Motivation: Convolutional neural networks (CNN) have outperformed conventional methods in modeling the sequence specificity of DNA–protein binding. Yet inappropriate CNN architectures can yield poorer performance than simpler models. Thus an in-depth understanding of how to match CNN architecture to a given task is needed to fully harness the power of CNNs for computational biology applications.Results: We present a systematic exploration of CNN architectures for predicting DNA sequence binding using a large compendium of transcription factor datasets. We identify the best-performing architectures by varying CNN width, depth and pooling designs. We find that adding convolutional kernels to a network is important for motif-based tasks. We show the benefits of CNNs in learning rich higher-order sequence features, such as secondary motifs and local sequence context, by comparing network performance on multiple modeling tasks ranging in difficulty. We also demonstrate how careful construction of sequence benchmark datasets, using approaches that control potentially confounding effects like positional or motif strength bias, is critical in making fair comparisons between competing methods. We explore how to establish the sufficiency of training data for these learning tasks, and we have created a flexible cloud-based framework that permits the rapid exploration of alternative neural network architectures for problems in computational biology.Availability and Implementation: All the models analyzed are available at http://cnn.csail.mit.edu.Contact: gifford@mit.eduSupplementary information: Supplementary data are available at Bioinformatics online.

The contribution of repetitive elements to quantitative human traits is largely unknown. Here, we report a genome-wide survey of the contribution of Short Tandem Repeats (STRs), one of the most polymorphic and abundant repeat classes, to gene expression in humans. Our survey identified 2,060 significant expression STRs (eSTRs). These eSTRs were replicable in orthogonal populations and expression assays. We used variance partitioning to disentangle the contribution of eSTRs from linked SNPs and indels and found that eSTRs contribute 10%–15% of the cis-heritability mediated by all common variants. Further functional genomic analyses showed that eSTRs are enriched in conserved regions, co-localize with regulatory elements, and can modulate certain histone modifications. By analyzing known GWAS hits and searching for new associations in 1,685 deeply-phenotyped whole-genomes, we found that eSTRs are enriched in various clinically-relevant conditions. These results highlight the contribution of short tandem repeats to the genetic architecture of quantitative human traits.

Expression quantitative trait loci (eQTLs) are a key tool to dissect cellular processes mediating complex diseases. However, little is known about the role of repetitive elements as eQTLs. We report a genome-wide survey of the contribution of Short Tandem Repeats (STRs), one of the most polymorphic and abundant repeat classes, to gene expression in humans. Our survey identified 2,060 significant expression STRs (eSTRs). These eSTRs were replicable in orthogonal populations and expression assays. We used variance partitioning to disentangle the contribution of eSTRs from linked SNPs and indels and found that eSTRs contribute 10%-15% of the cisheritability mediated by all common variants. Functional genomic analyses showed that eSTRs are enriched in conserved regions, co-localize with regulatory elements, and are predicted to modulate histone modifications. Our results show that eSTRs provide a novel set of regulatory variants and highlight the contribution of repeats to the genetic architecture of quantitative human traits.

Motivation The precise targeting of antibodies and other protein therapeutics is required for their proper function and the elimination of deleterious off-target effects. Often the molecular structure of a therapeutic target is unknown and randomized methods are used to design antibodies without a model that relates antibody sequence to desired properties. Results Here, we present Ens-Grad, a machine learning method that can design complementarity determining regions of human Immunoglobulin G antibodies with target affinities that are superior to candidates derived from phage display panning experiments. We also demonstrate that machine learning can improve target specificity by the modular composition of models from different experimental campaigns, enabling a new integrative approach to improving target specificity. Our results suggest a new path for the discovery of therapeutic molecules by demonstrating that predictive and differentiable models of antibody binding can be learned from high-throughput experimental data without the need for target structural data. Availability and implementation Sequencing data of the phage panning experiment are deposited at NIH’s Sequence Read Archive (SRA) under the accession number SRP158510. We make our code available at https://github.com/gifford-lab/antibody-2019. Supplementary information Supplementary data are available at Bioinformatics online.

ENCODE 3 (2012-2017) expanded production and added new types of assays 8 (Fig. 1, Extended Data Fig. 1), which revealed landscapes of RNA binding and the 3D organization of chromatin via methods such as chromatin interaction analysis by paired-end tagging (ChIA-PET) and Hi-C chromosome conformation capture. Phases 2 and 3 delivered 9,239 experiments (7,495 in human and 1,744 in mouse) in more than 500 cell types and tissues, including mapping of transcribed regions and transcript isoforms, regions of transcripts recognized by RNA-binding proteins, transcription factor binding regions, and regions that harbour specific histone modifications, open chromatin, and 3D chromatin interactions. The results of all of these experiments are available at the ENCODE portal (http://www.encodeproject.org). These efforts, combined with those of related projects and many other laboratories, have produced a greatly enhanced view of the human genome (Fig. 2), identifying 20,225 protein-coding and 37,595 noncoding genes