A Metastate HMM with Application to Gene Structure Identification in Eukaryotes

Abstract We introduce a generalized-clique hidden Markov model (HMM) and apply it to gene finding in eukaryotes (C. elegans). We demonstrate a HMM structure identification platform that is novel and robustly-performing in a number of ways. The generalized clique HMM begins by enlarging the primitive...
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Autor*in:	Winters-Hilt, Stephen [verfasserIn] Baribault, Carl [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2010

Schlagwörter:	Support Vector Machine Hide Markov Model Motif Discovery Viterbi Algorithm Dime State

Übergeordnetes Werk:	Enthalten in: EURASIP journal on advances in signal processing - Heidelberg : Springer, 2007, 2010(2010), 1 vom: 30. Nov.
Übergeordnetes Werk:	volume:2010 ; year:2010 ; number:1 ; day:30 ; month:11

Links:	Volltext

DOI / URN:	10.1155/2010/581373

Katalog-ID:	SPR031994709

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allfields	10.1155/2010/581373 doi (DE-627)SPR031994709 (SPR)581373-e DE-627 ger DE-627 rakwb eng 620 ASE 53.73 bkl Winters-Hilt, Stephen verfasserin aut A Metastate HMM with Application to Gene Structure Identification in Eukaryotes 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract We introduce a generalized-clique hidden Markov model (HMM) and apply it to gene finding in eukaryotes (C. elegans). We demonstrate a HMM structure identification platform that is novel and robustly-performing in a number of ways. The generalized clique HMM begins by enlarging the primitive hidden states associated with the individual base labels (as exon, intron, or junk) to substrings of primitive hidden states, or footprint states, having a minimal length greater than the footprint state length. The emissions are likewise expanded to higher order in the fundamental joint probability that is the basis of the generalized-clique, or "metastate", HMM. We then consider application to eukaryotic gene finding and show how such a metastate HMM improves the strength of coding/noncoding-transition contributions to gene-structure identification. We will describe situations where the coding/noncoding-transition modeling can effectively recapture the exon and intron heavy tail distribution modeling capability as well as manage the exon-start needle-in-the-haystack problem. In analysis of the C. elegans genome we show that the sensitivity and specificity (SN,SP) results for both the individual-state and full-exon predictions are greatly enhanced over the standard HMM when using the generalized-clique HMM. Support Vector Machine (dpeaa)DE-He213 Hide Markov Model (dpeaa)DE-He213 Motif Discovery (dpeaa)DE-He213 Viterbi Algorithm (dpeaa)DE-He213 Dime State (dpeaa)DE-He213 Baribault, Carl verfasserin aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2010(2010), 1 vom: 30. Nov. (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2010 year:2010 number:1 day:30 month:11 https://dx.doi.org/10.1155/2010/581373 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 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_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2522 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 53.73 ASE AR 2010 2010 1 30 11
spelling	10.1155/2010/581373 doi (DE-627)SPR031994709 (SPR)581373-e DE-627 ger DE-627 rakwb eng 620 ASE 53.73 bkl Winters-Hilt, Stephen verfasserin aut A Metastate HMM with Application to Gene Structure Identification in Eukaryotes 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract We introduce a generalized-clique hidden Markov model (HMM) and apply it to gene finding in eukaryotes (C. elegans). We demonstrate a HMM structure identification platform that is novel and robustly-performing in a number of ways. The generalized clique HMM begins by enlarging the primitive hidden states associated with the individual base labels (as exon, intron, or junk) to substrings of primitive hidden states, or footprint states, having a minimal length greater than the footprint state length. The emissions are likewise expanded to higher order in the fundamental joint probability that is the basis of the generalized-clique, or "metastate", HMM. We then consider application to eukaryotic gene finding and show how such a metastate HMM improves the strength of coding/noncoding-transition contributions to gene-structure identification. We will describe situations where the coding/noncoding-transition modeling can effectively recapture the exon and intron heavy tail distribution modeling capability as well as manage the exon-start needle-in-the-haystack problem. In analysis of the C. elegans genome we show that the sensitivity and specificity (SN,SP) results for both the individual-state and full-exon predictions are greatly enhanced over the standard HMM when using the generalized-clique HMM. Support Vector Machine (dpeaa)DE-He213 Hide Markov Model (dpeaa)DE-He213 Motif Discovery (dpeaa)DE-He213 Viterbi Algorithm (dpeaa)DE-He213 Dime State (dpeaa)DE-He213 Baribault, Carl verfasserin aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2010(2010), 1 vom: 30. Nov. (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2010 year:2010 number:1 day:30 month:11 https://dx.doi.org/10.1155/2010/581373 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 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_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2522 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 53.73 ASE AR 2010 2010 1 30 11
allfields_unstemmed	10.1155/2010/581373 doi (DE-627)SPR031994709 (SPR)581373-e DE-627 ger DE-627 rakwb eng 620 ASE 53.73 bkl Winters-Hilt, Stephen verfasserin aut A Metastate HMM with Application to Gene Structure Identification in Eukaryotes 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract We introduce a generalized-clique hidden Markov model (HMM) and apply it to gene finding in eukaryotes (C. elegans). We demonstrate a HMM structure identification platform that is novel and robustly-performing in a number of ways. The generalized clique HMM begins by enlarging the primitive hidden states associated with the individual base labels (as exon, intron, or junk) to substrings of primitive hidden states, or footprint states, having a minimal length greater than the footprint state length. The emissions are likewise expanded to higher order in the fundamental joint probability that is the basis of the generalized-clique, or "metastate", HMM. We then consider application to eukaryotic gene finding and show how such a metastate HMM improves the strength of coding/noncoding-transition contributions to gene-structure identification. We will describe situations where the coding/noncoding-transition modeling can effectively recapture the exon and intron heavy tail distribution modeling capability as well as manage the exon-start needle-in-the-haystack problem. In analysis of the C. elegans genome we show that the sensitivity and specificity (SN,SP) results for both the individual-state and full-exon predictions are greatly enhanced over the standard HMM when using the generalized-clique HMM. Support Vector Machine (dpeaa)DE-He213 Hide Markov Model (dpeaa)DE-He213 Motif Discovery (dpeaa)DE-He213 Viterbi Algorithm (dpeaa)DE-He213 Dime State (dpeaa)DE-He213 Baribault, Carl verfasserin aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2010(2010), 1 vom: 30. Nov. (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2010 year:2010 number:1 day:30 month:11 https://dx.doi.org/10.1155/2010/581373 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 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_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2522 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 53.73 ASE AR 2010 2010 1 30 11
allfieldsGer	10.1155/2010/581373 doi (DE-627)SPR031994709 (SPR)581373-e DE-627 ger DE-627 rakwb eng 620 ASE 53.73 bkl Winters-Hilt, Stephen verfasserin aut A Metastate HMM with Application to Gene Structure Identification in Eukaryotes 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract We introduce a generalized-clique hidden Markov model (HMM) and apply it to gene finding in eukaryotes (C. elegans). We demonstrate a HMM structure identification platform that is novel and robustly-performing in a number of ways. The generalized clique HMM begins by enlarging the primitive hidden states associated with the individual base labels (as exon, intron, or junk) to substrings of primitive hidden states, or footprint states, having a minimal length greater than the footprint state length. The emissions are likewise expanded to higher order in the fundamental joint probability that is the basis of the generalized-clique, or "metastate", HMM. We then consider application to eukaryotic gene finding and show how such a metastate HMM improves the strength of coding/noncoding-transition contributions to gene-structure identification. We will describe situations where the coding/noncoding-transition modeling can effectively recapture the exon and intron heavy tail distribution modeling capability as well as manage the exon-start needle-in-the-haystack problem. In analysis of the C. elegans genome we show that the sensitivity and specificity (SN,SP) results for both the individual-state and full-exon predictions are greatly enhanced over the standard HMM when using the generalized-clique HMM. Support Vector Machine (dpeaa)DE-He213 Hide Markov Model (dpeaa)DE-He213 Motif Discovery (dpeaa)DE-He213 Viterbi Algorithm (dpeaa)DE-He213 Dime State (dpeaa)DE-He213 Baribault, Carl verfasserin aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2010(2010), 1 vom: 30. Nov. (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2010 year:2010 number:1 day:30 month:11 https://dx.doi.org/10.1155/2010/581373 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 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_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2522 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 53.73 ASE AR 2010 2010 1 30 11
allfieldsSound	10.1155/2010/581373 doi (DE-627)SPR031994709 (SPR)581373-e DE-627 ger DE-627 rakwb eng 620 ASE 53.73 bkl Winters-Hilt, Stephen verfasserin aut A Metastate HMM with Application to Gene Structure Identification in Eukaryotes 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract We introduce a generalized-clique hidden Markov model (HMM) and apply it to gene finding in eukaryotes (C. elegans). We demonstrate a HMM structure identification platform that is novel and robustly-performing in a number of ways. The generalized clique HMM begins by enlarging the primitive hidden states associated with the individual base labels (as exon, intron, or junk) to substrings of primitive hidden states, or footprint states, having a minimal length greater than the footprint state length. The emissions are likewise expanded to higher order in the fundamental joint probability that is the basis of the generalized-clique, or "metastate", HMM. We then consider application to eukaryotic gene finding and show how such a metastate HMM improves the strength of coding/noncoding-transition contributions to gene-structure identification. We will describe situations where the coding/noncoding-transition modeling can effectively recapture the exon and intron heavy tail distribution modeling capability as well as manage the exon-start needle-in-the-haystack problem. In analysis of the C. elegans genome we show that the sensitivity and specificity (SN,SP) results for both the individual-state and full-exon predictions are greatly enhanced over the standard HMM when using the generalized-clique HMM. Support Vector Machine (dpeaa)DE-He213 Hide Markov Model (dpeaa)DE-He213 Motif Discovery (dpeaa)DE-He213 Viterbi Algorithm (dpeaa)DE-He213 Dime State (dpeaa)DE-He213 Baribault, Carl verfasserin aut Enthalten in EURASIP journal on advances in signal processing Heidelberg : Springer, 2007 2010(2010), 1 vom: 30. Nov. (DE-627)534054277 (DE-600)2364203-8 1687-6180 nnns volume:2010 year:2010 number:1 day:30 month:11 https://dx.doi.org/10.1155/2010/581373 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 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_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2522 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 53.73 ASE AR 2010 2010 1 30 11
language	English
source	Enthalten in EURASIP journal on advances in signal processing 2010(2010), 1 vom: 30. Nov. volume:2010 year:2010 number:1 day:30 month:11
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topic_facet	Support Vector Machine Hide Markov Model Motif Discovery Viterbi Algorithm Dime State
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container_title	EURASIP journal on advances in signal processing
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author	Winters-Hilt, Stephen
spellingShingle	Winters-Hilt, Stephen ddc 620 bkl 53.73 misc Support Vector Machine misc Hide Markov Model misc Motif Discovery misc Viterbi Algorithm misc Dime State A Metastate HMM with Application to Gene Structure Identification in Eukaryotes
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title	A Metastate HMM with Application to Gene Structure Identification in Eukaryotes
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title_full	A Metastate HMM with Application to Gene Structure Identification in Eukaryotes
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title_sort	metastate hmm with application to gene structure identification in eukaryotes
title_auth	A Metastate HMM with Application to Gene Structure Identification in Eukaryotes
abstract	Abstract We introduce a generalized-clique hidden Markov model (HMM) and apply it to gene finding in eukaryotes (C. elegans). We demonstrate a HMM structure identification platform that is novel and robustly-performing in a number of ways. The generalized clique HMM begins by enlarging the primitive hidden states associated with the individual base labels (as exon, intron, or junk) to substrings of primitive hidden states, or footprint states, having a minimal length greater than the footprint state length. The emissions are likewise expanded to higher order in the fundamental joint probability that is the basis of the generalized-clique, or "metastate", HMM. We then consider application to eukaryotic gene finding and show how such a metastate HMM improves the strength of coding/noncoding-transition contributions to gene-structure identification. We will describe situations where the coding/noncoding-transition modeling can effectively recapture the exon and intron heavy tail distribution modeling capability as well as manage the exon-start needle-in-the-haystack problem. In analysis of the C. elegans genome we show that the sensitivity and specificity (SN,SP) results for both the individual-state and full-exon predictions are greatly enhanced over the standard HMM when using the generalized-clique HMM.
abstractGer	Abstract We introduce a generalized-clique hidden Markov model (HMM) and apply it to gene finding in eukaryotes (C. elegans). We demonstrate a HMM structure identification platform that is novel and robustly-performing in a number of ways. The generalized clique HMM begins by enlarging the primitive hidden states associated with the individual base labels (as exon, intron, or junk) to substrings of primitive hidden states, or footprint states, having a minimal length greater than the footprint state length. The emissions are likewise expanded to higher order in the fundamental joint probability that is the basis of the generalized-clique, or "metastate", HMM. We then consider application to eukaryotic gene finding and show how such a metastate HMM improves the strength of coding/noncoding-transition contributions to gene-structure identification. We will describe situations where the coding/noncoding-transition modeling can effectively recapture the exon and intron heavy tail distribution modeling capability as well as manage the exon-start needle-in-the-haystack problem. In analysis of the C. elegans genome we show that the sensitivity and specificity (SN,SP) results for both the individual-state and full-exon predictions are greatly enhanced over the standard HMM when using the generalized-clique HMM.
abstract_unstemmed	Abstract We introduce a generalized-clique hidden Markov model (HMM) and apply it to gene finding in eukaryotes (C. elegans). We demonstrate a HMM structure identification platform that is novel and robustly-performing in a number of ways. The generalized clique HMM begins by enlarging the primitive hidden states associated with the individual base labels (as exon, intron, or junk) to substrings of primitive hidden states, or footprint states, having a minimal length greater than the footprint state length. The emissions are likewise expanded to higher order in the fundamental joint probability that is the basis of the generalized-clique, or "metastate", HMM. We then consider application to eukaryotic gene finding and show how such a metastate HMM improves the strength of coding/noncoding-transition contributions to gene-structure identification. We will describe situations where the coding/noncoding-transition modeling can effectively recapture the exon and intron heavy tail distribution modeling capability as well as manage the exon-start needle-in-the-haystack problem. In analysis of the C. elegans genome we show that the sensitivity and specificity (SN,SP) results for both the individual-state and full-exon predictions are greatly enhanced over the standard HMM when using the generalized-clique HMM.
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title_short	A Metastate HMM with Application to Gene Structure Identification in Eukaryotes
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A Metastate HMM with Application to Gene Structure Identification in Eukaryotes

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