Kristoffer Sahlin
@ksahlin.bsky.social
Associate Professor at the Department of Mathematics, Stockholm University, and a Scilifelab Fellow. Algorithms, Modeling, Transcriptomics, Genomics.
Hobby runner 5000m 18:48 | 10k 37:40 | HM 1:27:43 | M 3:39:06
Hobby runner 5000m 18:48 | 10k 37:40 | HM 1:27:43 | M 3:39:06
IMO it matters a lot as a 'first impression'
May 8, 2025 at 1:34 PM
IMO it matters a lot as a 'first impression'
I did only very minor impl. contributions, but from my (non-expert) view, I like that (1) it installs easily (also on a MacBook) and (2) no header files. Felt much easier to get started with than, e.g., C++. I never truly learned good .h/.cpp practices, and I could never get OpenMP/g++ working well
May 8, 2025 at 1:33 PM
I did only very minor impl. contributions, but from my (non-expert) view, I like that (1) it installs easily (also on a MacBook) and (2) no header files. Felt much easier to get started with than, e.g., C++. I never truly learned good .h/.cpp practices, and I could never get OpenMP/g++ working well
Also, it's in Rust! Tool available at github.com/aljpetri/isO...
GitHub - aljpetri/isONclust3: De novo clustering of long transcript reads into genes
De novo clustering of long transcript reads into genes - GitHub - aljpetri/isONclust3: De novo clustering of long transcript reads into genes
github.com
May 8, 2025 at 1:04 PM
Also, it's in Rust! Tool available at github.com/aljpetri/isO...
As for results, isONclust3 handles a 37M reads PacBio dataset from a revio machine in under 10h while other algorithms fail (>256Gb mem or >120h runtime). On the other datasets, isONclust3 has comparable or better accuracy than the other benchmarked tools.
May 8, 2025 at 1:04 PM
As for results, isONclust3 handles a 37M reads PacBio dataset from a revio machine in under 10h while other algorithms fail (>256Gb mem or >120h runtime). On the other datasets, isONclust3 has comparable or better accuracy than the other benchmarked tools.
The algorithm follows isONclust's algorithm in the general structure (greedy minimizer matching) but adds three key concepts: high confidence minimizers, on-the-fly cluster information update, and iterative (post-)cluster merging.
May 8, 2025 at 1:04 PM
The algorithm follows isONclust's algorithm in the general structure (greedy minimizer matching) but adds three key concepts: high confidence minimizers, on-the-fly cluster information update, and iterative (post-)cluster merging.
The motivation to develop this algorithm came from the inability of other algorithms to process recent large datasets (10-100M reads) from Revio or PromethION machines.
May 8, 2025 at 1:04 PM
The motivation to develop this algorithm came from the inability of other algorithms to process recent large datasets (10-100M reads) from Revio or PromethION machines.
Reposted by Kristoffer Sahlin
@tolyan.bsky.social is our very last speaker, on randstrobes ( high sensitivity seeds ) and their evolution the multi context seeds
April 25, 2025 at 7:39 AM
@tolyan.bsky.social is our very last speaker, on randstrobes ( high sensitivity seeds ) and their evolution the multi context seeds
Oh the good ol’ carnac/isonclust(1) times :)
April 25, 2025 at 8:07 AM
Oh the good ol’ carnac/isonclust(1) times :)
Reposted by Kristoffer Sahlin
2 in a row for @ksahlin.bsky.social (👋🏻👏🏻), first is @alexanderjpetri.bsky.social on de novo clustering of long read RNA, a problem that brings memories...
April 25, 2025 at 7:14 AM
2 in a row for @ksahlin.bsky.social (👋🏻👏🏻), first is @alexanderjpetri.bsky.social on de novo clustering of long read RNA, a problem that brings memories...