Biology subjects

Tsoy, O.

Publications and source records attributed to Tsoy, O..

15 recordsLinked to original sources

bioRxiv2025

AlterNet: Alternative splicing-aware gene regulatory networkinference

Gene regulatory networks (GRNs) help decode biological systems by identifying how genes interact and regulate cellular processes. However, conventional GRN inference methods operate at the gene-level, overlooking transcript-level variability introduced by alternative splicing (AS). In this work, we present AlterNet, the first GRN inference and annotation pipeline which considers isoform of transcription factors as distinct regulators in a GRN. AlterNet builds on the GRNBoost2 inference algorithm, and includes a transcript plausibility and annotation workflow. We applied AlterNet to expression data from heart tissue, including samples from donors with normal heart function and from patients with different types of cardiomyopathy. The resulting isoform-level GRNs uncovered highly relevant regulatory interactions not detectable at the gene-level. Overall, AlterNet infers transcript-level regulatory network which enable the discovery of novel, biologically relevant regulatory interactions that remain hidden in gene-level GRNs. The source code of AlterNet is available on GitHub https://github.com/bionetslab/AlterNet, and can be installed as a Python package.

bioinformatics↗

bioRxiv2025

Aberrant splicing in human cancer has a large-scale functional impact on transcription factors

Cancer shows aberrant alternative splicing (AS), which could functionally perturb proteins required for normal cellular behavior. Transcription factors (TFs) are proteins that regulate gene transcription. Some evidence about AS-driven perturbation of TFs in cancer has been documented. A comprehensive systematic analysis of how cancer-specific AS could affect functions of TFs is missing. Such an analysis could reveal the molecular mechanisms of cancer progression due to transcription misregulations, and thus could help identify therapeutic targets. Here, we systematically analyzed potential functional perturbations of TFs due to AS across 15 cancer types. By analyzing AS patterns, we identified 2 118 perturbed AS events (i.e., events showing significant AS pattern differences between normal and paired cancer samples) that affect 718 TFs across 14 cancer types. In 189 TFs, the perturbed AS events affected known functional domains of TFs. Indirect evidence for the functional impact of cancer-specific AS on TFs were also found: First, by relating AS patterns of the perturbed AS events with the DNA-binding and regulatory activity of TFs. Second, by using cancer dependency data to explore whether the affected TFs are essential for cancer cell line proliferation. Our findings show a large-scale functional perturbation of TFs due to cancer-specific AS.

bioinformatics↗

bioRxiv2025

Detection of alternative splicing: deep sequencing or deep learning?

Alternative splicing (AS) is a central mechanism of gene regulation that enables condition- and tissue-specific expression of gene isoforms. Its dysregulation plays a role in diseases such as cancer, neurological disorders, and metabolic conditions. Despite its importance, accurately identifying AS events remains challenging, especially in large-scale studies relying on publicly available RNA sequencing (RNA-seq) data. State-of-the-art AS event detection typically requires deep sequencing with over 100 million reads; however, much of the publicly accessible data is of lower sequencing depth. Recent advances, particularly deep learning models working with genomic sequences, offer new avenues for predicting AS without reliance on high sequencing depth data. Our study addresses the question: Can we utilize the vast repository of publicly available RNA-seq data for AS detection, despite often lacking the sequencing depth typically required? We show that sequence-based tools such as DeepSplice and SpliceAI show promising performance in retrieving novel and unannotated splice junctions, even when RNA-seq data are limited, but are not suitable for de novo splice junction detection. Our results demonstrate the potential of sequence-based tools for initial hypothesis development and as additional filters in standard RNA-seq 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 and RNA-seq data for prediction of tissue-/condition-specific AS in resource-limited settings.

bioinformatics↗

bioRxiv2025

Regulation of the transcriptome, miRNAs, and alternative splicing in a FSGS zebrafish injury model

BackgroundFocal Segmental Glomerulosclerosis (FSGS) is a severe kidney disorder with complex and not yet fully understood pathogenesis. Alternative splicing (AS) - the generation of distinct protein isoforms from the same gene - might play a critical role by the regulation of gene functions and disease development. MethodsTo investigate the role of AS in FSGS, we used a zebrafish model, which mimics key human FSGS features, including foot process effacement, matrix accumulation, podocyte detachment and parietal epithelial cell activation. We performed total RNA sequencing of isolated zebrafish glomeruli and whole larvae, followed by integrative bioinformatic analysis to identify AS events and regulatory miRNAs. ResultsOur data revealed a downregulation of essential podocyte genes (nphs1, nphs2, podxl, wt1) and an inhibition of pathways associated with nephron development and cytoskeletal organization. We also observed increased expression of the transcription factor stat3 and disease-associated miRNAs such as miR-21 and miR-193. AS analysis identified approximately [~]7,000 splicing events, primarily exon skipping ([~]80%), affecting genes such as nphs1, magi2, and ptpro. A total of 136 and 612 alternatively spliced genes were found at 5 and 6 days post-fertilization (dpf), respectively. Isoform switch analysis uncovered 70 genes affected by AS in FSGS, including epb41l5 (linked to podocyte adhesion), fgfr1a (fibroblast growth signaling), and members of the SRSF splicing factor family (e.g., srsf3a). ConclusionsThese findings emphasize the importance of transcriptional and post-transcriptional regulation, including AS, in FSGS pathogenesis. Furthermore, they support the zebrafish model as a valuable system for identifying novel mechanisms and potential therapeutic targets for kidney diseases.

cell biology↗

bioRxiv2025

Systematic Evaluation of Normalization Approaches in Tandem Mass Tag and Label-Free Protein Quantification Data Using PRONE

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 two batch effect correction methods, originally developed for pre-processing DNA microarray data but widely applied in proteomics, on six publicly available spike-in and three 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/.

bioinformatics↗

bioRxiv2024

RNA sequencing depth guidelines for the study of alternative splicing

A key parameter in the experimental design of RNA-seq projects is the choice of sequencing depth. Considering a limited budget, one needs to find a tradeoff between the number of samples and the sensitivity of the analysis, particularly concerning lowly expressed genes. While previous studies have proposed a lower bound for the comprehensive analysis of differential gene expression, for the analysis of alternative splicing, it has only been proposed for human adipose tissue. However, alternative splicing differs across tissues and conditions. We analyzed publicly available and newly generated deep-sequenced paired-end RNA-seq samples (between 150 and >500 million reads, read length 50-150 bp) from human buffy coat cells and diverse sets of tissues, including gluteal subcutaneous fat, heart, and hypothalamus. Our results show that the sequencing depth typically used in published cohorts is not sufficient to comprehensively capture the landscape of alternative splicing. This motivates the use of deeper sequencing or long-read technologies in future studies. Toward this goal, we offer guidelines for choosing a suitable sequencing depth. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=177 SRC="FIGDIR/small/617406v2_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@1aecd9org.highwire.dtl.DTLVardef@1b3d0fborg.highwire.dtl.DTLVardef@5d15c4org.highwire.dtl.DTLVardef@140035b_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2024

DREAMER: Exploring Common Mechanisms of Adverse Drug Reactions and Disease Phenotypes through Network-Based Analysis

Adverse drug reactions (ADRs) are a major concern in clinical healthcare, significantly affecting patient safety and drug development. The need for a deeper understanding of ADR mechanisms is crucial for improving drug safety profiles in drug design and drug repurposing. This study introduces DREAMER (Drug adverse REAction Mechanism ExplaineR), a novel network-based method for exploring the mechanisms underlying adverse drug reactions and disease phenotypes at a molecular level by leveraging a comprehensive knowledge graph obtained from various datasets. By considering drugs and diseases that cause similar phenotypes, and investigating their commonalities regarding their impact on specific modules of the protein-protein interaction network, DREAMER can robustly identify protein sets associated with the biological mechanisms underlying ADRs and unravel the causal relationships that contribute to the observed clinical outcomes. Applying DREAMER to 649 ADRs, we identified proteins associated with the mechanism of action for 67 ADRs across multiple organ systems, e.g., ventricular arrhythmia, metabolic acidosis, and interstitial pneumonitis. In particular, DREAMER highlights the importance of GABAergic signaling and proteins of the coagulation pathways for personality disorders and intracranial hemorrhage, respectively. We further demonstrate the application of DREAMER in drug repurposing and propose sotalol (targeting KCNH2), ranolazine (targeting SCN5A, currently under clinical trial), and diltiazem (indicated drug targeting CACNA1C and SCN3A) as candidate drugs to be repurposed for cardiac arrest. In summary, DREAMER effectively detects molecular mechanisms underlying phenotypes emphasizing the importance of network-based analyses with integrative data for enhancing drug safety and accelerating the discovery of novel therapeutic strategies.

systems biology↗

bioRxiv2024

Alternative splicing in mechanically stretched podocytes as a model of glomerular hypertension

BackgroundAlterations in pre-mRNA splicing play an important role in disease pathophysiology. However, the role of alternative splicing (AS) for podocytes in hypertensive nephropathy (HN) has not been investigated. The purpose of the Sys_CARE project was to identify AS events that play a role in the development and progression of HN. MethodsMurine podocytes were exposed to mechanical stretch, after which proteins and mRNA were analyzed by proteomics, RNA-Seq and several bioinformatic AS tools. ResultsBased on transcriptomics and proteomics analysis we could observe significant changes in gene expression and abundance of proteins under mechanical stretch compared to unstretched conditions. By RNA-Seq, we identified over 3,000 alternative spliced genes after mechanical stretch, including all types of AS events. We found 17 genes that showed an AS event in four different splicing analysis tools. From these, we focused on Myl6, a component of the myosin protein complex, and Shroom3, an actin-binding protein crucial for podocyte function. We found two Shroom3 isoforms that showed significant changes in expression upon mechanical stretch, which was verified by qRT-PCR and in situ hybridization. Furthermore, we observed an expression switch of two Myl6 isoforms after mechanical stretch. This switch is accompanied by a change in a C-terminally located amino acid sequence. ConclusionsIn summary, mechanical stretch of cultured podocytes is an excellent model to simulate hypertensive nephropathy. In depth RNA-Seq analysis disclosed alternative splicing events, such as in Shroom3 and Myl6, which may play a crucial role in the pathophysiology of hypertension-induced nephropathy.

cell biology↗

bioRxiv2024

The power and limits of predicting exon-exon interactions using protein 3D structures

Alternative splicing (AS) effects on cellular functions can be captured by studying changes in the underlying protein-protein interactions (PPIs). Because AS results in the gain or loss of exons, existing methods for predicting AS-related PPI changes utilize known PPI interfacing exon-exon interactions (EEIs), which only cover [~]5% of known human PPIs. Hence, there is a need to extend the existing limited EEI knowledge to advance the functional understanding of AS. In this study, we explore whether existing computational PPI interface prediction (PPIIP) methods, originally designed to predict residue-residue interactions (RRIs), can be used to predict EEIs. We evaluate three recent state-of-the-art PPIIP methods for the RRI- as well as EEI-prediction tasks using known protein complex structures, covering [~]230,000 RRIs and [~]27,000 EEIs. Our results provide the first evidence that existing PPIIP methods can be extended for the EEI prediction task, showing F-score, precision, and recall performances of up to [~]38%, [~]63%, and [~]28%, respectively, with a false discovery rate of less than 5%. Our study provides insights into the power and limits of existing PPIIP methods to predict EEIs, thus guiding future developments of computational methods for the EEI prediction task. We provide streamlined computational pipelines integrating each of the three considered PPIIP methods for the EEI prediction task to be utilized by the scientific community.

bioinformatics↗

bioRxiv2024

Comprehensive benchmark of differential transcript usage analysis for static and dynamic conditions

RNA sequencing offers unique insights into transcriptome diversity, and a plethora of tools have been developed to analyze alternative splicing. One important task is to detect changes in the relative transcript abundance in differential transcript usage (DTU) analysis. The choice of the right analysis tool is non-trivial and depends on experimental factors such as the availability of single- or paired-end and bulk or single-cell data. To help users select the most promising tool for their task, we performed a comprehensive benchmark of DTU detection tools. We cover a wide array of experimental settings, using simulated bulk and single-cell RNA-seq data as well as real transcriptomics datasets, including time-series data. Our results suggest that DEXSeq, edgeR, and LimmaDS are better choices for paired-end data, while DSGseq and DEXSeq can be used for single-end data. In single-cell simulation settings, we showed that satuRn performs better than DTUrtle. In addition, we showed that Spycone is optimal for time series DTU/IS analysis based on the evidence provided using GO terms enrichment analysis.

bioinformatics↗

bioRxiv2023

Alternative splicing impacts microRNA regulation within coding regions

MicroRNAs (miRNAs) are small non-coding RNA molecules that bind to target sites in different gene regions and regulate post-transcriptional gene expression. Approximately 95% of human multi-exon genes can be spliced alternatively, which enables the production of functionally diverse transcripts and proteins from a single gene. Through alternative splicing, transcripts might lose the exon with the miRNA target site and become unresponsive to miRNA regulation. To check this hypothesis, we studied the role of miRNA target sites in both coding and noncoding regions using six cancer data sets from The Cancer Genome Atlas (TCGA). First, we predicted miRNA target sites on mRNAs from their sequence using TarPmiR. To check whether alternative splicing interferes with this regulation, we trained linear regression models to predict miRNA expression from transcript expression. Using nested models, we compared the predictive power of transcripts with miRNA target sites in the coding regions to that of transcripts without target sites. Models containing transcripts with target sites perform significantly better. We conclude that alternative splicing does interfere with miRNA regulation by skipping exons with miRNA target sites within the coding region.

bioinformatics↗

bioRxiv2022

Systematic analysis of alternative splicing in time course data using Spycone

During disease progression or organism development, alternative splicing (AS) may lead to isoform switches (IS) that demonstrate similar temporal patterns and reflect the AS co-regulation of such genes. Tools for dynamic process analysis usually neglect AS. Here we propose Spycone (https://github.com/yollct/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.

bioinformatics↗

bioRxiv2022

Alternative splicing analysis benchmark with DICAST

Alternative splicing is a major contributor to transcriptome and proteome diversity in health and disease. A plethora of tools have been developed for studying alternative splicing in RNA-seq data. Previous benchmarks focused on isoform quantification and mapping. They neglected event detection tools, which arguably provide the most detailed insights into the alternative splicing process. DICAST offers a modular and extensible framework for the analysis of alternative splicing integrating 11 splice-aware mapping and eight event detection tools. We benchmark all tools extensively on simulated as well as whole blood RNA-seq data. STAR and HISAT2 demonstrated the best balance between performance and run time. The performance of event detection tools varies widely with no tool outperforming all others. DICAST allows researchers to employ a consensus approach to consider the most successful tools jointly for robust event detection. Furthermore, we propose the first reporting standard to unify existing formats and to guide future tool development.

bioinformatics↗

bioRxiv2021

Functional enrichment of alternative splicing events with NEASE reveals insights into tissue identity and diseases

Alternative splicing (AS) is an important aspect of gene regulation. Nevertheless, its role in molecular processes and pathobiology is far from understood. A roadblock is that tools for the functional analysis of AS-set events are lacking. To mitigate this, we developed NEASE, a tool integrating pathways with protein-protein and domain-domain interactions to functionally characterize AS events. We show in four application cases how NEASE can identify pathways contributing to tissue identity and cell type development, and how it highlights splicing-related biomarkers. With a unique view on AS, NEASE generates unique and meaningful biological insights complementary to classical pathways analysis.

bioinformatics↗

bioRxiv2021

Florigen revisited: proteins of the FT/CETS/PEBP/PKIP/YbhB family may be the enzymes of small molecule metabolism

Flowering signals are sensed in plant leaves and transmitted to the shoot apical meristems, where the formation of flowers is initiated. Searches for a diffusible hormone-like signaling entity ("florigen") went on for many decades, until in the 1990s a product of plant gene FT was identified as the key component of florigen, based on genetic evidence and protein localization studies. Sequence homologs of FT protein are found throughout prokaryotes and eukaryotes; some eukaryotic family members appear to bind phospholipids or interact with the components of the signal transduction cascades. We studied molecular features of the FT homologs in prokaryotes and analyzed their genome context, to find tentative evidence connecting the bacterial family members with small molecule metabolism, often involving sugar- or ribonucleoside-containing substrates. Most FT homologs share a constellation of five charged residues, three of which, i.e., two histidines and an aspartic acid, circumfere the rim of a well-defined cavity on the protein surface. We argue that this conserved feature is more likely to be an enzymatic active center than a catalytically inactive ligand-binding site. We propose that most of FT-related proteins are enzymes operating on small diffusible molecules, which may constitute an overlooked essential ingredient of the florigen signal.

bioinformatics↗