Computational biology with big -omics data and high-performance computing

Computing is the key for turning data into knowledge. We are interested in developing new algorithms and bioinformatics resources for analysis of big data from genomics, transcriptomics, proteomics and metabolomics. Our approaches are based on graph theory, computational statistics, and machine learning. We parallelize algorithms for supercomputers or cloud computing to scale our computation to large -omics datasets.

Selected relevant publications:
1. X. Guo, Z. Li, Q. Yao, R.S. Mueller, J.K. Eng, D.L. Tabb, W.J. Hervey, C. Pan. (2017) Sipros Ensemble improves database searching and filtering for complex metaproteomicsBioinformatics (Accepted)
2. T.-H. Ahn, J. Chai, and C. Pan. (2015) Sigma: Strain-level inference of genomes from metagenomic analysis for biosurveillanceBioinformatics 31, 170-177.
3. Y. Wang, G. Kora, B. P. Bowen, and C. Pan. (2014) MIDAS: A Database-Searching Algorithm for Metabolite Identification in MetabolomicsAnalytical Chemistry 86, 9496-9503.


Proteogenomics characterization of microbial communities

Microbial communities drive many biological processes in diverse ecosystems. We use metagenomics, metaproteomics, and stable isotope probing to study microbial communities ranging from human gastrointestinal tracts to coastal waters and forest soils in collaboration with experts of these ecosystems. The results can provide important insights into the ecology and evolutionary biology of microorganisms in their natural environments. The knowledge on microbial communities can be used to improve the health of human hosts and construct nutrient cycling models of terrestrial and aquatic ecosystems.

Selected relevant publications:
1. S. Bryson, Z. Li, F. Chavez, P. K. Weber, J. Pett-Ridge, R. L. Hettich, C. Pan, X. Mayali, and R. S. Mueller. (2017) Phylogenetically conserved resource partitioning in the coastal microbial loop, The ISME journal.
2. J. J. Marlow, C. T. Skennerton, Z. Li, K. Chourey, R. L. Hettich, C. Pan, and V. J. Orphan. (2016) Proteomic Stable Isotope Probing Reveals Biosynthesis Dynamics of Slow Growing Methane Based Microbial Communities, Frontiers in Microbiology 7.
3. Z. Li, Y. Wang, Q. Yao, N. B. Justice, T.-H. Ahn, D. Xu, R. L. Hettich, J. F. Banfield, and C. Pan. (2014) Diverse and divergent protein post-translational modifications in two growth stages of a natural microbial community, Nature Communications 5.


Systems biology of Eukaryotic organisms

Fungi, plants, animals, and humans are complex organisms with large genomes and sophisticated regulations of transcriptomes, proteomes and metabolomes. We use integrated -omics analyses to shed light on the metabolic and regulatory networks of these organisms in collaboration with domain experts.  We are also interested in studying the molecular -omics phenotypes of genomic variations stemming from somatic mutations, natural genetic polymorphisms, or genetic engineering. This may improve our understanding of the genetic basis of diseases and traits of plants and humans.

Selected relevant publications:
1. Z. Li, Q. Yao, S. P. Dearth, M. R. Entler, H. F. Castro Gonzalez, J. K. Uehling, R. J. Vilgalys, G. B. Hurst, S. R. Campagna, J. L. Labbe, and C. Pan. (2017) Integrated proteomics and metabolomics suggests symbiotic metabolism and multimodal regulation in a fungal-endobacterial system, Environmental Microbiology 19, 1041-1053.
2. A. C. Mosier, C. S. Miller, K. R. Frischkorn, R. A. Ohm, Z. Li, K. LaButti, A. Lapidus, A. Lipzen, C. Chen, J. Johnson, E. A. Lindquist, C. Pan, R. L. Hettich, I. V. Grigoriev, S. W. Singer, and J. F. Banfield. (2016) Fungi Contribute Critical but Spatially Varying Roles in Nitrogen and Carbon Cycling in Acid Mine Drainage, Frontiers in Microbiology 7.
3. Z. Li, O. Czarnecki, K. Chourey, J. Yang, G. A. Tuskan, G. B. Hurst, C. Pan, and J.-G. Chen. (2014) Strigolactone-Regulated Proteins Revealed by iTRAQ-Based Quantitative Proteomics in Arabidopsis, Journal of Proteome Research 13, 1359-1372.