Eintrag weiter verarbeiten
SPEPlip: the detection of signal peptide and lipoprotein cleavage sites
Gespeichert in:
| Zeitschriftentitel: | Bioinformatics |
|---|---|
| Personen und Körperschaften: | , , |
| In: | Bioinformatics, 19, 2003, 18, S. 2498-2499 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
Oxford University Press (OUP)
|
| Schlagwörter: |
| author_facet |
Fariselli, Piero Finocchiaro, Giacomo Casadio, Rita Fariselli, Piero Finocchiaro, Giacomo Casadio, Rita |
|---|---|
| author |
Fariselli, Piero Finocchiaro, Giacomo Casadio, Rita |
| spellingShingle |
Fariselli, Piero Finocchiaro, Giacomo Casadio, Rita Bioinformatics SPEPlip: the detection of signal peptide and lipoprotein cleavage sites Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability |
| author_sort |
fariselli, piero |
| spelling |
Fariselli, Piero Finocchiaro, Giacomo Casadio, Rita 1367-4811 1367-4803 Oxford University Press (OUP) Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability http://dx.doi.org/10.1093/bioinformatics/btg360 <jats:title>Abstract</jats:title> <jats:p>Summary: SPEPlip is a neural network-based method, trained and tested on a set of experimentally derived signal peptides from eukaryotes and prokaryotes. SPEPlip identifies the presence of sorting signals and predicts their cleavage sites. The accuracy in cross-validation is similar to that of other available programs: the rate of false positives is 4 and 6%, for prokaryotes and eukaryotes respectively and that of false negatives is 3% in both cases. When a set of 409 prokaryotic lipoproteins is predicted, SPEPlip predicts 97% of the chains in the signal peptide class. However, by integrating SPEPlip with a regular expression search utility based on the PROSITE pattern, we can successfully discriminate signal peptide-containing chains from lipoproteins. We propose the method for detecting and discriminating signal peptides containing chains and lipoproteins.</jats:p> <jats:p>Availability: It can be accessed through the web page at http://gpcr.biocomp.unibo.it/predictors/</jats:p> SPEPlip: the detection of signal peptide and lipoprotein cleavage sites Bioinformatics |
| doi_str_mv |
10.1093/bioinformatics/btg360 |
| facet_avail |
Online Free |
| finc_class_facet |
Mathematik Informatik Biologie Chemie und Pharmazie |
| format |
ElectronicArticle |
| fullrecord |
blob:ai-49-aHR0cDovL2R4LmRvaS5vcmcvMTAuMTA5My9iaW9pbmZvcm1hdGljcy9idGczNjA |
| id |
ai-49-aHR0cDovL2R4LmRvaS5vcmcvMTAuMTA5My9iaW9pbmZvcm1hdGljcy9idGczNjA |
| institution |
DE-D275 DE-Bn3 DE-Brt1 DE-Zwi2 DE-D161 DE-Gla1 DE-Zi4 DE-15 DE-Rs1 DE-Pl11 DE-105 DE-14 DE-Ch1 DE-L229 |
| imprint |
Oxford University Press (OUP), 2003 |
| imprint_str_mv |
Oxford University Press (OUP), 2003 |
| issn |
1367-4811 1367-4803 |
| issn_str_mv |
1367-4811 1367-4803 |
| language |
English |
| mega_collection |
Oxford University Press (OUP) (CrossRef) |
| match_str |
fariselli2003speplipthedetectionofsignalpeptideandlipoproteincleavagesites |
| publishDateSort |
2003 |
| publisher |
Oxford University Press (OUP) |
| recordtype |
ai |
| record_format |
ai |
| series |
Bioinformatics |
| source_id |
49 |
| title |
SPEPlip: the detection of signal peptide and lipoprotein cleavage sites |
| title_unstemmed |
SPEPlip: the detection of signal peptide and lipoprotein cleavage sites |
| title_full |
SPEPlip: the detection of signal peptide and lipoprotein cleavage sites |
| title_fullStr |
SPEPlip: the detection of signal peptide and lipoprotein cleavage sites |
| title_full_unstemmed |
SPEPlip: the detection of signal peptide and lipoprotein cleavage sites |
| title_short |
SPEPlip: the detection of signal peptide and lipoprotein cleavage sites |
| title_sort |
speplip: the detection of signal peptide and lipoprotein cleavage sites |
| topic |
Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability |
| url |
http://dx.doi.org/10.1093/bioinformatics/btg360 |
| publishDate |
2003 |
| physical |
2498-2499 |
| description |
<jats:title>Abstract</jats:title>
<jats:p>Summary: SPEPlip is a neural network-based method, trained and tested on a set of experimentally derived signal peptides from eukaryotes and prokaryotes. SPEPlip identifies the presence of sorting signals and predicts their cleavage sites. The accuracy in cross-validation is similar to that of other available programs: the rate of false positives is 4 and 6%, for prokaryotes and eukaryotes respectively and that of false negatives is 3% in both cases. When a set of 409 prokaryotic lipoproteins is predicted, SPEPlip predicts 97% of the chains in the signal peptide class. However, by integrating SPEPlip with a regular expression search utility based on the PROSITE pattern, we can successfully discriminate signal peptide-containing chains from lipoproteins. We propose the method for detecting and discriminating signal peptides containing chains and lipoproteins.</jats:p>
<jats:p>Availability: It can be accessed through the web page at http://gpcr.biocomp.unibo.it/predictors/</jats:p> |
| container_issue |
18 |
| container_start_page |
2498 |
| container_title |
Bioinformatics |
| container_volume |
19 |
| format_de105 |
Article, E-Article |
| format_de14 |
Article, E-Article |
| format_de15 |
Article, E-Article |
| format_de520 |
Article, E-Article |
| format_de540 |
Article, E-Article |
| format_dech1 |
Article, E-Article |
| format_ded117 |
Article, E-Article |
| format_degla1 |
E-Article |
| format_del152 |
Buch |
| format_del189 |
Article, E-Article |
| format_dezi4 |
Article |
| format_dezwi2 |
Article, E-Article |
| format_finc |
Article, E-Article |
| format_nrw |
Article, E-Article |
| _version_ |
1792339053855637510 |
| geogr_code |
not assigned |
| last_indexed |
2024-03-01T15:42:01.243Z |
| geogr_code_person |
not assigned |
| openURL |
url_ver=Z39.88-2004&ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fvufind.svn.sourceforge.net%3Agenerator&rft.title=SPEPlip%3A+the+detection+of+signal+peptide+and+lipoprotein+cleavage+sites&rft.date=2003-12-12&genre=article&issn=1367-4803&volume=19&issue=18&spage=2498&epage=2499&pages=2498-2499&jtitle=Bioinformatics&atitle=SPEPlip%3A+the+detection+of+signal+peptide+and+lipoprotein+cleavage+sites&aulast=Casadio&aufirst=Rita&rft_id=info%3Adoi%2F10.1093%2Fbioinformatics%2Fbtg360&rft.language%5B0%5D=eng |
| SOLR | |
| _version_ | 1792339053855637510 |
| author | Fariselli, Piero, Finocchiaro, Giacomo, Casadio, Rita |
| author_facet | Fariselli, Piero, Finocchiaro, Giacomo, Casadio, Rita, Fariselli, Piero, Finocchiaro, Giacomo, Casadio, Rita |
| author_sort | fariselli, piero |
| container_issue | 18 |
| container_start_page | 2498 |
| container_title | Bioinformatics |
| container_volume | 19 |
| description | <jats:title>Abstract</jats:title> <jats:p>Summary: SPEPlip is a neural network-based method, trained and tested on a set of experimentally derived signal peptides from eukaryotes and prokaryotes. SPEPlip identifies the presence of sorting signals and predicts their cleavage sites. The accuracy in cross-validation is similar to that of other available programs: the rate of false positives is 4 and 6%, for prokaryotes and eukaryotes respectively and that of false negatives is 3% in both cases. When a set of 409 prokaryotic lipoproteins is predicted, SPEPlip predicts 97% of the chains in the signal peptide class. However, by integrating SPEPlip with a regular expression search utility based on the PROSITE pattern, we can successfully discriminate signal peptide-containing chains from lipoproteins. We propose the method for detecting and discriminating signal peptides containing chains and lipoproteins.</jats:p> <jats:p>Availability: It can be accessed through the web page at http://gpcr.biocomp.unibo.it/predictors/</jats:p> |
| doi_str_mv | 10.1093/bioinformatics/btg360 |
| facet_avail | Online, Free |
| finc_class_facet | Mathematik, Informatik, Biologie, Chemie und Pharmazie |
| format | ElectronicArticle |
| format_de105 | Article, E-Article |
| format_de14 | Article, E-Article |
| format_de15 | Article, E-Article |
| format_de520 | Article, E-Article |
| format_de540 | Article, E-Article |
| format_dech1 | Article, E-Article |
| format_ded117 | Article, E-Article |
| format_degla1 | E-Article |
| format_del152 | Buch |
| format_del189 | Article, E-Article |
| format_dezi4 | Article |
| format_dezwi2 | Article, E-Article |
| format_finc | Article, E-Article |
| format_nrw | Article, E-Article |
| geogr_code | not assigned |
| geogr_code_person | not assigned |
| id | ai-49-aHR0cDovL2R4LmRvaS5vcmcvMTAuMTA5My9iaW9pbmZvcm1hdGljcy9idGczNjA |
| imprint | Oxford University Press (OUP), 2003 |
| imprint_str_mv | Oxford University Press (OUP), 2003 |
| institution | DE-D275, DE-Bn3, DE-Brt1, DE-Zwi2, DE-D161, DE-Gla1, DE-Zi4, DE-15, DE-Rs1, DE-Pl11, DE-105, DE-14, DE-Ch1, DE-L229 |
| issn | 1367-4811, 1367-4803 |
| issn_str_mv | 1367-4811, 1367-4803 |
| language | English |
| last_indexed | 2024-03-01T15:42:01.243Z |
| match_str | fariselli2003speplipthedetectionofsignalpeptideandlipoproteincleavagesites |
| mega_collection | Oxford University Press (OUP) (CrossRef) |
| physical | 2498-2499 |
| publishDate | 2003 |
| publishDateSort | 2003 |
| publisher | Oxford University Press (OUP) |
| record_format | ai |
| recordtype | ai |
| series | Bioinformatics |
| source_id | 49 |
| spelling | Fariselli, Piero Finocchiaro, Giacomo Casadio, Rita 1367-4811 1367-4803 Oxford University Press (OUP) Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability http://dx.doi.org/10.1093/bioinformatics/btg360 <jats:title>Abstract</jats:title> <jats:p>Summary: SPEPlip is a neural network-based method, trained and tested on a set of experimentally derived signal peptides from eukaryotes and prokaryotes. SPEPlip identifies the presence of sorting signals and predicts their cleavage sites. The accuracy in cross-validation is similar to that of other available programs: the rate of false positives is 4 and 6%, for prokaryotes and eukaryotes respectively and that of false negatives is 3% in both cases. When a set of 409 prokaryotic lipoproteins is predicted, SPEPlip predicts 97% of the chains in the signal peptide class. However, by integrating SPEPlip with a regular expression search utility based on the PROSITE pattern, we can successfully discriminate signal peptide-containing chains from lipoproteins. We propose the method for detecting and discriminating signal peptides containing chains and lipoproteins.</jats:p> <jats:p>Availability: It can be accessed through the web page at http://gpcr.biocomp.unibo.it/predictors/</jats:p> SPEPlip: the detection of signal peptide and lipoprotein cleavage sites Bioinformatics |
| spellingShingle | Fariselli, Piero, Finocchiaro, Giacomo, Casadio, Rita, Bioinformatics, SPEPlip: the detection of signal peptide and lipoprotein cleavage sites, Computational Mathematics, Computational Theory and Mathematics, Computer Science Applications, Molecular Biology, Biochemistry, Statistics and Probability |
| title | SPEPlip: the detection of signal peptide and lipoprotein cleavage sites |
| title_full | SPEPlip: the detection of signal peptide and lipoprotein cleavage sites |
| title_fullStr | SPEPlip: the detection of signal peptide and lipoprotein cleavage sites |
| title_full_unstemmed | SPEPlip: the detection of signal peptide and lipoprotein cleavage sites |
| title_short | SPEPlip: the detection of signal peptide and lipoprotein cleavage sites |
| title_sort | speplip: the detection of signal peptide and lipoprotein cleavage sites |
| title_unstemmed | SPEPlip: the detection of signal peptide and lipoprotein cleavage sites |
| topic | Computational Mathematics, Computational Theory and Mathematics, Computer Science Applications, Molecular Biology, Biochemistry, Statistics and Probability |
| url | http://dx.doi.org/10.1093/bioinformatics/btg360 |