Characterization of statistical features for plant microRNA prediction

Background Several tools are available to identify miRNAs from deep-sequencing data, however, only a few of them, like miRDeep, can identify novel miRNAs and are also available as a standalone application. Given the difference between plant and animal miRNAs, particularly in terms of distribution of...
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Autor*in:	Thakur, Vivek [verfasserIn] Wanchana, Samart Xu, Mercedes Bruskiewich, Richard Quick, William Paul Mosig, Axel Zhu, Xin-Guang

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2011

Schlagwörter:	miRNA Family Mature miRNAs Minimum Free Energy miRNA Precursor Plant miRNAs

Anmerkung:	© Thakur et al; licensee BioMed Central Ltd. 2011

Übergeordnetes Werk:	Enthalten in: BMC genomics - London : BioMed Central, 2000, 12(2011), 1 vom: 16. Feb.
Übergeordnetes Werk:	volume:12 ; year:2011 ; number:1 ; day:16 ; month:02

Links:	Volltext

DOI / URN:	10.1186/1471-2164-12-108

Katalog-ID:	SPR027064891

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520			\|a Background Several tools are available to identify miRNAs from deep-sequencing data, however, only a few of them, like miRDeep, can identify novel miRNAs and are also available as a standalone application. Given the difference between plant and animal miRNAs, particularly in terms of distribution of hairpin length and the nature of complementarity with its duplex partner (or miRNA star), the underlying (statistical) features of miRDeep and other tools, using similar features, are likely to get affected. Results The potential effects on features, such as minimum free energy, stability of secondary structures, excision length, etc., were examined, and the parameters of those displaying sizable changes were estimated for plant specific miRNAs. We found most of these features acquired a new set of values or distributions for plant specific miRNAs. While the length of conserved positions (nucleus) in mature miRNAs were relatively longer in plants, the difference in distribution of minimum free energy, between real and background hairpins, was marginal. However, the choice of source (species) of background sequences was found to affect both the minimum free energy and miRNA hairpin stability. The new parameters were tested on an Illumina dataset from maize seedlings, and the results were compared with those obtained using default parameters. The newly parameterized model was found to have much improved specificity and sensitivity over its default counterpart. Conclusions In summary, the present study reports behavior of few general and tool-specific statistical features for improving the prediction accuracy of plant miRNAs from deep-sequencing data.
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matchkey_str	article:14712164:2011----::hrceiainfttsiafauefrln
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allfields	10.1186/1471-2164-12-108 doi (DE-627)SPR027064891 (SPR)1471-2164-12-108-e DE-627 ger DE-627 rakwb eng Thakur, Vivek verfasserin aut Characterization of statistical features for plant microRNA prediction 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Thakur et al; licensee BioMed Central Ltd. 2011 Background Several tools are available to identify miRNAs from deep-sequencing data, however, only a few of them, like miRDeep, can identify novel miRNAs and are also available as a standalone application. Given the difference between plant and animal miRNAs, particularly in terms of distribution of hairpin length and the nature of complementarity with its duplex partner (or miRNA star), the underlying (statistical) features of miRDeep and other tools, using similar features, are likely to get affected. Results The potential effects on features, such as minimum free energy, stability of secondary structures, excision length, etc., were examined, and the parameters of those displaying sizable changes were estimated for plant specific miRNAs. We found most of these features acquired a new set of values or distributions for plant specific miRNAs. While the length of conserved positions (nucleus) in mature miRNAs were relatively longer in plants, the difference in distribution of minimum free energy, between real and background hairpins, was marginal. However, the choice of source (species) of background sequences was found to affect both the minimum free energy and miRNA hairpin stability. The new parameters were tested on an Illumina dataset from maize seedlings, and the results were compared with those obtained using default parameters. The newly parameterized model was found to have much improved specificity and sensitivity over its default counterpart. Conclusions In summary, the present study reports behavior of few general and tool-specific statistical features for improving the prediction accuracy of plant miRNAs from deep-sequencing data. miRNA Family (dpeaa)DE-He213 Mature miRNAs (dpeaa)DE-He213 Minimum Free Energy (dpeaa)DE-He213 miRNA Precursor (dpeaa)DE-He213 Plant miRNAs (dpeaa)DE-He213 Wanchana, Samart aut Xu, Mercedes aut Bruskiewich, Richard aut Quick, William Paul aut Mosig, Axel aut Zhu, Xin-Guang aut Enthalten in BMC genomics London : BioMed Central, 2000 12(2011), 1 vom: 16. Feb. (DE-627)326644954 (DE-600)2041499-7 1471-2164 nnns volume:12 year:2011 number:1 day:16 month:02 https://dx.doi.org/10.1186/1471-2164-12-108 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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 12 2011 1 16 02
spelling	10.1186/1471-2164-12-108 doi (DE-627)SPR027064891 (SPR)1471-2164-12-108-e DE-627 ger DE-627 rakwb eng Thakur, Vivek verfasserin aut Characterization of statistical features for plant microRNA prediction 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Thakur et al; licensee BioMed Central Ltd. 2011 Background Several tools are available to identify miRNAs from deep-sequencing data, however, only a few of them, like miRDeep, can identify novel miRNAs and are also available as a standalone application. Given the difference between plant and animal miRNAs, particularly in terms of distribution of hairpin length and the nature of complementarity with its duplex partner (or miRNA star), the underlying (statistical) features of miRDeep and other tools, using similar features, are likely to get affected. Results The potential effects on features, such as minimum free energy, stability of secondary structures, excision length, etc., were examined, and the parameters of those displaying sizable changes were estimated for plant specific miRNAs. We found most of these features acquired a new set of values or distributions for plant specific miRNAs. While the length of conserved positions (nucleus) in mature miRNAs were relatively longer in plants, the difference in distribution of minimum free energy, between real and background hairpins, was marginal. However, the choice of source (species) of background sequences was found to affect both the minimum free energy and miRNA hairpin stability. The new parameters were tested on an Illumina dataset from maize seedlings, and the results were compared with those obtained using default parameters. The newly parameterized model was found to have much improved specificity and sensitivity over its default counterpart. Conclusions In summary, the present study reports behavior of few general and tool-specific statistical features for improving the prediction accuracy of plant miRNAs from deep-sequencing data. miRNA Family (dpeaa)DE-He213 Mature miRNAs (dpeaa)DE-He213 Minimum Free Energy (dpeaa)DE-He213 miRNA Precursor (dpeaa)DE-He213 Plant miRNAs (dpeaa)DE-He213 Wanchana, Samart aut Xu, Mercedes aut Bruskiewich, Richard aut Quick, William Paul aut Mosig, Axel aut Zhu, Xin-Guang aut Enthalten in BMC genomics London : BioMed Central, 2000 12(2011), 1 vom: 16. Feb. (DE-627)326644954 (DE-600)2041499-7 1471-2164 nnns volume:12 year:2011 number:1 day:16 month:02 https://dx.doi.org/10.1186/1471-2164-12-108 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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 12 2011 1 16 02
allfields_unstemmed	10.1186/1471-2164-12-108 doi (DE-627)SPR027064891 (SPR)1471-2164-12-108-e DE-627 ger DE-627 rakwb eng Thakur, Vivek verfasserin aut Characterization of statistical features for plant microRNA prediction 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Thakur et al; licensee BioMed Central Ltd. 2011 Background Several tools are available to identify miRNAs from deep-sequencing data, however, only a few of them, like miRDeep, can identify novel miRNAs and are also available as a standalone application. Given the difference between plant and animal miRNAs, particularly in terms of distribution of hairpin length and the nature of complementarity with its duplex partner (or miRNA star), the underlying (statistical) features of miRDeep and other tools, using similar features, are likely to get affected. Results The potential effects on features, such as minimum free energy, stability of secondary structures, excision length, etc., were examined, and the parameters of those displaying sizable changes were estimated for plant specific miRNAs. We found most of these features acquired a new set of values or distributions for plant specific miRNAs. While the length of conserved positions (nucleus) in mature miRNAs were relatively longer in plants, the difference in distribution of minimum free energy, between real and background hairpins, was marginal. However, the choice of source (species) of background sequences was found to affect both the minimum free energy and miRNA hairpin stability. The new parameters were tested on an Illumina dataset from maize seedlings, and the results were compared with those obtained using default parameters. The newly parameterized model was found to have much improved specificity and sensitivity over its default counterpart. Conclusions In summary, the present study reports behavior of few general and tool-specific statistical features for improving the prediction accuracy of plant miRNAs from deep-sequencing data. miRNA Family (dpeaa)DE-He213 Mature miRNAs (dpeaa)DE-He213 Minimum Free Energy (dpeaa)DE-He213 miRNA Precursor (dpeaa)DE-He213 Plant miRNAs (dpeaa)DE-He213 Wanchana, Samart aut Xu, Mercedes aut Bruskiewich, Richard aut Quick, William Paul aut Mosig, Axel aut Zhu, Xin-Guang aut Enthalten in BMC genomics London : BioMed Central, 2000 12(2011), 1 vom: 16. Feb. (DE-627)326644954 (DE-600)2041499-7 1471-2164 nnns volume:12 year:2011 number:1 day:16 month:02 https://dx.doi.org/10.1186/1471-2164-12-108 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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 12 2011 1 16 02
allfieldsGer	10.1186/1471-2164-12-108 doi (DE-627)SPR027064891 (SPR)1471-2164-12-108-e DE-627 ger DE-627 rakwb eng Thakur, Vivek verfasserin aut Characterization of statistical features for plant microRNA prediction 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Thakur et al; licensee BioMed Central Ltd. 2011 Background Several tools are available to identify miRNAs from deep-sequencing data, however, only a few of them, like miRDeep, can identify novel miRNAs and are also available as a standalone application. Given the difference between plant and animal miRNAs, particularly in terms of distribution of hairpin length and the nature of complementarity with its duplex partner (or miRNA star), the underlying (statistical) features of miRDeep and other tools, using similar features, are likely to get affected. Results The potential effects on features, such as minimum free energy, stability of secondary structures, excision length, etc., were examined, and the parameters of those displaying sizable changes were estimated for plant specific miRNAs. We found most of these features acquired a new set of values or distributions for plant specific miRNAs. While the length of conserved positions (nucleus) in mature miRNAs were relatively longer in plants, the difference in distribution of minimum free energy, between real and background hairpins, was marginal. However, the choice of source (species) of background sequences was found to affect both the minimum free energy and miRNA hairpin stability. The new parameters were tested on an Illumina dataset from maize seedlings, and the results were compared with those obtained using default parameters. The newly parameterized model was found to have much improved specificity and sensitivity over its default counterpart. Conclusions In summary, the present study reports behavior of few general and tool-specific statistical features for improving the prediction accuracy of plant miRNAs from deep-sequencing data. miRNA Family (dpeaa)DE-He213 Mature miRNAs (dpeaa)DE-He213 Minimum Free Energy (dpeaa)DE-He213 miRNA Precursor (dpeaa)DE-He213 Plant miRNAs (dpeaa)DE-He213 Wanchana, Samart aut Xu, Mercedes aut Bruskiewich, Richard aut Quick, William Paul aut Mosig, Axel aut Zhu, Xin-Guang aut Enthalten in BMC genomics London : BioMed Central, 2000 12(2011), 1 vom: 16. Feb. (DE-627)326644954 (DE-600)2041499-7 1471-2164 nnns volume:12 year:2011 number:1 day:16 month:02 https://dx.doi.org/10.1186/1471-2164-12-108 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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 12 2011 1 16 02
allfieldsSound	10.1186/1471-2164-12-108 doi (DE-627)SPR027064891 (SPR)1471-2164-12-108-e DE-627 ger DE-627 rakwb eng Thakur, Vivek verfasserin aut Characterization of statistical features for plant microRNA prediction 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Thakur et al; licensee BioMed Central Ltd. 2011 Background Several tools are available to identify miRNAs from deep-sequencing data, however, only a few of them, like miRDeep, can identify novel miRNAs and are also available as a standalone application. Given the difference between plant and animal miRNAs, particularly in terms of distribution of hairpin length and the nature of complementarity with its duplex partner (or miRNA star), the underlying (statistical) features of miRDeep and other tools, using similar features, are likely to get affected. Results The potential effects on features, such as minimum free energy, stability of secondary structures, excision length, etc., were examined, and the parameters of those displaying sizable changes were estimated for plant specific miRNAs. We found most of these features acquired a new set of values or distributions for plant specific miRNAs. While the length of conserved positions (nucleus) in mature miRNAs were relatively longer in plants, the difference in distribution of minimum free energy, between real and background hairpins, was marginal. However, the choice of source (species) of background sequences was found to affect both the minimum free energy and miRNA hairpin stability. The new parameters were tested on an Illumina dataset from maize seedlings, and the results were compared with those obtained using default parameters. The newly parameterized model was found to have much improved specificity and sensitivity over its default counterpart. Conclusions In summary, the present study reports behavior of few general and tool-specific statistical features for improving the prediction accuracy of plant miRNAs from deep-sequencing data. miRNA Family (dpeaa)DE-He213 Mature miRNAs (dpeaa)DE-He213 Minimum Free Energy (dpeaa)DE-He213 miRNA Precursor (dpeaa)DE-He213 Plant miRNAs (dpeaa)DE-He213 Wanchana, Samart aut Xu, Mercedes aut Bruskiewich, Richard aut Quick, William Paul aut Mosig, Axel aut Zhu, Xin-Guang aut Enthalten in BMC genomics London : BioMed Central, 2000 12(2011), 1 vom: 16. Feb. (DE-627)326644954 (DE-600)2041499-7 1471-2164 nnns volume:12 year:2011 number:1 day:16 month:02 https://dx.doi.org/10.1186/1471-2164-12-108 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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 12 2011 1 16 02
language	English
source	Enthalten in BMC genomics 12(2011), 1 vom: 16. Feb. volume:12 year:2011 number:1 day:16 month:02
sourceStr	Enthalten in BMC genomics 12(2011), 1 vom: 16. Feb. volume:12 year:2011 number:1 day:16 month:02
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	miRNA Family Mature miRNAs Minimum Free Energy miRNA Precursor Plant miRNAs
isfreeaccess_bool	true
container_title	BMC genomics
authorswithroles_txt_mv	Thakur, Vivek @@aut@@ Wanchana, Samart @@aut@@ Xu, Mercedes @@aut@@ Bruskiewich, Richard @@aut@@ Quick, William Paul @@aut@@ Mosig, Axel @@aut@@ Zhu, Xin-Guang @@aut@@
publishDateDaySort_date	2011-02-16T00:00:00Z
hierarchy_top_id	326644954
id	SPR027064891
language_de	englisch
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author	Thakur, Vivek
spellingShingle	Thakur, Vivek misc miRNA Family misc Mature miRNAs misc Minimum Free Energy misc miRNA Precursor misc Plant miRNAs Characterization of statistical features for plant microRNA prediction
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topic_title	Characterization of statistical features for plant microRNA prediction miRNA Family (dpeaa)DE-He213 Mature miRNAs (dpeaa)DE-He213 Minimum Free Energy (dpeaa)DE-He213 miRNA Precursor (dpeaa)DE-He213 Plant miRNAs (dpeaa)DE-He213
topic	misc miRNA Family misc Mature miRNAs misc Minimum Free Energy misc miRNA Precursor misc Plant miRNAs
topic_unstemmed	misc miRNA Family misc Mature miRNAs misc Minimum Free Energy misc miRNA Precursor misc Plant miRNAs
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title	Characterization of statistical features for plant microRNA prediction
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title_full	Characterization of statistical features for plant microRNA prediction
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author_browse	Thakur, Vivek Wanchana, Samart Xu, Mercedes Bruskiewich, Richard Quick, William Paul Mosig, Axel Zhu, Xin-Guang
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title_sort	characterization of statistical features for plant microrna prediction
title_auth	Characterization of statistical features for plant microRNA prediction
abstract	Background Several tools are available to identify miRNAs from deep-sequencing data, however, only a few of them, like miRDeep, can identify novel miRNAs and are also available as a standalone application. Given the difference between plant and animal miRNAs, particularly in terms of distribution of hairpin length and the nature of complementarity with its duplex partner (or miRNA star), the underlying (statistical) features of miRDeep and other tools, using similar features, are likely to get affected. Results The potential effects on features, such as minimum free energy, stability of secondary structures, excision length, etc., were examined, and the parameters of those displaying sizable changes were estimated for plant specific miRNAs. We found most of these features acquired a new set of values or distributions for plant specific miRNAs. While the length of conserved positions (nucleus) in mature miRNAs were relatively longer in plants, the difference in distribution of minimum free energy, between real and background hairpins, was marginal. However, the choice of source (species) of background sequences was found to affect both the minimum free energy and miRNA hairpin stability. The new parameters were tested on an Illumina dataset from maize seedlings, and the results were compared with those obtained using default parameters. The newly parameterized model was found to have much improved specificity and sensitivity over its default counterpart. Conclusions In summary, the present study reports behavior of few general and tool-specific statistical features for improving the prediction accuracy of plant miRNAs from deep-sequencing data. © Thakur et al; licensee BioMed Central Ltd. 2011
abstractGer	Background Several tools are available to identify miRNAs from deep-sequencing data, however, only a few of them, like miRDeep, can identify novel miRNAs and are also available as a standalone application. Given the difference between plant and animal miRNAs, particularly in terms of distribution of hairpin length and the nature of complementarity with its duplex partner (or miRNA star), the underlying (statistical) features of miRDeep and other tools, using similar features, are likely to get affected. Results The potential effects on features, such as minimum free energy, stability of secondary structures, excision length, etc., were examined, and the parameters of those displaying sizable changes were estimated for plant specific miRNAs. We found most of these features acquired a new set of values or distributions for plant specific miRNAs. While the length of conserved positions (nucleus) in mature miRNAs were relatively longer in plants, the difference in distribution of minimum free energy, between real and background hairpins, was marginal. However, the choice of source (species) of background sequences was found to affect both the minimum free energy and miRNA hairpin stability. The new parameters were tested on an Illumina dataset from maize seedlings, and the results were compared with those obtained using default parameters. The newly parameterized model was found to have much improved specificity and sensitivity over its default counterpart. Conclusions In summary, the present study reports behavior of few general and tool-specific statistical features for improving the prediction accuracy of plant miRNAs from deep-sequencing data. © Thakur et al; licensee BioMed Central Ltd. 2011
abstract_unstemmed	Background Several tools are available to identify miRNAs from deep-sequencing data, however, only a few of them, like miRDeep, can identify novel miRNAs and are also available as a standalone application. Given the difference between plant and animal miRNAs, particularly in terms of distribution of hairpin length and the nature of complementarity with its duplex partner (or miRNA star), the underlying (statistical) features of miRDeep and other tools, using similar features, are likely to get affected. Results The potential effects on features, such as minimum free energy, stability of secondary structures, excision length, etc., were examined, and the parameters of those displaying sizable changes were estimated for plant specific miRNAs. We found most of these features acquired a new set of values or distributions for plant specific miRNAs. While the length of conserved positions (nucleus) in mature miRNAs were relatively longer in plants, the difference in distribution of minimum free energy, between real and background hairpins, was marginal. However, the choice of source (species) of background sequences was found to affect both the minimum free energy and miRNA hairpin stability. The new parameters were tested on an Illumina dataset from maize seedlings, and the results were compared with those obtained using default parameters. The newly parameterized model was found to have much improved specificity and sensitivity over its default counterpart. Conclusions In summary, the present study reports behavior of few general and tool-specific statistical features for improving the prediction accuracy of plant miRNAs from deep-sequencing data. © Thakur et al; licensee BioMed Central Ltd. 2011
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title_short	Characterization of statistical features for plant microRNA prediction
url	https://dx.doi.org/10.1186/1471-2164-12-108
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Characterization of statistical features for plant microRNA prediction

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Zugang & Verfügbarkeit

Vorhandene Bände

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