Notes

LncRNA KCNQ1OT1 marketed liver disease C virus-induced pyroptosis associated with β-cell by means of mediating the actual miR-223-3p/NLRP3 axis.
ProSeqViewer is an open-source TypeScript library compatible with state-of-the-art website environments. The source code and an extensive documentation including use cases are available from the URL https//github.com/BioComputingUP/ProSeqViewer.
ProSeqViewer is an open-source TypeScript library compatible with state-of-the-art website environments. The source code and an extensive documentation including use cases are available from the URL https//github.com/BioComputingUP/ProSeqViewer.
First-line data quality assessment and exploratory data analysis are integral parts of any data analysis workflow. In high-throughput quantitative omics experiments (e.g. click here transcriptomics, proteomics, metabolomics), after initial processing, the data are typically presented as a matrix of numbers (feature IDs × samples). Efficient and standardized data-quality metrics calculation and visualization are key to track the within-experiment quality of these rectangular data types and to guarantee for high-quality data sets and subsequent biological question-driven inference.

We present MatrixQCvis, which provides interactive visualization of data quality metrics at the per-sample and per-feature level using R's shiny framework. It provides efficient and standardized ways to analyze data quality of quantitative omics data types that come in a matrix-like format (features IDs × samples). MatrixQCvis builds upon the Bioconductor SummarizedExperiment S4 class and thus facilitates the integration into existing workflows.

MatrixQCVis is implemented in R. It is available via Bioconductor and released under the GPL v3.0 license.

Supplementary Information is available at Bioinformatics online.
Supplementary Information is available at Bioinformatics online.Individuals with high physical activity levels, such as athletes and military personnel, are likely to experience periods of low muscle glycogen content. link2 Reductions in glycogen stores are associated with impaired physical performance. Lower glycogen stores in these populations are likely due to sustained aerobic exercise coupled with sub-optimal carbohydrate or energy intake. Consuming exogenous carbohydrate during aerobic exercise may be an effective intervention to sustain physical performance during periods of low glycogen. However, research is limited in the area of carbohydrate recommendations to fuel performance during periods of sub-optimal carbohydrate and energy intake. Additionally, the studies that have investigated the effects of low glycogen stores on exogenous carbohydrate oxidation have yielded conflicting results. Discrepancies between studies may be the result of glycogen stores being lowered by restricting carbohydrate or restricting energy intake. This narrative review discusses the influence of low glycogen status resulting from carbohydrate restriction versus energy restriction on exogenous carbohydrate oxidation and examines the potential mechanism resulting in divergent responses in exogenous carbohydrate oxidation. Results from this review indicate that rates of exogenous carbohydrate oxidation can be maintained when glycogen content is lower following carbohydrate restrictions, but may be reduced following energy restriction. Reductions in exogenous carbohydrate oxidation following energy restriction appear to result from lower insulin sensitivity and glucose uptake. Exogenous carbohydrate may thus be an effective intervention to sustain performance following short-term energy adequate carbohydrate restriction, but may not be an effective ergogenic aid when glycogen stores are low due to energy restriction.
Persons with dementia often show circadian rhythm disturbances and sleep problems. Timed light exposure seems to be a promising non-pharmacological treatment option. In this review, meta-analyses were run on light effects on circadian activity rhythm parameters in persons with dementia measured with wrist actimetry. Further, we update a Cochrane review, published in 2014, on actigraphically measured light effects in nighttime sleep parameters in persons with dementia.

Four electronic databases were searched for randomized controlled trials. Effects in meta-analyses were summarized by using mean differences and 95% confidence intervals. We followed PRISMA guidelines to assess the risk of bias and registered the review protocol (PROSPERO CRD42020149001).

Thirteen trials met inclusion criteria, and either utilized light therapy devices, ambient room lighting systems, or dawn-dusk interventions. Eleven of these studies were subjected to meta-analyses. They did not reveal significant light effects on circadian activity parameters amplitude (p=.62; n=313), acrophase (p=.34; n=313), intradaily variability (p=.51; n=354), and interdaily stability (p=.38; n=354). Furthermore, no light effects were found on sleep parameters total sleep duration (p=.53; n=594), sleep efficiency (p=.63; n=333), wake after sleep onset (p=.95; n=212), and sleep onset latency (p=.26; n=156). Subgroup analyses, pooling data from three studies including persons with Alzheimer's dementia, also did not show light effects on circadian activity and sleep parameters. The overall risk of bias of included studies was high.

There is insufficient evidence for actigraphically measured circadian light effects in persons with dementia. More high-quality research is needed to recommend the application of adjunctive light.
There is insufficient evidence for actigraphically measured circadian light effects in persons with dementia. More high-quality research is needed to recommend the application of adjunctive light.
The MS2-MCP (MS2 coat protein) live imaging system allows for visualisation of transcription dynamics through the introduction of hairpin stem-loop sequences into a gene. A fluorescent signal at the site of nascent transcription in the nucleus quantifies mRNA production. Computational modelling can be used to infer the promoter states along with the kinetic parameters governing transcription, such as promoter switching frequency and polymerase loading rate. However, modelling of the fluorescent trace presents a challenge due its persistence; the observed fluorescence at a given time point depends on both current and previous promoter states. A compound state Hidden Markov Model (cpHMM) was recently introduced to allow inference of promoter activity from MS2-MCP data. However, the computational time for inference scales exponentially with gene length and the cpHMM is therefore not currently practical for application to many eukaryotic genes.

We present a scalable implementation of the cpHMM for fast inference of promoter activity and transcriptional kinetic parameters. This new method can model genes of arbitrary length through the use of a time-adaptive truncated compound state space. The truncated state space provides a good approximation to the full state space by retaining the most likely set of states at each time during the forward pass of the algorithm. Testing on MS2-MCP fluorescent data collected from early Drosophila melanogaster embryos indicates that the method provides accurate inference of kinetic parameters within a computationally feasible timeframe. The inferred promoter traces generated by the model can also be used to infer single-cell transcriptional parameters.

Python implementation available at https//github.com/ManchesterBioinference/burstInfer, along with code to reproduce the examples presented here.

Supplementary data are available at Bioinformatics online.
Supplementary data are available at Bioinformatics online.
Epistasis may play an etiologic role in complex diseases, but research has been hindered because identification of SNP-SNP interactions requires exploration of immense search spaces. Current approaches using nuclear families accommodate at most several hundred candidate SNPs.

GADGETS detects epistatic SNP-sets by applying a genetic algorithm to case-parent or case-sibling data. To allow for multiple epistatic sets, island sub-populations of SNP-sets evolve separately under selection for evident joint relevance to disease risk. The software evaluates the identified SNP-sets via permutation testing and provides graphical visualization. GADGETS correctly identified epistatic SNP-sets in realistically simulated case-parent triads with 10,000 candidate SNPs, far more SNPs than competitors can handle, and it outperformed competitors in simulations with many fewer SNPs. Applying GADGETS to family-based oral clefting data from dbGaP identified SNP-sets with possible epistatic effects on risk.

GADGETS is part of the epistasisGA package at https//github.com/mnodzenski/epistasisGA.

Supplementary data are available at Bioinformatics online.
Supplementary data are available at Bioinformatics online.
Significant effort has been spent by curators to create coding systems for phenotypes such as the Human Phenotype Ontology (HPO), as well as disease-phenotype annotations. We aim to support the discovery of literature-based phenotypes and integrate them into the knowledge discovery process.

PheneBank is a Web-portal for retrieving human phenotype-disease associations that have been text-mined from the whole of Medline. Our approach exploits state-of-the-art machine learning for concept identification by utilising an expert annotated rare disease corpus from the PMC Text Mining subset. Evaluation of the system for entities is conducted on a gold-standard corpus of rare disease sentences and for associations against the Monarch initiative data.

The PheneBank Web-portal freely available at http//www.phenebank.org. Annotated Medline data is available from Zenodo at DOI 10.5281/zenodo.1408800. Semantic annotation software is freely available for non-commercial use at GitHub https//github.com/pilehvar/phenebank.

Supplementary data is available at Bioinformatics online.
Supplementary data is available at Bioinformatics online.
Somatic mutations and gene fusions can produce immunogenic neoantigens mediating anticancer immune responses. However, their computational prediction from sequencing data requires complex computational workflows to identify tumor-specific aberrations, derive the resulting peptides, infer patients' Human Leukocyte Antigen (HLA) types, and predict neoepitopes binding to them, together with a set of features underlying their immunogenicity. Here, we present nextNEOpi, a comprehensive and fully-automated bioinformatic pipeline to predict tumor neoantigens from raw DNA and RNA sequencing data. In addition, nextNEOpi quantifies neoepitope- and patient-specific features associated with tumor immunogenicity and response to immunotherapy.

nextNEOpi source code and documentation are available at https//github.com/icbi-lab/nextNEOpi.

Supplementary data are available at Bioinformatics online.
Supplementary data are available at Bioinformatics online.
bollito is an automated, flexible and parallelizable computational pipeline for the comprehensive analysis of single-cell RNA-seq data. link3 Starting from FASTQ files or pre-processed expression matrices, bollito performs both basic and advanced tasks in single-cell analysis integrating >30 state-of-the-art tools. This includes quality control, read alignment, dimensionality reduction, clustering, cell-marker detection, differential expression, functional analysis, trajectory inference and RNA velocity. bollito is built using the Snakemake workflow management system, which easily connects each execution step and facilitates the reproducibility of results. bollito's modular design makes it easy to incorporate other packages into the pipeline enabling its expansion with new functionalities.

Source code is freely available at https//gitlab.com/bu_cnio/bollito under the MIT license.

Supplementary data are available at Bioinformatics online.
Supplementary data are available at Bioinformatics online.
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