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Jarrod Chapman

Researcher at Lawrence Berkeley National Laboratory

Publications -  12
Citations -  5824

Jarrod Chapman is an academic researcher from Lawrence Berkeley National Laboratory. The author has contributed to research in topics: Genome & Shotgun sequencing. The author has an hindex of 9, co-authored 12 publications receiving 5497 citations. Previous affiliations of Jarrod Chapman include University of California, Berkeley & Joint Genome Institute.

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Journal ArticleDOI

Community structure and metabolism through reconstruction of microbial genomes from the environment

TL;DR: Reconstruction of near-complete genomes of Leptospirillum group II and Ferroplasma type II and analysis of the gene complement for each organism revealed the pathways for carbon and nitrogen fixation and energy generation, and provided insights into survival strategies in an extreme environment.
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Phytophthora Genome Sequences Uncover Evolutionary Origins and Mechanisms of Pathogenesis

Brett M. Tyler, +68 more
- 01 Sep 2006 - 
TL;DR: Comparison of the two species' genomes reveals a rapid expansion and diversification of many protein families associated with plant infection such as hydrolases, ABC transporters, protein toxins, proteinase inhibitors, and, in particular, a superfamily of 700 proteins with similarity to known oömycete avirulence genes.
Journal ArticleDOI

Genome sequence of the lignocellulose degrading fungus Phanerochaete chrysosporium strain RP78

TL;DR: The sequenced genome of Phanerochaete chrysosporium strain RP78 reveals an impressive array of genes encoding secreted oxidases, peroxidases and hydrolytic enzymes that cooperate in wood decay, and provides a framework for further development of bioprocesses for biomass utilization, organopollutant degradation and fiber bleaching.
Proceedings ArticleDOI

Parallel de bruijn graph construction and traversal for de novo genome assembly

TL;DR: A novel algorithm is provided that leverages one-sided communication capabilities of the Unified Parallel C (UPC) to facilitate the requisite fine-grained parallelism and avoidance of data hazards, while analytically proving its scalability properties.