Runs of Homozygosity (RoHs) are popular among geneticists as the footprint of demographic processes, evolutionary forces and inbreeding in shaping our genome, and are known to confer risk of Mendelian and complex diseases. Notwithstanding growing interest in their study, there is unmet need for reliable and rapid methods for genomic analyses in large data sets. AUDACITY is a tool integrating novel RoH detection algorithm and autozygosity prediction score for prioritization of mutation-surrounding regions. It processes data in VCF file format, and outperforms existing methods in identifying RoHs of any size. Simulations and analysis of real exomes/genomes show its potential to foster future RoH studies in medical and population genomics. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.

AUDACITY: A comprehensive approach for the detection and classification of Runs of Homozygosity in medical and population genomics / Magi, Alberto; Giangregorio, Tania; Semeraro, Roberto; Carangelo, Giulia; Palombo, Flavia; Romeo, Giovanni; Seri, Marco; Pippucci, Tommaso. - In: COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL. - ISSN 2001-0370. - ELETTRONICO. - 18:(2020), pp. 1956-1967. [10.1016/j.csbj.2020.07.003]

AUDACITY: A comprehensive approach for the detection and classification of Runs of Homozygosity in medical and population genomics

Magi, Alberto;Semeraro, Roberto;Carangelo, Giulia;Pippucci, Tommaso
2020

Abstract

Runs of Homozygosity (RoHs) are popular among geneticists as the footprint of demographic processes, evolutionary forces and inbreeding in shaping our genome, and are known to confer risk of Mendelian and complex diseases. Notwithstanding growing interest in their study, there is unmet need for reliable and rapid methods for genomic analyses in large data sets. AUDACITY is a tool integrating novel RoH detection algorithm and autozygosity prediction score for prioritization of mutation-surrounding regions. It processes data in VCF file format, and outperforms existing methods in identifying RoHs of any size. Simulations and analysis of real exomes/genomes show its potential to foster future RoH studies in medical and population genomics. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.
18
1956
1967
Magi, Alberto; Giangregorio, Tania; Semeraro, Roberto; Carangelo, Giulia; Palombo, Flavia; Romeo, Giovanni; Seri, Marco; Pippucci, Tommaso
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2158/1280981
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