Supporting material for ECCMID 2015 Meet-The-Expert session.

1. What bioinformatic tools should I use for
analysis of high-throughput sequencing data
for molecular diagnostics?
Nick Loman

2. 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/VarScan
GATK
Custom script, snippy,
SnpEff, BRESEQ
Gubbins,
ClonalFrameML
FastTree, RaXML
SRST2
De novo approach
Assembly
MLST/Antibiogram
Annotation
Tree building
Population genomics
Pan-genome
Velvet
SPADES
Prokka
Harvest
BigsDB
Phyloviz
LS-BSR
mlst, Abricate

3. FastQC
• What: Analyse read-level sequence quality.
• Why: Determine serious errors in read quality
that might affect downstream analysis.
• Where:
http://www.bioinformatics.babraham.ac.uk/p
rojects/fastqc/

4. FastQC

5. Qualimap
• What: Analyse insert size distribution
• Why: Determine whether sequencing has
been effective, particularly for de novo
assembly, need for adaptor trimming
• Where: http://qualimap.bioinfo.cipf.es/

6. Trimmomatic
• What: One of several million read trimmers
• Why: To remove sequence adaptors which
may influence the results of de novo assembly
• Where:
http://www.usadellab.org/cms/?page=trimmo
matic

7. Species ID: BLAST
• What: Only the most famous bioinformatics
algorithm ever made
• Why: A few random BLAST searches will reveal
much important information about your data
before you start on a pipeline analysis
• Where: http://ncbi.nlm.nih.gov/BLAST

8. Species ID: Metaphlan
• What: Designed for metagenomics, this
algorithm will find “taxon-defining” genes to
identify what species are in a sample
• Why: Check for extent of sample
contamination, give an accurate species ID for
unknown samples
• Where:
https://bitbucket.org/biobakery/metaphlan2

9. Species ID: Kraken
• What: Similar to Metaphlan but even faster
and with a more complete database
• Why: Check for extent of sample
contamination, give an accurate species ID for
unknown samples
• Where: https://ccb.jhu.edu/software/kraken/

10. Species ID: MOCAT
• What: Uses a phylogenetic approach to
identify novel or divergent species by relying
on distances in conserved marker genes
• Why: Sometimes you sequence something
completely novel and want to know more
about its relationships
• Where: http://vm-
lux.embl.de/~kultima/MOCAT/
• Alternatives: Phylosift, rMLST

11. Sample QC: Blobology
• What: A simple method of plotting de novo
assembly contigs by GC, coverage and taxon
• Why: Characterise contamination, plasmids,
lytic phage in a sample
• Where:
https://github.com/blaxterlab/blobology

12. Reference approach

13. Alignment: BWA
• What: The standard method for aligning
Illumina sequences to a reference, use in
BWA-MEM mode which works well with most
read lengths
• Why: Finds the likely location of each
sequence read in a reference genome
• Where: https://github.com/lh3/bwa
• Alternatives: SMALT, Bowtie2 (beware
standard insert size parameters)

14. Variant calling: samtools&VarScan
• What: A way of calling SNPs against a
reference in one or more samples
• Why: VarScan permits easy filtering of SNPs by
allele frequency and strand, useful for getting
a precise dataset
• Where: http://www.htslib.org/
• http://varscan.sourceforge.net/
• Alternatives: GATK, snippy, Nesoni

15. Recombination filtering: Gubbins
• What: Detect regions which have undergone
recombination which will confound phylogenetic
reconstructions assuming clonality
• Why: Important when attempting phylogenetic
reconstructions from recombining organisms
• Where: http://sanger-
pathogens.github.io/gubbins/
• Alternatives: ClonalFrameML, BRATNextGen

16. Tree building: FastTree
• What: Phylogenetic reconstructions from SNP
data
• Why: Tree reconstructions are an effective way of
examining evolutionary relationships in isolates
and testing if they are from an outbreak, FastTree
• Note: Ensure you don’t hit the double-precision
bug!
(http://darlinglab.org/blog/2015/03/23/not-so-
fast-fasttree.html)
• Where:
http://meta.microbesonline.org/fasttree/Alternat
ives: RAxML (more thorough, slower), REALPHY
http://realphy.unibas.ch/fcgi/realphy

17. MLST & Antibiogram: SRST2
• What: Aligns reads against MLST and
antibiotic resistance databases
• Why: Permits MLST typing with genome data
and a rough prediction of antibiotic resistance
• Where: http://katholt.github.io/srst2/

18. De novo approach

19. De novo assembly: SPADES
• What: A reliable de novo assembler which
works well with multiple data types
• Why: Has in-built error corrector so no need
for read trimming, can use multiple values of k
so less need for experimentation, consistently
performs well in comparisons
• Where: http://bioinf.spbau.ru/spades

20. De novo assembly: Velvet
• What: The original short-read assembly
• Why: Extremely fast for draft assemblies,
particularly if just want to do MLST or
antibiograms
• Where:
https://www.ebi.ac.uk/~zerbino/velvet/
• Alternatives: MEGAHIT – even faster!

21. Annotation: Prokka
• What: Takes de novo assembly contig files and
annotates them with coding sequences and non-
coding features such as RNAs
• Why: A very sensible set of tools and reference
databases in a single package, produces usable
output for other software and database
submission
• Where:
http://www.vicbioinformatics.com/software.prok
ka.shtml
• Alternatives: xBASE annotation interface

22. Tree building: Harvest
• What: Takes de novo assembly contigs, performs
whole-genome alignment and permits
reconstruction of core genome phylogenies
• Why: Scaleable to hundreds of genomes on a
laptop and with an excellent viewer
• Where:
http://harvest.readthedocs.org/en/latest/index.h
tml
• Alternatives: Mauve

25. Population genomics: BIGSDB
• What: Takes de novo assembly contigs and
applies MLST-like schemes working on
hundreds or thousands of core genes
• Why: Scaleable to >1000s of genomes for
rapid population-level clustering
• Where:
http://pubmlst.org/software/database/bigsdb
/
• Alternatives: Bionumerics

26. Pan/accessory genomes: LS-BSR
• What: Takes de novo assembly contigs or
annotations and compares gene content
• Why: To determine differences in gene
content between 1 to 1000s of strains
• Where: https://github.com/jasonsahl/LS-BSR
• Alternatives: OrthoMCL

27. MLST/Antibiogram: mlst and Abricate
• What: Works on de novo assembly to give
mlst prediction and antibiotic resistance
perdiction
• Why: A very fast method
• Where: https://github.com/tseemann/mlst
• https://github.com/tseemann/abricate
• Alternatives: SRST2

28. 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

29. 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