Gene expression profiles and protein-protein interaction networks in THP-1 cells exposed to metal-based nanomaterials.

We analyzed gene expression in THP-1 cells exposed to metal-based nanomaterials (NMs) [TiO2 (NM-100), ZnO (NM-110), SiO2 (NM-200), Ag (NM-300 K)]. A functional enrichment analysis of the significant differentially expressed genes (DEGs) identified the key modulated biological processes and pathways. DEGs were used to construct protein-protein interaction networks. NM-110 and NM-300 K induced changes in the expression of genes involved in oxidative and genotoxic stress, immune response, alterations of cell cycle, detoxification of metal ions and regulation of redox-sensitive pathways. Both NMs shared a number of highly connected protein nodes (hubs) including CXCL8, ATF3, HMOX1, and IL1B. NM-200 induced limited transcriptional changes, mostly related to the immune response; however, several hubs (CXCL8, ATF3) were identical with NM-110 and NM-300 K. No effects of NM-100 were observed. Overall, soluble nanomaterials NM-110 and NM-300 K exerted a wide variety of toxic effects, while insoluble NM-200 induced immunotoxicity; NM-100 caused no detectable changes on the gene expression level.

Transcriptomic alterations in the olfactory bulb induced by exposure to air pollution: Identification of potential biomarkers and insights into olfactory system function.

This study evaluated how exposure to the ubiquitous air pollution component, ultrafine particles (UFPs), alters the olfactory bulb (OB) transcriptome. The study utilised a whole-body inhalation chamber to simulate real-life conditions and focused on UFPs due to their high translocation and deposition ability in OBs as well as their prevalence in ambient air. Female C57BL/6J mice were exposed to clean air or to freshly generated combustion derived UFPs for two weeks, after which OBs were dissected and mRNA transcripts were investigated using RNA sequencing analysis. For the first time, transcriptomics was applied to determine changes in mRNA expression levels occurring after subacute exposure to UFPs in the OBs. We found forty-five newly described mRNAs to be involved in air pollution-induced responses, including genes involved in odorant binding, synaptic regulation, and myelination signalling pathway, providing new gene candidates for future research. This study provides new insights for the environmental science and neuroscience fields and nominates future research directions.

Novel transcriptomic signatures associated with premature kidney allograft failure.

BACKGROUND: The power to predict kidney allograft outcomes based on non-invasive assays is limited. Assessment of operational tolerance (OT) patients allows us to identify transcriptomic signatures of true non-responders for construction of predictive models. METHODS: In this observational retrospective study, RNA sequencing of peripheral blood was used in a derivation cohort to identify a protective set of transcripts by comparing 15 OT patients (40% females), from the TOMOGRAM Study (NCT05124444), 14 chronic active antibody-mediated rejection (CABMR) and 23 stable graft function patients ≥15 years (STA). The selected differentially expressed transcripts between OT and CABMR were used in a validation cohort (n = 396) to predict 3-year kidney allograft loss at 3 time-points using RT-qPCR. FINDINGS: Archetypal analysis and classifier performance of RNA sequencing data showed that OT is clearly distinguishable from CABMR, but similar to STA. Based on significant transcripts from the validation cohort in univariable analysis, 2 multivariable Cox models were created. A 3-transcript (ADGRG3, ATG2A, and GNLY) model from POD 7 predicted graft loss with C-statistics (C) 0.727 (95% CI, 0.638-0.820). Another 3-transcript (IGHM, CD5, GNLY) model from M3 predicted graft loss with C 0.786 (95% CI, 0.785-0.865). Combining 3-transcripts models with eGFR at POD 7 and M3 improved C-statistics to 0.860 (95% CI, 0.778-0.944) and 0.868 (95% CI, 0.790-0.944), respectively. INTERPRETATION: Identification of transcripts distinguishing OT from CABMR allowed us to construct models predicting premature graft loss. Identified transcripts reflect mechanisms of injury/repair and alloimmune response when assessed at day 7 or with a loss of protective phenotype when assessed at month 3. FUNDING: Supported by the Ministry of Health of the Czech Republic under grant NV19-06-00031.

Improved recovery and annotation of genes in metagenomes through the prediction of fungal introns.

Metagenomics provides a tool to assess the functional potential of environmental and host-associated microbiomes based on the analysis of environmental DNA: assembly, gene prediction and annotation. While gene prediction is straightforward for most bacterial and archaeal taxa, it has limited applicability in the majority of eukaryotic organisms, including fungi that contain introns in gene coding sequences. As a consequence, eukaryotic genes are underrepresented in metagenomics datasets and our understanding of the contribution of fungi and other eukaryotes to microbiome functioning is limited. Here, we developed a machine intelligence-based algorithm that predicts fungal introns in environmental DNA with reasonable precision and used it to improve the annotation of environmental metagenomes. Intron removal increased the number of predicted genes by up to 9.1% and improved the annotation of several others. The proportion of newly predicted genes increased with the share of eukaryotic genes in the metagenome and-within fungal taxa-increased with the number of introns per gene. Our approach provides a tool named SVMmycointron for improved metagenome annotation, especially of microbiomes with a high proportion of eukaryotes. The scripts described in the paper are made publicly available and can be readily utilized by microbiome researchers analysing metagenomics data.

Expression of circular RNAs in myelodysplastic neoplasms and their association with mutations in the splicing factor gene SF3B1.

Mutations in the splicing factor 3b subunit 1 (SF3B1) gene are frequent in myelodysplastic neoplasms (MDS). Because the splicing process is involved in the production of circular RNAs (circRNAs), we investigated the impact of SF3B1 mutations on circRNA processing. Using RNA sequencing, we measured circRNA expression in CD34+ bone marrow MDS cells. We defined circRNAs deregulated in a heterogeneous group of MDS patients and described increased circRNA formation in higher-risk MDS. We showed that the presence of SF3B1 mutations did not affect the global production of circRNAs; however, deregulation of specific circRNAs was observed. Particularly, we demonstrated that strong upregulation of circRNAs processed from the zinc finger E-box binding homeobox 1 (ZEB1) transcription factor; this upregulation was exclusive to SF3B1-mutated patients and was not observed in those with mutations in other splicing factors or other recurrently mutated genes, or with other clinical variables. Furthermore, we focused on the most upregulated ZEB1-circRNA, hsa_circ_0000228, and, by its knockdown, we demonstrated that its expression is related to mitochondrial activity. Using microRNA analyses, we proposed miR-1248 as a direct target of hsa_circ_0000228. To conclude, we demonstrated that mutated SF3B1 leads to deregulation of ZEB1-circRNAs, potentially contributing to the defects in mitochondrial metabolism observed in SF3B1-mutated MDS.

circGPA: circRNA functional annotation based on probability-generating functions.

Recent research has already shown that circular RNAs (circRNAs) are functional in gene expression regulation and potentially related to diseases. Due to their stability, circRNAs can also be used as biomarkers for diagnosis. However, the function of most circRNAs remains unknown, and it is expensive and time-consuming to discover it through biological experiments. In this paper, we predict circRNA annotations from the knowledge of their interaction with miRNAs and subsequent miRNA-mRNA interactions. First, we construct an interaction network for a target circRNA and secondly spread the information from the network nodes with the known function to the root circRNA node. This idea itself is not new; our main contribution lies in proposing an efficient and exact deterministic procedure based on the principle of probability-generating functions to calculate the p-value of association test between a circRNA and an annotation term. We show that our publicly available algorithm is both more effective and efficient than the commonly used Monte-Carlo sampling approach that may suffer from difficult quantification of sampling convergence and subsequent sampling inefficiency. We experimentally demonstrate that the new approach is two orders of magnitude faster than the Monte-Carlo sampling, which makes summary annotation of large circRNA files feasible; this includes their reannotation after periodical interaction network updates, for example. We provide a summary annotation of a current circRNA database as one of our outputs. The proposed algorithm could be generalized towards other types of RNA in way that is straightforward.

RUNX1 mutations contribute to the progression of MDS due to disruption of antitumor cellular defense: a study on patients with lower-risk MDS.

Patients with lower-risk myelodysplastic syndromes (LR-MDS) have a generally favorable prognosis; however, a small proportion of cases progress rapidly. This study aimed to define molecular biomarkers predictive of LR-MDS progression and to uncover cellular pathways contributing to malignant transformation. The mutational landscape was analyzed in 214 LR-MDS patients, and at least one mutation was detected in 137 patients (64%). Mutated RUNX1 was identified as the main molecular predictor of rapid progression by statistics and machine learning. To study the effect of mutated RUNX1 on pathway regulation, the expression profiles of CD34 + cells from LR-MDS patients with RUNX1 mutations were compared to those from patients without RUNX1 mutations. The data suggest that RUNX1-unmutated LR-MDS cells are protected by DNA damage response (DDR) mechanisms and cellular senescence as an antitumor cellular barrier, while RUNX1 mutations may be one of the triggers of malignant transformation. Dysregulated DDR and cellular senescence were also observed at the functional level by detecting γH2AX expression and ß-galactosidase activity. Notably, the expression profiles of RUNX1-mutated LR-MDS resembled those of higher-risk MDS at diagnosis. This study demonstrates that incorporating molecular data improves LR-MDS risk stratification and that mutated RUNX1 is associated with a suppressed defense against LR-MDS progression.

Semantic clustering analysis of E3-ubiquitin ligases in gastrointestinal tract defines genes ontology clusters with tissue expression patterns.

BACKGROUND: Ubiquitin ligases (Ub-ligases) are essential intracellular enzymes responsible for the regulation of proteome homeostasis, signaling pathway crosstalk, cell differentiation and stress responses. Individual Ub-ligases exhibit their unique functions based on the nature of their substrates. They create a complex regulatory network with alternative and feedback pathways to maintain cell homeostasis, being thus important players in many physiological and pathological conditions. However, the functional classification of Ub-ligases needs to be revised and extended. METHODS: In the current study, we used a novel semantic biclustering technique for expression profiling of Ub-ligases and ubiquitination-related genes in the murine gastrointestinal tract (GIT). We accommodated a general framework of the algorithm for finding tissue-specific gene expression clusters in GIT. In order to test identified clusters in a biological system, we used a model of epithelial regeneration. For this purpose, a dextran sulfate sodium (DSS) mouse model, following with in situ hybridization, was used to expose genes with possible compensatory features. To determine cell-type specific distribution of Ub-ligases and ubiquitination-related genes, principal component analysis (PCA) and Uniform Manifold Approximation and Projection technique (UMAP) were used to analyze the Tabula Muris scRNA-seq data of murine colon followed by comparison with our clustering results. RESULTS: Our established clustering protocol, that incorporates the semantic biclustering algorithm, demonstrated the potential to reveal interesting expression patterns. In this manner, we statistically defined gene clusters consisting of the same genes involved in distinct regulatory pathways vs distinct genes playing roles in functionally similar signaling pathways. This allowed us to uncover the potentially redundant features of GIT-specific Ub-ligases and ubiquitination-related genes. Testing the statistically obtained results on the mouse model showed that genes clustered to the same ontology group simultaneously alter their expression pattern after induced epithelial damage, illustrating their complementary role during tissue regeneration. CONCLUSIONS: An optimized semantic clustering protocol demonstrates the potential to reveal a readable and unique pattern in the expression profiling of GIT-specific Ub-ligases, exposing ontologically relevant gene clusters with potentially redundant features. This extends our knowledge of ontological relationships among Ub-ligases and ubiquitination-related genes, providing an alternative and more functional gene classification. In a similar way, semantic cluster analysis could be used for studding of other enzyme families, tissues and systems.

Oxidative Stress and Antioxidant Response in Populations of the Czech Republic Exposed to Various Levels of Environmental Pollutants.

We aimed to identify the variables that modify levels of oxidatively damaged DNA and lipid peroxidation in subjects living in diverse localities of the Czech Republic (a rural area, a metropolitan locality, and an industrial region). The sampling of a total of 126 policemen was conducted twice in two sampling seasons. Personal characteristics, concentrations of particulate matter of aerodynamic diameter <2.5 µm and benzo[a]pyrene in the ambient air, activities of antioxidant mechanisms (superoxide dismutase, catalase, glutathione peroxidase, and antioxidant capacity), levels of pro-inflammatory cytokines (TNF-α, IL-1ß, and IL-6), concentrations of persistent organic pollutants in blood plasma, and urinary levels of polycyclic aromatic hydrocarbon metabolites were investigated as parameters potentially affecting the markers of DNA oxidation (8-oxo-7,8-dihydro-2'-deoxyguanosine) and lipid peroxidation (15-F2t-isoprostane). The levels of oxidative stress markers mostly differed between the localities in the individual sampling seasons. Multivariate linear regression analysis revealed IL-6, a pro-inflammatory cytokine, as a factor with the most pronounced effects on oxidative stress parameters. The role of other variables, including environmental pollutants, was minor. In conclusion, our study showed that oxidative damage to macromolecules was affected by processes related to inflammation; however, we did not identify a specific environmental factor responsible for the pro-inflammatory response in the organism.

Noncoding RNAs and Their Response Predictive Value in Azacitidine-treated Patients With Myelodysplastic Syndrome and Acute Myeloid Leukemia With Myelodysplasia-related Changes.

BACKGROUND/AIM: Prediction of response to azacitidine (AZA) treatment is an important challenge in hematooncology. In addition to protein coding genes (PCGs), AZA efficiency is influenced by various noncoding RNAs (ncRNAs), including long ncRNAs (lncRNAs), circular RNAs (circRNAs), and transposable elements (TEs). MATERIALS AND METHODS: RNA sequencing was performed in patients with myelodysplastic syndromes or acute myeloid leukemia before AZA treatment to assess contribution of ncRNAs to AZA mechanisms and propose novel disease prediction biomarkers. RESULTS: Our analyses showed that lncRNAs had the strongest predictive potential. The combined set of the best predictors included 14 lncRNAs, and only four PCGs, one circRNA, and no TEs. Epigenetic regulation and recombinational repair were suggested as crucial for AZA response, and network modeling defined three deregulated lncRNAs (CTC-482H14.5, RP11-419K12.2, and RP11-736I24.4) associated with these processes. CONCLUSION: The expression of various ncRNAs can influence the effect of AZA and new ncRNA-based predictive biomarkers can be defined.

The impact of extractable organic matter from gasoline and alternative fuel emissions on bronchial cell models (BEAS-2B, MucilAir™).

Air pollution caused by road traffic has an unfavorable impact on the environment and also on human health. It has previously been shown, that complete gasoline emissions lead to toxic effects in cell models originating from human airways. Here we focused on extractable organic matter (EOM) from particulate matter, collected from gasoline emissions from fuels with different ethanol content. We performed cytotoxicity evaluation, quantification of mucin and extracellular reactive oxygen species (ROS) production, DNA breaks detection, and selected gene deregulation analysis, after one and five days of exposure of human bronchial epithelial model (BEAS-2B) and a 3D model of the human airway (MucilAir™). Our data suggest that the longer exposure had more pronounced effects on the parameters of cytotoxicity and mucin production, while the impacts on ROS generation and DNA integrity were limited. In both cell models the expression of CYP1A1 was induced, regardless of the exposure period or EOM tested. Several other genes, including FMO2, IL1A, or TNF, were deregulated depending on the exposure time. In conclusion, ethanol content in the fuels did not significantly impact the toxicity of EOM. Biological effects were mostly linked to xenobiotics metabolism and inflammatory response. BEAS-2B cells were more sensitive to the treatment.

Predictive Potential of Flow Cytometry Crossmatching in Deceased Donor Kidney Transplant Recipients Subjected to Peritransplant Desensitization.

Recipient sensitization is a major risk factor of antibody-mediated rejection (ABMR) and inferior graft survival. The predictive effect of solid-phase human leukocyte antigen antibody testing and flow cytometry crossmatch (FCXM) in the era of peritransplant desensitization remains poorly understood. This observational retrospective single-center study with 108 donor-specific antibody (DSA)-positive deceased donor kidney allograft recipients who had undergone peritransplant desensitization aimed to analyze variables affecting graft outcome. ABMR rates were highest among patients with positive pretransplant FCXM vs. FCXM-negative (76 vs. 18.7%, p < 0.001) and with donor-specific antibody mean fluorescence intensity (DSA MFI) > 5,000 vs. <5,000 (54.5 vs. 28%, p = 0.01) despite desensitization. In univariable Cox regression, FCXM positivity, retransplantation, recipient gender, immunodominant DSA MFI, DSA number, and peak panel reactive antibodies were found to be associated with ABMR occurrence. In multivariable Cox regression adjusted for desensitization treatment (AUC = 0.810), only FCXM positivity (HR = 4.6, p = 0.001) and DSA number (HR = 1.47, p = 0.039) remained significant. In conclusion, our data suggest that pretransplant FCXM and DSA number, but not DSA MFI, are independent predictors of ABMR in patients who received peritransplant desensitization.

Transcription profiles in BEAS-2B cells exposed to organic extracts from particulate emissions produced by a port-fuel injection vehicle, fueled with conventional fossil gasoline and gasoline-ethanol blend.

Emissions from road traffic are among the major contributors to air pollution worldwide and represent a serious environmental health risk. Although traffic-related pollution has been most commonly associated with diesel engines, increasing evidence suggests that gasoline engines also produce a considerable amount of potentially hazardous particulate matter (PM). The primary objective of this study was to compare the intrinsic toxic properties of the organic components of PM, generated by a conventional gasoline engine fueled with neat gasoline (E0), or gasoline-ethanol blend (15 % ethanol, v/v, E15). Our results showed that while E15 has produced, compared to gasoline and per kg of fuel, comparable particle mass (µg PM/kg fuel) and slightly more particles by number, the organic extract from the particulate matter produced by E15 contained a larger amount of harmful polycyclic aromatic hydrocarbons (PAHs), as determined by the chemical analysis. To examine the toxicity, we monitored genome-wide gene expression changes in human lung BEAS-2B cells, exposed for 4 h and 24 h to a subtoxic dose of each PM extract. After 4 h exposure, numerous dysregulated genes and processes such as oxidative stress, lipid and steroid metabolism, PPARα signaling and immune response, were found to be common for both extract treatments. On the other hand, 24 h exposure resulted in more distinctive gene expression patterns. Although we identified several common modulated processes indicating the metabolism of PAHs and activation of aryl hydrocarbon receptor (AhR), E15 specifically dysregulated a variety of other genes and pathways related to cancer promotion and progression. Overall, our findings suggest that the ethanol addition to gasoline changed the intrinsic properties of PM emissions and increased the PAH content in PM organic extract, thus contributing to a more extensive toxic response particularly after 24 h exposure in BEAS-2B cells.

On transformative adaptive activation functions in neural networks for gene expression inference.

Gene expression profiling was made more cost-effective by the NIH LINCS program that profiles only ∼1, 000 selected landmark genes and uses them to reconstruct the whole profile. The D-GEX method employs neural networks to infer the entire profile. However, the original D-GEX can be significantly improved. We propose a novel transformative adaptive activation function that improves the gene expression inference even further and which generalizes several existing adaptive activation functions. Our improved neural network achieves an average mean absolute error of 0.1340, which is a significant improvement over our reimplementation of the original D-GEX, which achieves an average mean absolute error of 0.1637. The proposed transformative adaptive function enables a significantly more accurate reconstruction of the full gene expression profiles with only a small increase in the complexity of the model and its training procedure compared to other methods.

A prolonged exposure of human lung carcinoma epithelial cells to benzo[a]pyrene induces p21-dependent epithelial-to-mesenchymal transition (EMT)-like phenotype.

Deciphering the role of the aryl hydrocarbon receptor (AhR) in lung cancer cells may help us to better understand the role of toxic AhR ligands in lung carcinogenesis, including cancer progression. We employed human lung carcinoma A549 cells to investigate their fate after continuous two-week exposure to model AhR agonists, genotoxic benzo[a]pyrene (BaP; 1 µM) and non-genotoxic 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD; 10 nM). While TCDD increased proliferative rate of A549 cells, exposure to BaP decreased cell proliferation and induced epithelial-to-mesenchymal transition (EMT)-like phenotype, which was associated with enhanced cell migration, invasion, and altered cell morphology. Although TCDD also suppressed expression of E-cadherin and activated some genes linked to EMT, it did not induce the EMT-like phenotype. The results of transcriptomic analysis, and the opposite effects of BaP and TCDD on cell proliferation, indicated that a delay in cell cycle progression, together with a slight increase of senescence (when coupled with AhR activation), favors the induction of EMT-like phenotype. The shift towards EMT-like phenotype observed after simultaneous treatment with TCDD and mitomycin C (an inhibitor of cell proliferation) confirmed the hypothesis. Since BaP decreased cell proliferative rate via induction of p21 expression, we generated the A549 cell model with reduced p21 expression and exposed it to BaP for two weeks. The p21 knockdown suppressed the BaP-mediated EMT-like phenotype in A549 cells, thus confirming that a delayed cell cycle progression, together with p21-dependent induction of senescence-related chemokine CCL2, may contribute to induction of EMT-like cell phenotype in lung cells exposed to genotoxic AhR ligands.

On tower and checkerboard neural network architectures for gene expression inference.

BACKGROUND: One possible approach how to economically facilitate gene expression profiling is to use the L1000 platform which measures the expression of ∼1,000 landmark genes and uses a computational method to infer the expression of another ∼10,000 genes. One such method for the gene expression inference is a D-GEX which employs neural networks. RESULTS: We propose two novel D-GEX architectures that significantly improve the quality of the inference by increasing the capacity of a network without any increase in the number of trained parameters. The architectures partition the network into individual towers. Our best proposed architecture - a checkerboard architecture with a skip connection and five towers - together with minor changes in the training protocol improves the average mean absolute error of the inference from 0.134 to 0.128. CONCLUSIONS: Our proposed approach increases the gene expression inference accuracy without increasing the number of weights of the model and thus without increasing the memory footprint of the model that is limiting its usage.

Molecular patterns of isolated tubulitis differ from tubulitis with interstitial inflammation in early indication biopsies of kidney allografts.

The Banff 2019 kidney allograft pathology update excluded isolated tubulitis without interstitial inflammation (ISO-T) from the category of borderline (suspicious) for acute T cell-mediated rejection due to its proposed benign clinical outcome. In this study, we explored the molecular assessment of ISO-T. ISO-T or interstitial inflammation with tubulitis (I + T) was diagnosed in indication biopsies within the first 14 postoperative days. The molecular phenotype of ISO-T was compared to I + T either by using RNA sequencing (n = 16) or by Molecular Microscope Diagnostic System (MMDx, n = 51). RNA sequencing showed lower expression of genes related to interferon-y (p = 1.5 *10-16), cytokine signaling (p = 2.1 *10-20) and inflammatory response (p = 1.0*10-13) in the ISO-T group than in I + T group. Transcripts with increased expression in the I + T group overlapped significantly with previously described pathogenesis-based transcript sets associated with cytotoxic and effector T cell transcripts, and with T cell-mediated rejection (TCMR). MMDx classified 25/32 (78%) ISO-T biopsies and 12/19 (63%) I + T biopsies as no-rejection. ISO-T had significantly lower MMDx scores for interstitial inflammation (p = 0.014), tubulitis (p = 0.035) and TCMR (p = 0.016) compared to I + T. Fewer molecular signals of inflammation in isolated tubulitis suggest that this is also a benign phenotype on a molecular level.

Ordinary Gasoline Emissions Induce a Toxic Response in Bronchial Cells Grown at Air-Liquid Interface.

Gasoline engine emissions have been classified as possibly carcinogenic to humans and represent a significant health risk. In this study, we used MucilAir™, a three-dimensional (3D) model of the human airway, and BEAS-2B, cells originating from the human bronchial epithelium, grown at the air-liquid interface to assess the toxicity of ordinary gasoline exhaust produced by a direct injection spark ignition engine. The transepithelial electrical resistance (TEER), production of mucin, and lactate dehydrogenase (LDH) and adenylate kinase (AK) activities were analyzed after one day and five days of exposure. The induction of double-stranded DNA breaks was measured by the detection of histone H2AX phosphorylation. Next-generation sequencing was used to analyze the modulation of expression of the relevant 370 genes. The exposure to gasoline emissions affected the integrity, as well as LDH and AK leakage in the 3D model, particularly after longer exposure periods. Mucin production was mostly decreased with the exception of longer BEAS-2B treatment, for which a significant increase was detected. DNA damage was detected after five days of exposure in the 3D model, but not in BEAS-2B cells. The expression of CYP1A1 and GSTA3 was modulated in MucilAir™ tissues after 5 days of treatment. In BEAS-2B cells, the expression of 39 mRNAs was affected after short exposure, most of them were upregulated. The five days of exposure modulated the expression of 11 genes in this cell line. In conclusion, the ordinary gasoline emissions induced a toxic response in MucilAir™. In BEAS-2B cells, the biological response was less pronounced, mostly limited to gene expression changes.

Finding semantic patterns in omics data using concept rule learning with an ontology-based refinement operator.

BACKGROUND: Identification of non-trivial and meaningful patterns in omics data is one of the most important biological tasks. The patterns help to better understand biological systems and interpret experimental outcomes. A well-established method serving to explain such biological data is Gene Set Enrichment Analysis. However, this type of analysis is restricted to a specific type of evaluation. Abstracting from details, the analyst provides a sorted list of genes and ontological annotations of the individual genes; the method outputs a subset of ontological terms enriched in the gene list. Here, in contrary to enrichment analysis, we introduce a new tool/framework that allows for the induction of more complex patterns of 2-dimensional binary omics data. This extension allows to discover and describe semantically coherent biclusters. RESULTS: We present a new rapid method called sem1R that reveals interpretable hidden rules in omics data. These rules capture semantic differences between two classes: a target class as a collection of positive examples and a non-target class containing negative examples. The method is inspired by the CN2 rule learner and introduces a new refinement operator that exploits prior knowledge in the form of ontologies. In our work this knowledge serves to create accurate and interpretable rules. The novel refinement operator uses two reduction procedures: Redundant Generalization and Redundant Non-potential, both of which help to dramatically prune the rule space and consequently, speed-up the entire process of rule induction in comparison with the traditional refinement operator as is presented in CN2. CONCLUSIONS: Efficiency and effectivity of the novel refinement operator were tested on three real different gene expression datasets. Concretely, the Dresden Ovary Dataset, DISC, and m2816 were employed. The experiments show that the ontology-based refinement operator speeds-up the pattern induction drastically. The algorithm is written in C++ and is published as an R package available at http://github.com/fmalinka/sem1r.