Kingsford Group's Current Research Interests
We are interested in designing graph and optimization algorithms to extract insight from biological data. In particular, we focus on the following classes of problems:
- Genomics & genome assembly: RNA-seq expression quantification; genome assembly; large-scale sequence search, etc. This work is currently supported by a Data-Driven Invesgator grant from the Gordon and Betty Moore Foundation. It was previously supported by NIH grant 1R21HG006913, and NSF grant CCF-1319998.
- Chromatin structure and function: Algorithms for determining the spatial organization of eukaryotic genomes from Chromosome Conformation Capture data. Supported by NIH grant R01HG007104.
- Protein interactions and networks: Evolution of interactions; protein function prediction; clustering within networks; protein structure prediction. This work was supported by NSF grant EF-0849899 and by NSF grant CCF-1053918/CCF-1256087 (CAREER award).
Previous research interests include:
- Viral evolution: Reassortment in the influenza genome. This work was supported by NIH grant 1R21AI085376.
Recent Research Highlights
Sequence Bloom Trees
Armatus – Topological Domain Finder
Sailfish RNA-seq Quantification
Recent chromosome conformation capture experiments have led to the discovery of dense, contiguous, megabase-sized topological domains that are similar across cell types and conserved across species. These domains are strongly correlated with a number of chromatin markers and have since been included in a number of analyses. However, functionally-relevant domains…
RNA-seq expression estimates need not take longer than a cup of coffee The quantification of gene or isoform abundance is a fundamental step in many transcriptome analysis tasks, such as determining differential expression between biological samples. Yet, estimating isoform abundance from a large set of RNA-seq reads remains a computationally…
Querying a short read database for a transcript of interest is a fundamental problem in biology. Yet such queries are computationally intensive and scale linearly with the size of the data being searched. This leads to a computational bottleneck in which large databases of sequencing reads are compiled but never…
Sep. 2016: Dan DeBlasio, Ph.D. was elected to the ISCB Student Council Leadership (as Student Council Representative to ISCB Board of Directors). Sep. 2016: Hao Wang, Ph.D. successfully defended her Ph.D. August 2016: Hao Wang, Ph.D. student in the group, receives the CPCB Research award for her research contributions during…