Sequencing

11th Annual Sequencing, 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.
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