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Background The accurate screening of tumor genomic landscapes for somatic mutations using high-throughput sequencing involves a crucial step in precise clinical diagnosis and targeted therapy. However, the complex inherent features of cancer tissue, especially, tumor genetic intra-heterogeneity coupled with the problem of sequencing and alignment artifacts, makes somatic variant calling a challenging task. Current variant filtering strategies, such as rule-based filtering and consensus voting of different algorithms, have previously helped to increase specificity, although comes at the cost of sensitivity. Methods In light of this, we have developed the NeoMutate framework which incorporates 7 supervised machine learning (ML) algorithms to exploit the strengths of multiple variant callers, using a non-redundant set of biological and sequence features. We benchmarked NeoMutate by simulating more than 10,000 bona fide cancer-related mutations into three well-characterized Genome in a Bottle (GIAB) reference samples. Results A robust and exhaustive evaluation of NeoMutate’s performance based on 5-fold cross validation experiments, in addition to 3 independent tests, demonstrated a substantially improved variant detection accuracy compared to any of its individual composite variant callers and consensus calling of multiple tools. Conclusions We show here that integrating multiple tools in an ensemble ML layer optimizes somatic variant detection rates, leading to a potentially improved variant selection framework for the diagnosis and treatment of cancer. Electronic supplementary material The online version of this article (10.1186/s12920-019-0508-5) contains supplementary material, which is available to authorized users.

Las hojarascas de las especias caducifolias y perennifolias presentan procesos diferenciales de descomposición, debidos, en gran parte, a las diferencias en su composición química. El lixiviado es la primera de las etapas de descomposición. En esta etapa se liberan los materiales solubles presentes en las hojas. En el presente trabajo se ha llevado a cabo un experimento para determinar si existen diferencias significativas en el lixiviado de componentes entre hojas pertenecientes a especies caducifolias (Juglans regia y Quercus pyrenaica) y perennifolias (Quercus ilex y Pinus Sylvestris). Se discutirá la relación entre esta diferencia y la composición de los dos tipos de hoja.

BackgroundCheckpoint inhibitors (CPIs) have revolutionized the treatment of malignant melanoma. Although melanoma patients may experience deep and durable clinical responses to CPI treatment, the majority develop disease progression requiring additional therapy. Combining CPIs with therapeutic cancer vaccines may augment the anti-tumor immune response and thus improve clinical outcomes. UV1 is a therapeutic cancer vaccine based on synthetic antigens derived from the tumor-associated antigen telomerase reverse transcriptase (hTERT). hTERT is activated in 85–90% of all cancers and leads to replication of telomeric DNA, an essential mechanism for increased proliferation, immortality, and invasiveness of cancer cells. In this phase I/IIa study combining UV1 with ipilimumab in patients with melanoma (NCT02275416), we hypothesized that UV1 and ipilimumab might provide synergy in the expansion of vaccine-induced T cells. Furthermore, an augmented CD4+ Th1 immune response targeting a shared tumor antigen may increase T cell infiltration in the tumor microenvironment, promoting immune-mediated cancer cell death.MethodsThe immune response dynamics were assessed by longitudinal immunomonitoring for up to 5 years using a standard proliferation assay. The phenotype and functionality of vaccine-induced T cells were assessed by patient-derived T cell cloning and subsequent in vitro characterization by flow cytometry, peptide stimulation, and cytokine release assays. On available biopsies harvested at baseline and 12 weeks after treatment initiation, the tumor microenvironment was assessed based on whole-exome sequencing, RNA sequencing, and immunohistochemistry.ResultsTwelve patients with metastatic melanoma were enrolled in the study and received up to 9 vaccine doses over a 20-week period. A persistent (up to 5 years) vaccine-induced immune responses were demonstrated in 91% of evaluable patients (10/11). Vaccine-specific T cell clones were polyfunctional CD4+ Th1 cells, producing both IFN-γ and TNF-α upon in vitro peptide stimulation. Differential gene expression analysis and immunohistochemical characterization of biopsies at baseline and 12 weeks showed induction of an inflammatory ”hot” tumor microenvironment in clinical responders with available paired biopsies.ConclusionsUV1 in combination with ipilimumab leads to robust and long-lasting CD4+ Th1 anti-hTERT immune responses sculpting the local tumor microenvironment.Trial RegistrationNCT02275416Ethics ApprovalThe study was approved by Regional Ethical Committee South East (ID number 25 165). The patients provided their written informed consent to participate in the study.

Precise HLA genotyping is of great clinical importance, albeit a challenging bioinformatics endeavor because of the hyper polymorphism of the HLA region. The ever-increasing availability of next-generation sequencing (NGS) solutions has spurred the development of several computational methods for predicting HLA genotypes from NGS data. Although some of these tools genotype HLA Class I alleles reasonably well, there is a need to incorporate integrative parameters related to ethnicity frequency information, in order to improve performance for both Class I and Class II alleles. Here, we present a bioinformatics method that addresses some of the current shortfalls in HLA genotyping from NGS. First, reads that map to the HLA region is aligned against a comprehensive library of reference HLA alleles. The allele type was then subsequently determined on the basis of the distribution of aligned reads, and the prior probabilities of the ethnic frequencies of alleles. Three public NGS datasets were used to benchmark the approach against six similar tools. The method outlined in this manuscript displayed an overall accuracy of 98.73% for Class I and 96.37% for Class II alleles. We illustrate an improved integrative approach that outperforms existing tools and is able to predict HLA alleles with improved fidelity for both Class I and Class II alleles.

Long non-coding RNAs (lncRNAs) are functional non-translated molecules greater than 200 nt. Their roles are diverse and they are usually involved in transcriptional regulation. LncRNAs still remain largely uninvestigated in plants with few exceptions. Experimentally validated plant lncRNAs have been shown to regulate important agronomic traits such as phosphate starvation response, flowering time and interaction with symbiotic organisms, making them of great interest in plant biology and in breeding. There is still a lack of lncRNAs in most sequenced plant species, and in those where they have been annotated, different methods have been used, so making the lncRNAs less useful in comparisons within and between species. We developed a pipeline to annotate lncRNAs and applied it to 37 plant species and six algae, resulting in the annotation of more than 120 000 lncRNAs. To facilitate the study of lncRNAs for the plant research community, the information gathered is organised in the Green Non-Coding Database (GreeNC, http://greenc.sciencedesigners.com/).

Tomato is a high value crop and the primary model for fleshy fruit development and ripening. Breeding priorities include increased fruit quality, shelf life and tolerance to stresses. To contribute towards this goal, we re-sequenced the genomes of Corbarino (COR) and Lucariello (LUC) landraces, which both possess the traits of plant adaptation to water deficit, prolonged fruit shelf-life and good fruit quality. Through the newly developed pipeline Reconstructor, we generated the genome sequences of COR and LUC using datasets of 65.8 M and 56.4 M of 30–150 bp paired-end reads, respectively. New contigs including reads that could not be mapped to the tomato reference genome were assembled, and a total of 43, 054 and 44, 579 gene loci were annotated in COR and LUC. Both genomes showed novel regions with similarity to Solanum pimpinellifolium and Solanum pennellii. In addition to small deletions and insertions, 2, 000 and 1, 700 single nucleotide polymorphisms (SNPs) could exert potentially disruptive effects on 1, 371 and 1, 201 genes in COR and LUC, respectively. A detailed survey of the SNPs occurring in fruit quality, shelf life and stress tolerance related-genes identified several candidates of potential relevance. Variations in ethylene response components may concur in determining peculiar phenotypes of COR and LUC.

Fusarium verticillioides causes ear rot disease in maize and its contamination with fumonisins, mycotoxins harmful for humans and livestock. Lipids, and their oxidized forms, may drive the fate of this disease. In a previous study, we have explored the role of oxylipins in this interaction by deleting by standard transformation procedures a linoleate diol synthase-coding gene, lds1, in F. verticillioides. A profound phenotypic diversity in the mutants generated has prompted us to investigate more deeply the whole genome of two lds1-deleted strains. Bioinformatics analyses pinpoint significant differences in the genome sequences emerged between the wild type and the lds1-mutants further than those trivially attributable to the deletion of the lds1 locus, such as single nucleotide polymorphisms, small deletion/insertion polymorphisms and structural variations. Results suggest that the effect of a (theoretically) punctual transformation event might have enhanced the natural mechanisms of genomic variability and that transformation practices, commonly used in the reverse genetics of fungi, may potentially be responsible for unexpected, stochastic and henceforth off-target rearrangements throughout the genome.

The recent development of Sequence Capture methodology represents a powerful strategy for enhancing data generation to assess genetic variation of targeted genomic regions. Here, we present SUPER-CAP, a bioinformatics web tool aimed at handling Sequence Capture data, fine calculating the allele frequency of variations and building genotype-specific sequence of captured genes. The dataset used to develop this in silico strategy consists of 378 loci and related regulative regions in a collection of 44 tomato landraces. About 14,000 high-quality variants were identified. The high depth (>40×) of coverage and adopting the correct filtering criteria allowed identification of about 4,000 rare variants and 10 genes with a different copy number variation. We also show that the tool is capable to reconstruct genotype-specific sequences for each genotype by using the detected variants. This allows evaluating the combined effect of multiple variants in the same protein. The architecture and functionality of SUPER-CAP makes the software appropriate for a broad set of analyses including SNP discovery and mining. Its functionality, together with the capability to process large data sets and efficient detection of sequence variation, makes SUPER-CAP a valuable bioinformatics tool for genomics and breeding purposes.