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Staphylococcus aureus viewed from the
perspective of 40,000+ genomes
Robert A. Petit III and Timothy D. Read
Department of Medicine, Division of Infectious Diseases, Emory University School of Medicine,
Atlanta, GA, USA
ABSTRACT
Low-cost Illumina sequencing of clinically-important bacterial pathogens has
generated thousands of publicly available genomic datasets. Analyzing these
genomes and extracting relevant information for each pathogen and the associated
clinical phenotypes requires not only resources and bioinformatic skills but
organism-specific knowledge. In light of these issues, we created Staphopia, an
analysis pipeline, database and application programming interface, focused
on Staphylococcus aureus, a common colonizer of humans and a major
antibiotic-resistant pathogen responsible for a wide spectrum of hospital and
community-associated infections. Written in Python, Staphopia’s analysis pipeline
consists of submodules running open-source tools. It accepts raw FASTQ reads as an
input, which undergo quality control filtration, error correction and reduction to a
maximum of approximately 100chromosome coverage. This reduction
significantly reduces total runtime without detrimentally affecting the results.
The pipeline performs de novo assembly-based and mapping-based analysis.
Automated gene calling and annotation is performed on the assembled contigs.
Read-mapping is used to call variants (single nucleotide polymorphisms and
insertion/deletions) against a reference S. aureus chromosome (N315, ST5). We ran
the analysis pipeline on more than 43,000 S. aureus shotgun Illumina genome
projects in the public European Nucleotide Archive database in November 2017.
We found that only a quarter of known multi-locus sequence types (STs) were
represented but the top 10 STs made up 70% of all genomes. methicillin-resistant
S. aureus (MRSA) were 64% of all genomes. Using the Staphopia database we selected
380 high quality genomes deposited with good metadata, each from a different
multi-locus ST, as a non-redundant diversity set for studying S. aureus evolution.
In addition to answering basic science questions, Staphopia could serve as a potential
platform for rapid clinical diagnostics of S. aureus isolates in the future. The system
could also be adapted as a template for other organism-specific databases.
Subjects Bioinformatics, Genomics, Infectious Diseases, Public Health
Keywords Database, MSSA, MRSA, Antibiotic resistance, MLST, S. aureus
INTRODUCTION
Staphylococcus aureus is a common and deadly bacterial pathogen that has been frequently
investigated by whole genome sequencing over the last decade. It was the subject of
arguably the first large scale bacterial genomic epidemiology study using Illumina
sequencing technology (Harris et al., 2010). The cumulative number of Illumina shotgun
How to cite this article Petit and Read (2018), Staphylococcus aureus viewed from the perspective of 40,000+ genomes. PeerJ 6:e5261;
DOI 10.7717/peerj.5261
Submitted 25 April 2018
Accepted 28 June 2018
Published 12 July 2018
Corresponding author
Timothy D. Read, tread@emory.edu
Academic editor
Timothy Stinear
Additional Information and
Declarations can be found on
page 16
DOI 10.7717/peerj.5261
Copyright
2018 Petit and Read
Distributed under
Creative Commons CC-BY 4.0
genome projects deposited in public repositories (the National Center for Biotechnology
Information Short Read Archive (NCBI SRA) and the European Nucleotide Archive
(ENA)) had grown to almost 50,000 by March 2018 (Fig. 1). S. aureus is therefore on
the front edge of a cohort of bacterial species that are acquiring broad whole genome
shotgun coverage, offering possibilities of new types of large scale analysis.
Staphylococcus aureus is a Gram-positive bacterium with a chromosome of ∼2.8 Mbp.
Plasmid content varies between strains. A multi-locus sequence typing (MLST)
scheme that assigns each strain a “sequence type” (ST) based on seven genes has proven a
robust way of describing individual strain genotypes and membership of larger “clonal
complexes” (CCs) (Planet et al., 2017). The accumulated public S. aureus genome
datasets present an opportunity for investigating basic questions about how genetic
variations that cause antibiotic resistance evolve within populations and how long genes
traded by horizontal gene transfer persist in populations. However, there has been a
problem of access, as few published tools fill the niche of providing methods for fine-scale
querying of very large datasets from a pathogen species. For example, PATRIC
(Wattam et al., 2014) and BIGSdb (Jolley & Maiden, 2010) web based analysis sites
focus on high quality annotation and core genome MLST (cgMLST), respectively, while
Aureowiki (Fuchs et al., 2017) and PanX (Ding, Baumdicker & Neher, 2018) provide
very detailed information on a smaller number of strains. In this study we describe the
creation of Staphopia, an integrated analysis pipeline, database and application
programming interface (API) to analyze S. aureus genomes.
MATERIALS AND METHODS
Staphopia analysis pipeline
The Staphopia analysis pipeline (StAP) processed FASTQ (pFASTQ) files from a single
genome through quality control steps and bioinformatic analysis software. StAP
(DOI 10.5281/zenodo.1255310) consisted of custom Python3 scripts and open source
software organized by the Nextflow (Di Tommaso et al., 2017) (v0.28.2) workflow
292 1820
7410
13239
22921
31310
36234
42949
0
10000
20000
30000
40000
2010 2011 2012 2013 2014 2015 2016 2017
Year
Cumulative Count
Published
Unpublished
Figure 1 Cumulative submissions of Staphylococcus aureus genome projects 2010–2017 linked to
publications. There were 42,949 S. aureus genome projects investigated in this study. Of these sam-
ples, we have linked 11,921 to a publication. Full-size
DOI: 10.7717/peerj.5261/fig-1
Petit and Read (2018), PeerJ, DOI 10.7717/peerj.5261 2/20
management platform (Fig. 2). When available we used BioConda (Gru
¨ning et al., 2017)
to install the open source software. Summary statistics of the original input and
subsequent downstream results files were collected at each step of the pipeline. For
portability, StAP was wrapped in a Docker container. The version of the pipeline used in
this work was Docker Image Tag: 112017(https://hub.docker.com/r/rpetit3/staphopia/).
The input to StAP was either single- or paired-end FASTQ file (or files) generated
from Illumina technology. StAP contained an option that allowed FASTQ data to be
pulled from the ENA based on the experiment accession number (ena-dl v0.1,
https://github.com/rpetit3/ena-dl). A MD5 hash (md5sum) was generated from the input
FASTQ data and cross-referenced against a list generated from processed genomes to
prevent reanalysis of the same input. BBduk (Bushnell, 2016) (v37.66) was used to filter
out adapters associated with Illumina sequencing and trim reads based on quality. Read
errors were corrected using SPAdes (Bankevich et al., 2012) (v3.11.1). Based on the
corrected reads, low quality reads were filtered out and the total dataset was subsampled
to a maximum of 281 Mbases (100coverage of the N315 reference chromosome
(Kuroda et al., 2001)) with Illumina-cleanup (v0.3, https://github.com/rpetit3/
illumina-cleanup/). This file (or files, if paired-end) we termed “pFASTQ.”
Processed FASTQ reads were assembled de novo using SPAdes (Bankevich et al., 2012)
(v3.11.1). SPAdes also marked assembles as putative plasmids based on evidence such as
relative read coverage (Antipov et al., 2016). Summary statistics of the assembly were
created using the assembly-summary script (https://github.com/rpetit3/assembly-summary).
A BLAST+ nucleotide database was created from the assembled contigs to be used
Input Sample FASTQ
Quality Filtering,
Error Correction and
Sub-Sample to 100x Coverage
BBDuk, SPAdes, Illumina-Cleanup
Genome and Plasmid Assembly
SPAdes
Identify Variants
BWA, Picard Tools, GATK,
VCF-Annotator, Samtools
Count 31-mers
Jellyfish
Predict Antibiotic and
Virulence Profiles
Ariba
Predict MLST, cgMLST
BLAST+, Ariba, MentaLiST
Predict MRSA
BLAST+, BWA, bedtools,
Ariba
Annotate Genome
Prokka
Figure 2 Staphopia Analysis Pipeline (StAP) Workflow. The diagram describes basic operations of
the pipeline on a single genome input (FASTQ file) before uploading into the Postgres relational
database. Details of the programs used are in the ‘Methods’section and in DOI 10.5281/zenodo.1255310.
Green arrows indicate input from de novo assembled contigs; blue arrows were operations performed on
pFASTQ files. Full-size
DOI: 10.7717/peerj.5261/fig-2
Petit and Read (2018), PeerJ, DOI 10.7717/peerj.5261 3/20
subsequently for sequence query matching. Open reading frames and their putative
functions were predicted and annotated using Prokka (Seemann, 2014)(v1.12)andits
default database.
The S. aureus strain N315 (Kuroda et al., 2001) chromosome (ST5 methicillin-resistant
S. aureus (MRSA); accession NC_002745.2; length 2,814,816 bp) was used as a
reference for calling consensus single nucleotide polymorphisms (SNPs) and indels in
the pFASTQ reads using the GATK (McKenna et al., 2010) (v3.8.0) pipeline. GATK
pipeline also incorporated BWA (Li & Durbin, 2009) (v0.7.17), SAMtools (Li et al., 2009)
(v1.6) and Picard Tools (v2.14.1, http://broadinstitute.github.io/picard/) software.
Identified variants were annotated using the vcf-annotator script (v0.4, https://github.
com/rpetit3/vcf-annotator). Jellyfish (Marc¸ais & Kingsford, 2011) (v2.2.6) was used to
count k-mers of length 31 base pairs (31-mers) in the pFASTQ file. We used Ariba
(Hunt et al., 2017) (v2.10.2) to make antibiotic resistance and virulence predictions for
paired-end reads only. Resistance phenotypes were predicted using the MegaRes reference
database (Lakin et al., 2017) and virulence using the Virulence Factor Database
(Chen et al., 2016) core dataset.
Multi-locus sequence typing was determined by two or three methods depending
on the whether the pFASTQ was paired- or single-end. All methods used the S. aureus
MLST allele sequence database downloaded from https://pubmlst.org/saureus/
(November 2017). Alleles for each of the seven loci were aligned against the assembled
genome using BLAST+ (v2.7.1+) (Camacho et al., 2009). Alleles and ST were determined
based on perfect matches (100% nucleotide identity with no indels). We also used the
MentaLiST (Feijao et al., 2018) (v0.1.3) software to call MLST based on k-mer matching
of the alleles to the pFASTQ file. Unlike the BLAST+-based MLST method, MentaLiST
did not require exact matches to alleles to predict a ST. If the pFASTQ was paired-end,
Ariba (Hunt et al., 2017) (v2.10.2) also determined MLST alleles and ST. The default
ST call for each genome was determined in the following order: agreement between
each method, agreement between MentaLiST and Ariba, agreement between MentaLiST
and BLAST+, agreement between Ariba and BLAST+, Ariba alone without a novel or
uncertainty call, MentaLiST alone, and finally BLAST+ alone. cgMLST was determined
with MentaLiST using the S. aureus cgMLST scheme (Leopold et al., 2014) available
at http://www.cgmlst.org.
Evidence for Staphylococcal Cassette Chromosome mec (SCCmec) predictions were
based on multiple approaches. The primary approach aligned SCCmec typing primers,
downloaded from http://www.staphylococcus.net/, against the assembled genome
using BLAST+ (v.2.7.1+) (Camacho et al., 2009). Samples with a perfect match to
primer pairs for a given amplicon were assigned an SCCmec type following the
Kondo et al. (2007) algorithm. Genes and proteins associated with SCCmec mere
aligned against the assembled genome using BLASTN and TBLASTN. We also mapped
the pFASTQ to each SCCmec cassette using BWA (Li & Durbin, 2009) (v0.7.17).
The overall cassette and mec region coverage statistics were determined as well as the
per-base coverage for each cassette using genomeCoverageBed (Quinlan & Hall, 2010)
(v2.26.0). The methods described above were based on the 11 SCCmec types currently
Petit and Read (2018), PeerJ, DOI 10.7717/peerj.5261 4/20
listed in the http://www.sccmec.org (I–XI) and hence did not include recently
described types XII and XIII (Wu et al., 2015;Kaya et al., 2018). We labelled a genome
as “MRSA” only if each mecA typing primer (Kondo et al., 2007) had a perfect BLASTN
match on the de novo assembly, a predicted mecA gene ortholog had a BLAST score
ratio of at least 95%, or Ariba (Hunt et al., 2017), as previously described, predicted
reads in the paired-end pFASTQ file matching a mecA target.
Web application, Relational Database and application programming
interface
We used Django (v2.0), a Python web framework, to develop a PostgreSQL (v10.1) backed
relational database for storing the results from the analysis pipeline (Fig. S1). A Django
application was created for each module of the pipeline, automating the creation of
database tables for the results. Python scripts building off Django were developed for
insertion of results from each StAP module or the StAP as a whole. A web front-end
was developed (https://staphopia.emory.edu) using the Bootstrap (v4.0) and jQuery
(v3.2.1) web frameworks. We used the Django REST framework to develop an extensive
API that allowed users to create queries accessing multiple samples. We also developed an
R package, Staphopia-R (DOI 10.5281/zenodo.1255314), to programmatically access
the API. The API and its endpoints were documented to allow users to further develop
their own packages in a language of their choice. The source code for our web
application was made available at DOI 10.5281/zenodo.1255312.
Processing public data
We used the Cancer Genomics Cloud (CGC) Platform, powered by Seven Bridges
(http://www.cancergenomicscloud.org/), to process S. aureus genomes through StAP in
November 2017. CGC allows users to create custom workflows based on Docker
containers, then execute these workflows on the Amazon Web Services (AWS) cloud
platform. We obtained a list of publicly available S. aureus sequencing projects from
the ENA web API using the following search term:
“tax_tree(1280) AND library_source=GENOMIC AND (library_strategy=OTHER
OR library_strategy=WGS OR library_strategy=WGA) AND
(library_selection=MNase OR library_selection=RANDOM OR
library_selection=unspecified OR library_selection=“size fractionation”).”
We only processed projects which used Illumina sequencing technology. CGC
opened AWS r3.xlarge instances (30.5 GB RAM, four processors) that downloaded
FASTQ files from the ENA, using ena-dl for each genome, and ran the StAP pipeline.
Ouput files were returned to the CGC, then uploaded into the Staphopia database server.
Metadata collection
We used the ENA API to download and store any information linked to the “Experiment,”
“Study,” “Run” and “BioSample” accessions into the database for each genome. We also
determined each sample’s publication status using three approaches.
Petit and Read (2018), PeerJ, DOI 10.7717/peerj.5261 5/20
The first approach identified existing links between SRA, a mirror of ENA, and
PubMed using NCBI’s Entrez Programming Utilities web API (Entrez Programming
Utilities Help, 2010). For any links identified, we used the corresponding PubMed ID
to extract information corresponding to the publication and stored them in the database.
The second approach searched for accessions within the text of scientific articles.
We searched PubMed using the term, “S. aureus,” limited to the years between and
including 2010 (the date of the first publicly available Illumina data upload), and 2017.
The saved results, stored as XML, were then loaded into Paperpile, a subscription-based
reference management tool, and the corresponding main-text PDFs were automatically
downloaded. This process did not include supplementary information files, which
required a manual operation. For those articles in which a PDF could not be automatically
downloaded, attempts to manually acquire the PDF were made. Using the text search
program “mdfind,” available on Apple OS X, each accession (BioSample, Experiment,
Study and Run) in the Staphopia database was used as a separate query to search all
the PDF files. Experiment accessions with a corresponding PubMed ID were then stored
in the database. In cases where a Study, BioSample or Run accession was identified in
PDF text, each associated Experiment accession was linked to the corresponding
PubMed ID.
For the third approach, a collection of PubMed articles with primary descriptions of
S. aureus genome sequencing studies was manually curated. For these studies, the PDF
and all available supplementary information were downloaded. The process of text-
mining the articles and linking Experiment information to PubMed ID was repeated
as described in the second approach. A list of these articles is available in BibTeX
format from the code repository.
Creating non-redundant S. aureus diversity set
Using available metadata, we selected a non-redundant diversity (NRD) set of
genomes that were “Gold” quality (see “Results”), linked to a publication and each had a
unique ST. When more than one strain from a ST was available, we randomly selected
one individual giving priority to samples with collection date, site of isolation and
location of isolation fields filled.
Using predicted variants against N315, we extracted a list of genes that had complete
sequence coverage (i.e., “core” genes) but no predicted indels. For each reference gene
sequence, we created an alternative gene sequence with SNPs predicted in each sample.
If all the 31-mers of the alternative gene sequences were present in a 31-mer database
of the pFASTQ file of genome created using the Jellyfish (Marc¸ais & Kingsford, 2011) tool,
the reconstructed sequence was considered “validated.” Validated reconstructed gene
sequences or all genomes were stored in the database and made available through the
API for rapid phylogenetic comparisons.
A total of 380 genomes were selected for phylogenetic analysis as representatives of
unique STs with good quality metadata. The set of validated genes were extracted and
concatenated into a single sequence for each genome and saved in multi-FASTA and
PHYLIP formats. A guide tree was generated with IQ-Tree (Nguyen et al., 2015)
Petit and Read (2018), PeerJ, DOI 10.7717/peerj.5261 6/20
(v8.2.11, -fast option) for identification of recombination events with ClonalFrameML
(Didelot & Wilson, 2015) (v1.11). A recombination free alignment was created with
maskrc-svg (https://github.com/kwongj/maskrc-svg). We used IQ-Tree to generate the
final maximum likelihood tree with the GTR model and bootstrap support. Bootstrap
support was generated from 1000 UFBoot2 (Hoang et al., 2018) (ultrafast bootstrap)
replicates. We annotated the tree using iTOL (Letunic & Bork, 2016).
RESULTS
Design of the Staphopia analysis pipeline and processing 43,000+
genomes
The StAP (Fig. 2) was written to automate processing of individual S. aureus genomes
from Illumina shotgun data. The pipeline was designed as a series of modules running
individual software packages, organized by the Nextflow (Di Tommaso et al., 2017)
workflow language, which made it possible to run the entire pipeline or individual
components as needed. The first step of the pipeline was to import single- or paired-end
FASTQ files either as local files, or from the ENA database. Following quality-based
trimming and down selection of the FASTQ to 281 Mbases (∼100coverage of the
N315 reference chromosome (Kuroda et al., 2001), NC_002745.2), analyses were run
on the raw pFASTQ files directly, or on de novo genome assemblies constructed by the
SPAdes program (see “Methods” for more details). We decided to downsample the input
FASTQ files for two reasons: to manage the computational burdenwhen running thousands
of genome projects and also to achieve genome datasets with consistently sized pFASTQ
input files. The threshold of ∼100coverage was chosen after preliminary studies
(https://github.com/staphopia/staphopia-paper/blob/master/analysis/07-supplementary.pdf)
showed that there was either small or no improvements in outcome for downstream
assembly and remapping steps for input files >100but large increases in processing
time and memory requirement. We created a Postgres database to store results from the
StAP analysis and a web front end and a web API for mining the data. An R package
(Staphopia-R) was written for interacting with the API and was used for most analysis
presented in the results.
In November 2017 there were 44,012 publicly-available shotgun sequencing projects
with FASTQ files in ENA. Illumina technology was the dominant platform, accounting
for 99% of samples (N= 43,972). A total of 81% (N= 35,580) of them had at least
281 Mbases sequence data. We processed all Illumina genomes in parallel through the
StAP using cloud servers (see “Methods”). On parallel r3.xlarge instances with 30.5 Gb
RAM and four processors, the mean time to process a genome was 52 min with an
interquartile range of 47–56 min (Fig. S2).
Sequence and assembly quality trends
We identified samples that were likely mixed-samples or not S. aureus whole genome
shotgun projects and/or were of low technical quality and marked them to not be
included in subsequent analysis. We removed genomes that failed to match to any known
allele of the seven MLST loci (323 genomes), had a total assembly size that differed
Petit and Read (2018), PeerJ, DOI 10.7717/peerj.5261 7/20
by more than one Mb from a typical S. aureus chromosome (<1.8Mb or >3.8Mb; 764
genomes), or had a GC content differing more than 5% (<28% or >38%; 467 genomes) of
the expected 33% GC content. Failure to complete the StAP pipeline due to poor data
quality, and coverages less than 20were flagged in 101 and 142 genomes, respectively.
In total, we removed 1,023 genome projects, leaving 42,949 for further analysis.
We placed genomes into an arbitrary ranking of 1–3 (“Bronze,” “Silver” and “Gold”)
based on the pFASTQ coverage and average sequencing quality. Paired-end
genomes that had read lengths exceeding 100 bp, a coverage of 100and an average
per base quality score of at least 30 were given a Gold rank. The purpose of the
Gold rank was to group together high-quality samples with near-identical coverage.
Paired-end genomes with similar read length and quality cutoffs but a lower
sequence coverage (between 50and 100) were classified as Silver. The remaining
samples were given a rank of Bronze. Single-end reads were classified as Bronze no
matter the read length, quality or coverage. More than 70% of the samples were of rank
Gold (N= 31,014). There were 5,931 Silver and 6,004 Bronze rank samples. Each year
since 2012, the number of Gold ranked genomes have exceeded Silver and Bronze
(Fig. 3).
Changes in sequence quality and de novo genome assembly metrics over time reflected
the development of Illumina technology. Mean per-base quality scores increased from
∼32 in 2010 to >35 in 2012 and have stayed at that level since. The mean sequence read
length rose in steps from <50 in 2010 to ∼150 bp in 2017. Assembly metrics such as
N50 (Earl et al., 2011), and mean and maximum contig length have gradually increased
since 2010. Bronze ranked genome projects had similar (or sometimes even higher) mean
per read quality scores than Gold and Silver since 2011. However, Silver and Gold
assembly metrics such as N50 and mean contig size were generally quite similar and higher
than Bronze.
00
292 55
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3480
984
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645
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2010 2011 2012 2013 2014 2015 2016 2017
Year
Count
Bronze
Gold
Silver
Figure 3 Sequencing quality ranks per year 2010–2017. Genome projects were grouped into three
increasing quality ranks: Bronze, Silver and Gold. The rank was based on coverage, read length and per-
read quality (please see “Results”). The highest rank, Gold, represented 72% (N= 31,014) of the available
S. aureus genome projects. The remaining genomes were almost evenly split between Silver (N= 5,931)
and Bronze (N= 6,004). Full-size
DOI: 10.7717/peerj.5261/fig-3
Petit and Read (2018), PeerJ, DOI 10.7717/peerj.5261 8/20
Genetic diversity measured by MLST
We obtained a view into the genetic diversity of the sequenced S. aureus genomes by
in silico MLSTusing Ariba (Hunt et al., 2017), MentaLiST (Feijao et al., 2018) (both taking
pFASTQ as input, but using different algorithms) and BLASTNagainst assembled contigs.
A ST was assigned to 42,337 (98.6%) genomes. Of these, 41,226 (97.3%) calls were in
agreement between MentaLiST, BLAST+ and (if paired-end) Ariba methods; 828 had
agreement between two methods and a no-call on the other, and 189 were supported by
one program with no-calls from the other two. Of the remaining 612 genomes not
assigned to a known ST, 306 were predicted to be in a novel ST based on matches to
known alleles of each of the seven loci. The remaining 306 genomes had 1–6 known
S. aureus MLST alleles.
The 42,337 genomes assigned to existing STs represented only 1,090 STs of 4,466 in
the PubMLST S. aureus database (November 2017, https://pubmlst.org/saureus/).
The abundance distribution was weighted toward common strains, with the top 10
(STs 22, 8, 5, 239, 398, 30, 45, 15, 36 and 105) representing 70% (N= 29,851) of the
genomes (Fig. 4).
The cgMLST set of 1861 loci (November 2017, https://www.cgmlst.org/) were assigned
to the genome set using MentaLiST. There were 38,677 distinct patterns, with only
0
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22 8 5 239 398 30 45 15 36 105
Sequence Type
Count
A
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Sequence Type
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Sequence Type
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6000
22 8 5 239 398 30 45 15 36 105
Sequence Type
Count
D
Resistant Susceptible
Figure 4 Resistance genes to methicillin (MRSA), aminoglycoside, fosfomycin and macrolide-lincosamide-streptogramin (MLS) antibiotic in
the top 10 STs. The presence of resistance genes was predicted by Ariba (Hunt et al., 2017) using the reference MegaRes (Lakin et al., 2017) database.
The distribution MegaRes resistance classes for methicillin (A), aminoglycosides (B), fosfomycin (C) and macrolide-lincosamide-streptogramin
(D) are presented for the top 10 sequence types (ST). The top 10 STs represent 70% (N= 29,851) of the genomes analyzed in this study.
A breakdown of each remaining resistance class tested is available in the code repository. Full-size
DOI: 10.7717/peerj.5261/fig-4
Petit and Read (2018), PeerJ, DOI 10.7717/peerj.5261 9/20
1,850 patterns found in more than one sample, the remaining 36,827 patterns were
represented by a single genome.
Antibiotic resistance genes
Treatment of S. aureus infections has been complicated by the evolution of strains
resistant to many commonly used antibiotics (Foster, 2017). In particular, MRSA, carrying
the mecA gene encoding the PBP2a protein that confers resistance to beta-lactam
antibiotics, has become a global problem. We designated a genome as MRSA if each
mecA typing primer (Kondo et al., 2007) had a perfect BLASTN match on the de novo
assemblies (26,743 strains), a predicted mecA gene ortholog had a BLASTN score ratio
of at least 95% (26,430 strains), or the Ariba (Hunt et al., 2017) algorithm predicted
reads in the paired-end pFASTQ file matching a mecA target in the MegaRes (Lakin
et al., 2017) database (27,120 strains). The number of genomes having at least one of these
criteria (27,628) was 64% of the total number. Of these, 95% (26,340) of the samples
had agreement between each of the criteria. The top five most common STs had a large
portion of MRSA strains (Fig. 4), which reflects the selection bias of the research
community in investigating these significant hospital and community pathogen strains
over other S. aureus.
The mecA gene is usually horizontally acquired as part of a mobile genetic element
called SCCmec (Katayama, Ito & Hiramatsu, 2000). SCCmec elements have been classified
into at least 11 classes that vary in composition of mec genes, ccr cassette recombinase
genes and spacer regions (http://www.sccmec.org). Knowledge of the SCCmec type
can be useful for high-level characterization of MRSA strain types (Kaya et al., 2018).
We showed that 10 of the 11 cassettes in the current schema map to at least one genome
with highest coverage (an approximate method for assigning SCCmec type) (Table 1).
Of the 26,462 (26,185 paired-end) genomes with at least 50% cassette coverage, 96%,
96% and 99% are MRSA based on primer BLASTN, protein BLASTN or MegaRes,
Table 1 Predicted SCCmec cassette type representation.
SCCmec type Count
I 689
II 5,183
III 2,807
IV 14,526
V 1,684
VI 171
VII 19
VIII 468
IX 0
X 20
XI 895
Note:
There were 26,462 samples with reads mapped to at least 50% of a SCCmec cassette. The table is a breakdown of the
SCCmec cassettes with the highest percent match for each sample.
Petit and Read (2018), PeerJ, DOI 10.7717/peerj.5261 10/20
respectively. All type XI cassettes were mecA negative by primer BLASTN because these
contained the mecC allele (Garcı
´a-A
´lvarez et al., 2011;Shore et al., 2011), which was
sufficiently different to be outside the normal distance for a positive match. Additionally,
we found 53 genomes which matched to at least 50% of a SCCmec cassette but
were not MRSA and had no reads mapping to the mec region of the cassette.
In addition to mecA, we found numerous other classes of non-core antibiotic resistance
genes using the MegaRes (Lakin et al., 2017) class designations (Table 2). We did not
consider SNPs/indels in core genes associated with resistance for this analysis.
The most common class of resistance genes were beta-lactamases found in 37,758
genomes. Following this, the most common were the genes putatively conferring
fosfomycin, macrolide-lincosamide-streptogramin and aminoglycosides resistance
(24,205, 22,322, 17,968 genomes respectively). As with MRSA, the other common
resistance genes were not distributed evenly among the top ST groups (Fig. 4), reflecting
sampling ascertainment bias and also possibly differences in geographic distribution
and prevalence of healthcare-isolated strains in the most common genotypes.
Publication, metadata and strain geographic distribution
One challenge to using publicly available datasets through ENA or SRA is determining
whether there is a published article describing the sequenced genome. We found
through NCBI’s Entrez Tools (eLink) that 6,712 genomes were linked to 48 publications
in PubMed (March 2018). We attempted to add to the number by using text-mining
methods to find S. aureus accession numbers in PDFs of S. aureus genome publications,
ascertaining an additional 5,209 genomes in 30 publications. Therefore, of the 42,949
Table 2 Antibiotic resistance classes predicted by non-core genes.
Antibiotic resistance class Count
Aminocoumarin 46
Aminoglycoside 17,968
Beta-lactam 37,758
Fluoroquinolone 69
Fosfomycin 24,205
Fusidic Acid 346
Glycopeptide 5,777
Lipopeptide 44
Macrolide-lincosamide-streptogramin (MLS) 22,322
Multi-drug resistance 13,653
Phenicol 852
Rifampin 46
Sulfonamide 36
Tetracycline 8,638
Trimethoprim 6,605
Note:
Number of genomes with genes of resistance classes predicted by Ariba using the reference MegaRes database naming
scheme.
Petit and Read (2018), PeerJ, DOI 10.7717/peerj.5261 11/20
samples deposited between 2010 and 2017, only 28% (N= 11,921) could be linked to a
publication (Fig. 1). Since many genomes have been deposited in the last 1–3 years, this
reflected the often significant time lag between depositing sequence data and final
publication.
We noted that collection of metadata from public sequencing projects was another
challenge. When submitting genome sequences to databases only a limited number of
metadata fields are required, leading to the bulk of the information needing to be extracted
manually from a publication, if it can be found. Only 40% (N= 17,034) genomes had a
collection date, 35% (N= 14,983) had a geographic location and 35% (N= 14,768) had
isolate source metadata. Using the available geographic data to geocode the sites of collection,
we found that strains were from five continents and at least 40 countries. There was a
strong bias toward strains from Europe (N= 7,314) and North America (N= 5,882),
reflecting where the funding for most of the early sequencing studies had originated.
A non-redundant S. aureus diversity set
The number of SNPs compared to the N315 reference strain varied from 6 to 141,893
within our collection of 42,949 genomes. The stepped pattern of the distribution (Fig. 5)
reflected the organization of S. aureus into CCs. Apart from CC5 strains closely related
to N315, the majority of S. aureus had ∼50–50,000 SNPs and ∼500–1500 indels called
by the GATK pipeline (McKenna et al., 2010). There were a group of 240 most distant
strains with >55,000 SNP (Fig. 5) that were found to be closer to the sister species,
S. argenteus (Holt et al., 2011) based on ANI imputed by mash (Ondov et al., 2016),
although 230 of these were assigned a S. aureus ST.
Of the 6,904 S. aureus genomes of Gold rank linked to a publication we selected a
group of 380, each having a distinct ST as a NRD set of genomes. Of the 2,756
annotated N315 genes (excluding RNAs), 1,113 genes had no indels when reads from
S. argenteus
S. aureus
0
20,000
40,000
60,000
80,000
100,000
120,000
140,000
0 5000 10000 15000 20000 25000 30000 35000 40000
Samples
Count
Figure 5 Staphylococcus aureus SNP distance from reference S. aureus N315. For each genome, the
number of SNPs found by mapping reads to the N315 reference using GATK (McKenna et al., 2010) was
plotted, with genomes ordered from least to most SNPs. A total of 240 genomes with >55,000 SNPs
(dotted line) that had best matches to S. argenteus using mash (Ondov et al., 2016) were indicated by
silver bars, the rest were S. aureus (gold). Full-size
DOI: 10.7717/peerj.5261/fig-5
Petit and Read (2018), PeerJ, DOI 10.7717/peerj.5261 12/20
each genome in the NRD dataset were mapped. Of these, 878 were “core” genes found
in every genome. We reconstructed these genes for each of the NRD genomes starting
with the N315 sequence and substituting predicted SNPs. These predicted sequences
were then validated by decomposing into 31-mers and cross-checking whether each k-mer
was present in pFASTQ files processed by Jellyfish (Marc¸ais & Kingsford, 2011). We
concatenated the 878 genes for each member of the NRD set and created a tree based
on the 44,377 variant SNP positions (Fig. S3). The structure of the unrooted species
tree resembles previous S. aureus phylogenies (Planet et al., 2017). The assembled
genomes and concatenated gene sequences from the NRD dataset is available at
DOI 10.6084/m9.figshare.6263435.
DISCUSSION
The huge public library of genome sequence projects of S. aureus and other pathogens are
a resource for microbiologists for testing genetic hypotheses in silico. Unfortunately,
this has been a library of blank covers: most projects cannot be browsed to identify
features such as underlying data quality, ST, key SNPs and non-core genes. Staphopia
makes the library searchable for a number of important attributes, and we have described
example workflows in the results section. Staphopia is unique in allowing both stand-
alone analysis of S. aureus genomes through the StAP in a Docker container as well as
access to the database of >40,000 processed genomes through more than 350 API
endpoints. Furthermore, we have introduced a “Gold” rank for Illumina shotgun
projects of 100coverage and an average per base quality score of Q30 that allows
comparison of more than 30,000 of the genomes with near-identical underlying
data quality.
We used three strategies for analysis of raw sequence data: mapping reads to a
reference chromosome to identify variants; de novo genome assembly, and direct
analysis of the reads. Each has its strengths and weaknesses. Reference mapping retains
quality information about variant calls but is limited to regions of the core genome and
accuracy is reduced as genetic distance increases between the query and the reference.
De novo assembly allows for discovery of novel accessory genes and is reference
independent but could be affected by genomic contamination, and with Illumina short
read data small portions of the sequence could be lost in gaps between contigs. Direct
analysis of reads based on k-mer decomposition approaches allows examination of
sequence independent of mapping and assembly algorithms but is also susceptible to
false results arising from contamination and random sequence error. Using different
approaches to cross-validate wherever possible builds confidence and we showed that
MLST and MRSA/MSSA identification were robust with different underlying data types
collected.
We showed that S. aureus genomes sequenced to date are heavily over-represented by a
small number of common STs (Fig. 4), with a large tail of rarer strains. This is similar
to what is seen in the PubMLST database, where the 10 most common STs were also
the top 10 STs in Staphopia. It is tempting to assume that these numbers are a reflection of
the true population structure of S. aureus, and that this is dominated by a relatively
Petit and Read (2018), PeerJ, DOI 10.7717/peerj.5261 13/20
small number of recently expanded lineages. However, strains selected for sequencing are
far from a random sample of the population, and have strong biases in geography and
toward those found in hospitals. For this reason, we selected a non-redundant set from
within the Staphopia genomes for investigators to use for cross-species bioinformatic
studies. A total of 3,371 of PubMed STs were found not represented in Staphopia. Of
these STs, 98.5% were represented by one or two isolates in PubMLST. ST390, with
36 samples from Pleven, Bulgaria between 1995 and 1999, was the highest represented
ST without assignment in Staphopia. The large number of rare PubMLST STs missing
from Staphopia could be a combination of sequencing errors in MLST data creating
spurious STs, systematic undersampling of rare and geographically diverse strains in
early Illumina genome projects, or simply that the population structure of S. aureus has
a large number of rare STs.
There are many possible avenues for future extensions of the project. New tools for
efficient direct querying of raw reads have recently become available (e.g., BigSI
(Bradley et al., 2017), and mash (Ondov et al., 2016)) and we plan to incorporate them
in future iterations of the pipeline. Some of the principal improvements need to be in
protein functional annotation. For speed and simplicity, we elected to map genes called
from de novo assemblies against the included Prokka (Seemann, 2014) RefSeq database.
This has the advantage of giving consistent proteins naming that can be linked to
many functional annotation databases through UniProt cross-references. However, for
fine resolution studies of sets of genomes from Staphopia, we recommend reprocessing
with Roary (Page et al., 2015) to incorporate paralog detection and to use more
extensive databases for homology matching. Even then, specific modules would need
to be incorporated to improve naming of intrinsically hard to annotate protein
families (e.g., microbial surface components recognizing adhesive matrix molecules
(Foster et al., 2014)).
A key problem highlighted in this study is the difficulty in tracing publications
linked to public genome data and finding typical metadata on strains (date and place
of isolation, body site). We were able here to link thousands of records to publications
through searching text in PDFs. For this reason, we urge researchers publishing
microbial genomes to quote the Project ID (i.e., the PRJN ID) of publicly submitted
datainthefulltextofthepublication.Extracting metadata from publications presented
a more complicated process. Metadata is often available as spreadsheets, documents or
PDFs which are not easily parsed. We believe that journals need to start enforcing
machine readable standards for metadata associated with deposited strains. The routine
usage of BioSample ID (https://www.ncbi.nlm.nih.gov/books/NBK169436/), which
links strains to genomic information, would be a major step forward.
Staphopia was designed with Illumina shotgun data in mind but increased use of
alternative sequencing technologies in the future may necessitate new development.
“Long read” technologies (e.g., PacBio, Oxford Nanopore) tend to have assemblies
with fewer gaps, higher per base errors and lower coverage. A “gold standard” PacBio
assembly will have a different quality profile to Illumina technology data (which itself
is also evolving). Another challenge for automated assembly of public data will be to
Petit and Read (2018), PeerJ, DOI 10.7717/peerj.5261 14/20
identify projects sequenced with multiple technologies and assembled as hybrids
(e.g., as demonstrated by the Unicycler tool (Wick et al., 2017)). To do this would
mean altering the pipeline to perform hybrid assembly when experiments with
multiple technologies are associated with a strain. Currently, within ENA (and SRA) a
BioSample can be associated with multiple Experiments, but an Experiment can
only be associated with a single BioSample. When a BioSample was linked to more than
one Experiment, it was difficult to determine in an automated way if it is actually
the same genomic DNA input to multiple experiments or, in rare cases, a mistaken
assignment of a set of genetically non-identical isolates with the BioSample (e.g., all isolates
from a study given the common strain name “USA300”). Because of this, Staphopia treated
each ENA Experiment as a unique sample, rather than the BioSample.
It is unclear at this time whether the approach of processing of every public dataset
will be sustainable as sequencing data production grows in the future. It would only be
possible if storage and processing costs fall faster than the accumulation of new data,
and multi-genome database queries may still be prohibitively slow. An alternative strategy
to processing all strains, would be to filter the isolates for redundancy, by removing
isolates that are less than nSNPs from any member of a canonical genome set.
However, there is still information in deep sequencing studies that can be captured
from distributions of reads and k-mer distribution, even if the consensus sequences of
the strains are identical. The plasmid copy number may differ between clones grown
under different conditions, and the distribution of reads across the genome can itself
be used to infer relative growth rate (Brown et al., 2016). No two shotgun genome
sequencing projects are identical, and all have some potential value, especially if they
have strong supporting metadata.
CONCLUSION
We analyzed 43,972 S. aureus public Illumina genome projects using the newly
developed “Staphopia” analysis pipeline and database. 42,949 genomes were
retained for subsequent analysis after filtering against low quality.
The data quality was high overall: 36,945 (86%) were from paired-end projects with
greater than 50-fold coverage and 30 average base quality (“Gold” and “Silver” quality).
There has been a great concentration of effort on a sequencing a small number of STs:
only 1,090 STs of 4,466 previously collected STs were recovered and 10 STs make up
70% of all genomes.
26,340–27,628 genomes were predicted MRSA depending on the criteria used for
classification.
We could link only 28% of the genomes to a PubMed referenced publication.
We identified 380 non-redundant highly quality published genomes as a reference
subset for diversity within the species.
We identified 878 core genes that can be reliably used for rapid tree building based
on SNPs compared to the reference N315 genome.
Petit and Read (2018), PeerJ, DOI 10.7717/peerj.5261 15/20
ACKNOWLEDGEMENTS
We would like to thank Tauqeer Alam, Jim Hogan, Santiago Castillo-Ramı
´rez. Michelle
Su, Michael Frisch and Erik Lehnert for their helpful suggestions. We would also
like to acknowledge our gratitude to the many scientists and their funders who
provided genome sequences to the public domain, ENA and SRA for storing and
organizing the data, and the authors of the open source software tools and databases
used in this work.
ADDITIONAL INFORMATION AND DECLARATIONS
Funding
Funding was from Emory University, Amazon AWS in Education Grant Program, and
NIH grants AI091827 and AI121860. The Seven Bridges NCI Cancer Genomics Cloud
pilot was supported in part by the funds from the National Cancer Institute, National
Institutes of Health, Department of Health and Human Services, under Contract No.
HHSN261201400008C. The funders had no role in study design, data collection and
analysis, decision to publish, or preparation of the manuscript.
Grant Disclosures
The following grant information was disclosed by the authors:
Emory University, Amazon AWS in Education Grant Program, and NIH: grants AI091827
and AI121860.
National Cancer Institute, National Institutes of Health, Department of Health and
Human Services: under Contract No. HHSN261201400008C.
Competing Interests
Timothy D. Read is an Academic Editor for PeerJ.
Author Contributions
Robert A. Petit III conceived and designed the experiments, performed the experiments,
analyzed the data, contributed reagents/materials/analysis tools, prepared figures and/or
tables, authored or reviewed drafts of the paper, approved the final draft.
Timothy D. Read conceived and designed the experiments, performed the experiments,
analyzed the data, contributed reagents/materials/analysis tools, prepared figures and/or
tables, authored or reviewed drafts of the paper, approved the final draft.
Data Availability
The following information was supplied regarding data availability:
Code for most analysis described in the Results section—DOI 10.5281/zenodo.
1296448,
Staphopia—https://staphopia.emory.edu,
R Package—DOI 10.5281/zenodo.1255314,
StAP—DOI 10.5281/zenodo.1255310,
Web Package—DOI 10.5281/zenodo.1255312,
Petit and Read (2018), PeerJ, DOI 10.7717/peerj.5261 16/20
Docker Image—https://hub.docker.com/r/rpetit3/staphopia/,
NRD Dataset—DOI 10.6084/m9.figshare.6263435.
Supplemental Information
Supplemental information for this article can be found online at http://dx.doi.org/
10.7717/peerj.5261#supplemental-information.
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