MANASIG: Python Package to Manipulate Nanopore Signals From Sequencing Files

Barton, Vojtech; Nykrýnová, Markéta; Škutková, Helena

doi:10.1109/bibm52615.2021.9669821

Cited by 3 publications

(1 citation statement)

References 8 publications

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“…For preprocessing the raw sequencing fast5 files and dataset preparation, the internally developed MANASIG (Barton et al, 2021 ) package was used and is available on GitHub .…”

Section: Resultsmentioning

confidence: 99%

Using deep learning for gene detection and classification in raw nanopore signals

et al. 2022

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Recently, nanopore sequencing has come to the fore as library preparation is rapid and simple, sequencing can be done almost anywhere, and longer reads are obtained than with next-generation sequencing. The main bottleneck still lies in data postprocessing which consists of basecalling, genome assembly, and localizing significant sequences, which is time consuming and computationally demanding, thus prolonging delivery of crucial results for clinical practice. Here, we present a neural network-based method capable of detecting and classifying specific genomic regions already in raw nanopore signals—squiggles. Therefore, the basecalling process can be omitted entirely as the raw signals of significant genes, or intergenic regions can be directly analyzed, or if the nucleotide sequences are required, the identified squiggles can be basecalled, preferably to others. The proposed neural network could be included directly in the sequencing run, allowing real-time squiggle processing.

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“…For preprocessing the raw sequencing fast5 files and dataset preparation, the internally developed MANASIG (Barton et al, 2021 ) package was used and is available on GitHub .…”

Section: Resultsmentioning

confidence: 99%

Using deep learning for gene detection and classification in raw nanopore signals

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

Using Deep Learning for Gene Detection and Classification in Raw Nanopore Signals

Nykrýnová

Barton

Jakubíček

et al. 2021

Preprint

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Recently, nanopore sequencing has come to the fore as library preparation is rapid and simple, sequencing can be done almost anywhere, and longer reads are obtained than with next-generation sequencing. The main bottleneck still lies in data postprocessing which consists of basecalling, genome assembly, and localizing significant sequences, which is time consuming and computationally demanding, thus prolonging delivery of crucial results for clinical practice. Here, we present a neural network-based method capable of detecting and classifying specific genomic regions already in raw nanopore signals - squiggles. Therefore, the basecalling process can be omitted entirely as the raw signals of significant genes, or intergenic regions can be directly analysed, or if the nucleotide sequences are required, the identified squiggles can be basecalled, preferably to others. The proposed neural network could be included directly in the sequencing run, allowing real-time squiggle processing.

show abstract