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Computational Identification of Novel Genes: Current and Future Perspectives

While it has long been thought that all genomic novelties are derived from the existing material, many genes lacking homology to known genes were found in recent genome projects. Some of these novel genes were proposed to have evolved de novo, ie, out of noncoding sequences, whereas some have been s...

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Autores principales: Klasberg, Steffen, Bitard-Feildel, Tristan, Mallet, Ludovic
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Libertas Academica 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4970615/
https://www.ncbi.nlm.nih.gov/pubmed/27493475
http://dx.doi.org/10.4137/BBI.S39950
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author Klasberg, Steffen
Bitard-Feildel, Tristan
Mallet, Ludovic
author_facet Klasberg, Steffen
Bitard-Feildel, Tristan
Mallet, Ludovic
author_sort Klasberg, Steffen
collection PubMed
description While it has long been thought that all genomic novelties are derived from the existing material, many genes lacking homology to known genes were found in recent genome projects. Some of these novel genes were proposed to have evolved de novo, ie, out of noncoding sequences, whereas some have been shown to follow a duplication and divergence process. Their discovery called for an extension of the historical hypotheses about gene origination. Besides the theoretical breakthrough, increasing evidence accumulated that novel genes play important roles in evolutionary processes, including adaptation and speciation events. Different techniques are available to identify genes and classify them as novel. Their classification as novel is usually based on their similarity to known genes, or lack thereof, detected by comparative genomics or against databases. Computational approaches are further prime methods that can be based on existing models or leveraging biological evidences from experiments. Identification of novel genes remains however a challenging task. With the constant software and technologies updates, no gold standard, and no available benchmark, evaluation and characterization of genomic novelty is a vibrant field. In this review, the classical and state-of-the-art tools for gene prediction are introduced. The current methods for novel gene detection are presented; the methodological strategies and their limits are discussed along with perspective approaches for further studies.
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spelling pubmed-49706152016-08-04 Computational Identification of Novel Genes: Current and Future Perspectives Klasberg, Steffen Bitard-Feildel, Tristan Mallet, Ludovic Bioinform Biol Insights Review While it has long been thought that all genomic novelties are derived from the existing material, many genes lacking homology to known genes were found in recent genome projects. Some of these novel genes were proposed to have evolved de novo, ie, out of noncoding sequences, whereas some have been shown to follow a duplication and divergence process. Their discovery called for an extension of the historical hypotheses about gene origination. Besides the theoretical breakthrough, increasing evidence accumulated that novel genes play important roles in evolutionary processes, including adaptation and speciation events. Different techniques are available to identify genes and classify them as novel. Their classification as novel is usually based on their similarity to known genes, or lack thereof, detected by comparative genomics or against databases. Computational approaches are further prime methods that can be based on existing models or leveraging biological evidences from experiments. Identification of novel genes remains however a challenging task. With the constant software and technologies updates, no gold standard, and no available benchmark, evaluation and characterization of genomic novelty is a vibrant field. In this review, the classical and state-of-the-art tools for gene prediction are introduced. The current methods for novel gene detection are presented; the methodological strategies and their limits are discussed along with perspective approaches for further studies. Libertas Academica 2016-08-01 /pmc/articles/PMC4970615/ /pubmed/27493475 http://dx.doi.org/10.4137/BBI.S39950 Text en © 2016 the author(s), publisher and licensee Libertas Academica Ltd. This is an open-access article distributed under the terms of the Creative Commons CC-BY-NC 3.0 License.
spellingShingle Review
Klasberg, Steffen
Bitard-Feildel, Tristan
Mallet, Ludovic
Computational Identification of Novel Genes: Current and Future Perspectives
title Computational Identification of Novel Genes: Current and Future Perspectives
title_full Computational Identification of Novel Genes: Current and Future Perspectives
title_fullStr Computational Identification of Novel Genes: Current and Future Perspectives
title_full_unstemmed Computational Identification of Novel Genes: Current and Future Perspectives
title_short Computational Identification of Novel Genes: Current and Future Perspectives
title_sort computational identification of novel genes: current and future perspectives
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4970615/
https://www.ncbi.nlm.nih.gov/pubmed/27493475
http://dx.doi.org/10.4137/BBI.S39950
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