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.
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.
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|
|V3 high output||180000000||2×100||16||11||36||576||52|
|V4 high output||250000000||2×125||16||6||62.5||1000||167|
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.