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Adam M. Phillippy

Researcher at National Institutes of Health

Publications -  195
Citations -  44155

Adam M. Phillippy is an academic researcher from National Institutes of Health. The author has contributed to research in topics: Genome & Sequence assembly. The author has an hindex of 62, co-authored 174 publications receiving 31138 citations. Previous affiliations of Adam M. Phillippy include University of Maryland, Baltimore & Battelle Memorial Institute.

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Versatile and open software for comparing large genomes

TL;DR: The newest version of MUMmer easily handles comparisons of large eukaryotic genomes at varying evolutionary distances, as demonstrated by applications to multiple genomes.
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Canu: scalable and accurate long-read assembly via adaptive k-mer weighting and repeat separation.

TL;DR: Canu, a successor of Celera Assembler that is specifically designed for noisy single-molecule sequences, is presented, demonstrating that Canu can reliably assemble complete microbial genomes and near-complete eukaryotic chromosomes using either Pacific Biosciences or Oxford Nanopore technologies.
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High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries.

TL;DR: FastANI is developed, a method to compute ANI using alignment-free approximate sequence mapping, and it is shown 95% ANI is an accurate threshold for demarcating prokaryotic species by analyzing about 90,000 proKaryotic genomes.
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Evolution of genes and genomes on the Drosophila phylogeny.

Andrew G. Clark, +429 more
- 08 Nov 2007 - 
TL;DR: These genome sequences augment the formidable genetic tools that have made Drosophila melanogaster a pre-eminent model for animal genetics, and will further catalyse fundamental research on mechanisms of development, cell biology, genetics, disease, neurobiology, behaviour, physiology and evolution.
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Mash: fast genome and metagenome distance estimation using MinHash.

TL;DR: Mash extends the MinHash dimensionality-reduction technique to include a pairwise mutation distance and P value significance test, enabling the efficient clustering and search of massive sequence collections.