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Abstract Background Antimicrobial resistance (AMR) is a major threat to global public health because it makes standard treatments ineffective and contributes to the spread of infections. It is important to understand AMR’s biological mechanisms for the development of new drugs and more rapid and accurate clinical diagnostics. The increasing availability of whole-genome SNP (single nucleotide polymorphism) information, obtained from whole-genome sequence data, along with AMR profiles provides an opportunity to use feature selection in machine learning to find AMR-associated mutations. This work describes the use of a supervised feature selection approach using deep neural networks to detect AMR-associated genetic factors from whole-genome SNP data. Results The proposed method, DNP-AAP (deep neural pursuit – average activation potential), was tested on a Neisseria gonorrhoeae dataset with paired whole-genome sequence data and resistance profiles to five commonly used antibiotics including penicillin, tetracycline, azithromycin, ciprofloxacin, and cefixime. The results show that DNP-AAP can effectively identify known AMR-associated genes in N. gonorrhoeae, and also provide a list of candidate genomic features (SNPs) that might lead to the discovery of novel AMR determinants. Logistic regression classifiers were built with the identified SNPs and the prediction AUCs (area under the curve) for penicillin, tetracycline, azithromycin, ciprofloxacin, and cefixime were 0.974, 0.969, 0.949, 0.994, and 0.976, respectively. Conclusions DNP-AAP can effectively identify known AMR-associated genes in N. gonorrhoeae. It also provides a list of candidate genes and intergenic regions that might lead to novel AMR factor discovery. More generally, DNP-AAP can be applied to AMR analysis of any bacterial species with genomic variants and phenotype data. It can serve as a useful screening tool for microbiologists to generate genetic candidates for further lab experiments.

Abstract Background Synthetic lethal interactions (SLIs) that occur between gene pairs are exploited for cancer therapeutics. Studies in the model eukaryote yeast have identified ~ 550,000 negative genetic interactions that have been extensively studied, leading to characterization of novel pathways and gene functions. This resource can be used to predict SLIs that can be relevant to cancer therapeutics. Methods We used patient data to identify genes that are down-regulated in breast cancer. InParanoid orthology mapping was performed to identify yeast orthologs of the down-regulated genes and predict their corresponding SLIs in humans. The predicted network graphs were drawn with Cytoscape. CancerRXgene database was used to predict drug response. Results Harnessing the vast available knowledge of yeast genetics, we generated a Humanized Yeast Genetic Interaction Network (HYGIN) for 1009 human genes with 10,419 interactions. Through the addition of patient-data from The Cancer Genome Atlas (TCGA), we generated a breast cancer specific subnetwork. Specifically, by comparing 1009 genes in HYGIN to genes that were down-regulated in breast cancer, we identified 15 breast cancer genes with 130 potential SLIs. Interestingly, 32 of the 130 predicted SLIs occurred with FBXW7, a well-known tumor suppressor that functions as a substrate-recognition protein within a SKP/CUL1/F-Box ubiquitin ligase complex for proteasome degradation. Efforts to validate these SLIs using chemical genetic data predicted that patients with loss of FBXW7 may respond to treatment with drugs like Selumitinib or Cabozantinib. Conclusions This study provides a patient-data driven interpretation of yeast SLI data. HYGIN represents a novel strategy to uncover therapeutically relevant cancer drug targets and the yeast SLI data offers a major opportunity to mine these interactions.

Abstract Background Recent adva1nces in whole genome sequencing (WGS) based technologies have facilitated multi-step applications for predicting antimicrobial resistance (AMR) and investigating the molecular epidemiology of Neisseria gonorrhoeae. However, generating full scaffolds of N. gonorrhoeae genomes from short reads, and the assignment of molecular epidemiological information (NG-MLST, NG-MAST, and NG-STAR) to multiple assembled samples, is challenging due to required manual tasks such as annotating antimicrobial resistance determinants with standard nomenclature for a large number of genomes. Results We present Gen2Epi, a pipeline that assembles short reads into full scaffolds and automatically assigns molecular epidemiological and AMR information to the assembled genomes. Gen2Epi is a command-line tool integrating third-party software and tailored specifically for N. gonorrhoeae. For its evaluation, the Gen2Epi pipeline successfully assembled the WGS short reads from 1484 N. gonorrhoeae samples into full-length genomes for both chromosomes and plasmids and was able to assign in silico molecular determinant information to each dataset automatically. The assemblies were generated using raw as well as trimmed short reads. The median genome coverage of full-length scaffolds and “N” statistics (N50, NG50, and NGA50) were higher than, or comparable to, previously published results and the scaffolding process improved the quality of the draft genome assemblies. Molecular antimicrobial resistant (AMR) determinants identified by Gen2Epi reproduced information for the 1484 samples as previously reported, including NG-MLST, NG-MAST, and NG-STAR molecular sequence types. Conclusions Gen2Epi can be used to assemble short reads into full-length genomes and assign accurate molecular marker and AMR information automatically from NG-STAR, NG-MAST, and NG-MLST. Gen2Epi is publicly available under “CC BY-NC 2.0 CA” Creative Commons licensing as a VirtualBox image containing the constituent software components running on the LINUX operating system (CentOS 7). The image and associated documentation are available via anonymous FTP at ftp://www.cs.usask.ca/pub/combi or ftp://ftp.cs.usask.ca/pub/combi

Abstract Objectives Hi-C is a proximity-based ligation reaction used to detect regions of the genome that are close in 3D space (or “interacting”). Typically, results from Hi-C experiments (contact maps) are visualized as heatmaps or Circos plots. While informative, these visualizations do not directly represent genomic structure and folding, making the interpretation of the underlying 3D genomic organization obscured. Our objective was to generate a graph-based contact map representation that leads to a more intuitive structural visualization. Results Normalized contact maps were converted into undirected graphs where each vertex represented a genomic region and each edge represented a detected (intra- and inter-chromosomal) or known (linear) interaction between two regions. Each edge was weighted by the inverse of the linear distance (Hi-C experimental resolution) or the interaction frequency from the contact map. Graphs were generated based on this representation scheme for contact maps from existing fission yeast datasets. Originally, these datasets were used to (1) identify specific principles influencing fission yeast genome organization and (2) uncover changes in fission yeast genome organization during the cell cycle. When compared to the equivalent heatmaps and/or Circos plots, the graph-based visualizations more intuitively depicted the changes in genome organization described in the original studies.