Deep learning describes a class of machine learning algorithms that are capable of combining
raw inputs into layers of intermediate features. These algorithms have recently shown …

With the completion of the human genome sequence, attention turned to identifying and
annotating its functional DNA elements. As a complement to genetic and comparative genomics …

Transcriptome-wide association studies (TWAS) integrate genome-wide association studies
(GWAS) and gene expression datasets to identify gene–trait associations. In this Perspective…

The reference human genome sequence set the stage for studies of genetic variation and
its association with human disease, but epigenomic studies lack a similar reference. To …

We present Omni-ATAC, an improved ATAC-seq protocol for chromatin accessibility
profiling that works across multiple applications with substantial improvement of signal-to-…

The purported “black box” nature of neural networks is a barrier to adoption in applications
where interpretability is essential. Here we present DeepLIFT (Deep Learning Important …

The human and mouse genomes contain instructions that specify RNAs and proteins and
govern the timing, magnitude, and cellular context of their production. To better delineate these …

Chromatin immunoprecipitation (ChIP) followed by high-throughput DNA sequencing (ChIP-seq)
has become a valuable and widely used approach for mapping the genomic location of …

Transcription factors bind in a combinatorial fashion to specify the on-and-off states of genes;
the ensemble of these binding events forms a regulatory network, constituting the wiring …

Functional genomics assays based on high-throughput sequencing greatly expand our ability
to understand the genome. Here, we define the ENCODE blacklist- a comprehensive set …