2020
DOI: 10.1101/2020.02.03.932517
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FAN-C: A Feature-rich Framework for the Analysis and Visualisation of C data

Abstract: Chromosome conformation capture data, particularly from high-throughput approaches such as Hi-C and its derivatives, are typically very complex to analyse. Existing analysis tools are often 15 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 commandline tool and powerful Python API with a broad feature set covering matrix generation, analysis, and visualisation for C-like data (https… Show more

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Cited by 14 publications
(24 citation statements)
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“…Hi-C data was analysed using (Kruse et al, 2020). Paired-end reads were scanned to identify ligation junctions, split at ligation junctions if any were present, and then aligned independently to the dm6 genome using BWA-MEM (version 0.7.17-r1188) (Li and Durbin, 2009).…”
Section: Competing Interestsmentioning
confidence: 99%
See 1 more Smart Citation
“…Hi-C data was analysed using (Kruse et al, 2020). Paired-end reads were scanned to identify ligation junctions, split at ligation junctions if any were present, and then aligned independently to the dm6 genome using BWA-MEM (version 0.7.17-r1188) (Li and Durbin, 2009).…”
Section: Competing Interestsmentioning
confidence: 99%
“…Aggregate compartment, domain, and loop plots were created using FAN-C. Compartments were identified using the first eigenvector of the correlation matrix of the normalised Hi-C data, using GC content to orient the eigenvector. The compartment eigenvector for the 3-4 hpf Hi-C data from Hug et al 2017 was used as the reference for the aggregate compartment plots ("saddle plots") (Flyamer et al, 2017;Gassler et al, 2017;Imakaev et al, 2012;Kruse et al, 2020). Domain aggregates were also created using the domains identified in the hpf Hi-C data.…”
Section: Competing Interestsmentioning
confidence: 99%
“…Consistent with these results, we find that regions of the genome which consistently form more contacts in normalized Hi-C are enriched for heterochromatin marks. We have found the bias in raw GAM datasets to be uniformly lower than that found in raw Hi-C, yet both methods have their own specific biases and improved normalisation algorithms have the potential to bridge the divergences between the two methods Chandradoss et al, 2020;Kruse et al, 2020;Liu and Wang, 2019).…”
Section: Discussionmentioning
confidence: 89%
“…The resulting filtered pairs file was converted to a tsv file that was used as input for Juicer Tools Pre 69 , which generated multiresolution hic files. HiC matrices at 10 and 500 Kb resolution, normalized with the Knight-Ruiz (KR) method 70 , were extracted for downstream analysis using the FAN-C toolkit 71 . Visualization of normalized HiC matrices and other values described below, such as insulation scores, TAD boundaries, aggregate TAD and loop analysis, Pearson’s correlation matrices and eigenvectors, were calculated and visualized using FAN-C.…”
Section: Methodsmentioning
confidence: 99%