NANOSLAST: Nanopore Signal Local Alignment and Segmentation Tool

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F26%3A0198637" target="_blank" >RIV/00216305:26220/26:0198637 - isvavai.cz</a>

Výsledek na webu

<a href="https://international-aset.net/avestia.com/EECSS2025_Proceedings/files/paper/ICBES/ICBES_151.pdf" target="_blank" >https://international-aset.net/avestia.com/EECSS2025_Proceedings/files/paper/ICBES/ICBES_151.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.11159/icbes25.151" target="_blank" >10.11159/icbes25.151</a>

Jazyk výsledku

angličtina

Název v původním jazyce

NANOSLAST: Nanopore Signal Local Alignment and Segmentation Tool

Popis výsledku v původním jazyce

Nanopore sequencing provided by Oxford Nanopore Technologies enables real-time analysis of native DNA or RNA molecules based on measured electrical signals called squiggles. While basecalling algorithms translate squiggles into nucleotide sequences, much of the raw signal information remains unexplored. Although several tools exist for inspecting the squiggles and offer simple signal visualization, they do not provide integrated workflows that allow users to search for specific nucleotide sequences and directly extract or visualize the corresponding raw signal segments. To address this gap, we developed NANOSLAST – a Nanopore Signal Local Alignment and Search Tool. This Python-based tool links local sequence similarity searches (via BLAST) to raw nanopore signals using basecalled SAM files and signal data from FAST5 or POD5 formats. The tool can be used to extract signals corresponding to specific motifs or functional elements, compare raw signal characteristics between different flowcells or sequencers or extract signal– sequence pairs suitable for training machine learning models focused on basecalling, methylation detection or other purposes.

Název v anglickém jazyce

NANOSLAST: Nanopore Signal Local Alignment and Segmentation Tool

Popis výsledku anglicky

Nanopore sequencing provided by Oxford Nanopore Technologies enables real-time analysis of native DNA or RNA molecules based on measured electrical signals called squiggles. While basecalling algorithms translate squiggles into nucleotide sequences, much of the raw signal information remains unexplored. Although several tools exist for inspecting the squiggles and offer simple signal visualization, they do not provide integrated workflows that allow users to search for specific nucleotide sequences and directly extract or visualize the corresponding raw signal segments. To address this gap, we developed NANOSLAST – a Nanopore Signal Local Alignment and Search Tool. This Python-based tool links local sequence similarity searches (via BLAST) to raw nanopore signals using basecalled SAM files and signal data from FAST5 or POD5 formats. The tool can be used to extract signals corresponding to specific motifs or functional elements, compare raw signal characteristics between different flowcells or sequencers or extract signal– sequence pairs suitable for training machine learning models focused on basecalling, methylation detection or other purposes.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science

ISBN

978-1-990800-61-0

ISSN

—

e-ISSN

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Počet stran výsledku

4

Strana od-do

—

Název nakladatele

Avestia Publishing

Místo vydání

—

Místo konání akce

Paříž

Datum konání akce

17. 8. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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