MP3: a software tool for the prediction of pathogenic proteins in genomic and metagenomic data.
The identification of virulent proteins in any de-novo sequenced genome is useful in estimating its pathogenic ability and understanding the mechanism of pathogenesis. Similarly, the identification of such proteins could be valuable in comparing the metagenome of healthy and diseased individuals and...
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Public Library of Science (PLoS)
2014-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0093907 |
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| author | Ankit Gupta Rohan Kapil Darshan B Dhakan Vineet K Sharma |
| author_facet | Ankit Gupta Rohan Kapil Darshan B Dhakan Vineet K Sharma |
| author_sort | Ankit Gupta |
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| description | The identification of virulent proteins in any de-novo sequenced genome is useful in estimating its pathogenic ability and understanding the mechanism of pathogenesis. Similarly, the identification of such proteins could be valuable in comparing the metagenome of healthy and diseased individuals and estimating the proportion of pathogenic species. However, the common challenge in both the above tasks is the identification of virulent proteins since a significant proportion of genomic and metagenomic proteins are novel and yet unannotated. The currently available tools which carry out the identification of virulent proteins provide limited accuracy and cannot be used on large datasets. Therefore, we have developed an MP3 standalone tool and web server for the prediction of pathogenic proteins in both genomic and metagenomic datasets. MP3 is developed using an integrated Support Vector Machine (SVM) and Hidden Markov Model (HMM) approach to carry out highly fast, sensitive and accurate prediction of pathogenic proteins. It displayed Sensitivity, Specificity, MCC and accuracy values of 92%, 100%, 0.92 and 96%, respectively, on blind dataset constructed using complete proteins. On the two metagenomic blind datasets (Blind A: 51-100 amino acids and Blind B: 30-50 amino acids), it displayed Sensitivity, Specificity, MCC and accuracy values of 82.39%, 97.86%, 0.80 and 89.32% for Blind A and 71.60%, 94.48%, 0.67 and 81.86% for Blind B, respectively. In addition, the performance of MP3 was validated on selected bacterial genomic and real metagenomic datasets. To our knowledge, MP3 is the only program that specializes in fast and accurate identification of partial pathogenic proteins predicted from short (100-150 bp) metagenomic reads and also performs exceptionally well on complete protein sequences. MP3 is publicly available at http://metagenomics.iiserb.ac.in/mp3/index.php. |
| format | Article |
| id | doaj-art-e3fd30a8dc6f48148458d4367c2a812a |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Public Library of Science (PLoS) |
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| series | PLoS ONE |
| spelling | doaj-art-e3fd30a8dc6f48148458d4367c2a812a2025-08-20T03:44:38ZengPublic Library of Science (PLoS)PLoS ONE1932-62032014-01-0194e9390710.1371/journal.pone.0093907MP3: a software tool for the prediction of pathogenic proteins in genomic and metagenomic data.Ankit GuptaRohan KapilDarshan B DhakanVineet K SharmaThe identification of virulent proteins in any de-novo sequenced genome is useful in estimating its pathogenic ability and understanding the mechanism of pathogenesis. Similarly, the identification of such proteins could be valuable in comparing the metagenome of healthy and diseased individuals and estimating the proportion of pathogenic species. However, the common challenge in both the above tasks is the identification of virulent proteins since a significant proportion of genomic and metagenomic proteins are novel and yet unannotated. The currently available tools which carry out the identification of virulent proteins provide limited accuracy and cannot be used on large datasets. Therefore, we have developed an MP3 standalone tool and web server for the prediction of pathogenic proteins in both genomic and metagenomic datasets. MP3 is developed using an integrated Support Vector Machine (SVM) and Hidden Markov Model (HMM) approach to carry out highly fast, sensitive and accurate prediction of pathogenic proteins. It displayed Sensitivity, Specificity, MCC and accuracy values of 92%, 100%, 0.92 and 96%, respectively, on blind dataset constructed using complete proteins. On the two metagenomic blind datasets (Blind A: 51-100 amino acids and Blind B: 30-50 amino acids), it displayed Sensitivity, Specificity, MCC and accuracy values of 82.39%, 97.86%, 0.80 and 89.32% for Blind A and 71.60%, 94.48%, 0.67 and 81.86% for Blind B, respectively. In addition, the performance of MP3 was validated on selected bacterial genomic and real metagenomic datasets. To our knowledge, MP3 is the only program that specializes in fast and accurate identification of partial pathogenic proteins predicted from short (100-150 bp) metagenomic reads and also performs exceptionally well on complete protein sequences. MP3 is publicly available at http://metagenomics.iiserb.ac.in/mp3/index.php.https://doi.org/10.1371/journal.pone.0093907 |
| spellingShingle | Ankit Gupta Rohan Kapil Darshan B Dhakan Vineet K Sharma MP3: a software tool for the prediction of pathogenic proteins in genomic and metagenomic data. PLoS ONE |
| title | MP3: a software tool for the prediction of pathogenic proteins in genomic and metagenomic data. |
| title_full | MP3: a software tool for the prediction of pathogenic proteins in genomic and metagenomic data. |
| title_fullStr | MP3: a software tool for the prediction of pathogenic proteins in genomic and metagenomic data. |
| title_full_unstemmed | MP3: a software tool for the prediction of pathogenic proteins in genomic and metagenomic data. |
| title_short | MP3: a software tool for the prediction of pathogenic proteins in genomic and metagenomic data. |
| title_sort | mp3 a software tool for the prediction of pathogenic proteins in genomic and metagenomic data |
| url | https://doi.org/10.1371/journal.pone.0093907 |
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