Advanced perspectives of biocomputational gene discovery

Gene discovery is one of the most significant processes in understanding and analysis of an organism’s genome after its sequencing. The method comprises determining the location of intron structures, exon structures, and open reading frames (ORFs). It computationally specifies all of the genes with near 100% reliability. It can substantially reduce the amount of wet lab experimentation. In eukaryotic genomes, mosaic organization occurs when a gene is divided into exons by intervening noncoding regions (called introns). Several methods are available for gene finding such as laboratory-based approaches, feature-based approaches homology-based approaches, and statistical and HMM-based approaches. In this paper, we aim to discuss in silco approaches for gene prediction with available bioinformatics tools for gene finding.

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Publication Details

Journal
Indian journal of animal genetics and breeding.
Published
2024-11-18
DOI
https://doi.org/10.59317/efnfpt15
Primary Topic
Genetics, Bioinformatics, and Biomedical Research
Type
article
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article

Advanced perspectives of biocomputational gene discovery

Sandeep Tiwari, Chandra Sekhar Mukhopadhyay
Indian journal of animal genetics and breeding.
Genetics, Bioinformatics, and Biomedical Research
article

Advanced perspectives of biocomputational gene discovery

Sandeep Tiwari, Chandra Sekhar Mukhopadhyay
article en

Abstract

Gene discovery is one of the most significant processes in understanding and analysis of an organism’s genome after its sequencing. The method comprises determining the location of intron structures, exon structures, and open reading frames (ORFs). It computationally specifies all of the genes with near 100% reliability. It can substantially reduce the amount of wet lab experimentation. In eukaryotic genomes, mosaic organization occurs when a gene is divided into exons by intervening noncoding regions (called introns). Several methods are available for gene finding such as laboratory-based approaches, feature-based approaches homology-based approaches, and statistical and HMM-based approaches. In this paper, we aim to discuss in silco approaches for gene prediction with available bioinformatics tools for gene finding.

Indian journal of animal genetics and breeding.Vol. 43(1,2)
Guru Angad Dev Veterinary and Animal Sciences University (IN)
Openalex Percentile: Top 81%
Genetics, Bioinformatics, and Biomedical Research
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