BACKGROUND: There has been a simultaneous increase in demand and accessibility across genomics, transcriptomics, proteomics and metabolomics data, known as omics data. This has encouraged widespread application of omics data in life sciences, from personalized medicine to the discovery of underlying pathophysiology of diseases. Causal analysis of omics data may provide important insight into the underlying biological mechanisms. Existing causal analysis methods yield promising results when identifying potential general causes of an observed outcome based on omics data. However, they may fail to discover the causes specific to a particular stratum of individuals and missing from others. METHODS: To fill this gap, we introduce the problem of stratified causal discovery and propose a method, Aristotle, for solving it. Aristotle addresses the two challenges intrinsic to omics data: high dimensionality and hidden stratification. It employs existing biological knowledge and a state-of-the-art patient stratification method to tackle the above challenges and applies a quasi-experimental design method to each stratum to find stratum-specific potential causes. RESULTS: Evaluation based on synthetic data shows better performance for Aristotle in discovering true causes under different conditions compared to existing causal discovery methods. Experiments on a real dataset on Anthracycline Cardiotoxicity indicate that Aristotle's predictions are consistent with the existing literature. Moreover, Aristotle makes additional predictions that suggest further investigations.

Temporal causal discovery aims to find cause-effect relationships between time-series. However, none of the existing techniques is able to identify the causal profile, the temporal pattern that the causal variable needs to follow in order to trigger the most significant change in the outcome. Toward a new horizon, this study introduces the novel problem of Causal Profile Discovery, which is crucial for many applications such as adverse drug reaction and cyber-attack detection. This work correspondingly proposes Heidegger to discover causal profiles, comprised of a flexible randomized block design for hypothesis evaluation and an efficient profile search via on-the-fly graph construction and entropy-based pruning. Heidegger's performance is demonstrated/evaluated extensively on both synthetic and real-world data. The experimental results show the proposed method is robust to noise and flexible at detecting complex patterns.

Variable-Number Tandem Repeats (VNTR) are genomic regions where a short sequence of DNA is repeated with no space in between repeats. While a fixed set of VNTRs is typically identified for a given species, the copy number at each VNTR varies between individuals within a species. Although VNTRs are found in both prokaryotic and eukaryotic genomes, the methodology called multi-locus VNTR analysis (MLVA) is widely used to distinguish different strains of bacteria, as well as cluster strains that might be epidemiologically related and investigate evolutionary rates. We propose PRINCE (Processing Reads to Infer the Number of Copies via Estimation), an algorithm that is able to accurately estimate the copy number of a VNTR given the sequence of a single repeat unit and a set of short reads from a whole-genome sequence (WGS) experiment. This is a challenging problem, especially in the cases when the repeat region is longer than the expected read length. Our proposed method computes a statistical approximation of the local coverage inside the repeat region. This approximation is then mapped to the copy number using a linear function whose parameters are fitted to simulated data. We test PRINCE on the genomes of three datasets of Mycobacterium tuberculosis strains and show that it is more than twice as accurate as a previous method. An implementation of PRINCE in the Python language is freely available at https://github.com/WGS-TB/PythonPRINCE.

Motivation: Adverse drug reactions are one of the major factors that affect the wellbeing of patients and financial costs of healthcare systems. Genetic variations of patients have been shown to be a key factor in the occurrence and severity of many ADRs. However, the large number of confounding drugs and genetic biomarkers for each adverse reaction case demands a method that evaluates all potential genetic causes of ADRs simultaneously. Results: To address this challenge, we propose HUME, a multi-phase algorithm that recommends genetic factors for ADRs that are causally supported by the patient record data. HUME consists of the construction of a network from co-prevalence between significant genetic biomarkers and ADRs, a link score phase for predicting candidate relations based on the Adamic-Adar measure, and a causal refinement phase based on multiple hypothesis testing of quasi experimental designs for evaluating evidence and counter evidence of candidate relations in the patient records. Supplementary information: Supplementary data are available at Bioinformatics online.