Alternative splicing is a crucial mechanism of gene regulation that enables condition- and tissue-specific expression of gene isoforms. Its dysregulation plays a role in various diseases such as cancer, neurological disorders, and metabolic conditions. Despite its importance, accurate detection of alternative splicing events remains challenging. Comprehensive alternative splicing event detection typically requires deep sequencing with over 100 million reads; however, much of the publicly accessible RNA sequencing data is of lower sequencing depth. Recent advances, particularly deep learning models working with genomic sequences, offer new avenues for predicting alternative splicing without reliance on high sequencing depth data. Our study addresses the question: Can we utilize the vast repository of publicly available RNA sequencing data for comprehensive alternative splicing detection, despite the low sequencing depth? Our results demonstrate the potential of sequence-based deep learning tools such as AlphaGenome, SpliceAI and DeepSplice for initial hypothesis development and as additional filters in standard RNA sequencing pipelines, especially when sequencing depth is limited. Nonetheless, validation with higher sequencing depths remains essential for confirmation of splice events. Overall, our findings underscore the need for integrative methods combining genomic sequence data and RNA sequencing data for the prediction of tissue- and condition-specific alternative splicing in resource-limited settings.

Despite the significant progress in accuracy and reliability in mass spectrometry technology, as well as the development of strategies based on isotopic labeling or internal standards in recent decades, systematic biases originating from non-biological factors remain a significant challenge in data analysis. In addition, the wide range of available normalization methods renders the choice of a suitable normalization method challenging. We systematically evaluated 17 normalization and 2 batch effect correction methods, originally developed for preprocessing DNA microarray data but widely applied in proteomics, on 6 publicly available spike-in and 3 label-free and tandem mass tag datasets. Opposed to state-of-the-art normalization practice, we found that a reduction in intragroup variation is not directly related to the effectiveness of the normalization methods. Furthermore, our results demonstrated that the methods RobNorm and Normics, specifically developed for proteomics data, in line with LoessF performed consistently well across the spike-in datasets, while EigenMS exhibited a high false-positive rate. Finally, based on experimental data, we show that normalization substantially impacts downstream analyses, and the impact is highly dataset-specific, emphasizing the importance of use-case-specific evaluations for novel proteomics datasets. For this, we developed the PROteomics Normalization Evaluator (PRONE), a unifying R package enabling comparative evaluation of normalization methods, including their impact on downstream analyses, while offering considerable flexibility, acknowledging the lack of universally accepted standards. PRONE is available on Bioconductor with a web application accessible at https://exbio.wzw.tum.de/prone/.

MOTIVATION: During disease progression or organism development, alternative splicing may lead to isoform switches that demonstrate similar temporal patterns and reflect the alternative splicing co-regulation of such genes. Tools for dynamic process analysis usually neglect alternative splicing. RESULTS: Here, we propose Spycone, a splicing-aware framework for time course data analysis. Spycone exploits a novel IS detection algorithm and offers downstream analysis such as network and gene set enrichment. We demonstrate the performance of Spycone using simulated and real-world data of SARS-CoV-2 infection. AVAILABILITY AND IMPLEMENTATION: The Spycone package is available as a PyPI package. The source code of Spycone is available under the GPLv3 license at https://github.com/yollct/spycone and the documentation at https://spycone.readthedocs.io/en/latest/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: A plethora of tools exist for RNA-Seq data analysis with a focus on alternative splicing (AS). However, appropriate data for their comparative evaluation is missing. The R package ASimulatoR simulates gold standard RNA-Seq datasets with fine-grained control over the distribution of AS events, which allow for evaluating alternative splicing tools, e.g. to study the effect of sequencing depth on the performance of AS event detection. AVAILABILITY AND IMPLEMENTATION: ASimulatoR is freely available at https://github.com/biomedbigdata/ASimulatoR as an R package under GPL-3 license.