On the influence of several factors on pathway enrichment analysis

A workflow for human health hazard evaluation using transcriptomic data and Key Characteristics-based gene sets

Key characteristics (KCs) are properties of chemicals that are associated with different types of human health hazards. KCs are used for systematic reviews in support of hazard identification. Transcriptomic data are a rich source of mechanistic data and are frequently interpreted through "enriched" pathways/gene sets. Such analyses may be challenging to interpret in regulatory science because of redundancy among pathways, complex data analyses, and unclear relevance to hazard identification. We hypothesized that by cross-mapping pathways/gene sets and KCs, the interpretability of transcriptomic data can be improved. We summarized 72 published KCs across 7 hazard traits into 34 umbrella KC terms. Gene sets from Reactome and Kyoto Encyclopedia of Genes and Genomes (KEGG) were mapped to these, resulting in "KC gene sets." These sets exhibit minimal overlap and vary in the number of genes. Comparisons of the same KC gene sets mapped from Reactome and KEGG revealed low similarity, indicating complementarity. Performance of these KC gene sets was tested using publicly available transcriptomic datasets of chemicals with known organ-specific toxicity: benzene and 2,3,7,8-tetrachlorodibenzo-p-dioxin tested in mouse liver and drugs sunitinib and amoxicillin tested in human-induced pluripotent stem cell-derived cardiomyocytes. We found that KC terms related to the mechanisms affected by tested compounds were highly enriched, while the negative control (amoxicillin) showed limited enrichment with marginal significance. This study's impact is in presenting a computational approach based on KCs for the analysis of toxicogenomic data and facilitating transparent interpretation of these data in the process of chemical hazard identification.

Identification of biomarkers associated with inflammatory response in Parkinson's disease by bioinformatics and machine learning

Parkinson's disease (PD) is a common and debilitating neurodegenerative disorder. The inflammatory response is essential in the pathogenesis and progression of PD. The goal of this study is to combine bioinformatics and machine learning to screen for biomarker genes related to the inflammatory response in PD. First, differentially expressed genes associated with inflammatory response were screened, PPI networks were constructed and enriched for analysis. LASSO, SVM-RFE and Random Forest algorithms were used to screen biomarker genes. Then, ROC curves were drawn and PD risk predicting models were constructed on the basis of the biomarker genes. Finally, drug sensitivity analysis, mRNA-miRNA network construction and single-cell transcriptome data analysis were performed. The experimental results showed that we screened 31 differentially expressed genes related to inflammatory response. Signaling pathways such as cytokine activity were associated with these genes. Three biomarkers were identified using machine learning algorithms: IL18R1, NMUR1 and RELA. Seventeen co-associated miRNAs were identified by the mRNA-miRNA network as possible regulatory nodes in PD. Finally, these three biomarkers were found to be closely associated with T cells, Endothelial cells, excitatory neurons, inhibitory neurons, and other cells in single-cell transcriptomic analysis. In conclusion, IL18R1, NMUR1 and RELA could be potential therapeutic targets for PD in inflammatory response and new biomarkers for PD diagnosis.

Transcriptomic profiling revealed the regulatory pathways and key genes associated with cold tolerance in two eel gobies

Closely related species of the eel goby family (Gobiidae) have evolved divergent resistance to low temperatures, but the molecular mechanisms remain poorly understood. This study used a comparative transcriptomic approach to identify key pathways and genes associated with cold tolerance in two eel goby species. Expression profiles of the cold-tolerant O. lacepedii and the cold-sensitive O. rebecca in control (23 °C) and cold stress groups (15 °C and 11 °C) were analyzed. Differentially expressed genes closely linked to interspecific cold tolerance divergence were identified through transcriptome profiling and Venn diagram analysis. GO and KEGG enrichment analyses revealed that processes related to cellular homeostasis, the PPAR signaling pathway, cellular respiration, and oxidative phosphorylation were activated during the cold tolerance response of eel gobies. WGCNA analysis indicated that the hub genes related to thermogenesis and microtubular stability, specifically PPARGC1A and α-tubulin, may contribute to the high cold tolerance in O. lacepedii. These findings provide key clues for dissection of the molecular mechanisms behind the formation of cold tolerance in eel gobies.

Mitochondrial Dysfunction: Unraveling the Elusive Biology Behind Anti-TNF Response During Ulcerative Colitis

BACKGROUND

Recent studies hint at mitochondrial genes influencing UC patient response to anti-TNF treatment. We evaluated this hypothesis by following a targeted strategy to identify gene expression that captures the relationship between mitochondrial dysregulation and response to treatment. Our objective was to initially examine this relationship in colon samples and subsequently assess whether the resulting signal persists in the bloodstream.

METHODS

We analyzed the transcriptome of colon samples from an anti-TNF-treated murine model characterized by impaired mitochondrial activity and treatment resistance. We then transferred the findings that linked mitochondrial dysfunction and compromised treatment response to an anti-TNF-treated UC human cohort. We next matched differential expression in the blood using monocytes from the peripheral blood of controls and IBD patients, and we evaluated a classification process at baseline with whole blood samples from UC patients.

RESULTS

In human colon samples, the derived gene set from the murine model showed differential expression, primarily enriched metabolic pathways, and exhibited similar classification capacity as genes enriching inflammatory pathways. Moreover, the evaluation of the classification signal using blood samples from UC patients at baseline highlighted the involvement of mitochondrial homeostasis in treatment response.

CONCLUSIONS

Our results highlight the involvement of metabolic pathways and mitochondrial homeostasis in determining treatment response and their ability to provide promising classification signals with detection levels in both the colon and the bloodstream.

ASS1 is a hub gene and possible therapeutic target for regulating metabolic dysfunction-associated steatotic liver disease modulated by a carbohydrate-restricted diet

Metabolic dysfunction-associated steatotic liver disease (MASLD) is the leading cause of chronic liver disease globally. A low-carbohydrate diet (LCD) offers benefits to MASLD patients, albeit its exact mechanism is not fully understood. Using public liver transcriptome data from MASLD patients before/after LCD intervention, we applied differential expression analysis and machine learning to identify key genes. We initially identified 162 differentially expressed genes in the GSE107650 dataset. Secondly, employing two machine learning algorithms, we found that PRAMENP, LEAP2, LOC105379013, and argininosuccinate synthetase 1 (ASS1) are potential hub genes. Additionally, protein-protein interaction and single-cell RNA location analyses suggested that ASS1 was the most crucial hub gene. Then, L1000CDS2 analysis of the gene-expression signatures was employed for drug repurposing studies. CGP71683, an appetite suppressant, was predicted to improve MASLD and may mimic the ASS1 expression pattern induced by an LCD. Molecular dynamics confirmed spontaneous, stable CGP71683-ASS1 complex formation. Overall, this work based on analysis of machine learning algorithms, essential gene identification, and drug repurposing studies suggested that ASS1 is an essential gene in MASLD and CGP71683 is a potential drug candidate for treating MASLD by targeting ASS1 and mimicking the beneficial effects of an LCD. However, due to the inherent limitations of a purely computational approach, further experimental investigation is necessary to validate the anticipated results.

Plasma proteins and coronary atherosclerosis: A Mendelian randomization study

Coronary atherosclerosis (AS) is a complicated and severe chronic pathological process that contributes to the basis of various cardiovascular diseases, which causes a serious challenge to the global healthcare system. AS is the underlying physiopathological mechanism. Despite recent advances in the research of biomarkers and therapeutic targets for AS, there remain significant limitations in the current targeted therapies for AS. This study utilizes Mendelian randomization analysis to leverage genetic variations in order to identify plasma proteins with causal relationships to coronary AS. Utilizing publicly available genome-wide association study datasets, 4907 plasma proteins were assessed as exposure factors, with coronary AS being the outcome variable. The primary analytical method employed was the inverse variance weighted approach to ensure the robustness and accuracy of the causal relationships. In addition, to verify the reliability of the results, we employed several complementary methods, including the weighted median, Mendelian randomization-Egger, weighted mode, and simple mode approaches, to thoroughly assess the heterogeneity and pleiotropy of the findings. To ensure the robustness of the results and to exclude potential biases, a leave-one-out sensitivity analysis was performed. Twenty potential therapeutic targets were analyzed and identified (P < .05), combined with multiple bioinformatic analyses; among them, fibronectin 1 was identified as a key target. These findings may provide a new theoretical basis for future research in coronary AS drug development and therapeutic strategies.

Current approaches and outstanding challenges of functional annotation of metabolites: a comprehensive review

Metabolite profiling is a powerful approach for the clinical diagnosis of complex diseases, ranging from cardiometabolic diseases, cancer, and cognitive disorders to respiratory pathologies and conditions that involve dysregulated metabolism. Because of the importance of systems-level interpretation, many methods have been developed to identify biologically significant pathways using metabolomics data. In this review, we first describe a complete metabolomics workflow (sample preparation, data acquisition, pre-processing, downstream analysis, etc.). We then comprehensively review 24 approaches capable of performing functional analysis, including those that combine metabolomics data with other types of data to investigate the disease-relevant changes at multiple omics layers. We discuss their availability, implementation, capability for pre-processing and quality control, supported omics types, embedded databases, pathway analysis methodologies, and integration techniques. We also provide a rating and evaluation of each software, focusing on their key technique, software accessibility, documentation, and user-friendliness. Following our guideline, life scientists can easily choose a suitable method depending on method rating, available data, input format, and method category. More importantly, we highlight outstanding challenges and potential solutions that need to be addressed by future research. To further assist users in executing the reviewed methods, we provide wrappers of the software packages at https://github.com/tinnlab/metabolite-pathway-review-docker.

FORWARD: A Learning Framework for Logical Network Perturbations to Prioritize Targets for Drug Development

Despite advances in artificial intelligence (AI), target-based drug development remains a costly, complex and imprecise process. We introduce F.O.R.W.A.R.D [ Framework for Outcome-based Research and Drug Development ], a network-based target prioritization approach and test its utility in the challenging therapeutic area of Inflammatory Bowel Diseases (IBD), which is a chronic condition of multifactorial origin. F.O.R.W.A.R.D leverages real-world outcomes, using a machine-learning classifier trained on transcriptomic data from seven prospective randomized clinical trials involving four drugs. It establishes a molecular signature of remission as the therapeutic goal and computes, by integrating principles of network connectivity, the likelihood that a drug's action on its target(s) will induce the remission-associated genes. Benchmarking F.O.R.W.A.R.D against 210 completed clinical trials on 52 targets showed a perfect predictive accuracy of 100%. The success of F.O.R.W.A.R.D was achieved despite differences in targets, mechanisms, and trial designs. F.O.R.W.A.R.D-driven in-silico phase '0' trials revealed its potential to inform trial design, justify re-trialing failed drugs, and guide early terminations. With its extendable applications to other therapeutic areas and its iterative refinement with emerging trials, F.O.R.W.A.R.D holds the promise to transform drug discovery by generating foresight from hindsight and impacting research and development as well as human-in-the-loop clinical decision-making.

Assessment of Gene Set Enrichment Analysis using curated RNA-seq-based benchmarks

Pathway enrichment analysis is a ubiquitous computational biology method to interpret a list of genes (typically derived from the association of large-scale omics data with phenotypes of interest) in terms of higher-level, predefined gene sets that share biological function, chromosomal location, or other common features. Among many tools developed so far, Gene Set Enrichment Analysis (GSEA) stands out as one of the pioneering and most widely used methods. Although originally developed for microarray data, GSEA is nowadays extensively utilized for RNA-seq data analysis. Here, we quantitatively assessed the performance of a variety of GSEA modalities and provide guidance in the practical use of GSEA in RNA-seq experiments. We leveraged harmonized RNA-seq datasets available from The Cancer Genome Atlas (TCGA) in combination with large, curated pathway collections from the Molecular Signatures Database to obtain cancer-type-specific target pathway lists across multiple cancer types. We carried out a detailed analysis of GSEA performance using both gene-set and phenotype permutations combined with four different choices for the Kolmogorov-Smirnov enrichment statistic. Based on our benchmarks, we conclude that the classic/unweighted gene-set permutation approach offered comparable or better sensitivity-vs-specificity tradeoffs across cancer types compared with other, more complex and computationally intensive permutation methods. Finally, we analyzed other large cohorts for thyroid cancer and hepatocellular carcinoma. We utilized a new consensus metric, the Enrichment Evidence Score (EES), which showed a remarkable agreement between pathways identified in TCGA and those from other sources, despite differences in cancer etiology. This finding suggests an EES-based strategy to identify a core set of pathways that may be complemented by an expanded set of pathways for downstream exploratory analysis. This work fills the existing gap in current guidelines and benchmarks for the use of GSEA with RNA-seq data and provides a framework to enable detailed benchmarking of other RNA-seq-based pathway analysis tools.

PESSA: A web tool for pathway enrichment score-based survival analysis in cancer

The activation levels of biologically significant gene sets are emerging tumor molecular markers and play an irreplaceable role in the tumor research field; however, web-based tools for prognostic analyses using it as a tumor molecular marker remain scarce. We developed a web-based tool PESSA for survival analysis using gene set activation levels. All data analyses were implemented via R. Activation levels of The Molecular Signatures Database (MSigDB) gene sets were assessed using the single sample gene set enrichment analysis (ssGSEA) method based on data from the Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), The European Genome-phenome Archive (EGA) and supplementary tables of articles. PESSA was used to perform median and optimal cut-off dichotomous grouping of ssGSEA scores for each dataset, relying on the survival and survminer packages for survival analysis and visualisation. PESSA is an open-access web tool for visualizing the results of tumor prognostic analyses using gene set activation levels. A total of 238 datasets from the GEO, TCGA, EGA, and supplementary tables of articles; covering 51 cancer types and 13 survival outcome types; and 13,434 tumor-related gene sets are obtained from MSigDB for pre-grouping. Users can obtain the results, including Kaplan-Meier analyses based on the median and optimal cut-off values and accompanying visualization plots and the Cox regression analyses of dichotomous and continuous variables, by selecting the gene set markers of interest. PESSA (https://smuonco.shinyapps.io/PESSA/ OR http://robinl-lab.com/PESSA) is a large-scale web-based tumor survival analysis tool covering a large amount of data that creatively uses predefined gene set activation levels as molecular markers of tumors.

Proteome of spores from biological indicators in sterilization processes: Bacillus pumilus and Bacillus atrophaeus

Bacillus atrophaeus and Bacillus pumilus spores are widely used as biological indicators to assess the effectiveness of decontamination procedures. Spores are intricate, multi-layered cellular structures primarily composed of proteins, which significantly contribute to their extreme resistance. Therefore, conducting a comprehensive proteome analysis of spores is crucial to identify the specific proteins conferring spore resistance. Here, we employed a high-throughput shotgun proteomic approach to compare the spore proteomes of B. atrophaeus DSM675 and B. pumilus DSM492, identifying 1312 and 1264 proteins, respectively. While the overall number of proteins found in both strains is roughly equivalent, a closer examination of a subset of 54 spore-specific proteins revealed noteworthy distinctions. Among these 54 proteins, 23 were exclusively detected in one strain, while others were shared between both. Notably, of the 31 proteins detected in both strains, 10 exhibited differential abundance levels, including key coat layer morphogenetic proteins. The exploration of these 54 proteins, considering their presence, absence, and differential abundance, provides a unique molecular signature that may elucidate the differences in sensitivity/resistance profiles between the two strains.

Shatavari supplementation in postmenopausal women alters the skeletal muscle proteome and pathways involved in training adaptation

PURPOSE

Shatavari is an understudied, widely available herbal supplement. It contains steroidal saponins and phytoestrogens. We previously showed that six weeks of shatavari supplementation improved handgrip strength and increased markers of myosin contractile function. Mechanistic insights into shatavari's actions are limited. Therefore, we performed proteomics on vastus lateralis (VL) samples that remained from our original study.

METHODS

In a randomised double-blind trial, women (68.5 ± 6 years) ingested either placebo or shatavari (equivalent to 26,500 mg/d fresh weight) for six weeks. Tandem mass tag global proteomic analysis of VL samples was conducted (N = 7 shatavari, N = 5 placebo). Data were normalized to total peptides and scaled using a reference sample. Data were filtered using a 5% FDR. For each protein, the pre to post supplementation difference was expressed as log2 fold change. Welch's t tests with Benjamini-Hochberg corrections were performed for each protein. Pathway enrichment (PADOG, CAMERA) was interrogated in Reactome (v85).

RESULTS

No individual protein was significantly different between supplementation conditions. Both PADOG and CAMERA indicated that pathways related to (1) Integrin/MAPK signalling, (2) metabolism/insulin secretion; (3) cell proliferation/senescence/DNA repair/cell death; (4) haemostasis/platelets/fibrin; (5) signal transduction; (6) neutrophil degranulation and (7) chemical synapse function were significantly upregulated. CAMERA indicated pathways related to translation/amino acid metabolism, viral infection, and muscle contraction were downregulated.

CONCLUSION

Our analyses indicate that shatavari may support muscle adaptation responses to exercise. These data provide useful signposts for future investigation of shatavari's utility in conserving and enhancing musculoskeletal function in older age.

TRIAL REGISTRATION

NCT05025917 30/08/21, retrospectively registered.

Generalized reporter score-based enrichment analysis for omics data

Enrichment analysis contextualizes biological features in pathways to facilitate a systematic understanding of high-dimensional data and is widely used in biomedical research. The emerging reporter score-based analysis (RSA) method shows more promising sensitivity, as it relies on P-values instead of raw values of features. However, RSA cannot be directly applied to multi-group and longitudinal experimental designs and is often misused due to the lack of a proper tool. Here, we propose the Generalized Reporter Score-based Analysis (GRSA) method for multi-group and longitudinal omics data. A comparison with other popular enrichment analysis methods demonstrated that GRSA had increased sensitivity across multiple benchmark datasets. We applied GRSA to microbiome, transcriptome and metabolome data and discovered new biological insights in omics studies. Finally, we demonstrated the application of GRSA beyond functional enrichment using a taxonomy database. We implemented GRSA in an R package, ReporterScore, integrating with a powerful visualization module and updatable pathway databases, which is available on the Comprehensive R Archive Network (https://cran.r-project.org/web/packages/ReporterScore). We believe that the ReporterScore package will be a valuable asset for broad biomedical research fields.

Robustness evaluations of pathway activity inference methods on gene expression data

BACKGROUND

With the exponential growth of high-throughput technologies, multiple pathway analysis methods have been proposed to estimate pathway activities from gene expression profiles. These pathway activity inference methods can be divided into two main categories: non-Topology-Based (non-TB) and Pathway Topology-Based (PTB) methods. Although some review and survey articles discussed the topic from different aspects, there is a lack of systematic assessment and comparisons on the robustness of these approaches.

RESULTS

Thus, this study presents comprehensive robustness evaluations of seven widely used pathway activity inference methods using six cancer datasets based on two assessments. The first assessment seeks to investigate the robustness of pathway activity in pathway activity inference methods, while the second assessment aims to assess the robustness of risk-active pathways and genes predicted by these methods. The mean reproducibility power and total number of identified informative pathways and genes were evaluated. Based on the first assessment, the mean reproducibility power of pathway activity inference methods generally decreased as the number of pathway selections increased. Entropy-based Directed Random Walk (e-DRW) distinctly outperformed other methods in exhibiting the greatest reproducibility power across all cancer datasets. On the other hand, the second assessment shows that no methods provide satisfactory results across datasets.

CONCLUSION

However, PTB methods generally appear to perform better in producing greater reproducibility power and identifying potential cancer markers compared to non-TB methods.

PathIntegrate: Multivariate modelling approaches for pathway-based multi-omics data integration

As terabytes of multi-omics data are being generated, there is an ever-increasing need for methods facilitating the integration and interpretation of such data. Current multi-omics integration methods typically output lists, clusters, or subnetworks of molecules related to an outcome. Even with expert domain knowledge, discerning the biological processes involved is a time-consuming activity. Here we propose PathIntegrate, a method for integrating multi-omics datasets based on pathways, designed to exploit knowledge of biological systems and thus provide interpretable models for such studies. PathIntegrate employs single-sample pathway analysis to transform multi-omics datasets from the molecular to the pathway-level, and applies a predictive single-view or multi-view model to integrate the data. Model outputs include multi-omics pathways ranked by their contribution to the outcome prediction, the contribution of each omics layer, and the importance of each molecule in a pathway. Using semi-synthetic data we demonstrate the benefit of grouping molecules into pathways to detect signals in low signal-to-noise scenarios, as well as the ability of PathIntegrate to precisely identify important pathways at low effect sizes. Finally, using COPD and COVID-19 data we showcase how PathIntegrate enables convenient integration and interpretation of complex high-dimensional multi-omics datasets. The PathIntegrate Python package is available at https://github.com/cwieder/PathIntegrate.

A tiered testing strategy based on in vitro phenotypic and transcriptomic data for selecting representative petroleum UVCBs for toxicity evaluation in vivo

Hazard evaluation of substances of "unknown or variable composition, complex reaction products and biological materials" (UVCBs) remains a major challenge in regulatory science because their chemical composition is difficult to ascertain. Petroleum substances are representative UVCBs and human cell-based data have been previously used to substantiate their groupings for regulatory submissions. We hypothesized that a combination of phenotypic and transcriptomic data could be integrated to make decisions as to selection of group-representative worst-case petroleum UVCBs for subsequent toxicity evaluation in vivo. We used data obtained from 141 substances from 16 manufacturing categories previously tested in 6 human cell types (induced pluripotent stem cell [iPSC]-derived hepatocytes, cardiomyocytes, neurons, and endothelial cells, and MCF7 and A375 cell lines). Benchmark doses for gene-substance combinations were calculated, and both transcriptomic and phenotype-derived points of departure (PODs) were obtained. Correlation analysis and machine learning were used to assess associations between phenotypic and transcriptional PODs and to determine the most informative cell types and assays, thus representing a cost-effective integrated testing strategy. We found that 2 cell types-iPSC-derived-hepatocytes and -cardiomyocytes-contributed the most informative and protective PODs and may be used to inform selection of representative petroleum UVCBs for further toxicity evaluation in vivo. Overall, although the use of new approach methodologies to prioritize UVCBs has not been widely adopted, our study proposes a tiered testing strategy based on iPSC-derived hepatocytes and cardiomyocytes to inform selection of representative worst-case petroleum UVCBs from each manufacturing category for further toxicity evaluation in vivo.

From vision toward best practices: Evaluating in vitro transcriptomic points of departure for application in risk assessment using a uniform workflow

The growing number of chemicals in the current consumer and industrial markets presents a major challenge for regulatory programs faced with the need to assess the potential risks they pose to human and ecological health. The increasing demand for hazard and risk assessment of chemicals currently exceeds the capacity to produce the toxicity data necessary for regulatory decision making, and the applied data is commonly generated using traditional approaches with animal models that have limited context in terms of human relevance. This scenario provides the opportunity to implement novel, more efficient strategies for risk assessment purposes. This study aims to increase confidence in the implementation of new approach methods in a risk assessment context by using a parallel analysis to identify data gaps in current experimental designs, reveal the limitations of common approaches deriving transcriptomic points of departure, and demonstrate the strengths in using high-throughput transcriptomics (HTTr) to derive practical endpoints. A uniform workflow was applied across six curated gene expression datasets from concentration-response studies containing 117 diverse chemicals, three cell types, and a range of exposure durations, to determine tPODs based on gene expression profiles. After benchmark concentration modeling, a range of approaches was used to determine consistent and reliable tPODs. High-throughput toxicokinetics were employed to translate in vitro tPODs (µM) to human-relevant administered equivalent doses (AEDs, mg/kg-bw/day). The tPODs from most chemicals had AEDs that were lower (i.e., more conservative) than apical PODs in the US EPA CompTox chemical dashboard, suggesting in vitro tPODs would be protective of potential effects on human health. An assessment of multiple data points for single chemicals revealed that longer exposure duration and varied cell culture systems (e.g., 3D vs. 2D) lead to a decreased tPOD value that indicated increased chemical potency. Seven chemicals were flagged as outliers when comparing the ratio of tPOD to traditional POD, thus indicating they require further assessment to better understand their hazard potential. Our findings build confidence in the use of tPODs but also reveal data gaps that must be addressed prior to their adoption to support risk assessment applications.

On the correspondence between the transcriptomic response of a compound and its effects on its targets

Better understanding the transcriptomic response produced by a compound perturbing its targets can shed light on the underlying biological processes regulated by the compound. However, establishing the relationship between the induced transcriptomic response and the target of a compound is non-trivial, partly because targets are rarely differentially expressed. Therefore, connecting both modalities requires orthogonal information (e.g., pathway or functional information). Here, we present a comprehensive study aimed at exploring this relationship by leveraging thousands of transcriptomic experiments and target data for over 2000 compounds. Firstly, we confirm that compound-target information does not correlate as expected with the transcriptomic signatures induced by a compound. However, we reveal how the concordance between both modalities increases by connecting pathway and target information. Additionally, we investigate whether compounds that target the same proteins induce a similar transcriptomic response and conversely, whether compounds with similar transcriptomic responses share the same target proteins. While our findings suggest that this is generally not the case, we did observe that compounds with similar transcriptomic profiles are more likely to share at least one protein target and common therapeutic applications. Finally, we demonstrate how to exploit the relationship between both modalities for mechanism of action deconvolution by presenting a case scenario involving a few compound pairs with high similarity.

Interpreting omics data with pathway enrichment analysis

Pathway enrichment analysis is indispensable for interpreting omics datasets and generating hypotheses. However, the foundations of enrichment analysis remain elusive to many biologists. Here, we discuss best practices in interpreting different types of omics data using pathway enrichment analysis and highlight the importance of considering intrinsic features of various types of omics data. We further explain major components that influence the outcomes of a pathway enrichment analysis, including defining background sets and choosing reference annotation databases. To improve reproducibility, we describe how to standardize reporting methodological details in publications. This article aims to serve as a primer for biologists to leverage the wealth of omics resources and motivate bioinformatics tool developers to enhance the power of pathway enrichment analysis.

Comprehensive investigation of pathway enrichment methods for functional interpretation of LC-MS global metabolomics data

BACKGROUND

Global or untargeted metabolomics is widely used to comprehensively investigate metabolic profiles under various pathophysiological conditions such as inflammations, infections, responses to exposures or interactions with microbial communities. However, biological interpretation of global metabolomics data remains a daunting task. Recent years have seen growing applications of pathway enrichment analysis based on putative annotations of liquid chromatography coupled with mass spectrometry (LC-MS) peaks for functional interpretation of LC-MS-based global metabolomics data. However, due to intricate peak-metabolite and metabolite-pathway relationships, considerable variations are observed among results obtained using different approaches. There is an urgent need to benchmark these approaches to inform the best practices.

RESULTS

We have conducted a benchmark study of common peak annotation approaches and pathway enrichment methods in current metabolomics studies. Representative approaches, including three peak annotation methods and four enrichment methods, were selected and benchmarked under different scenarios. Based on the results, we have provided a set of recommendations regarding peak annotation, ranking metrics and feature selection. The overall better performance was obtained for the mummichog approach. We have observed that a ~30% annotation rate is sufficient to achieve high recall (~90% based on mummichog), and using semi-annotated data improves functional interpretation. Based on the current platforms and enrichment methods, we further propose an identifiability index to indicate the possibility of a pathway being reliably identified. Finally, we evaluated all methods using 11 COVID-19 and 8 inflammatory bowel diseases (IBD) global metabolomics datasets.

Ten quick tips for computational analysis of medical images

Medical imaging is a great asset for modern medicine, since it allows physicians to spatially interrogate a disease site, resulting in precise intervention for diagnosis and treatment, and to observe particular aspect of patients' conditions that otherwise would not be noticeable. Computational analysis of medical images, moreover, can allow the discovery of disease patterns and correlations among cohorts of patients with the same disease, thus suggesting common causes or providing useful information for better therapies and cures. Machine learning and deep learning applied to medical images, in particular, have produced new, unprecedented results that can pave the way to advanced frontiers of medical discoveries. While computational analysis of medical images has become easier, however, the possibility to make mistakes or generate inflated or misleading results has become easier, too, hindering reproducibility and deployment. In this article, we provide ten quick tips to perform computational analysis of medical images avoiding common mistakes and pitfalls that we noticed in multiple studies in the past. We believe our ten guidelines, if taken into practice, can help the computational-medical imaging community to perform better scientific research that eventually can have a positive impact on the lives of patients worldwide.

A brief survey of tools for genomic regions enrichment analysis

Functional enrichment analysis or pathway enrichment analysis (PEA) is a bioinformatics technique which identifies the most over-represented biological pathways in a list of genes compared to those that would be associated with them by chance. These biological functions are found on bioinformatics annotated databases such as The Gene Ontology or KEGG; the more abundant pathways are identified through statistical techniques such as Fisher’s exact test. All PEA tools require a list of genes as input. A few tools, however, read lists of genomic regions as input rather than lists of genes, and first associate these chromosome regions with their corresponding genes. These tools perform a procedure called genomic regions enrichment analysis, which can be useful for detecting the biological pathways related to a set of chromosome regions. In this brief survey, we analyze six tools for genomic regions enrichment analysis (BEHST, g:Profiler g:GOSt, GREAT, LOLA, Poly-Enrich, and ReactomePA), outlining and comparing their main features. Our comparison results indicate that the inclusion of data for regulatory elements, such as ChIP-seq, is common among these tools and could therefore improve the enrichment analysis results.

Nine quick tips for pathway enrichment analysis

Pathway enrichment analysis (PEA) is a computational biology method that identifies biological functions that are overrepresented in a group of genes more than would be expected by chance and ranks these functions by relevance. The relative abundance of genes pertinent to specific pathways is measured through statistical methods, and associated functional pathways are retrieved from online bioinformatics databases. In the last decade, along with the spread of the internet, higher availability of computational resources made PEA software tools easy to access and to use for bioinformatics practitioners worldwide. Although it became easier to use these tools, it also became easier to make mistakes that could generate inflated or misleading results, especially for beginners and inexperienced computational biologists. With this article, we propose nine quick tips to avoid common mistakes and to out a complete, sound, thorough PEA, which can produce relevant and robust results. We describe our nine guidelines in a simple way, so that they can be understood and used by anyone, including students and beginners. Some tips explain what to do before starting a PEA, others are suggestions of how to correctly generate meaningful results, and some final guidelines indicate some useful steps to properly interpret PEA results. Our nine tips can help users perform better pathway enrichment analyses and eventually contribute to a better understanding of current biology.