PubTransformer

A site to transform Pubmed publications into these bibliographic reference formats: ADS, BibTeX, EndNote, ISI used by the Web of Knowledge, RIS, MEDLINE, Microsoft's Word 2007 XML.

Incorporating evolution of transcription factor binding sites into annotated alignments.

Abstract Identifying transcription factor binding sites (TFBSs) is essential to elucidate putative regulatory mechanisms. A common strategy is to combine cross-species conservation with single sequence TFBS annotation to yield "conserved TFBSs". Most current methods in this field adopt a multi-step approach that segregates the two aspects. Again, it is widely accepted that the evolutionary dynamics of binding sites differ from those of the surrounding sequence. Hence, it is desirable to have an approach that explicitly takes this factor into account. Although a plethora of approaches have been proposed for the prediction of conserved TFBSs, very few explicitly model TFBS evolutionary properties, while additionally being multi-step. Recently, we introduced a novel approach to simultaneously align and annotate conserved TFBSs in a pair of sequences. Building upon the standard Smith-Waterman algorithm for local alignments, SimAnn introduces additional states for profiles to output extended alignments or annotated alignments. That is, alignments with parts annotated as gaplessly aligned TFBSs (pair-profile hits)are generated. Moreover,the pair- profile related parameters are derived in a sound statistical framework. In this article, we extend this approach to explicitly incorporate evolution of binding sites in the SimAnn framework. We demonstrate the extension in the theoretical derivations through two position-specific evolutionary models, previously used for modelling TFBS evolution. In a simulated setting, we provide a proof of concept that the approach works given the underlying assumptions,as compared to the original work. Finally, using a real dataset of experimentally verified binding sites in human-mouse sequence pairs,we compare the new approach (eSimAnn) to an existing multi-step tool that also considers TFBS evolution. Although it is widely accepted that binding sites evolve differently from the surrounding sequences, most comparative TFBS identification methods do not explicitly consider this.Additionally, prediction of conserved binding sites is carried out in a multi-step approach that segregates alignment from TFBS annotation. In this paper, we demonstrate how the simultaneous alignment and annotation approach of SimAnn can be further extended to incorporate TFBS evolutionary relationships. We study how alignments and binding site predictions interplay at varying evolutionary distances and for various profile qualities.
PMID
Related Publications

CORE_TF: a user-friendly interface to identify evolutionary conserved transcription factor binding sites in sets of co-regulated genes.

Identification of functional transcription factor binding sites using closely related Saccharomyces species.

Regulatory motif finding by logic regression.

Simultaneous alignment and annotation of cis-regulatory regions.

Alignment and prediction of cis-regulatory modules based on a probabilistic model of evolution.

Authors

Mayor MeshTerms

Computational Biology

Evolution, Molecular

Sequence Alignment

Sequence Analysis, RNA

Keywords
Journal Title journal of biosciences
Publication Year Start
%A Bais, Abha S.; Grossmann, Stefen; Vingron, Martin
%T Incorporating evolution of transcription factor binding sites into annotated alignments.
%J Journal of biosciences, vol. 32, no. 5, pp. 841-850
%D 08/2007
%V 32
%N 5
%M eng
%B Identifying transcription factor binding sites (TFBSs) is essential to elucidate putative regulatory mechanisms. A common strategy is to combine cross-species conservation with single sequence TFBS annotation to yield "conserved TFBSs". Most current methods in this field adopt a multi-step approach that segregates the two aspects. Again, it is widely accepted that the evolutionary dynamics of binding sites differ from those of the surrounding sequence. Hence, it is desirable to have an approach that explicitly takes this factor into account. Although a plethora of approaches have been proposed for the prediction of conserved TFBSs, very few explicitly model TFBS evolutionary properties, while additionally being multi-step. Recently, we introduced a novel approach to simultaneously align and annotate conserved TFBSs in a pair of sequences. Building upon the standard Smith-Waterman algorithm for local alignments, SimAnn introduces additional states for profiles to output extended alignments or annotated alignments. That is, alignments with parts annotated as gaplessly aligned TFBSs (pair-profile hits)are generated. Moreover,the pair- profile related parameters are derived in a sound statistical framework. In this article, we extend this approach to explicitly incorporate evolution of binding sites in the SimAnn framework. We demonstrate the extension in the theoretical derivations through two position-specific evolutionary models, previously used for modelling TFBS evolution. In a simulated setting, we provide a proof of concept that the approach works given the underlying assumptions,as compared to the original work. Finally, using a real dataset of experimentally verified binding sites in human-mouse sequence pairs,we compare the new approach (eSimAnn) to an existing multi-step tool that also considers TFBS evolution. Although it is widely accepted that binding sites evolve differently from the surrounding sequences, most comparative TFBS identification methods do not explicitly consider this.Additionally, prediction of conserved binding sites is carried out in a multi-step approach that segregates alignment from TFBS annotation. In this paper, we demonstrate how the simultaneous alignment and annotation approach of SimAnn can be further extended to incorporate TFBS evolutionary relationships. We study how alignments and binding site predictions interplay at varying evolutionary distances and for various profile qualities.
%K Algorithms, Animals, Binding Sites, Computational Biology, Computer Simulation, Evolution, Molecular, Humans, Mice, Models, Genetic, Sequence Alignment, Sequence Analysis, RNA, Transcription Factors
%P 841
%L 850
%W PHY
%G AUTHOR
%R 2007.......32..841B

@Article{Bais2007,
author="Bais, Abha S.
and Grossmann, Stefen
and Vingron, Martin",
title="Incorporating evolution of transcription factor binding sites into annotated alignments.",
journal="Journal of biosciences",
year="2007",
month="Aug",
volume="32",
number="5",
pages="841--850",
keywords="Algorithms",
keywords="Animals",
keywords="Binding Sites",
keywords="Computational Biology",
keywords="Computer Simulation",
keywords="Evolution, Molecular",
keywords="Humans",
keywords="Mice",
keywords="Models, Genetic",
keywords="Sequence Alignment",
keywords="Sequence Analysis, RNA",
keywords="Transcription Factors",
abstract="Identifying transcription factor binding sites (TFBSs) is essential to elucidate putative regulatory mechanisms. A common strategy is to combine cross-species conservation with single sequence TFBS annotation to yield ``conserved TFBSs''. Most current methods in this field adopt a multi-step approach that segregates the two aspects. Again, it is widely accepted that the evolutionary dynamics of binding sites differ from those of the surrounding sequence. Hence, it is desirable to have an approach that explicitly takes this factor into account. Although a plethora of approaches have been proposed for the prediction of conserved TFBSs, very few explicitly model TFBS evolutionary properties, while additionally being multi-step. Recently, we introduced a novel approach to simultaneously align and annotate conserved TFBSs in a pair of sequences. Building upon the standard Smith-Waterman algorithm for local alignments, SimAnn introduces additional states for profiles to output extended alignments or annotated alignments. That is, alignments with parts annotated as gaplessly aligned TFBSs (pair-profile hits)are generated. Moreover,the pair- profile related parameters are derived in a sound statistical framework. In this article, we extend this approach to explicitly incorporate evolution of binding sites in the SimAnn framework. We demonstrate the extension in the theoretical derivations through two position-specific evolutionary models, previously used for modelling TFBS evolution. In a simulated setting, we provide a proof of concept that the approach works given the underlying assumptions,as compared to the original work. Finally, using a real dataset of experimentally verified binding sites in human-mouse sequence pairs,we compare the new approach (eSimAnn) to an existing multi-step tool that also considers TFBS evolution. Although it is widely accepted that binding sites evolve differently from the surrounding sequences, most comparative TFBS identification methods do not explicitly consider this.Additionally, prediction of conserved binding sites is carried out in a multi-step approach that segregates alignment from TFBS annotation. In this paper, we demonstrate how the simultaneous alignment and annotation approach of SimAnn can be further extended to incorporate TFBS evolutionary relationships. We study how alignments and binding site predictions interplay at varying evolutionary distances and for various profile qualities.",
issn="0250-5991",
url="http://www.ncbi.nlm.nih.gov/pubmed/17914226",
language="eng"
}

%0 Journal Article
%T Incorporating evolution of transcription factor binding sites into annotated alignments.
%A Bais, Abha S.
%A Grossmann, Stefen
%A Vingron, Martin
%J Journal of biosciences
%D 2007
%8 Aug
%V 32
%N 5
%@ 0250-5991
%G eng
%F Bais2007
%X Identifying transcription factor binding sites (TFBSs) is essential to elucidate putative regulatory mechanisms. A common strategy is to combine cross-species conservation with single sequence TFBS annotation to yield "conserved TFBSs". Most current methods in this field adopt a multi-step approach that segregates the two aspects. Again, it is widely accepted that the evolutionary dynamics of binding sites differ from those of the surrounding sequence. Hence, it is desirable to have an approach that explicitly takes this factor into account. Although a plethora of approaches have been proposed for the prediction of conserved TFBSs, very few explicitly model TFBS evolutionary properties, while additionally being multi-step. Recently, we introduced a novel approach to simultaneously align and annotate conserved TFBSs in a pair of sequences. Building upon the standard Smith-Waterman algorithm for local alignments, SimAnn introduces additional states for profiles to output extended alignments or annotated alignments. That is, alignments with parts annotated as gaplessly aligned TFBSs (pair-profile hits)are generated. Moreover,the pair- profile related parameters are derived in a sound statistical framework. In this article, we extend this approach to explicitly incorporate evolution of binding sites in the SimAnn framework. We demonstrate the extension in the theoretical derivations through two position-specific evolutionary models, previously used for modelling TFBS evolution. In a simulated setting, we provide a proof of concept that the approach works given the underlying assumptions,as compared to the original work. Finally, using a real dataset of experimentally verified binding sites in human-mouse sequence pairs,we compare the new approach (eSimAnn) to an existing multi-step tool that also considers TFBS evolution. Although it is widely accepted that binding sites evolve differently from the surrounding sequences, most comparative TFBS identification methods do not explicitly consider this.Additionally, prediction of conserved binding sites is carried out in a multi-step approach that segregates alignment from TFBS annotation. In this paper, we demonstrate how the simultaneous alignment and annotation approach of SimAnn can be further extended to incorporate TFBS evolutionary relationships. We study how alignments and binding site predictions interplay at varying evolutionary distances and for various profile qualities.
%K Algorithms
%K Animals
%K Binding Sites
%K Computational Biology
%K Computer Simulation
%K Evolution, Molecular
%K Humans
%K Mice
%K Models, Genetic
%K Sequence Alignment
%K Sequence Analysis, RNA
%K Transcription Factors
%U http://www.ncbi.nlm.nih.gov/pubmed/17914226
%P 841-850

PT Journal
AU Bais, AS
   Grossmann, S
   Vingron, M
TI Incorporating evolution of transcription factor binding sites into annotated alignments.
SO Journal of biosciences
JI J. Biosci.
PD Aug
PY 2007
BP 841
EP 850
VL 32
IS 5
LA eng
DE Algorithms; Animals; Binding Sites; Computational Biology; Computer Simulation; Evolution, Molecular; Humans; Mice; Models, Genetic; Sequence Alignment; Sequence Analysis, RNA; Transcription Factors
AB Identifying transcription factor binding sites (TFBSs) is essential to elucidate putative regulatory mechanisms. A common strategy is to combine cross-species conservation with single sequence TFBS annotation to yield "conserved TFBSs". Most current methods in this field adopt a multi-step approach that segregates the two aspects. Again, it is widely accepted that the evolutionary dynamics of binding sites differ from those of the surrounding sequence. Hence, it is desirable to have an approach that explicitly takes this factor into account. Although a plethora of approaches have been proposed for the prediction of conserved TFBSs, very few explicitly model TFBS evolutionary properties, while additionally being multi-step. Recently, we introduced a novel approach to simultaneously align and annotate conserved TFBSs in a pair of sequences. Building upon the standard Smith-Waterman algorithm for local alignments, SimAnn introduces additional states for profiles to output extended alignments or annotated alignments. That is, alignments with parts annotated as gaplessly aligned TFBSs (pair-profile hits)are generated. Moreover,the pair- profile related parameters are derived in a sound statistical framework. In this article, we extend this approach to explicitly incorporate evolution of binding sites in the SimAnn framework. We demonstrate the extension in the theoretical derivations through two position-specific evolutionary models, previously used for modelling TFBS evolution. In a simulated setting, we provide a proof of concept that the approach works given the underlying assumptions,as compared to the original work. Finally, using a real dataset of experimentally verified binding sites in human-mouse sequence pairs,we compare the new approach (eSimAnn) to an existing multi-step tool that also considers TFBS evolution. Although it is widely accepted that binding sites evolve differently from the surrounding sequences, most comparative TFBS identification methods do not explicitly consider this.Additionally, prediction of conserved binding sites is carried out in a multi-step approach that segregates alignment from TFBS annotation. In this paper, we demonstrate how the simultaneous alignment and annotation approach of SimAnn can be further extended to incorporate TFBS evolutionary relationships. We study how alignments and binding site predictions interplay at varying evolutionary distances and for various profile qualities.
ER

PMID- 17914226
OWN - NLM
STAT- MEDLINE
DA  - 20071004
DCOM- 20071113
LR  - 20091103
IS  - 0250-5991 (Print)
IS  - 0250-5991 (Linking)
VI  - 32
IP  - 5
DP  - 2007 Aug
TI  - Incorporating evolution of transcription factor binding sites into annotated
      alignments.
PG  - 841-50
AB  - Identifying transcription factor binding sites (TFBSs) is essential to elucidate 
      putative regulatory mechanisms. A common strategy is to combine cross-species
      conservation with single sequence TFBS annotation to yield "conserved TFBSs".
      Most current methods in this field adopt a multi-step approach that segregates
      the two aspects. Again, it is widely accepted that the evolutionary dynamics of
      binding sites differ from those of the surrounding sequence. Hence, it is
      desirable to have an approach that explicitly takes this factor into account.
      Although a plethora of approaches have been proposed for the prediction of
      conserved TFBSs, very few explicitly model TFBS evolutionary properties, while
      additionally being multi-step. Recently, we introduced a novel approach to
      simultaneously align and annotate conserved TFBSs in a pair of sequences.
      Building upon the standard Smith-Waterman algorithm for local alignments, SimAnn 
      introduces additional states for profiles to output extended alignments or
      annotated alignments. That is, alignments with parts annotated as gaplessly
      aligned TFBSs (pair-profile hits)are generated. Moreover,the pair- profile
      related parameters are derived in a sound statistical framework. In this article,
      we extend this approach to explicitly incorporate evolution of binding sites in
      the SimAnn framework. We demonstrate the extension in the theoretical derivations
      through two position-specific evolutionary models, previously used for modelling 
      TFBS evolution. In a simulated setting, we provide a proof of concept that the
      approach works given the underlying assumptions,as compared to the original work.
      Finally, using a real dataset of experimentally verified binding sites in
      human-mouse sequence pairs,we compare the new approach (eSimAnn) to an existing
      multi-step tool that also considers TFBS evolution. Although it is widely
      accepted that binding sites evolve differently from the surrounding sequences,
      most comparative TFBS identification methods do not explicitly consider
      this.Additionally, prediction of conserved binding sites is carried out in a
      multi-step approach that segregates alignment from TFBS annotation. In this
      paper, we demonstrate how the simultaneous alignment and annotation approach of
      SimAnn can be further extended to incorporate TFBS evolutionary relationships. We
      study how alignments and binding site predictions interplay at varying
      evolutionary distances and for various profile qualities.
FAU - Bais, Abha S
AU  - Bais AS
AD  - Max Planck Institute for Molecular Genetics, Berlin, Germany. [email protected]
FAU - Grossmann, Stefen
AU  - Grossmann S
FAU - Vingron, Martin
AU  - Vingron M
LA  - eng
PT  - Comparative Study
PT  - Journal Article
PL  - India
TA  - J Biosci
JT  - Journal of biosciences
JID - 8100809
RN  - 0 (Transcription Factors)
SB  - IM
MH  - Algorithms
MH  - Animals
MH  - Binding Sites/genetics
MH  - *Computational Biology/methods
MH  - Computer Simulation
MH  - *Evolution, Molecular
MH  - Humans
MH  - Mice
MH  - Models, Genetic
MH  - *Sequence Alignment
MH  - *Sequence Analysis, RNA
MH  - Transcription Factors/chemistry/*metabolism
EDAT- 2007/10/05 09:00
MHDA- 2007/11/14 09:00
CRDT- 2007/10/05 09:00
PST - ppublish
SO  - J Biosci. 2007 Aug;32(5):841-50.
TY  - JOUR
AU  - Bais, Abha S.
AU  - Grossmann, Stefen
AU  - Vingron, Martin
PY  - 2007/Aug/
TI  - Incorporating evolution of transcription factor binding sites into annotated alignments.
T2  - J. Biosci.
JO  - Journal of biosciences
SP  - 841
EP  - 850
VL  - 32
IS  - 5
KW  - Algorithms
KW  - Animals
KW  - Binding Sites
KW  - Computational Biology
KW  - Computer Simulation
KW  - Evolution, Molecular
KW  - Humans
KW  - Mice
KW  - Models, Genetic
KW  - Sequence Alignment
KW  - Sequence Analysis, RNA
KW  - Transcription Factors
N2  - Identifying transcription factor binding sites (TFBSs) is essential to elucidate putative regulatory mechanisms. A common strategy is to combine cross-species conservation with single sequence TFBS annotation to yield "conserved TFBSs". Most current methods in this field adopt a multi-step approach that segregates the two aspects. Again, it is widely accepted that the evolutionary dynamics of binding sites differ from those of the surrounding sequence. Hence, it is desirable to have an approach that explicitly takes this factor into account. Although a plethora of approaches have been proposed for the prediction of conserved TFBSs, very few explicitly model TFBS evolutionary properties, while additionally being multi-step. Recently, we introduced a novel approach to simultaneously align and annotate conserved TFBSs in a pair of sequences. Building upon the standard Smith-Waterman algorithm for local alignments, SimAnn introduces additional states for profiles to output extended alignments or annotated alignments. That is, alignments with parts annotated as gaplessly aligned TFBSs (pair-profile hits)are generated. Moreover,the pair- profile related parameters are derived in a sound statistical framework. In this article, we extend this approach to explicitly incorporate evolution of binding sites in the SimAnn framework. We demonstrate the extension in the theoretical derivations through two position-specific evolutionary models, previously used for modelling TFBS evolution. In a simulated setting, we provide a proof of concept that the approach works given the underlying assumptions,as compared to the original work. Finally, using a real dataset of experimentally verified binding sites in human-mouse sequence pairs,we compare the new approach (eSimAnn) to an existing multi-step tool that also considers TFBS evolution. Although it is widely accepted that binding sites evolve differently from the surrounding sequences, most comparative TFBS identification methods do not explicitly consider this.Additionally, prediction of conserved binding sites is carried out in a multi-step approach that segregates alignment from TFBS annotation. In this paper, we demonstrate how the simultaneous alignment and annotation approach of SimAnn can be further extended to incorporate TFBS evolutionary relationships. We study how alignments and binding site predictions interplay at varying evolutionary distances and for various profile qualities.
SN  - 0250-5991
UR  - http://www.ncbi.nlm.nih.gov/pubmed/17914226
ID  - Bais2007
ER  - 
<?xml version="1.0" encoding="UTF-8"?>
<b:Sources SelectedStyle="" xmlns:b="http://schemas.openxmlformats.org/officeDocument/2006/bibliography"  xmlns="http://schemas.openxmlformats.org/officeDocument/2006/bibliography" >
<b:Source>
<b:Tag>Bais2007</b:Tag>
<b:SourceType>ArticleInAPeriodical</b:SourceType>
<b:Year>2007</b:Year>
<b:Month>Aug</b:Month>
<b:PeriodicalName>Journal of biosciences</b:PeriodicalName>
<b:Volume>32</b:Volume>
<b:Issue>5</b:Issue>
<b:Pages>841-850</b:Pages>
<b:Author>
<b:Author><b:NameList>
<b:Person><b:Last>Bais</b:Last><b:First>Abha</b:First><b:Middle>S</b:Middle></b:Person>
<b:Person><b:Last>Grossmann</b:Last><b:First>Stefen</b:First></b:Person>
<b:Person><b:Last>Vingron</b:Last><b:First>Martin</b:First></b:Person>
</b:NameList></b:Author>
</b:Author>
<b:Title>Incorporating evolution of transcription factor binding sites into annotated alignments.</b:Title>
 <b:ShortTitle>J. Biosci.</b:ShortTitle>
<b:Comments>Identifying transcription factor binding sites (TFBSs) is essential to elucidate putative regulatory mechanisms. A common strategy is to combine cross-species conservation with single sequence TFBS annotation to yield &quot;conserved TFBSs&quot;. Most current methods in this field adopt a multi-step approach that segregates the two aspects. Again, it is widely accepted that the evolutionary dynamics of binding sites differ from those of the surrounding sequence. Hence, it is desirable to have an approach that explicitly takes this factor into account. Although a plethora of approaches have been proposed for the prediction of conserved TFBSs, very few explicitly model TFBS evolutionary properties, while additionally being multi-step. Recently, we introduced a novel approach to simultaneously align and annotate conserved TFBSs in a pair of sequences. Building upon the standard Smith-Waterman algorithm for local alignments, SimAnn introduces additional states for profiles to output extended alignments or annotated alignments. That is, alignments with parts annotated as gaplessly aligned TFBSs (pair-profile hits)are generated. Moreover,the pair- profile related parameters are derived in a sound statistical framework. In this article, we extend this approach to explicitly incorporate evolution of binding sites in the SimAnn framework. We demonstrate the extension in the theoretical derivations through two position-specific evolutionary models, previously used for modelling TFBS evolution. In a simulated setting, we provide a proof of concept that the approach works given the underlying assumptions,as compared to the original work. Finally, using a real dataset of experimentally verified binding sites in human-mouse sequence pairs,we compare the new approach (eSimAnn) to an existing multi-step tool that also considers TFBS evolution. Although it is widely accepted that binding sites evolve differently from the surrounding sequences, most comparative TFBS identification methods do not explicitly consider this.Additionally, prediction of conserved binding sites is carried out in a multi-step approach that segregates alignment from TFBS annotation. In this paper, we demonstrate how the simultaneous alignment and annotation approach of SimAnn can be further extended to incorporate TFBS evolutionary relationships. We study how alignments and binding site predictions interplay at varying evolutionary distances and for various profile qualities.</b:Comments>
</b:Source>
</b:Sources>