Biology subjects

Liu-Wei, W.

Publications and source records attributed to Liu-Wei, W..

5 recordsLinked to original sources

bioRxiv2025

WarpDemuX-tRNA: barcode multiplexing for nanopore tRNA sequencing

Transfer RNA (tRNA) plays an essential role in protein translation and tRNA modifications are important to their function. Recently, nanopore direct RNA sequencing (dRNA-seq) has shown promising results in the detection of complex tRNA modifications. However, its wider adoption in the tRNA field has been limited by a lack of (de)multiplexing solutions. Here, we present WarpDemuX-tRNA: an extension to the WarpDemuX method specifically optimized for multiplexed nanopore tRNA sequencing. Using consensus-based signal analysis using (soft) Dynamic Time Warping and barycenter averaging, our approach improves barcode feature generation and achieves more robust barcode identification. WarpDemuX-tRNA outperforms the original method and achieves 99% precision and 95% recovery for four barcodes, while reducing computational complexity and runtime to six minutes per one million reads. WarpDemuX-tRNA is an open-source and free-to-use solution to high-throughput nanopore tRNA sequencing, facilitating more accessible, cost-effective and high-throughput studies of tRNA modifications and their regulatory mechanisms.

bioinformatics↗

bioRxiv2024

Demultiplexing and barcode-specific adaptive sampling for nanopore direct RNA sequencing

Nanopore direct RNA sequencing (dRNA-seq) enables unique insights into (epi-)transcriptomics. However, applications are currently limited by the lack of accurate and cost-effective sample multiplexing. We introduce WarpDemuX, an ultra-fast and highly accurate adapter-barcoding and demultiplexing approach. WarpDemuX enhances speed and accuracy by fast processing of the raw nanopore signal, use of a lightweight machine-learning algorithm and design of optimized barcode sets. We demonstrate its utility by performing a rapid phenotypic profiling of different SARS-CoV-2 viruses, crucial for pandemic prevention and response, through multiplexed sequencing of longitudinal samples on a single flowcell. This identifies systematic differences in transcript abundance and poly(A) tail lengths during infection. Additionally, integrating WarpDemuX into sequencing control software enables real-time enrichment of target molecules through barcode-specific adaptive sampling, which we demonstrate by enriching low abundance viral RNA. In summary, WarpDemuX is a broadly applicable, high-performance, and economical multiplexing solution for nanopore dRNA-seq, facilitating advanced (epi-)transcriptomic research.

bioinformatics↗

bioRxiv2022

FrameRate: learning the coding potential of unassembled metagenomic reads

MotivationMetagenomic assembly is a slow and computationally intensive process and despite needing iterative rounds for improvement and completeness the resulting assembly often fails to incorporate many of the input sequencing reads. This is further complicated when there is reduced read-depth and/or artefacts which result in chimeric assemblies both of which are especially prominent in the assembly of metagenomic datasets. Many of these limitations could potentially be overcome by exploiting the information content stored in the reads directly and thus eliminating the need for assembly in a number of situations. ResultsWe explored the prediction of coding potential of DNA reads by training a machine learning model on existing protein sequences. Named FrameRate, this model can predict the coding frame(s) from unassembled DNA sequencing reads directly, thus greatly reducing the computational resources required for genome assembly and similarity-based inference to pre-computed databases. Using the eggNOG-mapper function annotation tool, the predicted coding frames from FrameRate were functionally verified by comparing to the results from full-length protein sequences reconstructed with an established metagenome assembly and gene prediction pipeline from the same metagenomic sample. FrameRate captured equivalent functional profiles from the coding frames while reducing the required storage and time resources significantly. FrameRate was also able to annotate reads that were not represented in the assembly, capturing this missing information. As an ultra-fast read-level assembly-free coding profiler, FrameRate enables rapid characterisation of almost every sequencing read directly, whether it can be assembled or not, and thus circumvent many of the problems caused by contemporary assembly workflows. Availabilityhttps://github.com/NickJD/FrameRate Contactliuwei.wang@fu-berlin.de and nicholas@dimonaco.co.uk

bioinformatics↗

bioRxiv2020

Prediction of novel virus-host interactions by integrating clinical symptoms and protein sequences

MotivationInfectious diseases from novel viruses have become a major public health concern. Rapid identification of virus-host interactions can reveal mechanistic insights into infectious diseases and shed light on potential treatments. Current computational prediction methods for novel viruses are based mainly on protein sequences. However, it is not clear to what extent other important features, such as the symptoms caused by the viruses, could contribute to a predictor. Disease phenotypes (i.e., signs and symptoms) are readily accessible from clinical diagnosis and we hypothesize that they may act as a potential proxy and an additional source of information for the underlying molecular interactions between the pathogens and hosts. ResultsWe developed DeepViral, a deep learning based method that predicts protein-protein interactions (PPI) between humans and viruses. Motivated by the potential utility of infectious disease phenotypes, we first embedded human proteins and viruses in a shared space using their associated phenotypes and functions, supported by formalized background knowledge from biomedical ontologies. By jointly learning from protein sequences and phenotype features, DeepViral significantly improves over existing sequence-based methods for intra- and inter-species PPI prediction. Lastly, we propose a novel experimental setup to realistically evaluate prediction methods for novel viruses. Availabilityhttps://github.com/bio-ontology-research-group/DeepViral Contactrobert.hoehndorf@kaust.edu.sa

bioinformatics↗

bioRxiv2018

Phenotypic, functional and taxonomic features predict host-pathogen interactions

MotivationIdentification of host-pathogen interactions (HPIs) can reveal mechanistic insights of infectious diseases for potential treatments and drug discoveries. Current computational methods for the prediction of HPIs often rely on our knowledge on the sequences and functions of pathogen proteins, which is limited for many species, especially for emerging pathogens. Matching the phenotypes elicited by pathogens with phenotypes associated with host proteins might improve the prediction of HPIs. ResultsWe developed an ontology-based machine learning method that predicts potential interaction protein partners for pathogens. Our method exploits information about disease mechanisms through features learned from phenotypic, functional and taxonomic knowledge about pathogens and human proteins. Additionally, by embedding the phenotypic information of the pathogens within a formal representation of pathogen taxonomy, we demonstrate that our model can accurately predict interaction partners for pathogens without known phenotypes, using a combination of their taxonomic relationships with other pathogens and information from ontologies as background knowledge. Our results show that the integration of phenotypic, functional and taxonomic knowledge not only improves the prediction of HPIs, but also enables us to investigate novel pathogens in emerging infectious diseases. Availabilityhttps://github.com/bio-ontology-research-group/hpi-predict Contactrobert.hoehndorf@kaust.edu.sa

bioinformatics↗