scholarly journals Chromosome conformation capture assay combined with biotin enrichment for hyperthermophilic archaea

2021 ◽  
Vol 2 (2) ◽  
pp. 100576
Author(s):  
Naomichi Takemata ◽  
Stephen D. Bell
2021 ◽  
Vol 10 (17) ◽  
Author(s):  
Quentin Lamy-Besnier ◽  
Romain Koszul ◽  
Laurent Debarbieux ◽  
Martial Marbouty

ABSTRACT The Oligo-Mouse-Microbiota (OMM12) gnotobiotic murine model is an increasingly popular model in microbiota studies. However, following Illumina and PacBio sequencing, the genomes of the 12 strains could not be closed. Here, we used genomic chromosome conformation capture (Hi-C) data to reorganize, close, and improve the quality of these 12 genomes.


2020 ◽  
Author(s):  
Marlies E. Oomen ◽  
Adam K. Hedger ◽  
Jonathan K. Watts ◽  
Job Dekker

Abstract Current chromosome conformation capture techniques are not able to distinguish sister chromatids. Here we describe the protocol of SisterC1: a novel Hi-C technique that leverages BrdU incorporation and UV/Hoechst-induced single strand breaks to identify interactions along and between sister chromatids. By synchronizing cells, BrdU is incorporated only on the newly replicated strand, which distinguishes the two sister chromatids2,3. This is followed by Hi-C4 of cells that can be arrested in different stages of the cell cycle, e.g. in mitosis. Before final amplification of the Hi-C library, strands containing BrdU are specifically depleted by UV/Hoechst treatment. SisterC libraries are then sequenced using 50bp paired end reads, followed by mapping using standard Hi-C processing tools. Interactions can then be assigned as inter- or intra-sister interactions based on read orientation.


BMC Genomics ◽  
2016 ◽  
Vol 17 (1) ◽  
Author(s):  
Idan Gabdank ◽  
Sreejith Ramakrishnan ◽  
Anne M. Villeneuve ◽  
Andrew Z. Fire

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Kai Kruse ◽  
Clemens B. Hug ◽  
Juan M. Vaquerizas

AbstractChromosome conformation capture data, particularly from high-throughput approaches such as Hi-C, are typically very complex to analyse. Existing analysis tools are often single-purpose, or limited in compatibility to a small number of data formats, frequently making Hi-C analyses tedious and time-consuming. Here, we present FAN-C, an easy-to-use command-line tool and powerful Python API with a broad feature set covering matrix generation, analysis, and visualisation for C-like data (https://github.com/vaquerizaslab/fanc). Due to its compatibility with the most prevalent Hi-C storage formats, FAN-C can be used in combination with a large number of existing analysis tools, thus greatly simplifying Hi-C matrix analysis.


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