Regmex: a statistical tool for exploring motifs in ranked sequence lists from genomics experiments
Abstract Background Motif analysis methods have long been central for studying biological function of nucleotide sequences. Functional genomics experiments extend their potential. They typically generate sequence lists ranked by an experimentally acquired functional property such as gene expression...
Main Authors: | , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
BMC
2018-12-01
|
Series: | Algorithms for Molecular Biology |
Online Access: | http://link.springer.com/article/10.1186/s13015-018-0135-2 |
id |
doaj-61a03bdbb36e4f6dbd5ccac65365961b |
---|---|
record_format |
Article |
spelling |
doaj-61a03bdbb36e4f6dbd5ccac65365961b2020-11-25T01:23:40ZengBMCAlgorithms for Molecular Biology1748-71882018-12-0113111110.1186/s13015-018-0135-2Regmex: a statistical tool for exploring motifs in ranked sequence lists from genomics experimentsMorten Muhlig Nielsen0Paula Tataru1Tobias Madsen2Asger Hobolth3Jakob Skou Pedersen4Department of Molecular Medicine (MOMA), Aarhus University HospitalBioinformatics Research Centre, Aarhus UniversityDepartment of Molecular Medicine (MOMA), Aarhus University HospitalBioinformatics Research Centre, Aarhus UniversityDepartment of Molecular Medicine (MOMA), Aarhus University HospitalAbstract Background Motif analysis methods have long been central for studying biological function of nucleotide sequences. Functional genomics experiments extend their potential. They typically generate sequence lists ranked by an experimentally acquired functional property such as gene expression or protein binding affinity. Current motif discovery tools suffer from limitations in searching large motif spaces, and thus more complex motifs may not be included. There is thus a need for motif analysis methods that are tailored for analyzing specific complex motifs motivated by biological questions and hypotheses rather than acting as a screen based motif finding tool. Methods We present Regmex (REGular expression Motif EXplorer), which offers several methods to identify overrepresented motifs in ranked lists of sequences. Regmex uses regular expressions to define motifs or families of motifs and embedded Markov models to calculate exact p-values for motif observations in sequences. Biases in motif distributions across ranked sequence lists are evaluated using random walks, Brownian bridges, or modified rank based statistics. A modular setup and fast analytic p value evaluations make Regmex applicable to diverse and potentially large-scale motif analysis problems. Results We demonstrate use cases of combined motifs on simulated data and on expression data from micro RNA transfection experiments. We confirm previously obtained results and demonstrate the usability of Regmex to test a specific hypothesis about the relative location of microRNA seed sites and U-rich motifs. We further compare the tool with an existing motif discovery tool and show increased sensitivity. Conclusions Regmex is a useful and flexible tool to analyze motif hypotheses that relates to large data sets in functional genomics. The method is available as an R package (https://github.com/muhligs/regmex).http://link.springer.com/article/10.1186/s13015-018-0135-2 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Morten Muhlig Nielsen Paula Tataru Tobias Madsen Asger Hobolth Jakob Skou Pedersen |
spellingShingle |
Morten Muhlig Nielsen Paula Tataru Tobias Madsen Asger Hobolth Jakob Skou Pedersen Regmex: a statistical tool for exploring motifs in ranked sequence lists from genomics experiments Algorithms for Molecular Biology |
author_facet |
Morten Muhlig Nielsen Paula Tataru Tobias Madsen Asger Hobolth Jakob Skou Pedersen |
author_sort |
Morten Muhlig Nielsen |
title |
Regmex: a statistical tool for exploring motifs in ranked sequence lists from genomics experiments |
title_short |
Regmex: a statistical tool for exploring motifs in ranked sequence lists from genomics experiments |
title_full |
Regmex: a statistical tool for exploring motifs in ranked sequence lists from genomics experiments |
title_fullStr |
Regmex: a statistical tool for exploring motifs in ranked sequence lists from genomics experiments |
title_full_unstemmed |
Regmex: a statistical tool for exploring motifs in ranked sequence lists from genomics experiments |
title_sort |
regmex: a statistical tool for exploring motifs in ranked sequence lists from genomics experiments |
publisher |
BMC |
series |
Algorithms for Molecular Biology |
issn |
1748-7188 |
publishDate |
2018-12-01 |
description |
Abstract Background Motif analysis methods have long been central for studying biological function of nucleotide sequences. Functional genomics experiments extend their potential. They typically generate sequence lists ranked by an experimentally acquired functional property such as gene expression or protein binding affinity. Current motif discovery tools suffer from limitations in searching large motif spaces, and thus more complex motifs may not be included. There is thus a need for motif analysis methods that are tailored for analyzing specific complex motifs motivated by biological questions and hypotheses rather than acting as a screen based motif finding tool. Methods We present Regmex (REGular expression Motif EXplorer), which offers several methods to identify overrepresented motifs in ranked lists of sequences. Regmex uses regular expressions to define motifs or families of motifs and embedded Markov models to calculate exact p-values for motif observations in sequences. Biases in motif distributions across ranked sequence lists are evaluated using random walks, Brownian bridges, or modified rank based statistics. A modular setup and fast analytic p value evaluations make Regmex applicable to diverse and potentially large-scale motif analysis problems. Results We demonstrate use cases of combined motifs on simulated data and on expression data from micro RNA transfection experiments. We confirm previously obtained results and demonstrate the usability of Regmex to test a specific hypothesis about the relative location of microRNA seed sites and U-rich motifs. We further compare the tool with an existing motif discovery tool and show increased sensitivity. Conclusions Regmex is a useful and flexible tool to analyze motif hypotheses that relates to large data sets in functional genomics. The method is available as an R package (https://github.com/muhligs/regmex). |
url |
http://link.springer.com/article/10.1186/s13015-018-0135-2 |
work_keys_str_mv |
AT mortenmuhlignielsen regmexastatisticaltoolforexploringmotifsinrankedsequencelistsfromgenomicsexperiments AT paulatataru regmexastatisticaltoolforexploringmotifsinrankedsequencelistsfromgenomicsexperiments AT tobiasmadsen regmexastatisticaltoolforexploringmotifsinrankedsequencelistsfromgenomicsexperiments AT asgerhobolth regmexastatisticaltoolforexploringmotifsinrankedsequencelistsfromgenomicsexperiments AT jakobskoupedersen regmexastatisticaltoolforexploringmotifsinrankedsequencelistsfromgenomicsexperiments |
_version_ |
1725120759528947712 |