School of Computer Science › Ray and Stephanie Lane Computational Biology Department › People › Carl Kingsford

Carl Kingsford

Herbert A. Simon Professor of Computer Science and co-Director of the Joint Carnegie Mellon-University of Pittsburgh Ph.D. Program in Computational Biology, Ray and Stephanie Lane Computational Biology Department

Address:

Ray and Stephanie Lane Computational Biology Department, SCS
Carnegie Mellon University
5000 Forbes Avenue
Pittsburgh, PA 15213
Gates Hillman Center Room 7719

Work Phone: 412-268-1769

Administrative Assistant: Janet Garrand

Dr. Kingsford’s research is focused on developing new, efficient algorithms for extracting knowledge from large biological data sets, particularly high-throughput DNA and RNA sequencing data. He has worked recently on algorithms for accurately quantifying gene expression, identifying compact regions of chromatin, and large-scale sequence search, among other topics. His group typically explores solutions using optimization, graph algorithms, and machine learning.

Lab Members

Shiyi Zoe Du

Ph.D. Student

C. Shane Elder

Ph.D Student

Jiayi Li

Ph.D. Student

Guillaume Marçais

Senior System Scientist

Highlighted Publications

Hongyu Zheng, Carl Kingsford, Guillaume Marçais (2020). Lower density selection schemes via small universal hitting sets with short remaining path length. In Proceedings of RECOMB 2020, pages 202-217 (2020). [Journal version in J Comp. Biol. 28(4):395-409 (2021).]

Guillaume Marçais, Dan DeBlasio, Prashant Pandey, Carl Kingsford (2019). Locality sensitive hashing for the edit distance. In Proceedings of ISMB 2019, in Bioinformatics 35(14):i127-i135 (2019).

G. Marçais, D. DeBlasio, C. Kingsford. Asymptotically optimal minimizers schemes. In Proceedings of ISMB 2018. Bioinformatics 34(13):i13-i22, 2018.

C. Ma, M. Shao, and C. Kingsford. SQUID: Transcriptomic structural variation Detection from RNA-seq. Genome Biology 19:52 (2018).

R. Patro, G. Duggal, M. I Love, R. A Irizarry, C. Kingsford. Salmon provides accurate, fast, and bias-aware transcript expression estimates using dual-phase inference. Nature Methods 14:417-419 (2017) doi: http://dx.doi.org/10.1101/021592

Mingfu Shao and Carl Kingsford. Accurate assembly of transcripts through phase-preserving graph decomposition. Nature Biotechnology 35:1167–1169 (2017).

B. Solomon and C. Kingsford. Fast search of thousands of short-read sequencing experiments. Nature Biotechnology 34:300–302 (2016).

R. Patro, S. M. Mount, and C. Kingsford. Sailfish enables alignment-free isoform quantification from RNA-seq reads using lightweight algorithms. Nature Biotechnology 32:462-464 (2014).

D. Filippova, R. Patro, G. Duggal, and C. Kingsford. Identification of alternative topological domains in chromatin. Algorithms for Molecular Biology 9:14 (2014).

G. Marçais and C. Kingsford. A fast, lock-free approach for efficient parallel counting of occurrences of k-mers. Bioinformatics 27(6):764-770 (2011).

S. Navlakha and C. Kingsford. Network archaeology: Uncovering ancient networks from present-day interactions. PLoS Computational Biology 7(4):e1001119.

Google Scholar

Highlighted Software

Salmon - https://github.com/COMBINE-lab/salmon - Fast, accurate, flexible gene expression quantification.

Sailfish - https://github.com/kingsfordgroup/sailfish - First fast quantifier for gene expression (use Salmon, which is improved in every respect).

Scallop - https://github.com/Kingsford-Group/scallop - Transcript assembler.

Kourami - https://github.com/Kingsford-Group/kourami - Graph-guided assembly for novel HLA allele discovery.

VariantStore - https://github.com/Kingsford-Group/variantstore - Efficient graph-based storage and search of genomic variants.

Biblint - https://github.com/Kingsford-Group/biblint - Clean and normalized BibTeX databases.

Armatus - https://github.com/kingsfordgroup/armatus - Identify topologically associated domains (TADs) from Hi-C data.

Jellyfish - https://github.com/gmarcais/Jellyfish - Fast, parallel kmer counting.