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Background: Eukaryotic gene expression is controlled by cis-regulatory elements (CREs), including promoters and enhancers, which are bound by transcription factors (TFs). Differential expression of TFs and their binding affinity at putative CREs determine tissue- and developmental-specific transcriptional activity. Consolidating genomic data sets can offer further insights into the accessibility of CREs, TF activity, and, thus, gene regulation. However, the integration and analysis of multi-modal data sets are hampered by considerable technical challenges. While methods for highlighting differential TF activity from combined chromatin state data (e.g., ChIP-seq, ATAC-seq, or DNase-seq) and RNA-seq data exist, they do not offer convenient usability, have limited support for large-scale data processing, and provide only minimal functionality for visually interpreting results. Results: We developed TF-Prioritizer, an automated pipeline that prioritizes condition-specific TFs from multi-modal data and generates an interactive web report. We demonstrated its potential by identifying known TFs along with their target genes, as well as previously unreported TFs active in lactating mouse mammary glands. Additionally, we studied a variety of ENCODE data sets for cell lines K562 and MCF-7, including twelve histone modification ChIP-seq as well as ATAC-seq and DNase-seq datasets, where we observe and discuss assay-specific differences. Conclusion: TF-Prioritizer accepts ATAC-seq, DNase-seq, or ChIP-seq and RNA-seq data as input and identifies TFs with differential activity, thus offering an understanding of genome-wide gene regulation, potential pathogenesis, and therapeutic targets in biomedical research.

MicroRNAs (miRNAs) are small non-coding RNA molecules that regulate post-transcriptional gene expression by binding to specific target sites. 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. In complex diseases, such as cancer, gene but also miRNA dysregulation plays a significant role. According to most studies miRNAs preferably bind to 3’-untranslated regions of mRNA. However, through alternative splicing, transcripts might lose exons harboring miRNA target sites and, hence, 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. For our analysis, we focused on miRNAs whose expression was negatively correlated with gene expression (as evidence for active regulation) as well as genes that were at least moderately expressed and showed evidence of alternative splicing. We chose different subsets of transcripts to differentiate the effects of target sites in different gene regions. To check whether alternative splicing interferes with miRNA 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 to that of transcripts without target sites in the investigated gene region. For all six cancer data sets and all subsets, models containing transcripts with target sites predicted miRNA abundance significantly better. We conclude that alternative splicing does interfere with miRNA regulation by skipping exons with miRNA target sites within the coding region.

Alternative splicing (AS) can impact protein structure and lead to protein-protein interaction (PPI) rewiring. Available PPI networks neglect alternative splicing isoforms: as interactions might happen only between a subset of isoforms, the PPI network contains both false-positive and false-negative interactions. Since it is not feasible to validate all isoform-isoform interactions experimentally, we present a set of tools to investigate AS 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 residuelevel 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 that demonstrate similar temporal splicing patterns and reflect 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.

Drug repurposing is the use of previously developed and tested pharmaceutical agents in new application cases and lately often used as a solution to the increasing drug development costs. Cancers are extremely heterogeneous disorders demonstrating a wide variability of drug responses due to diverse subtypes, quickly evolving and acquiring drug resistance. Therefore, the identification of compounds that can effectively combat a specific tumor type is crucial. Drug candidates that are potentially effective against a specific tumor can be chosen based on the set of driver mutations acquired by this tumor. For optimal treatment, it is important to consider targeted anti-cancer therapies and drugs initially developed to treat non-cancerous diseases. To overcome this hurdle, we present CADDIE (Cancer Driver Drug Interaction Explorer), a web platform to identify oncological drug repurposing candidates. CADDIE’s biomedical knowledge base integrates a multitude of gene-gene and drug-gene interaction datasets, detailed anticancer drug information and cancer biology data such as cancer driver genes, mutation frequencies and gene expressions. For the purpose of locating drug targets and candidates for drug repurposing, CADDIE makes network medicine algorithms available to the researchers. It guides the users from the choice of seed genes through the discovery of therapeutic targets or drug candidates. Network medicine also provides indirect strategies that take into account other functionally relevant targets in the gene interaction network since potential cancer driver genes may be inaccessible for direct targeting. We demonstrate the application of CADDIE in different cancer subtypes such as sarcoma and ovarian cancer with a detailed analysis of the found drug targets and chemical compounds. CADDIE is available online at https://exbio.wzw.tum.de/caddie/ and as a python package at https://pypi.org/project/caddiepy/.

For complex diseases, most drugs are highly ineffective, and the success rate of drug discovery is in constant decline. While low quality, reproducibility issues, and translational irrelevance of most basic and preclinical research have contributed to this, the current organ-centricity of medicine and the 'one disease-one target-one drug' dogma obstruct innovation in the most profound manner. Systems and network medicine and their therapeutic arm, network pharmacology, revolutionize how we define, diagnose, treat, and, ideally, cure diseases. Descriptive disease phenotypes are replaced by endotypes defined by causal, multitarget signaling modules that also explain respective comorbidities. Precise and effective therapeutic intervention is achieved by synergistic multicompound network pharmacology and drug repurposing, obviating the need for drug discovery and speeding up clinical translation.

Background Eukaryotic gene expression is controlled by cis-regulatory elements (CREs) including promoters and enhancers which are bound by transcription factors (TFs). Differential expression of TFs and their putative binding sites on CREs cause tissue and developmental-specific transcriptional activity. Consolidating genomic data sets can offer further insights into the accessibility of CREs, TF activity, and thus gene regulation. However, the integration and analysis of multi-modal data sets are hampered by considerable technical challenges. While methods for highlighting differential TF activity from combined ChIP-seq and RNA-seq data exist, they do not offer good usability, have limited support for large-scale data processing, and provide only minimal functionality for visual result interpretation. Results We developed TF-Prioritizer, an automated java pipeline to prioritize condition-specific TFs derived from multi-modal data. TF-Prioritizer creates an interactive, feature-rich, and user-friendly web report of its results. To showcase the potential of TF-Prioritizer, we identified known active TFs (e.g., Stat5, Elf5, Nfib, Esr1), their target genes (e.g., milk proteins and cell-cycle genes), and newly classified lactating mammary gland TFs (e.g., Creb1, Arnt). Conclusion TF-Prioritizer accepts ChIP-seq and RNA-seq data, as input and suggests TFs with differential activity, thus offering an understanding of genome-wide gene regulation, potential pathogenesis, and therapeutic targets in biomedical research.

For complex diseases, most drugs are highly ineffective, and the success rate of drug discovery is in constant decline. While low quality, reproducibility issues, and translational irrelevance of most basic and preclinical research have contributed to this, the current organ-centricity of medicine and the 'one disease-one target-one drug' dogma obstruct innovation in the most profound manner. Systems and network medicine and their therapeutic arm, network pharmacology, revolutionize how we define, diagnose, treat, and, ideally, cure diseases. Descriptive disease phenotypes are replaced by endotypes defined by causal, multitarget signaling modules that also explain respective comorbidities. Precise and effective therapeutic intervention is achieved by synergistic multicompound network pharmacology and drug repurposing, obviating the need for drug discovery and speeding up clinical translation.

SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) is a novel virus of the family Coronaviridae. The virus causes the infectious disease COVID-19. The biology of coronaviruses has been studied for many years. However, bioinformatics tools designed explicitly for SARS-CoV-2 have only recently been developed as a rapid reaction to the need for fast detection, understanding and treatment of COVID-19. To control the ongoing COVID-19 pandemic, it is of utmost importance to get insight into the evolution and pathogenesis of the virus. In this review, we cover bioinformatics workflows and tools for the routine detection of SARS-CoV-2 infection, the reliable analysis of sequencing data, the tracking of the COVID-19 pandemic and evaluation of containment measures, the study of coronavirus evolution, the discovery of potential drug targets and development of therapeutic strategies. For each tool, we briefly describe its use case and how it advances research specifically for SARS-CoV-2. All tools are free to use and available online, either through web applications or public code repositories. Contact:evbc@unj-jena.de

Machine Learning (ML) and Artificial Intelligence (AI) have shown promising results in many areas and are driven by the increasing amount of available data. However, this data is often distributed across different institutions and cannot be shared due to privacy concerns. Privacy-preserving methods, such as Federated Learning (FL), allow for training ML models without sharing sensitive data, but their implementation is time-consuming and requires advanced programming skills. Here, we present the FeatureCloud AI Store for FL as an all-in-one platform for biomedical research and other applications. It removes large parts of this complexity for developers and end-users by providing an extensible AI Store with a collection of ready-to-use apps. We show that the federated apps produce similar results to centralized ML, scale well for a typical number of collaborators and can be combined with Secure Multiparty Computation (SMPC), thereby making FL algorithms safely and easily applicable in biomedical and clinical environments.

SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) is a novel virus of the family Coronaviridae. The virus causes the infectious disease COVID-19. The biology of coronaviruses has been studied for many years. However, bioinformatics tools designed explicitly for SARS-CoV-2 have only recently been developed as a rapid reaction to the need for fast detection, understanding and treatment of COVID-19. To control the ongoing COVID-19 pandemic, it is of utmost importance to get insight into the evolution and pathogenesis of the virus. In this review, we cover bioinformatics workflows and tools for the routine detection of SARS-CoV-2 infection, the reliable analysis of sequencing data, the tracking of the COVID-19 pandemic and evaluation of containment measures, the study of coronavirus evolution, the discovery of potential drug targets and development of therapeutic strategies. For each tool, we briefly describe its use case and how it advances research specifically for SARS-CoV-2. All tools are free to use and available online, either through web applications or public code repositories. Contact:evbc@unj-jena.de

Biomolecular networks such as protein–protein interaction networks provide a static picture of the interplay of genes and their products, and, consequently, they fail to capture dynamic changes taking place during the development of complex diseases. KeyPathwayMiner is a software platform designed to fill this gap by integrating previous knowledge captured in molecular interaction networks with OMICS datasets (DNA microarrays, RNA sequencing, genome-wide methylation studies, etc.) to extract connected subnetworks with a high number of deregulated genes. This protocol describes how to use KeyPathwayMiner for integrated analysis of multi-omics datasets in the network analysis tool Cytoscape and in a stand-alone web application available at https://keypathwayminer.compbio.sdu.dk.

Breast cancer is a heterogeneous disease for which various clinically relevant subtypes have been reported. These subtypes are characterized by molecular differences which direct treatment selection. The state of the art for breast cancer subtyping utilizes histochemistry or gene expression to measure a few selected markers. However, classification based on molecular pathways (rather than individual markers) is a more robust way to classify breast cancer samples into known subtypes. Here, we present PathClass, a web application that allows its users to predict breast cancer subtypes using various traditional as well as advanced methods. This includes methods based on classical gene expression panels as well as de novo pathway-based predictors. Users can predict labels for datasets in the Gene Expression Omnibus or upload their own expression profiling data. Availability: https://pathclass.compbio.sdu.dk/.

Drug research, therapy development, and other areas of pharmacology and medicine can benefit from simulations and optimization of mathematical models that contain a mathematical description of interactions between systems elements at the cellular, tissue, organ, body, and population level. This approach is the foundation of systems medicine and precision medicine. Here, simulated experiments are performed with computers (in silico) first, and they are then replicated through lab experiments (in vivo or in vitro) or clinical studies. In turn, these experiments and studies can be used to validate or improve the models. This iterative loop of dry and wet lab work is successful when biomedical researchers tightly collaborate with data scientists and modelers. From an educational point of view, the interdisciplinary research in systems biology can be sustained most effectively when specialists have been trained to have both a strong background in the disciplines of biology or modeling and strong communication skills, which make them able to communicate with other specialists. This overview addresses possible interdisciplinary communication gaps. Focusing our attention on biomedical researchers, we describe the reasons for using modeling and ways to collaborate with modelers, including their needs for specific biological expertise and data. This review includes an introduction to the principles of several widely used mechanistic modeling methods, focusing on their areas of applicability as well as their limitations. A potential complementary role of machine-learning methods in the development of mechanistic models is also discussed. The descriptions of the methods also include links to corresponding modeling software tools as well as practical examples of their application. Finally, we also explicitly address different aspects of multiscale modeling approaches that allow a more complete and holistic perspective of the human body

Breast cancer is a heterogeneous disease for which various clinically relevant subtypes have been reported. These subtypes are characterized by molecular differences which direct treatment selection. The state of the art for breast cancer subtyping utilizes histochemistry or gene expression to measure a few selected markers. However, classification based on molecular pathways (rather than individual markers) is a more robust way to classify breast cancer samples into known subtypes. Here, we present PathClass, a web application that allows its users to predict breast cancer subtypes using various traditional as well as advanced methods. This includes methods based on classical gene expression panels as well as de novo pathway-based predictors. Users can predict labels for datasets in the Gene Expression Omnibus or upload their own expression profiling data. Availability: https://pathclass.compbio.sdu.dk/.

Biomolecular networks such as protein–protein interaction networks provide a static picture of the interplay of genes and their products, and, consequently, they fail to capture dynamic changes taking place during the development of complex diseases. KeyPathwayMiner is a software platform designed to fill this gap by integrating previous knowledge captured in molecular interaction networks with OMICS datasets (DNA microarrays, RNA sequencing, genome-wide methylation studies, etc.) to extract connected subnetworks with a high number of deregulated genes. This protocol describes how to use KeyPathwayMiner for integrated analysis of multi-omics datasets in the network analysis tool Cytoscape and in a stand-alone web application available at https://keypathwayminer.compbio.sdu.dk.

Drug research, therapy development, and other areas of pharmacology and medicine can benefit from simulations and optimization of mathematical models that contain a mathematical description of interactions between systems elements at the cellular, tissue, organ, body, and population level. This approach is the foundation of systems medicine and precision medicine. Here, simulated experiments are performed with computers (in silico) first, and they are then replicated through lab experiments (in vivo or in vitro) or clinical studies. In turn, these experiments and studies can be used to validate or improve the models. This iterative loop of dry and wet lab work is successful when biomedical researchers tightly collaborate with data scientists and modelers. From an educational point of view, the interdisciplinary research in systems biology can be sustained most effectively when specialists have been trained to have both a strong background in the disciplines of biology or modeling and strong communication skills, which make them able to communicate with other specialists. This overview addresses possible interdisciplinary communication gaps. Focusing our attention on biomedical researchers, we describe the reasons for using modeling and ways to collaborate with modelers, including their needs for specific biological expertise and data. This review includes an introduction to the principles of several widely used mechanistic modeling methods, focusing on their areas of applicability as well as their limitations. A potential complementary role of machine-learning methods in the development of mechanistic models is also discussed. The descriptions of the methods also include links to corresponding modeling software tools as well as practical examples of their application. Finally, we also explicitly address different aspects of multiscale modeling approaches that allow a more complete and holistic perspective of the human body

Artificial intelligence (AI) has been successfully applied in numerous scientific domains. In biomedicine, AI has already shown tremendous potential, e.g. in the interpretation of next-generation sequencing data and in the design of clinical decision support systems. However, training an AI model on sensitive data raises concerns about the privacy of individual participants. For example, summary statistics of a genome-wide association study can be used to determine the presence or absence of an individual in a given dataset. This considerable privacy risk has led to restrictions in accessing genomic and other biomedical data, which is detrimental for collaborative research and impedes scientific progress. Hence, there has been a substantial effort to develop AI methods that can learn from sensitive data while protecting individuals' privacy. This paper provides a structured overview of recent advances in privacy-preserving AI techniques in biomedicine. It places the most important state-of-the-art approaches within a unified taxonomy and discusses their strengths, limitations, and open problems. As the most promising direction, we suggest combining federated machine learning as a more scalable approach with other additional privacy preserving techniques. This would allow to merge the advantages to provide privacy guarantees in a distributed way for biomedical applications. Nonetheless, more research is necessary as hybrid approaches pose new challenges such as additional network or computation overhead., Comment: 17 pages, 3 figures, 3 tables. Methods of Information in Medicine (2022)

Federated learning has faced performance and network communication challenges, especially in the environments where the data is not independent and identically distributed (IID) across the clients. To address the former challenge, we introduce the federated-centralized concordance property and show that the federated single-mini-batch training approach can achieve comparable performance as the corresponding centralized training in the Non-IID environments. To deal with the latter, we present the federated multi-mini-batch approach and illustrate that it can establish a trade-off between the performance and communication efficiency and outperforms federated averaging in the Non-IID settings.

Hepatic lipid deposition and inflammation represent risk factors for hepatocellular carcinoma (HCC). The mRNA-binding protein tristetraprolin (TTP, gene name ZFP36) has been suggested as a tumor suppressor in several malignancies, but it increases insulin resistance. The aim of this study was to elucidate the role of TTP in hepatocarcinogenesis and HCC progression. Employing liver-specific TTP-knockout (lsTtp-KO) mice in the diethylnitrosamine (DEN) hepatocarcinogenesis model, we observed a significantly reduced tumor burden compared to wild-type animals. Upon short-term DEN treatment, modelling early inflammatory processes in hepatocarcinogenesis, lsTtp-KO mice exhibited a reduced monocyte/macrophage ratio as compared to wild-type mice. While short-term DEN strongly induced an abundance of saturated and poly-unsaturated hepatic fatty acids, lsTtp-KO mice did not show these changes. These findings suggested anti-carcinogenic actions of TTP deletion due to effects on inflammation and metabolism. Interestingly, though, investigating effects of TTP on different hallmarks of cancer suggested tumor-suppressing actions: TTP inhibited proliferation, attenuated migration, and slightly increased chemosensitivity. In line with a tumor-suppressing activity, we observed a reduced expression of several oncogenes in TTP-overexpressing cells. Accordingly, ZFP36 expression was downregulated in tumor tissues in three large human data sets. Taken together, this study suggests that hepatocytic TTP promotes hepatocarcinogenesis, while it shows tumor-suppressive actions during hepatic tumor progression.

Background/Aims: Hepatocellular carcinoma (HCC) represents the second most common cause of cancer-related deaths worldwide, not least due to its high chemoresistance. The long non-coding RNA nuclear paraspeckle assembly transcript 1 (NEAT1), localised in nuclear paraspeckles, has been shown to enhance chemoresistance in several cancer types. Since data on NEAT1 in HCC chemosensitivity are completely lacking and chemoresistance is linked to poor prognosis, we aimed to study NEAT1 expression in HCC chemoresistance and its link to HCC prognosis. Methods: NEAT1 expression was determined in either sensitive, or sorafenib, or doxorubicin resistant HepG2, PLC/PRF/5, and Huh7 cells by qPCR. Paraspeckles were detected by immunostaining of paraspeckle component 1 (PSPC1) in cell culture and in a cohort of HCC patients. PSPC1 expression was correlated with clinical data. The expression of transcript variants of NEAT1 and transcripts encoding the paraspeckle-associated proteins was analysed in the TCGA liver cancer data set. Results: NEAT1 was overexpressed in all three sorafenib and doxorubicin resistant cell lines. Paraspeckles were present in all chemoresistant cells, whereas no signal was detected in the sensitive cells. Expression of NEAT1 transcripts as well as transcripts encoding PSPC1, NONO, and RBM14 was increased in tumour tissue. Expression of PSPC1, NONO, and RBM14 transcripts was significantly associated with poor survival, whereas NEAT1 expression was not. Immunohistochemical analysis revealed that nuclear and cytoplasmic PSPC1-positivity was significantly associated with shorter overall survival of HCC patients. Conclusion: Our data show an induction of NEAT1 in HCC chemoresistance and a high correlation of transcripts encoding paraspeckle-associated proteins with poor survival in HCC. Therefore, NEAT1, PSPC1, NONO, and RBM14 might be promising targets for novel HCC therapies, and the paraspeckle-associated proteins might be clinical markers and predictors for p

Motivation: International consortia such as the Genotype-Tissue Expression (GTEx) project, The Cancer Genome Atlas (TCGA) or the International Human Epigenetics Consortium (IHEC) have produced a wealth of genomic datasets with the goal of advancing our understanding of cell differentiation and disease mechanisms. However, utilizing all of these data effectively through integrative analysis is hampered by batch effects, large cell type heterogeneity and low replicate numbers. To study if batch effects across datasets can be observed and adjusted for, we analyze RNA-seq data of 215 samples from ENCODE, Roadmap, BLUEPRINT and DEEP as well as 1336 samples from GTEx and TCGA. While batch effects are a considerable issue, it is non-trivial to determine if batch adjustment leads to an improvement in data quality, especially in cases of low replicate numbers. Results: We present a novel method for assessing the performance of batch effect adjustment methods on heterogeneous data. Our method borrows information from the Cell Ontology to establish if batch adjustment leads to a better agreement between observed pairwise similarity and similarity of cell types inferred from the ontology. A comparison of state-of-the art batch effect adjustment methods suggests that batch effects in heterogeneous datasets with low replicate numbers cannot be adequately adjusted. Better methods need to be developed, which can be assessed objectively in the framework presented here.

Gene expression profiles have been extensively discussed as an aid to guide the therapy by predicting disease outcome for the patients suffering from complex diseases, such as cancer. However, prediction models built upon single-gene (SG) features show poor stability and performance on independent datasets. Attempts to mitigate these drawbacks have led to the development of network-based approaches that integrate pathway information to produce meta-gene (MG) features. Also, MG approaches have only dealt with the two-class problem of good versus poor outcome prediction. Stratifying patients based on their molecular subtypes can provide a detailed view of the disease and lead to more personalized therapies. We propose and discuss a novel MG approach based on de novo pathways, which for the first time have been used as features in a multi-class setting to predict cancer subtypes. Comprehensive evaluation in a large cohort of breast cancer samples from The Cancer Genome Atlas (TCGA) revealed that MGs are considerably more stable than SG models, while also providing valuable insight into the cancer hallmarks that drive them. In addition, when tested on an independent benchmark non-TCGA dataset, MG features consistently outperformed SG models. We provide an easy-to-use web service at http://pathclass.compbio.sdu.dk where users can upload their own gene expression datasets from breast cancer studies and obtain the subtype predictions from all the classifiers.

Gene expression profiles have been extensively discussed as an aid to guide the therapy by predicting disease outcome for the patients suffering from complex diseases, such as cancer. However, prediction models built upon single-gene (SG) features show poor stability and performance on independent datasets. Attempts to mitigate these drawbacks have led to the development of network-based approaches that integrate pathway information to produce meta-gene (MG) features. Also, MG approaches have only dealt with the two-class problem of good versus poor outcome prediction. Stratifying patients based on their molecular subtypes can provide a detailed view of the disease and lead to more personalized therapies. We propose and discuss a novel MG approach based on de novo pathways, which for the first time have been used as features in a multi-class setting to predict cancer subtypes. Comprehensive evaluation in a large cohort of breast cancer samples from The Cancer Genome Atlas (TCGA) revealed that MGs are considerably more stable than SG models, while also providing valuable insight into the cancer hallmarks that drive them. In addition, when tested on an independent benchmark non-TCGA dataset, MG features consistently outperformed SG models. We provide an easy-to-use web service at http://pathclass.compbio.sdu.dk where users can upload their own gene expression datasets from breast cancer studies and obtain the subtype predictions from all the classifiers.