Scoring personalized molecular portraits identify Systemic Lupus Erythematosus subtypes and predict individualized drug responses, symptomatology and disease progression.

OBJECTIVES: Systemic Lupus Erythematosus is a complex autoimmune disease that leads to significant worsening of quality of life and mortality. Flares appear unpredictably during the disease course and therapies used are often only partially effective. These challenges are mainly due to the molecular heterogeneity of the disease, and in this context, personalized medicine-based approaches offer major promise. With this work we intended to advance in that direction by developing MyPROSLE, an omic-based analytical workflow for measuring the molecular portrait of individual patients to support clinicians in their therapeutic decisions. METHODS: Immunological gene-modules were used to represent the transcriptome of the patients. A dysregulation score for each gene-module was calculated at the patient level based on averaged z-scores. Almost 6100 Lupus and 750 healthy samples were used to analyze the association among dysregulation scores, clinical manifestations, prognosis, flare and remission events and response to Tabalumab. Machine learning-based classification models were built to predict around 100 different clinical parameters based on personalized dysregulation scores. RESULTS: MyPROSLE allows to molecularly summarize patients in 206 gene-modules, clustered into nine main lupus signatures. The combination of these modules revealed highly differentiated pathological mechanisms. We found that the dysregulation of certain gene-modules is strongly associated with specific clinical manifestations, the occurrence of relapses or the presence of long-term remission and drug response. Therefore, MyPROSLE may be used to accurately predict these clinical outcomes. CONCLUSIONS: MyPROSLE (https://myprosle.genyo.es) allows molecular characterization of individual Lupus patients and it extracts key molecular information to support more precise therapeutic decisions.

Molecular characterisation of lupus low disease activity state (LLDAS) and DORIS remission by whole-blood transcriptome-based pathways in a pan-European systemic lupus erythematosus cohort.

OBJECTIVES: To unveil biological milieus underlying low disease activity (LDA) and remission versus active systemic lupus erythematosus (SLE). METHODS: We determined differentially expressed pathways (DEPs) in SLE patients from the PRECISESADS project (NTC02890121) stratified into patients fulfilling and not fulfilling the criteria of (1) Lupus LDA State (LLDAS), (2) Definitions of Remission in SLE remission, and (3) LLDAS exclusive of remission. RESULTS: We analysed data from 321 patients; 40.8% were in LLDAS, and 17.4% in DORIS remission. After exclusion of patients in remission, 28.3% were in LLDAS. Overall, 604 pathways differed significantly in LLDAS versus non-LLDAS patients with an false-discovery rate-corrected p (q)<0.05 and a robust effect size (dr)≥0.36. Accordingly, 288 pathways differed significantly between DORIS remitters and non-remitters (q<0.05 and dr≥0.36). DEPs yielded distinct molecular clusters characterised by differential serological, musculoskeletal, and renal activity. Analysis of partially overlapping samples showed no DEPs between LLDAS and DORIS remission. Drug repurposing potentiality for treating SLE was unveiled, as were important pathways underlying active SLE whose modulation could aid attainment of LLDAS/remission, including toll-like receptor (TLR) cascades, Bruton tyrosine kinase (BTK) activity, the cytotoxic T lymphocyte antigen 4 (CTLA-4)-related inhibitory signalling, and the nucleotide-binding oligomerization domain leucine-rich repeat-containing protein 3 (NLRP3) inflammasome pathway. CONCLUSIONS: We demonstrated for the first time molecular signalling pathways distinguishing LLDAS/remission from active SLE. LLDAS/remission was associated with reversal of biological processes related to SLE pathogenesis and specific clinical manifestations. DEP clustering by remission better grouped patients compared with LLDAS, substantiating remission as the ultimate treatment goal in SLE; however, the lack of substantial pathway differentiation between the two states justifies LLDAS as an acceptable goal from a biological perspective.

Continental-scale genomic analysis suggests shared post-admixture adaptation in the Americas.

American populations are one of the most interesting examples of recently admixed groups, where ancestral components from three major continental human groups (Africans, Eurasians and Native Americans) have admixed within the last 15 generations. Recently, several genetic surveys focusing on thousands of individuals shed light on the geography, chronology and relevance of these events. However, even though gene flow could drive adaptive evolution, it is unclear whether and how natural selection acted on the resulting genetic variation in the Americas. In this study, we analysed the patterns of local ancestry of genomic fragments in genome-wide data for ~ 6000 admixed individuals from 10 American countries. In doing so, we identified regions characterized by a divergent ancestry profile (DAP), in which a significant over or under ancestral representation is evident. Our results highlighted a series of genomic regions with DAPs associated with immune system response and relevant medical traits, with the longest DAP region encompassing the human leukocyte antigen locus. Furthermore, we found that DAP regions are enriched in genes linked to cancer-related traits and autoimmune diseases. Then, analysing the biological impact of these regions, we showed that natural selection could have acted preferentially towards variants located in coding and non-coding transcripts and characterized by a high deleteriousness score. Taken together, our analyses suggest that shared patterns of post admixture adaptation occurred at a continental scale in the Americas, affecting more often functional and impactful genomic variants.

A survey of gene expression meta-analysis: methods and applications.

The increasing use of high-throughput gene expression quantification technologies over the last two decades and the fact that most of the published studies are stored in public databases has triggered an explosion of studies available through public repositories. All this information offers an invaluable resource for reuse to generate new knowledge and scientific findings. In this context, great interest has been focused on meta-analysis methods to integrate and jointly analyze different gene expression datasets. In this work, we describe the main steps in the gene expression meta-analysis, from data preparation to the state-of-the art statistical methods. We also analyze the main types of applications and problems that can be approached in gene expression meta-analysis studies and provide a comparative overview of the available software and bioinformatics tools. Moreover, a practical guide for choosing the most appropriate method in each case is also provided.

Distinct gene dysregulation patterns herald precision medicine potentiality in systemic lupus erythematosus.

OBJECTIVES: We aimed at investigating the whole-blood transcriptome, expression quantitative trait loci (eQTLs), and levels of selected serological markers in patients with SLE versus healthy controls (HC) to gain insight into pathogenesis and identify drug targets. METHODS: We analyzed differentially expressed genes (DEGs) and dysregulated gene modules in a cohort of 350 SLE patients and 497 HC from the European PRECISESADS project (NTC02890121), split into a discovery (60%) and a replication (40%) set. Replicated DEGs qualified for eQTL, pathway enrichment, regulatory network, and druggability analysis. For validation purposes, a separate gene module analysis was performed in an independent cohort (GSE88887). RESULTS: Analysis of 521 replicated DEGs identified multiple enriched interferon signaling pathways through Reactome. Gene module analysis yielded 18 replicated gene modules in SLE patients, including 11 gene modules that were validated in GSE88887. Three distinct gene module clusters were defined i.e., "interferon/plasma cells", "inflammation", and "lymphocyte signaling". Predominant downregulation of the lymphocyte signaling cluster denoted renal activity. By contrast, upregulation of interferon-related genes indicated hematological activity and vasculitis. Druggability analysis revealed several potential drugs interfering with dysregulated genes within the "interferon" and "PLK1 signaling events" modules. STAT1 was identified as the chief regulator in the most enriched signaling molecule network. Drugs annotated to 15 DEGs associated with cis-eQTLs included bortezomib for its ability to modulate CTSL activity. Belimumab was annotated to TNFSF13B (BAFF) and daratumumab was annotated to CD38 among the remaining replicated DEGs. CONCLUSIONS: Modulation of interferon, STAT1, PLK1, B and plasma cell signatures showed promise as viable approaches to treat SLE, pointing to their importance in SLE pathogenesis.

PeacoQC: Peak-based selection of high quality cytometry data.

In cytometry analysis, a large number of markers is measured for thousands or millions of cells, resulting in high-dimensional datasets. During the measurement of these samples, erroneous events can occur such as clogs, speed changes, slow uptake of the sample etc., which can influence the downstream analysis and can even lead to false discoveries. As these issues can be difficult to detect manually, an automated approach is recommended. In order to filter these erroneous events out, we created a novel quality control algorithm, Peak Extraction And Cleaning Oriented Quality Control (PeacoQC), that allows for automated cleaning of cytometry data. The algorithm will determine density peaks per channel on which it will remove low quality events based on their position in the isolation tree and on their mean absolute deviation distance to these density peaks. To evaluate PeacoQC's cleaning capability, it was compared to three other existing quality control algorithms (flowAI, flowClean and flowCut) on a wide variety of datasets. In comparison to the other algorithms, PeacoQC was able to filter out all different types of anomalies in flow, mass and spectral cytometry data, while the other methods struggled with at least one type. In the quantitative comparison, PeacoQC obtained the highest median balanced accuracy and a similar running time compared to the other algorithms while having a better scalability for large files. To ensure that the parameters chosen in the PeacoQC algorithm are robust, the cleaning tool was run on 16 public datasets. After inspection, only one sample was found where the parameters should be further optimized. The other 15 datasets were analyzed correctly indicating a robust parameter choice. Overall, we present a fast and accurate quality control algorithm that outperforms existing tools and ensures high-quality data that can be used for further downstream analysis. An R implementation is available.

A comprehensive database for integrated analysis of omics data in autoimmune diseases.

BACKGROUND: Autoimmune diseases are heterogeneous pathologies with difficult diagnosis and few therapeutic options. In the last decade, several omics studies have provided significant insights into the molecular mechanisms of these diseases. Nevertheless, data from different cohorts and pathologies are stored independently in public repositories and a unified resource is imperative to assist researchers in this field. RESULTS: Here, we present Autoimmune Diseases Explorer ( https://adex.genyo.es ), a database that integrates 82 curated transcriptomics and methylation studies covering 5609 samples for some of the most common autoimmune diseases. The database provides, in an easy-to-use environment, advanced data analysis and statistical methods for exploring omics datasets, including meta-analysis, differential expression or pathway analysis. CONCLUSIONS: This is the first omics database focused on autoimmune diseases. This resource incorporates homogeneously processed data to facilitate integrative analyses among studies.

Stabilization of Human Whole Blood Samples for Multicenter and Retrospective Immunophenotyping Studies.

Whole blood is often collected for large-scale immune monitoring studies to track changes in cell frequencies and responses using flow (FC) or mass cytometry (MC). In order to preserve sample composition and phenotype, blood samples should be analyzed within 24 h after bleeding, restricting the recruitment, analysis protocols, as well as biobanking. Herein, we have evaluated two whole blood preservation protocols that allow rapid sample processing and long-term stability. Two fixation buffers were used, Phosphoflow Fix and Lyse (BD) and Proteomic Stabilizer (PROT) to fix and freeze whole blood samples for up to 6 months. After analysis by an 8-plex panel by FC and a 26-plex panel by MC, manual gating of circulating leukocyte populations and cytokines was performed. Additionally, we tested the stability of a single sample over a 13-months period using 45 consecutive aliquots and a 34-plex panel by MC. We observed high correlation and low bias toward any cell population when comparing fresh and 6 months frozen blood with FC and MC. This correlation was confirmed by hierarchical clustering. Low coefficients of variation (CV) across studied time points indicate good sample preservation for up to 6 months. Cytokine detection stability was confirmed by low CVs, with some differences between fresh and fixed conditions. Thirteen months regular follow-up of PROT samples showed remarkable sample stability. Whole blood can be preserved for phenotyping and cytokine-response studies provided the careful selection of a compatible antibody panel. However, possible changes in cell morphology, differences in antibody affinity, and changes in cytokine-positive cell frequencies when compared to fresh blood should be considered. Our setting constitutes a valuable tool for multicentric and retrospective studies. © 2020 International Society for Advancement of Cytometry.

Precision medicine in autoimmune diseases: fact or fiction.

Much is said about precision medicine, but its real significance and potential are far from certain. Several studies in each of the autoimmune diseases have provided important insights into molecular pathways, but the use of molecular studies, particularly those looking into transcriptome pathways, has seldom approached the possibility of using the data for disease stratification and then for prediction, or for diagnosis. Only the type I IFN signature has been considered for therapeutic purposes, particularly in the case of SLE. This review provides an update on precision medicine, on what can be translated into clinical practice and on what single-cell molecular studies contribute to our knowledge of autoimmune diseases, focusing on a few examples. The main message is that we should try to move from precision medicine of established diseases to preventive medicine in order to predict the development of disease.

B Cells and Microbiota in Autoimmunity.

Trillions of microorganisms inhabit the mucosal membranes maintaining a symbiotic relationship with the host's immune system. B cells are key players in this relationship because activated and differentiated B cells produce secretory immunoglobulin A (sIgA), which binds commensals to preserve a healthy microbial ecosystem. Mounting evidence shows that changes in the function and composition of the gut microbiota are associated with several autoimmune diseases suggesting that an imbalanced or dysbiotic microbiota contributes to autoimmune inflammation. Bacteria within the gut mucosa may modulate autoimmune inflammation through different mechanisms from commensals ability to induce B-cell clones that cross-react with host antigens or through regulation of B-cell subsets' capacity to produce cytokines. Commensal signals in the gut instigate the differentiation of IL-10 producing B cells and IL-10 producing IgA+ plasma cells that recirculate and exert regulatory functions. While the origin of the dysbiosis in autoimmunity is unclear, compelling evidence shows that specific species have a remarkable influence in shaping the inflammatory immune response. Further insight is necessary to dissect the complex interaction between microorganisms, genes, and the immune system. In this review, we will discuss the bidirectional interaction between commensals and B-cell responses in the context of autoimmune inflammation.

Metabolic Disturbances in Urinary and Plasma Samples from Seven Different Systemic Autoimmune Diseases Detected by HPLC-ESI-QTOF-MS.

Systemic autoimmune diseases (SADs) are characterized by dysfunctioning of the immune system, which causes damage in several tissues and organs. Among these pathologies are systemic lupus erythematosus (SLE), systemic sclerosis or scleroderma, Sjögren's syndrome, rheumatoid arthritis, primary antiphospholipid syndrome (PAPS), mixed connective tissue disease (MCTD), and undifferentiated connective tissue disease (UCTD). Early diagnosis is difficult due to similarity in symptoms, signs, and clinical test results. Hence, our aim was to search for differentiating metabolites of these diseases in plasma and urine samples. We performed metabolomic profiling by liquid chromatography-mass spectrometry (LC-MS) of samples from 228 SADs patients and 55 healthy volunteers. Multivariate PLS models were applied to investigate classification accuracies and identify metabolites differentiating SADs and healthy controls. Furthermore, we specifically investigated UCTD against the other SADs. PLS models were able to classify most SADs vs healthy controls (area under the roc curve (AUC) > 0.7), with the exception of MCTD and PAPS. Differentiating metabolites consisted predominantly of unsaturated fatty acids, acylglycines, acylcarnitines, and amino acids. In accordance with the difficulties in defining UCTD, the UCTD metabolome did not differentiate well from the other SADs. However, most UCTD cases were classified as SLE, suggesting that metabolomics may provide a tool to reassess UCTD diagnosis into other conditions for more well-informed therapeutic strategies.

Genetic fine mapping of systemic lupus erythematosus MHC associations in Europeans and African Americans.

Genetic variation within the major histocompatibility complex (MHC) contributes substantial risk for systemic lupus erythematosus, but high gene density, extreme polymorphism and extensive linkage disequilibrium (LD) have made fine mapping challenging. To address the problem, we compared two association techniques in two ancestrally diverse populations, African Americans (AAs) and Europeans (EURs). We observed a greater number of Human Leucocyte Antigen (HLA) alleles in AA consistent with the elevated level of recombination in this population. In EUR we observed 50 different A-C-B-DRB1-DQA-DQB multilocus haplotype sequences per hundred individuals; in the AA sample, these multilocus haplotypes were twice as common compared to Europeans. We also observed a strong narrow class II signal in AA as opposed to the long-range LD observed in EUR that includes class I alleles. We performed a Bayesian model choice of the classical HLA alleles and a frequentist analysis that combined both single nucleotide polymorphisms (SNPs) and classical HLA alleles. Both analyses converged on a similar subset of risk HLA alleles: in EUR HLA- B*08:01 + B*18:01 + (DRB1*15:01 frequentist only) + DQA*01:02 + DQB*02:01 + DRB3*02 and in AA HLA-C*17:01 + B*08:01 + DRB1*15:03 + (DQA*01:02 frequentist only) + DQA*02:01 + DQA*05:01+ DQA*05:05 + DQB*03:19 + DQB*02:02. We observed two additional independent SNP associations in both populations: EUR rs146903072 and rs501480; AA rs389883 and rs114118665. The DR2 serotype was best explained by DRB1*15:03 + DQA*01:02 in AA and by DRB1*15:01 + DQA*01:02 in EUR. The DR3 serotype was best explained by DQA*05:01 in AA and by DQB*02:01 in EUR. Despite some differences in underlying HLA allele risk models in EUR and AA, SNP signals across the extended MHC showed remarkable similarity and significant concordance in direction of effect for risk-associated variants.

ImaGEO: integrative gene expression meta-analysis from GEO database.

SUMMARY: The Gene Expression Omnibus (GEO) database provides an invaluable resource of publicly available gene expression data that can be integrated and analyzed to derive new hypothesis and knowledge. In this context, gene expression meta-analysis (geMAs) is increasingly used in several fields to improve study reproducibility and discovering robust biomarkers. Nevertheless, integrating data is not straightforward without bioinformatics expertise. Here, we present ImaGEO, a web tool for geMAs that implements a complete and comprehensive meta-analysis workflow starting from GEO dataset identifiers. The application integrates GEO datasets, applies different meta-analysis techniques and provides functional analysis results in an easy-to-use environment. ImaGEO is a powerful and useful resource that allows researchers to integrate and perform meta-analysis of GEO datasets to lead robust findings for biomarker discovery studies. AVAILABILITY AND IMPLEMENTATION: ImaGEO is accessible at http://bioinfo.genyo.es/imageo/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Antiphospholipid autoantibody detection is important in all patients with systemic autoimmune diseases.

Antiphospholipid (aPL) autoantibodies are uncommon in systemic autoimmune diseases (SADs). However, the European PRECISESADS study provides the opportunity to better characterize this rare association. The study was composed of 1818 patients with SADs including 453 with systemic lupus erythematosus (SLE), 359 with rheumatoid arthritis (RA), 385 with systemic sclerosis (SSc), 367 with Sjögren's syndrome (SjS), 94 with mixed connective tissue disease (MCTD), and 160 with undifferentiated connective tissue disease (UCTD). Assays used for aPL determination include the lupus anticoagulant (LAC) analysis using the dilute Russell's viper venom time (dRVVT) assay plus anti-cardiolipin (aCL) and anti-aß2GPI autoantibodies of IgG and IgM isotype. Information regarding clinical and biological characteristics of SAD patients was available. Among SAD patients, the prevalence of aPL differs significantly between two groups: SLE (57.6%) and non-SLE SADs (13.7%, p < 10-4). Next, association between aPL plus thrombosis and miscarriage were observed in both SLE and non-SLE patients. Thrombosis was best predicted in SLE patients by dRVVT (OR = 6.1; IC95:3.5-10.3) and miscarriage by aCL±ß2GPI IgG (OR = 2.5; IC95:1.2-5.2); while in non-SLE SADs the best predictors were aCL±ß2GPI IgG for thrombosis (OR = 6.6; IC95:2.4-18.4) and aCL±ß2GPI IgM for miscarriage (OR = 2.9; IC95:1.2-6.8). In the case of multiple positivity of aPL, the risk for thrombosis and miscarriage was increased. Central nervous system involvement characterized the SLE patients, in contrast to pulmonary and skin fibrosis, valve lesions, hypertension, elevated creatinemia, C4 fraction reduction, platelet reduction and inflammation that characterized the non-SLE SAD patients. Anti-PL determination remains important in SADs patients and should not be restricted to only SLE patients.

An update on the genetics of systemic lupus erythematosus.

PURPOSE OF REVIEW: The aim of this study is to update on the most recent findings on the genetics of systemic lupus erythematosus. RECENT FINDINGS: Our overview focuses particularly on results from expression quantitative trait loci, exome sequencing, and rare variants and their impact on disease. SUMMARY: Systemic lupus erythematosus is a systemic autoimmune disease for which a significant number of susceptibility genes have been identified. Several genome-wide association studies were recently published in different populations that provide a better picture of the molecular mechanisms. It is becoming clear that the genetic architecture of lupus is quite well established but more information is required on the role of rare variants.

New Attempts to Define and Clarify Lupus.

PURPOSE OF REVIEW: The purpose is to discuss the advances that genetics and genomics have provided to better understand the molecular mechanisms behind SLE and how to solve its heterogeneity. I propose new ideas that can help us stratify lupus in order to find the best therapies for each patient, and the idea of substituting clinical diagnosis with molecular diagnosis according to their molecular patterns, an idea that may not only include lupus but also other diseases. RECENT FINDINGS: The study of rare mutations may provide insight into groups of lupus patients where type I interferon signature is important and help understand those with an atypical clinical presentation. Recent papers used longitudinal blood transcriptome data correlating with disease activity scores to stratify lupus into molecular clusters. The implication of neutrophils in the risk to develop nephritis was established, but also that neutrophils and lymphocytes may correlate with activity differentiating the mechanisms of flares and separating patients into clinically separate groups. The role of type I interferon signature is important; however, the stratification of SLE patients according to the genes and cellular compartments being modulated during disease activity may be even more important to define those patients who may benefit the most with new anti-type I IFN receptor therapies.

Epigenome-wide association studies for systemic autoimmune diseases: The road behind and the road ahead.

Epigenetics is known to be an important mechanism in the pathogenesis of autoimmune diseases. Epigenetic variations can act as integrators of environmental and genetic exposures and propagate activated states in immune cells. Studying epigenetic alterations by means of genome-wide approaches promises to unravel novel molecular mechanisms related to disease etiology, disease progression, clinical manifestations and treatment responses. This paper reviews what we have learned in the last five years from epigenome-wide studies for three systemic autoimmune diseases, namely systemic lupus erythematosus, primary Sjögren's syndrome, and rheumatoid arthritis. We examine the degree of epigenetic sharing between different diseases and the possible mediating role of epigenetic associations in genetic and environmental risks. Finally, we also shed light into the use of epigenetic markers towards a better precision medicine regarding disease prediction, prevention and personalized treatment in systemic autoimmunity.

Lupus Risk Variant Increases pSTAT1 Binding and Decreases ETS1 Expression.

Genetic variants at chromosomal region 11q23.3, near the gene ETS1, have been associated with systemic lupus erythematosus (SLE), or lupus, in independent cohorts of Asian ancestry. Several recent studies have implicated ETS1 as a critical driver of immune cell function and differentiation, and mice deficient in ETS1 develop an SLE-like autoimmunity. We performed a fine-mapping study of 14,551 subjects from multi-ancestral cohorts by starting with genotyped variants and imputing to all common variants spanning ETS1. By constructing genetic models via frequentist and Bayesian association methods, we identified 16 variants that are statistically likely to be causal. We functionally assessed each of these variants on the basis of their likelihood of affecting transcription factor binding, miRNA binding, or chromatin state. Of the four variants that we experimentally examined, only rs6590330 differentially binds lysate from B cells. Using mass spectrometry, we found more binding of the transcription factor signal transducer and activator of transcription 1 (STAT1) to DNA near the risk allele of rs6590330 than near the non-risk allele. Immunoblot analysis and chromatin immunoprecipitation of pSTAT1 in B cells heterozygous for rs6590330 confirmed that the risk allele increased binding to the active form of STAT1. Analysis with expression quantitative trait loci indicated that the risk allele of rs6590330 is associated with decreased ETS1 expression in Han Chinese, but not other ancestral cohorts. We propose a model in which the risk allele of rs6590330 is associated with decreased ETS1 expression and increases SLE risk by enhancing the binding of pSTAT1.

Metagene projection characterizes GEN2.2 and CAL-1 as relevant human plasmacytoid dendritic cell models.

MOTIVATION: Plasmacytoid dendritic cells (pDC) play a major role in the regulation of adaptive and innate immunity. Human pDC are difficult to isolate from peripheral blood and do not survive in culture making the study of their biology challenging. Recently, two leukemic counterparts of pDC, CAL-1 and GEN2.2, have been proposed as representative models of human pDC. Nevertheless, their relationship with pDC has been established only by means of particular functional and phenotypic similarities. With the aim of characterizing GEN2.2 and CAL-1 in the context of the main circulating immune cell populations we have performed microarray gene expression profiling of GEN2.2 and carried out an integrated analysis using publicly available gene expression datasets of CAL-1 and the main circulating primary leukocyte lineages. RESULTS: Our results show that GEN2.2 and CAL-1 share common gene expression programs with primary pDC, clustering apart from the rest of circulating hematopoietic lineages. We have also identified common differentially expressed genes that can be relevant in pDC biology. In addition, we have revealed the common and differential pathways activated in primary pDC and cell lines upon CpG stimulatio. AVAILABILITY AND IMPLEMENTATION: R code and data are available in the supplementary material. CONTACT: pedro.carmona@genyo.es or concepcion.maranon@genyo.es. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.