Biology subjects

Gifford, D. K.

Publications and source records attributed to Gifford, D. K..

23 records · Page 2Linked to original sources

bioRxiv2020

Identification of determinants of differential chromatin accessibility through a massively parallel genome-integrated reporter assay

A key mechanism in cellular regulation is the ability of the transcriptional machinery to physically access DNA. Pioneer transcription factors interact with DNA to open chromatin, which subsequently enables changes to gene expression during development, disease, or as a response to environmental stimuli. However, the regulation of DNA accessibility via the recruitment of transcription factors is difficult to understand in the context of the native genome because every genomic site is distinct in multiple ways. Here we introduce the Multiplexed Integrated Accessibility Assay (MIAA), a multiplexed parallel reporter assay which measures changes to genome accessibility as a result of the integration of synthetic oligonucleotide phrase libraries into a controlled, natively inaccessible genomic context. We apply MIAA to measure the effects of sequence motifs on cell type-specific DNA accessibility between mouse embryonic stem cells and embryonic stem cell-derived definitive endoderm cells, screening a total of 7,905 distinct phrases. MIAA is able to recapitulate differential accessibility patterns of 100-nt sequences derived from natively differential genomic regions, identifying the presence of E-box motifs common to epithelial-mesenchymal transition driver transcription factors in stem cell-specific accessible regions that become repressed during differentiation to endoderm. We further present causal evidence that a single binding motif for a key regulatory transcription factor is sufficient to open chromatin, and classify sets of stem cell-specific, endoderm-specific, and shared pioneer factor motifs. We also demonstrate that over-expression of two definitive endoderm transcription factors, Brachyury and FoxA2, results in changes to accessibility in phrases containing their respective DNA-binding motifs. Finally, we use MIAA results to explore the order of motif interactions and identify preferential motif ordering arrangements that appear to have an effect on accessibility.

genomics↗

bioRxiv2019

IDR2D identifies reproducible genomic interactions

Chromatin interaction data from protocols such as ChIA-PET, HiChIP, and HiC provide valuable insights into genome organization and gene regulation, but can include spurious interactions that do not reflect underlying genome biology. We introduce a generalization of the Irreproducible Discovery Rate (IDR) method called IDR2D that identifies replicable interactions shared by chromatin interaction experiments. IDR2D provides a principled set of interactions and eliminates artifacts from single experiments. The method is available as a Bioconductor package for the R community, as well as an online service at https://idr2d.mit.edu.

bioinformatics↗

bioRxiv2019

Antibody Complementarity Determining Region Design Using High-Capacity Machine Learning

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. Here we present a machine learning method that can design human Immunoglobulin G (IgG) antibodies with target affinities that are superior to candidates from phage display panning experiments within a limited design budget. 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.\n\nSignificanceAntibody based therapeutics must meet both affinity and specificity metrics, and existing in vitro methods for meeting these metrics are based upon randomization and empirical testing. We demonstrate that with sufficient target-specific training data machine learning can suggest novel antibody variable domain sequences that are superior to those observed during training. Our machine learning method does not require any target structural information. We further show that data from disparate antibody campaigns can be combined by machine learning to improve antibody specificity.

immunology↗

bioRxiv2018

High resolution discovery of chromatin interactions

Chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) is a method for the genome-wide de novo discovery of chromatin interactions. Existing computational methods typically fail to detect weak or dynamic interactions because they use a peak-calling step that ignores paired-end linkage information. We have developed a novel computational method called Chromatin Interaction Discovery (CID) to overcome this limitation with an unbiased clustering approach for interaction discovery. CID outperforms existing chromatin interaction detection methods with improved sensitivity, replicate consistency, and concordance with other chromatin interaction datasets. In addition, CID also outperforms other methods in discovering chromatin interactions from HiChIP data. We expect that the CID method will be valuable in characterizing 3D chromatin interactions and in understanding the functional consequences of disease-associated distal genetic variations.

bioinformatics↗

bioRxiv2017

A novel k-mer set memory (KSM) motif representation improves regulatory variant prediction

The representation and discovery of transcription factor (TF) sequence binding specificities is critical for understanding gene regulatory networks and interpreting the impact of disease-associated non-coding genetic variants. We present a novel TF binding motif representation, the K-mer Set Memory (KSM), which consists of a set of aligned k-mers that are over-represented at TF binding sites, and a new method called KMAC for de novo discovery of KSMs. We find that KSMs more accurately predict in vivo binding sites than position weight matrix models (PWMs) and other more complex motif models across a large set of ChIP-seq experiments. KMAC also identifies correct motifs in more experiments than four state-of-the-art motif discovery methods. In addition, KSM derived features outperform both PWM and deep learning model derived sequence features in predicting differential regulatory activities of expression quantitative trait loci (eQTL) alleles. Finally, we have applied KMAC to 1488 ENCODE TF ChIP-seq datasets and created a public resource of KSM and PWM motifs. We expect that the KSM representation and KMAC method will be valuable in characterizing TF binding specificities and in interpreting the effects of non-coding genetic variations.

genomics↗