DGLinker: flexible knowledge-graph prediction of disease-gene associations

As a result of the advent of high-throughput technologies, there has been rapid progress in our understanding of the genetics underlying biological processes. However, despite such advances, the genetic landscape of human diseases has only marginally been disclosed. Exploiting the present availability of large amounts of biological and phenotypic data, we can use our current understanding of disease genetics to train machine learning models to predict novel genetic factors associated with the disease. To this end, we developed DGLinker, a webserver for the prediction of novel candidate genes for human diseases given a set of known disease genes. DGLinker has a user-friendly interface that allows non-expert users to exploit biomedical information from a wide range of biological and phenotypic databases, and/or to upload their own data, to generate a knowledge-graph and use machine learning to predict new disease-associated genes. The webserver includes tools to explore and interpret the results and generates publication-ready figures. DGLinker is available at https://dglinker.rosalind.kcl.ac.uk. The webserver is free and open to all users without the need for registration

DNAscan2: a versatile, scalable, and user-friendly analysis pipeline for human next-generation sequencing data

SUMMARY: The current widespread adoption of next-generation sequencing (NGS) in all branches of basic research and clinical genetics fields means that users with highly variable informatics skills, computing facilities and application purposes need to process, analyse, and interpret NGS data. In this landscape, versatility, scalability, and user-friendliness are key characteristics for an NGS analysis software. We developed DNAscan2, a highly flexible, end-to-end pipeline for the analysis of NGS data, which (i) can be used for the detection of multiple variant types, including SNVs, small indels, transposable elements, short tandem repeats, and other large structural variants; (ii) covers all standard steps of NGS analysis, from quality control of raw data and genome alignment to variant calling, annotation, and generation of reports for the interpretation and prioritization of results; (iii) is highly adaptable as it can be deployed and run via either a graphic user interface for non-bioinformaticians and a command line tool for personal computer usage; (iv) is scalable as it can be executed in parallel as a Snakemake workflow, and; (v) is computationally efficient by minimizing RAM and CPU time requirements. AVAILABILITY AND IMPLEMENTATION: DNAscan2 is implemented in Python3 and is available at https://github.com/KHP-Informatics/DNAscanv2

DGLinker: flexible knowledge-graph prediction of disease-gene associations

As a result of the advent of high-throughput technologies, there has been rapid progress in our understanding of the genetics underlying biological processes. However, despite such advances, the genetic landscape of human diseases has only marginally been disclosed. Exploiting the present availability of large amounts of biological and phenotypic data, we can use our current understanding of disease genetics to train machine learning models to predict novel genetic factors associated with the disease. To this end, we developed DGLinker, a webserver for the prediction of novel candidate genes for human diseases given a set of known disease genes. DGLinker has a user-friendly interface that allows non-expert users to exploit biomedical information from a wide range of biological and phenotypic databases, and/or to upload their own data, to generate a knowledge-graph and use machine learning to predict new disease-associated genes. The webserver includes tools to explore and interpret the results and generates publication-ready figures. DGLinker is available at https://dglinker.rosalind.kcl.ac.uk. The webserver is free and open to all users without the need for registration

Author Correction: A HML6 endogenous retrovirus on chromosome 3 is upregulated in amyotrophic lateral sclerosis motor cortex

There is increasing evidence that endogenous retroviruses (ERVs) play a significant role in central nervous system diseases, including amyotrophic lateral sclerosis (ALS). Studies of ALS have consistently identified retroviral enzyme reverse transcriptase activity in patients. Evidence indicates that ERVs are the cause of reverse transcriptase activity in ALS, but it is currently unclear whether this is due to a specific ERV locus or a family of ERVs. We employed a combination of bioinformatic methods to identify whether specific ERVs or ERV families are associated with ALS. Using the largest post-mortem RNA-sequence datasets available we selectively identified ERVs that closely resembled full-length proviruses. In the discovery dataset there was one ERV locus (HML6_3p21.31c) that showed significant increased expression in post-mortem motor cortex tissue after multiple-testing correction. Using six replication post-mortem datasets we found HML6_3p21.31c was consistently upregulated in ALS in motor cortex and cerebellum tissue. In addition, HML6_3p21.31c showed significant co-expression with cytokine binding and genes involved in EBV, HTLV-1 and HIV type-1 infections. There were no significant differences in ERV family expression between ALS and controls. Our results support the hypothesis that specific ERV loci are involved in ALS pathology

A HML6 endogenous retrovirus on chromosome 3 is upregulated in amyotrophic lateral sclerosis motor cortex.

There is increasing evidence that endogenous retroviruses (ERVs) play a significant role in central nervous system diseases, including amyotrophic lateral sclerosis (ALS). Studies of ALS have consistently identified retroviral enzyme reverse transcriptase activity in patients. Evidence indicates that ERVs are the cause of reverse transcriptase activity in ALS, but it is currently unclear whether this is due to a specific ERV locus or a family of ERVs. We employed a combination of bioinformatic methods to identify whether specific ERVs or ERV families are associated with ALS. Using the largest post-mortem RNA-sequence datasets available we selectively identified ERVs that closely resembled full-length proviruses. In the discovery dataset there was one ERV locus (HML6_3p21.31c) that showed significant increased expression in post-mortem motor cortex tissue after multiple-testing correction. Using six replication post-mortem datasets we found HML6_3p21.31c was consistently upregulated in ALS in motor cortex and cerebellum tissue. In addition, HML6_3p21.31c showed significant co-expression with cytokine binding and genes involved in EBV, HTLV-1 and HIV type-1 infections. There were no significant differences in ERV family expression between ALS and controls. Our results support the hypothesis that specific ERV loci are involved in ALS pathology

Commissioning and operation of the Cherenkov detector for proton Flux Measurement of the UA9 Experiment

The UA9 Experiment at CERN-SPS investigates channeling processes in bent silicon crystals with the aim to manipulate hadron beams. Monitoring and characterization of channeled beams in the high energy accelerators environment ideally requires in-vacuum and radiation hard detectors. For this purpose the Cherenkov detector for proton Flux Measurement (CpFM) was designed and developed. It is based on thin fused silica bars in the beam pipe vacuum which intercept charged particles and generate Cherenkov light. The first version of the CpFM is installed since 2015 in the crystal-assisted collimation setup of the UA9 experiment. In this paper the procedures to make the detector operational and fully integrated in the UA9 setup are described. The most important standard operations of the detector are presented. They have been used to commission and characterize the detector, providing moreover the measurement of the integrated channeled beam profile and several functionality tests as the determination of the crystal bending angle. The calibration has been performed with Lead (Pb) and Xenon (Xe) beams and the results are applied to the flux measurement discussed here in detail.Comment: 25 pages, 14 figure

DNAscan: personal computer compatible NGS analysis, annotation and visualisation.

BACKGROUND: Next Generation Sequencing (NGS) is a commonly used technology for studying the genetic basis of biological processes and it underpins the aspirations of precision medicine. However, there are significant challenges when dealing with NGS data. Firstly, a huge number of bioinformatics tools for a wide range of uses exist, therefore it is challenging to design an analysis pipeline. Secondly, NGS analysis is computationally intensive, requiring expensive infrastructure, and many medical and research centres do not have adequate high performance computing facilities and cloud computing is not always an option due to privacy and ownership issues. Finally, the interpretation of the results is not trivial and most available pipelines lack the utilities to favour this crucial step. RESULTS: We have therefore developed a fast and efficient bioinformatics pipeline that allows for the analysis of DNA sequencing data, while requiring little computational effort and memory usage. DNAscan can analyse a whole exome sequencing sample in 1 h and a 40x whole genome sequencing sample in 13 h, on a midrange computer. The pipeline can look for single nucleotide variants, small indels, structural variants, repeat expansions and viral genetic material (or any other organism). Its results are annotated using a customisable variety of databases and are available for an on-the-fly visualisation with a local deployment of the gene.iobio platform. DNAscan is implemented in Python. Its code and documentation are available on GitHub: https://github.com/KHP-Informatics/DNAscan . Instructions for an easy and fast deployment with Docker and Singularity are also provided on GitHub. CONCLUSIONS: DNAscan is an extremely fast and computationally efficient pipeline for analysis, visualization and interpretation of NGS data. It is designed to provide a powerful and easy-to-use tool for applications in biomedical research and diagnostic medicine, at minimal computational cost. Its comprehensive approach will maximise the potential audience of users, bringing such analyses within the reach of non-specialist laboratories, and those from centres with limited funding available

Characterisation of retrotransposon insertion polymorphisms in whole genome sequencing data from individuals with amyotrophic lateral sclerosis

The genetics of an individual is a crucial factor in understanding the risk of developing the neurodegenerative disease amyotrophic lateral sclerosis (ALS). There is still a large proportion of the heritability of ALS, particularly in sporadic cases, to be understood. Among others, active transposable elements drive inter-individual variability, and in humans long interspersed element 1 (LINE1, L1), Alu and SINE-VNTR-Alu (SVA) retrotransposons are a source of polymorphic insertions in the population. We undertook a pilot study to characterise the landscape of non-reference retrotransposon insertion polymorphisms (non-ref RIPs) in 15 control and 15 ALS individuals’ whole genomes from Project MinE, an international project to identify potential genetic causes of ALS. The combination of two bioinformatics tools (mobile element locator tool (MELT) and TEBreak) identified on average 1250 Alu, 232 L1 and 77 SVA non-ref RIPs per genome across the 30 analysed. Further PCR validation of individual polymorphic retrotransposon insertions showed a similar level of accuracy for MELT and TEBreak. Our preliminary study did not identify a specific RIP or a significant difference in the total number of non-ref RIPs in ALS compared to control genomes. The use of multiple bioinformatic tools improved the accuracy of non-ref RIP detection and our study highlights the potential importance of studying these elements further in ALS

A 1 m3^3 Gas Time Projection Chamber with Optical Readout for Directional Dark Matter Searches: the CYGNO Experiment

The aim of the CYGNO project is the construction and operation of a 1~m$^3$ gas TPC for directional dark matter searches and coherent neutrino scattering measurements, as a prototype toward the 100-1000~m$^3$ (0.15-1.5 tons) CYGNUS network of underground experiments. In such a TPC, electrons produced by dark-matter- or neutrino-induced nuclear recoils will drift toward and will be multiplied by a three-layer GEM structure, and the light produced in the avalanche processes will be readout by a sCMOS camera, providing a 2D image of the event with a resolution of a few hundred micrometers. Photomultipliers will also provide a simultaneous fast readout of the time profile of the light production, giving information about the third coordinate and hence allowing a 3D reconstruction of the event, from which the direction of the nuclear recoil and consequently the direction of the incoming particle can be inferred. Such a detailed reconstruction of the event topology will also allow a pure and efficient signal to background discrimination. These two features are the key to reach and overcome the solar neutrino background that will ultimately limit non-directional dark matter searches.Comment: 5 page, 7 figures, contribution to the Conference Records of 2018 IEEE NSS/MI