Nabil-Fareed Alikhan
@happykhan.mstdn.science.ap.brid.gy
Admin for @mstdn.science. #bioinformatician. Interested in #microbiology and #genomics.
[bridged from https://mstdn.science/@happykhan on the fediverse by https://fed.brid.gy/ ]
[bridged from https://mstdn.science/@happykhan on the fediverse by https://fed.brid.gy/ ]
🔍 Why the question matters
Workflow systems evolve rapidly. What made Nextflow attractive in 2020 may or may not still hold in 2025.
New tools, frameworks, cloud infrastructures, and AI-assisted code generation are pushing the boundaries of reproducibility and scalability.
For teams jumping in […]
Workflow systems evolve rapidly. What made Nextflow attractive in 2020 may or may not still hold in 2025.
New tools, frameworks, cloud infrastructures, and AI-assisted code generation are pushing the boundaries of reproducibility and scalability.
For teams jumping in […]
Original post on mstdn.science
mstdn.science
October 17, 2025 at 6:59 AM
🔍 Why the question matters
Workflow systems evolve rapidly. What made Nextflow attractive in 2020 may or may not still hold in 2025.
New tools, frameworks, cloud infrastructures, and AI-assisted code generation are pushing the boundaries of reproducibility and scalability.
For teams jumping in […]
Workflow systems evolve rapidly. What made Nextflow attractive in 2020 may or may not still hold in 2025.
New tools, frameworks, cloud infrastructures, and AI-assisted code generation are pushing the boundaries of reproducibility and scalability.
For teams jumping in […]
Why it matters:
✅ Genome assemblies underpin genotyping, AMR detection, and pathogen surveillance.
✅ Current QC tools are species-agnostic, but bacterial genomes vary widely.
✅ Our approach sets species-specific, reproducible thresholds using machine learning and large-scale datasets.
✅ Genome assemblies underpin genotyping, AMR detection, and pathogen surveillance.
✅ Current QC tools are species-agnostic, but bacterial genomes vary widely.
✅ Our approach sets species-specific, reproducible thresholds using machine learning and large-scale datasets.
September 19, 2025 at 8:15 AM
Why it matters:
✅ Genome assemblies underpin genotyping, AMR detection, and pathogen surveillance.
✅ Current QC tools are species-agnostic, but bacterial genomes vary widely.
✅ Our approach sets species-specific, reproducible thresholds using machine learning and large-scale datasets.
✅ Genome assemblies underpin genotyping, AMR detection, and pathogen surveillance.
✅ Current QC tools are species-agnostic, but bacterial genomes vary widely.
✅ Our approach sets species-specific, reproducible thresholds using machine learning and large-scale datasets.