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Clifton L. Dalgard

dblp:271/8634 · DBLP profile ↗
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2ranked-venue papers
0as first author
1since 2021 · last 2022
0000-0003-2025-8239ORCID · corroborated

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

Applied, interdisciplinary, general and emerging computing · 2 · 1 since 2021

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
1 paper
Bioinformatics and computational biology · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › genomics
genotyping
0.612022
ImmunoTyper-SR: A Novel Computational Approach for Genotyping Immunoglobulin Heavy Chain Variable Genes Using Short Read Data · RECOMB 2022
Bioinformatics and computational biology
sequence analysis
0.612022
ImmunoTyper-SR: A Novel Computational Approach for Genotyping Immunoglobulin Heavy Chain Variable Genes Using Short Read Data · RECOMB 2022

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

computational genotyping · 0.6
YearPublicationVenuePosition
2022 ImmunoTyper-SR: A Novel Computational Approach for Genotyping Immunoglobulin Heavy Chain Variable Genes Using Short Read Data
Michael K. B. Ford, Ananth Hari, Oscar Rodriguez, Junyan Xu, Justin Lack, Cihan Oguz, Sarah Weber, Mary Magliocco, Jason Barnett, Sandhya Xirasagar, Smilee Samuel, Luisa Imberti, Paolo Bonfanti, Andrea Biondi, Clifton L. Dalgard, Stephen J. Chanock, Lindsey Rosen, Steven Holland, Helen Su, Luigi Notarangelo, Uzi Vishkin, Corey Watson, Süleyman Cenk Sahinalp
RECOMB16
2020 MutEnricher: a flexible toolset for somatic mutation enrichment analysis of tumor whole genomes
abstract
BACKGROUND: Analysis of somatic mutations from tumor whole exomes has fueled discovery of novel cancer driver genes. However, ~ 98% of the genome is non-coding and includes regulatory elements whose normal cellular functions can be disrupted by mutation. Whole genome sequencing (WGS), on the other hand, allows for identification of non-coding somatic variation and expanded estimation of background mutation rates, yet fewer computational tools exist for specific interrogation of this space. RESULTS: We present MutEnricher, a flexible toolset for investigating somatic mutation enrichment in both coding and non-coding genomic regions from WGS data. MutEnricher contains two distinct modules for these purposes that provide customizable options for calculating sample- and feature-specific background mutation rates. Additionally, both MutEnricher modules calculate feature-level and local, or "hotspot," somatic mutation enrichment statistics. CONCLUSIONS: MutEnricher is a flexible software package for investigating somatic mutation enrichment that is implemented in Python, is freely available, can be efficiently parallelized, and is highly configurable to researcher's specific needs. MutEnricher is available online at https://github.com/asoltis/MutEnricher .
Anthony R. Soltis, Clifton L. Dalgard, Harvey B. Pollard, Matthew D. Wilkerson
BMC Bioinform.2