Carnegie Mellon University ———
Mellon College of Science  ›  Computational Cancer Research  ›  Faculty  ›  Carl Kingsford

Carl Kingsford

Herbert A. Simon Professor of Computer Science

Address:
7719 Gates and Hillman Centers Email:
Computational Biology Department
Carnegie Mellon University
Pittsburgh, PA 15213

Email

Carl Kingsford
We are interested in designing algorithms to extract insight from biological data, particularly large collections of genomic and gene expression sequencing data. Of particular interest is developing methods for detecting large-scale mutations (structural variants) that are common in various types of cancers and also developing methods to estimate the change in expression of various genes in cancer tumors. We have developed software called SQUID to identify structural variant in tumors from gene expression data and also the software SALMON that quantifies gene expression levels and is widely used in cancer analyses. We have also developed several computational methods to study how the 3D structure of the genome changes in cancer.

Highlighted Publications

Ma, Cong, and Carl Kingsford. "Detecting, Categorizing, and Correcting Coverage Anomalies of RNA-Seq Quantification." Cell Systems 9.6 (2019): 589-599.

Qiu, Yutong, et al. "Detecting Transcriptomic Structural Variants in Heterogeneous Contexts via the Multiple Compatible Arrangements Problem." BioRxiv (2019): 697367. 19th International Workshop on Algorithms in Bioinformatics (WABI 2019) 18:1–18:19 (2019).

Sauerwald, Natalie, Akshat Singhal, and Carl Kingsford. "Analysis of the structural variability of topologically associated domains as revealed by Hi-C." NAR genomics and bioinformatics 2.1 (2020): lqz008.

Sauerwald, Natalie, Yihang Shen, and Carl Kingsford. "Topological Data Analysis Reveals Principles of Chromosome Structure in Cellular Differentiation." 19th International Workshop on Algorithms in Bioinformatics (WABI 2019). Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik, 2019.

Lee, Heewook, and Carl Kingsford. "Kourami: graph-guided assembly for novel human leukocyte antigen allele discovery." Genome biology 19.1 (2018): 16.

Sauerwald, Natalie, and Carl Kingsford. "Quantifying the similarity of topological domains across normal and cancer human cell types." Bioinformatics 34.13 (2018): i475-i483.

Ma, Cong, Mingfu Shao, and Carl Kingsford. "SQUID: transcriptomic structural variation detection from RNA-seq." Genome biology 19.1 (2018): 52.

GOOGLE SCHOLAR

Software

SAD - detect expression coverage anomalies

SQUID - detect structural variants in RNA-seq

DiploSQUID - detect structural variants in RNA-seq from heterogenous samples

Scallop - assemble transcripts

Salmon - RNA-seq expression quantification

Localtadsim - compare topologically associated domains using Hi-C