Kumar Lab | Research
Statistical and Computational Methods in Molecular Phylogenetics
Comparative analysis of DNA and amino acid sequences is now routinely employed in tracing the origins, patterns, and evolutionary relationships of homologous sequences. Due to recent advances in DNA sequencing technologies, these datasets now contain increasingly larger numbers of sequences and there is an increasing need for computationally efficient and statistically rigorous tools for data analysis. We are conducting computer simulations involving biologically realistic parameters as well as empirical data analyses to identify substitution patterns and topological models that associate with greatest errors and differences among (computationally feasible) algorithms applied to infer phylogeny and divergence times in large data sets.
Evolutionary Bioinformatics of Human Mutations
Personal Genomics is a branch of genomics where the genome of a person is analyzed using bioinformatics techniques with an aim to predict their association with diseases and adaptation. Personal Genomics is a prerequisite for Personalized Medicine, because all drugs, treatments and remedies need to be dictated by the genomic profiles of patients’ own genomes. We are now investigating how, where, why, and when specific mutations occurred in our genomes, and what their functional significance is to us, as well as to future generations. We are also developing bioinformatics tools to diagnose human mutations in an effort to distinguish personal mutations with functional consequences (negative or adaptive) from those with no functional effects during a person’s life history.
Development of Molecular Evolutionary Genetics Analysis (MEGA) software
Evolutionary Bioinformatics is a powerful tool for conducting in silico functional analysis of DNA and protein sequences from genes and genomes of diverse organisms. We are developing advanced MEGA software, which contains facilities for conducting automatic and manual sequence alignment, inferring phylogenetic trees, mining web-based databases, estimating rates of molecular evolution, inferring ancestral sequences, and testing evolutionary hypotheses. This software will assist biologists in better understanding the evolutionary dynamics of human genomes, our evolutionary relatives, and pathogens.
Building a TimeTree of Life Web-resource
TimeTree is a public knowledge-base for information on the evolutionary timescale of life. A search utility allows exploration of the thousands of divergence times among organisms in the published literature. A tree-based (hierarchical) system is used to identify all published molecular time estimates bearing on the divergence of two chosen taxa, such as species, compute summary statistics, and present the results. Those most likely to find the search utility in TimeTree useful will be researchers who already have some knowledge of evolutionary biology and wish to mine the available published data, which often require interpretation.
Development of Fruit Fly Gene Expression (FlyExpress) knowledgebase
Translating sequence information to gene function and interaction is greatly facilitated by the growing collection of spatial and temporal gene expression patterns in the model organism, Drosophila melanogaster. These patterns are links between a gene’s primary sequence and its influence on the phenotype, as their overlaps provide the initial clues to functional, genetic, or regulatory interactions. We are developing computational methods and bioinformatics tools to build a comprehensive framework for analyzing these expression patterns. This system will fulfill needs of basic and applied researchers as well as students in many areas of molecular biology crucial in human health research, including computational genomics, molecular genetics, developmental biology, genetics, and evolution.
A Phylogenetic Approach to Metagenomic Analysis
Metagenomic analysis has emerged as a powerful tool to analyze genetic, and thus organismal, compositions of microbial communities that inhabit our planet and our bodies. The proposed statistical and computational research will result in the development of an evolutionary phylogenetic framework for an advanced analysis of the metagenomic data, which will improve the application of metagenomics to understanding microorganisms, both harmful and beneficial to humans.