$Identifying a high fraction of the human genome to be under selective constraint using GERP++.$
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Identifying a high fraction of the human genome to be under selective constraint using GERP++.

Computational efforts to identify functional elements within genomes leverage comparative sequence information by looking for regions that exhibit evidence of selective constraint. One way of detecting constrained elements is to follow a bottom-up approach by computing constraint scores for individu...

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Autores principales:	Eugene V Davydov, David L Goode, Marina Sirota, Gregory M Cooper, Arend Sidow, Serafim Batzoglou
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2010
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/c3537d52341745768f626395e71118d1
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spelling	oai:doaj.org-article:c3537d52341745768f626395e71118d12021-11-18T05:50:50ZIdentifying a high fraction of the human genome to be under selective constraint using GERP++.1553-734X1553-735810.1371/journal.pcbi.1001025https://doaj.org/article/c3537d52341745768f626395e71118d12010-12-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/21152010/pdf/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Computational efforts to identify functional elements within genomes leverage comparative sequence information by looking for regions that exhibit evidence of selective constraint. One way of detecting constrained elements is to follow a bottom-up approach by computing constraint scores for individual positions of a multiple alignment and then defining constrained elements as segments of contiguous, highly scoring nucleotide positions. Here we present GERP++, a new tool that uses maximum likelihood evolutionary rate estimation for position-specific scoring and, in contrast to previous bottom-up methods, a novel dynamic programming approach to subsequently define constrained elements. GERP++ evaluates a richer set of candidate element breakpoints and ranks them based on statistical significance, eliminating the need for biased heuristic extension techniques. Using GERP++ we identify over 1.3 million constrained elements spanning over 7% of the human genome. We predict a higher fraction than earlier estimates largely due to the annotation of longer constrained elements, which improves one to one correspondence between predicted elements with known functional sequences. GERP++ is an efficient and effective tool to provide both nucleotide- and element-level constraint scores within deep multiple sequence alignments.Eugene V DavydovDavid L GoodeMarina SirotaGregory M CooperArend SidowSerafim BatzoglouPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 6, Iss 12, p e1001025 (2010)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Eugene V Davydov David L Goode Marina Sirota Gregory M Cooper Arend Sidow Serafim Batzoglou Identifying a high fraction of the human genome to be under selective constraint using GERP++.
description	Computational efforts to identify functional elements within genomes leverage comparative sequence information by looking for regions that exhibit evidence of selective constraint. One way of detecting constrained elements is to follow a bottom-up approach by computing constraint scores for individual positions of a multiple alignment and then defining constrained elements as segments of contiguous, highly scoring nucleotide positions. Here we present GERP++, a new tool that uses maximum likelihood evolutionary rate estimation for position-specific scoring and, in contrast to previous bottom-up methods, a novel dynamic programming approach to subsequently define constrained elements. GERP++ evaluates a richer set of candidate element breakpoints and ranks them based on statistical significance, eliminating the need for biased heuristic extension techniques. Using GERP++ we identify over 1.3 million constrained elements spanning over 7% of the human genome. We predict a higher fraction than earlier estimates largely due to the annotation of longer constrained elements, which improves one to one correspondence between predicted elements with known functional sequences. GERP++ is an efficient and effective tool to provide both nucleotide- and element-level constraint scores within deep multiple sequence alignments.
format	article
author	Eugene V Davydov David L Goode Marina Sirota Gregory M Cooper Arend Sidow Serafim Batzoglou
author_facet	Eugene V Davydov David L Goode Marina Sirota Gregory M Cooper Arend Sidow Serafim Batzoglou
author_sort	Eugene V Davydov
title	Identifying a high fraction of the human genome to be under selective constraint using GERP++.
title_short	Identifying a high fraction of the human genome to be under selective constraint using GERP++.
title_full	Identifying a high fraction of the human genome to be under selective constraint using GERP++.
title_fullStr	Identifying a high fraction of the human genome to be under selective constraint using GERP++.
title_full_unstemmed	Identifying a high fraction of the human genome to be under selective constraint using GERP++.
title_sort	identifying a high fraction of the human genome to be under selective constraint using gerp++.
publisher	Public Library of Science (PLoS)
publishDate	2010
url	https://doaj.org/article/c3537d52341745768f626395e71118d1
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Identifying a high fraction of the human genome to be under selective constraint using GERP++.

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