Machine Learning and Integrative Analysis of Biomedical Big Data.

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues

Transcription, signaling receptor activity, oxidative phosphorylation, and fatty acid metabolism mediate the presence of closely related species in distinct intertidal and cold-seep habitats

Bathyal cold seeps are isolated extreme deep-sea environments characterized by low species diversity while biomass can be high. The Hakon Mosby mud volcano (Barents Sea, 1,280 m) is a rather stable chemosynthetic driven habitat characterized by prominent surface bacterial mats with high sulfide concentrations and low oxygen levels. Here, the nematode Halomonhystera hermesithrives in high abundances (11,000 individuals 10 cm(-2)). Halomonhystera hermesi is a member of the intertidal Halomonhystera disjuncta species complex that includes five cryptic species (GD 1-5). GD1-5's common habitat is characterized by strong environmental fluctuations. Here, we compared the transcriptomes of H. hermesi and GD1, H. hermesi's closest relative. Genes encoding proteins involved in oxidative phosphorylation are more strongly expressed in H. hermesi than in GD1, and many genes were only observed in H. hermesi while being completely absent in GD1. Both observations could in part be attributed to high sulfide concentrations and low oxygen levels. Additionally, fatty acid elongation was also prominent in H. hermesi confirming the importance of highly unsaturated fatty acids in this species. Significant higher amounts of transcription factors and genes involved in signaling receptor activity were observed in GD1 (many of which were completely absent in H. hermesi), allowing fast signaling and transcriptional reprogramming which can mediate survival in dynamic intertidal environments. GC content was approximately 8% higher in H. hermesi coding unigenes resulting in differential codon usage between both species and a higher proportion of amino acids with GC-rich codons in H. hermesi. In general our results showed that most pathways were active in both environments and that only three genes are under natural selection. This indicates that also plasticity should be taken in consideration in the evolutionary history of Halomonhystera species. Such plasticity, as well as possible preadaptation to low oxygen and high sulfide levels might have played an important role in the establishment of a cold-seep Halomonhystera population

Mapping genomic and transcriptomic alterations spatially in epithelial cells adjacent to human breast carcinoma.

Almost all genomic studies of breast cancer have focused on well-established tumours because it is technically challenging to study the earliest mutational events occurring in human breast epithelial cells. To address this we created a unique dataset of epithelial samples ductoscopically obtained from ducts leading to breast carcinomas and matched samples from ducts on the opposite side of the nipple. Here, we demonstrate that perturbations in mRNA abundance, with increasing proximity to tumour, cannot be explained by copy number aberrations. Rather, we find a possibility of field cancerization surrounding the primary tumour by constructing a classifier that evaluates where epithelial samples were obtained relative to a tumour (cross-validated micro-averaged AUC = 0.74). We implement a spectral co-clustering algorithm to define biclusters. Relating to over-represented bicluster pathways, we further validate two genes with tissue microarrays and in vitro experiments. We highlight evidence suggesting that bicluster perturbation occurs early in tumour development

MutationDistiller: user-driven identification of pathogenic DNA variants

MutationDistiller is a freely available online tool for user-driven analyses of Whole Exome Sequencing data. It offers a user-friendly interface aimed at clinicians and researchers, who are not necessarily bioinformaticians. MutationDistiller combines Mutation- Taster’s pathogenicity predictions with a phenotypebased approach. Phenotypic information is not limited to symptoms included in the Human Phenotype Ontology (HPO), but may also comprise clinical diagnoses and the suspected mode of inheritance. The search can be restricted to lists of candidate genes (e.g. virtual gene panels) and by tissue-specific gene expression. The inclusion of GeneOntology (GO) and metabolic pathways facilitates the discovery of hitherto unknown disease genes. In a novel approach, we trained MutationDistiller’s HPO-based prioritization on authentic genotype–phenotype sets obtained from ClinVar and found it to match or outcompete current prioritization tools in terms of accuracy. In the output, the program provides a list of potential disease mutations ordered by the likelihood of the affected genes to cause the phenotype. MutationDistiller provides links to gene-related information from various resources. It has been extensively tested by clinicians and their suggestions have been valued in many iterative cycles of revisions. The tool, a comprehensive documentation and examples are freely available at https://www.mutationdistiller.org

Principles for the post-GWAS functional characterisation of risk loci

Several challenges lie ahead in assigning functionality to susceptibility SNPs. For example, most effect sizes are small relative to effects seen in monogenic diseases, with per allele odds ratios usually ranging from 1.15 to 1.3. It is unclear whether current molecular biology methods have enough resolution to differentiate such small effects. Our objective here is therefore to provide a set of recommendations to optimize the allocation of effort and resources in order maximize the chances of elucidating the functional contribution of specific loci to the disease phenotype. It has been estimated that 88% of currently identified disease-associated SNP are intronic or intergenic. Thus, in this paper we will focus our attention on the analysis of non-coding variants and outline a hierarchical approach for post-GWAS functional studies