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Adina Howe

Researcher at Iowa State University

Publications -  80
Citations -  4060

Adina Howe is an academic researcher from Iowa State University. The author has contributed to research in topics: Biology & Metagenomics. The author has an hindex of 20, co-authored 66 publications receiving 3027 citations. Previous affiliations of Adina Howe include New York Academy of Sciences & University of Illinois at Urbana–Champaign.

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Revealing the Bacterial Butyrate Synthesis Pathways by Analyzing (Meta)genomic Data

TL;DR: A complete database of genes from major known butyrate-producing pathways is provided, using in-depth genomic analysis of publicly available genomes, filling an important gap to accurately assess the butyrATE-producing potential of complex microbial communities from “-omics”-derived data.
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The khmer software package: enabling efficient nucleotide sequence analysis.

Michael R. Crusoe, +60 more
- 25 Sep 2015 - 
TL;DR: Khmer as discussed by the authors is a free software library for working efficiently with fixed length DNA words, or k-mers, which provides implementations of a probabilistic k-mer counting data structure, a compressible De Bruijn graph representation, De Bruhen graph partitioning, and digital normalization.
Journal ArticleDOI

Tackling soil diversity with the assembly of large, complex metagenomes

TL;DR: This work combines two preassembly filtering approaches—digital normalization and partitioning—to generate previously intractable large metagenome assemblies, which result in assemblies nearly identical to assemblies from unprocessed data.
Journal ArticleDOI

Microbial activity in forest soil reflects the changes in ecosystem properties between summer and winter

TL;DR: The activity of the abundant ectomycorrhizal fungi was reduced in winter, which indicates that plant photosynthetic production was likely one of the major drivers of changes in the functioning of microbial communities in this coniferous forest.
Posted Content

A Reference-Free Algorithm for Computational Normalization of Shotgun Sequencing Data

TL;DR: Digital normalization is described, a single-pass computational algorithm that systematizes coverage in shotgun sequencing data sets, thereby decreasing sampling variation, discarding redundant data, and removing the majority of errors.