Motivation: Annotation of genomic variants is an increasingly important and complex part of the analysis of sequence-based genomic analyses. Computational predictions of variant function are routinely incorporated into gene-based analyses of rare-variants, though to date most studies use limited information for assessing variant function that is often agnostic of the disease being studied. Results: In this work, we outline an annotation process motivated by the Alzheimer's Disease Sequencing Project, illustrate the impact of including tissue-specific transcript sets and sources of gene regulatory information and assess the potential impact of changing genomic builds on the annotation process. While these factors only impact a small proportion of total variant annotations (∼5%), they influence the potential analysis of a large fraction of genes (∼25%). Availability and implementation: Individual variant annotations are available via the NIAGADS GenomicsDB, at https://www.niagads.org/genomics/ tools-and-software/databases/genomics-database. Annotations are also available for bulk download at https://www.niagads.org/datasets. Annotation processing software is available at http://www.icompbio.net/resources/software-and-downloads/. Supplementary information: Supplementary data are available at Bioinformatics online.

SUMMARY: Reading frame altering genomic variants can impact gene expression levels and the structure of protein products, thus potentially inducing disease phenotypes. Current annotation approaches report the impact of such variants in the context of altered DNA sequence only; attributes of the resulting transcript, reading frame and translated protein product are not reported. To remedy this shortcoming, we present a new genetic annotation approach termed Codon Consequence Scanner (COCOS). Implemented as an Ensembl variant effect predictor (VEP) plugin, COCOS captures amino acid sequence alterations stemming from variants that produce an altered reading frame, such as stop-lost variants and small insertions and deletions (InDels). To highlight its significance, COCOS was applied to data from the 1000 Genomes Project. Transcripts affected by stop-lost variants introduce a median of 15 amino acids, while InDels have a more extensive impact with a median of 66 amino acids being incorporated. Captured sequence alterations are written out in FASTA format and can be further analyzed for impact on the underlying protein structure. AVAILABILITY AND IMPLEMENTATION: COCOS is available to all users on github: https://github.com/butkiem/COCOS. CONTACT: [email protected].

MOTIVATION: We present an update to the pathway enrichment analysis tool 'Pathway Analysis by Randomization Incorporating Structure (PARIS)' that determines aggregated association signals generated from genome-wide association study results. Pathway-based analyses highlight biological pathways associated with phenotypes. PARIS uses a unique permutation strategy to evaluate the genomic structure of interrogated pathways, through permutation testing of genomic features, thus eliminating many of the over-testing concerns arising with other pathway analysis approaches. RESULTS: We have updated PARIS to incorporate expanded pathway definitions through the incorporation of new expert knowledge from multiple database sources, through customized user provided pathways, and other improvements in user flexibility and functionality. AVAILABILITY AND IMPLEMENTATION: PARIS is freely available to all users at https://ritchielab.psu.edu/software/paris-download CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

In this case study, a ligand-based virtual high throughput screening suite, bcl::ChemInfo, was applied to screen for activation of the protein target 17-beta hydroxysteroid dehydrogenase type 10 (HSD) involved in Alzheimer's Disease. bcl::ChemInfo implements a diverse set of machine learning techniques such as artificial neural networks (ANN), support vector machines (SVM) with the extension for regression, kappa nearest neighbor (KNN), and decision trees (DT). Molecular structures were converted into a distinct collection of descriptor groups involving 2D- and 3D-autocorrelation, and radial distribution functions. A confirmatory high-throughput screening data set contained over 72,000 experimentally validated compounds, available through PubChem. Here, the systematical model development was achieved through optimization of feature sets and algorithmic parameters resulting in a theoretical enrichment of 11 (44% of maximal enrichment), and an area under the ROC curve (AUC) of 0.75 for the best performing machine learning technique on an independent data set. In addition, consensus combinations of all involved predictors were evaluated and achieved the best enrichment of 13 (50%), and AUC of 0.86. All models were computed in silico and represent a viable option in guiding the drug discovery process through virtual library screening and compound prioritization a priori to synthesis and biological testing. The best consensus predictor will be made accessible for the academic community at www.meilerlab.org.

Quantitative structure activity relationship (QSAR) modeling using high-throughput screening (HTS) data is a powerful technique which enables the construction of predictive models. These models are utilized for the in silico screening of libraries of molecules for which experimental screening methods are both cost- and time-expensive. Machine learning techniques excel in QSAR modeling where the relationship between structure and activity is often complex and non-linear. As these HTS data sets continue to increase in number of compounds screened, extensive feature selection and cross validation becomes computationally expensive. Leveraging massively parallel architectures such as graphics processing units (GPUs) to accelerate the training algorithms for these machine learning techniques is a cost-efficient manner in which to combat this problem. In this work, several machine learning techniques are ported in OpenCL for GPU-acceleration to enable construction of QSAR ensemble models using HTS data. We report computational performance numbers using several HTS data sets freely available from PubChem database. We also report results of a case study using HTS data for a target of pharmacological and pharmaceutical relevance, cytochrome P450 3A4, for which an enrichment of 94% of the theoretical maximum is achieved.

Several machine learning techniques were evaluated for the prediction of logP. The algorithms used include artificial neural networks (ANN), support vector machines (SVM) with the extension for regression, and kappa nearest neighbor (k-NN). Molecules were described using optimized feature sets derived from a series of scalar, two- and three-dimensional descriptors including 2-D and 3-D autocorrelation, and radial distribution function. Feature optimization was performed as a sequential forward feature selection. The data set contained over 25,000 molecules with experimentally determined logP values collected from the Reaxys and MDDR databases, as well as data mining through SciFinder. LogP, the logarithm of the equilibrium octanol-water partition coefficient for a given substance is a metric of the hydrophobicity. This property is an important metric for drug absorption, distribution, metabolism, and excretion (ADME). In this work, models were built by systematically optimizing feature sets and algorithmic parameters that predict logP with a root mean square deviation (rmsd) of 0.86 for compounds in an independent test set. This result presents a substantial improvement over XlogP, an incremental system that achieves a rmsd of 1.41 over the same dataset. The final models were 5-fold cross-validated. These fully in silico models can be useful in guiding early stages of drug discovery, such as virtual library screening and analogue prioritization prior to synthesis and biological testing. These models are freely available for academic use.

Machine Learning techniques are successfully applied to establish quantitative relations between chemical structure and biological activity (QSAR), i.e. classify compounds as active or inactive with respect to a specific target biological system. This paper presents a comparison of artificial neural networks (ANN), support vector machines (SVM), and decision trees (DT) in an effort to identify potentiators of metabotropic glutamate receptor 5 (mGluR5), compounds that have potential as novel treatments against schizophrenia. When training and testing each of the three techniques on the same dataset enrichments of 61, 64, and 43 were obtained and an area under the curve (AUC) of 0.77, 0.78, and 0.63 was determined for ANNs, SVMs, and DTs, respectively. For the top percentile of predicted active compounds, the true positives for all three methods were highly similar, while the inactives were diverse offering the potential use of jury approaches to improve prediction accuracy.