Journal Article2023Cited by 11

Integrated transcriptome catalog of Tenualosa ilisha as a resource for gene discovery and expression profiling

Md Arko Ayon Chowdhury, Md Rakibul Islam, Al Amin, Sadia Noor Mou, Kazi Newaz Ullah, Abdul Baten, Mohammad Shoyaib, Amin Ahsan Ali, Farhana Tasnim Chowdhury, Md Lifat Rahi, H Khan, MA Amin, MR Islam

Scientific Data

Nature Publishing Group UK London, Vol. 10, Issue 1, pp. 214, ISBN: 2052-4463

DOI: 10.1038/s41597-023-02132-zURL

Abstract

AbstractThe silver pride of Bangladesh, migratory shad, Tenualosa ilisha (Hilsa), makes the highest contribution to the total fish production of Bangladesh. Despite its noteworthy contribution, a well-annotated transcriptome data is not available. Here we report a transcriptomic catalog of Hilsa, constructed by assembling RNA-Seq reads from different tissues of the fish including brain, gill, kidney, liver, and muscle. Hilsa fish were collected from different aquatic habitats (fresh, brackish, and sea water) and the sequencing was performed in the next generation sequencing (NGS) platform. De novo assembly of the sequences obtained from 46 cDNA libraries revealed 462,085 transcript isoforms that were subsequently annotated using the Universal Protein Resource Knowledgebase (UniPortKB) as a reference. Starting from the sampling to final annotation, all the steps along with the workflow are reported here. This study will provide a significant resource for ongoing and future research on Hilsa for transcriptome based expression profiling and identification of candidate genes.

Keywords

  • Tenualosa ilisha
  • de novo assembly
  • genome annotation
  • tissue-specific gene expression
  • hilsa
  • candidate gene identification
  • Transcriptome
  • Gene expression profiling
  • mRNA isoforms

CCDS Authors

Md Rakibul Islam, PhD
Md Rakibul Islam, PhD

Md Rakibul Islam, PhD

  • Collaborator, HCI Wing

Assistant Professor

Lamar University

Published at

Scientific Data

Research Interests

Human Aspects in Software Engineering, Software Security, Source Code Analysis, Natural Language Processing, Bioinformatics

Amin Ahsan Ali, PhD
Amin Ahsan Ali, PhD

Amin Ahsan Ali, PhD

  • Director, AI/ML Wing

Professor

Department of Computer Science and Engineering

Independent University, Bangladesh

Published at

ACL, CHI, UbiComp, ECCV, IJCAI, ECAI, WACV, IPSN, PAKDD, LREC, IJCNN, ICPR, ICIP, BEA, BLP, ICMLA, Scientific Reports, Scientific Data, Pattern Recognition, Information Sciences, Ad Hoc Networks, Drug and Alcohol Dependence, PLOS ONE

Research Interests

Artificial Intelligence, Machine Learning

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