Performance of neural network basecalling tools for Oxford Nanopore sequencing
Wick RR, Judd LM, Holt KE. · 2019
Read Performance of neural network basecalling tools for Oxford Nanopore sequencing by Wick RR, Judd LM, Holt KE. (2019) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-6591954.txt.
Description
Ryan Wick, Louise Judd, and Kathryn Holt compared nanopore basecalling tools in 2019. Basecalling turns the raw electrical signal from a nanopore sequencer into DNA letters. Every later result depends on getting those letters right.
The team ran several neural network callers on the same bacterial data sets. They measured accuracy, speed, and how errors cluster in hard regions. Newer callers clearly beat older ones, but none was perfect everywhere. Their benchmarks help labs pick a caller and set honest expectations for assembly and variant work. The authors share their test data so future callers can be judged on the same ground.
Catalog record
| CREATOR | Wick RR, Judd LM, Holt KE. |
|---|---|
| YEAR | 2019 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 38 KB |
| LICENSE | Creative Commons Attribution License |
| RIGHTS | CC BY |
| SOURCE | PubMed Central |