Sequencing

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11th Annual <strong>Sequencing</strong>, Finishing, and Analysis in the Future Meeting AVERAGE NUCLEOTIDE IDENTITY: A FAST WHOLE GENOME SEQUENCE-BASED METHOD FOR SPECIES IDENTIFICATION OF ESCHERICHIA COLI, E. ALBERTII AND E. FERGUSONII Wednesday, 1st June 20:00 La Fonda NM Room (1st floor) Poster (PS‐1b.19) Sung Im, Heather Carleton, Lee Katz, Andrew Huang, Rebecca Lindsey Centers for Disease Control and Prevention In the context of national reference laboratory strain testing and foodborne pathogen surveillance it is important to quickly and accurately identify the genus and species of unknown bacterial whole genome sequences (WGS). Average nucleotide Identity (ANI) is an in‐silico method that calculates the average similarity between two WGS by aligning and comparing the nucleotide bases between two genome assemblies. Two common algorithms are used to compute the ANI between two genomes: NCBI’s BLASTn and MUMmer’s DNAdiff. While the BLASTn method is computationally more expensive, it provides higher specificity as each WGS comparison is reverse analyzed to provide a two‐way reciprocal best hit similarity value. The MUMmer based method calculates the ANI more rapidly using preselected high quality reference genomes for comparison. In a previous ANI analysis, 170 strains from 5 Escherichia species (E. coli/Shigella, E. albertii, E. fergusonii, E. hermanii & E. vulneris) were compared in an all‐against‐all pairwise fashion using the BLASTn algorithm (ANIb). This experiment determined that an ANI value of >95% is required to correctly identify an unknown Escherichia genome to its species. The results of this anal‐ ysis were used to select three complete Escherichia reference strains for the MUMmer based ANI (ANIm) method: Escherichia albertii KF1 (NZ_CP007025.1), Escherichia fergusonii ATCC_35469 (NC_011740.1) and Escherichia coli 08‐4006. The accuracy of ANIm was tested with WGS from 100 Escherichia and 100 additional non‐Escherichia enteric bacteria previously characterized by traditional methods. Additionally, a down‐sampling experiment was conducted to test the coverage depth (from 40x 1x) at which species identification using ANIm became unreliable. For each Escherichia strain tested, reads were systematically removed from the original fastq sequence files. The down sampled read pairs were assembled and re‐analyzed against the reference strains. The initial ANIb analysis, averaging a run time of 4 minutes per comparison, yielded a defined genomic space from which Escherichia strains representing their respective genera were selected. Using the selected reference strains ANIm analysis of 100 Escherichia WGS correctly identified 81 E. coli, 12 E. albertii and 7 E. fergusonii. The 100 non‐Escherichia WGS all tested negative for the Escherichia genus while testing positive to their respective organisms. The down‐sampling experiment revealed that ANIm was accurate to
11th Annual <strong>Sequencing</strong>, Finishing, and Analysis in the Future Meeting MINING FREQUENT SUBPATHS IN PAN-GENOMES Wednesday, 1st June 20:00 La Fonda NM Room (1st floor) Poster (PS‐1b.20) AlanCleary 1 , Thiruvarangan Ramaraj 2 , Joann Mudge 2 , Brendan Mumey 1 1 Montana State University, 2 National Center for Genome Resources (NCGR) In an era where next generation sequencing has enabled genome sequencing and assembly of multiple accessions per species, the need for pan‐genomic algorithms to mine biological information from multiple genomes has become critical. As a pan‐genome can be represented at the nucleotide resolution by a de Bruijn graph or at the gene resolution with a directed gene graph, we have implemented an approximate frequent subpaths algorithm to identify syntenic stretches between genomes. Similar to approximate frequent itemset mining algorithms, our algorithm bounds the minimum number of supporting paths (transactions), as well as the amount of error allowed in each supporting path and the fraction of support for each node (item). Additionally, our algorithm bounds the length of divergent sequences inserted into supporting paths. By exploiting the structure of the graph, our algorithm achieves an efficient running time. Biological applications of this algorithm include the identification of genes, the inference of phylogenies, the recognition of signatures of local adaptation, and gene clustering, among other things. Additionally, these data can be used to characterize interesting portions of the pan‐genome when sketching a representative visualization, as the graph is typically too dense to draw verbatim. This algorithm could be used, for example, to identify core genes for a species, differences in assemblies run with different algorithms, and regions of the genome responding to selection pressures, such as genomic signatures of climate adaptation that can be used to predict future responses to climate change or genomic regions driving specialization such as those driving industrial specialization of yeast. 86
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Reliable solutions for focused NGS
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Sequencing

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