Three-dimensional folding and functional organization principles of the Drosophila genome.

Chromosomes are the physical realization of genetic information and thus form the basis for its readout and propagation. Here we present a high-resolution chromosomal contact map derived from a modified genome-wide chromosome conformation capture approach applied to Drosophila embryonic nuclei. The data show that the entire genome is linearly partitioned into well-demarcated physical domains that overlap extensively with active and repressive epigenetic marks. Chromosomal contacts are hierarchically organized between domains. Global modeling of contact density and clustering of domains show that inactive domains are condensed and confined to their chromosomal territories, whereas active domains reach out of the territory to form remote intra- and interchromosomal contacts. Moreover, we systematically identify specific long-range intrachromosomal contacts between Polycomb-repressed domains. Together, these observations allow for quantitative prediction of the Drosophila chromosomal contact map, laying the foundation for detailed studies of chromosome structure and function in a genetically tractable system.

Robust 4C-seq data analysis to screen for regulatory DNA interactions.

Regulatory DNA elements can control the expression of distant genes via physical interactions. Here we present a cost-effective methodology and computational analysis pipeline for robust characterization of the physical organization around selected promoters and other functional elements using chromosome conformation capture combined with high-throughput sequencing (4C-seq). Our approach can be multiplexed and routinely integrated with other functional genomics assays to facilitate physical characterization of gene regulation.

Personalized lab test models to quantify disease potentials in healthy individuals.

Standardized lab tests are central for patient evaluation, differential diagnosis and treatment. Interpretation of these data is nevertheless lacking quantitative and personalized metrics. Here we report on the modeling of 2.1 billion lab measurements of 92 different lab tests from 2.8 million adults over a span of 18 years. Following unsupervised filtering of 131 chronic conditions and 5,223 drug-test pairs we performed a virtual survey of lab tests distributions in healthy individuals. Age and sex alone explain less than 10% of the within-normal test variance in 89 out of 92 tests. Personalized models based on patients' history explain 60% of the variance for 17 tests and over 36% for half of the tests. This allows for systematic stratification of the risk for future abnormal test levels and subsequent emerging disease. Multivariate modeling of within-normal lab tests can be readily implemented as a basis for quantitative patient evaluation.

MetaCell: analysis of single-cell RNA-seq data using K-nn graph partitions.

scRNA-seq profiles each represent a highly partial sample of mRNA molecules from a unique cell that can never be resampled, and robust analysis must separate the sampling effect from biological variance. We describe a methodology for partitioning scRNA-seq datasets into metacells: disjoint and homogenous groups of profiles that could have been resampled from the same cell. Unlike clustering analysis, our algorithm specializes at obtaining granular as opposed to maximal groups. We show how to use metacells as building blocks for complex quantitative transcriptional maps while avoiding data smoothing. Our algorithms are implemented in the MetaCell R/C++ software package.