Pairtools: From sequencing data to chromosome contacts

,; Abdennur, Nezar; Fudenberg, Geoffrey; Flyamer, Ilya M.; Galitsyna, Aleksandra A.; Goloborodko, Anton; Imakaev, Maxim; Venev, Sergey V.

doi:10.1371/journal.pcbi.1012164

Cited by 14 publications

(2 citation statements)

References 63 publications

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“…The pairs files of the technical replicates of the same micro-C BR ( Supplementary Table S2 ) were merged to create a pairs file for each BR using pairtools ’s (v1.0.2) function ( 19 ). Similarly, the pairs files of the same cell line were merged to create a pairs file of pooled BRs for each cell line.…”

Section: Methodsmentioning

confidence: 99%

ENT3C: an entropy-based similarity measure for Hi-C and micro-C derived contact matrices

Lainscsek,

Taher

2024

NAR Genomics and Bioinformatics

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Hi-C and micro-C sequencing have shed light on the profound importance of 3D genome organization in cellular function by probing 3D contact frequencies across the linear genome. The resulting contact matrices are extremely sparse and susceptible to technical- and sequence-based biases, making their comparison challenging. The development of reliable, robust and efficient methods for quantifying similarity between contact matrices is crucial for investigating variations in the 3D genome organization in different cell types or under different conditions, as well as evaluating experimental reproducibility. We present a novel method, ENT3C, which measures the change in pattern complexity in the vicinity of contact matrix diagonals to quantify their similarity. ENT3C provides a robust, user-friendly Hi-C or micro-C contact matrix similarity metric and a characteristic entropy signal that can be used to gain detailed biological insights into 3D genome organization.

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Section: Methodsmentioning

confidence: 99%

ENT3C: an entropy-based similarity measure for Hi-C and micro-C derived contact matrices

Lainscsek,

Taher

2024

NAR Genomics and Bioinformatics

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“…Data preprocessing: We followed the preprocessing described in prior research using the Akita framework [19] for the 6 mouse and 5 human datasets in Supplemental Table 1. Briefly, we reprocessed these datasets using the distiller pipeline (https://github.com/open2c/distiller-nf, [56]), extracting contacts with pairtools [57], binning each dataset to 2,048bp cooler files (https://github.com/open2c/cooler, [58]) and performing genome-wide iterative correction [59]. Individual target matrices were extracted from genome-wide cooler files for regions corresponding to 1,310,720bp of input sequence, 25% larger than the original 1,048,576bp.…”

Section: Methodsmentioning

confidence: 99%

Interpreting the CTCF-mediated sequence grammar of genome folding with AkitaV2

Smaruj,

Kamulegeya,

Kelley

et al. 2024

Preprint

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Interphase mammalian genomes are folded in 3D with complex locus-specific patterns that impact gene regulation. CTCF (CCCTC-binding factor) is a key architectural protein that binds specific DNA sites, halts cohesin-mediated loop extrusion, and enables long-range chromatin interactions. There are hundreds of thousands of annotated CTCF-binding sites in mammalian genomes; disruptions of some result in distinct phenotypes, while others have no visible effect. Despite their importance, the determinants of which CTCF sites are necessary for genome folding and gene regulation remain unclear. Here, we update and utilize Akita, a convolutional neural network model, to extract the sequence preferences and grammar of CTCF contributing to genome folding. Our analyses of individual CTCF sites reveal four predictions: (i) only a small fraction of genomic sites are impactful, (ii) insulation strength is highly dependent on sequences flanking the core CTCF binding motif, (iii) core and flanking sequences are broadly compatible, and (iv) core and flanking nucleotides contribute largely additively to overall strength. Our analysis of collections of CTCF sites make two predictions for multi-motif grammar: (i) insulation strength depends on the number of CTCF sites within a cluster, and (ii) pattern formation is governed by the orientation and spacing of these sites, rather than any inherent specialization of the CTCF motifs themselves. In sum, we present a framework for using neural network models to probe the sequences instructing genome folding and provide a number of predictions to guide future experimental inquiries.Author SummaryMammalian genomes are spatially organized in 3D with profound consequences for all processes involving DNA. CTCF is a key genome organizer, recognizing numerous sites and creating a variety of contact patterns across the genome. Despite the importance of CTCF, the sequence determinants and grammar of how individual sites collectively instruct genome folding remain unclear. This work leverages the ability of Akita, a deep neural network, to make high-throughput predictions for genome folding after DNA sequence perturbations. Using Akita, we make several experimentally testable predictions. First, only a minority of annotated sites individually impact folding, and flanking DNA sequences greatly modulate their impact. Second, multiple sites together influence folding based on their number, orientation, and spacing. In sum, we provide a roadmap for interpreting neural networks to better understand genome folding and important considerations for the design of experiments.

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CRISPR screen of venetoclax response-associated genes identifies transcription factor ZNF740 as a key functional regulator

Zhang,

Zhou,

Aryal

et al. 2024

Cell Death Dis

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BCL-2 inhibitors such as venetoclax offer therapeutic promise in acute myeloid leukemia (AML) and other cancers, but drug resistance poses a significant challenge. It is crucial to understand the mechanisms that regulate venetoclax response. While correlative studies have identified numerous genes linked to venetoclax sensitivity, their direct impact on the drug response remains unclear. In this study, we targeted around 1400 genes upregulated in venetoclax-sensitive primary AML samples and carried out a CRISPR knockout screen to evaluate their direct effects on venetoclax response. Our screen identified the transcription factor ZNF740 as a critical regulator, with its expression consistently predicting venetoclax sensitivity across subtypes of the FAB classification. ZNF740 depletion leads to increased resistance to ventoclax, while its overexpression enhances sensitivity to the drug. Mechanistically, our integrative transcriptomic and genomic analysis identifies NOXA as a direct target of ZNF740, which negatively regulates MCL-1 protein stability. Loss of ZNF740 downregulates NOXA and increases the steady state protein levels of MCL-1 in AML cells. Restoring NOXA expression in ZNF740-depleted cells re-sensitizes AML cells to venetoclax treatment. Furthermore, we demonstrated that dual targeting of MCL-1 and BCL-2 effectively treats ZNF740-deficient AML in vivo. Together, our work systematically elucidates the causal relationship between venetoclax response signature genes and establishes ZNF740 as a novel transcription factor regulating venetoclax sensitivity.

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Pairtools: From sequencing data to chromosome contacts

Cited by 14 publications

References 63 publications

ENT3C: an entropy-based similarity measure for Hi-C and micro-C derived contact matrices

ENT3C: an entropy-based similarity measure for Hi-C and micro-C derived contact matrices

Interpreting the CTCF-mediated sequence grammar of genome folding with AkitaV2

CRISPR screen of venetoclax response-associated genes identifies transcription factor ZNF740 as a key functional regulator

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