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Alternative splicing (AS) is a key transcriptional regulation pathway. Recent studies have shown that AS events are associated with the occurrence of complex diseases. Various computational approaches have been developed for the detection of disease-associated AS events. In this review, we first describe the metrics used for quantitative characterization of AS events. Second, we review and discuss the three types of methods for detecting disease-associated splicing events, which are differential splicing analysis, aberrant splicing detection and splicing-related network analysis. Third, to further exploit the genetic mechanism of disease-associated AS events, we describe the methods for detecting genetic variants that potentially regulate splicing. For each type of methods, we conducted experimental comparison to illustrate their performance. Finally, we discuss the limitations of these methods and point out potential ways to address them. We anticipate that this review provides a systematic understanding of computational approaches for the analysis of disease-associated splicing. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) has a strong genetic predisposition. However, its risk genes remain incompletely identified. We developed an Alzheimer's brain gene network-based approach to predict AD-associated genes by leveraging the functional pattern of known AD-associated genes. Our constructed network outperformed existing networks in predicting AD genes. We then systematically validated the predictions using independent genetic, transcriptomic, proteomic data, neuropathological and clinical data. First, top-ranked genes were enriched in AD-associated pathways. Second, using external gene expression data from the Mount Sinai Brain Bank study, we found that the top-ranked genes were significantly associated with neuropathological and clinical traits, including the Consortium to Establish a Registry for Alzheimer's Disease score, Braak stage score and clinical dementia rating. The analysis of Alzheimer's brain single-cell RNA-seq data revealed cell-type-specific association of predicted genes with early pathology of AD. Third, by interrogating proteomic data in the Religious Orders Study and Memory and Aging Project and Baltimore Longitudinal Study of Aging studies, we observed a significant association of protein expression level with cognitive function and AD clinical severity. The network, method and predictions could become a valuable resource to advance the identification of risk genes for AD. [ABSTRACT FROM AUTHOR]

In single-cell RNA-seq (scRNA-seq) data analysis, a fundamental problem is to determine the number of cell clusters based on the gene expression profiles. However, the performance of current methods is still far from satisfactory, presumably due to their limitations in capturing the expression variability among cell clusters. Batch effects represent the undesired variability between data measured in different batches. When data are obtained from different labs or protocols batch effects occur. Motivated by the practice of batch effect removal, we considered cell clusters as batches. We hypothesized that the number of cell clusters (i.e. batches) could be correctly determined if the variances among clusters (i.e. batch effects) were removed. We developed a new method, namely, removal of batch effect and testing (REBET), for determining the number of cell clusters. In this method, cells are first partitioned into k clusters. Second, the batch effects among these k clusters are then removed. Third, the quality of batch effect removal is evaluated with the average range of normalized mutual information (ARNMI), which measures how uniformly the cells with batch-effects-removal are mixed. By testing a range of k values, the k value that corresponds to the lowest ARNMI is determined to be the optimal number of clusters. We compared REBET with state-of-the-art methods on 32 simulated datasets and 14 published scRNA-seq datasets. The results show that REBET can accurately and robustly estimate the number of cell clusters and outperform existing methods. Contact: H.D.L. (hongdong@csu.edu.cn) or Q.S.X. (qsxu@csu.edu.cn) [ABSTRACT FROM AUTHOR]

Motivation Gene-centric bioinformatics studies frequently involve the calculation or the extraction of various features of genes such as splice sites, promoters, independent introns and untranslated regions (UTRs) through manipulation of gene models. Gene models are often annotated in gene transfer format (GTF) files. The features are essential for subsequent analysis such as intron retention detection, DNA-binding site identification and computing splicing strength of splice sites. Some features such as independent introns and splice sites are not provided in existing resources including the commonly used BioMart database. A package that implements and integrates functions to analyze various features of genes will greatly ease routine analysis for related bioinformatics studies. However, to the best of our knowledge, such a package is not available yet. Results We introduce GTFtools, a stand-alone command-line software that provides a set of functions to calculate various gene features, including splice sites, independent introns, transcription start sites (TSS)-flanking regions, UTRs, isoform coordination and length, different types of gene lengths, etc. It takes the ENSEMBL or GENCODE GTF files as input and can be applied to both human and non-human gene models like the lab mouse. We compare the utilities of GTFtools with those of two related tools: Bedtools and BioMart. GTFtools is implemented in Python and not dependent on any third-party software, making it very easy to install and use. Availability and implementation GTFtools is freely available at www.genemine.org/gtftools.php as well as pyPI and Bioconda. [ABSTRACT FROM AUTHOR]

Motivation Alzheimer's disease (AD) is a complex brain disorder with risk genes incompletely identified. The candidate genes are dominantly obtained by computational approaches. In order to obtain biological insights of candidate genes or screen genes for experimental testing, it is essential to assess their relevance to AD. A platform that integrates different types of omics data and approaches would facilitate the analysis of candidate genes and is in great need. Results We report AlzCode, a platform for multiview analysis of genes related to AD. First, this platform integrates a rich collection of functional genomic data, including expression data of AD samples (gene expression, single-cell RNA-seq data and protein expression), AD-specific biological networks (co-expression networks and functional gene networks), neuropathological and clinical traits (CERAD score, Braak staging score, Clinical Dementia Rating, cognitive function and clinical severity) and general data such as protein–protein interaction, regulatory networks, sequence similarity and miRNA-target interactions. These data provide basis for analyzing genes from different views. Second, the platform integrates multiple approaches designed for the various types of data. We implement functions to analyze both individual genes and gene sets. We also compare AlzCode with two existing platforms for AD analysis, which are Agora and AD Atlas. We pinpoint the features of each platform and highlight their differences. This platform would be valuable to the understanding of AD genetics and pathological mechanisms. Availability and implementation AlzCode is freely available at: http://www.alzcode.xyz. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Motivation Single-cell RNA sequencing (scRNA-seq) offers a powerful tool to dissect the complexity of biological tissues through cell sub-population identification in combination with clustering approaches. Feature selection is a critical step for improving the accuracy and interpretability of single-cell clustering. Existing feature selection methods underutilize the discriminatory potential of genes across distinct cell types. We hypothesize that incorporating such information could further boost the performance of single cell clustering. Results We develop CellBRF, a feature selection method that considers genes' relevance to cell types for single-cell clustering. The key idea is to identify genes that are most important for discriminating cell types through random forests guided by predicted cell labels. Moreover, it proposes a class balancing strategy to mitigate the impact of unbalanced cell type distributions on feature importance evaluation. We benchmark CellBRF on 33 scRNA-seq datasets representing diverse biological scenarios and demonstrate that it substantially outperforms state-of-the-art feature selection methods in terms of clustering accuracy and cell neighborhood consistency. Furthermore, we demonstrate the outstanding performance of our selected features through three case studies on cell differentiation stage identification, non-malignant cell subtype identification, and rare cell identification. CellBRF provides a new and effective tool to boost single-cell clustering accuracy. Availability and implementation All source codes of CellBRF are freely available at https://github.com/xuyp-csu/CellBRF. [ABSTRACT FROM AUTHOR]

In astronomy, the demand for high-resolution imaging and high-efficiency observation requires telescopes that are maintained at peak performance. To improve telescope performance, it is useful to conduct real-time monitoring of the telescope status and detailed recordings of the operational data of the telescope. In this paper, we provide a method based on machine learning to monitor the telescope performance in real-time. First, we use picture features and the random forest algorithm to select normal pictures captured by the acquisition camera or science camera. Next, we cut out the source image of the picture and use convolutional neural networks to recognize star shapes. Finally, we monitor the telescope performance based on the relationship between the source image shape and telescope performance. Through this method, we achieve high-performance real-time monitoring with the Large Sky Area Multi-Object Fibre Spectroscopic Telescope, including guiding system performance, focal surface defocus, submirror performance, and active optics system performance. The ultimate performance detection accuracy can reach up to 96.7 per cent. [ABSTRACT FROM AUTHOR]

Objective Our objective was to determine whether initiation of febuxostat during an acute gout flare prolongs the current episode. Methods In this randomized, placebo-controlled, single-blinded, multicentre trial, patients with acute gout flares within 72 h were randomized (1:1) to the placebo and febuxostat (40 mg/day) groups. All patients were administered diclofenac (150 mg/day) for 7 days and then open-labelled on the eighth day. Febuxostat 40 mg daily and diclofenac 75 mg daily were administered from day 8 through 28 for the remission period. The dose of diclofenac was 150 mg/day before remission in both arms, and the original protocol was maintained until remission. The primary outcome was 'days to resolution'. Results We randomized 140 patients, 70 into each arm. The mean days to resolution was 5.98 days [median 7.00, interquartile range (IQR) 2.45 days] for the placebo and 6.50 days (median 7.00, IQR 3.67 days) for the febuxostat group (P  = 0.578). The rate of resolution within 7 days was 84.38% for the placebo group and 76.92% for the febuxostat group (P  = 0.284). There were no statistically significant differences in joint pain, swelling, tenderness and erythema scores at days 1, 3, 5 and 7. The mean serum uric acid levels were 507.54 and 362.62 μmol/l for the placebo and febuxostat group, respectively, on day 7 (P  = 0.000). The rate of recurrent gout flares was 10.00% for the placebo group and 6.56% for the febuxostat group from day 8 through 28 (P  = 0.492). Conclusion Initiation of febuxostat administration during an acute gout flare did not prolong the duration of acute flares. Trial registration Chinese Clinical Trial Registry, http://www.chictr.org.cn/ , ChiCTR1800015962 [ABSTRACT FROM AUTHOR]

Zearalenone (ZEA) is a mycotoxin produced by various Fusarium fungi, which has been shown to cause several cases of mycotoxicosis in farm animals and humans. However, there is no evidence regarding the effect of ZEA on mouse egg developmental competence. In this study, we found that the activation rate of maturated oocytes was affected in mice by ZEA treatment, indicating that ZEA affects egg developmental competence. And we explored possible mechanisms of low mouse maturated oocyte developmental competence after ZEA treatment from an epigenetic modification perspective. The fluorescence intensity analysis showed that 5-methyl cytosine level increased after ZEA treatment, indicating that the general DNA methylation level increased in the treated eggs. Moreover, histone methylations were also altered: H3K4me2 as well as H3K9me3 and H4K20me1, me2, me3 levels decreased in eggs that were cultured in high-dose ZEA medium. Thus, our results indicated that ZEA decreased egg developmental competence by affecting the epigenetic modifications. [ABSTRACT FROM PUBLISHER]