Carl Kingsford

About

Carl Kingsford is the Herbert A. Simon Professor of Computer Science in the Ray and Stephanie Lane Computational Biology Department at Carnegie Mellon University. He is recognized as a trailblazer in computational molecular biology, showcasing sustained innovation in scalable algorithmic approaches. His research focuses on the development of computational methods and algorithms for biological data analysis, including genome graph construction, sequence analysis, and the study of genomic variation. Kingsford's contributions have significantly advanced the understanding of genome structure and function through algorithmic innovations, and he has been honored as an ISCB Fellow for his impactful work in the field.

Research topics

• Computer Science
• Biology
• Artificial Intelligence
• Computational biology
• Data Mining
• Anatomy
• Cell biology
• Algorithm

Selected publications

• Alignment and mapping methodology influence transcript abundance estimation

  Genome biology · 2020 · 192 citations

  BACKGROUND: The accuracy of transcript quantification using RNA-seq data depends on many factors, such as the choice of alignment or mapping method and the quantification model being adopted. While the choice of quantification model has been shown to be important, considerably less attention has been given to comparing the effect of various read alignment approaches on quantification accuracy. RESULTS: We investigate the influence of mapping and alignment on the accuracy of transcript quantifica…

  DOI

• Advances and prospects for the Human BioMolecular Atlas Program (HuBMAP)

  Nature Cell Biology · 2023 · 170 citations

  PublisherOA PDFDOI

• Improved design and analysis of practical minimizers

  Bioinformatics · 2020 · 67 citations

  Abstract Motivation Minimizers are methods to sample k-mers from a string, with the guarantee that similar set of k-mers will be chosen on similar strings. It is parameterized by the k-mer length k, a window length w and an order on the k-mers. Minimizers are used in a large number of softwares and pipelines to improve computation efficiency and decrease memory usage. Despite the method’s popularity, many theoretical questions regarding its performance remain open. The core metric for measuring…

  DOI

• How Much Data Is Sufficient to Learn High-Performing Algorithms?

  Journal of the ACM · 2024 · 13 citations

  Algorithms often have tunable parameters that impact performance metrics such as runtime and solution quality. For many algorithms used in practice, no parameter settings admit meaningful worst-case bounds, so the parameters are made available for the user to tune. Alternatively, parameters may be tuned implicitly within the proof of a worst-case approximation ratio or runtime bound. Worst-case instances, however, may be rare or nonexistent in practice. A growing body of research has demonstrate…

  PublisherOA PDFDOI

• <i>k</i> -nonical space: sketching with reverse complements

  Bioinformatics · 2024-10-17 · 7 citations

  articleOpen accessSenior author

  MOTIVATION: Sequences equivalent to their reverse complements (i.e. double-stranded DNA) have no analogue in text analysis and non-biological string algorithms. Despite this striking difference, algorithms designed for computational biology (e.g. sketching algorithms) are designed and tested in the same way as classical string algorithms. Then, as a post-processing step, these algorithms are adapted to work with genomic sequences by folding a k-mer and its reverse complement into a single sequen…

  PublisherDOI

Recent grants

• Algorithms for Managing Uncertainty in Chromosome Conformation Capture Data

  NIH · $1.3M · 2013–2017

• CAREER: Model-based Reconstruction of Ancient Biological Networks

  NSF · $177k · 2011–2012

• Data Discovery: Computational Methods for Searching Short-Read Sequencing Experiments - Administrative Supplement

  NIH · $1.2M · 2017–2022

Frequent coauthors

• Rob Patro

  University of Maryland, College Park

  42 shared

• Guillaume Marçais

  Carnegie Mellon University

  38 shared

• Mingfu Shao

  Pennsylvania State University

  25 shared

• Dan DeBlasio

  Carnegie Mellon University

  21 shared

• Cong Ma

  Northwestern Polytechnical University

  19 shared

• Geet Duggal

  DNAnexus (United States)

  19 shared

• Charlotte Soneson

  SIB Swiss Institute of Bioinformatics

  17 shared

• Darya Filippova

  16 shared

Labs

• Kingsford GroupPI

Similar researchers at Carnegie Mellon University

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• Jian Mah-60
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