Briefings in bioinformatics
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Brief. Bioinformatics · Nov 2021
Deep learning-based advances and applications for single-cell RNA-sequencing data analysis.
The rapid development of single-cell RNA-sequencing (scRNA-seq) technology has raised significant computational and analytical challenges. The application of deep learning to scRNA-seq data analysis is rapidly evolving and can overcome the unique challenges in upstream (quality control and normalization) and downstream (cell-, gene- and pathway-level) analysis of scRNA-seq data. ⋯ Moreover, the future perspectives and challenges of deep-learning techniques regarding the appropriate analysis and interpretation of scRNA-seq data were investigated. The present study aimed to provide evidence supporting the biomedical application of deep learning-based tools and may aid biologists and bioinformaticians in navigating this exciting and fast-moving area.
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Brief. Bioinformatics · Mar 2021
Comparative StudyComparison of the binding characteristics of SARS-CoV and SARS-CoV-2 RBDs to ACE2 at different temperatures by MD simulations.
Temperature plays a significant role in the survival and transmission of SARS-CoV (severe acute respiratory syndrome coronavirus) and SARS-CoV-2. To reveal the binding differences of SARS-CoV and SARS-CoV-2 receptor-binding domains (RBDs) to angiotensin-converting enzyme 2 (ACE2) at different temperatures at atomic level, 20 molecular dynamics simulations were carried out for SARS-CoV and SARS-CoV-2 RBD-ACE2 complexes at five selected temperatures, i.e. 200, 250, 273, 300 and 350 K. ⋯ Finally, the hotspot residues facilitating the binding of SARS-CoV and SARS-CoV-2 RBDs to ACE2, the key differential residues contributing to the difference in binding and the interaction mechanism of differential residues that exist at all investigated temperatures were analyzed and compared in depth. The current work would provide a molecular basis for better understanding of the high infectiousness of SARS-CoV-2 and offer better theoretical guidance for the design of inhibitors targeting infectious diseases caused by SARS-CoV-2.
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Brief. Bioinformatics · Dec 2020
LiBis: an ultrasensitive alignment augmentation for low-input bisulfite sequencing.
The cell-free DNA (cfDNA) methylation profile in liquid biopsy has been utilized to diagnose early-stage disease and estimate therapy response. However, typical clinical procedures are capable of purifying only very small amounts of cfDNA. Whole-genome bisulfite sequencing (WGBS) is the gold standard for measuring DNA methylation; however, WGBS using small amounts of fragmented DNA introduces a critical challenge for data analysis, namely a low-mapping ratio. ⋯ By substantially increasing the mapping ratio by up to 88%, LiBis dramatically improved the number of informative CpGs and the precision in quantifying the methylation status of individual CpG sites. LiBis significantly improved the cost efficiency of low-input WGBS experiments by dynamically removing contamination introduced by random priming. The high sensitivity and cost effectiveness afforded by LiBis for low-input samples will allow the discovery of genetic and epigenetic features suitable for downstream analysis and biomarker identification using liquid biopsy.
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Brief. Bioinformatics · May 2020
In silico prediction of host-pathogen protein interactions in melioidosis pathogen Burkholderia pseudomallei and human reveals novel virulence factors and their targets.
The aerobic, Gram-negative motile bacillus, Burkholderia pseudomallei is a facultative intracellular bacterium causing melioidosis, a critical disease of public health importance, which is widely endemic in the tropics and subtropical regions of the world. Melioidosis is associated with high case fatality rates in animals and humans; even with treatment, its mortality is 20-50%. It also infects plants and is designated as a biothreat agent. ⋯ The current study is the first report on developing a genome-scale host-pathogen protein interaction networks between the human and B. pseudomallei, a critical biothreat agent. We have identified novel virulence factors and their interacting partners in the human proteome. These PPIs can be further validated by high-throughput experiments and may give new insights on how B. pseudomallei interacts with its host, which will help medical researchers in developing better prevention measures.
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Brief. Bioinformatics · Jul 2019
Modeling biological problems in computer science: a case study in genome assembly.
As computer scientists working in bioinformatics/computational biology, we often face the challenge of coming up with an algorithm to answer a biological question. This occurs in many areas, such as variant calling, alignment and assembly. ⋯ We show the modeling process step-by-step, including all the intermediate failed attempts. Please note this is not an introduction to how genome assembly algorithms work and, if treated as such, would be incomplete and unnecessarily long-winded.