MiRFinder: an improved approach and software implementation for genome-wide fast microRNA precursor scans

<p<Abstract</p< <p<Background</p< <p<MicroRNAs (miRNAs) are recognized as one of the most important families of non-coding RNAs that serve as important sequence-specific post-transcriptional regulators of gene expression. Identification of miRNAs is an important require...
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Autor*in:	Li Kui [verfasserIn] Hu Zhi-Liang [verfasserIn] Rothschild Max F [verfasserIn] Fan Bin [verfasserIn] Huang Ting-Hua [verfasserIn] Zhao Shu-Hong [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2007

Übergeordnetes Werk:	In: BMC Bioinformatics - BMC, 2003, 8(2007), 1, p 341
Übergeordnetes Werk:	volume:8 ; year:2007 ; number:1, p 341

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1186/1471-2105-8-341

Katalog-ID:	DOAJ06601753X

Internformat


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912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4326
912			\|a GBV_ILN_4335
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Indexfelder

author_variant	l k lk h z l hzl r m f rmf f b fb h t h hth z s h zsh
matchkey_str	article:14712105:2007----::ifneaipoeapocadotaemlmnainognmwdf
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publishDate	2007
allfields	10.1186/1471-2105-8-341 doi (DE-627)DOAJ06601753X (DE-599)DOAJ940741288dbe46b286360fa88c67621c DE-627 ger DE-627 rakwb eng R858-859.7 QH301-705.5 Li Kui verfasserin aut MiRFinder: an improved approach and software implementation for genome-wide fast microRNA precursor scans 2007 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Abstract</p< <p<Background</p< <p<MicroRNAs (miRNAs) are recognized as one of the most important families of non-coding RNAs that serve as important sequence-specific post-transcriptional regulators of gene expression. Identification of miRNAs is an important requirement for understanding the mechanisms of post-transcriptional regulation. Hundreds of miRNAs have been identified by direct cloning and computational approaches in several species. However, there are still many miRNAs that remain to be identified due to lack of either sequence features or robust algorithms to efficiently identify them.</p< <p<Results</p< <p<We have evaluated features valuable for pre-miRNA prediction, such as the local secondary structure differences of the stem region of miRNA and non-miRNA hairpins. We have also established correlations between different types of mutations and the secondary structures of pre-miRNAs. Utilizing these features and combining some improvements of the current pre-miRNA prediction methods, we implemented a computational learning method SVM (support vector machine) to build a high throughput and good performance computational pre-miRNA prediction tool called MiRFinder. The tool was designed for genome-wise, pair-wise sequences from two related species. The method built into the tool consisted of two major steps: 1) genome wide search for hairpin candidates and 2) exclusion of the non-robust structures based on analysis of 18 parameters by the SVM method. Results from applying the tool for chicken/human and D. melanogaster/D. pseudoobscura pair-wise genome alignments showed that the tool can be used for genome wide pre-miRNA predictions.</p< <p<Conclusion</p< <p<The MiRFinder can be a good alternative to current miRNA discovery software. This tool is available at <url<http://www.bioinformatics.org/mirfinder/</url<.</p< Computer applications to medicine. Medical informatics Biology (General) Hu Zhi-Liang verfasserin aut Rothschild Max F verfasserin aut Fan Bin verfasserin aut Huang Ting-Hua verfasserin aut Zhao Shu-Hong verfasserin aut In BMC Bioinformatics BMC, 2003 8(2007), 1, p 341 (DE-627)326644814 (DE-600)2041484-5 14712105 nnns volume:8 year:2007 number:1, p 341 https://doi.org/10.1186/1471-2105-8-341 kostenfrei https://doaj.org/article/940741288dbe46b286360fa88c67621c kostenfrei http://www.biomedcentral.com/1471-2105/8/341 kostenfrei https://doaj.org/toc/1471-2105 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_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_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_370 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2007 1, p 341
spelling	10.1186/1471-2105-8-341 doi (DE-627)DOAJ06601753X (DE-599)DOAJ940741288dbe46b286360fa88c67621c DE-627 ger DE-627 rakwb eng R858-859.7 QH301-705.5 Li Kui verfasserin aut MiRFinder: an improved approach and software implementation for genome-wide fast microRNA precursor scans 2007 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Abstract</p< <p<Background</p< <p<MicroRNAs (miRNAs) are recognized as one of the most important families of non-coding RNAs that serve as important sequence-specific post-transcriptional regulators of gene expression. Identification of miRNAs is an important requirement for understanding the mechanisms of post-transcriptional regulation. Hundreds of miRNAs have been identified by direct cloning and computational approaches in several species. However, there are still many miRNAs that remain to be identified due to lack of either sequence features or robust algorithms to efficiently identify them.</p< <p<Results</p< <p<We have evaluated features valuable for pre-miRNA prediction, such as the local secondary structure differences of the stem region of miRNA and non-miRNA hairpins. We have also established correlations between different types of mutations and the secondary structures of pre-miRNAs. Utilizing these features and combining some improvements of the current pre-miRNA prediction methods, we implemented a computational learning method SVM (support vector machine) to build a high throughput and good performance computational pre-miRNA prediction tool called MiRFinder. The tool was designed for genome-wise, pair-wise sequences from two related species. The method built into the tool consisted of two major steps: 1) genome wide search for hairpin candidates and 2) exclusion of the non-robust structures based on analysis of 18 parameters by the SVM method. Results from applying the tool for chicken/human and D. melanogaster/D. pseudoobscura pair-wise genome alignments showed that the tool can be used for genome wide pre-miRNA predictions.</p< <p<Conclusion</p< <p<The MiRFinder can be a good alternative to current miRNA discovery software. This tool is available at <url<http://www.bioinformatics.org/mirfinder/</url<.</p< Computer applications to medicine. Medical informatics Biology (General) Hu Zhi-Liang verfasserin aut Rothschild Max F verfasserin aut Fan Bin verfasserin aut Huang Ting-Hua verfasserin aut Zhao Shu-Hong verfasserin aut In BMC Bioinformatics BMC, 2003 8(2007), 1, p 341 (DE-627)326644814 (DE-600)2041484-5 14712105 nnns volume:8 year:2007 number:1, p 341 https://doi.org/10.1186/1471-2105-8-341 kostenfrei https://doaj.org/article/940741288dbe46b286360fa88c67621c kostenfrei http://www.biomedcentral.com/1471-2105/8/341 kostenfrei https://doaj.org/toc/1471-2105 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_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_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_370 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2007 1, p 341
allfields_unstemmed	10.1186/1471-2105-8-341 doi (DE-627)DOAJ06601753X (DE-599)DOAJ940741288dbe46b286360fa88c67621c DE-627 ger DE-627 rakwb eng R858-859.7 QH301-705.5 Li Kui verfasserin aut MiRFinder: an improved approach and software implementation for genome-wide fast microRNA precursor scans 2007 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Abstract</p< <p<Background</p< <p<MicroRNAs (miRNAs) are recognized as one of the most important families of non-coding RNAs that serve as important sequence-specific post-transcriptional regulators of gene expression. Identification of miRNAs is an important requirement for understanding the mechanisms of post-transcriptional regulation. Hundreds of miRNAs have been identified by direct cloning and computational approaches in several species. However, there are still many miRNAs that remain to be identified due to lack of either sequence features or robust algorithms to efficiently identify them.</p< <p<Results</p< <p<We have evaluated features valuable for pre-miRNA prediction, such as the local secondary structure differences of the stem region of miRNA and non-miRNA hairpins. We have also established correlations between different types of mutations and the secondary structures of pre-miRNAs. Utilizing these features and combining some improvements of the current pre-miRNA prediction methods, we implemented a computational learning method SVM (support vector machine) to build a high throughput and good performance computational pre-miRNA prediction tool called MiRFinder. The tool was designed for genome-wise, pair-wise sequences from two related species. The method built into the tool consisted of two major steps: 1) genome wide search for hairpin candidates and 2) exclusion of the non-robust structures based on analysis of 18 parameters by the SVM method. Results from applying the tool for chicken/human and D. melanogaster/D. pseudoobscura pair-wise genome alignments showed that the tool can be used for genome wide pre-miRNA predictions.</p< <p<Conclusion</p< <p<The MiRFinder can be a good alternative to current miRNA discovery software. This tool is available at <url<http://www.bioinformatics.org/mirfinder/</url<.</p< Computer applications to medicine. Medical informatics Biology (General) Hu Zhi-Liang verfasserin aut Rothschild Max F verfasserin aut Fan Bin verfasserin aut Huang Ting-Hua verfasserin aut Zhao Shu-Hong verfasserin aut In BMC Bioinformatics BMC, 2003 8(2007), 1, p 341 (DE-627)326644814 (DE-600)2041484-5 14712105 nnns volume:8 year:2007 number:1, p 341 https://doi.org/10.1186/1471-2105-8-341 kostenfrei https://doaj.org/article/940741288dbe46b286360fa88c67621c kostenfrei http://www.biomedcentral.com/1471-2105/8/341 kostenfrei https://doaj.org/toc/1471-2105 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_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_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_370 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2007 1, p 341
allfieldsGer	10.1186/1471-2105-8-341 doi (DE-627)DOAJ06601753X (DE-599)DOAJ940741288dbe46b286360fa88c67621c DE-627 ger DE-627 rakwb eng R858-859.7 QH301-705.5 Li Kui verfasserin aut MiRFinder: an improved approach and software implementation for genome-wide fast microRNA precursor scans 2007 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Abstract</p< <p<Background</p< <p<MicroRNAs (miRNAs) are recognized as one of the most important families of non-coding RNAs that serve as important sequence-specific post-transcriptional regulators of gene expression. Identification of miRNAs is an important requirement for understanding the mechanisms of post-transcriptional regulation. Hundreds of miRNAs have been identified by direct cloning and computational approaches in several species. However, there are still many miRNAs that remain to be identified due to lack of either sequence features or robust algorithms to efficiently identify them.</p< <p<Results</p< <p<We have evaluated features valuable for pre-miRNA prediction, such as the local secondary structure differences of the stem region of miRNA and non-miRNA hairpins. We have also established correlations between different types of mutations and the secondary structures of pre-miRNAs. Utilizing these features and combining some improvements of the current pre-miRNA prediction methods, we implemented a computational learning method SVM (support vector machine) to build a high throughput and good performance computational pre-miRNA prediction tool called MiRFinder. The tool was designed for genome-wise, pair-wise sequences from two related species. The method built into the tool consisted of two major steps: 1) genome wide search for hairpin candidates and 2) exclusion of the non-robust structures based on analysis of 18 parameters by the SVM method. Results from applying the tool for chicken/human and D. melanogaster/D. pseudoobscura pair-wise genome alignments showed that the tool can be used for genome wide pre-miRNA predictions.</p< <p<Conclusion</p< <p<The MiRFinder can be a good alternative to current miRNA discovery software. This tool is available at <url<http://www.bioinformatics.org/mirfinder/</url<.</p< Computer applications to medicine. Medical informatics Biology (General) Hu Zhi-Liang verfasserin aut Rothschild Max F verfasserin aut Fan Bin verfasserin aut Huang Ting-Hua verfasserin aut Zhao Shu-Hong verfasserin aut In BMC Bioinformatics BMC, 2003 8(2007), 1, p 341 (DE-627)326644814 (DE-600)2041484-5 14712105 nnns volume:8 year:2007 number:1, p 341 https://doi.org/10.1186/1471-2105-8-341 kostenfrei https://doaj.org/article/940741288dbe46b286360fa88c67621c kostenfrei http://www.biomedcentral.com/1471-2105/8/341 kostenfrei https://doaj.org/toc/1471-2105 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_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_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_370 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2007 1, p 341
allfieldsSound	10.1186/1471-2105-8-341 doi (DE-627)DOAJ06601753X (DE-599)DOAJ940741288dbe46b286360fa88c67621c DE-627 ger DE-627 rakwb eng R858-859.7 QH301-705.5 Li Kui verfasserin aut MiRFinder: an improved approach and software implementation for genome-wide fast microRNA precursor scans 2007 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Abstract</p< <p<Background</p< <p<MicroRNAs (miRNAs) are recognized as one of the most important families of non-coding RNAs that serve as important sequence-specific post-transcriptional regulators of gene expression. Identification of miRNAs is an important requirement for understanding the mechanisms of post-transcriptional regulation. Hundreds of miRNAs have been identified by direct cloning and computational approaches in several species. However, there are still many miRNAs that remain to be identified due to lack of either sequence features or robust algorithms to efficiently identify them.</p< <p<Results</p< <p<We have evaluated features valuable for pre-miRNA prediction, such as the local secondary structure differences of the stem region of miRNA and non-miRNA hairpins. We have also established correlations between different types of mutations and the secondary structures of pre-miRNAs. Utilizing these features and combining some improvements of the current pre-miRNA prediction methods, we implemented a computational learning method SVM (support vector machine) to build a high throughput and good performance computational pre-miRNA prediction tool called MiRFinder. The tool was designed for genome-wise, pair-wise sequences from two related species. The method built into the tool consisted of two major steps: 1) genome wide search for hairpin candidates and 2) exclusion of the non-robust structures based on analysis of 18 parameters by the SVM method. Results from applying the tool for chicken/human and D. melanogaster/D. pseudoobscura pair-wise genome alignments showed that the tool can be used for genome wide pre-miRNA predictions.</p< <p<Conclusion</p< <p<The MiRFinder can be a good alternative to current miRNA discovery software. This tool is available at <url<http://www.bioinformatics.org/mirfinder/</url<.</p< Computer applications to medicine. Medical informatics Biology (General) Hu Zhi-Liang verfasserin aut Rothschild Max F verfasserin aut Fan Bin verfasserin aut Huang Ting-Hua verfasserin aut Zhao Shu-Hong verfasserin aut In BMC Bioinformatics BMC, 2003 8(2007), 1, p 341 (DE-627)326644814 (DE-600)2041484-5 14712105 nnns volume:8 year:2007 number:1, p 341 https://doi.org/10.1186/1471-2105-8-341 kostenfrei https://doaj.org/article/940741288dbe46b286360fa88c67621c kostenfrei http://www.biomedcentral.com/1471-2105/8/341 kostenfrei https://doaj.org/toc/1471-2105 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_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_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_370 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2007 1, p 341
language	English
source	In BMC Bioinformatics 8(2007), 1, p 341 volume:8 year:2007 number:1, p 341
sourceStr	In BMC Bioinformatics 8(2007), 1, p 341 volume:8 year:2007 number:1, p 341
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Computer applications to medicine. Medical informatics Biology (General)
isfreeaccess_bool	true
container_title	BMC Bioinformatics
authorswithroles_txt_mv	Li Kui @@aut@@ Hu Zhi-Liang @@aut@@ Rothschild Max F @@aut@@ Fan Bin @@aut@@ Huang Ting-Hua @@aut@@ Zhao Shu-Hong @@aut@@
publishDateDaySort_date	2007-01-01T00:00:00Z
hierarchy_top_id	326644814
id	DOAJ06601753X
language_de	englisch
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callnumber-first	R - Medicine
author	Li Kui
spellingShingle	Li Kui misc R858-859.7 misc QH301-705.5 misc Computer applications to medicine. Medical informatics misc Biology (General) MiRFinder: an improved approach and software implementation for genome-wide fast microRNA precursor scans
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topic_title	R858-859.7 QH301-705.5 MiRFinder: an improved approach and software implementation for genome-wide fast microRNA precursor scans
topic	misc R858-859.7 misc QH301-705.5 misc Computer applications to medicine. Medical informatics misc Biology (General)
topic_unstemmed	misc R858-859.7 misc QH301-705.5 misc Computer applications to medicine. Medical informatics misc Biology (General)
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title	MiRFinder: an improved approach and software implementation for genome-wide fast microRNA precursor scans
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title_full	MiRFinder: an improved approach and software implementation for genome-wide fast microRNA precursor scans
author_sort	Li Kui
journal	BMC Bioinformatics
journalStr	BMC Bioinformatics
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lang_code	eng
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author_browse	Li Kui Hu Zhi-Liang Rothschild Max F Fan Bin Huang Ting-Hua Zhao Shu-Hong
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author-letter	Li Kui
doi_str_mv	10.1186/1471-2105-8-341
author2-role	verfasserin
title_sort	mirfinder: an improved approach and software implementation for genome-wide fast microrna precursor scans
callnumber	R858-859.7
title_auth	MiRFinder: an improved approach and software implementation for genome-wide fast microRNA precursor scans
abstract	<p<Abstract</p< <p<Background</p< <p<MicroRNAs (miRNAs) are recognized as one of the most important families of non-coding RNAs that serve as important sequence-specific post-transcriptional regulators of gene expression. Identification of miRNAs is an important requirement for understanding the mechanisms of post-transcriptional regulation. Hundreds of miRNAs have been identified by direct cloning and computational approaches in several species. However, there are still many miRNAs that remain to be identified due to lack of either sequence features or robust algorithms to efficiently identify them.</p< <p<Results</p< <p<We have evaluated features valuable for pre-miRNA prediction, such as the local secondary structure differences of the stem region of miRNA and non-miRNA hairpins. We have also established correlations between different types of mutations and the secondary structures of pre-miRNAs. Utilizing these features and combining some improvements of the current pre-miRNA prediction methods, we implemented a computational learning method SVM (support vector machine) to build a high throughput and good performance computational pre-miRNA prediction tool called MiRFinder. The tool was designed for genome-wise, pair-wise sequences from two related species. The method built into the tool consisted of two major steps: 1) genome wide search for hairpin candidates and 2) exclusion of the non-robust structures based on analysis of 18 parameters by the SVM method. Results from applying the tool for chicken/human and D. melanogaster/D. pseudoobscura pair-wise genome alignments showed that the tool can be used for genome wide pre-miRNA predictions.</p< <p<Conclusion</p< <p<The MiRFinder can be a good alternative to current miRNA discovery software. This tool is available at <url<http://www.bioinformatics.org/mirfinder/</url<.</p<
abstractGer	<p<Abstract</p< <p<Background</p< <p<MicroRNAs (miRNAs) are recognized as one of the most important families of non-coding RNAs that serve as important sequence-specific post-transcriptional regulators of gene expression. Identification of miRNAs is an important requirement for understanding the mechanisms of post-transcriptional regulation. Hundreds of miRNAs have been identified by direct cloning and computational approaches in several species. However, there are still many miRNAs that remain to be identified due to lack of either sequence features or robust algorithms to efficiently identify them.</p< <p<Results</p< <p<We have evaluated features valuable for pre-miRNA prediction, such as the local secondary structure differences of the stem region of miRNA and non-miRNA hairpins. We have also established correlations between different types of mutations and the secondary structures of pre-miRNAs. Utilizing these features and combining some improvements of the current pre-miRNA prediction methods, we implemented a computational learning method SVM (support vector machine) to build a high throughput and good performance computational pre-miRNA prediction tool called MiRFinder. The tool was designed for genome-wise, pair-wise sequences from two related species. The method built into the tool consisted of two major steps: 1) genome wide search for hairpin candidates and 2) exclusion of the non-robust structures based on analysis of 18 parameters by the SVM method. Results from applying the tool for chicken/human and D. melanogaster/D. pseudoobscura pair-wise genome alignments showed that the tool can be used for genome wide pre-miRNA predictions.</p< <p<Conclusion</p< <p<The MiRFinder can be a good alternative to current miRNA discovery software. This tool is available at <url<http://www.bioinformatics.org/mirfinder/</url<.</p<
abstract_unstemmed	<p<Abstract</p< <p<Background</p< <p<MicroRNAs (miRNAs) are recognized as one of the most important families of non-coding RNAs that serve as important sequence-specific post-transcriptional regulators of gene expression. Identification of miRNAs is an important requirement for understanding the mechanisms of post-transcriptional regulation. Hundreds of miRNAs have been identified by direct cloning and computational approaches in several species. However, there are still many miRNAs that remain to be identified due to lack of either sequence features or robust algorithms to efficiently identify them.</p< <p<Results</p< <p<We have evaluated features valuable for pre-miRNA prediction, such as the local secondary structure differences of the stem region of miRNA and non-miRNA hairpins. We have also established correlations between different types of mutations and the secondary structures of pre-miRNAs. Utilizing these features and combining some improvements of the current pre-miRNA prediction methods, we implemented a computational learning method SVM (support vector machine) to build a high throughput and good performance computational pre-miRNA prediction tool called MiRFinder. The tool was designed for genome-wise, pair-wise sequences from two related species. The method built into the tool consisted of two major steps: 1) genome wide search for hairpin candidates and 2) exclusion of the non-robust structures based on analysis of 18 parameters by the SVM method. Results from applying the tool for chicken/human and D. melanogaster/D. pseudoobscura pair-wise genome alignments showed that the tool can be used for genome wide pre-miRNA predictions.</p< <p<Conclusion</p< <p<The MiRFinder can be a good alternative to current miRNA discovery software. This tool is available at <url<http://www.bioinformatics.org/mirfinder/</url<.</p<
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title_short	MiRFinder: an improved approach and software implementation for genome-wide fast microRNA precursor scans
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