Assessment of selection pressure exerted on genes from complete pangenomes helps to improve the accuracy in the prediction of new genes.

Bacterial genomes are massively sequenced, and they provide valuable data to better know the complete set of genes of a species. The analysis of thousands of bacterial strains can identify both shared genes and those appearing only in the pathogenic ones. Current computational gene finders facilitate this task but often miss some existing genes. However, the present availability of different genomes from the same species is useful to estimate the selective pressure applied on genes of complete pangenomes. It may assist in evaluating gene predictions either by checking the certainty of a new gene or annotating it as a gene under positive selection. Here, we estimated the selective pressure of 19 271 genes that are part of the pangenome of the human opportunistic pathogen Acinetobacter baumannii and found that most genes in this bacterium are subject to negative selection. However, 23% of them showed values compatible with positive selection. These latter were mainly uncharacterized proteins or genes required to evade the host defence system including genes related to resistance and virulence whose changes may be favoured to acquire new functions. Finally, we evaluated the utility of measuring selection pressure in the detection of sequencing errors and the validation of gene prediction.

Lipid turnover through lipophagy in the newly identified extremophilic green microalga Chlamydomonas urium.

Autophagy is a central degradative pathway highly conserved among eukaryotes, including microalgae, which remains unexplored in extremophilic organisms. In this study, we described and characterized autophagy in the newly identified extremophilic green microalga Chlamydomonas urium, which was isolated from an acidic environment. The nuclear genome of C. urium was sequenced, assembled and annotated in order to identify autophagy-related genes. Transmission electron microscopy, immunoblotting, metabolomic and photosynthetic analyses were performed to investigate autophagy in this extremophilic microalga. The analysis of the C. urium genome revealed the conservation of core autophagy-related genes. We investigated the role of autophagy in C. urium by blocking autophagic flux with the vacuolar ATPase inhibitor concanamycin A. Our results indicated that inhibition of autophagic flux in this microalga resulted in a pronounced accumulation of triacylglycerols and lipid droplets (LDs). Metabolomic and photosynthetic analyses indicated that C. urium cells with impaired vacuolar function maintained an active metabolism. Such effects were not observed in the neutrophilic microalga Chlamydomonas reinhardtii. Inhibition of autophagic flux in C. urium uncovered an active recycling of LDs through lipophagy, a selective autophagy pathway for lipid turnover. This study provided the metabolic basis by which extremophilic algae are able to catabolize lipids in the vacuole.

Serial co-expression analysis of host factors from SARS-CoV viruses highly converges with former high-throughput screenings and proposes key regulators.

The current genomics era is bringing an unprecedented growth in the amount of gene expression data, only comparable to the exponential growth of sequences in databases during the last decades. This data allow the design of secondary analyses that take advantage of this information to create new knowledge. One of these feasible analyses is the evaluation of the expression level for a gene through a series of different conditions or cell types. Based on this idea, we have developed Automatic and Serial Analysis of CO-expression, which performs expression profiles for a given gene along hundreds of heterogeneous and normalized transcriptomics experiments and discover other genes that show either a similar or an inverse behavior. It might help to discover co-regulated genes, and common transcriptional regulators in any biological model. The present severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic is an opportunity to test this novel approach due to the wealth of data that are being generated, which could be used for validating results. Thus, we have identified 35 host factors in the literature putatively involved in the infectious cycle of SARS-CoV viruses and searched for genes tightly co-expressed with them. We have found 1899 co-expressed genes whose assigned functions are strongly related to viral cycles. Moreover, this set of genes heavily overlaps with those identified by former laboratory high-throughput screenings (with P-value near 0). Our results reveal a series of common regulators, involved in immune and inflammatory responses that might be key virus targets to induce the coordinated expression of SARS-CoV-2 host factors.

Influence of Agaricus bisporus establishment and fungicidal treatments on casing soil metataxonomy during mushroom cultivation.

The cultivation of edible mushroom is an emerging sector with a potential yet to be discovered. Unlike plants, it is a less developed agriculture where many studies are lacking to optimize the cultivation. In this work we have employed high-throughput techniques by next generation sequencing to screen the microbial structure of casing soil employed in mushroom cultivation (Agaricus bisporus) while sequencing V3-V4 of the 16S rRNA gene for bacteria and the ITS2 region of rRNA for. In addition, the microbiota dynamics and evolution (bacterial and fungal communities) in peat-based casing along the process of incubation of A. bisporus have been studied, while comparing the effect of fungicide treatment (chlorothalonil and metrafenone). Statistically significant changes in populations of bacteria and fungi were observed. Microbial composition differed significantly based on incubation day, changing radically from the original communities in the raw material to a specific microbial composition driven by the A. bisporus mycelium growth. Chlorothalonil treatment seems to delay casing colonization by A. bisporus. Proteobacteria and Bacteroidota appeared as the most dominant bacterial phyla. We observed a great change in the structure of the bacteria populations between day 0 and the following days. Fungi populations changed more gradually, with A. bisporus displacing the rest of the species as the cultivation cycle progresses. A better understanding of the microbial communities in the casing will hopefully allow us to increase the biological efficiency of the crop.

Isolation and genomic characterization of the ibuprofen-degrading bacterium Sphingomonas strain MPO218.

The presence of pharmaceutical compounds in waters and soils is of particular concern because these compounds can be biologically active, even at environmental concentrations. Most pharmaceutical contaminants result from inefficient removal of these compounds during wastewater treatment. Although microorganisms able to biodegrade pharmaceuticals compounds have been described, the isolation and characterization of new bacterial strains capable of degrading drugs remain important to improve the removal of this pollutant. In this work, we describe the Sphingomonas wittichii strain MPO218 as able to use ibuprofen as the sole carbon and energy source. The genome of MPO218 consists of a circular chromosome and two circular plasmids. Our analysis shows that the largest plasmid, named pIBU218, is conjugative and can horizontally transfer the capability of growing on ibuprofen after conjugation with another related bacterium, Sphingopyxis granuli TFA. This plasmid appears to be unstable since it undergoes different deletions in absence of selection when growth on ibuprofen is not selected. This is the first described example of a natural and conjugative plasmid that enables growth on ibuprofen and is another example of how horizontal gene transfer plays a crucial role in the evolution of bacteria.

Ancient evolutionary signals of protein-coding sequences allow the discovery of new genes in the Drosophila melanogaster genome.

BACKGROUND: The current growth in DNA sequencing techniques makes of genome annotation a crucial task in the genomic era. Traditional gene finders focus on protein-coding sequences, but they are far from being exhaustive. The number of this kind of genes continuously increases due to new experimental data and development of improved bioinformatics algorithms. RESULTS: In this context, AnABlast represents a novel in silico strategy, based on the accumulation of short evolutionary signals identified by protein sequence alignments of low score. This strategy potentially highlights protein-coding regions in genomic sequences regardless of traditional homology or translation signatures. Here, we analyze the evolutionary information that the accumulation of these short signals encloses. Using the Drosophila melanogaster genome, we stablish optimal parameters for the accurate gene prediction with AnABlast and show that this new strategy significantly contributes to add genes, exons and pseudogenes regions, yet to be discovered in both already annotated and new genomes. CONCLUSIONS: AnABlast can be freely used to analyze genomic regions of whole genomes where it contributes to complete the previous annotation.

Autophagic dysfunction in patients with Papillon-Lefèvre syndrome is restored by recombinant cathepsin C treatment.

BACKGROUND: Cathepsin C (CatC) is a lysosomal enzyme involved in activation of serine proteases from immune and inflammatory cells. Several loss-of-function mutations in the CatC gene have been shown to be the genetic mark of Papillon-Lefèvre syndrome (PLS), a rare autosomal recessive disease characterized by severe early-onset periodontitis, palmoplantar hyperkeratosis, and increased susceptibility to infections. Deficiencies or dysfunction in other cathepsin family proteins, such as cathepsin B or D, have been associated with autophagic and lysosomal disorders. OBJECTIVES: Here we characterized the basis for autophagic dysfunction in patients with PLS by analyzing skin fibroblasts derived from patients with several mutations in the CatC gene and reduced enzymatic activity. METHODS: Skin fibroblasts were isolated from patients with PLS assessed by using genetic analysis. Authophagic flux dysfunction was evaluated by examining accumulation of p62/SQSTM1 and a bafilomycin assay. Ultrastructural analysis further confirmed abnormal accumulation of autophagic vesicles in mutant cells. A recombinant CatC protein was produced by a baculovirus system in insect cell cultures. RESULTS: Mutant fibroblasts from patients with PLS showed alterations in oxidative/antioxidative status, reduced oxygen consumption, and a marked autophagic dysfunction associated with autophagosome accumulation. These alterations were accompanied by lysosomal permeabilization, cathepsin B release, and NLR family pyrin domain containing 3 (NLRP3) inflammasome activation. Treatment of mutant fibroblasts with recombinant CatC improved cell growth and autophagic flux and partially restored lysosomal permeabilization. CONCLUSIONS: Our data provide a novel molecular mechanism underlying PLS. Impaired autophagy caused by insufficient lysosomal function might represent a new therapeutic target for PLS.

Automated selection of homologs to track the evolutionary history of proteins.

BACKGROUND: The selection of distant homologs of a query protein under study is a usual and useful application of protein sequence databases. Such sets of homologs are often applied to investigate the function of a protein and the degree to which experimental results can be transferred from one organism to another. In particular, a variety of databases facilitates static browsing for orthologs. However, these resources have a limited power when identifying orthologs between taxonomically distant species. In addition, in some situations, for a given query protein, it is advantageous to compare the sets of orthologs from different specific organisms: this recursive step-wise search might give an idea of the evolutionary path of the protein as a series of consecutive steps, for example gaining or losing domains. However, a step-wise orthology search is a time-consuming task if the number of steps is high. RESULTS: To illustrate a solution for this problem, we present the web tool ProteinPathTracker, which allows to track the evolutionary history of a query protein by locating homologs in selected proteomes along several evolutionary paths. Additional functionalities include locking a region of interest to follow its evolution in the discovered homologous sequences and the study of the protein function evolution by analysis of the annotations of the homologs. CONCLUSIONS: ProteinPathTracker is an easy-to-use web tool that automatises the practice of looking for selected homologs in distant species in a straightforward way for non-expert users.

Sma3s: A universal tool for easy functional annotation of proteomes and transcriptomes.

The current cheapening of next-generation sequencing has led to an enormous growth in the number of sequenced genomes and transcriptomes, allowing wet labs to get the sequences from their organisms of study. To make the most of these data, one of the first things that should be done is the functional annotation of the protein-coding genes. But it used to be a slow and tedious step that can involve the characterization of thousands of sequences. Sma3s is an accurate computational tool for annotating proteins in an unattended way. Now, we have developed a completely new version, which includes functionalities that will be of utility for fundamental and applied science. Currently, the results provide functional categories such as biological processes, which become useful for both characterizing particular sequence datasets and comparing results from different projects. But one of the most important implemented innovations is that it has now low computational requirements, and the complete annotation of a simple proteome or transcriptome usually takes around 24 hours in a personal computer. Sma3s has been tested with a large amount of complete proteomes and transcriptomes, and it has demonstrated its potential in health science and other specific projects.

Genomic analysis of the nitrate-respiring Sphingopyxis granuli (formerly Sphingomonas macrogoltabida) strain TFA.

BACKGROUND: Sphingomonads are Alphaproteobacteria that belong to the Sphingomonas, Novosphingobium, Sphingopyxis or Sphingobium genera, They are physiologically diverse and broadly distributed in nature, playing important roles in oligotrophic environments and in the degradation of recalcitrant polyaromatic compounds, Sphingopyxis is a poorly studied genus of which only one representative (S. alaskensis RB2256) has been deeply characterized. In this paper we analyze the genomic features of S. granuli strain TFA (formerly Sphingomonas macrogoltabida) in comparison with the available Sphingopyxis sequenced genomes, to describe common characteristics of this genus and to highlight unique characteristics of strain TFA. RESULTS: The TFA genome has been assembled in a single circular chromosome of 4.7 Mb. Genomic sequence analysis and proteome comparison re-assigned the TFA strain to the Sphingopyxis genus and the S. granuli species. Some regions of the TFA genome show high similarity (ca. 100%) to other bacteria and several genomic islands have been detected. Pathways for aromatic compound degradation have been predicted but no growth of TFA has been detected using these as carbon or nitrogen sources. Genes for nitrate respiration have been identified as TFA exclusive. Experimental data on anaerobic growth of TFA using nitrate as a terminal electron acceptor are also provided. CONCLUSIONS: Sphingopyxis representatives form a compact phylogenetic group (with the exception of S. baekryungensis DSM 16222) that share several characteristics, such as being naturally resistant to streptomycin, having only one ribosomal operon, a low number of prophages and CRISPR sequences, absence of selenoproteins and presence of ectoin and other biosynthesis pathways for secondary metabolites. Moreover, the TFA genome organization shows evidence of the presence of putative integrative and conjugative elements (ICE) responsible for the acquisition of several characteristics by horizontal transfer mechanisms. Sphingopyxis representatives have been described as strict aerobes but anaerobic growth using nitrate as a terminal electron acceptor might confer an environmental advantage to the first S. granuli strain characterized at genomic level.

Proteome-wide search for PP2A substrates in fission yeast.

PP2A (protein phosphatase 2A) is a major phosphatase in eukaryotic cells that plays an essential role in many processes. PP2A mutations in Schizosaccharomyces pombe result in defects of cell cycle control, cytokinesis and morphogenesis. Which PP2A substrates are responsible for these changes is not known. In this work, we searched for PP2A substrates in S. pombe using two approaches, 2D-DIGE analysis of PP2A complex mutants and identification of PP2A interacting proteins. In both cases, we used MS to identify proteins of interest. In the DIGE experiment, we compared proteomes of wild-type S. pombe, deletion of pta2, the phosphoactivator of the PP2A catalytic subunit, and pab1-4, a mutant of B-type PP2A regulatory subunit. A total of 1742 protein spots were reproducibly resolved by 2D-DIGE and 51 spots demonstrated significant changes between PP2A mutants and the wild-type control. MS analysis of these spots identified 27 proteins that include key regulators of glycerol synthesis, carbon metabolism, amino acid biosyntesis, vitamin production, and protein folding. Importantly, we independently identified a subset of these proteins as PP2A binding partners by affinity precipitation, suggesting they may be direct targets of PP2A. We have validated our approach by demonstrating that phosphorylation of Gpd1, a key enzyme in glycerol biogenesis, is regulated by PP2A and that ability of cells to respond to osmotic stress by synthesizing glycerol is compromised in the PP2A mutants. Our work contributes to a better understanding of PP2A function and identifies potential PP2A substrates.

Identification of O-mannosylated virulence factors in Ustilago maydis.

The O-mannosyltransferase Pmt4 has emerged as crucial for fungal virulence in the animal pathogens Candida albicans or Cryptococcus neoformans as well as in the phytopathogenic fungus Ustilago maydis. Pmt4 O-mannosylates specific target proteins at the Endoplasmic Reticulum. Therefore a deficient O-mannosylation of these target proteins must be responsible for the loss of pathogenicity in pmt4 mutants. Taking advantage of the characteristics described for Pmt4 substrates in Saccharomyces cerevisiae, we performed a proteome-wide bioinformatic approach to identify putative Pmt4 targets in the corn smut fungus U. maydis and validated Pmt4-mediated glycosylation of candidate proteins by electrophoretic mobility shift assays. We found that the signalling mucin Msb2, which regulates appressorium differentiation upstream of the pathogenicity-related MAP kinase cascade, is O-mannosylated by Pmt4. The epistatic relationship of pmt4 and msb2 showed that both are likely to act in the same pathway. Furthermore, constitutive activation of the MAP kinase cascade restored appressorium development in pmt4 mutants, suggesting that during the initial phase of infection the failure to O-mannosylate Msb2 is responsible for the virulence defect of pmt4 mutants. On the other hand we demonstrate that during later stages of pathogenic development Pmt4 affects virulence independently of Msb2, probably by modifying secreted effector proteins. Pit1, a protein required for fungal spreading inside the infected leaf, was also identified as a Pmt4 target. Thus, O-mannosylation of different target proteins affects various stages of pathogenic development in U. maydis.

The most exposed regions of SARS-CoV-2 structural proteins are subject to strong positive selection and gene overlap may locally modify this behavior.

The SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) pandemic that emerged in 2019 has been an unprecedented event in international science, as it has been possible to sequence millions of genomes, tracking their evolution very closely. This has enabled various types of secondary analyses of these genomes, including the measurement of their sequence selection pressure. In this work, we have been able to measure the selective pressure of all the described SARS-CoV-2 genes, even analyzed by sequence regions, and we show how this type of analysis allows us to separate the genes between those subject to positive selection (usually those that code for surface proteins or those exposed to the host immune system) and those subject to negative selection because they require greater conservation of their structure and function. We have also seen that when another gene with an overlapping reading frame appears within a gene sequence, the overlapping sequence between the two genes evolves under a stronger purifying selection than the average of the non-overlapping regions of the main gene. We propose this type of analysis as a useful tool for locating and analyzing all the genes of a viral genome when an adequate number of sequences are available.IMPORTANCEWe have analyzed the selection pressure of all severe acute respiratory syndrome coronavirus 2 genes by means of the nonsynonymous (Ka) to synonymous (Ks) substitution rate. We found that protein-coding genes are exposed to strong positive selection, especially in the regions of interaction with other molecules (host receptor and genome of the virus itself). However, overlapping coding regions are more protected and show negative selection. This suggests that this measure could be used to study viral gene function as well as overlapping genes.

ASACO: Automatic and Serial Analysis of CO-expression to discover gene modifiers with potential use in drug repurposing.

Massive gene expression analyses are widely used to find differentially expressed genes under specific conditions. The results of these experiments are often available in public databases that are undergoing a growth similar to that of molecular sequence databases in the past. This now allows novel secondary computational tools to emerge that use such information to gain new knowledge. If several genes have a similar expression profile across heterogeneous transcriptomics experiments, they could be functionally related. These associations are usually useful for the annotation of uncharacterized genes. In addition, the search for genes with opposite expression profiles is useful for finding negative regulators and proposing inhibitory compounds in drug repurposing projects. Here we present a new web application, Automatic and Serial Analysis of CO-expression (ASACO), which has the potential to discover positive and negative correlator genes to a given query gene, based on thousands of public transcriptomics experiments. In addition, examples of use are presented, comparing with previous contrasted knowledge. The results obtained propose ASACO as a useful tool to improve knowledge about genes associated with human diseases and noncoding genes. ASACO is available at http://www.bioinfocabd.upo.es/asaco/.

Classification of protein motifs based on subcellular localization uncovers evolutionary relationships at both sequence and functional levels.

BACKGROUND: Most proteins have evolved in specific cellular compartments that limit their functions and potential interactions. On the other hand, motifs define amino acid arrangements conserved between protein family members and represent powerful tools for assigning function to protein sequences. The ideal motif would identify all members of a protein family but in practice many motifs identify both family members and unrelated proteins, referred to as True Positive (TP) and False Positive (FP) sequences, respectively. RESULTS: To address the relationship between protein motifs, protein function and cellular localization, we systematically assigned subcellular localization data to motif sequences from the comprehensive PROSITE sequence motif database. Using this data we analyzed relationships between localization and function. We find that TPs and FPs have a strong tendency to localize in different compartments. When multiple localizations are considered, TPs are usually distributed between related cellular compartments. We also identified cases where FPs are concentrated in particular subcellular regions, indicating possible functional or evolutionary relationships with TP sequences of the same motif. CONCLUSIONS: Our findings suggest that the systematic examination of subcellular localization has the potential to uncover evolutionary and functional relationships between motif-containing sequences. We believe that this type of analysis complements existing motif annotations and could aid in their interpretation. Our results shed light on the evolution of cellular organelles and potentially establish the basis for new subcellular localization and function prediction algorithms.

Analysis of bacterial pangenomes reduces CRISPR dark matter and reveals strong association between membranome and CRISPR-Cas systems.

CRISPR-Cas systems are prokaryotic acquired immunity mechanisms, which are found in 40% of bacterial genomes. They prevent viral infections through small DNA fragments called spacers. However, the vast majority of these spacers have not yet been associated with the virus they recognize, and it has been named CRISPR dark matter. By analyzing the spacers of tens of thousands of genomes from six bacterial species, we have been able to reduce the CRISPR dark matter from 80% to as low as 15% in some of the species. In addition, we have observed that, when a genome presents CRISPR-Cas systems, this is accompanied by particular sets of membrane proteins. Our results suggest that when bacteria present membrane proteins that make it compete better in its environment and these proteins are, in turn, receptors for specific phages, they would be forced to acquire CRISPR-Cas.

A biclustering algorithm for extracting bit-patterns from binary datasets.

MOTIVATION: Binary datasets represent a compact and simple way to store data about the relationships between a group of objects and their possible properties. In the last few years, different biclustering algorithms have been specially developed to be applied to binary datasets. Several approaches based on matrix factorization, suffix trees or divide-and-conquer techniques have been proposed to extract useful biclusters from binary data, and these approaches provide information about the distribution of patterns and intrinsic correlations. RESULTS: A novel approach to extracting biclusters from binary datasets, BiBit, is introduced here. The results obtained from different experiments with synthetic data reveal the excellent performance and the robustness of BiBit to density and size of input data. Also, BiBit is applied to a central nervous system embryonic tumor gene expression dataset to test the quality of the results. A novel gene expression preprocessing methodology, based on expression level layers, and the selective search performed by BiBit, based on a very fast bit-pattern processing technique, provide very satisfactory results in quality and computational cost. The power of biclustering in finding genes involved simultaneously in different cancer processes is also shown. Finally, a comparison with Bimax, one of the most cited binary biclustering algorithms, shows that BiBit is faster while providing essentially the same results. AVAILABILITY: The source and binary codes, the datasets used in the experiments and the results can be found at: http://www.upo.es/eps/bigs/BiBit.html CONTACT: dsrodbae@upo.es SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Mpg2 interacts and cooperates with Mpg1 to maintain yeast glycosylation.

Using a yeast two-hybrid screen, we isolated a gene from Schizosaccharomyces pombe, whose product interacts with Mpg1, a GDP-mannose-1-phosphate guanylyltransferase involved in the maintenance of cell wall integrity and glycosylation. We have designated this gene mpg2 based on its similarity to Mpg1. Mpg2 is evolutionarily conserved in higher eukaryotes. In the absence of Mpg2, defects in cell growth and sensitivity to hygromycin B are observed. When mpg1 is depleted, the lack of mpg2 causes a synthetic enhancement of the growth defect, the sensitivity to hygromycin B and the cell cycle phenotype previously reported for mpg1 mutant. Finally, Mpg1 overexpression complements the Δmpg2 mutant phenotypes. Taken together, these results indicate that mpg1 and mpg2 function together in glycosylation and septum formation.

MOWServ: a web client for integration of bioinformatic resources.

The productivity of any scientist is affected by cumbersome, tedious and time-consuming tasks that try to make the heterogeneous web services compatible so that they can be useful in their research. MOWServ, the bioinformatic platform offered by the Spanish National Institute of Bioinformatics, was released to provide integrated access to databases and analytical tools. Since its release, the number of available services has grown dramatically, and it has become one of the main contributors of registered services in the EMBRACE Biocatalogue. The ontology that enables most of the web-service compatibility has been curated, improved and extended. The service discovery has been greatly enhanced by Magallanes software and biodataSF. User data are securely stored on the main server by an authentication protocol that enables the monitoring of current or already-finished user's tasks, as well as the pipelining of successive data processing services. The BioMoby standard has been greatly extended with the new features included in the MOWServ, such as management of additional information (metadata such as extended descriptions, keywords and datafile examples), a qualified registry, error handling, asynchronous services and service replication. All of them have increased the MOWServ service quality, usability and robustness. MOWServ is available at http://www.inab.org/MOWServ/ and has a mirror at http://www.bitlab-es.com/MOWServ/.

E-learning strategies from a bioinformatics postgraduate programme to improve student engagement and completion rate.

Motivation: E-learning is the standard solution adopted in transnational study programmes for which multiple face-to-face learning places are not an option. Bioinformatics is compatible with e-learning because its resource requirements are low. Online learning, however, is usually associated with high dropout rates because students start from a very low computational level and/or they need support to conduct practical analyses on their own. Results: In this article, we analyse the academic results of an online bioinformatics educational programme based on learning communities. The programme has been offered by the Spanish Pablo de Olavide University for more than 5 years with a completion rate of close to 90%. Learning bioinformatics requires technical and operational competencies that can only be acquired through a practical methodology. We have thus developed a student-centred and problem-based constructivist learning model; the model uses faculty and peer mentoring to drive individual work and retain students. Regarding our innovative learning model, the recruitment level (i.e. the number of applicants per available places and international origin), the results obtained (i.e. the retention index and learning outcomes) as well as the satisfaction index expressed by students and faculty lead us to regard this programme as a successful strategy for online graduate learning in bioinformatics. Availability and implementation: All data and results for this article are available in the figures and supplementary files. The current syllabus (Supplementary File S7) and other details of the course are available at: https://www.upo.es/postgrado/Diploma-de-Especializacion-Analisis-Bioinformatico and https://www.upo.es/postgrado/Master-Analisis-Bioinformatico-Avanzado. Supplementary information: Supplementary data are available at Bioinformatics Advances online.