One pipeline to predict them all? On the prediction of alternative splicing from RNA-Seq data

A novel splicing graph allows a direct comparison between exon-based and splice junction-based approaches to alternative splicing detection

There are primarily two computational approaches to alternative splicing (AS) detection using short reads: splice junction-based and exon-based approaches. Despite their shared goal of addressing the same biological problem, these approaches have not been reconciled before. We devised a novel graph structure and algorithm aimed at mapping between the exonic parts and splicing events detected by the two different methods. Through simulations, we demonstrated disparities in sensitivity and specificity between splice junction-based and exon-based methods. When applied to empirical data, there were large discrepancies in the results, suggesting that the methods are complementary. With the discrepancies localized to individual events and exonic parts, we were able to gain insights into the strengths and weaknesses inherent in each approach. Finally, we integrated the results to generate a comprehensive list of both common and unique AS events detected by both methodologies.

Alternative splicing landscape in mouse skeletal muscle and adipose tissue: Effects of intermittent fasting and exercise

Alternative splicing contributes to diversify the cellular protein landscape, but aberrant splicing is implicated in many diseases. To which extent mis-splicing contributes to insulin resistance as the causal defect of type 2 diabetes and whether this can be reversed by lifestyle interventions is largely unknown. Therefore, RNA sequencing data from skeletal muscle and adipose tissue of diabetes-susceptible NZO mice treated with or without intermittent fasting and of healthy C57BL/6J mice subjected to exercise were analyzed for alternative splicing differences using Whippet and rMATS. Diet and exercise interventions triggered comparable levels of splicing changes, although the splicing profile of skeletal muscle appeared to be more flexible than that of adipose tissue, with 72-114 differential splicing events in muscle and less than 25 in adipose tissue. Splicing changes induced by time-restricted feeding, alternate-day fasting and exercise were generally mild, with a maximal percent spliced in (PSI) difference of 67%, indicating that alternative splicing plays a rather minor role in lifestyle-induced adaptations of muscle and adipose tissue in mice. However, intron retention contributed to the regulation of gene expression, influencing genes whose expression was directly linked to phenotypic parameters (e.g. Eno2 and Pan2). Alternate-day fasting promoted skipping of exon 7 in Mlxipl (coding for ChREBP), thereby affecting the glucose sensing module of this carbohydrate-responsive transcription factor. Both intermittent fasting and exercise training led to alternative splicing of known diabetes-related GWAS genes (e.g. Abcc8, Ifnar2, Smarcad1), highlighting the potential metabolic relevance of these changes.

Next-generation data filtering in the genomics era

Genomic data are ubiquitous across disciplines, from agriculture to biodiversity, ecology, evolution and human health. However, these datasets often contain noise or errors and are missing information that can affect the accuracy and reliability of subsequent computational analyses and conclusions. A key step in genomic data analysis is filtering - removing sequencing bases, reads, genetic variants and/or individuals from a dataset - to improve data quality for downstream analyses. Researchers are confronted with a multitude of choices when filtering genomic data; they must choose which filters to apply and select appropriate thresholds. To help usher in the next generation of genomic data filtering, we review and suggest best practices to improve the implementation, reproducibility and reporting standards for filter types and thresholds commonly applied to genomic datasets. We focus mainly on filters for minor allele frequency, missing data per individual or per locus, linkage disequilibrium and Hardy-Weinberg deviations. Using simulated and empirical datasets, we illustrate the large effects of different filtering thresholds on common population genetics statistics, such as Tajima's D value, population differentiation (FST), nucleotide diversity (π) and effective population size (Ne).

Systematic identification of post-transcriptional regulatory modules

In our cells, a limited number of RNA binding proteins (RBPs) are responsible for all aspects of RNA metabolism across the entire transcriptome. To accomplish this, RBPs form regulatory units that act on specific target regulons. However, the landscape of RBP combinatorial interactions remains poorly explored. Here, we perform a systematic annotation of RBP combinatorial interactions via multimodal data integration. We build a large-scale map of RBP protein neighborhoods by generating in vivo proximity-dependent biotinylation datasets of 50 human RBPs. In parallel, we use CRISPR interference with single-cell readout to capture transcriptomic changes upon RBP knockdowns. By combining these physical and functional interaction readouts, along with the atlas of RBP mRNA targets from eCLIP assays, we generate an integrated map of functional RBP interactions. We then use this map to match RBPs to their context-specific functions and validate the predicted functions biochemically for four RBPs. This study provides a detailed map of RBP interactions and deconvolves them into distinct regulatory modules with annotated functions and target regulons. This multimodal and integrative framework provides a principled approach for studying post-transcriptional regulatory processes and enriches our understanding of their underlying mechanisms.

A comprehensive workflow for optimizing RNA-seq data analysis

BACKGROUND

Current RNA-seq analysis software for RNA-seq data tends to use similar parameters across different species without considering species-specific differences. However, the suitability and accuracy of these tools may vary when analyzing data from different species, such as humans, animals, plants, fungi, and bacteria. For most laboratory researchers lacking a background in information science, determining how to construct an analysis workflow that meets their specific needs from the array of complex analytical tools available poses a significant challenge.

RESULTS

By utilizing RNA-seq data from plants, animals, and fungi, it was observed that different analytical tools demonstrate some variations in performance when applied to different species. A comprehensive experiment was conducted specifically for analyzing plant pathogenic fungal data, focusing on differential gene analysis as the ultimate goal. In this study, 288 pipelines using different tools were applied to analyze five fungal RNA-seq datasets, and the performance of their results was evaluated based on simulation. This led to the establishment of a relatively universal and superior fungal RNA-seq analysis pipeline that can serve as a reference, and certain standards for selecting analysis tools were derived for reference. Additionally, we compared various tools for alternative splicing analysis. The results based on simulated data indicated that rMATS remained the optimal choice, although consideration could be given to supplementing with tools such as SpliceWiz.

CONCLUSION

The experimental results demonstrate that, in comparison to the default software parameter configurations, the analysis combination results after tuning can provide more accurate biological insights. It is beneficial to carefully select suitable analysis software based on the data, rather than indiscriminately choosing tools, in order to achieve high-quality analysis results more efficiently.

Identification of differentially expressed genes and splicing events in early-onset colorectal cancer

Background

The incidence of colorectal cancer (CRC) has been steadily increasing in younger individuals over the past several decades for reasons that are incompletely defined. Identifying differences in gene expression profiles, or transcriptomes, in early-onset colorectal cancer (EOCRC, < 50 years old) patients versus later-onset colorectal cancer (LOCRC, > 50 years old) patients is one approach to understanding molecular and genetic features that distinguish EOCRC.

Methods

We performed RNA-sequencing (RNA-seq) to characterize the transcriptomes of patient-matched tumors and adjacent, uninvolved (normal) colonic segments from EOCRC (n=21) and LOCRC (n=22) patients. The EOCRC and LOCRC cohorts were matched for demographic and clinical characteristics. We used The Cancer Genome Atlas Colon Adenocarcinoma (TCGA-COAD) database for validation. We used a series of computational and bioinformatic tools to identify EOCRC-specific differentially expressed genes, molecular pathways, predicted cell populations, differential gene splicing events, and predicted neoantigens.

Results

We identified an eight-gene signature in EOCRC comprised of ALDOB, FBXL16, IL1RN, MSLN, RAC3, SLC38A11, WBSCR27 and WNT11, from which we developed a score predictive of overall CRC patient survival. On the entire set of genes identified in normal tissues and tumors, cell type deconvolution analysis predicted a differential abundance of immune and non-immune populations in EOCRC versus LOCRC. Gene set enrichment analysis identified increased expression of splicing machinery in EOCRC. We further found differences in alternative splicing (AS) events, including one within the long non-coding RNA, HOTAIRM1. Additional analysis of AS found seven events specific to EOCRC that encode potential neoantigens.

Conclusion

Our transcriptome analyses identified genetic and molecular features specific to EOCRC which may inform future screening, development of prognostic indicators, and novel drug targets.

Deciphering the role of alternative splicing as a potential regulator in fat-tail development of sheep: a comprehensive RNA-seq based study

Although research on alternative splicing (AS) has been widely conducted in mammals, no study has investigated the splicing profiles of genes involved in fat-tail formation in sheep. Here, for the first time, a comprehensive study was designed to investigate the profile of AS events and their involvement in fat-tail development of sheep. In total, 45 RNA-Seq samples related to seven different studies, which have compared the fat-tailed vs thin-tailed sheep breeds, were analyzed. Two independent tools, rMATS and Whippet, along with a set of stringent filters were applied to identify differential AS (DAS) events between the breeds per each study. Only DAS events that were detected by both tools as well as in at least three datasets with the same ΔPSI trend (percent spliced in), were considered as the final high-confidence set of DAS genes. Final results revealed 130 DAS skipped exon events (69 negative and 61 positive ΔPSI) belonged to 124 genes. Functional enrichment analysis highlighted the importance of the genes in the underlying molecular mechanisms of fat metabolism. Moreover, protein-protein interaction network analysis revealed that DAS genes are significantly connected. Of DAS genes, five transcription factors were found that were enriched in the biological process associated with lipid metabolism like "Fat Cell Differentiation". Further investigations of the findings along with a comprehensive literature review provided a reliable list of candidate genes that may potentially contribute to fat-tail formation including HSD11B1, SIRT2, STRN3 and TCF7L2. Based on the results, it can be stated that the AS patterns may have evolved, during the evolution of sheep breeds, as another layer of regulation to contribute to biological complexity by reprogramming the gene regulatory networks. This study provided the theoretical basis of the molecular mechanisms behind the sheep fat-tail development in terms of AS.

Alternative Transcripts Diversify Genome Function for Phenome Relevance to Health and Diseases

Manipulation using alternative exon splicing (AES), alternative transcription start (ATS), and alternative polyadenylation (APA) sites are key to transcript diversity underlying health and disease. All three are pervasive in organisms, present in at least 50% of human protein-coding genes. In fact, ATS and APA site use has the highest impact on protein identity, with their ability to alter which first and last exons are utilized as well as impacting stability and translation efficiency. These RNA variants have been shown to be highly specific, both in tissue type and stage, with demonstrated importance to cell proliferation, differentiation and the transition from fetal to adult cells. While alternative exon splicing has a limited effect on protein identity, its ubiquity highlights the importance of these minor alterations, which can alter other features such as localization. The three processes are also highly interwoven, with overlapping, complementary, and competing factors, RNA polymerase II and its CTD (C-terminal domain) chief among them. Their role in development means dysregulation leads to a wide variety of disorders and cancers, with some forms of disease disproportionately affected by specific mechanisms (AES, ATS, or APA). Challenges associated with the genome-wide profiling of RNA variants and their potential solutions are also discussed in this review.