and pathways with higher vertebrates.

查看原文本刊更多论文

印度洋鱿鱼 Uroteuthis duvaucelii (Orbigny, 1848) 的全新转录组分析。

头足纲动物统治海洋已有数亿年之久，毫无疑问处于软体动物进化的顶峰。大型大脑和复杂感官系统的发展为其成功做出了巨大贡献。因此，它被认为是趋同进化的最佳范例，吸引了生物学各学科科学家的关注。本研究旨在构建印度鱿鱼 Uroteuthis duvaucelii 的参考转录组，以深入了解头足类动物的进化过程，并丰富现有的头足类动物数据库。研究人员从大脑、眼睛、鳃、心脏和性腺等五个不同组织中生成了约 7200 万个 Illumina 短读数，并使用 Trinity 汇编器进行了汇编。根据组装的转录本注释了约 26230 条蛋白质编码序列。与 Mollusca_Odb10 基因组相比，组装的 BUSCO 完整性为 71.71%。KEGG和REACTOME通路分析表明，U. duvaucelii与高等脊椎动物共享许多基因和通路。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

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.