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Price, Morgan N.; Deutschbauer, Adam M.; Arkin, Adam P.
Filling gaps in bacterial catabolic pathways with computation and high-throughput genetics Journal Article
In: PLoS Genet, vol. 18, no. 4, 2022, ISSN: 1553-7404.
Abstract | Links | BibTeX | Tags: Behavior and Systematics, Cancer Research, Ecology, Evolution, Genetics, Genetics (clinical), Molecular Biology
@article{Price2022b,
title = {Filling gaps in bacterial catabolic pathways with computation and high-throughput genetics},
author = {Morgan N. Price and Adam M. Deutschbauer and Adam P. Arkin},
editor = {Bernhard O. Palsson},
doi = {10.1371/journal.pgen.1010156},
issn = {1553-7404},
year = {2022},
date = {2022-04-13},
journal = {PLoS Genet},
volume = {18},
number = {4},
publisher = {Public Library of Science (PLoS)},
abstract = {To discover novel catabolic enzymes and transporters, we combined high-throughput genetic data from 29 bacteria with an automated tool to find gaps in their catabolic pathways. GapMind for carbon sources automatically annotates the uptake and catabolism of 62 compounds in bacterial and archaeal genomes. For the compounds that are utilized by the 29 bacteria, we systematically examined the gaps in GapMind’s predicted pathways, and we used the mutant fitness data to find additional genes that were involved in their utilization. We identified novel pathways or enzymes for the utilization of glucosamine, citrulline, myo-inositol, lactose, and phenylacetate, and we annotated 299 diverged enzymes and transporters. We also curated 125 proteins from published reports. For the 29 bacteria with genetic data, GapMind finds high-confidence paths for 85% of utilized carbon sources. In diverse bacteria and archaea, 38% of utilized carbon sources have high-confidence paths, which was improved from 27% by incorporating the fitness-based annotations and our curation. GapMind for carbon sources is available as a web server (http://papers.genomics.lbl.gov/carbon ) and takes just 30 seconds for the typical genome. },
keywords = {Behavior and Systematics, Cancer Research, Ecology, Evolution, Genetics, Genetics (clinical), Molecular Biology},
pubstate = {published},
tppubtype = {article}
}
Skerker, Jeffrey M; Pianalto, Kaila M; Mondo, Stephen J; Yang, Kunlong; Arkin, Adam P; Keller, Nancy P; Grigoriev, Igor V; Glass, N Louise
Chromosome assembled and annotated genome sequence of Aspergillus flavus NRRL 3357 Journal Article
In: vol. 11, no. 8, 2021, ISSN: 2160-1836.
Abstract | Links | BibTeX | Tags: Genetics, Genetics (clinical), Molecular Biology
@article{Skerker2021,
title = {Chromosome assembled and annotated genome sequence of \textit{Aspergillus flavus} NRRL 3357},
author = {Jeffrey M Skerker and Kaila M Pianalto and Stephen J Mondo and Kunlong Yang and Adam P Arkin and Nancy P Keller and Igor V Grigoriev and N Louise Glass},
editor = {J C Dunlap},
doi = {10.1093/g3journal/jkab213},
issn = {2160-1836},
year = {2021},
date = {2021-08-07},
volume = {11},
number = {8},
publisher = {Oxford University Press (OUP)},
abstract = {Abstract
Aspergillus flavus is an opportunistic pathogen of crops, including peanuts and maize, and is the second leading cause of aspergillosis in immunocompromised patients. A. flavus is also a major producer of the mycotoxin, aflatoxin, a potent carcinogen, which results in significant crop losses annually. The A. flavus isolate NRRL 3357 was originally isolated from peanut and has been used as a model organism for understanding the regulation and production of secondary metabolites, such as aflatoxin. A draft genome of NRRL 3357 was previously constructed, enabling the development of molecular tools and for understanding population biology of this particular species. Here, we describe an updated, near complete, telomere-to-telomere assembly and re-annotation of the eight chromosomes of A. flavus NRRL 3357 genome, accomplished via long-read PacBio and Oxford Nanopore technologies combined with Illumina short-read sequencing. A total of 13,715 protein-coding genes were predicted. Using RNA-seq data, a significant improvement was achieved in predicted 5’ and 3’ untranslated regions, which were incorporated into the new gene models. },
keywords = {Genetics, Genetics (clinical), Molecular Biology},
pubstate = {published},
tppubtype = {article}
}



