Another work from our group which came sometime back. Please do write to (ankitgmeister@gmail.com) in case of any problem. Comments are welcome.
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.
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