Biology subjects

Ragel, R.

Publications and source records attributed to Ragel, R..

3 recordsLinked to original sources

bioRxiv2024

A new compression strategy to reduce the size of nanopore sequencing data

Nanopore sequencing is an increasingly central tool for genomics. Despite rapid advances in the field, large data volumes and computational bottlenecks continue to pose major challenges. Here we introduce ex-zd, a new data compression strategy that helps address the large size of raw signal data generated during nanopore experiments. Ex-zd encompasses both a lossless compression method, which modestly outperforms all current methods for nanopore signal data compression, and a lossy method, which can be used to achieve dramatic additional savings. The latter component works by reducing the number of bits used to encode signal data. We show that the three least significant bits in signal data generated on instruments from Oxford Nanopore Technologies (ONT) predominantly encode noise. Their removal reduces file sizes by half without impacting downstream analyses, including basecalling and detection of DNA methylation. Ex-zd compression saves hundreds of gigabytes on a single ONT sequencing experiment, thereby increasing the scalability, portability and accessibility of nanopore sequencing.

bioinformatics↗

bioRxiv2022

DeepSelectNet: Deep Neural Network Based Selective Sequencing for Oxford Nanopore Sequencing

BackgroundNanopore sequencing allows selective sequencing, the ability to programmatically reject unwanted reads in a sample. Selective sequencing has many present and future applications in genomics research and the classification of species from a pool of species is an example. Existing methods for selective sequencing for species classification are still immature and the accuracy highly varies depending on the datasets. For the five datasets we tested, the accuracy of existing methods varied in the range of ~77%-97% (average accuracy <89%). Here we present DeepSelectNet, an accurate deep-learning-based method that can directly classify nanopore current signals belonging to a particular species. DeepSelectNet utilizes novel data preprocessing techniques and improved neural network architecture for regularization. ResultsFor the five datasets tested, DeepSelectNets accuracy varied between ~91%-99% (average accuracy ~95%). At its best performance, DeepSelectNet achieved a nearly 12% accuracy increase compared to its deep learning-based predecessor SquiggleNet. Furthermore, precision and recall evaluated for DeepSelectNet on average were always >89% (average ~95%). In terms of execution performance, DeepSelectNet outperformed SquiggleNet by ~13% on average. Thus, DeepSelectNet is a practically viable method to improve the effectiveness of selective sequencing. ConclusionsCompared to base alignment and deep learning predecessors, DeepSelectNet can significantly improve the accuracy to enable real-time species classification using selective sequencing. The source code of DeepSelectNet is available at https://github.com/AnjanaSenanayake/DeepSelectNet.

bioinformatics↗

bioRxiv2020

F5N : Nanopore Sequence Analysis Toolkit for Android Smartphones

F5N is the first ever Android application for nanopore sequence analysis on a mobile phone, comprised of popular tools for read alignment (Minimap2), sequence data manipulation (Samtools) and methylation calling (F5C/Nanopolish). On NA12878 nanopore data, F5N can perform a complete methylation calling pipeline on a mobile phone in [~]15 minutes for a batch of 4000 nanopore reads ([~]34 megabases). F5N is not only a toolkit but also a framework for integrating existing C/C++ based command line tools to run on Android. F5N will enable performing nanopore sequence analysis on-site when used with an ultra-portable nanopore sequencer (eg: MinION or the anticipated smidgION), consequently reducing the cost for special computers and high-speed Internet. Availability and implementationF5N Android application is available on Google Play store at https://play.google.com/store/apps/details?id=com.mobilegenomics.genopo&hl=en and the source code is available on Github at https://github.com/SanojPunchihewa/f5n. Contacthirunas@eng.pdn.ac.lk

bioinformatics↗