Ontology-oriented retrieval of putative microRNAs in Vitis vinifera via GrapeMiRNA: a web database of de novo predicted grape microRNAs

Background Two complete genome sequences are available for Vitis vinifera Pinot noir. Based on the sequence and gene predictions produced by the IASMA, we performed an in silico detection of putative microRNA genes and of their targets, and collected the most reliable microRNA predictions in a web d...
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Autor*in:	Lazzari, Barbara [verfasserIn] Caprera, Andrea Cestaro, Alessandro Merelli, Ivan Del Corvo, Marcello Fontana, Paolo Milanesi, Luciano Velasco, Riccardo Stella, Alessandra

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2009

Schlagwörter:	Gene Ontology miRNA Family miRNA Gene Hairpin Structure Mature Sequence

Anmerkung:	© Lazzari et al; licensee BioMed Central Ltd. 2009

Übergeordnetes Werk:	Enthalten in: BMC plant biology - London : BioMed Central, 2001, 9(2009), 1 vom: 29. Juni
Übergeordnetes Werk:	volume:9 ; year:2009 ; number:1 ; day:29 ; month:06

Links:	Volltext

DOI / URN:	10.1186/1471-2229-9-82

Katalog-ID:	SPR027282333

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520			\|a Background Two complete genome sequences are available for Vitis vinifera Pinot noir. Based on the sequence and gene predictions produced by the IASMA, we performed an in silico detection of putative microRNA genes and of their targets, and collected the most reliable microRNA predictions in a web database. The application is available at http://www.itb.cnr.it/ptp/grapemirna/. Description The program FindMiRNA was used to detect putative microRNA genes in the grape genome. A very high number of predictions was retrieved, calling for validation. Nine parameters were calculated and, based on the grape microRNAs dataset available at miRBase, thresholds were defined and applied to FindMiRNA predictions having targets in gene exons. In the resulting subset, predictions were ranked according to precursor positions and sequence similarity, and to target identity. To further validate FindMiRNA predictions, comparisons to the Arabidopsis genome, to the grape Genoscope genome, and to the grape EST collection were performed. Results were stored in a MySQL database and a web interface was prepared to query the database and retrieve predictions of interest. Conclusion The GrapeMiRNA database encompasses 5,778 microRNA predictions spanning the whole grape genome. Predictions are integrated with information that can be of use in selection procedures. Tools added in the web interface also allow to inspect predictions according to gene ontology classes and metabolic pathways of targets. The GrapeMiRNA database can be of help in selecting candidate microRNA genes to be validated.
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author_variant	b l bl a c ac a c ac i m im c m d cm cmd p f pf l m lm r v rv a s as
matchkey_str	article:14712229:2009----::nooyretdereaopttvmconsniivnfrvarpmrawbaaae
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publishDate	2009
allfields	10.1186/1471-2229-9-82 doi (DE-627)SPR027282333 (SPR)1471-2229-9-82-e DE-627 ger DE-627 rakwb eng Lazzari, Barbara verfasserin aut Ontology-oriented retrieval of putative microRNAs in Vitis vinifera via GrapeMiRNA: a web database of de novo predicted grape microRNAs 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Lazzari et al; licensee BioMed Central Ltd. 2009 Background Two complete genome sequences are available for Vitis vinifera Pinot noir. Based on the sequence and gene predictions produced by the IASMA, we performed an in silico detection of putative microRNA genes and of their targets, and collected the most reliable microRNA predictions in a web database. The application is available at http://www.itb.cnr.it/ptp/grapemirna/. Description The program FindMiRNA was used to detect putative microRNA genes in the grape genome. A very high number of predictions was retrieved, calling for validation. Nine parameters were calculated and, based on the grape microRNAs dataset available at miRBase, thresholds were defined and applied to FindMiRNA predictions having targets in gene exons. In the resulting subset, predictions were ranked according to precursor positions and sequence similarity, and to target identity. To further validate FindMiRNA predictions, comparisons to the Arabidopsis genome, to the grape Genoscope genome, and to the grape EST collection were performed. Results were stored in a MySQL database and a web interface was prepared to query the database and retrieve predictions of interest. Conclusion The GrapeMiRNA database encompasses 5,778 microRNA predictions spanning the whole grape genome. Predictions are integrated with information that can be of use in selection procedures. Tools added in the web interface also allow to inspect predictions according to gene ontology classes and metabolic pathways of targets. The GrapeMiRNA database can be of help in selecting candidate microRNA genes to be validated. Gene Ontology (dpeaa)DE-He213 miRNA Family (dpeaa)DE-He213 miRNA Gene (dpeaa)DE-He213 Hairpin Structure (dpeaa)DE-He213 Mature Sequence (dpeaa)DE-He213 Caprera, Andrea aut Cestaro, Alessandro aut Merelli, Ivan aut Del Corvo, Marcello aut Fontana, Paolo aut Milanesi, Luciano aut Velasco, Riccardo aut Stella, Alessandra aut Enthalten in BMC plant biology London : BioMed Central, 2001 9(2009), 1 vom: 29. Juni (DE-627)335489060 (DE-600)2059868-3 1471-2229 nnns volume:9 year:2009 number:1 day:29 month:06 https://dx.doi.org/10.1186/1471-2229-9-82 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_224 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 9 2009 1 29 06
spelling	10.1186/1471-2229-9-82 doi (DE-627)SPR027282333 (SPR)1471-2229-9-82-e DE-627 ger DE-627 rakwb eng Lazzari, Barbara verfasserin aut Ontology-oriented retrieval of putative microRNAs in Vitis vinifera via GrapeMiRNA: a web database of de novo predicted grape microRNAs 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Lazzari et al; licensee BioMed Central Ltd. 2009 Background Two complete genome sequences are available for Vitis vinifera Pinot noir. Based on the sequence and gene predictions produced by the IASMA, we performed an in silico detection of putative microRNA genes and of their targets, and collected the most reliable microRNA predictions in a web database. The application is available at http://www.itb.cnr.it/ptp/grapemirna/. Description The program FindMiRNA was used to detect putative microRNA genes in the grape genome. A very high number of predictions was retrieved, calling for validation. Nine parameters were calculated and, based on the grape microRNAs dataset available at miRBase, thresholds were defined and applied to FindMiRNA predictions having targets in gene exons. In the resulting subset, predictions were ranked according to precursor positions and sequence similarity, and to target identity. To further validate FindMiRNA predictions, comparisons to the Arabidopsis genome, to the grape Genoscope genome, and to the grape EST collection were performed. Results were stored in a MySQL database and a web interface was prepared to query the database and retrieve predictions of interest. Conclusion The GrapeMiRNA database encompasses 5,778 microRNA predictions spanning the whole grape genome. Predictions are integrated with information that can be of use in selection procedures. Tools added in the web interface also allow to inspect predictions according to gene ontology classes and metabolic pathways of targets. The GrapeMiRNA database can be of help in selecting candidate microRNA genes to be validated. Gene Ontology (dpeaa)DE-He213 miRNA Family (dpeaa)DE-He213 miRNA Gene (dpeaa)DE-He213 Hairpin Structure (dpeaa)DE-He213 Mature Sequence (dpeaa)DE-He213 Caprera, Andrea aut Cestaro, Alessandro aut Merelli, Ivan aut Del Corvo, Marcello aut Fontana, Paolo aut Milanesi, Luciano aut Velasco, Riccardo aut Stella, Alessandra aut Enthalten in BMC plant biology London : BioMed Central, 2001 9(2009), 1 vom: 29. Juni (DE-627)335489060 (DE-600)2059868-3 1471-2229 nnns volume:9 year:2009 number:1 day:29 month:06 https://dx.doi.org/10.1186/1471-2229-9-82 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_224 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 9 2009 1 29 06
allfields_unstemmed	10.1186/1471-2229-9-82 doi (DE-627)SPR027282333 (SPR)1471-2229-9-82-e DE-627 ger DE-627 rakwb eng Lazzari, Barbara verfasserin aut Ontology-oriented retrieval of putative microRNAs in Vitis vinifera via GrapeMiRNA: a web database of de novo predicted grape microRNAs 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Lazzari et al; licensee BioMed Central Ltd. 2009 Background Two complete genome sequences are available for Vitis vinifera Pinot noir. Based on the sequence and gene predictions produced by the IASMA, we performed an in silico detection of putative microRNA genes and of their targets, and collected the most reliable microRNA predictions in a web database. The application is available at http://www.itb.cnr.it/ptp/grapemirna/. Description The program FindMiRNA was used to detect putative microRNA genes in the grape genome. A very high number of predictions was retrieved, calling for validation. Nine parameters were calculated and, based on the grape microRNAs dataset available at miRBase, thresholds were defined and applied to FindMiRNA predictions having targets in gene exons. In the resulting subset, predictions were ranked according to precursor positions and sequence similarity, and to target identity. To further validate FindMiRNA predictions, comparisons to the Arabidopsis genome, to the grape Genoscope genome, and to the grape EST collection were performed. Results were stored in a MySQL database and a web interface was prepared to query the database and retrieve predictions of interest. Conclusion The GrapeMiRNA database encompasses 5,778 microRNA predictions spanning the whole grape genome. Predictions are integrated with information that can be of use in selection procedures. Tools added in the web interface also allow to inspect predictions according to gene ontology classes and metabolic pathways of targets. The GrapeMiRNA database can be of help in selecting candidate microRNA genes to be validated. Gene Ontology (dpeaa)DE-He213 miRNA Family (dpeaa)DE-He213 miRNA Gene (dpeaa)DE-He213 Hairpin Structure (dpeaa)DE-He213 Mature Sequence (dpeaa)DE-He213 Caprera, Andrea aut Cestaro, Alessandro aut Merelli, Ivan aut Del Corvo, Marcello aut Fontana, Paolo aut Milanesi, Luciano aut Velasco, Riccardo aut Stella, Alessandra aut Enthalten in BMC plant biology London : BioMed Central, 2001 9(2009), 1 vom: 29. Juni (DE-627)335489060 (DE-600)2059868-3 1471-2229 nnns volume:9 year:2009 number:1 day:29 month:06 https://dx.doi.org/10.1186/1471-2229-9-82 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_224 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 9 2009 1 29 06
allfieldsGer	10.1186/1471-2229-9-82 doi (DE-627)SPR027282333 (SPR)1471-2229-9-82-e DE-627 ger DE-627 rakwb eng Lazzari, Barbara verfasserin aut Ontology-oriented retrieval of putative microRNAs in Vitis vinifera via GrapeMiRNA: a web database of de novo predicted grape microRNAs 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Lazzari et al; licensee BioMed Central Ltd. 2009 Background Two complete genome sequences are available for Vitis vinifera Pinot noir. Based on the sequence and gene predictions produced by the IASMA, we performed an in silico detection of putative microRNA genes and of their targets, and collected the most reliable microRNA predictions in a web database. The application is available at http://www.itb.cnr.it/ptp/grapemirna/. Description The program FindMiRNA was used to detect putative microRNA genes in the grape genome. A very high number of predictions was retrieved, calling for validation. Nine parameters were calculated and, based on the grape microRNAs dataset available at miRBase, thresholds were defined and applied to FindMiRNA predictions having targets in gene exons. In the resulting subset, predictions were ranked according to precursor positions and sequence similarity, and to target identity. To further validate FindMiRNA predictions, comparisons to the Arabidopsis genome, to the grape Genoscope genome, and to the grape EST collection were performed. Results were stored in a MySQL database and a web interface was prepared to query the database and retrieve predictions of interest. Conclusion The GrapeMiRNA database encompasses 5,778 microRNA predictions spanning the whole grape genome. Predictions are integrated with information that can be of use in selection procedures. Tools added in the web interface also allow to inspect predictions according to gene ontology classes and metabolic pathways of targets. The GrapeMiRNA database can be of help in selecting candidate microRNA genes to be validated. Gene Ontology (dpeaa)DE-He213 miRNA Family (dpeaa)DE-He213 miRNA Gene (dpeaa)DE-He213 Hairpin Structure (dpeaa)DE-He213 Mature Sequence (dpeaa)DE-He213 Caprera, Andrea aut Cestaro, Alessandro aut Merelli, Ivan aut Del Corvo, Marcello aut Fontana, Paolo aut Milanesi, Luciano aut Velasco, Riccardo aut Stella, Alessandra aut Enthalten in BMC plant biology London : BioMed Central, 2001 9(2009), 1 vom: 29. Juni (DE-627)335489060 (DE-600)2059868-3 1471-2229 nnns volume:9 year:2009 number:1 day:29 month:06 https://dx.doi.org/10.1186/1471-2229-9-82 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_224 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 9 2009 1 29 06
allfieldsSound	10.1186/1471-2229-9-82 doi (DE-627)SPR027282333 (SPR)1471-2229-9-82-e DE-627 ger DE-627 rakwb eng Lazzari, Barbara verfasserin aut Ontology-oriented retrieval of putative microRNAs in Vitis vinifera via GrapeMiRNA: a web database of de novo predicted grape microRNAs 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Lazzari et al; licensee BioMed Central Ltd. 2009 Background Two complete genome sequences are available for Vitis vinifera Pinot noir. Based on the sequence and gene predictions produced by the IASMA, we performed an in silico detection of putative microRNA genes and of their targets, and collected the most reliable microRNA predictions in a web database. The application is available at http://www.itb.cnr.it/ptp/grapemirna/. Description The program FindMiRNA was used to detect putative microRNA genes in the grape genome. A very high number of predictions was retrieved, calling for validation. Nine parameters were calculated and, based on the grape microRNAs dataset available at miRBase, thresholds were defined and applied to FindMiRNA predictions having targets in gene exons. In the resulting subset, predictions were ranked according to precursor positions and sequence similarity, and to target identity. To further validate FindMiRNA predictions, comparisons to the Arabidopsis genome, to the grape Genoscope genome, and to the grape EST collection were performed. Results were stored in a MySQL database and a web interface was prepared to query the database and retrieve predictions of interest. Conclusion The GrapeMiRNA database encompasses 5,778 microRNA predictions spanning the whole grape genome. Predictions are integrated with information that can be of use in selection procedures. Tools added in the web interface also allow to inspect predictions according to gene ontology classes and metabolic pathways of targets. The GrapeMiRNA database can be of help in selecting candidate microRNA genes to be validated. Gene Ontology (dpeaa)DE-He213 miRNA Family (dpeaa)DE-He213 miRNA Gene (dpeaa)DE-He213 Hairpin Structure (dpeaa)DE-He213 Mature Sequence (dpeaa)DE-He213 Caprera, Andrea aut Cestaro, Alessandro aut Merelli, Ivan aut Del Corvo, Marcello aut Fontana, Paolo aut Milanesi, Luciano aut Velasco, Riccardo aut Stella, Alessandra aut Enthalten in BMC plant biology London : BioMed Central, 2001 9(2009), 1 vom: 29. Juni (DE-627)335489060 (DE-600)2059868-3 1471-2229 nnns volume:9 year:2009 number:1 day:29 month:06 https://dx.doi.org/10.1186/1471-2229-9-82 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_224 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 9 2009 1 29 06
language	English
source	Enthalten in BMC plant biology 9(2009), 1 vom: 29. Juni volume:9 year:2009 number:1 day:29 month:06
sourceStr	Enthalten in BMC plant biology 9(2009), 1 vom: 29. Juni volume:9 year:2009 number:1 day:29 month:06
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Gene Ontology miRNA Family miRNA Gene Hairpin Structure Mature Sequence
isfreeaccess_bool	true
container_title	BMC plant biology
authorswithroles_txt_mv	Lazzari, Barbara @@aut@@ Caprera, Andrea @@aut@@ Cestaro, Alessandro @@aut@@ Merelli, Ivan @@aut@@ Del Corvo, Marcello @@aut@@ Fontana, Paolo @@aut@@ Milanesi, Luciano @@aut@@ Velasco, Riccardo @@aut@@ Stella, Alessandra @@aut@@
publishDateDaySort_date	2009-06-29T00:00:00Z
hierarchy_top_id	335489060
id	SPR027282333
language_de	englisch
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Results were stored in a MySQL database and a web interface was prepared to query the database and retrieve predictions of interest. Conclusion The GrapeMiRNA database encompasses 5,778 microRNA predictions spanning the whole grape genome. Predictions are integrated with information that can be of use in selection procedures. Tools added in the web interface also allow to inspect predictions according to gene ontology classes and metabolic pathways of targets. 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author	Lazzari, Barbara
spellingShingle	Lazzari, Barbara misc Gene Ontology misc miRNA Family misc miRNA Gene misc Hairpin Structure misc Mature Sequence Ontology-oriented retrieval of putative microRNAs in Vitis vinifera via GrapeMiRNA: a web database of de novo predicted grape microRNAs
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topic_title	Ontology-oriented retrieval of putative microRNAs in Vitis vinifera via GrapeMiRNA: a web database of de novo predicted grape microRNAs Gene Ontology (dpeaa)DE-He213 miRNA Family (dpeaa)DE-He213 miRNA Gene (dpeaa)DE-He213 Hairpin Structure (dpeaa)DE-He213 Mature Sequence (dpeaa)DE-He213
topic	misc Gene Ontology misc miRNA Family misc miRNA Gene misc Hairpin Structure misc Mature Sequence
topic_unstemmed	misc Gene Ontology misc miRNA Family misc miRNA Gene misc Hairpin Structure misc Mature Sequence
topic_browse	misc Gene Ontology misc miRNA Family misc miRNA Gene misc Hairpin Structure misc Mature Sequence
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title	Ontology-oriented retrieval of putative microRNAs in Vitis vinifera via GrapeMiRNA: a web database of de novo predicted grape microRNAs
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title_full	Ontology-oriented retrieval of putative microRNAs in Vitis vinifera via GrapeMiRNA: a web database of de novo predicted grape microRNAs
author_sort	Lazzari, Barbara
journal	BMC plant biology
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author_browse	Lazzari, Barbara Caprera, Andrea Cestaro, Alessandro Merelli, Ivan Del Corvo, Marcello Fontana, Paolo Milanesi, Luciano Velasco, Riccardo Stella, Alessandra
container_volume	9
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author-letter	Lazzari, Barbara
doi_str_mv	10.1186/1471-2229-9-82
title_sort	ontology-oriented retrieval of putative micrornas in vitis vinifera via grapemirna: a web database of de novo predicted grape micrornas
title_auth	Ontology-oriented retrieval of putative microRNAs in Vitis vinifera via GrapeMiRNA: a web database of de novo predicted grape microRNAs
abstract	Background Two complete genome sequences are available for Vitis vinifera Pinot noir. Based on the sequence and gene predictions produced by the IASMA, we performed an in silico detection of putative microRNA genes and of their targets, and collected the most reliable microRNA predictions in a web database. The application is available at http://www.itb.cnr.it/ptp/grapemirna/. Description The program FindMiRNA was used to detect putative microRNA genes in the grape genome. A very high number of predictions was retrieved, calling for validation. Nine parameters were calculated and, based on the grape microRNAs dataset available at miRBase, thresholds were defined and applied to FindMiRNA predictions having targets in gene exons. In the resulting subset, predictions were ranked according to precursor positions and sequence similarity, and to target identity. To further validate FindMiRNA predictions, comparisons to the Arabidopsis genome, to the grape Genoscope genome, and to the grape EST collection were performed. Results were stored in a MySQL database and a web interface was prepared to query the database and retrieve predictions of interest. Conclusion The GrapeMiRNA database encompasses 5,778 microRNA predictions spanning the whole grape genome. Predictions are integrated with information that can be of use in selection procedures. Tools added in the web interface also allow to inspect predictions according to gene ontology classes and metabolic pathways of targets. The GrapeMiRNA database can be of help in selecting candidate microRNA genes to be validated. © Lazzari et al; licensee BioMed Central Ltd. 2009
abstractGer	Background Two complete genome sequences are available for Vitis vinifera Pinot noir. Based on the sequence and gene predictions produced by the IASMA, we performed an in silico detection of putative microRNA genes and of their targets, and collected the most reliable microRNA predictions in a web database. The application is available at http://www.itb.cnr.it/ptp/grapemirna/. Description The program FindMiRNA was used to detect putative microRNA genes in the grape genome. A very high number of predictions was retrieved, calling for validation. Nine parameters were calculated and, based on the grape microRNAs dataset available at miRBase, thresholds were defined and applied to FindMiRNA predictions having targets in gene exons. In the resulting subset, predictions were ranked according to precursor positions and sequence similarity, and to target identity. To further validate FindMiRNA predictions, comparisons to the Arabidopsis genome, to the grape Genoscope genome, and to the grape EST collection were performed. Results were stored in a MySQL database and a web interface was prepared to query the database and retrieve predictions of interest. Conclusion The GrapeMiRNA database encompasses 5,778 microRNA predictions spanning the whole grape genome. Predictions are integrated with information that can be of use in selection procedures. Tools added in the web interface also allow to inspect predictions according to gene ontology classes and metabolic pathways of targets. The GrapeMiRNA database can be of help in selecting candidate microRNA genes to be validated. © Lazzari et al; licensee BioMed Central Ltd. 2009
abstract_unstemmed	Background Two complete genome sequences are available for Vitis vinifera Pinot noir. Based on the sequence and gene predictions produced by the IASMA, we performed an in silico detection of putative microRNA genes and of their targets, and collected the most reliable microRNA predictions in a web database. The application is available at http://www.itb.cnr.it/ptp/grapemirna/. Description The program FindMiRNA was used to detect putative microRNA genes in the grape genome. A very high number of predictions was retrieved, calling for validation. Nine parameters were calculated and, based on the grape microRNAs dataset available at miRBase, thresholds were defined and applied to FindMiRNA predictions having targets in gene exons. In the resulting subset, predictions were ranked according to precursor positions and sequence similarity, and to target identity. To further validate FindMiRNA predictions, comparisons to the Arabidopsis genome, to the grape Genoscope genome, and to the grape EST collection were performed. Results were stored in a MySQL database and a web interface was prepared to query the database and retrieve predictions of interest. Conclusion The GrapeMiRNA database encompasses 5,778 microRNA predictions spanning the whole grape genome. Predictions are integrated with information that can be of use in selection procedures. Tools added in the web interface also allow to inspect predictions according to gene ontology classes and metabolic pathways of targets. The GrapeMiRNA database can be of help in selecting candidate microRNA genes to be validated. © Lazzari et al; licensee BioMed Central Ltd. 2009
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container_issue	1
title_short	Ontology-oriented retrieval of putative microRNAs in Vitis vinifera via GrapeMiRNA: a web database of de novo predicted grape microRNAs
url	https://dx.doi.org/10.1186/1471-2229-9-82
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author2	Caprera, Andrea Cestaro, Alessandro Merelli, Ivan Del Corvo, Marcello Fontana, Paolo Milanesi, Luciano Velasco, Riccardo Stella, Alessandra
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Ontology-oriented retrieval of putative microRNAs in Vitis vinifera via GrapeMiRNA: a web database of de novo predicted grape microRNAs

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