Zakaria Louadi

Background and Purpose: Gene regulation is frequently altered in diseases in unique and patient‐specific ways. Hence, personalised strategies have been proposed to infer patient‐specific gene‐regulatory networks. However, existing methods do not scale well because they often require recomputing the entire network per sample. Moreover, they do not account for clinically important confounding factors such as age, sex or treatment history. Finally, a user‐friendly implementation for the analysis and interpretation of such networks is missing. Experimental Approach: We present DysRegNet, a method for inferring patient‐specific regulatory alterations (dysregulations) from bulk gene expression profiles. We compared DysRegNet to the well‐known SSN method, considering patient clustering, promoter methylation, mutations and cancer‐stage data. Key Results: We demonstrate that both SSN and DysRegNet produce interpretable and biologically meaningful networks across various cancer types. In contrast to SSN, DysRegNet can scale to arbitrary sample numbers and highlights the importance of confounders in network inference, revealing an age‐specific bias in gene regulation in breast cancer. DysRegNet is available as a Python package ( https://github.com/biomedbigdata/DysRegNet_package ), and analysis results for 11 TCGA cancer types are available through an interactive web interface ( https://exbio.wzw.tum.de/dysregnet ). Conclusion and Implications: DysRegNet introduces a novel bioinformatics tool enabling confounder‐aware and patient‐specific network analysis to unravel regulatory alteration in complex diseases. LINKED ARTICLES: This article is part of a themed issue Network Medicine and Systems Pharmacology. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v183.8/issuetoc

Alternative splicing (AS) impacts protein function and structure and lead to protein-protein interaction (PPI) rewiring. However, available PPI networks neglect alternative splicing isoforms. Since it is not feasible to validate all isoform-isoform interactions experimentally, we present a set of tools to investigate AS impact on a network level: DIGGER to map splicing to the PPI network, as well as NEASE and Spycone to evaluate the functional consequences of network rewiring. DIGGER (https://exbio.wzw.tum.de/digger) integrates PPIs, domain-domain, and residue-level interactions - the structures that might be spliced in or out and result in interaction gain or loss. Users can explore possible rewiring for an isoform or exon of interest and extract relevant subnetworks. NEASE (https://github.com/louadi/NEASE) identifies pathways that are significantly affected by network rewiring. NEASE extends classic gene set enrichment analysis by considering isoform-specific interactions affecting pathways. Spycone (https://github.com/yollct/spycone) addresses the time-course changes in AS. It searches for isoforms demonstrating similar temporal splicing patterns and reflecting the splicing co-regulation. Spycone further integrates gene set, network, and splicing-aware NEASE enrichment. Overall, we offer a splicing-focused network analysis toolkit that allows for studying the mechanistic consequences of AS.

Gene regulation is frequently altered in diseases in unique and often patient-specific ways. Hence, personalized strategies have been proposed to infer patient-specific gene-regulatory networks. However, existing methods do not focus on disease-specific dysregulation or lack assessment of statistical significance. Moreover, they do not account for clinically important confounding factors such as age, sex, or treatment history. To overcome these shortcomings, we present DysRegNet, a novel method for inferring patient-specific regulatory alterations (dysregulations) from gene expression profiles. We compared DysRegNet to SSN, the original sample-specific network approach, and demonstrated that DysRegNet produces more interpretable and biologically meaningful networks. We apply DysRegNet to eleven TCGA cancer types and illustrate how the inferred networks can be used for downstream analysis. We show that unique as well as cancer-type-specific dysregulation patterns exist and highlight immune-related mechanisms that are not obvious when focusing on individual genes rather than their interactions.

Gene regulation is frequently altered in diseases in unique and patient-specific ways. Hence, personalized strategies have been proposed to infer patient-specific gene-regulatory networks. However, existing methods do not scale well as they often require recomputing the entire network per sample. Moreover, they do not account for clinically important confounding factors such as age, sex, or treatment history. Finally, a user-friendly implementation for the analysis and interpretation of such net-works is missing. We present DysRegNet, a method for inferring patient-specific regulatory alterations (dysregulations) from bulk gene expression profiles. We compared DysRegNet to SSN, a well-known sample-specific network approach. We demonstrate that both SSN and DysRegNet produce interpretable and biologically meaningful networks across various cancer types. In contrast to SSN, DysRegNet can scale to arbitrary sample numbers and highlights the importance of confounders in network inference, revealing an age-specific bias in gene regulation in breast cancer. DysRegNet is available as a Python package ( https://github.com/biomedbigdata/DysRegNet_package ), and analysis results for eleven TCGA cancer types are available through an interactive web interface ( https://exbio.wzw.tum.de/dysregnet ).

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.

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.

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.

Alternative splicing plays a major role in regulating the functional repertoire of the proteome. However, isoform-specific effects to protein-protein interactions (PPIs) are usually overlooked, making it impossible to judge the functional role of individual exons on a systems biology level. We overcome this barrier by integrating protein-protein interactions, domain-domain interactions and residue-level interactions information to lift exon expression analysis to a network level. Our user-friendly database DIGGER is available at https://exbio.wzw.tum.de/digger and allows users to seamlessly switch between isoform and exon-centric views of the interactome and to extract sub-networks of relevant isoforms, making it an essential resource for studying mechanistic consequences of alternative splicing.

Alternative splicing (AS) is the process of combining different parts of the pre-mRNA to produce diverse transcripts and eventually different protein products from a single gene. In computational biology field, researchers try to understand AS behavior and regulation using computational models known as “Splicing Codes”. The final goal of these algorithms is to make an in-silico prediction of AS outcome from genomic sequence. Here, we develop a deep learning approach, called Deep Splicing Code (DSC), for categorizing the well-studied classes of AS namely alternatively skipped exons, alternative 5’ss, alternative 3’ss, and constitutively spliced exons based only on the sequence of the exon junctions. The proposed approach significantly improves the prediction and the obtained results reveal that constitutive exons have distinguishable local characteristics from alternatively spliced exons. Using the motif visualization technique, we show that the trained models learned to search for competitive alternative splice sites as well as motifs of important splicing factors with high precision. Thus, the proposed approach greatly expands the opportunities to improve alternative splicing modeling. In addition, a web-server for AS events prediction has been developed based on the proposed method.