Illumina have done it again, disrupted their own market under no competiton and produced some wonderful new machines with higher throughput and lower run times.  Below is a brief summary of what I have learned so far.

HiSeq X 5

Pretty basic, this is half of an X ten, but the reagents etc are going to be more expensive.  $6million caital for an X5 and the headline figure appears to be $1400 per 30X human genome.  The headline figure for X10 is $1000 per genome, so X5 may be 40% more expensive.

HiSeq 3000/4000

The 3000 is to the 4000 as the 1000 was to the 2000 and the 1500 to the 2500 – it’s a 4000 that can only run one flowcell instead of two.  I expect it to be as popular as the 1000/1500s were – i.e. not very.  No-one goes to a funder for capital investment and says “Give me millions of dollars so I can buy the second best machine”.

Details are scarce, but the 4000 (judging by the stats) will have 2 flowcells with 6 8 lanes each, do 2x150bp sequencing, it seems around 375 312 million clusters per lane in 3.5 days.

Here is how it stacks up against the other HiSeq systems:

Clusters per lane Read length Lanes Days Gb per lane Gb total Gb per day
V1 rapid 150000000 2×150 4 2 45 180 90
V2 rapid 150000000 2×250 4 2.5 75 300 120
V3 high output 180000000 2×100 16 11 36 576 52
V4 high output 250000000 2×125 16 6 62.5 1000 167
HiSeq 4000 312000000 2×150 16 3.5 93.6 1500 428
HiSeq X 375000000 2×150 16 3 112.5 1800 600

These are headine figures and contain some guesses. How the machines behave in reality might differ.

If any of my figures are wrong, please leave a comment!

UPDATE: there appears to be some confusion over the exact config of the HiSeq 4000.  The spec sheet says that 5 billion reads per run pass filter.  The RNA-Seq dataset has 378million reads “from one lane”.  5 billion / 378 million is ~ 13 (lanes).  My contact at Illumina says there are 8 lanes per flowcell.  5 billion clusters / 16 lanes would give us 312 million reads per lane.  Possible the RNA-Seq dataset is overclustered!

A 387million paired RNA-Seq data set is here.