VariantAlert: A web-based tool to notify updates in genetic variant annotations.

The reinterpretation of variants based on updated annotations is part of the routine work of research laboratories: the more data is collected about a specific variant, the higher the probability to reinterpret its classification. To support this task, we developed VariantAlert, a web-based tool to help researchers and clinicians to be constantly informed about changes in variant annotations extracted from multiple sources. VariantAlert provides daily re-annotation of variants using external resources accessed through application programming interface, such as MyVariant.info providing in turn links to gnomAD, catalogue of somatic mutations In cancer (COSMIC), ClinVar, CIViC, and many others. Researchers and clinicians can submit one or more lists of variants. If a change is detected for the annotation of a variant due to the upgrade of the underlying resource (e.g., change in gnomAD allele frequency, presence in COSMIC database, change in ClinVar classification) the user is notified by email and updated annotations are stored on the web-site. VariantAlert is freely available at https://github.com/next-crs4/VariantAlert. Installation and deployment are easy thanks to the use of the Docker platform. A Makefile allows you to easily bootstrap VariantAlert. VariantAlert is also available as a web service at https://variant-alert.crs4.it/.

Bi-allelic Mutations in KLHL7 Cause a Crisponi/CISS1-like Phenotype Associated with Early-Onset Retinitis Pigmentosa.

Crisponi syndrome (CS)/cold-induced sweating syndrome type 1 (CISS1) is a very rare autosomal-recessive disorder characterized by a complex phenotype with high neonatal lethality, associated with the following main clinical features: hyperthermia and feeding difficulties in the neonatal period, scoliosis, and paradoxical sweating induced by cold since early childhood. CS/CISS1 can be caused by mutations in cytokine receptor-like factor 1 (CRLF1). However, the physiopathological role of CRLF1 is still poorly understood. A subset of CS/CISS1 cases remain yet genetically unexplained after CRLF1 sequencing. In five of them, exome sequencing and targeted Sanger sequencing identified four homozygous disease-causing mutations in kelch-like family member 7 (KLHL7), affecting the Kelch domains of the protein. KLHL7 encodes a BTB-Kelch-related protein involved in the ubiquitination of target proteins for proteasome-mediated degradation. Mono-allelic substitutions in other domains of KLHL7 have been reported in three families affected by a late-onset form of autosomal-dominant retinitis pigmentosa. Retinitis pigmentosa was also present in two surviving children reported here carrying bi-allelic KLHL7 mutations. KLHL7 mutations are thus associated with a more severe phenotype in recessive than in dominant cases. Although these data further support the pathogenic role of KLHL7 mutations in a CS/CISS1-like phenotype, they do not explain all their clinical manifestations and highlight the high phenotypic heterogeneity associated with mutations in KLHL7.

BITS 2017: the annual meeting of the Italian Society of Bioinformatics.

This preface introduces the content of the BioMed Central journal Supplement related to the 14th annual meeting of the Bioinformatics Italian Society, held in Cagliari, Italy, from the 5th to the 7th of July, 2017.

Novel action of FOXL2 as mediator of Col1a2 gene autoregulation.

FOXL2 belongs to the evolutionarily conserved forkhead box (FOX) superfamily and is a master transcription factor in a spectrum of developmental pathways, including ovarian and eyelid development and bone, cartilage and uterine maturation. To analyse its action, we searched for proteins that interact with FOXL2. We found that FOXL2 interacts with specific C-terminal propeptides of several fibrillary collagens. Because these propeptides can participate in feedback regulation of collagen biosynthesis, we inferred that FOXL2 could thereby affect the transcription of the cognate collagen genes. Focusing on COL1A2, we found that FOXL2 indeed affects collagen synthesis, by binding to a DNA response element located about 65Kb upstream of this gene. According to our hypothesis we found that in Foxl2(-/-) mouse ovaries, Col1a2 was elevated from birth to adulthood. The extracellular matrix (ECM) compartmentalizes the ovary during folliculogenesis, (with type I, type III and type IV collagens as primary components), and ECM composition changes during the reproductive lifespan. In Foxl2(-/-) mouse ovaries, in addition to up-regulation of Col1a2, Col3a1, Col4a1 and fibronectin were also upregulated, while laminin expression was reduced. Thus, by regulating levels of extracellular matrix components, FOXL2 may contribute to both ovarian histogenesis and the fibrosis attendant on depletion of the follicle reserve during reproductive aging and menopause.

Orione, a web-based framework for NGS analysis in microbiology.

UNLABELLED: End-to-end next-generation sequencing microbiology data analysis requires a diversity of tools covering bacterial resequencing, de novo assembly, scaffolding, bacterial RNA-Seq, gene annotation and metagenomics. However, the construction of computational pipelines that use different software packages is difficult owing to a lack of interoperability, reproducibility and transparency. To overcome these limitations we present Orione, a Galaxy-based framework consisting of publicly available research software and specifically designed pipelines to build complex, reproducible workflows for next-generation sequencing microbiology data analysis. Enabling microbiology researchers to conduct their own custom analysis and data manipulation without software installation or programming, Orione provides new opportunities for data-intensive computational analyses in microbiology and metagenomics. AVAILABILITY AND IMPLEMENTATION: Orione is available online at http://orione.crs4.it.

Pervasive structural heterogeneity rewires glioblastoma chromosomes to sustain patient-specific transcriptional programs.

Glioblastoma multiforme (GBM) encompasses brain malignancies marked by phenotypic and transcriptional heterogeneity thought to render these tumors aggressive, resistant to therapy, and inevitably recurrent. However, little is known about how the spatial organization of GBM genomes underlies this heterogeneity and its effects. Here, we compile a cohort of 28 patient-derived glioblastoma stem cell-like lines (GSCs) known to reflect the properties of their tumor-of-origin; six of these were primary-relapse tumor pairs from the same patient. We generate and analyze 5 kbp-resolution chromosome conformation capture (Hi-C) data from all GSCs to systematically map thousands of standalone and complex structural variants (SVs) and the multitude of neoloops arising as a result. By combining Hi-C, histone modification, and gene expression data with chromatin folding simulations, we explain how the pervasive, uneven, and idiosyncratic occurrence of neoloops sustains tumor-specific transcriptional programs via the formation of new enhancer-promoter contacts. We also show how even moderately recurrent neoloops can relate to patient-specific vulnerabilities. Together, our data provide a resource for dissecting GBM biology and heterogeneity, as well as for informing therapeutic approaches.

Next Generation Sequencing for miRNA Detection on the Exhaled Breath Condensate: A Pilot Study.

Introduction: Exhaled breath condensate (EBC) sampling has been suggested as a less-invasive and cost-effective method to detect biological macromolecules, including miRNA. To explore the feasibility of its use as a biomarker of early effects of asbestos exposure, we conducted a preliminary test on male volunteers by comparing the miRNA profile in the EBC and the plasma using 2 different sequencing platforms. Methods: Six male volunteers, all retired and unexposed to dust or fumes, participated in the test. RNA was extracted from 200 µL EBC samples and same-size plasma samples. Sample aliquots were processed in 2 laboratories using 2 different sequencing platforms: a MiSeq Illumina® platform and a more performing HiSeq Illumina® platform. Results: The HiSeq3000® sequencing platform identified twice as many unique molecular indexes (UMI)-validated miRNA as the MiSeq® platform. The Spearman's correlation coefficient between EBC counts and plasma counts was significant in 5/6 subjects with either platform (MiSeq® = 0.128-0.508, P = .026-<.001; HiSeq® = 0.156-0.412, P = .001-<.001). The intraclass correlation coefficient confirmed the consistency of the miRNA profile over the 6 participants with both biospecimens. Exploring the agreement between the EBC and plasma samples with Bland-Altman plots showed that using the HiSeq3000® platform substantially improved the EBC miRNA detection rate. Conclusion: Our preliminary study confirms that, when using the HiSeq® sequencing platform, EBC sampling is a suitable, non-invasive method to detect the miRNA profile in healthy subjects.

Bacterial Genomic Data Analysis in the Next-Generation Sequencing Era.

Bacterial genome sequencing is now an affordable choice for many laboratories for applications in research, diagnostic, and clinical microbiology. Nowadays, an overabundance of tools is available for genomic data analysis. However, tools differ for algorithms, languages, hardware requirements, and user interface, and combining them as it is necessary for sequence data interpretation often requires (bio)informatics skills which can be difficult to find in many laboratories. In addition, multiple data sources, as well as exceedingly large dataset sizes, and increasingly computational complexity further challenge the accessibility, reproducibility, and transparency of the entire process. In this chapter we will cover the main bioinformatics steps required for a complete bacterial genome analysis using next-generation sequencing data, from the raw sequence data to assembled and annotated genomes. All the tools described are available in the Orione framework ( http://orione.crs4.it ), which uniquely combines in a transparent way the most used open source bioinformatics tools for microbiology, allowing microbiologist without any specific hardware or informatics skill to conduct data-intensive computational analyses from quality control to microbial gene annotation.

Simulating Cardiac Electrophysiology Using Unstructured All-Hexahedra Spectral Elements.

We discuss the application of the spectral element method to the monodomain and bidomain equations describing propagation of cardiac action potential. Models of cardiac electrophysiology consist of a system of partial differential equations coupled with a system of ordinary differential equations representing cell membrane dynamics. The solution of these equations requires solving multiple length scales due to the ratio of advection to diffusion that varies among the different equations. High order approximation of spectral elements provides greater flexibility in resolving multiple length scales. Furthermore, spectral elements are extremely efficient to model propagation phenomena on complex shapes using fewer degrees of freedom than its finite element equivalent (for the same level of accuracy). We illustrate a fully unstructured all-hexahedra approach implementation of the method and we apply it to the solution of full 3D monodomain and bidomain test cases. We discuss some key elements of the proposed approach on some selected benchmarks and on an anatomically based whole heart human computational model.

Simulation of cardiac electrophysiology on next-generation high-performance computers.

Models of cardiac electrophysiology consist of a system of partial differential equations (PDEs) coupled with a system of ordinary differential equations representing cell membrane dynamics. Current software to solve such models does not provide the required computational speed for practical applications. One reason for this is that little use is made of recent developments in adaptive numerical algorithms for solving systems of PDEs. Studies have suggested that a speedup of up to two orders of magnitude is possible by using adaptive methods. The challenge lies in the efficient implementation of adaptive algorithms on massively parallel computers. The finite-element (FE) method is often used in heart simulators as it can encapsulate the complex geometry and small-scale details of the human heart. An alternative is the spectral element (SE) method, a high-order technique that provides the flexibility and accuracy of FE, but with a reduced number of degrees of freedom. The feasibility of implementing a parallel SE algorithm based on fully unstructured all-hexahedra meshes is discussed. A major computational task is solution of the large algebraic system resulting from FE or SE discretization. Choice of linear solver and preconditioner has a substantial effect on efficiency. A fully parallel implementation based on dynamic partitioning that accounts for load balance, communication and data movement costs is required. Each of these methods must be implemented on next-generation supercomputers in order to realize the necessary speedup. The problems that this may cause, and some of the techniques that are beginning to be developed to overcome these issues, are described.

Inferring gene networks: dream or nightmare?

We describe several algorithms with winning performance in the Dialogue for Reverse Engineering Assessments and Methods (DREAM2) Reverse Engineering Competition 2007. After the gold standards for the challenges were released, the performance of the algorithms could be thoroughly evaluated under different parameters or alternative ways of solving systems of equations. For the analysis of Challenge 4, the "In-silico" challenges, we employed methods to explicitly deal with perturbation data and time-series data. We show that original methods used to produce winning submissions could easily be altered to substantially improve performance. For Challenge 5, the genome-scale Escherichia coli network, we evaluated a variety of measures of association. These data are troublesome, and no good solutions could be produced, either by us or by any other teams. Our best results were obtained when analyzing subdatasets instead of considering the dataset as a whole.