Title: Evolving technology for clinical sequencing
[no blogging of clinical data – the rest is fair game. good policy.]
Review of sequencing technology, and intresting point that we’re now running towards smaller system for faster runs with smaller number of reads, which will lead to clinical or diagnostic use.
- increase cluster density
- minimise gc bias
- increase ata yeld
- increase flowcell area
- increase chemistry
Increased representation of GC-rich features – much improved representation with new chemistry. Slide showing same region over time, goes from gaps to complete coverage.
unique paired read alignment also makes a big impact, particularly with paired ends.
Contrast of HiSeq to MiSeq. Usual things – push button, all on board, etc etc… [different clients for each tool set, I would think.] Run time 10 Days @600Gb vs 1Gb@ less than one day.
All methods are interchangeable between platforms.
Power of deep sequencing. With sufficiently deep sequencing, it’s easy to see minor variants… (1.47% detectable in a 750k depth??? [That just sounds odd… maybe I got it wrong.])
Covering Chronic Lmphocytic Leukaemia (CLL) example [- will not blog this part.]
- New Bayesian caller: HYRAX
- indels/SV de novo assembled with GROUPER
- CNVs use recursive partitioning….[using the software published by Sergii Ivankhno, which I totally did not understand last night.]
- First description of progression of CLL
- Limited number of NS mutations and CNV occur
- Candidates involved in regulation of innate immune response and cancer progression
- relapse drivers are actually present in pre-treatement samples
- profiling identifies mutations eradicated by or resistant to treatment
- Quantification of mutational burden by deep sequencing reveals clusters.
Spectrum of seuqencing: Targetted test <===> Whole Genomes.