In #JPM2023 news, $TWST Twist Biosciences also presented on Monday, and here are some highlights of their presentation: first slide is the obligatory comparison between Twist 1M oligos per chip technology compared to the plate-based methods.
They have a new factory which they expect to start shipping products in January 2023 (now!) and would mean doubling up their current capacity.
Given that $ILMN Illumina recently presented their NovaSeq X Plus technology and $200/genome price point, now Twist has their slide also updated and they see more sample growth in their NGS business given the lower costs of sequencing.
They put a table of the new numbers for exome and cancer panels, pre- and post-NovaSeq X (NovaX). Interesting to see that 50,000x coverage seems to be the norm for cancer panels. Have people settled on this number?
They see Liquid Biopsy and MRD growing rapidly, from their current $0.3B to $2.2B by 2027. A reminder that in previous #JPM2023 threads for both $ILMN Illumina and $GH Guardant Health, we saw some spectacular numbers for TAM in Liquid Biopsy, the largest being the $100B by $GH.
Twist classes their End-to-End #Antibody Discovery Service as a Premium Solution in the Biopharma segment. Will they show premium customer success for this premium solution?
In their #DNAWrite segment, they have a strong plan for terabyte level chips. Their POC Chip at 256M oligos is 256x denser than the 1M production chip. Expected to be road-tested in 2023.
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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.