ChExMix: A Method for Identifying and Classifying Protein–DNA Interaction Subtypes

Yamada, Norimasa; Kuntala, Prashant K; Pugh, B. Franklin; Mahony, Shaun

doi:10.1089/cmb.2019.0466

Cited by 4 publications

(1 citation statement)

References 8 publications

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“…For sequence-specific factors, peak calling was performed using GeneTrack (s5, e20, F1) and ChExMix algorithm for the 30°C and 42°C data sets ( Albert et al 2008 ; Yamada et al 2020 ). Unique peaks from both GeneTrack and ChExMix were combined for each sequence-specific factor from the 30°C and 42°C shock data sets.…”

Section: Methodsmentioning

confidence: 99%

Genome-wide promoter assembly in E. coli measured at single-base resolution

et al. 2022

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When detected at single-base-pair resolution, the genome-wide location, occupancy level, and structural organization of DNA-binding proteins provide mechanistic insights into genome regulation. Here we use ChIP-exo to provide a near-base-pair resolution view of the epigenomic organization of the Escherichia coli transcription machinery and nucleoid structural proteins at the time when cells are growing exponentially and upon rapid reprogramming (acute heat shock). We examined the site specificity of three sigma factors (RpoD/σ70, RpoH/σ32, and RpoN/σ54), RNA polymerase (RNAP or RpoA, -B, -C), and two nucleoid proteins (Fis and IHF). We suggest that DNA shape at the flanks of cognate motifs helps drive site specificity. We find that although RNAP and sigma factors occupy active cognate promoters, RpoH and RpoN can occupy quiescent promoters without the presence of RNAP. Thus, promoter-bound sigma factors can be triggered to recruit RNAP by a mechanism that is distinct from an obligatory cycle of free sigma binding RNAP followed by promoter binding. These findings add new dimensions to how sigma factors achieve promoter specificity through DNA sequence and shape, and further define mechanistic steps in regulated genome-wide assembly of RNAP at promoters in E. coli.

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Section: Methodsmentioning

confidence: 99%

Genome-wide promoter assembly in E. coli measured at single-base resolution

et al. 2022

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MCM2-7 loading-dependent ORC release ensures genome-wide origin licensing

Reuter,

Khadayate,

Mossler

et al. 2024

Nat Commun

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Origin recognition complex (ORC)-dependent loading of the replicative helicase MCM2-7 onto replication origins in G1-phase forms the basis of replication fork establishment in S-phase. However, how ORC and MCM2-7 facilitate genome-wide DNA licensing is not fully understood. Mapping the molecular footprints of budding yeast ORC and MCM2-7 genome-wide, we discovered that MCM2-7 loading is associated with ORC release from origins and redistribution to non-origin sites. Our bioinformatic analysis revealed that origins are compact units, where a single MCM2-7 double hexamer blocks repetitive loading through steric ORC binding site occlusion. Analyses of A-elements and an improved B2-element consensus motif uncovered that DNA shape, DNA flexibility, and the correct, face-to-face spacing of the two DNA elements are hallmarks of ORC-binding and efficient helicase loading sites. Thus, our work identified fundamental principles for MCM2-7 helicase loading that explain how origin licensing is realised across the genome.

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Deep-learning optimized DEOCSU suite provides an iterable pipeline for accurate ChIP-exo peak calling

Bang

Lee

Park

et al. 2023

Briefings in Bioinformatics

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Recognizing binding sites of DNA-binding proteins is a key factor for elucidating transcriptional regulation in organisms. ChIP-exo enables researchers to delineate genome-wide binding landscapes of DNA-binding proteins with near single base-pair resolution. However, the peak calling step hinders ChIP-exo application since the published algorithms tend to generate false-positive and false-negative predictions. Here, we report the development of DEOCSU (DEep-learning Optimized ChIP-exo peak calling SUite), a novel machine learning-based ChIP-exo peak calling suite. DEOCSU entails the deep convolutional neural network model which was trained with curated ChIP-exo peak data to distinguish the visualized data of bona fide peaks from false ones. Performance validation of the trained deep-learning model indicated its high accuracy, high precision and high recall of over 95%. Applying the new suite to both in-house and publicly available ChIP-exo datasets obtained from bacteria, eukaryotes and archaea revealed an accurate prediction of peaks containing canonical motifs, highlighting the versatility and efficiency of DEOCSU. Furthermore, DEOCSU can be executed on a cloud computing platform or the local environment. With visualization software included in the suite, adjustable options such as the threshold of peak probability, and iterable updating of the pre-trained model, DEOCSU can be optimized for users’ specific needs.

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ChExMix: A Method for Identifying and Classifying Protein–DNA Interaction Subtypes

Cited by 4 publications

References 8 publications

Genome-wide promoter assembly in E. coli measured at single-base resolution

Genome-wide promoter assembly in E. coli measured at single-base resolution

MCM2-7 loading-dependent ORC release ensures genome-wide origin licensing

Deep-learning optimized DEOCSU suite provides an iterable pipeline for accurate ChIP-exo peak calling

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