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Transposase-Accessible Chromatin followed by sequencing (ATAC-seq) is a simple protocol for detection of open chromatin. Computational footprinting, the search for regions with depletion of cleavage events due to transcription factor binding, is poorly understood for ATAC-seq. We propose the first footprinting method considering ATAC-seq protocol artifacts. HINT-ATAC uses a position dependency model to learn the cleavage preferences of the transposase. We observe strand-specific cleavage patterns around transcription factor binding sites, which are determined by local nucleosome architecture. By incorporating all these biases, HINT-ATAC is able to significantly outperform competing methods in the prediction of transcription factor binding sites with footprints.

Combinatorial binding of transcription factors to regulatory DNA underpins gene regulation in all organisms. Genetic variation in regulatory regions has been connected with diseases and diverse phenotypic traits 1 , but it remains challenging to distinguish variants that affect regulatory function 2 . Genomic DNase I footprinting enables the quantitative, nucleotide-resolution delineation of sites of transcription factor occupancy within native chromatin 3 – 6 . However, only a small fraction of such sites have been precisely resolved on the human genome sequence 6 . Here, to enable comprehensive mapping of transcription factor footprints, we produced high-density DNase I cleavage maps from 243 human cell and tissue types and states and integrated these data to delineate about 4.5 million compact genomic elements that encode transcription factor occupancy at nucleotide resolution. We map the fine-scale structure within about 1.6 million DNase I-hypersensitive sites and show that the overwhelming majority are populated by well-spaced sites of single transcription factor–DNA interaction. Cell-context-dependent cis -regulation is chiefly executed by wholesale modulation of accessibility at regulatory DNA rather than by differential transcription factor occupancy within accessible elements. We also show that the enrichment of genetic variants associated with diseases or phenotypic traits in regulatory regions 1 , 7 is almost entirely attributable to variants within footprints, and that functional variants that affect transcription factor occupancy are nearly evenly partitioned between loss- and gain-of-function alleles. Unexpectedly, we find increased density of human genetic variation within transcription factor footprints, revealing an unappreciated driver of cis -regulatory evolution. Our results provide a framework for both global and nucleotide-precision analyses of gene regulatory mechanisms and functional genetic variation. A high-density DNase I cleavage map from 243 human cell and tissue types provides a genome-wide, nucleotide-resolution map of human transcription factor footprints.

DNA–protein interactions are essential for several molecular and cellular mechanisms, such as transcription, transcriptional regulation, DNA modifications, among others. For many decades scientists tried to unravel how DNA links to proteins, forming complex and vital interactions. However, the high number of techniques developed for the study of these interactions made the choice of the appropriate technique a difficult task. This review intends to provide a historical context and compile the methods that describe DNA–protein interactions according to the purpose of each approach, summarise the respective advantages and disadvantages and give some examples of recent uses for each technique. The final aim of this work is to help in deciding which technique to perform according to the objectives and capacities of each research team. Considering the DNA–binding proteins characterisation, filter binding assay and EMSA are easy in vitro methods that rapidly identify nucleic acid-protein binding interactions. To find DNA-binding sites, DNA-footprinting is indeed an easier, faster and reliable approach, however, techniques involving base analogues and base-site selection are more precise. Concerning binding kinetics and affinities, filter binding assay and EMSA are useful and easy methods, although SPR and spectroscopy techniques are more sensitive. Finally, relatively to genome-wide studies, ChIP–seq is the desired method, given the coverage and resolution of the technique. In conclusion, although some experiments are easier and faster than others, when designing a DNA–protein interaction study several concerns should be taken and different techniques may need to be considered, since different methods confer different precisions and accuracies.

Hydroxyl-radical footprinting (HRF) is a powerful method for probing structures of nucleic acid–protein complexes with single-nucleotide resolution in solution. To tap the full quantitative potential of HRF, we describe a protocol, hydroxyl-radical footprinting interpretation for DNA (HYDROID), to quantify HRF data and integrate them with atomistic structural models. The stages of the HYDROID protocol are extraction of the lane profiles from gel images, quantification of the DNA cleavage frequency at each nucleotide and theoretical estimation of the DNA cleavage frequency from atomistic structural models, followed by comparison of experimental and theoretical results. Example scripts for each step of HRF data analysis and interpretation are provided for several nucleosome systems; they can be easily adapted to analyze user data. As input, HYDROID requires polyacrylamide gel electrophoresis (PAGE) images of HRF products and optionally can use a molecular model of the DNA–protein complex. The HYDROID protocol can be used to quantify HRF over DNA regions of up to 100 nucleotides per gel image. In addition, it can be applied to the analysis of RNA–protein complexes and free RNA or DNA molecules in solution. Compared with other methods reported to date, HYDROID is unique in its ability to simultaneously integrate HRF data with the analysis of atomistic structural models. HYDROID is freely available. The complete protocol takes ~3 h. Users should be familiar with the command-line interface, the Python scripting language and Protein Data Bank (PDB) file formats. A graphical user interface (GUI) with basic functionality (HYDROID_GUI) is also available.

We introduce CUT&RUNTools as a flexible, general pipeline for facilitating the identification of chromatin-associated protein binding and genomic footprinting analysis from antibody-targeted CUT&RUN primary cleavage data. CUT&RUNTools extracts endonuclease cut site information from sequences of short-read fragments and produces single-locus binding estimates, aggregate motif footprints, and informative visualizations to support the high-resolution mapping capability of CUT&RUN. CUT&RUNTools is available at https://bitbucket.org/qzhudfci/cutruntools/ .

While footprinting analysis of ATAC-seq data can theoretically enable investigation of transcription factor (TF) binding, the lack of a computational tool able to conduct different levels of footprinting analysis has so-far hindered the widespread application of this method. Here we present TOBIAS, a comprehensive, accurate, and fast footprinting framework enabling genome-wide investigation of TF binding dynamics for hundreds of TFs simultaneously. We validate TOBIAS using paired ATAC-seq and ChIP-seq data, and find that TOBIAS outperforms existing methods for bias correction and footprinting. As a proof-of-concept, we illustrate how TOBIAS can unveil complex TF dynamics during zygotic genome activation in both humans and mice, and propose how zygotic Dux activates cascades of TFs, binds to repeat elements and induces expression of novel genetic elements. Footprinting analysis allows genome-wide investigation of transcription factor (TF) binding on chromatin. Here the authors developed a framework termed TOBIAS aimed at identifying footprints of chromatin-associated proteins from ATAC-seq accessibility profiles and apply it to zygotic development datasets.

Introduction and AimsThe healthcare industry has a substantial carbon footprint and the recent 2022 Health and Care Act has committed the NHS to net zero carbon by 2040. In 2022, BASL published its Sustainability Strategy and committed to reduce the Association’s carbon footprint. The sustainability of different ways of presenting posters is not known. The aim of this study was, therefore, to analyse and compare the carbon footprint of three different options of organising and displaying posters during the 2022 BASL annual conference.MethodsA comparative analysis of the carbon footprint of three different models of poster display was performed using a cradle to grave process-based carbon footprinting methodology; namelyIndividual delegate printing and transporting to the venueCentral printing of all posters in the host cityElectronic displaysThis included raw material extraction, production, delivery, use and disposal of items. This was based on the 80 posters displayed. Of note only 66 of the posters displayed in Leeds were printed centrally and the analysis was split into option 2a (this scenario) and option 2b (all 80 posters printed centrally). It is important to note that local printing was on fully recycled material that was then recycled at the end of the meeting.ResultsThe carbon footprint of option 1 (individual delegate printing) was estimated at 117 kg of carbon dioxide equivalent (kgCO2e); the equivalent of driving 345 miles. Option 2a had a carbon footprint of 46 kgCO2e, 40% of option 1’s carbon footprint. Option 2b reduced the footprint further to 31 kgCO2e (figure 1). Displaying 80 posters on 6 digital screens over 3 days had an estimated carbon footprint of 38 kgCO2e (or 112 miles; a return trip from London to Oxford). In all three options, the majority of the carbon footprint burden was the result of transportation of materials to and from the venue.Discussion and ConclusionsSubstantial carbon saving can be achieved by moving away from the traditional model of individual delegates printing and transporting their own posters. Surprisingly, local printing and display on poster boards had a better carbon footprint than electronic displays. In addition, this model has the added advantage of allowing researchers to continuously stand by their posters and interact/network with other meeting delegates. This data underlines the importance of not making assumptions about relative environmental impacts of current and future practices without undertaking specific analyses comparing them.

Hydroxyl radical protein footprinting (HRPF) in combination with mass spectrometry reveals the relative solvent exposure of labeled residues within a protein, thereby providing insight into protein tertiary structure. HRPF labels nineteen residues with varying degrees of reliability and reactivity. Here, we are presenting a dynamics-driven HRPF-guided algorithm for protein structure prediction. In a benchmark test of our algorithm, usage of the dynamics data in a score term resulted in notable improvement of the root-mean-square deviations of the lowest-scoring ab initio models and improved the funnel-like metric P near for all benchmark proteins. We identified models with accurate atomic detail for three of the four benchmark proteins. This work suggests that HRPF data along with side chain dynamics sampled by a Rosetta mover ensemble can be used to accurately predict protein structure. Mass spectrometry-based covalent labeling techniques such as hydroxyl radical protein footprinting (HRPF) provide information about protein tertiary structures. Here, the authors present a dynamics driven HRPF-guided algorithm for protein structure prediction that is incorporated in the Rosetta software suite and only requires the protein sequence and HRPF data as input and they demonstrate its successful application to four benchmark proteins.

The advent of single-cell chromatin accessibility profiling has accelerated the ability to map gene regulatory landscapes but has outpaced the development of scalable software to rapidly extract biological meaning from these data. Here we present a software suite for single-cell analysis of regulatory chromatin in R (ArchR; https://www.archrproject.com/ ) that enables fast and comprehensive analysis of single-cell chromatin accessibility data. ArchR provides an intuitive, user-focused interface for complex single-cell analyses, including doublet removal, single-cell clustering and cell type identification, unified peak set generation, cellular trajectory identification, DNA element-to-gene linkage, transcription factor footprinting, mRNA expression level prediction from chromatin accessibility and multi-omic integration with single-cell RNA sequencing (scRNA-seq). Enabling the analysis of over 1.2 million single cells within 8 h on a standard Unix laptop, ArchR is a comprehensive software suite for end-to-end analysis of single-cell chromatin accessibility that will accelerate the understanding of gene regulation at the resolution of individual cells. ArchR is a software suite that enables efficient and end-to-end analysis of single-cell chromatin accessibility data (scATAC-seq).

Background DNase-seq and ATAC-seq are broadly used methods to assay open chromatin regions genome-wide. The single nucleotide resolution of DNase-seq has been further exploited to infer transcription factor binding sites (TFBSs) in regulatory regions through footprinting. Recent studies have demonstrated the sequence bias of DNase I and its adverse effects on footprinting efficiency. However, footprinting and the impact of sequence bias have not been extensively studied for ATAC-seq. Results Here, we undertake a systematic comparison of the two methods and show that a modification to the ATAC-seq protocol increases its yield and its agreement with DNase-seq data from the same cell line. We demonstrate that the two methods have distinct sequence biases and correct for these protocol-specific biases when performing footprinting. Despite the differences in footprint shapes, the locations of the inferred footprints in ATAC-seq and DNase-seq are largely concordant. However, the protocol-specific sequence biases in conjunction with the sequence content of TFBSs impact the discrimination of footprint from the background, which leads to one method outperforming the other for some TFs. Finally, we address the depth required for reproducible identification of open chromatin regions and TF footprints. Conclusions We demonstrate that the impact of bias correction on footprinting performance is greater for DNase-seq than for ATAC-seq and that DNase-seq footprinting leads to better performance. It is possible to infer concordant footprints by using replicates, highlighting the importance of reproducibility assessment. The results presented here provide an overview of the advantages and limitations of footprinting analyses using ATAC-seq and DNase-seq.