The GenRot program takes a typical Gaussian .gjf file (connectivity written after XYZ) as input, and generates new XYZ data based on inputs from the user. After opening the file in the program, the user clicks on 4 atoms to define a dihedral angle and determines the degree of rotation they wish for that angle. This is repeated until all the dihedral angles that the user wants to rotate have been defined. The program will rotate each of the dihedral angles 360/n times, where n is the rotation degree from the user input. If n, is, for example, 33, then 360/33=10.9, the number will be rounded down to 10. If the user wants to rotate four dihedral angles 120 degrees each, the program generates (360/120)^4 = 81 rotamers. It is possible for the user to change the route section of the Gaussian input file, in case there was an error, and also to append text to the end of the file. When all the dihedral angles have been chosen, and the desired rotation defined, one simply clicks “Generate files”, and all files are generated into folder specified by the user. While the program reads Gaussian type input file only, it can in principle generate files with any information before and after the XYZ data, and may be suited to generate input files for other quantum calculation software.

Recognition of different genomic signals and regions (GSRs) in the DNA is helpful in gaining knowledge to understand genome organization and gene regulation as well as gene function. Accurate recognition of GSRs enables better genome and gene annotation. Although many methods have been developed to recognize GSRs, their pure computational identification remains challenging. Moreover, various GSRs usually require a specialized set of features for developing robust recognition models. Recently, deep learning (DL) methods have been shown to generate more accurate prediction models than the ‘shallow’ methods without the need to develop specialized features for the problems in question. Here, we explore the potential use of DL for the recognition of GSRs. We developed DeepGSR, an optimized DL architecture for the prediction of different types of GSRs. The performance of the DeepGSR structure is evaluated on the recognition of polyadenylation signals (PAS) and translation initiation sites (TIS) of different organisms: human, mouse, bovine and fruit fly. The results show that DeepGSR outperformed the state-of-the-art methods, reducing the classification error rate of the PAS and TIS prediction in the human genome by up to 29% and 86%, respectively. Moreover, the cross-organisms and genome-wide analyses we performed, confirmed the robustness of DeepGSR and provided new insights into the conservation of examined GSRs across species.

Abstract: Discriminating the causative disease variant(s) for individuals with inherited or de novo mutations present one of the main challenges faced by the clinical genetics community today. Computational approaches for variant prioritization include machine learning methods utilizing a large number of features, including molecular information, interaction networks, or phenotypes. Here, we demonstrate the PhenomeNET Variant Predictor (PVP) system that exploits semantic technologies and automated reasoning over genotype-phenotype relations to filter and prioritize variants in whole exome and whole genome sequencing datasets. We demonstrate the performance of PVP in identifying causative variants on a large number of synthetic whole-exome and whole-genome sequences, covering a wide range of diseases and syndromes. In a retrospective study, we further illustrate the application of PVP for the interpretation of whole-exome sequencing data in patients suffering from congenital hypothyroidism. We find that PVP accurately identifies causative variants in whole exome and whole genome sequencing datasets and provides a powerful resource for the discovery of causal variants. PhenomeNet Variant Predictor (PVP) - User Guide A phenotype-based tool to annotate and prioritize disease variants in WES and WGS data This user guide have been tested on Ubuntu version 16.04. For details regarding model training and evaluation, please refer to dev/ directory above. Hardware requirements At least 32 GB RAM. At least 1TB free disk space to process and accommodate the necessary databases for annotation Software requirements (for native installation) Any Unix-based operating system Java 8 Python 2.7 (as a system default version) and install the dependencies (for Python 2.7) with: pip install -r requirements.txt Run python 2 for the script test.py (available above) to test the installation of the python dependencies. If the script fails, please try again to install the required dependencies ( using "pip2" instead of "pip", checking for permissions, or try the docker image instead). Native Installation Download the distribution file phenomenet-vp-2.1.zip Download the data files phenomenet-vp-2.1-data.zip Extract the distribution files phenomenet-vp-2.1.zip Extract the data files data.tar.gz inside the directory phenomenet-vp-2.1 cd phenomenet-vp-2.1 Run the command: bin/phenomenet-vp to display help and parameters. Database requirements Download CADD database file. Download and run the script generate.sh (Requires TABIX). Copy the generated files cadd.txt.gz and cadd.txt.gz.tbi to directory phenomenet-vp-1.0/data/db. Download DANN database file and its indexed file to directory phenomenet-vp-1.0/data/db. Rename the DANN files as dann.txt.gz and dann.txt.gz.tbi respectively. Docker Container Install Docker Download the data files phenomenet-vp-2.1-data.zip and database requirements Build phenomenet-vp docker image: docker build -t phenomenet-vp . Run phenomenet docker run -v $(pwd)/data:/data phenomenet-vp -f data/Miller.vcf -o OMIM:263750 Parameters --file, -f Path to VCF file --outfile, -of Path to results file --inh, -i Mode of inheritance Default: unknown --json, -j Path to PhenoTips JSON file containing phenotypes --omim, -o OMIM ID --phenotypes, -p List of phenotype ids separated by commas --human, -h Propagate human disease phenotypes to genes only Default: false --sp, -s Propagate mouse and fish disease phenotypes to genes only Default: false --digenic, -d Rank digenic combinations Default: false --trigenic, -t Rank trigenic combinations Default: false --combination, -c Maximum Number of variant combinations to prioritize (for digenic and trigenic cases only) Default: 1000 --ngenes, -n Number of genes in oligogenic combinations (more than three) Default: 4 --oligogenic, -og Rank oligogenic combinations Default: false --python, -y Path to Python executable (ex. /usr/bin/python) Default: python Usage: To run the tool, the user needs to provide a VCF file along with either an OMIM ID of the disease or a list of phenotypes (HPO or MPO terms). a) Prioritize disease-causing variants using an OMIM ID: bin/phenomenet-vp -f data/Miller.vcf -o OMIM:263750 b) Prioritize digenic disease-causing variants using an OMIM ID, and gene-to-phenotype datta from human studies only: bin/phenomenet-vp -f data/Miller.vcf -o OMIM:263750 --human --digenic c) Prioritize disease-causing variants using a set of phenotypes, and recessive inheritance mode bin/phenomenet-vp -f data/Miller.vcf -p HP:0000007,HP:0000028,HP:0000054,HP:0000077,HP:0000175 -i recessive The result file will be at the directory containg the input file. The output file has the same name as input file with .res extension. For digenic, trigenic or oligogenic prioritization, the result file will have .digenic, .trigenic, or .oligogenic extension repectivly. Analysis of Rare Variants: In order to effectively analysis rare variants, it is strongly recommended to filter the input VCF files by MAF prior to running phenomenet-vp on it. To do so, follow the instructions below: a) Install VCFtools. b) Run the following command using VCFtools on your input VCF file to filter out variants with MAF > 1%: vcftools --vcf input_file.vcf --recode --max-maf 0.01 --out filtered c) Run PVP on the output file filtered.recode.vcf generated from the command above. PVP 1.0 The original random-forest-based PVP tool is available to download here along with its required data files here. The prepared set of exomes and genomes used for the analysis and results are provided here. DeepPVP The updated neural-network model, DeepPVP is available to download here along with its required data files here. The prepared set of exomes used for the analysis and comparative results are provided here. The comparison with PVP is based on PVP-1.1 available here along with its required data files here. OligoPVP OligoPVP is provided as part of DeepPVP tool using the parameters --digenic, --trigenicm and --oligogenic for ranking candidate disease-causing variant pairs and triples. Our prepared set of synthetic genomes digenic combinations are available here using data from the DIgenic diseases DAtabase (DIDA). The comparison results with other methods are also provided. Results were obtained using DeepPVP v2.0. People PVP is jointly developed by researchers at the University of Birmingham (Prof George Gkoutos and his team), University of Cambridge (Dr Paul Schofield and his team), and King Abdullah University of Science and Technology (Prof Vladimir Bajic, Robert Hoehndorf, and teams). Publications [1] Boudellioua I, Mahamad Razali RB, Kulmanov M, Hashish Y, Bajic VB, Goncalves-Serra E, Schoenmakers N, Gkoutos GV., Schofield PN., and Hoehndorf R. (2017) Semantic prioritization of novel causative genomic variants. PLOS Computational Biology. https://doi.org/10.1371/journal.pcbi.1005500 [2] Boudellioua I, Kulmanov M, Schofield PN., Gkoutos GV., and Hoehndorf R . (2018) OligoPVP: Phenotype-driven analysis of individual genomic information to prioritize oligogenic disease variants. Scientific Reports. https://doi.org/10.1038/s41598-018-32876-3 [3] Boudellioua I, Kulmanov M, Schofield PN., Gkoutos GV., and Hoehndorf R . (2019) DeepPVP: phenotype-based prioritization of causative variants using deep learning. BMC Bioinformatics. https://doi.org/10.1186/s12859-019-2633-8 License Copyright (c) 2016-2018, King Abdullah University of Science and Technology All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. All advertising materials mentioning features or use of this software must display the following acknowledgment: This product includes software developed by the King Abdullah University of Science and Technology. 4. 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Background Mining high-throughput screening (HTS) assays is key for enhancing decisions in the area of drug repositioning and drug discovery. However, many challenges are encountered in the process of developing suitable and accurate methods for extracting useful information from these assays. Virtual screening and a wide variety of databases, methods, and solutions proposed to-date, did not completely overcome these challenges. This study is based on a multi-label classification (MLC) technique for modeling correlations between several HTS assays, meaning that a single prediction represents a subset of assigned correlated labels instead of one label. Thus, the devised method provides an increased probability for more accurate predictions of compounds that were not tested in particular assays. Results Here we present DRABAL, a novel MLC solution that incorporates structure learning of a Bayesian network as a step to the model dependency between the HTS assays. In this study, DRABAL was used to process more than 1.4 million interactions of over 400,000 compounds and analyze the existing relationships between five large HTS assays from the PubChem BioAssay Database. Compared to different MLC methods, DRABAL significantly improves the F1Score by about 22%, on average. We further illustrated usefulness and utility of DRABAL through screening FDA approved drugs and reported ones that have a high probability to interact with several targets, thus enabling drug-multi-target repositioning. Specifically, DRABAL suggests the Thiabendazole drug as a common activator of the NCP1 and Rab-9A proteins, both of which are designed to identify treatment modalities for the Niemann–Pick type C disease. Conclusion We developed a novel MLC solution based on a Bayesian active learning framework to overcome the challenge of lacking fully labeled training data and exploit actual dependencies between the HTS assays. The solution is motivated by the need to model dependencies between existing experimental confirmatory HTS assays and improve prediction performance. We have pursued extensive experiments over several HTS assays and have shown the advantages of DRABAL. The datasets and programs can be downloaded from https://figshare.com/articles/DRABAL/3309562.