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Daniel N. Baker
Researcher at Johns Hopkins University
Publications - 5
Citations - 143
Daniel N. Baker is an academic researcher from Johns Hopkins University. The author has contributed to research in topics: GENCODE & Bioconductor. The author has an hindex of 3, co-authored 5 publications receiving 66 citations.
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Journal ArticleDOI
Dashing: fast and accurate genomic distances with HyperLogLog
Daniel N. Baker,Ben Langmead +1 more
TL;DR: Dashing is a fast and accurate software tool for estimating similarities of genomes or sequencing datasets that uses the HyperLogLog sketch together with cardinality estimation methods that are specialized for set unions and intersections.
Posted ContentDOI
Dashing: Fast and Accurate Genomic Distances with HyperLogLog
Daniel N. Baker,Ben Langmead +1 more
TL;DR: Dashing is a fast and accurate software tool for estimating similarities of genomes or sequencing datasets that uses the HyperLogLog sketch together with cardinality estimation methods that specialize in set unions and intersections.
Journal ArticleDOI
Megadepth: efficient coverage quantification for BigWigs and BAMs
Christopher Wilks,Omar G. Ahmed,Daniel N. Baker,David Zhang,David Zhang,Leonardo Collado-Torres,Ben Langmead +6 more
TL;DR: For example, Megadepth as mentioned in this paper is a fast tool for quantifying alignments and coverage for BigWig and BAM/CRAM input files, using substantially less memory than the next-fastest competitor.
Posted ContentDOI
Megadepth: efficient coverage quantification for BigWigs and BAMs
Christopher Wilks,Omar G. Ahmed,Daniel N. Baker,David Zhang,Leonardo Collado-Torres,Ben Langmead +5 more
TL;DR: Megadepth is a fast tool for quantifying alignments and coverage for BigWig and BAM/CRAM input files, using substantially less memory than the next-fastest competitor.
Proceedings ArticleDOI
Fast and memory-efficient scRNA-seq k-means clustering with various distances
TL;DR: Minicore as discussed by the authors is an open source library for efficient k-means++ center finding and kmeans clustering of single-cell RNA-seq data using Euclidean distance, Jensen-Shannon divergence, Kullback-Leibler divergence, and Bhattacharyya distance.