A peek inside the bioinformatics black box

A/Prof. Torsten Seemann

Victorian Life Sciences Computation Initiative (VLSCI)Doherty Centre for Applied Microbial Genomics (DCAMG)

The University of Melbourne

DCAMG Symposium - Melbourne, AU - Mon 20 July 2015

Bioinformatics

Bioinformatics

What’s inside the black box?

It’s black boxes, all the way down.

Data

Algorithms

Software

Analyses

We use real black boxes too!

The data

The currency of genomics

Reads

Reads are stored in FASTQ files

Genome

What data do we really have?

Isolate genomeSequenced reads

Other isolates in sequencing run

Contamination

Unsequenced regions

What we want

Metadata

■ Genome data itself is of limited value

■ Needs “extra” information

□ location: Australia 37.8S,145.0E □ date: 2015 2015-07-20□ source: human 60yo male faecal swab□ etc.

Got my reads, now what?

De novo genome assembly

Draft vs. Finished genomes

250 bp - Illumina - $100 8000 bp - Pacbio - $1000

Compare to already assembled genomes

AGTCTGATTAGCTTAGCTTGTAGCGCTATATTATAGTCTGATTAGCTTAGAT

ATTAGCTTAGATTGTAG

CTTAGATTGTAGC-C

TGATTAGCTTAGATTGTAGC-CTATAT

TAGCTTAGATTGTAGC-CTATATT

TAGATTGTAGC-CTATATTA

TAGATTGTAGC-CTATATTAT

SNP Deletion

Reference

Reads

Best practice

■ Use both approaches□ reference-based + de novo

■ Best of both worlds□ and worst of both worlds - interpretation is non-trivial

■ Still need□ good epidemiology, metadata and domain knowledge!

Comparing genomes

Core vs. Pan genome

Core is common to all & has similar sequence.

Pan genome analysis

Rows are genomes, columns are genes.

Phylogenomics

Inferring transmission

■ Identical sequence does not imply transmission

■ Easier to rule out than in

Conclusion

The future

■ Genomics is delivering on the promise□ still not maximally exploited

■ Directions

□ more use of pan-genome□ understanding recombination / horizontal transfer□ dynamics of microevolution□ useful visualization of large data sets□ open science: data sharing, open source software