Welcome !

Guda lab 2015
From left to right: Kristin Wipfler, Nitish Mishra, Matthew Cserhati, Simarjeet Negi, Adam Cornish, Suleyman Vural, You Li, Sanjit Pandey, Xiaosheng Wang, Peng Xiao, Babu Guda

Our laboratory nurtures a wide range of research areas related to bioinformatics and systems biology. Our research projects can be broadly categorized under novel algorithm development, downstream analysis of genomic and proteomic datasets, data mining and knowledge discovery, software and web application development, and using machine learning tools for data classification tasks.

The core strengths of our research lie in the computational biology area with special emphasis on developing novel algorithms/methods/tools for predicting the unknown based on the known data. Over the past 15 years, we have developed a number of novel methods for aligning protein structures in the protein data bank (PDB), predicting the subcellular localization of proteins, and predicting the functional significance of domain-domain interactions in protein-protein interaction networks. We also developed a number of data analysis pipelines using existing tools for pattern recognition and phylogenetic analysis of protein families, tracing the evolutionary origin of the domains of human proteome, reconstructing the amino acid pathways in mitochondria, comparative genome analysis, etc.

With the advent of Next-Gen sequencing (NGS) technology, we have expanded our research focus to analyzing the genomic and metagenomic datasets. Current NGS-based projects in our lab include developing new classification methods for detecting cancer subtypes, RNASeq and splice-variant analysis, and the identification and quantification of metagenomic samples from gut microbiome. On the systems biology front, we have been working to understand the network modules (sub-networks that correspond to functional units) involved in cancer development and progression. We have developed graph-theory-based algorithms to carryout network-to-network comparisons and identify functionally relevant network modules that are either common across many cancers or specific to a particular cancer type. Additional active research projects include using machine-learning algorithms to predict enzymes and enzymatic reactions, developing a database of signal peptides associated with subcellular localization and analyzing the structural variation in the exome sequences of normal and tumor samples.