Andrew Quitadamo

TL;DR: The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations, and has reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-generation sequencing, deep exome sequencing, and dense microarray genotyping.

TL;DR: The 1000 Genomes Project as mentioned in this paper provided a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations, and reported the completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole genome sequencing, deep exome sequencing and dense microarray genotyping.

TL;DR: In this paper, the authors describe an integrated set of eight structural variant classes comprising both balanced and unbalanced variants, which are constructed using short-read DNA sequencing data and statistically phased onto haplotype blocks in 26 human populations.

TL;DR: This study demonstrates that deep learning is appropriate for tackling another genomic problem, i.e., building predictive models to understand genotypes’ contribution to gene expression, by providing a deep auto-encoder model for predicting gene expression from SNP genotypes.

TL;DR: An integrated network approach that integrates multiple data sources at a systems level was demonstrated, and a network that provided a more inclusive view of miRNA and gene expression in ovarian cancer was constructed that included four separate types of interactions among miRNAs and genes.