Scoring Targets of Transcription in Bacteria Rather than Focusing on Individual Binding Sites

Reliable identification of targets of bacterial regulators is necessary to understand bacterial gene expression regulation. These targets are commonly predicted by searching for high-scoring binding sites in the upstream genomic regions, which typically leads to a large number of false positives. In...
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Autor*in:	Marko Djordjevic [verfasserIn] Magdalena Djordjevic [verfasserIn] Evgeny Zdobnov [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2017

Schlagwörter:	direct target gene predictions transcription factor binding site predictions transcription regulation position specific weight matrices transcription targets transcription start starts

Übergeordnetes Werk:	In: Frontiers in Microbiology - Frontiers Media S.A., 2011, 8(2017)
Übergeordnetes Werk:	volume:8 ; year:2017

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fmicb.2017.02314

Katalog-ID:	DOAJ014766566

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520			\|a Reliable identification of targets of bacterial regulators is necessary to understand bacterial gene expression regulation. These targets are commonly predicted by searching for high-scoring binding sites in the upstream genomic regions, which typically leads to a large number of false positives. In contrast to the common approach, here we propose a novel concept, where overrepresentation of the scoring distribution that corresponds to the entire searched region is assessed, as opposed to predicting individual binding sites. We explore two implementations of this concept, based on Kolmogorov–Smirnov (KS) and Anderson–Darling (AD) tests, which both provide straightforward P-value estimates for predicted targets. This approach is implemented for pleiotropic bacterial regulators, including σ70 (bacterial housekeeping σ factor) target predictions, which is a classical bioinformatics problem characterized by low specificity. We show that KS based approach is both faster and more accurate, departing from the current paradigm of AD being slower, but more accurate. Moreover, KS approach leads to a significant increase in the search accuracy compared to the standard approach, while at the same time straightforwardly assigning well established P-values to each potential target. Consequently, the new KS based method proposed here, which assigns P-values to fixed length upstream regions, provides a fast and accurate approach for predicting bacterial transcription targets.
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allfieldsSound	10.3389/fmicb.2017.02314 doi (DE-627)DOAJ014766566 (DE-599)DOAJab30ac00824248c89eb93999fafbc3f6 DE-627 ger DE-627 rakwb eng QR1-502 Marko Djordjevic verfasserin aut Scoring Targets of Transcription in Bacteria Rather than Focusing on Individual Binding Sites 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Reliable identification of targets of bacterial regulators is necessary to understand bacterial gene expression regulation. These targets are commonly predicted by searching for high-scoring binding sites in the upstream genomic regions, which typically leads to a large number of false positives. In contrast to the common approach, here we propose a novel concept, where overrepresentation of the scoring distribution that corresponds to the entire searched region is assessed, as opposed to predicting individual binding sites. We explore two implementations of this concept, based on Kolmogorov–Smirnov (KS) and Anderson–Darling (AD) tests, which both provide straightforward P-value estimates for predicted targets. This approach is implemented for pleiotropic bacterial regulators, including σ70 (bacterial housekeeping σ factor) target predictions, which is a classical bioinformatics problem characterized by low specificity. We show that KS based approach is both faster and more accurate, departing from the current paradigm of AD being slower, but more accurate. Moreover, KS approach leads to a significant increase in the search accuracy compared to the standard approach, while at the same time straightforwardly assigning well established P-values to each potential target. Consequently, the new KS based method proposed here, which assigns P-values to fixed length upstream regions, provides a fast and accurate approach for predicting bacterial transcription targets. direct target gene predictions transcription factor binding site predictions transcription regulation position specific weight matrices transcription targets transcription start starts Microbiology Magdalena Djordjevic verfasserin aut Evgeny Zdobnov verfasserin aut In Frontiers in Microbiology Frontiers Media S.A., 2011 8(2017) (DE-627)642889384 (DE-600)2587354-4 1664302X nnns volume:8 year:2017 https://doi.org/10.3389/fmicb.2017.02314 kostenfrei https://doaj.org/article/ab30ac00824248c89eb93999fafbc3f6 kostenfrei http://journal.frontiersin.org/article/10.3389/fmicb.2017.02314/full kostenfrei https://doaj.org/toc/1664-302X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2017
language	English
source	In Frontiers in Microbiology 8(2017) volume:8 year:2017
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topic_facet	direct target gene predictions transcription factor binding site predictions transcription regulation position specific weight matrices transcription targets transcription start starts Microbiology
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container_title	Frontiers in Microbiology
authorswithroles_txt_mv	Marko Djordjevic @@aut@@ Magdalena Djordjevic @@aut@@ Evgeny Zdobnov @@aut@@
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callnumber-first	Q - Science
author	Marko Djordjevic
spellingShingle	Marko Djordjevic misc QR1-502 misc direct target gene predictions misc transcription factor binding site predictions misc transcription regulation misc position specific weight matrices misc transcription targets misc transcription start starts misc Microbiology Scoring Targets of Transcription in Bacteria Rather than Focusing on Individual Binding Sites
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topic_title	QR1-502 Scoring Targets of Transcription in Bacteria Rather than Focusing on Individual Binding Sites direct target gene predictions transcription factor binding site predictions transcription regulation position specific weight matrices transcription targets transcription start starts
topic	misc QR1-502 misc direct target gene predictions misc transcription factor binding site predictions misc transcription regulation misc position specific weight matrices misc transcription targets misc transcription start starts misc Microbiology
topic_unstemmed	misc QR1-502 misc direct target gene predictions misc transcription factor binding site predictions misc transcription regulation misc position specific weight matrices misc transcription targets misc transcription start starts misc Microbiology
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title	Scoring Targets of Transcription in Bacteria Rather than Focusing on Individual Binding Sites
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title_full	Scoring Targets of Transcription in Bacteria Rather than Focusing on Individual Binding Sites
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title_sort	scoring targets of transcription in bacteria rather than focusing on individual binding sites
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title_auth	Scoring Targets of Transcription in Bacteria Rather than Focusing on Individual Binding Sites
abstract	Reliable identification of targets of bacterial regulators is necessary to understand bacterial gene expression regulation. These targets are commonly predicted by searching for high-scoring binding sites in the upstream genomic regions, which typically leads to a large number of false positives. In contrast to the common approach, here we propose a novel concept, where overrepresentation of the scoring distribution that corresponds to the entire searched region is assessed, as opposed to predicting individual binding sites. We explore two implementations of this concept, based on Kolmogorov–Smirnov (KS) and Anderson–Darling (AD) tests, which both provide straightforward P-value estimates for predicted targets. This approach is implemented for pleiotropic bacterial regulators, including σ70 (bacterial housekeeping σ factor) target predictions, which is a classical bioinformatics problem characterized by low specificity. We show that KS based approach is both faster and more accurate, departing from the current paradigm of AD being slower, but more accurate. Moreover, KS approach leads to a significant increase in the search accuracy compared to the standard approach, while at the same time straightforwardly assigning well established P-values to each potential target. Consequently, the new KS based method proposed here, which assigns P-values to fixed length upstream regions, provides a fast and accurate approach for predicting bacterial transcription targets.
abstractGer	Reliable identification of targets of bacterial regulators is necessary to understand bacterial gene expression regulation. These targets are commonly predicted by searching for high-scoring binding sites in the upstream genomic regions, which typically leads to a large number of false positives. In contrast to the common approach, here we propose a novel concept, where overrepresentation of the scoring distribution that corresponds to the entire searched region is assessed, as opposed to predicting individual binding sites. We explore two implementations of this concept, based on Kolmogorov–Smirnov (KS) and Anderson–Darling (AD) tests, which both provide straightforward P-value estimates for predicted targets. This approach is implemented for pleiotropic bacterial regulators, including σ70 (bacterial housekeeping σ factor) target predictions, which is a classical bioinformatics problem characterized by low specificity. We show that KS based approach is both faster and more accurate, departing from the current paradigm of AD being slower, but more accurate. Moreover, KS approach leads to a significant increase in the search accuracy compared to the standard approach, while at the same time straightforwardly assigning well established P-values to each potential target. Consequently, the new KS based method proposed here, which assigns P-values to fixed length upstream regions, provides a fast and accurate approach for predicting bacterial transcription targets.
abstract_unstemmed	Reliable identification of targets of bacterial regulators is necessary to understand bacterial gene expression regulation. These targets are commonly predicted by searching for high-scoring binding sites in the upstream genomic regions, which typically leads to a large number of false positives. In contrast to the common approach, here we propose a novel concept, where overrepresentation of the scoring distribution that corresponds to the entire searched region is assessed, as opposed to predicting individual binding sites. We explore two implementations of this concept, based on Kolmogorov–Smirnov (KS) and Anderson–Darling (AD) tests, which both provide straightforward P-value estimates for predicted targets. This approach is implemented for pleiotropic bacterial regulators, including σ70 (bacterial housekeeping σ factor) target predictions, which is a classical bioinformatics problem characterized by low specificity. We show that KS based approach is both faster and more accurate, departing from the current paradigm of AD being slower, but more accurate. Moreover, KS approach leads to a significant increase in the search accuracy compared to the standard approach, while at the same time straightforwardly assigning well established P-values to each potential target. Consequently, the new KS based method proposed here, which assigns P-values to fixed length upstream regions, provides a fast and accurate approach for predicting bacterial transcription targets.
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title_short	Scoring Targets of Transcription in Bacteria Rather than Focusing on Individual Binding Sites
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