Chittibabu (Babu) Guda, Ph.D.

Associate Professor, Director of Bioinformatics and Systems Biology Core

Chittibabu (Babu) Guda, Ph.D.

Ph. D. in Molecular Biology, Auburn University, 1997
Post-doc training in Computational Biology
University of California at San Diego, 2001

Phone: 402-559-5954
Fax: 402-559-7328
Email: babu.guda@unmc.edu
URL: Lab webpage

Research - Overall Goals:

My laboratory nurtures a wide variety of research areas related to bioinformatics. Research topics can be broadly grouped under novel method development, data mining and knowledge discovery, and the application of machine learning tools to solve biological problems. In addition, we have been developing and supporting web servers and software tools for bioinformatic applications. Major current research projects in my laboratory are the following:

(i) Comparative analysis of cancer protein interaction networks: In this study, we compare protein-protein interaction networks of different cancer types to identify their common and distinct functional modules. We used gene-lists associated with six common cancer types from the NCI Cancer Gene Index and mapped them against known interacting proteins to build cancer-specific interaction networks. Our goal is to computationally identify the overlapping sub-networks across multiple cancers that represent the common functional modules of cancer disease. Also, the sub-graphs that don’t overlap with any other cancer network represent functional modules that are characteristic of a given cancer type. We believe that this work would allow us to identify functionally-relevant sub-graphs in cancer networks, which can be advanced to experimental validation to further our understanding of the complex cancer biology.

(ii) Inferring domain-domain interactions from protein-protein interactions: Knowledge of specific domain-domain interactions (DDIs) is essential for understanding the functional significance of protein-protein interaction networks (PPIs). We have developed a top-down approach to accurately infer functionally relevant DDIs from a comprehensive, non-redundant dataset of 209,165 experimentally-derived PPIs. We introduced a novel combination of a set of five orthogonal scoring features covering the probabilistic, evolutionary, evidence-based, spatial and functional properties of interacting domains, which can map the interacting propensity of two domains in many dimensions. A set of 25,287 high-confidence DDIs from human proteome have been mapped onto cancer-associated proteins to understand the functional significance of PPIs at the modular level. Experimental validation of novel DDIs found in a core set of DNA repair proteins representing tumor suppressors is underway.

(iii) Cataloguing the organellar proteomes of eukaryotes: The structural organization of eukaryotic cells exemplify the concept of ‘division of labor’, where each subcellular organelle or location carryout and coordinate a set of specialized functions in the global context of the cellular function. To a large extent, subcellular proteomes are genetically programmed to localize and function in a given subcellular compartment at a steady cellular state. Nevertheless, the concept of an organellar proteome is rather dynamic depending upon the spatial (tissue-specific), temporal (stage-specific) and physiological state of a cell. Mislocalization of proteins to unintended organelles has been implicated as the causative factor for many human diseases. In this study, we used our ngLOC-X method to classify the localizations of whole proteomes of eight eukaryotic organisms and estimate the fraction of proteins localized to fourteen distinct subcellular compartments. Our results show that in lower eukaryotes the relative fractions of organellar proteomes fluctuate considerably between evolutionarily neighboring species. In higher eukaryotes, the fractions of the larger organellar proteomes have stabilized and those of the moderate or tiny organellar proteomes have gradually increased. Organelles with increased proteomes are related to either cell structure (cytoskeleton, cell junction, cell projection) or to vesicular transport system (Golgi, endosomes, lysosomes).

Publications listed in PubMed

Selected Recent Publications:

  1. Shen R, Guda C. (2014) Applied graph-mining algorithms to study biomolecular interaction networks. BioMed Research International. (In press)
  2. Goonesekhere N, Wang X, Ludwig L, Guda C. (2014) A meta analysis of pancreatic microarray datasets yields new targets as cancer genes and biomarkers. PLoS ONE (In press)
  3. Wang X, Guda C. (2014) Computational analysis of transcriptional circuitries in human embryonic stem cells reveals multiple and independent networks. BioMed Research International. 2014;2014:725780. [PubMed]
  4. Srinivasan SM, Guda C. (2013) MetaID: A novel method for identification and quantification of metagenomic samples. BMC Genomics, [PubMed]
  5. Read BA, Kegel J, … Emiliana huxleyi Annotation Consortium (Guda C.) … Grigoriev IV (2013) Pan genome of the phytoplankton Emiliania underpins its global distribution. Nature 499, 209-213.[PubMed]
  6. Guda C. (2013) Bioinformatic methods and resources for neuroscience research. In: Current Laboratory Methods in Neuroscience Research, Xiong H and Gendelman HE Eds. Springer publishing (In press)
  7. Ozturk F, Li Y, Zhu X,  Guda C, Nawshad A. (2013) Systematic analysis of palatal transcriptome to identify cleft palate genes within TGFbeta3-knockout mice alleles: RNA-Seq analysis of TGFbeta3 Mice. BMC Genomics, 14:113. [PubMed]
  8. Srinivasan SM, Vural S, King BR, Guda C. (2013) Mining for class-specific motifs in protein sequence classification.  BMC Bioinformatics, 14:96. [PubMed]
  9. King BR, Vural S, Pandey S, Barteau A, Guda C. (2012) ngLOC: software and web server for predicting protein subcellular localization in prokaryotes and eukaryotes. BMC Research Notes, 5:351.[PubMed]
  10. Mohibi S, Gurumurthy CB, Nag A, Wang J, Mirza S, Mian Y, Quinn M, Katafiasz B, Eudy J, Pandey S, Guda C, Naramura M, Band H, Band V. (2012) Mammalian alteration/deficiency in activation 3 (Ada3) is essential for embryonic development and cell cycle progression. J Biol Chem., 287:29442-56. [PubMed]
  11. Shen R, Gooneshkere NCW, Guda C. (2012) Mining functional subgraphs from cancer protein-protein interaction networks. BMC Systems Biology, 6:S2
  12. Gu, SQ, Bakthavachalu B, Han J, Patil D, Otsuka Y, Guda C, Schoenberg DR. (2012) Identification of the human PMR1 mRNA endonuclease as an alternatively processed product of the gene for peroxidasin-like protein. RNA 18:1186-1196.[PubMed]
  13. Zhu X, Ozturk F, Pandey S, Guda C, Nowshad A. (2012) Implications of TGF-B on transcriptome and cellular biofunctions of palatal mesenchyme. Frontiers in Physiology, 3:1-22. [PubMed]
  14. Mohammed A, Guda C. (2011) Computational approaches for automated classification of enzyme sequences. Journal of Proteomics and Bioinformatics [PubMed]
  15. Mukherjee A, Reisdorph N, Guda C, Pandey S, Roy SK. (2012) Changes in ovarian protein expression during primordial follicle formation in the hamster. Molecular and Cellular Endocrinology, 348:87-94. [PubMed].

;