Search

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. This study was funded by project RTA2017-00054-C03-01, RTA2017-00054-C03-02 and RTA2017-00054-C03-03 funded from MCIU/AEI/FEDER, UE. IGC is funded by a predoctoral fellowship from INIA

[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.

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

Gene expression analyses of non-model organisms must start with the construction of a high accurate de novo transcriptome as a reference. The best way to determine the suitability of any de novo transcriptome assembling is its comparison with other well-known “reference” transcriptomes. In this study, we took six complete plant transcriptomes (Arabidopsis thaliana, Vitis vinifera, Zea mays, Populus trichocarpa, Triticum aestivum and Oryza sativa) and compared all of them using a series of metrics system for a principal component analysis, resulting that A. thaliana and P. trichocarpa were the best references. This has been automated using AutoFlow. A primary assembly of short reads from Illumina Platform (50 nt, single reads) and long reads from Roche-454 technology from Castanea sativa was performed individually using k-mers from 25 to 35 and different assemblers (Oases v2, SOAPdenovoTrans, RAY, MIRA4 and MINIMUS). The resulting contigs were then reconciled with the aim of obtaining the best transcriptome. Oases and SOAP were used for the assembling of short reads, MIRA and MINIMUS for the assembling of long reads or the reconciliations, and RAY, that can compute de novo transcript assembling from heterogeneous (long and short reads) next-generation sequencing data, was included to avoid the reconciliation step. A total of 90 different assemblies were generated in a single run of the pipeline. A hierarchical clustering on the PCA components (HCPC) was implemented to automatically identify the best assembling strategies based on the shortest distance in HCPC to the two plant reference transcriptomes is selected. In this approach, reconciliation of Roche/454 long reads with Illumina contigs produce more complete and accurate gene reconstructions than other combinations. Surprisingly, reconstructions based only on Illumina and the ones creates with RAY seem to be less accurate. For this specific study, the most complete and accurate transcriptome corresponds to the Illumina contigs obtained with SOAPdenovoTrans and reassembled with 454 long reads using MIRA4. This is only a one example of a transcriptome building. Many other assembling can be performed just changing parameters, k-mers, sequencing technology, assemblers, reference organisms, etc. The pipeline in AutoFlow is easily customizable for those purposes.

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.

Differential gene expression based on RNA-seq is widely used. Bioinformatics skills are required since no algorithm is appropriate for all experimental designs. Moreover, when working with organisms without reference genome, functional analysis is less than straightforward in most situations. DEgenes Hunter, an attempt to automate the process, is based on two independent scripts, one for differential expression and one for functional interpretation. Based on replicates, the R script decides which of the edgeR, DEseq2, NOISeq and limma algorithms are appropriate. It performs quality control calculations and provides the prevalent, most reliable, set of differentially expressed genes, and lists all other possible candidates for further functional interpretation. It also provides a combined P-value that allows differentially expressed genes ranking. It has been tested with synthetic and real-world datasets, showing in both cases ease of use and reliable results. With real data, DEgenes Hunter offers straightforward functional interpretation.

Background In the era of DNA throughput sequencing, assembling and understanding gymnosperm mega-genomes remains a challenge. Although drafts of three conifer genomes have recently been published, this number is too low to understand the full complexity of conifer genomes. Using techniques focused on specific genes, gene models can be established that can aid in the assembly of gene-rich regions, and this information can be used to compare genomes and understand functional evolution. Results In this study, gene capture technology combined with BAC isolation and sequencing was used as an experimental approach to establish de novo gene structures without a reference genome. Probes were designed for 866 maritime pine transcripts to sequence genes captured from genomic DNA. The gene models were constructed using GeneAssembler, a new bioinformatic pipeline, which reconstructed over 82 % of the gene structures, and a high proportion (85 %) of the captured gene models contained sequences from the promoter regulatory region. In a parallel experiment, the P. pinaster BAC library was screened to isolate clones containing genes whose cDNA sequence were already available. BAC clones containing the asparagine synthetase, sucrose synthase and xyloglucan endotransglycosylase gene sequences were isolated and used in this study. The gene models derived from the gene capture approach were compared with the genomic sequences derived from the BAC clones. This combined approach is a particularly efficient way to capture the genomic structures of gene families with a small number of members. Conclusions The experimental approach used in this study is a valuable combined technique to study genomic gene structures in species for which a reference genome is unavailable. It can be used to establish exon/intron boundaries in unknown gene structures, to reconstruct incomplete genes and to obtain promoter sequences that can be used for transcriptional studies. A bioinformatics algorithm (GeneAssembler) is also provided as a Ruby gem for this class of analyses. Electronic supplementary material The online version of this article (doi:10.1186/s12864-016-2490-z) contains supplementary material, which is available to authorized users.