High sensitivity TSS prediction: estimates of locations where TSS cannot occur.

<h4>Background</h4>Although transcription in mammalian genomes can initiate from various genomic positions (e.g., 3'UTR, coding exons, etc.), most locations on genomes are not prone to transcription initiation. It is of practical and theoretical interest to be able to estimate such...

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Autores principales:	Ulf Schaefer, Rimantas Kodzius, Chikatoshi Kai, Jun Kawai, Piero Carninci, Yoshihide Hayashizaki, Vladimir B Bajic
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2010
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/771d124439074b98abc5b3c6bfbe6807
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spelling	oai:doaj.org-article:771d124439074b98abc5b3c6bfbe68072021-11-18T07:36:48ZHigh sensitivity TSS prediction: estimates of locations where TSS cannot occur.1932-620310.1371/journal.pone.0013934https://doaj.org/article/771d124439074b98abc5b3c6bfbe68072010-11-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/21085627/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4>Although transcription in mammalian genomes can initiate from various genomic positions (e.g., 3'UTR, coding exons, etc.), most locations on genomes are not prone to transcription initiation. It is of practical and theoretical interest to be able to estimate such collections of non-TSS locations (NTLs). The identification of large portions of NTLs can contribute to better focusing the search for TSS locations and thus contribute to promoter and gene finding. It can help in the assessment of 5' completeness of expressed sequences, contribute to more successful experimental designs, as well as more accurate gene annotation.<h4>Methodology</h4>Using comprehensive collections of Cap Analysis of Gene Expression (CAGE) and other transcript data from mouse and human genomes, we developed a methodology that allows us, by performing computational TSS prediction with very high sensitivity, to annotate, with a high accuracy in a strand specific manner, locations of mammalian genomes that are highly unlikely to harbor transcription start sites (TSSs). The properties of the immediate genomic neighborhood of 98,682 accurately determined mouse and 113,814 human TSSs are used to determine features that distinguish genomic transcription initiation locations from those that are not likely to initiate transcription. In our algorithm we utilize various constraining properties of features identified in the upstream and downstream regions around TSSs, as well as statistical analyses of these surrounding regions.<h4>Conclusions</h4>Our analysis of human chromosomes 4, 21 and 22 estimates ∼46%, ∼41% and ∼27% of these chromosomes, respectively, as being NTLs. This suggests that on average more than 40% of the human genome can be expected to be highly unlikely to initiate transcription. Our method represents the first one that utilizes high-sensitivity TSS prediction to identify, with high accuracy, large portions of mammalian genomes as NTLs. The server with our algorithm implemented is available at http://cbrc.kaust.edu.sa/ddm/.Ulf SchaeferRimantas KodziusChikatoshi KaiJun KawaiPiero CarninciYoshihide HayashizakiVladimir B BajicPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 5, Iss 11, p e13934 (2010)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ulf Schaefer Rimantas Kodzius Chikatoshi Kai Jun Kawai Piero Carninci Yoshihide Hayashizaki Vladimir B Bajic High sensitivity TSS prediction: estimates of locations where TSS cannot occur.
description	<h4>Background</h4>Although transcription in mammalian genomes can initiate from various genomic positions (e.g., 3'UTR, coding exons, etc.), most locations on genomes are not prone to transcription initiation. It is of practical and theoretical interest to be able to estimate such collections of non-TSS locations (NTLs). The identification of large portions of NTLs can contribute to better focusing the search for TSS locations and thus contribute to promoter and gene finding. It can help in the assessment of 5' completeness of expressed sequences, contribute to more successful experimental designs, as well as more accurate gene annotation.<h4>Methodology</h4>Using comprehensive collections of Cap Analysis of Gene Expression (CAGE) and other transcript data from mouse and human genomes, we developed a methodology that allows us, by performing computational TSS prediction with very high sensitivity, to annotate, with a high accuracy in a strand specific manner, locations of mammalian genomes that are highly unlikely to harbor transcription start sites (TSSs). The properties of the immediate genomic neighborhood of 98,682 accurately determined mouse and 113,814 human TSSs are used to determine features that distinguish genomic transcription initiation locations from those that are not likely to initiate transcription. In our algorithm we utilize various constraining properties of features identified in the upstream and downstream regions around TSSs, as well as statistical analyses of these surrounding regions.<h4>Conclusions</h4>Our analysis of human chromosomes 4, 21 and 22 estimates ∼46%, ∼41% and ∼27% of these chromosomes, respectively, as being NTLs. This suggests that on average more than 40% of the human genome can be expected to be highly unlikely to initiate transcription. Our method represents the first one that utilizes high-sensitivity TSS prediction to identify, with high accuracy, large portions of mammalian genomes as NTLs. The server with our algorithm implemented is available at http://cbrc.kaust.edu.sa/ddm/.
format	article
author	Ulf Schaefer Rimantas Kodzius Chikatoshi Kai Jun Kawai Piero Carninci Yoshihide Hayashizaki Vladimir B Bajic
author_facet	Ulf Schaefer Rimantas Kodzius Chikatoshi Kai Jun Kawai Piero Carninci Yoshihide Hayashizaki Vladimir B Bajic
author_sort	Ulf Schaefer
title	High sensitivity TSS prediction: estimates of locations where TSS cannot occur.
title_short	High sensitivity TSS prediction: estimates of locations where TSS cannot occur.
title_full	High sensitivity TSS prediction: estimates of locations where TSS cannot occur.
title_fullStr	High sensitivity TSS prediction: estimates of locations where TSS cannot occur.
title_full_unstemmed	High sensitivity TSS prediction: estimates of locations where TSS cannot occur.
title_sort	high sensitivity tss prediction: estimates of locations where tss cannot occur.
publisher	Public Library of Science (PLoS)
publishDate	2010
url	https://doaj.org/article/771d124439074b98abc5b3c6bfbe6807
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High sensitivity TSS prediction: estimates of locations where TSS cannot occur.

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