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

Loci specific epigenetic drug sensitivity.

Therapeutic targeting of epigenetic modulators offers a novel approach to the treatment of multiple diseases. The cellular consequences of chemical compounds that target epigenetic regulators (epi-drugs) are complex. Epi-drugs affect global cellular phenotypes and cause local changes to gene expression due to alteration of a gene chromatin environment. Despite increasing use in the clinic, the mechanisms responsible for cellular changes are unclear. Specifically, to what degree the effects are a result of cell-wide changes or disease related locus specific effects is unknown. Here we developed a platform to systematically and simultaneously investigate the sensitivity of epi-drugs at hundreds of genomic locations by combining DNA barcoding, unique split-pool encoding, and single cell expression measurements. Internal controls are used to isolate locus specific effects separately from any global consequences these drugs have. Using this platform we discovered wide-spread loci specific sensitivities to epi-drugs for three distinct epi-drugs that target histone deacetylase, DNA methylation and bromodomain proteins. By leveraging ENCODE data on chromatin modification, we identified features of chromatin environments that are most likely to be affected by epi-drugs. The measurements of loci specific epi-drugs sensitivities will pave the way to the development of targeted therapy for personalized medicine

Short- and long-term effects of 56Fe irradiation on cognition and hippocampal DNA methylation and gene expression.

BackgroundAstronauts are exposed to 56Fe ions that may pose a significant health hazard during and following prolonged missions in deep space. We showed previously that object recognition requiring the hippocampus, a structure critical for cognitive function, is affected in 2-month-old mice irradiated with 56Fe ions. Here we examined object recognition in 6-month-old mice irradiated with 56Fe ions, a biological age more relevant to the typical ages of astronauts. Moreover, because the mechanisms mediating the detrimental effects of 56Fe ions on hippocampal function are unclear, we examined changes in hippocampal networks involved in synaptic plasticity and memory, gene expression, and epigenetic changes in cytosine methylation (5mC) and hydroxymethylation (5hmC) that could accompany changes in gene expression. We assessed the effects of whole body 56Fe ion irradiation at early (2 weeks) and late (20 weeks) time points on hippocampus-dependent memory and hippocampal network stability, and whether these effects are associated with epigenetic changes in hippocampal DNA methylation (both 5mC and 5hmC) and gene expression.ResultsAt the two-week time point, object recognition and network stability were impaired following irradiation at the 0.1 and 0.4 Gy dose, but not following irradiation at the 0.2 Gy dose. No impairments in object recognition or network stability were seen at the 20-week time point at any irradiation dose used. Consistent with this pattern, the significance of pathways for gene categories for 5hmC was lower, though not eliminated, at the 20-week time point compared to the 2-week time point. Similarly, significant changes were observed for 5mC gene pathways at the 2-week time point, but no significant gene categories were observed at the 20-week time point. Only the 5hmC changes tracked with gene expression changes.ConclusionsDose- and time-dependent epigenomic remodeling in the hippocampus following 56Fe ion exposure correlates with behavioral changes

Assessing the Gene Content of the Megagenome: Sugar Pine (Pinus lambertiana).

Sugar pine (Pinus lambertiana Douglas) is within the subgenus Strobus with an estimated genome size of 31 Gbp. Transcriptomic resources are of particular interest in conifers due to the challenges presented in their megagenomes for gene identification. In this study, we present the first comprehensive survey of the P. lambertiana transcriptome through deep sequencing of a variety of tissue types to generate more than 2.5 billion short reads. Third generation, long reads generated through PacBio Iso-Seq have been included for the first time in conifers to combat the challenges associated with de novo transcriptome assembly. A technology comparison is provided here to contribute to the otherwise scarce comparisons of second and third generation transcriptome sequencing approaches in plant species. In addition, the transcriptome reference was essential for gene model identification and quality assessment in the parallel project responsible for sequencing and assembly of the entire genome. In this study, the transcriptomic data were also used to address questions surrounding lineage-specific Dicer-like proteins in conifers. These proteins play a role in the control of transposable element proliferation and the related genome expansion in conifers