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11th Annual <strong>Sequencing</strong>, Finishing, and Analysis in the Future Meeting BACTERIAL PATHOGEN NEXT-GENERATION SEQUENCING DATA TRIMMING, CORRECTION, AND SNPS DISCOVERY Wednesday, 1st June 20:00 La Fonda Mezzanine (2nd Floor) Poster (PS‐2b.03) Darlene Wagner 1 , Lee Katz 2 , Eija Trees 2 , Heather Carleton 2 1 IHRC, 2 Centers for Disease Control and Prevention Background: Next‐generation sequencing (NGS) allows rapid in‐house sequencing of bacterial strains implicated in local or multi‐state outbreaks. Single‐nucleotide polymorphisms (SNPs) analyses incorporating NGS reads can aid outbreak cluster identification. This study evaluated effects of read trimming and correction (healing) on SNPs analysis quality. Methods: NGS reads representing outbreak clusters from Salmonella enterica serovar Bareilly, Shiga toxinproducing Escherichia coli (STEC) serogroup O157, and Campylobacter jejuni, were cleaned using nine healing methods. Methods used to implement healing included prinseq, fastx_trimmer, BayesHammer, BayesHammer with fastx_trimmer, Musket, Quake, Blue, CG‐Pipeline with quality trimming, and CG‐Pipeline with quality cutoff masking. Forward‐read (R1) and reverse‐read (R2) errors were estimated through base‐call qualities (Phred) and ambiguous nucleotide (N) counts. SNPs discovery through Lyve‐SET (https://github.com/lskatz/lyve‐SET) was assessed by counting informative aligned positions. Possible false positive/negative SNPs were inferred by counting SNPs shared across results of the different healing methods. Results: R1 and R2 reads from Salmonella ser. Bareilly averaged 300,000 ambiguous nucleo‐tide reads while R2 reads exhibited average Phred scores as low as 25.8 (median 29.6). Un‐healed Bareilly reads yielded 56 SNP positions through Lyve‐SET. BayesHammer, an edit‐distance‐based method, and kmers‐based methods, Quake and Blue, raised Phred scores in the Bareilly set above 30.0. Blue, along with Musket, another kmers method, increased Bareilly SNP positions to 122 and 123, respectively, with 1 potential false positive site each. BayesHammer, fastx_trimmer, and BayesHammer with fastx_trimmer increased Bareilly SNP positions to 94, 99, and 110, respectively, without false positives. The STEC O157 outbreak clus‐ter R2 reads exhibited median quality of 29.3 with up to 1.07x106 reads containing ambiguous nucleotides. Unhealed O157 reads yielded 62 SNP positions, which increased to 66 positions with no false positives after healing through Quake. R1 and R2 reads of the Campylobacter cluster had quality scores well above 30.0 but with an average of 11,000 ambiguous‐nucleotide reads in R2. Unhealed Campylobacter reads yielded 137 SNP positions while fastx_trimmer, Blue, and BayesHammer with fastx_trimmer increased SNPs to 150, 152, and 153, respectively, with no inferred false positives. Across all three organism/outbreak sets, prinseq failed to in‐crease SNP counts, while the CG‐Pipeline‐based methods reduced SNP counts in the Bareilly and Campylobacter sets. Musket, Quake, and Blue, consistently increased SNP counts, but each produced possible false positive/negative SNP sites in at least one organism set. Conclusions: An optimal read trimming or cleaning method should increase the number of SNP positions without adding false positives, thus enhancing outbreak phylogeny. This study has shown that Musket, Blue, and Quake increase numbers of SNP positions for NGS reads with high ambiguous base‐calls and Phred scores < 30.0. Yet, the kmers‐based methods occasionally introduced SNPs not shared across methods within the organism set, indicating possible false positive/negative positions. BayesHammer, fastx_trimmer, and the two methods combined all increased counts of SNPs which were reproducible across more than one healing method. For future studies, outbreak sets with validated SNP sites will be used for more accurate assessment of false discovery or false exclusion of SNPs. 91
11th Annual <strong>Sequencing</strong>, Finishing, and Analysis in the Future Meeting IDENTIFYING STRUCTURAL VARIATION, COMPONENT ISSUES AND OTHER SEQUENCE ARTEFACTS BY INTEGRATING LONG RANGE GENOME MAPS IN A WEB-BASED GENOME BROWSER Wednesday, 1st June 20:00 La Fonda Mezzanine (2nd Floor) Poster (PS‐2b.04) William Chow, Kerstin Howe Wellcome Trust Sanger Institute The web‐based genome evaluation browser gEVAL (geval.sanger.ac.uk) has been used extensively to curate and improve assemblies from reference consensus standards to novel draft genomes. However despite the wealth of evidential datasets available to aid in improving assemblies to gold standard such as clone ends, genetic markers or transcript models, there are still difficult regions in the genome that can’t be sequenced, finished and resolved. The single molecule genome mapping technology developed by Bionano Genomics, provides unbiased physical maps indicative of the genomic structure. By comparing these maps to an assembly, it can aid in both long range assembly correction as well as structural variant detection. gEVAL has integrated and aligned genome map datasets against the various human, mouse and zebrafish assemblies presently available in the browser. Whilst we also provide trackhubs (ngs.sanger.ac.uk/production/grit/track_hub/hub.txt), gEVAL’s representation of the aligned data was developed to allow users to easily identify regions of discordance between map(s) and assembly. This has been helpful in quickly sizing sequence gaps and resolving problematic regions such as those caused by repetitive elements. gEVAL hosts in‐house generated maps, as well as datasets for 4 human individuals (one trio plus one individual) from the Genome In A Bottle initiative (https://sites.stanford.edu/abms/giab). This aids not only the improvement of the human reference genome, but also provides a resource to help in identifying potential structural variation. 92
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Reliable solutions for focused NGS
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Sequencing

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