So I have sequenced my organism … what do I do now?

Nick Loman

Oh dear

Sequence some more

Sensible

Useful things

Whole-genome sequencing:utility in clinical microbiology

• Diagnostics– Species, subspecies, strain identification– In silico antibiogram– In silico virulence profile

• Surveillance• Typing (including backwards compatibility with MLST and

serotype)• What strains and resistance elements are lurking in my

hospital/community?

• Forensic epidemiology – Is there an outbreak?

• Who gave what to who?

Common types of sequencing

• Paired-end Illumina (typically 150 – 300 bases)

• Single-end Ion Torrent (typically 300-400 bases)

– Can be treated more or less the same

• Pacific Biosciences or Oxford Nanopore

– Requires special handling, not covered today

Quality Control: Questions to Ask

• Did my sequencing work?

• What are the fragment lengths?

• Is my sample what I think it is?

• Is my sample contaminated?

Read QC

Adaptor/quality trimming

Species ID

Sample QC

FastQC, Qualimap, Kraken, BLAST

Trimmomatic

BLAST, Metaphlan, MOCAT

Blobology

Did my sequencing work?

• FastQC:

What coverage do I have?

• SNP calling: >10x (>15x better)

• De novo assembly: >30x (50x probably better)

• Absolutely no benefits over about 100x for standard applications and slows everything down and takes more disk space

• (BTW, FASTQ files are probably a waste of space)

What are the fragment lengths?

• Qualimap (or just BWA)

BadFragment length < read

length

OKFragment length > read

length

GoodFragment length > 2x read

length

You are in dangerous territory dealing with repetitive regions longer than the fragment length, regardless of read depth coverage

Repetitive regions

This is important because repeat-containing are often the most interesting parts of the genome! Think:

• Insertion elements

• Transposons

• Plasmids

• Ribosomal RNA

REPEAT: You are in dangerous territory dealing with repetitive regions longer than the fragment length, regardless of read depth coverage

Do not trust the computer

Bioinformatics software will do its best to look like it is dealing with repeats in a rational way, but it is in fact plotting aggressively to ruin your analysis without telling you.

Computers are just like that!

If repeats are important to your analysis, you need an alternative sequencing strategy: long mate-pairs, long reads (Pacific Biosciences or Oxford Nanopore). Don’t drive yourself mad making short reads do what they can’t.

Adaptor trim reads

• With Nextera libraries, failing to adaptor trim will KILL your assemblies.

• Particularly important when mean fragment length < read length.

• Many trimmers available: I like to use Trimmomatic

• Quality trimming not important with modern tools (BWA and Spades)

For more explanation: http://nickloman.github.io/high-throughput%20sequencing/genomics/bioinformatics/2013/04/17/adaptor-trim-or-die-experiences-with-nextera-libraries/

Is my sample what I think it is?

• BLASTing a few random reads usually very efficient quality control check, as well as helping identify a reference genome

• Kraken or Metaphlan can give rapid organism report

Species identification

• Methods:

– 16S rDNA extraction (typically following de novo assembly and annotation) and BLAST

– Taxon-defining genes (e.g. Metaphlan)

– Phylogenetic approach (e.g. MOCAT, Phylosift)

For more explanation: http://nickloman.github.io/high-throughput%20sequencing/genomics/bioinformatics/2013/04/17/adaptor-trim-or-die-experiences-with-nextera-libraries/

Isolate genome

Sequence reads

Other samples on sequencing run

Contamination

Unsequencedregions

Sources of contamination

• Accidental multiple colony picks or mixed liquid culture– Same or different organism

– E.g. Achromobacter & Pseudomonas aeruginosa in CF

• Reagent contamination (DNA extractions)

• Sequencer “carry-over” (0.2%?)

• PhiX control sequence <- don’t be this guy

• Barcode “cross-over” (bad pipetting technique or contaminated reagents)

Blobology

Contamination

Adaptor trim reads

• With Nextera libraries, failing to adaptor trim will KILL your assemblies.

• Particularly important when mean fragment length < read length.

• Many trimmers available: I like to use Trimmomatic

For more explanation: http://nickloman.github.io/high-throughput%20sequencing/genomics/bioinformatics/2013/04/17/adaptor-trim-or-die-experiences-with-nextera-libraries/

Reference-based or de novo?

Reference-based or de novo?

• Reference-based

– Implies ALIGNMENT to reference

– Implies you HAVE a reference

– Allows exquisitely sensitive and specific SNP calling (forensic SNP calling to single mutation precision)

– Important for looking at CHAINS OF TRANSMISSION

– Can only call in parts of the genome COMMON between your SAMPLES and REFERENCE: the CORE

Reference-based or de novo?

• De-novo– Implies de novo assembly

– Does NOT require a reference

– Gives access to the entire PAN-genome

– E.g.• Unexpected antibiotic resistance genes

• Virulence factors

– Can give misleading results in REPEAT sequences

– Not suitable for very fine-resolution SNP analysis

In practice

• Most people will want to do both.

• And if you have no reference, you can use a draft de novo assembly AS your reference

– But exercise caution

Reference-based approach

Alignment

Variant calling

SNP extraction & filter

Recombination filtering

Tree building

MLST/Antibiogram

Read QC

Adaptor/quality trimming

Species ID

Sample QC

FastQC, Qualimap, Kraken, BLAST

Trimmomatic

BLAST, Metaphlan, MOCAT

Blobology

BWA

Samtools/VarScanGATK

Custom script, snippy, snpEff, BRESEQ

Gubbins, ClonalFrameML

FastTree, RaXML

SRST2

Analysis choice highly species dependent: not one size fits all!

• What is the mode and tempo of evolution?

• Monomorphic organisms:– Characterised by vertical pattern of inheritance

– Isolates differ by few mutations

• Highly recombinogenic organisms– Mutations dominated by recombination

– May have vast differences in gene content, gene order

– “Clonal frame” may be obscured or absent

Different species require different analysis strategies

Variation

M. tuberculosis

S. aureus

B. anthracis

E. coli

P. aeruginosa

N. meningitidis

S. pneumoniae

Clonal population structureBranching phylogenies

Open pan-genomeHorizontal gene transfer

Salmonella

High rates of recombinationPhylogenetic networks

Tips for picking a reference

• The higher quality the better (aim for pre-NGS Sanger genomes, e.g. <2001)

• Ideally single contig, no gaps

• Canonical strains have most portable and referenced gene references, e.g. TB H37Rv, PAO1, E. coli K-12 etc.

• For SNP calling specificity: more closely related is better

The core genome

• The core genome used to call SNPs will reduce as more genomes are added

• Particularly noticeable in species with highly plastic genomes: E. coli

• Has significance for forensic applications

Is my reference good enough?

• Assess core genome size

– Harvest will do this for you

• Or look at samtools flagstat (?)

• Between-sample SNP calling efficiency goes down with reference divergence

• Luxury option: get a Pacific Biosciences complete reference done for each “clone” in your dataset (for some definition of clone)

Effect of closer reference on P. aeruginosa genotyping

SNPs Indels Mapped

PAO1Reference

23 4 77%

PacBioReference

40 5 97%

Quick, Loman et al. BMJ Open 2014

SNP filtering

• Specific SNP dataset is vital for effective phylogenetic reconstructions and outbreak tracing

• Most SNP calling errors come from– A) misalignment (sequence present in sample but not

in reference, align)

– B) copy number variation (2 copies in sample, 1 copy in reference)

• NOT from sequencing error (at least with Illumina: systematic errors with other platforms)

SNP filtering (2)

• Allele frequency filter is most effective SNP filter– AF > 0.9 (90%) works very well empirically

• Strand filter also very useful to prevent SNPs around structural variations

• Filtering for low coverage not that helpful:– 1/1000 error (Q30) * minimum of 3 coverage =

.000000001 chance of an error per position = < 1 error per genome

• Avoid SNPs at ends of contigs as these may be mismapping

Detecting recombination

• Simple algorithms rely on SNP density, more complex ones asssess impact on “clonal frame”

Normal SNP density Recombining region

Impact of recombination filtering

De novo approach

• Interrogate the accessory genome

– Novel genes

• Some important applications take contigsrather than reads as primary input

• SNP calling with de novo assembly is fundamentally less reliable due to lack of allele frequency information; but fine for broad-scale clustering

Reference-based approach

Alignment

Variant calling

SNP extraction & filter

Recombination filtering

Tree building

MLST/Antibiogram

Read QC

Adaptor/quality trimming

Species ID

Sample QC

FastQC, Qualimap

Trimmomatic

BLAST, Metaphlan, MOCAT

Blobology, Kraken, BLAST

BWA

Samtools/VarScanGATK

Custom script, snippy

Gubbins, ClonalFrameML

FastTree, RaXML

SRST2

De novo approach

Assembly

MLST/Antibiogram

Annotation

Tree building

Population genomics

Pan-genome

VelvetSPADES

Prokka

Harvest

BigsDBPhyloviz

LS-BSR

mlst, Abricate

Concluding thoughts

1. Don’t trust your sequencing data (or others’) – sense-check and validate each step

2. Make extensive use of visualisation tools to do this

3. There’s more than one way to do any one task

CLoud Infrastructure for Microbial Bioinformatics (CLIMB)

• MRC funded project to develop Cloud Infrastructure for microbial bioinformatics

• £4M of hardware, capable of supporting >1000 individual virtual servers

• Amazon/Google cloud for Academics

Meet-The-Expert

• Meet-The-Expert: Joao Carrico and I

• Tomorrow (Monday)

• 07:45 (really)

• Hall M

• Session ME11 What bioinformatics tools do I use for whole-genome sequence (WGS)-based bacterial diagnostics and typing?

Acknowledgements

• Twitter comments:

– Tom Connor, Alan McNally, Torsten Seemann, C. Titus Brown, Heng Li, Christoffer Flensburg, Matt MacManes, Rachel Glover, Willem van Schaik, Bill Hanage, Jennifer Gardy, Mick Watson, Alan McNally, Esther Robinson, Nicola Fawcett, Aziz Aboobaker, Ruth Massey