A novel scoring schema for peptide identification by searching protein sequence databases using tandem mass spectrometry data
<p>Abstract</p> <p>Background</p> <p>Tandem mass spectrometry (MS/MS) is a powerful tool for protein identification. Although great efforts have been made in scoring the correlation between tandem mass spectra and an amino acid sequence database, improvements could be m...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2006-04-01
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| Series: | BMC Bioinformatics |
| Online Access: | http://www.biomedcentral.com/1471-2105/7/222 |
| _version_ | 1818757188676485120 |
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| author | Yu Chungong Liu Xiaofei Chang Suhua Zhu Xiaopeng Sun Shiwei Zhang Zhuo Bu Dongbo Chen Runsheng |
| author_facet | Yu Chungong Liu Xiaofei Chang Suhua Zhu Xiaopeng Sun Shiwei Zhang Zhuo Bu Dongbo Chen Runsheng |
| author_sort | Yu Chungong |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>Tandem mass spectrometry (MS/MS) is a powerful tool for protein identification. Although great efforts have been made in scoring the correlation between tandem mass spectra and an amino acid sequence database, improvements could be made in three aspects, including characterization ofpeaks in spectra, adoption of effective scoring functions and access to thereliability of matching between peptides and spectra.</p> <p>Results</p> <p>A novel scoring function is presented, along with criteria to estimate the performance confidence of the function. Through learning the typesof product ions and the probability of generating them, a hypothetic spectrum was generated for each candidate peptide. Then relative entropy was introduced to measure the similarity between the hypothetic and the observed spectra. Based on the extreme value distribution (EVD) theory, a threshold was chosen to distinguish a true peptide assignment from a random one. Tests on a public MS/MS dataset demonstrated that this method performs better than the well-known SEQUEST.</p> <p>Conclusion</p> <p>A reliable identification of proteins from the spectra promises a more efficient application of tandem mass spectrometry to proteomes with high complexity.</p> |
| first_indexed | 2024-12-18T06:06:58Z |
| format | Article |
| id | doaj.art-6758bc738b414caea1e0a0546da7adc0 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-18T06:06:58Z |
| publishDate | 2006-04-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-6758bc738b414caea1e0a0546da7adc02022-12-21T21:18:31ZengBMCBMC Bioinformatics1471-21052006-04-017122210.1186/1471-2105-7-222A novel scoring schema for peptide identification by searching protein sequence databases using tandem mass spectrometry dataYu ChungongLiu XiaofeiChang SuhuaZhu XiaopengSun ShiweiZhang ZhuoBu DongboChen Runsheng<p>Abstract</p> <p>Background</p> <p>Tandem mass spectrometry (MS/MS) is a powerful tool for protein identification. Although great efforts have been made in scoring the correlation between tandem mass spectra and an amino acid sequence database, improvements could be made in three aspects, including characterization ofpeaks in spectra, adoption of effective scoring functions and access to thereliability of matching between peptides and spectra.</p> <p>Results</p> <p>A novel scoring function is presented, along with criteria to estimate the performance confidence of the function. Through learning the typesof product ions and the probability of generating them, a hypothetic spectrum was generated for each candidate peptide. Then relative entropy was introduced to measure the similarity between the hypothetic and the observed spectra. Based on the extreme value distribution (EVD) theory, a threshold was chosen to distinguish a true peptide assignment from a random one. Tests on a public MS/MS dataset demonstrated that this method performs better than the well-known SEQUEST.</p> <p>Conclusion</p> <p>A reliable identification of proteins from the spectra promises a more efficient application of tandem mass spectrometry to proteomes with high complexity.</p>http://www.biomedcentral.com/1471-2105/7/222 |
| spellingShingle | Yu Chungong Liu Xiaofei Chang Suhua Zhu Xiaopeng Sun Shiwei Zhang Zhuo Bu Dongbo Chen Runsheng A novel scoring schema for peptide identification by searching protein sequence databases using tandem mass spectrometry data BMC Bioinformatics |
| title | A novel scoring schema for peptide identification by searching protein sequence databases using tandem mass spectrometry data |
| title_full | A novel scoring schema for peptide identification by searching protein sequence databases using tandem mass spectrometry data |
| title_fullStr | A novel scoring schema for peptide identification by searching protein sequence databases using tandem mass spectrometry data |
| title_full_unstemmed | A novel scoring schema for peptide identification by searching protein sequence databases using tandem mass spectrometry data |
| title_short | A novel scoring schema for peptide identification by searching protein sequence databases using tandem mass spectrometry data |
| title_sort | novel scoring schema for peptide identification by searching protein sequence databases using tandem mass spectrometry data |
| url | http://www.biomedcentral.com/1471-2105/7/222 |
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