Martin Hölzer
@martinhoelzer.bsky.social
Bioinformatics and translational research. Team lead at the Genome Competence Center at RKI, Berlin, Germany.
https://hoelzer.github.io
https://hoelzer.github.io
Hey, we are not involved in the platform, but we got in touch with the maintainers while publishing our data set. You can surely get in touch with them directly!
squidbase.org
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squidbase.org
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SquiDBase
SquiDBase is an innovative platform designed to manage and share vast amounts of microbial paired squiggle-sequence data, enhancing the capabilities of researchers and scientists in pathogen detection...
squidbase.org
December 8, 2025 at 1:15 PM
Hey, we are not involved in the platform, but we got in touch with the maintainers while publishing our data set. You can surely get in touch with them directly!
squidbase.org
(scroll down)
squidbase.org
(scroll down)
Want an easy place to publish POD5 despite the size? Check out SquiDBase — a community platform built exactly for sharing raw microbial nanopore data + metadata. Super aligned with open, FAIR squiggle science. 🙌 squidbase.org
SquiDBase
SquiDBase is an innovative platform designed to manage and share vast amounts of microbial paired squiggle-sequence data, enhancing the capabilities of researchers and scientists in pathogen…
squidbase.org
December 8, 2025 at 8:02 AM
Want an easy place to publish POD5 despite the size? Check out SquiDBase — a community platform built exactly for sharing raw microbial nanopore data + metadata. Super aligned with open, FAIR squiggle science. 🙌 squidbase.org
We really hope this nudges the field toward a norm: share your raw POD5 when possible.
FASTQ alone ages fast as basecallers improve; raw signals let others revisit old runs with new models and new questions.
FASTQ alone ages fast as basecallers improve; raw signals let others revisit old runs with new models and new questions.
December 8, 2025 at 8:02 AM
We really hope this nudges the field toward a norm: share your raw POD5 when possible.
FASTQ alone ages fast as basecallers improve; raw signals let others revisit old runs with new models and new questions.
FASTQ alone ages fast as basecallers improve; raw signals let others revisit old runs with new models and new questions.
Dataset highlights:
• R10.4.1 flow cells + V14 chemistry
• ~16 Gb per flow cell on avg
• 12 isolates sequenced in triplicate across 3 labs → great for reproducibility & method comparisons.
• R10.4.1 flow cells + V14 chemistry
• ~16 Gb per flow cell on avg
• 12 isolates sequenced in triplicate across 3 labs → great for reproducibility & method comparisons.
December 8, 2025 at 8:02 AM
Dataset highlights:
• R10.4.1 flow cells + V14 chemistry
• ~16 Gb per flow cell on avg
• 12 isolates sequenced in triplicate across 3 labs → great for reproducibility & method comparisons.
• R10.4.1 flow cells + V14 chemistry
• ~16 Gb per flow cell on avg
• 12 isolates sequenced in triplicate across 3 labs → great for reproducibility & method comparisons.
Why raw signals? Because POD5 keeps the full ion-current information beyond FASTQ.
That means you can:
• re-basecall with new models
• study nucleotide modifications / methylation
• benchmark error correction
• improve SNP & cgMLST surveillance workflows.
That means you can:
• re-basecall with new models
• study nucleotide modifications / methylation
• benchmark error correction
• improve SNP & cgMLST surveillance workflows.
December 8, 2025 at 8:02 AM
Why raw signals? Because POD5 keeps the full ion-current information beyond FASTQ.
That means you can:
• re-basecall with new models
• study nucleotide modifications / methylation
• benchmark error correction
• improve SNP & cgMLST surveillance workflows.
That means you can:
• re-basecall with new models
• study nucleotide modifications / methylation
• benchmark error correction
• improve SNP & cgMLST surveillance workflows.