Transcriptomic analysis identifies dysregulated pathways and therapeutic targets in PMM2-CDG.

PMM2-CDG (MIM # 212065), the most common congenital disorder of glycosylation, is caused by the deficiency of phosphomannomutase 2 (PMM2). It is a multisystemic disease of variable severity that particularly affects the nervous system; however, its molecular pathophysiology remains poorly understood. Currently, there is no effective treatment. We performed an RNA-seq based transcriptomic study using patient-derived fibroblasts to gain insight into the mechanisms underlying the clinical symptomatology and to identify druggable targets. Systems biology methods were used to identify cellular pathways potentially affected by PMM2 deficiency, including Senescence, Bone regulation, Cell adhesion and Extracellular Matrix (ECM) and Response to cytokines. Functional validation assays using patients' fibroblasts revealed defects related to cell proliferation, cell cycle, the composition of the ECM and cell migration, and showed a potential role of the inflammatory response in the pathophysiology of the disease. Furthermore, treatment with a previously described pharmacological chaperone reverted the differential expression of some of the dysregulated genes. The results presented from transcriptomic data might serve as a platform for identifying therapeutic targets for PMM2-CDG, as well as for monitoring the effectiveness of therapeutic strategies, including pharmacological candidates and mannose-1-P, drug repurposing.

Exploring miRNA-target gene pair detection in disease with coRmiT.

A wide range of approaches can be used to detect micro RNA (miRNA)-target gene pairs (mTPs) from expression data, differing in the ways the gene and miRNA expression profiles are calculated, combined and correlated. However, there is no clear consensus on which is the best approach across all datasets. Here, we have implemented multiple strategies and applied them to three distinct rare disease datasets that comprise smallRNA-Seq and RNA-Seq data obtained from the same samples, obtaining mTPs related to the disease pathology. All datasets were preprocessed using a standardized, freely available computational workflow, DEG_workflow. This workflow includes coRmiT, a method to compare multiple strategies for mTP detection. We used it to investigate the overlap of the detected mTPs with predicted and validated mTPs from 11 different databases. Results show that there is no clear best strategy for mTP detection applicable to all situations. We therefore propose the integration of the results of the different strategies by selecting the one with the highest odds ratio for each miRNA, as the optimal way to integrate the results. We applied this selection-integration method to the datasets and showed it to be robust to changes in the predicted and validated mTP databases. Our findings have important implications for miRNA analysis. coRmiT is implemented as part of the ExpHunterSuite Bioconductor package available from https://bioconductor.org/packages/ExpHunterSuite.

Genetic signature detected in T cell receptors from patients with severe COVID-19.

Characterization of host genetic factors contributing to COVID-19 severity promises advances on drug discovery to fight the disease. Most genetic analyses to date have identified genome-wide significant associations involving loss-of-function variants for immune response pathways. Despite accumulating evidence supporting a role for T cells in COVID-19 severity, no definitive genetic markers have been found to support an involvement of T cell responses. We analyzed 205 whole exomes from both a well-characterized cohort of hospitalized severe COVID-19 patients and controls. Significantly enriched high impact alleles were found for 25 variants within the T cell receptor beta (TRB) locus on chromosome 7. Although most of these alleles were found in heterozygosis, at least three or more in TRBV6-5, TRBV7-3, TRBV7-6, TRBV7-7, and TRBV10-1 suggested a possible TRB loss of function via compound heterozygosis. This loss-of-function in TRB genes supports suboptimal or dysfunctional T cell responses as a major contributor to severe COVID-19 pathogenesis.

Integrating differential expression, co-expression and gene network analysis for the identification of common genes associated with tumor angiogenesis deregulation.

Angiogenesis is essential for tumor growth and cancer metastasis. Identifying the molecular pathways involved in this process is the first step in the rational design of new therapeutic strategies to improve cancer treatment. In recent years, RNA-seq data analysis has helped to determine the genetic and molecular factors associated with different types of cancer. In this work we performed integrative analysis using RNA-seq data from human umbilical vein endothelial cells (HUVEC) and patients with angiogenesis-dependent diseases to find genes that serve as potential candidates to improve the prognosis of tumor angiogenesis deregulation and understand how this process is orchestrated at the genetic and molecular level. We downloaded four RNA-seq datasets (including cellular models of tumor angiogenesis and ischaemic heart disease) from the Sequence Read Archive. Our integrative analysis includes a first step to determine differentially and co-expressed genes. For this, we used the ExpHunter Suite, an R package that performs differential expression, co-expression and functional analysis of RNA-seq data. We used both differentially and co-expressed genes to explore the human gene interaction network and determine which genes were found in the different datasets that may be key for the angiogenesis deregulation. Finally, we performed drug repositioning analysis to find potential targets related to angiogenesis inhibition. We found that that among the transcriptional alterations identified, SEMA3D and IL33 genes are deregulated in all datasets. Microenvironment remodeling, cell cycle, lipid metabolism and vesicular transport are the main molecular pathways affected. In addition to this, interacting genes are involved in intracellular signaling pathways, especially in immune system and semaphorins, respiratory electron transport and fatty acid metabolism. The methodology presented here can be used for finding common transcriptional alterations in other genetically-based diseases.

SpPdp11 Administration in Diet Modified the Transcriptomic Response and Its Microbiota Associated in Mechanically Induced Wound Sparus aurata Skin.

Skin lesions are a frequent fact associated with intensive conditions affecting farmed fish. Knowing that the use of probiotics can improve fish skin health, SpPdp11 dietary administration has demonstrated beneficial effects for farmed fish, so its potential on the skin needs to be studied more deeply. The wounded specimens that received the diet with SpPdp11 showed a decrease in the abundance of Enterobacteriaceae, Photobacterium and Achromobacter related to bacterial biofilm formation, as well as the overexpression of genes involved in signaling mechanisms (itpr3), cell migration and differentiation (panxa, ttbk1a, smpd3, vamp5); and repression of genes related to cell proliferation (vstm4a, areg), consistent with a more efficient skin healing processes than that observed in the wounded control group. In addition, among the groups of damaged skin with different diets, Achromobacter, f_Ruminococcaceae, p_Bacteroidetes, Fluviicola and Flavobacterium genera with significant differences showed positive correlations with genes related to cell migration and negative correlations with inflammation and cell proliferation and may be the target of future studies.

Advancing in Schaaf-Yang syndrome pathophysiology: from bedside to subcellular analyses of truncated MAGEL2.

BACKGROUND: Schaaf-Yang syndrome (SYS) is caused by truncating mutations in MAGEL2, 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. METHODS: We performed an extensive phenotypic and mutational revision of previously reported patients with SYS. We analysed the secretion levels of amyloid-ß 1-40 peptide (Aß1-40) 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. RESULTS: Functional studies show significantly decreased levels of secreted Aß1-40 and intracellular glutamine in SYS fibroblasts compared with WT. We also identified 132 differentially expressed genes, including non-coding RNAs (ncRNAs) such as HOTAIR, 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. CONCLUSION: A truncated MAGEL2 protein is stable and localises mainly in the nucleus, where it might exert a pathogenic neomorphic effect. Aß1-40 secretion levels and HOTAIR mRNA levels might be promising biomarkers for SYS. Our findings may improve SYS understanding and clinical management.

Pathogenic strains of Shewanella putrefaciens contain plasmids that are absent in the probiotic strain Pdp11.

Shewanella putrefaciens Pdp11 is a strain described as a probiotic for use in aquaculture. However, S. putrefaciens includes strains reported to be pathogenic or saprophytic to fish. Although the probiotic trait has been related to the presence of a group of genes in its genome, the existence of plasmids that could determine the probiotic or pathogenic character of this bacterium is unknown. In the present work, we searched for plasmids in several strains of S. putrefaciens that differ in their pathogenic and probiotic character. Under the different conditions tested, plasmids were only found in two of the five pathogenic strains, but not in the probiotic strain nor in the two saprophytic strains tested. Using a workflow integrating Sanger and Illumina reads, the complete consensus sequences of the plasmids were obtained. Plasmids differed in one ORF and encoded a putative replication initiator protein of the repB family, as well as proteins related to plasmid stability and a toxin-antitoxin system. Phylogenetic analysis showed some similarity to functional repB proteins of other Shewanella species. The implication of these plasmids in the probiotic or pathogenic nature of S. putrefaciens is discussed.

CoMent: Relationships Between Biomedical Concepts Inferred From the Scientific Literature.

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.

Deepening the knowledge of rare diseases dependent on angiogenesis through semantic similarity clustering and network analysis.

BACKGROUND: Angiogenesis is regulated by multiple genes whose variants can lead to different disorders. Among them, rare diseases are a heterogeneous group of pathologies, most of them genetic, whose information may be of interest to determine the still unknown genetic and molecular causes of other diseases. In this work, we use the information on rare diseases dependent on angiogenesis to investigate the genes that are associated with this biological process and to determine if there are interactions between the genes involved in its deregulation. RESULTS: We propose a systemic approach supported by the use of pathological phenotypes to group diseases by semantic similarity. We grouped 158 angiogenesis-related rare diseases in 18 clusters based on their phenotypes. Of them, 16 clusters had traceable gene connections in a high-quality interaction network. These disease clusters are associated with 130 different genes. We searched for genes associated with angiogenesis througth ClinVar pathogenic variants. Of the seven retrieved genes, our system confirms six of them. Furthermore, it allowed us to identify common affected functions among these disease clusters. AVAILABILITY: https://github.com/ElenaRojano/angio_cluster. CONTACT: seoanezonjic@uma.es and elenarojano@uma.es.

Transcriptional changes in dendritic cells underlying allergen specific induced tolerance in a mouse model.

To investigate food allergy-tolerance mechanisms induced through allergen-specific immunotherapy we used RNA-Sequencing to measure gene expression in lymph-node-derived dendritic cells from Pru p 3-anaphylactic mice after immunotherapy with glycodendropeptides at 2 nM and 5 nM, leading to permanent tolerance and short-term desensitization, respectively. Gene expression was also measured in mice receiving no immunotherapy (anaphylaxis); and in which anaphylaxis could never occur (antigen-only). Compared to anaphylaxis, the antigen-only group showed the greatest number of expression-changes (411), followed by tolerant (186) and desensitized (119). Only 29 genes changed in all groups, including Il12b, Cebpb and Ifngr1. The desensitized group showed enrichment for genes related to chronic inflammatory response, secretory granule, and regulation of interleukin-12 production; the tolerant group showed genes related to cytokine receptor activity and glucocorticoid receptor binding, suggesting distinct pathways for similar outcomes. We identified genes and processes potentially involved in the restoration of long-term tolerance via allergen-specific immunotherapy, representing potential prognostic biomarkers.

Gene expression analysis method integration and co-expression module detection applied to rare glucide metabolism disorders using ExpHunterSuite.

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 .

Demography, genetic diversity and expansion load in the colonizing species Leontodon longirostris (Asteraceae) throughout its native range.

Unravelling the evolutionary processes underlying range expansions is fundamental to understand the distribution of organisms, as well as to predict their future responses to environmental change. Predictions for range expansions include a loss of genetic diversity and an accumulation of deleterious alleles along the expansion axis, which can decrease fitness at the range-front (expansion load). In plants, empirical studies supporting expansion load are scarce, and its effects remain to be tested outside a few model species. Leontodon longirostris is a colonizing Asteraceae with a widespread distribution in the Western Mediterranean, providing a particularly interesting system to gain insight into the factors that can enhance or mitigate expansion load. In this study, we produced a first genome draft for the species, covering 418 Mbp (~53% of the genome). Although incomplete, this draft was suitable to design a targeted sequencing of ~1.5 Mbp in 238 L. longirostris plants from 21 populations distributed along putative colonization routes in the Iberian Peninsula. Inferred demographic history supports a range expansion from southern Iberia around 40,000 years ago, reaching northern Iberia around 25,000 years ago. The expansion was accompanied by a loss of genetic diversity and a significant increase in the proportion of putatively deleterious mutations. However, levels of expansion load in L. longirostris were smaller than those found in other plant species, which can be explained, at least partially, by its high dispersal ability, the self-incompatible mating system, and the fact that the expansion occurred along a strong environmental cline.

Systematic identification of genetic systems associated with phenotypes in patients with rare genomic copy number variations.

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.

Development of whole-genome multiplex assays and construction of an integrated genetic map using SSR markers in Senegalese sole.

The Senegalese sole (Solea senegalensis) is an economically important flatfish species. In this study, a genome draft was analyzed to identify microsatellite (SSR) markers for whole-genome genotyping. A subset of 224 contigs containing SSRs were preselected and validated by using a de novo female hybrid assembly. Overall, the SSR density in the genome was 886.7 markers per megabase of genomic sequences and the dinucleotide motif was the most abundant (52.4%). In silico comparison identified a set of 108 SSRs (with di-, tetra- or pentanucleotide motifs) widely distributed in the genome and suitable for primer design. A total of 106 markers were structured in thirteen multiplex PCR assays (with up to 10-plex) and the amplification conditions were optimized with a high-quality score. Main genetic diversity statistics and genotyping reliability were assessed. A subset of 40 high polymorphic markers were selected to optimize four supermultiplex PCRs (with up to 11-plex) for pedigree analysis. Theoretical exclusion probabilities and real parentage allocation tests using parent-offspring information confirmed their robustness and effectiveness for parental assignment. These new SSR markers were combined with previously published SSRs (in total 229 makers) to construct a new and improved integrated genetic map containing 21 linkage groups that matched with the expected number of chromosomes. Synteny analysis with respect to C. semilaevis provided new clues on chromosome evolution in flatfish and the formation of metacentric and submetacentric chromosomes in Senegalese sole.

An improved de novo assembling and polishing of Solea senegalensis transcriptome shed light on retinoic acid signalling in larvae.

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.

Phenotype-genotype comorbidity analysis of patients with rare disorders provides insight into their pathological and molecular bases.

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 haustorial transcriptome of the cucurbit pathogen Podosphaera xanthii reveals new insights into the biotrophy and pathogenesis of powdery mildew fungi.

BACKGROUND: Podosphaera xanthii is the main causal agent of powdery mildew disease in cucurbits and is responsible for important yield losses in these crops worldwide. Powdery mildew fungi are obligate biotrophs. In these parasites, biotrophy is determined by the presence of haustoria, which are specialized structures of parasitism developed by these fungi for the acquisition of nutrients and the delivery of effectors. Detailed molecular studies of powdery mildew haustoria are scarce due mainly to difficulties in their isolation. Therefore, their analysis is considered an important challenge for powdery mildew research. The aim of this work was to gain insights into powdery mildew biology by analysing the haustorial transcriptome of P. xanthii. RESULTS: Prior to RNA isolation and massive-scale mRNA sequencing, a flow cytometric approach was developed to isolate P. xanthii haustoria free of visible contaminants. Next, several commercial kits were used to isolate total RNA and to construct the cDNA and Illumina libraries that were finally sequenced by the Illumina NextSeq system. Using this approach, the maximum amount of information from low-quality RNA that could be obtained was used to accomplish the de novo assembly of the P. xanthii haustorial transcriptome. The subsequent analysis of this transcriptome and comparison with the epiphytic transcriptome allowed us to identify the importance of several biological processes for haustorial cells such as protection against reactive oxygen species, the acquisition of different nutrients and genetic regulation mediated by non-coding RNAs. In addition, we could also identify several secreted proteins expressed exclusively in haustoria such as cell adhesion proteins that have not been related to powdery mildew biology to date. CONCLUSIONS: This work provides a novel approach to study the molecular aspects of powdery mildew haustoria. In addition, the results of this study have also allowed us to identify certain previously unknown processes and proteins involved in the biology of powdery mildews that could be essential for their biotrophy and pathogenesis.

TarSynFlow, a workflow for bacterial genome comparisons that revealed genes putatively involved in the probiotic character of Shewanella putrefaciens strain Pdp11.

Probiotic microorganisms are of great interest in clinical, livestock and aquaculture. Knowledge of the genomic basis of probiotic characteristics can be a useful tool to understand why some strains can be pathogenic while others are probiotic in the same species. An automatized workflow called TarSynFlow (Targeted Synteny Workflow) has been then developed to compare finished or draft bacterial genomes based on a set of proteins. When used to analyze the finished genome of the probiotic strain Pdp11 of Shewanella putrefaciens and genome drafts from seven known non-probiotic strains of the same species obtained in this work, 15 genes were found exclusive of Pdp11. Their presence was confirmed by PCR using Pdp11-specific primers. Functional inspection of the 15 genes allowed us to hypothesize that Pdp11 underwent genome rearrangements spurred by plasmids and mobile elements. As a result, Pdp11 presents specific proteins for gut colonization, bile salt resistance and gut pathogen adhesion inhibition, which can explain some probiotic features of Pdp11.

Regulatory variants: from detection to predicting impact.

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.

TransFlow: a modular framework for assembling and assessing accurate de novo transcriptomes in non-model organisms.

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.