Long read sequencing - LSCC lab talk - fri 5 june 2015

Long read sequencing

Torsten Seemann

VLSCI LSCC Lab Talk - Melbourne, AU - Fri 5 June 2015

The good, the bad, and the really cool.

Why do we need long reads?

Repeats!

Long reads untangle graphs

Completed genomes

Phased haplotypes

Structural variationThe missing heritability - not just SNPs & indels

Long read instruments

Pacific Biosciences RSII

2015 ARC LIEFw/ Tim Stinear

Installed this week.

Passed testing!

Oxford Nanopore MinION MkI

Successor to Mk0

MinION Access Program Round 2

The up & comer!

PacBio It’s already here and it works.

PacBio - the device∷ It’s big!

∷ Three chunks: compute (left): robotics (top): sequencing (bottom)

∷ A cushion of N2 gas

PacBio - technology∷ Polymerase bound to

bottom of ZMW μ-well

∷ Fluorescent nucleotide incorporation measured in real time

∷ 3 hour “movies”

PacBio: read lengths

Needs careful library prep to ensure DNA is

not overly fragmented!

PacBio: error rate

Single read: 86% 30x Consensus: 99.999%

PacBio: main applications

∷ Finished microbial genomes

∷ Full length cDNA (mRNA isoforms)

∷ Extreme GC sequence

∷ HLA / MHC / KIR haplotyping

∷ Base modifications (methylation)

PacBio: bioinformatics

∷ All in GitHub∷ SMRT Portal

: Nice GUI: Cloud ready: Linux backend: Cluster ready

∷ Cmdline too!

Oxford NanoporeThe new kid on the block.

MinION - the device

PromethION - large scale

∷ 48 separate

flow cells

∷ On board ASIC

∷ Runs Python

Nanopore - technology

Nanopore - types of reads“1D reads”

∷ Template 1D﹕ only fwd stran

∷ Complement 1D﹕ only rev strand

“2D reads”

∷ Normal 2D﹕ mostly fwd, some rev

∷ Full 2D﹕ most of fwd & rev﹕ these are high quality

Nanopore - read lengths

Read length is not limited by technology but by library preparation.

Can get >100kbp reads.

Read length

Nanopore - error rate

∷ 5-mer errors∷ Not modelling

base mods yet∷ Basically

where PacBio was a few years ago!

Percent identity (aligned)

MinION - applications

∷ Same as PacBio plus....

∷ Portable sequencing: in the field eg. Josh Quick in Guinea for Ebola: in hospitals - infection control: monitoring - water/food supply, production facilities: at the GP - pathogen test in 10 min from blood prick?: spit in a home device every morning?

MinION - bioinformatics

∷ Event space -vs- base space: MinION MkI - base calling in cloud (Metrichor): MinION MkII - on device?: PromethION - can choose on-device add-on

∷ Mostly 3rd-party tools - lots of activity: poretools, poRe : minoTour, nanoPolish

Disruptive technologyJust another sequencer?

“Run until” Dynamically adjust sequencing yield

“Read until”

∷ Can access events/bases during reading: remember reads are long 40 kbp: examine first 100 bp say: can decide to stop reading and eject molecule!

∷ This is a killer app!: only want pathogens? eject if human DNA: only want exome? eject if not exonic looking: controlled with Python code

VolTRAX - library prep

A new business model

∷ No capital or reagent costs: Instrument will be free: Flow cells will be free: Only pay for what you want to sequence: Min. $20 and ~$1000 for a 100x human genome

∷ But I’ll scam the system!: Flowcell stats sent back to base: Won’t send you new flow cells if they look unused

How will our job change?

Some things never change

∷ Don’t worry!: 50% of our job will always be converting file formats ☺

∷ But things are improving: Pacbio: HDF5: MinION: HDF5 / FAST5

∷ Can convert .h5/.hd5 to .fastq easily

Read alignment

∷ PacBio: BLASR - Basic Local Alignment + Successive Refinement: BWA MEM - bwa mem -x pacbio

∷ MinION: MarginAlign - sum over possible alignments, HMMs: BWA MEM - bwa mem -x ont

∷ Need to modify variant caller parameters

De novo assembly

∷ Pacbio: HGAP, HGAP2, Falcon, Spades, Celera Assembler

∷ MinION: Spades, Celera Assembler, NanoPolish

∷ Lots of convergence: Similar error models (indels): Long reads, lower coverage - back to the future!

Streaming analysis

∷ We are not going to keep all this data

∷ Extract info we need and discard

∷ Cheaper to resequence?

∷ Need to think streaming analyses

∷ Lots of new applications

Conclusion

Exciting times!

∷ Genomics is changing all the time: new technologies: changing attributes/properties of current technology

∷ Bioinformaticians need to be able to adapt: focus on key skills not specific apps

∷ Pipelines are often short lived: except maybe clinical / accredited ones

Contact

∷ tseemann.github.io

∷ t.seemann@unimelb.edu.au

∷ @torstenseemann