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Fig. 1 | BMC Genomics

Fig. 1

From: HiC-bench: comprehensive and reproducible Hi-C data analysis designed for parameter exploration and benchmarking

Fig. 1

HiC-bench workflow. Raw reads (input fastq files) are aligned and then filtered (align and filter tasks). Filtered reads are used for the creation of Hi-C track files (tracks) that can be directly uploaded to the WashU Epigenome Browser [27]. A report with a statistics summary of filtered Hi-C reads, is also automatically generated (filter-stats). Raw Hi-C matrices (matrix-filtered) are normalized using (a) scaling (matrix-prep), (b) iterative correction (matrix-ic) [9] or (c) HiCNorm (matrix-hicnorm) [28]. A report with the plots of the normalized Hi-C counts as function of the distance between the interacting partners (matrix-stats) is automatically generated for all methods. The resulting matrices are compared across all samples in terms of Pearson and Spearman correlation (compare-matrices and compare-matrices-stats). Boundary scores are calculated and the corresponding report with the Principal Component Analysis (PCA) is automatically generated (boundary-scores and boundary-scores-pca). Domains are identified using various TAD calling algorithms (domains) followed by comparison of TAD boundaries (compare-boundaries and compare-boundaries-stats). A report with the statistics of boundary comparison is also automatically generated. Hi-C visualization of user-defined genomic regions is performed using HiCPlotter (hicplotter) [23]. Specific chromatin interactions (interactions) are detected and annotated (annotations). Finally, enrichment of top interactions in certain chromatin marks, transcription factors etc. provided by the user, is automatically calculated (annotations-stats)

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