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BackgroundSchaaf-Yang syndrome (SYS) is caused by truncating mutations inMAGEL2, mapping to the Prader-Willi region (15q11-q13), with an observed phenotype partially overlapping that of Prader-Willi syndrome. MAGEL2 plays a role in retrograde transport and protein recycling regulation. Our aim is to contribute to the characterisation of SYS pathophysiology at clinical, genetic and molecular levels.MethodsWe performed an extensive phenotypic and mutational revision of previously reported patients with SYS. We analysed the secretion levels of amyloid-β 1–40 peptide (Aβ1-40) and performed targeted metabolomic and transcriptomic profiles in fibroblasts of patients with SYS (n=7) compared with controls (n=11). We also transfected cell lines with vectors encoding wild-type (WT) or mutated MAGEL2 to assess stability and subcellular localisation of the truncated protein.ResultsFunctional studies show significantly decreased levels of secreted Aβ1-40and intracellular glutamine in SYS fibroblasts compared with WT. We also identified 132 differentially expressed genes, including non-coding RNAs (ncRNAs) such asHOTAIR, and many of them related to developmental processes and mitotic mechanisms. The truncated form of MAGEL2 displayed a stability similar to the WT but it was significantly switched to the nucleus, compared with a mainly cytoplasmic distribution of the WT MAGEL2. Based on the updated knowledge, we offer guidelines for the clinical management of patients with SYS.ConclusionA truncated MAGEL2 protein is stable and localises mainly in the nucleus, where it might exert a pathogenic neomorphic effect. Aβ1-40secretion levels andHOTAIRmRNA levels might be promising biomarkers for SYS. Our findings may improve SYS understanding and clinical management.

Figure S1. Rarefaction curves of microbiota skin samples Figure S2. Relative abundance (%) of bacteria at the phylum in the skin mucus microbiota of fish feed with control diet without (C) and with wound (CW); fish feed with probiotic diet without (P) and with wound (PW) Figure S3. Relative abundance (%) of bacteria at class in the skin mucus microbiota of fish feed with control diet without (C) and with wound (CW); fish feed with probiotic diet without (P) and with wound (PW). c__ non taxonomy classify OTUs at this categorise Figure S4. Relative abundance (%) of bacteria at genera level in the skin mucus microbiota of fish feed with control diet without (C) and with wound (CW); fish feed with probiotic diet without (P) and with wound (PW). g__ non taxonomy classify OTUs at this categorise Figure S5. Volcano plots of differentially expressed genes (DEGs) between groups, expression data plotted with correct p value cutoff FDR < 0.05 and log2 fold change absolute value >= 1.3. Fish feed with control diet without (C) and with wound (CW). Fish feed with probiotic diet without (P) and with wound (PW). Figure S6.Significant functional enrichment network (A) of the "Cellular Component" and (B) "Molecular Function"GO categories (with their adjusted p), from the DEGs obtained in the comparison of PW vs CW. Brown nodes indicates functional categories. Red to blue nodes indicate the log2 fold change values. Figure S7. Functional enrichment network of the KEGG terms from the DEGs obtained in the comparison of PW vs CW. The brown nodes indicate the KEGG categories, red and blue nodes show the repression overexpression and repression genes in the skin of the ulcerated probiotic group. Figure S8. Functional enrichment network of the Reactome categories from the DEGs obtained in the comparison of PW vs CW. The brown nodes indicate the Reactome categories, red and blue nodes show the repression overexpression and repression genes in the skin of the ulcerated probiotic group. Histogram shows that the categories were significantly enriched (p adjust > 0.05) Table S1.Results for two-way ANOVA calculation of alpha diversity indices between groups.Fish feed with control diet without (C - N) and with wound (C - W). Fish feed withprobiotic diet without (P - N) and with wound (P - W). Non significance differences were showed(p > 0.05) Table S2. DESeq2 results in P vs PW comparison at OTU level, all the taxonomy categories were showed Table S3. RNA-seq reads summary. 1-3 biological replicate of fish feed with control diet without (C) and with wound (CW). Fish feed with probiotic diet without (P) and with wound (PW). % Respect to total reads in each case. Table S4. Correlations of CW-PW DEGs with the genera of skin microbiota in the same tissue

Populations of RNA and ssDNA viruses within their hosts contain a heterogeneous collection of variant genomes known as quasispecies. Large variability in mitochondrial DNA has also been found within the same organism, drawing an interesting parallel between the two situations. The advent of next-generation sequencing technologies facilitated studying genetic variation, but many open-source bioinformatic tools have to be combined in a non-trivial approach. Here it is presented QuasiFlow, a workflow based on well-stablished software that extracts reliable mutations and recombinations, even at low frequencies (~10-4), provided that at least 250 million nucleotides are analysed. Accurate prediction of mutations and recombinations has been demonstrated with synthetic reads and with in vitro rolling-circle amplification of a plant geminivirus. An in-depth analysis of viral quasispecies was performed and QuasiFlow revealed the coexistence in the plant of three virus genomes and distinct recombinations between some of them. Human mitochondrial variants were also investigated and high level of heteroplasmy (75%) was confirmed, and the relation between low-frequency heteroplasmy (0.1- 0.2%) and some human diseases, regardless of sex, was established. Hence, we propose that QuasiFlow may find use with known and emerging viruses to reveal evolutionary jumps and co-infections, with mitochondrial DNA to detect relevant heteroplasmy would otherwise be elusive, or even in other population studies such as those considering single cell sequencing., This work was supported by Consejería de Economía, Innovación y Ciencia, Junta de Andalucía with assistance from the European Regional Development Fund (ERDF) and the European Social Fund (ESF) [grant to Research Groups BIO-264 and BIO-267; P10594 -CVI- 6561 and UMA18-FEDERJA178 to A.G.-P., E.V.M; UMA20-FEDERJA-029 to MGC; and a predoctoral fellowship to L.D.-M. A.G-P. acknowledges support of Plan Propio de Investigación y Transferencia of the University of Málaga. Open access charges was funded by University of Málaga. The authors also thankfully acknowledge the computer resources and the technical support provided by the Plataforma Andaluza de Bioinformática of the University of Málaga.

The mining of the massive amounts of biomedical information is hindered by the still scarce representation of these data using formal vocabularies and ontologies, which is necessary for cross-linking conceptual entities between different resources and, in general, representing the information in a computer-tractable way. Basic things such as retrieving a comprehensive list of associations between complex diseases and their reported symptoms or underlying biological processes, given in terms of formal identifiers, are not trivial and, in many cases, these have to be generated by manual curation or inferred/predicted from indirect evidences. In this work, using a text-mining approach based on detecting significant co-mentions in the scientific literature, we generated a resource with millions of relationships between thousands of terms representing diseases, symptoms, biological processes, molecular functions and cellular compartments, all given in terms of formal identifiers of these terms in the main resources dealing with them. We show some examples that highlight the differences between these relationships and those that are available in other resources. These relationships can be queried and inspected in an interactive web interface freely available at: https://sysbiol.cnb.csic.es/CoMent.

Copy number variation (CNV) related disorders tend to show complex phenotypic profiles that do not match known diseases. This makes it difficult to ascertain their underlying molecular basis. A potential solution is to compare the affected genomic regions for multiple patients that share a pathological phenotype, looking for commonalities. Here, we present a novel approach to associate phenotypes with functional systems, in terms of GO categories and KEGG and Reactome pathways, based on patient data. The approach uses genomic and phenomic data from the same patients, finding shared genomic regions between patients with similar phenotypes. These regions are mapped to genes to find associated functional systems. We applied the approach to analyse patients in the DECIPHER database with de novo CNVs, finding functional systems associated with most phenotypes, often due to mutations affecting related genes in the same genomic region. Manual inspection of the ten top-scoring phenotypes found multiple FunSys connections supported by the previous studies for seven of them. The workflow also produces reports focussed on the genes and FunSys connected to the different phenotypes, alongside patient-specific reports, which give details of the associated genes and FunSys for each individual in the cohort. These can be run in "confidential" mode, preserving patient confidentiality. The workflow presented here can be used to associate phenotypes with functional systems using data at the level of a whole cohort of patients, identifying important connections that could not be found when considering them individually. The full workflow is available for download, enabling it to be run on any patient cohort for which phenotypic and CNV data are available.

12 páginas, 7 figuras, 2 tablas. Te online version contains supplementary material available at https://doi.org/ 10.1038/s41598-021-94343-w . Code is available at the bioconductor landing page https://bioconductor.org/packages/ExpHunterSuite. Te latest version of the code can be found at https://github.com/seoanezonjic/ExpHunterSuite. Tere is a specifc github site for the simulated data and case-studies at: https://github.com/fmjabato/ExpHunterSuiteExamples. Te dataset supporting the results of this article are available in the Sequence Read Archive SRA [https://www.ncbi.nlm.nih.gov/sra/PRJNA746239 (Lafora Disease) and ttps://www.ncbi.nlm.nih.gov/sra/PRJNA747153 (PMM2-CDG)] ; all FASTQ fles as well as important processed data necessary to repeat analysis have been made available., High-throughput gene expression analysis is widely used. However, analysis is not straightforward. Multiple approaches should be applied and methods to combine their results implemented and investigated. We present methodology for the comprehensive analysis of expression data, including co-expression module detection and result integration via data-fusion, threshold based methods, and a Naïve Bayes classifier trained on simulated data. Application to rare-disease model datasets confirms existing knowledge related to immune cell infiltration and suggest novel hypotheses including the role of calcium channels. Application to simulated and spike-in experiments shows that combining multiple methods using consensus and classifiers leads to optimal results. ExpHunter Suite is implemented as an R/Bioconductor package available from https://bioconductor.org/packages/ExpHunterSuite . It can be applied to model and non-model organisms and can be run modularly in R; it can also be run from the command line, allowing scalability with large datasets. Code and reports for the studies are available from https://github.com/fmjabato/ExpHunterSuiteExamples, Tis work was supported by Te Spanish Ministry of Economy and Competitiveness with European Regional Development Fund [PID2019-108096RB-C21]; the Andalusian Government with European Regional Development Fund [projects: UMA18-FEDERJA-102 and PAIDI-2020-PY20-00372]; biomedicine research project [PI-0075-2017] (Fundación Progreso y Salud); the Carlos III Health Institue [PI19/01155]; the Ramón Areces foundation for rare disease investigation (National call for research on life and material sciences, XIX edition); the National Institute of Health (NIH-NINDS) [P01NS097197], which established the Lafora Epilepsy Cure Initiative (LECI); the Madrid Government [B2017/BMD-3721], and the Fundación Isabel Gemio/Fundacion La Caixa [LCF/PR/PR16/11110018]. Te CIBERER is an initiative from the Carlos III Health Institute (Instituto de Salud Carlos III).

[Background]: The advances in high-throughput sequencing technologies are allowing more and more de novo assembling of transcriptomes from many new organisms. Some degree of automation and evaluation is required to warrant reproducibility, repetitivity and the selection of the best possible transcriptome. Workflows and pipelines are becoming an absolute requirement for such a purpose, but the issue of assembling evaluation for de novo transcriptomes in organisms lacking a sequenced genome remains unsolved. An automated, reproducible and flexible framework called TransFlow to accomplish this task is described., [Results]: TransFlow with its five independent modules was designed to build different workflows depending on the nature of the original reads. This architecture enables different combinations of Illumina and Roche/454 sequencing data, and can be extended to other sequencing platforms. Its capabilities are illustrated with the selection of reliable plant reference transcriptomes and the assembling six transcriptomes (three case studies for grapevine leaves, olive tree pollen, and chestnut stem, and other three for haustorium, epiphytic structures and their combination for the phytopathogenic fungus Podosphaera xanthii). Arabidopsis and poplar transcriptomes revealed to be the best references. A common result regarding de novo assemblies is that Illumina paired-end reads of 100 nt in length assembled with OASES can provide reliable transcriptomes, while the contribution of longer reads is noticeable only when they complement a set of short, single-reads., [Conclusions]:TransFlow can handle up to 181 different assembling strategies. Evaluation based on principal component analyses allows its self-adaptation to different sets of reads to provide a suitable transcriptome for each combination of reads and assemblers. As a result, each case study has its own behaviour, prioritises evaluation parameters, and gives an objective and automated way for detecting the best transcriptome within a pool of them. Sequencing data type and quantity (preferably several hundred millions of 2×100 nt or longer), assemblers (OASES for Illumina, MIRA4 and EULER-SR reconciled with CAP3 for Roche/454) and strategy (preferably scaffolding with OASES, and probably merging with Roche/454 when available) arise as the most impacting factors., This work was supported by co-funding by the European Union through the European Regional Development Fund (ERDF) 2014-2020 “Programa Operativo de Crecimiento Inteligente” together with Spanish AEI “Agencia Estatal de Investigación” (BFU2016-77243-P, RTC-2015-4181-2, RTC-2016-4824-2, AGL2013-41939-R and AGL2016-76216-C2-1-R), AEI-INIA (RTA2013-00023-C02 and RTA2013-00068-C03), and Junta de Andalucía (P2011-CVI-7487), as well as CSIC grant 201540E065. PS received a postdoctoral fellowship from Junta de Andalucía linked to grant P10-CVI-6075. Publication costs were funded by the mentioned RTA2013-00068-C03 grant.

Senegalese sole is an economically important flatfish species in aquaculture and an attractive model to decipher the molecular mechanisms governing the severe transformations occurring during metamorphosis, where retinoic acid seems to play a key role in tissue remodeling. In this study, a robust sole transcriptome was envisaged by reducing the number of assembled libraries (27 out of 111 available), fine-tuning a new automated and reproducible set of workflows for de novo assembling based on several assemblers, and removing low confidence transcripts after mapping onto a sole female genome draft. From a total of 96 resulting assemblies, two “raw” transcriptomes, one containing only Illumina reads and another with Illumina and GS-FLX reads, were selected to provide SOLSEv5.0, the most informative transcriptome with low redundancy and devoid of most single-exon transcripts. It included both Illumina and GS-FLX reads and consisted of 51,348 transcripts of which 22,684 code for 17,429 different proteins described in databases, where 9527 were predicted as complete proteins. SOLSEv5.0 was used as reference for the study of retinoic acid (RA) signalling in sole larvae using drug treatments (DEAB, a RA synthesis blocker, and TTNPB, a RA-receptor agonist) for 24 and 48 h. Differential expression and functional interpretation were facilitated by an updated version of DEGenes Hunter. Acute exposure of both drugs triggered an intense, specific and transient response at 24 h but with hardly observable differences after 48 h at least in the DEAB treatments. Activation of RA signalling by TTNPB specifically increased the expression of genes in pathways related to RA degradation, retinol storage, carotenoid metabolism, homeostatic response and visual cycle, and also modified the expression of transcripts related to morphogenesis and collagen fibril organisation. In contrast, DEAB mainly decreased genes related to retinal production, impairing phototransduction signalling in the retina. A total of 755 transcripts mainly related to lipid metabolism, lipid transport and lipid homeostasis were altered in response to both treatments, indicating non-specific drug responses associated with intestinal absorption. These results indicate that a new assembling and transcript sieving were both necessary to provide a reliable transcriptome to identify the many aspects of RA action during sole development that are of relevance for sole aquaculture.

Background: Protein function prediction remains a key challenge. Domain composition is key to understanding protein function, and domain-based prediction methods consistently perform well in challenges such as CAFA. Here we present DomFun, a Ruby gem that uses associations between protein domains and functions, calculated using multiple indices based on tripartite network analysis. These domain-function associations are combined at the protein level, to generate protein-function predictions. Results: We analysed 14 tripartite networks connecting homologous superfamily and FunFam domains from CATH-Gene3D with functional annotations from the Gene Ontology, KEGG, Reactome and the Human Phenotype Ontology. We validated the results using the CAFA 2 benchmark platform for GO and HPO annotation, finding Simpson's index combined with Stouffer's method led to the best performance in almost all scenarios. We also found that FunFams led to better performance than superfamilies, and better results were found for GO molecular function compared to GO biological process terms. Results were similar to other high-performing domain-based methods in CAFA 2. We also implemented our own benchmark procedure, Pathway Prediction Performance (PPP), which can be used to validate function prediction for additional annotations sources, such as KEGG and Reactome. Using PPP, we found similar results to those found with CAFA 2 for GO, moreover we found good performance for the other annotation sources. As with CAFA 2, Simpson's index with Stouffer's method led to the top performance in most scenarios. Conclusions: DomFun shows comparable performance to other methods evaluated in CAFA 2 when predicting human proteins function with GO. Through our own benchmark procedure, PPP we have shown it can also make accurate predictions for KEGG and Reactome. It performs best when using FunFams, combining Simpson's index derived domain-function associations combined using Stouffer's method. The tool has been implemented so that it could be easily adapted to incorporate other protein features, such as domain data from other sources. The DomFun Ruby gem is available from https://rubygems.org/gems/DomFun and its code is available at https://github.com/ElenaRojano/DomFun .

© 2020 Díaz-Santiago et al., Genetic and molecular analysis of rare disease is made difficult by the small numbers of affected patients. Phenotypic comorbidity analysis can help rectify this by combining information from individuals with similar phenotypes and looking for overlap in terms of shared genes and underlying functional systems. However, few studies have combined comorbidity analysis with genomic data. We present a computational approach that connects patient phenotypes based on phenotypic co-occurence and uses genomic information related to the patient mutations to assign genes to the phenotypes, which are used to detect enriched functional systems. These phenotypes are clustered using network analysis to obtain functionally coherent phenotype clusters. We applied the approach to the DECIPHER database, containing phenotypic and genomic information for thousands of patients with heterogeneous rare disorders and copy number variants. Validity was demonstrated through overlap with known diseases, co-mention within the biomedical literature, semantic similarity measures, and patient cluster membership. These connected pairs formed multiple phenotype clusters, showing functional coherence, and mapped to genes and systems involved in similar pathological processes. Examples include claudin genes from the 22q11 genomic region associated with a cluster of phenotypes related to DiGeorge syndrome and genes related to the GO term anterior/posterior pattern specification associated with abnormal development. The clusters generated can help with the diagnosis of rare diseases, by suggesting additional phenotypes for a given patient and potential underlying functional systems. Other tools to find causal genes based on phenotype were also investigated. The approach has been implemented as a workflow, named PhenCo, which can be adapted to any set of patients for which phenomic and genomic data is available. Full details of the analysis, including the clusters formed, their constituent functional systems and underlying genes are given. Code to implement the workflow is available from GitHub., The study was funded by grants from the The Spanish Ministry of Science and Innovation with European Regional Development Fund [PID2019-108096RB-C21 to J.A.G and PID2019- 108096RB-C22 to F.P.]; the Andalusian Government (Junta de Andalucia) with European Regional Development Fund [UMA18-FEDERJA102], and Biomedicine Research project [PI-0075- 2017] (Fundacion Progreso y Salud); and the Ramo´n Areces foundation, which funds project for the investigation of rare disease (National call for research on life and material sciences, XIX edition); Ramo´n y Cajal I3 projects through the Research Plan of the University of Malaga (Ayudas del I Plan Propio). The CIBERER is an initiative from the Carlos III Health Institute (Instituto de Salud Carlos III; ISCIII).

Systemic approaches based on network analysis have been successful in associating pathological phenotypes observed in patients with their affected genomic regions. Previously we have used phenotype-genotype associations to determine the genetic causes that lead to pathological phenotypes observed in patients with rare and complex disorders. However, these studies were limited as many of these associations had low specificity, frequently associating pathological phenotypes such as intellectual disability or growth abnormality with multiple regions of the genome. To help solve this problem, we propose that the phenotypic characterisation of patients using more specific terms will substantially improve the determination of the genetic causes that produce them. In this work we present the Patient Exploration Tools Suite (PETS), which includes three tools to: (1) determine the quality of information within a patient cohort; (2) associate genomic regions with their pathological phenotypes based on the cohort data; and (3) predict the possible genetic variants that cause the clinically observed pathological phenotypes using phenotype-genotype association values. This tool has been developed to be used by the clinical community, to facilitate patient characterisation, help identify where data quality can be improved within a cohort and help diagnose patients with complex disease.

The latest advances in biotechnology are increasing the size and number of biological databases, specially those related to “omics” sciences. This data can be used to generate complex interaction networks, which analysis allows to extract biological information. Network analysis comprises a current bioinformatics challenge and the implementation of kernels offers a potential procedure to perform this analysis. Kernel algebraic functions have been used to study interaction networks and they are of major interest in new applications to improve machine learning studies. To manage these interaction networks, the NetAnalyzer tool was developed with the purpose of analysing multi-layer networks, calculating different probabilistic indices to establish the association between pairs of nodes. In this study we implement different kernel operations using several programming languages to inspect their reliability to perform these operations in different scenarios. Best performances have been included as a kernel functional module into NetAnalyzer, and we used them over gene interactions networks and gene-disease knowledge to identify disease causing genes.

Variants within non-coding genomic regions can greatly affect disease. In recent years, increasing focus has been given to these variants, and how they can alter regulatory elements, such as enhancers, transcription factor binding sites and DNA methylation regions. Such variants can be considered regulatory variants. Concurrently, much effort has been put into establishing international consortia to undertake large projects aimed at discovering regulatory elements in different tissues, cell lines and organisms, and probing the effects of genetic variants on regulation by measuring gene expression. Here, we describe methods and techniques for discovering disease-associated non-coding variants using sequencing technologies. We then explain the computational procedures that can be used for annotating these variants using the information from the aforementioned projects, and prediction of their putative effects, including potential pathogenicity, based on rule-based and machine learning approaches. We provide the details of techniques to validate these predictions, by mapping chromatin–chromatin and chromatin–protein interactions, and introduce Clustered Regularly Interspaced Short Palindromic Repeats-Associated Protein 9 (CRISPR-Cas9) technology, which has already been used in this field and is likely to have a big impact on its future evolution. We also give examples of regulatory variants associated with multiple complex diseases. This review is aimed at bioinformaticians interested in the characterization of regulatory variants, molecular biologists and geneticists interested in understanding more about the nature and potential role of such variants from a functional point of views, and clinicians who may wish to learn about variants in non-coding genomic regions associated with a given disease and find out what to do next to uncover how they impact on the underlying mechanisms.

Exhaustive and comprehensive analysis of pathological traits is essential to understanding genetic diseases, performing precise diagnosis and prescribing personalized treatments. It is particularly important for disease cohorts, as thoroughly detailed phenotypic profiles allow patients to be compared and contrasted. However, many disease cohorts contain patients that have been ascribed low numbers of very general and relatively uninformative phenotypes. We present Cohort Analyzer, a tool that measures the phenotyping quality of patient cohorts. It calculates multiple statistics to give a general overview of the cohort status in terms of the depth and breadth of phenotyping, allowing us to detect less well-phenotyped patients for re-examining or excluding from further analyses. In addition, it performs clustering analysis to find subgroups of patients that share similar phenotypic profiles. We used it to analyse three cohorts of genetic diseases patients with very different properties. We found that cohorts with the most specific and complete phenotypic characterization give more potential insights into the disease than those that were less deeply characterised by forming more informative clusters. For two of the cohorts, we also analysed genomic data related to the patients, and linked the genomic data to the patient-subgroups by mapping shared variants to genes and functions. The work highlights the need for improved phenotyping in this era of personalized medicine. The tool itself is freely available alongside a workflow to allow the analyses shown in this work to be applied to other datasets., The Spanish Ministry of Economy and Competitiveness with European Regional Development Fund [PID2019-108096RB-C21]; the Andalusian Government with European Regional Development Fund [UMA18-FEDERJA-102 and PAIDI 2020:PY20-00372]; biomedicine research project [PI-0075-2017] (Fundación Progreso y Salud); the Carlos III Health Institute [PI19/01155]; the Madrid Government [B2017/BMD-3721]; the Ramón Areces and foundation Generalitat de Catalunya [PERIS LT008/18/00194]. The CIBERER is an initiative from the Carlos III Health Institute (Instituto de Salud Carlos III)

[Background] Gene expression analyses demand appropriate reference genes (RGs) for normalization, in order to obtain reliable assessments. Ideally, RG expression levels should remain constant in all cells, tissues or experimental conditions under study. Housekeeping genes traditionally fulfilled this requirement, but they have been reported to be less invariant than expected; therefore, RGs should be tested and validated for every particular situation. Microarray data have been used to propose new RGs, but only a limited set of model species and conditions are available; on the contrary, RNA-seq experiments are more and more frequent and constitute a new source of candidate RGs., [Results] An automated workflow based on mapped NGS reads has been constructed to obtain highly and invariantly expressed RGs based on a normalized expression in reads per mapped million and the coefficient of variation. This workflow has been tested with Roche/454 reads from reproductive tissues of olive tree (Olea europaea L.), as well as with Illumina paired-end reads from two different accessions of Arabidopsis thaliana and three different human cancers (prostate, small-cell cancer lung and lung adenocarcinoma). Candidate RGs have been proposed for each species and many of them have been previously reported as RGs in literature. Experimental validation of significant RGs in olive tree is provided to support the algorithm., [Conclusion] Regardless sequencing technology, number of replicates, and library sizes, when RNA-seq experiments are designed and performed, the same datasets can be analyzed with our workflow to extract suitable RGs for subsequent PCR validation. Moreover, different subset of experimental conditions can provide different suitable RGs., This research was supported by co-funding from the European Union through the ERDF 2014–2020 “Programa Operativo de Crecimiento Inteligente” to the projects RTA2013-00068-C03 and RTA2013-00023-C02 of the Spanish INIA; BFU2011-22779 and RECUPERA2020-3.1.4 from the Spanish MINECO, P11-CVI-7487 from the regional PAI and NEUMOSUR grant 12/2015 entitled “Expresión de retrotransposones en pacientes con adenocarcinoma intervenido. Comparación entre tejjido sano y tumoral”. Publication costs were funded by the mentioned grants

Recent advances in sequencing technologies allow researchers to investigate diseases resulting of genomic variation. This allows us to further develop the concept of precision medicine and determine the best treatment for each patient. We have focused on developing tools for studying genomic loci associated to pathological traits from the perspective of network analysis. We have obtained from DECIPHER database patient information which includes their affected genomic regions by Copy Number Variations (CNV) and their pathologies described as Human Phenotype Ontology phenotypes. We have used different metrics for calculating association values between phenotypes and affected genomic regions to determine which method fits better to our data. The results obtained in this work, can be used in prediction systems for determining and ranking which genomic regions are associated to a concrete phenotype, in order to help clinicians with their diagnosis.

The use of workflows to automate routine tasks is an absolute requirement in many bioinformatics fields. Current workflow manager systems usually compromise between providing a user-friendly interface and constructing complex, scalable pipelines. We present AutoFlow, a Ruby-based workflow engine devoid of graphic interface and tool repository, that is useful in most computer systems and most workflow requirements in any scientific field. It accepts any local or remote command-line software and converts one workflow into a series of independent tasks. It has been supplied with control patterns that allow for iterative task capability, supporting static and dynamic variables for decision-making or chaining workflows, as well as debugging utilities that include graphs, file searching, functional consistency and timing. Two proof-of-concept cases are presented to illustrate AutoFlow capabilities, and a case-of-use illustrates the automated construction of the best transcriptome for a non-model species (Vicia faba) after analysis of several combinations of Illumina reads and Sanger sequences with different assemblers and different parameters in a complex and repetitive workflow where branching and convergent tasks were used and internal, automated decisions were taken. The workflow finally produced an optimal transcriptome of 118,188 transcripts, of which 38,004 were annotated, 10,516 coded for a complete protein, 3,314 were putatively new faba-specific transcripts, and 23,727 were considered the representative transcriptome of V. faba.

Population genetic studies in tropical plants are often challenging because of limited information on taxonomy, phylogenetic relationships and distribution ranges, scarce genomic information and logistic challenges in sampling. We describe a strategy to develop robust and widely applicable genetic markers based on a modest development of genomic resources in the ancient tropical tree species Symphonia globulifera L.f. (Clusiaceae), a keystone species in African and Neotropical rainforests. We provide the first low-coverage (11X) fragmented draft genome sequenced on an individual from Cameroon, covering 1.027 Gbp or 67.5% of the estimated genome size. Annotation of 565 scaffolds (7.57 Mbp) resulted in the prediction of 1046 putative genes (231 of them containing a complete open reading frame) and 1523 exact simple sequence repeats (SSRs, microsatellites). Aligning a published transcriptome of a French Guiana population against this draft genome produced 923 high-quality single nucleotide polymorphisms. We also preselected genic SSRs in silico that were conserved and polymorphic across a wide geographical range, thus reducing marker development tests on rare DNA samples. Of 23 SSRs tested, 19 amplified and 18 were successfully genotyped in four S. globulifera populations from South America (Brazil and French Guiana) and Africa (Cameroon and São Tomé island, FST = 0.34). Most loci showed only population-specific deviations from Hardy–Weinberg proportions, pointing to local population effects (e.g. null alleles). The described genomic resources are valuable for evolutionary studies in Symphonia and for comparative studies in plants. The methods are especially interesting for widespread tropical or endangered taxa with limited DNA availability. © 2016 John Wiley & Sons Ltd

Expression analyses such as quantitative and/or real-time PCR require the use of reference genes for normalization in order to obtain reliable assessments. The expression levels of these reference genes must remain constant in all different experimental conditions and/or tissues under study. Traditionally, housekeeping genes have been used for this purpose, but most of them have been reported to vary their expression levels under some experimental conditions. Consequently, the election of the best reference genes should be tested and validated in every experimental scenario. Microarray data are not always available for the search of appropriate reference genes, but NGS experiments are increasingly common. For this reason, an automatic workflow based on mapped NGS reads is presented with the aim of obtaining putative reference genes for a giving species in the experimental conditions of interest. The calculation of the coefficient of variation (CV) and a simple, normalized expression value such as RPKM per transcript allows for filtering and selecting those transcripts expressed homogeneously and consistently in all analyzed conditions. This workflow has been tested with Roche/454 reads obtained from olive (Olea europaea L.) pollen and pistil at different developmental stages, as well as with Illumina paired-end reads from two different accessions of Arabidopsis thaliana. Some of the putative candidate reference genes have been experimentally validated.

Transcriptome databases are an important source of structural and functional information about an organism, for example, plants without a sequenced genome. This is the case of the olive tree (Olea europaea L.), one of the most important oil-producing plant species all over the world. In addition, reproductive tissues and seeds are the less studied part of these plant species in spite of their importance in allergies, germination success, plant sterility, as well as being an important source of valuable components for agro-food industries, including seed storage proteins and trialcylglycerides. Therefore, an automated workflow has been developed using our tool AutoFlow to construct an annotated transcriptome from raw reads (Sanger, Illumina or Roche/454 or a combination of them) combining open source software (Bowtie2, CAP2, Euler-SR, MIRA3, Velvet/Oases, AutoFact, MREPS, GigaBayes) with software developed by our group (SeqTrimNext, Full-LengtherNext, Sma3). The resulting transcriptomes were used to build a database ReprOlive (http://reprolive.eez.csic.es) where descriptions, GO terms, InterPro signatures, EC numbers, graphical localization of enzymes in KEGG pathways, ORFs, SSRs, and the corresponding orthologues in Arabidopsis thaliana from TAIR and RefSeq can be browsed. Finally, expression data can be accessed and, in addition to a BLAST search, a the semantic conceptualization using RDF allowing for Linked Data search was implemented to extract the most updated information related to enzymes, interactions, allergens, and structures. The olive tree reproductive transcriptome was constructed from 2,077,309 raw reads (454/Roche Titanium+) and 1,549 Sanger sequences from different stages of pollen and stigma development, resulting in 72,846 contigs, of which 63,965 (87.8%) included at least one functional annotation, and 55,356 (75.9%) had an orthologue. Using different seed stages, 1,425,911 raw reads (454/Roche Titanium+) are in use for obtaining the seed transcriptome. Uses of these transcriptomes can be found in communications by Carmona et al. and JImnez-Quesada et al. in this congress. This work was supported by co-funding from the ERDF and Spanish MINECO and Andalusian PAIDI to the grants BFU2011-22779, TIN2011-25840, TIN2014-58304-R, P10-CVI-6075, P10-AGR-6274, P11-CVI-7487, P11-TIC-7529 and P12-TIC-1519. Authors also acknowledge the use of the SCBI facilities of UMA.

The use of RNA-Seq has transformed the way sequencing reads are analyzed, allowing for qualitative and quantitative studies of transcriptomes. These studies always include an important collection (usually > 40%) of unknown transcripts. In this study, we improve the capability of Full-LengtherNext, an algorithm developed in our laboratory to annotate, analyze and correct de novo transcriptomes, to detect of potentially coding sequences. Here we analyze five software implementations of coding sequence predictors and show that the use of high-quality sequences at the training stage, proper threshold selection during score interrogation and the algorithm adaptation to its input type have a profound effect on the accuracy of the prediction. TransDecoder, the best performing algorithm in our tests, was thus added to the Full-LenghterNext pipeline, significantly improving its coding prediction reliability. Moreover, these analyses served to make inferences about the quality of the sample and to extract the subset of species specific (perhaps novel) genes discovered in the transcriptome assembly. Indirectly, we also demonstrated that Full-LentherNext sequence classification is appropriate and worth taking into consideration.