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Duncan T. Odom

dblp:68/6133 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2019
0000-0001-6201-5599ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 2

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
2 papers
Bioinformatics and computational biology · 100%

Topics — the 6 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › genome editing
CRISPR guide RNA design
0.412019
Crisflash: open-source software to generate CRISPR guide RNAs against genomes annotated with individual variation · Bioinform. 2019
Bioinformatics and computational biology
genomics
0.412019
Crisflash: open-source software to generate CRISPR guide RNAs against genomes annotated with individual variation · Bioinform. 2019
Bioinformatics and computational biology › sequence analysis
motif discovery
0.112006
A hypothesis-based approach for identifying the binding specificity of regulatory proteins from chromatin immunoprecipitation data · Bioinform. 2006
Bioinformatics and computational biology › gene regulation
regulatory genomics
0.112006
A hypothesis-based approach for identifying the binding specificity of regulatory proteins from chromatin immunoprecipitation data · Bioinform. 2006
Bioinformatics and computational biology › gene regulation
transcription factor binding site prediction
0.112006
A hypothesis-based approach for identifying the binding specificity of regulatory proteins from chromatin immunoprecipitation data · Bioinform. 2006
Bioinformatics and computational biology › epigenomics
ChIP-chip analysis
0.012006
A hypothesis-based approach for identifying the binding specificity of regulatory proteins from chromatin immunoprecipitation data · Bioinform. 2006

Methods — techniques the papers use, named apart from their topics

variant integration · 0.4sequence indexing · 0.4cross-validation · 0.1constrained local optimization · 0.1
YearPublicationVenuePosition
2019 Crisflash: open-source software to generate CRISPR guide RNAs against genomes annotated with individual variation
abstract
SUMMARY: CRISPR/Cas9 system requires short guide RNAs (sgRNAs) to direct genome modification. Most currently available tools for sgRNA design operate only with standard reference genomes, and are best suited for small-scale projects. To address these limitations, we developed Crisflash, a software tool for fast sgRNA design and potential off-target discovery, built for performance and flexibility. Crisflash can rapidly design CRISPR guides against any sequenced genome or genome sequences, and can optimize guide accuracy by incorporating user-supplied variant data. Crisflash is over an order of magnitude faster than comparable tools, even using a single CPU core, and efficiently and robustly scores the potential off-targeting of all possible candidate CRISPR guide oligonucleotides. AVAILABILITY AND IMPLEMENTATION: https://github.com/crisflash. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Adrien L. S. Jacquin, Duncan T. Odom, Margus Lukk
Bioinform.2
2006 A hypothesis-based approach for identifying the binding specificity of regulatory proteins from chromatin immunoprecipitation data
abstract
MOTIVATION: Genome-wide chromatin-immunoprecipitation (ChIP-chip) detects binding of transcriptional regulators to DNA in vivo at low resolution. Motif discovery algorithms can be used to discover sequence patterns in the bound regions that may be recognized by the immunoprecipitated protein. However, the discovered motifs often do not agree with the binding specificity of the protein, when it is known. RESULTS: We present a powerful approach to analyzing ChIP-chip data, called THEME, that tests hypotheses concerning the sequence specificity of a protein. Hypotheses are refined using constrained local optimization. Cross-validation provides a principled standard for selecting the optimal weighting of the hypothesis and the ChIP-chip data and for choosing the best refined hypothesis. We demonstrate how to derive hypotheses for proteins from 36 domain families. Using THEME together with these hypotheses, we analyze ChIP-chip datasets for 14 human and mouse proteins. In all the cases the identified motifs are consistent with the published data with regard to the binding specificity of the proteins.
Kenzie D. MacIsaac, D. Benjamin Gordon, Lena Nekludova, Duncan T. Odom, Joerg Schreiber, David K. Gifford, Richard A. Young, Ernest Fraenkel
Bioinform.4