Identification of copy number variations and translocations in cancer cells from Hi-C data

Abstract

Motivation: Eukaryotic chromosomes adapt a complex and highly dynamic three-dimensional (3D) structure, which profoundly affects different cellular functions and outcomes including changes in epigenetic landscape and in gene expression. Making the scenario even more complex, cancer cells harbor chromosomal abnormalities (e.g., copy number variations (CNVs) and translocations) altering their genomes both at the sequence level and at the level of 3D organization. High-throughput chromosome conformation capture techniques (e.g., Hi-C), which are originally developed for decoding the 3D structure of the chromatin, provide a great opportunity to simultaneously identify the locations of genomic rearrangements and to investigate the 3D genome organization in cancer cells. Even though Hi-C data has been used for validating known rearrangements, computational methods that can distinguish rearrangement signals from the inherent biases of Hi-C data and from the actual 3D conformation of chromatin, and can precisely detect rearrangement locations de novo have been missing. Results:In this work, we characterize how intra and inter-chromosomal Hi-C contacts are distributed for normal and rearranged chromosomes to devise a new set of algorithms (i) to identify genomic segments that correspond to CNV regions such as amplifications and deletions (HiCnv), (ii) to call inter-chromosomal translocations and their boundaries (HiCtrans) from Hi-C experiments, and (iii) to simulate Hi-C data from genomes with desired rearrangements and abnormalities (AveSim) in order to select optimal parameters for and to benchmark the accuracy of our methods. Our results on 10 different cancer cell lines with Hi-C data show that we identify a total number of 105 amplifications and 45 deletions together with 90 translocations, whereas we identify virtually no such events for two karyotypically normal cell lines. Our CNV predictions correlate very well with whole genome sequencing (WGS) data among chromosomes with CNV events for a breast cancer cell line (r=0.89) and capture most of the CNVs we simulate using Avesim. For HiCtrans predictions, we report evidence from the literature for 30 out of 90 translocations for eight of our cancer cell lines. Furthermore, we show that our tools identify and correctly classify relatively understudied rearrangements such as double minutes (DMs) and homogeneously staining regions (HSRs). Conclusions:Considering the inherent limitations of existing techniques for karyotyping (i.e., missing balanced rearrangements and those near repetitive regions), the accurate identification of CNVs and translocations in a cost-effective and high-throughput setting is still a challenge. Our results show that the set of tools we develop effectively utilize moderately sequenced Hi-C libraries (100-300 million reads) to identify known and de novo chromosomal rearrangements/abnormalities in well-established cancer cell lines. With the decrease in required number of cells and the increase in attainable resolution, we believe that ...

DLO Hi-C Tool for Digestion-Ligation-Only Hi-C Chromosome Conformation Capture Data Analysis

“…9. We confirmed the authenticity of these two translocations by the software hic_breakfinder and Hi-Ctrans [15,16]. The heatmap generated by DLO Hi-C Tool will provide a visual theoretical basis for chromatin translocation calling.…”

No kissing in the nucleus: Unbiased analysis reveals no evidence of trans chromosomal regulation of mammalian immune development

“…Consistent with previous literature (16), these transchromosomal interactions are enriched in gene--rich, centrally located chromosomes (Figure 1 A, Supplemental Figure 1 C). However, closer examination of these interactions reveals that a high percentage (74--90% in mouse and 82--94% in human) contain regions recommended to be removed, or 'blacklisted', from analyses due to their high or low mappability, repeated nature, location within telomeres or centromeres, among others (17,18). After application of blacklisting the majority of transchromosomal interactions are removed (Figure 1 B--C, Supplemental Table 2).…”

Identification and Utilization of Copy Number Information for Correcting Hi-C Contact Map of Cancer Cell Line

“…Hi-C sequencing reads can be utilized to estimate the read depth (RD) signal and discovery of CNVs without additional cost of performing WGS. Recently, HiCnv tool has attempted to identify CNVs from Hi-C reads [20]. However, it can only identify large CNVs (> 1Mb) which leave a lot of room for improvement in identifying smaller CNVs.…”