Hicberg

Hicberg reconstructs contact signals from repeated elements in genomic data, particularly Hi-C data, using statistical inference to enable understanding of the impact of repeated elements on genome organization.

Key Features:

• Statistical inference for read reassignment: Computes probability distributions to precisely reassign the positions of reads from repeated sequences in paired omics data such as Hi-C data.
• Contact map reconstruction: Generates complete chromosome contact maps by reconstructing contact signals from repeated elements that are typically excluded from standard high-throughput sequencing pipelines.
• Trend computation on unambiguous genome regions: Computes statistical trends on the unambiguous part of the genome prior to inference-based read reassignment.
• Multi-mapping read handling: Reassigns the position of multi-mapping reads that cannot be unambiguously localized on reference genomes due to their origin from repeated sequences.

Scientific Applications:

• Repeated element impact analysis: Reveals new observations on the impact of repeated elements on chromosome architecture, including duplicated genes, low-complexity sequences, transposons, ribosomal DNA, and tandem repeats.
• Retrotransposon-cohesin relationship investigation: Enables study of the involvement of certain retrotransposons in the positioning of cohesins, the molecular motors behind chromosome loops.
• Genome spatial organization research: Generates insights into the spatial organisation of genomes, particularly in microorganisms, by processing repeated sequences that current high-throughput sequencing pipelines cannot handle directly.

Methodology:

Hicberg computes statistical trends on the unambiguous part of the genome and then, by statistical inference with the computation of probability distributions, reassigns the position of multi-mapping reads from repeated sequences in paired omics data such as Hi-C data.