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In the recent years, the improvement of software and hardware performance has made biomolecular simulations a mature tool for the study of biological processes. Simulation length and the size and complexity of the analyzed systems make simulations both complementary and compatible with other bioinformatics disciplines. However, the characteristics of the software packages used for simulation have prevented the adoption of the technologies accepted in other bioinformatics fields like automated deployment systems, workflow orchestration, or the use of software containers. We present here a comprehensive exercise to bring biomolecular simulations to the “bioinformatics way of working”. The exercise has led to the development of the BioExcel Building Blocks (BioBB) library. BioBB’s are built as Python wrappers to provide an interoperable architecture. BioBB’s have been integrated in a chain of usual software management tools to generate data ontologies, documentation, installation packages, software containers and ways of integration with workflow managers, that make them usable in most computational environments. We thank The Barcelona Supercomputing Center for providing all the HPC resources to launch the test executions presented in this manuscript. Te project was supported by the following grants: BioExcel Center of Excellence (Horizon 2020 Framework Programme. Grants 675728, and 823830). Elixir-Excelerate (Horizon 2020 Framework Programme. Grant 676559). Spanish National Institute for Bioinformatics (Institute of Health Carlos III. Grants PT13/0001/0019, PT13/0001/0028, PT17/0009/0001). Spanish Government, Severo Ochoa Grant SEV2015-0493, TIN2015-65316-P, Generalitat de Catalunya. 2014-SGR-1051, 2017-SGR-1110.

The production of antibodies to anti-tumor necrosis factor alpha (TNF) agents is one of the main causes of treatment failure in Crohn’s disease (CD). To date, however, the contribution of genetics to anti-TNF immunogenicity in CD is still unknown. The objective of the present study was to identify genetic variation associated with anti-TNF immunogenicity in CD. We performed a two-stage genome-wide association study in a cohort of 96 and 123 adalimumab-treated patients, respectively. In the discovery stage, we identified a genome-wide significant association between the CD96 locus and the production of antibodies to anti-TNF treatment (P = 1.88e–09). This association was validated in the replication stage (P

Multiscale Genomics (MuG) Virtual Research Environment (MuGVRE) is a cloud-based computational infrastructure created to support the deployment of software tools addressing the various levels of analysis in 3D/4D genomics. Integrated tools tackle needs ranging from high computationally demanding applications (e.g. molecular dynamics simulations) to high-throughput data analysis applications (like the processing of next generation sequencing). The MuG Infrastructure is based on openNebula cloud systems implemented at the Institute for research in Biomedicine, and the Barcelona Supercomputing Center, and has specific interfaces for users and developers. Interoperability of the tools included in MuGVRE is maintained through a rich set of metadata allowing the system to associate tools and data in a transparent manner. Execution scheduling is based in a traditional queueing system to handle demand peaks in applications of fixed needs, and an elastic and multi-scale programming model (pyCOMPSs, controlled by the PMES scheduler), for complex workflows requiring distributed or multi-scale executions schemes. MuGVRE is available athttps://vre.multiscalegenomics.euand documentation and general information athttps://www.multiscalegenomics.eu. The infrastructure is open and freely accessible.

Objective Rheumatoid factor (RF) is a well-established diagnostic and prognostic biomarker in rheumatoid arthritis (RA). However, ∼20% of RA patients are negative for this anti-IgG antibody. To date, only variation at the HLA-DRB1 gene has been associated with the presence of RF. This study was undertaken to identify additional genetic variants associated with RF positivity. Methods A genome-wide association study (GWAS) for RF positivity was performed using an Illumina Quad610 genotyping platform. A total of 937 RF-positive and 323 RF-negative RA patients were genotyped for >550,000 single-nucleotide polymorphisms (SNPs). Association testing was performed using an allelic chi-square test implemented in Plink software. An independent cohort of 472 RF-positive and 190 RF-negative RA patients was used to validate the most significant findings. Results In the discovery stage, a SNP in the IRX1 locus on chromosome 5p15.3 (SNP rs1502644) showed a genome-wide significant association with RF positivity (P = 4.13 × 10(-8) , odds ratio [OR] 0.37 [95% confidence interval (95% CI) 0.26-0.53]). In the validation stage, the association of IRX1 with RF was replicated in an independent group of RA patients (P = 0.034, OR 0.58 [95% CI 0.35-0.97] and combined P = 1.14 × 10(-8) , OR 0.43 [95% CI 0.32-0.58]). Conclusion To our knowledge, this is the first GWAS of RF positivity in RA. Variation at the IRX1 locus on chromosome 5p15.3 is associated with the presence of RF. Our findings indicate that IRX1 and HLA-DRB1 are the strongest genetic factors for RF production in RA.

OBJECTIVE RA patients with serum ACPA have a strong and specific genetic background. The objective of the study was to identify new susceptibility genes for ACPA-positive RA using a genome-wide association approach. METHODS A total of 924 ACPA-positive RA patients with joint damage in hands and/or feet, and 1524 healthy controls were genotyped in 582 591 single-nucleotide polymorphisms (SNPs) in the discovery phase. In the validation phase, the most significant SNPs in the genome-wide association study representing new candidate loci for RA were tested in an independent cohort of 863 ACPA-positive patients with joint damage and 1152 healthy controls. All individuals from the discovery and validation cohorts were Caucasian and of Southern European ancestry. RESULTS In the discovery phase, 60 loci not previously associated with RA risk showed evidence for association at P < 5×10(-4) and were tested for replication in the validation cohort. A total of 12 loci were replicated at the nominal level (P < 0.05, same direction of effect as in the discovery phase). When combining the discovery and validation cohorts, an intronic SNP in the Solute Carrier family 8 gene (SLC8A3) was found to be associated with ACPA-positive RA at a genome-wide level of significance RA [odds ratio (95% CI): 1.42 (1.25, 1.6), Pcombined = 3.19×10(-8)]. CONCLUSIONS SLC8A3 was identified as a new risk locus for ACPA-positive RA. This study demonstrates the advantage of analysing relevant subsets of RA patients to identify new genetic risk variants.

We present Nucleosome Dynamics, a suite of programs integrated into a virtual research environment and created to define nucleosome architecture and dynamics from noisy experimental data. The package allows both the definition of nucleosome architectures and the detection of changes in nucleosomal organization due to changes in cellular conditions. Results are displayed in the context of genomic information thanks to different visualizers and browsers, allowing the user a holistic, multidimensional view of the genome/transcriptome. The package shows good performance for both locating equilibrium nucleosome architecture and nucleosome dynamics and provides abundant useful information in several test cases, where experimental data on nucleosome position (and for some cases expression level) have been collected for cells under different external conditions (cell cycle phase, yeast metabolic cycle progression, changes in nutrients or difference in MNase digestion level). Nucleosome Dynamics is a free software and is provided under several distribution models. M.O. is an ICREA (Institució Catalana de Recerca i Estudis Avancats) academia researcher; Spanish Ministry of Science [RTI2018-096704-B-100]; Catalan Government [2017-SGR-134]; Instituto de Salud Carlos III–Instituto Nacional de Bioinformática, the European Union's Horizon 2020 research and innovation program, and the Biomolecular and Bioinformatics Resources Platform [ISCIII PT 17/0009/0007 co-funded by the Fondo Europeo de Desarrollo Regional FEDER; Grants Elixir-Excelerate: 676559 and BioExcel2: 823830; ERC:812850; MuG-676566]; MINECO Severo Ochoa Award of Excellence from the Government of Spain (awarded to IRB Barcelona). Funding for open access charge: Spanish Ministry of Science [RTI2018-096704-B-100].

ObjectivePsoriatic arthritis (PsA) is a chronic inflammatory arthritis affecting up to 30% of patients with psoriasis (Ps). To date, most of the known risk loci for PsA are shared with Ps, and identifying disease-specific variation has proven very challenging. The objective of the present study was to identify genetic variation specific for PsA.MethodsWe performed a genome-wide association study in a cohort of 835 patients with PsA and 1558 controls from Spain. Genetic association was tested at the single marker level and at the pathway level. Meta-analysis was performed with a case–control cohort of 2847 individuals from North America. To confirm the specificity of the genetic associations with PsA, we tested the associated variation using a purely cutaneous psoriasis cohort (PsC, n=614) and a rheumatoid arthritis cohort (RA, n=1191). Using network and drug-repurposing analyses, we further investigated the potential of the PsA-specific associations to guide the development of new drugs in PsA.ResultsWe identified a new PsA risk single-nucleotide polymorphism at B3GNT2 locus (p=1.10e-08). At the pathway level, we found 14 genetic pathways significantly associated with PsA (pFDRConclusionThese findings provide insights into the biological mechanisms that are specific for PsA and could contribute to develop more effective therapies.

Background Tumour Necrosis Factor (TNF) inhibitors have improved the management of many patients with rheumatoid arthritis (RA). However,~30% of anti-TNF treated patients do not show a significant clinical improvement. To date, little is known on the biological mechanisms underlying the differential response to anti-TNF agents. Objectives We sought to identify genetic variation associated with the anti-TNF response in RA using a sequential multi-omics approach. Methods First, we aimed to identify gene coexpression modules (GCMs) associated with anti-TNF response. For this objective, we extracted the RNA from synovial biopsies of 13 RA patients starting anti-TNF therapy and determined the expression profiles using Illumina microarrays. GCMs were identified using the WGCNA approach. The association between GCMs and anti-TNF response was performed using the eigengene of each GCM. Clinical response was defined using the EULAR criteria at week 14. To analyse the association of GCMs with anti-TNF response at the genetic level, we used 348 anti-TNF treated RA patients from Spain. The statistical analysis was performed using GWAS data and the set-based test in PLINK. The GCMs that were significantly associated with anti-TNF response were subsequently tested for validation in an independent cohort of 2706 anti-TNF treated RA patients. The functional implication of the validated GCMs was studied via pathway and cell type epigenetic enrichment analyses. Results We identified 148 GCMs in the RA synovium. From these, 15 GCMs were found to be associated with anti-TNF response (p Conclusions Our study shows the existence of a drug-specific genetic basis for the anti-TNF response. Therefore, this molecular diversity should be considered for biomarker research in RA. Disclosure of Interest None declared

Objective Genome-wide association studies (GWAS) have identified multiple risk loci for Crohn9s disease (CD). However, the cumulative risk exerted by these loci is low, and the likelihood that additional, as-yet undiscovered loci contribute to the risk of CD is very high. We performed a GWAS on a southern European population to identify new CD risk loci. Design We genotyped 620 901 genome markers on 1341 CD patients and 1518 controls from Spain. The top association signals representing new candidate risk loci were subsequently analysed in an independent replication cohort of 1365 CD patients and 1396 controls. Results We identified a genome-wide significant association on chromosome 22q13.2 in the intergenic region between the RBX1 and EP300 genes (single nucleotide polymorphism rs4820425, OR 1.27, 95% CI 1.17 to 1.38, p=3.42E-8). We also found suggestive evidence for the association of the IFNGR2 (21q22.11), FOXP2 (7q31), MACROD2 (20p12.1) and AIF1 (6p21.3) loci with CD risk. Conclusions In this GWAS performed on a southern European cohort, we have identified a new risk locus for CD between RBX1 and EP300 . This study demonstrates that using populations of different ancestry is a useful strategy to identify new risk loci for CD.

Rheumatoid factor (RF) is a well-established diagnostic and prognostic biomarker in rheumatoid arthritis (RA). However, ∼20% of RA patients are negative for this anti-IgG antibody. To date, only variation at the HLA-DRB1 gene has been associated with the presence of RF. This study was undertaken to identify additional genetic variants associated with RF positivity. A genome-wide association study (GWAS) for RF positivity was performed using an Illumina Quad610 genotyping platform. A total of 937 RF-positive and 323 RF-negative RA patients were genotyped for >550,000 single-nucleotide polymorphisms (SNPs). Association testing was performed using an allelic chi-square test implemented in Plink software. An independent cohort of 472 RF-positive and 190 RF-negative RA patients was used to validate the most significant findings. In the discovery stage, a SNP in the IRX1 locus on chromosome 5p15.3 (SNP rs1502644) showed a genome-wide significant association with RF positivity (P = 4.13 × 10(-8) , odds ratio [OR] 0.37 [95% confidence interval (95% CI) 0.26-0.53]). In the validation stage, the association of IRX1 with RF was replicated in an independent group of RA patients (P = 0.034, OR 0.58 [95% CI 0.35-0.97] and combined P = 1.14 × 10(-8) , OR 0.43 [95% CI 0.32-0.58]). To our knowledge, this is the first GWAS of RF positivity in RA. Variation at the IRX1 locus on chromosome 5p15.3 is associated with the presence of RF. Our findings indicate that IRX1 and HLA-DRB1 are the strongest genetic factors for RF production in RA.

Background: Immune-mediated inflammatory diseases (IMIDs) are a group of complex and prevalent diseases where disease diagnostic and activity monitoring is highly challenging. The determination of the metabolite profiles of biological samples is becoming a powerful approach to identify new biomarkers of clinical utility. In order to identify new metabolite biomarkers of diagnosis and disease activity, we have performed the first large-scale profiling of the urine metabolome of the six most prevalent IMIDs: rheumatoid arthritis, psoriatic arthritis, psoriasis, systemic lupus erythematosus, Crohn's disease, and ulcerative colitis. Methods: Using nuclear magnetic resonance, we analyzed the urine metabolome in a discovery cohort of 1210 patients and 100 controls. Within each IMID, two patient subgroups were recruited representing extreme disease activity (very high vs. very low). Metabolite association analysis with disease diagnosis and disease activity was performed using multivariate linear regression in order to control for the effects of clinical, epidemiological, or technical variability. After multiple test correction, the most significant metabolite biomarkers were validated in an independent cohort of 1200 patients and 200 controls. Results: In the discovery cohort, we identified 28 significant associations between urine metabolite levels and disease diagnosis and three significant metabolite associations with disease activity (P-FDR < 0.05). Using the validation cohort, we validated 26 of the diagnostic associations and all three metabolite associations with disease activity (PFDR < 0.05). Combining all diagnostic biomarkers using multivariate classifiers we obtained a good disease prediction accuracy in all IMIDs and particularly high in inflammatory bowel diseases. Several of the associated metabolites were found to be commonly altered in multiple IMIDs, some of which can be considered as hub biomarkers. The analysis of the metabolic reactions connecting the IMID-associated metabolites showed an overrepresentation of citric acid cycle, phenylalanine, and glycine-serine metabolism pathways. Conclusions: This study shows that urine is a source of biomarkers of clinical utility in IMIDs. We have found that IMIDs show similar metabolic changes, particularly between clinically similar diseases and we have found, for the first time, the presence of hub metabolites. These findings represent an important step in the development of more efficient and less invasive diagnostic and disease monitoring methods in IMIDs.

Molecular dynamics simulation (MD) is, just behind genomics, the bioinformatics tool that generates the largest amounts of data, and that is using the largest amount of CPU time in supercomputing centres. MD trajectories are obtained after months of calculations, analysed in situ, and in practice forgotten. Several projects to generate stable trajectory databases have been developed for proteins, but no equivalence exists in the nucleic acids world. We present here a novel database system to store MD trajectories and analyses of nucleic acids. The initial data set available consists mainly of the benchmark of the new molecular dynamics force-field, parmBSC1. It contains 156 simulations, with over 120 of total simulation time. A deposition protocol is available to accept the submission of new trajectory data. The database is based on the combination of two NoSQL engines, Cassandra for storing trajectories and MongoDB to store analysis results and simulation metadata. The analyses available include backbone geometries, helical analysis, NMR observables and a variety of mechanical analyses. Individual trajectories and combined meta-trajectories can be downloaded from the portal. The system is accessible through http: //mmb.irbbarcelona.org/BIGNASim/. Supplementary Material is also available on-line at http://mmb. irbbarcelona.org/BIGNASim/SuppMaterial/. Spanish Ministry of Science [BIO2012-32868, SEV-2011-00067, TIN2012-34557]; Catalan Government [2014-SGR-134, 2014-SGR-1051]; Institut Català de Recerca I Estudis Avanc¸ats, ICREA Academia [to M.O.], Instituto de Salud Carlos III-Instituto Nacional de Bioinformática [PT13/0001/0019, PT13/0001/0028]; European Research Council [ERC SimDNA]; European Union, H2020 programme [Elixir-Excellerate: 676559; BioExcel: 674728, MuG: 676566]; PEDECIBA and SNI (ANII, Uruguay) [to P.D.D.]. Funding for open access charge: European Union [MuG: 676566].

Psoriasis is a chronic inflammatory disease with a complex genetic architecture. To date, the psoriasis heritability is only partially explained. However, there is increasing evidence that the missing heritability in psoriasis could be explained by multiple genetic variants of low effect size from common genetic pathways. The objective of this study was to identify new genetic variation associated with psoriasis risk at the pathway level. We genotyped 598,258 single nucleotide polymorphisms in a discovery cohort of 2,281 case-control individuals from Spain. We performed a genome-wide pathway analysis using 1,053 reference biological pathways. A total of 14 genetic pathways (PFDR ≤ 2.55 × 10(-2)) were found to be significantly associated with psoriasis risk. Using an independent validation cohort of 7,353 individuals from the UK, a total of 6 genetic pathways were significantly replicated (PFDR ≤ 3.46 × 10(-2)). We found genetic pathways that had not been previously associated with psoriasis risk such as retinol metabolism (Pcombined = 1.84 × 10(-4)), the transport of inorganic ions and amino acids (Pcombined = 1.57 × 10(-7)), and post-translational protein modification (Pcombined = 1.57 × 10(-7)). In the latter pathway, MGAT5 showed a strong network centrality, and its association with psoriasis risk was further validated in an additional case-control cohort of 3,429 individuals (P < 0.05). These findings provide insights into the biological mechanisms associated with psoriasis susceptibility. This study was funded by of the Spanish Ministry of Economy and Competitiveness, grant numbers: PSE-010000-2006-6 and IPT-010000-2010-36

Maya V. Georgieva et al., Protein tagging is widely used in approaches ranging from affinity purification to fluorescence-based detection in live cells. However, an intrinsic limitation of tagging is that the native function of the protein may be compromised or even abolished by the presence of the tag. Here we describe and characterize a set of small, innocuous protein tags (inntags) that we anticipate will find application in a variety of biological techniques. © 2015 Nature America, Inc., This work was funded by the Ministry of Economy and Competitiveness of Spain (INNPACTO IPT-010000-2010-19), Consolider-Ingenio 2010 (CSD2007-15), the Instituto Nacional de Bioinformática (INB) and the European Union (FEDER) and received financial support from Antibody BCN and Immunostep. M.O. acknowledges support from Institució Catalana de Recerca i Estudis Avançats