Upcoming Events

July 19-23, 2013: 21st Annual International Conference on Intelligent Systems for Molecular Biology (ISMB).

April 7-10, 2013: 17th Annual International Conference on Research in Computational Molecular Biology (RECOMB).

April 15-19, 2013: IEEE Symposium on Computational Intelligence in Bioinformatics & Computational Biology.

March 4-6, 2013: 5th international conference on Bioinformatics & Computational Biology (BICoB).

More events....


Oct 18, 2012: A new Postdoctoral Fellow, Dr. Sitanshu Sekhar Sahu has joined our lab.

Aug 13-17, 2012: A comprehensive 1-week Bioinformatics Workshop was organized on campus; co-organized by OSU's iCREST center. Visit facebook page for details.

Apr 23, 2012: Co-hosted Dr. James Tiedje (Director, NSF Center for Microbial Ecology, Michigan State University) as an invited iCREST speaker; see flyer for details.

Apr 13, 2012: World renowned Computational Biologist, Dr. Eugene Koonin (NCBI) visited our lab, and delivered an invited lecture on campus as part of iCREST speaker series; see flyer for details. Video on YouTube.

Mar 16, 2012: We welcome Dr. Chris Town (Group leader, Plant Genomics, JCVI) as an invited iCREST speaker; see flyer for details.

Feb 14, 2012: KBL receives new grant from OCAST to develop bioinformatics systems for plant-microbe interaction networks; immediate Postdoc opening available.

Oct 21, 2011: We welcome Dr. Patrick X. Zhao (Head, Bioinformatics Lab, Noble Foundation) as an invited iCREST speaker; see flyer for details.

Sep 17, 2011: Tyler Weirick joins our lab (under iCREST) as a Graduate Research Assistant.

Aug 17, 2011: Robyn Kelley, a new master's student joins our lab as a Graduate Research Assistant.

July 21, 2011: KBL receives OSU funding to establish an iCREST center for Bioinformatics and Computational Biology.

June 08, 2011: KBL welcomes its first student, Kalpana Varala to work as a summer scholar in lab.


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Welcome to AP-iNET

AP-iNET is a computational prediction system for host-pathogen protein-protein interactions in the Arabidopsis-Pseudomonas model system. This webtool allows users to input host and pathogen protein sequences to predict whether they are interacting within a host-pathogen network.

Protein-protein interactions (PPIs) play a crucial role in host-pathogen interactions and pathogenesis and maintaining infection. Successful prediction of PPIs for a well-studied host-pathogen system will lead to better understanding of mechanisms of pathogenesis on a genome-wide scale.

Several support vector machine(SVM) models were developed using experimentally proved Arabidopsis – Pseudomonas PPIs from different well curated host-pathogen interaction (HPI) databases. These models are based on Amino acid composition (AAC), Dipeptide composition (DIPEP), Conjoint Triad composition (CT) and Composition-Transtion-Distribuition (CTD) features. The SVM models are validated through a five-fold training dataset and an independent dataset. The user has a choice of training individual descriptors or a combination for prediction of the host-pathogen interactions. A homology based model was also developed to assess the predictions in Arabidopsis - Pseudomonas system in genome-wide. More information about these models can be found on the Help page.

There are three options for interaction prediction: “Host Only”, “Pathogen Only” or “Host and Pathogen”. User can submit the protein sequences in FASTA format by either uploading a file or through a text box. After the prediction model has been selected and the sequences uploaded, the submitted sequences are tested against trained files of the selected model. If both host and pathogen sequences are input, all possible combinations of host-pathogen sequence pairs will be created and tested against the trained SVM model. After the prediction is complete, the prediction results will be displayed in a table of all posible pairs with their SVM scores and the reamrk of interacting or non-interacting. The output can also be downloaded in various fourmats (CSV,XML,TSV) and the network of interaction can be viewed through "View Cytoscape".