I'll be live tweeting the $ILMN Illumina #IGF with running commentary. I won't intend to be comprehensive with the slide deck screenshots, but cover the main headlines.
NovaSeq 6000 Dx, same as NovaSeq, but regulated FDA and CE (Europe).
6Tb in 44 hours, this hasn't changed
Infinity closes the remaining 5% of the genome that is not accessible by short-reads
Long-read human WGS assay in Q1 2023, 6-7kb N50, longest reads at 30Kb. Use cases in rare disease, as an example. Ultra-rare variants for children with fatal diseases. Second half of 2023 with an enrichment panel equivalent.
XLEAP-SBS chemistry (commercial name of Chemistry X)
XLEAP will be available on NextSeq 1000/2000 in 2024 (I got it right, I thought it was gonna be the NextSeq first). P4 flowcell 500gb.
NovaSeq X (also got the name right).
Now it will be the biggest instrument in the portfolio (physically and in specs). X and X Plus.
Up to 16 addressable lanes per run, up to 16Tb
20000 WGS per year, 2.5x more than NovaSeq
NovaSeq X Plus: 16Tb, 2X faster, 3X higher accuracy, $2/Gb
NextSeq Chemistry X in 2024
The computing power of 3x NovaSeqs. The flowcells on the screen look like Wall-E's eyes.
Summary of the two new big products, the NextSeq 1000/2000 P4 flowcell and the NovaSeq X Plus in the #ngsspecs table now (need to confirm run time and other flowcell sizes).
Nano-level precision of image scanning, 2x faster basecalling, 24 hr in NovaSeq X.
It seems the runtime is reduced from 44hr to 24hr, I may not have heard this correctly, so pending confirmation.
Variant calling on board, specs shown based on the DRAGEN 4.0 pipeline.
The Broad and deCODE have already had access to NovaSeq X. Tempus as well. Macrogen in S. Korea. $REGN Regeneron some sort of access as well.
• • •
Missing some Tweet in this thread? You can try to
force a refresh
On library prep at #Nanoporeconf, a description for PCR-free methods showing the difference between ligation (max output) and rapid mode (10minutes, minimal lab equipment needed). Ultralong reads (ULR) also enabled, all Kit14.
Rapid ULR. Current record is about 4 megabases.
PCR expansion kits enable the use of samples with low input amount.
I did a deep dive on the different workflow management (WFM) tools for #Bioinformatics Data Analysis a few years ago, and since then there have been a few extra entrants in this segment, still mostly concentrated in serving the Next-Generation Sequencing field.
A few years ago, there were two communities dominating the open-source WFM ecosystem in NextFlow and SnakeMake, and two platforms dominating the the commercial offerings in DNAnexus and Illumina BaseSpace.
Since then, a company out of the founders of Nextflow has started offering enterprise support for Nextflow workflows in the cloud: Seqera Labs. They offer the extra level of support that some organizations require to run Nextflow on their Data Analysis setups.
More interesting Next-Generation Sequencing knowledge in the ASeq Discord channel (by @new299). Illumina patterned flowcells and the etching process to "print" the wells into the flowcell. Could be down to 350nm diameter for some flowcell configurations now.
If I remember correctly, Illumina started with a 600nm diameter for the patterned flowcell, in the HiSeq X and then later on in the evolution of the platform that used these patterned flowcells.
They then said to have gone down to 500nm, and what you are showing seems to indicate that it's at 350nm now, at least for the NextSeq 2000? I am not sure if they claimed that for NovaSeq X?
There have been some acquisitions in #CancerDiagnostics and #CancerScreening recently, some of which signify a trend towards consolidation that is worth describing:
$A Agilent is moving towards some more vertical integration in Cancer Dx and Cancer screening
by recently acquiring both announcing a partnership with Akoya Bio and announcing the acquisition of Avida Biomed.
Some may ask: isn’t $A Agilent too small to go into this field? Would they be able to compete against $ILMN Illumina/GrailBio or $GH Guardant Health or $EXAS Exact Sciences?
It is likely that as Spatial Biology tools become more robust and user-friendly, they will become increasingly popular and widely adopted in the scientific community.
This may lead to a shift in the balance between single-cell and Spatial Biology approaches, with the latter eventually becoming more prevalent.
Additionally, as more and more datasets are generated using Spatial Biology techniques, the field of Machine Learning and Artificial Intelligence will likely play an increasingly important role in analysing and interpreting this data.