Browsing by Author "Huang, Chengjie"

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    LINbase: a web server for genome-based identification of prokaryotes as members of crowdsourced taxa
    Tian, Long; Huang, Chengjie; Mazloom, Reza; Heath, Lenwood S.; Vinatzer, Boris A. (Oxford University Press, 2020-03-30)
    High throughput DNA sequencing in combination with efficient algorithms could provide the basis for a highly resolved, genome phylogeny-based and digital prokaryotic taxonomy. However, current taxonomic practice continues to rely on cumbersome journal publications for the description of new species, which still constitute the smallest taxonomic units. In response, we introduce LINbase, a web server that allows users to genomically circumscribe any group of prokaryotes with measurable DNA similarity and that uses the individual isolate as smallest unit. Since LINbase leverages the concept of Life Identification Numbers (LINs), which are codes assigned to individual genomes based on reciprocal average nucleotide identity, we refer to groups circumscribed in LINbase as LINgroups. Users can associate with each LINgroup a name, a short description, and a URL to a peer-reviewed publication. As soon as a LINgroup is circumscribed, any user can immediately identify query genomes as members and submit comments about the LINgroup. Most genomes currently in LINbase were imported from GenBank, but users can upload their own genome sequences as well. In conclusion, LINbase combines the resolution of LINs with the power of crowdsourcing in support of a highly resolved, genome phylogeny-based digital taxonomy. LINbase is available at http://www.LINbase.org.
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    Strain-level identification of bacterial tomato pathogens directly from metagenomic sequences
    Mechan Llontop, Marco Enrique; Sharma, Parul; Aguilera Flores, Marcela; Yang, Shu; Pollock, Jill; Tian, Long; Huang, Chengjie; Rideout, Steven L.; Heath, Lenwood S.; Li, Song; Vinatzer, Boris A. (Scientific Societies, 2019-12-12)
    Routine strain-level identification of plant pathogens directly from symptomatic tissue could significantly improve plant disease control and prevention. Here we tested the Oxford Nanopore Technologies (ONT) MinIONTM sequencer for metagenomic sequencing of tomato plants either artificially inoculated with a known strain of the bacterial speck pathogen Pseudomonas syringae pv. tomato (Pto), or collected in the field and showing bacterial spot symptoms caused by either one of four Xanthomonas species. After species-level identification using ONT's WIMP software and the third party tools Sourmash and MetaMaps, we used Sourmash and MetaMaps with a custom database of representative genomes of bacterial tomato pathogens to attempt strain-level identification. In parallel, each metagenome was assembled and the longest contigs were used as query with the genome-based microbial identification Web service LINbase. Both the read-based and assembly-based approaches correctly identified Pto strain T1 in the artificially inoculated samples. The pathogen strain in most field samples was identified as a member of Xanthomonas perforans group 2. This result was confirmed by whole genome sequencing of colonies isolated from one of the samples. Although in our case, metagenome-based pathogen identification at the strain-level was achieved, caution still needs to be exerted when interpreting strain-level results because of the challenges inherent to assigning reads to specific strains and the error rate of nanopore sequencing.