Goh, Y. | Search bioRxiv

bioRxiv2020

Three-dimensional Genome Organization Maps in Normal Haematopoietic Stem Cells and Acute Myeloid Leukemia

Acute Myeloid Leukemia (AML) is a highly lethal blood cancer arising due to aberrant differentiation of haematopoietic stem cells. Here we obtained 3D genome organization maps by Hi-C in the CD34+ haematopoietic stem cells from three healthy individuals and eight individuals with AML, and found that AML have increased loops to oncogenes compared with normal CD34+ cells. The MEIS1 oncogenic transcription factor is regulated by a Frequently Interacting Region (FIRE). This FIRE is only present in normal bone marrow samples, and four of eight AML sample. FIRE presence is associated with MEIS1 expression. CRISPR excision of a FIRE boundary led to loss of MEIS1 and reduced cell growth. Moreover, MEIS1 can bind to the promoter of HOXA9, and HOXA9 shows gain of Acute Myeloid Leukemia-specific super-enhancers that loop to the HOXA9 promoter. SignificanceWe found that Acute Myeloid Leukemias have more chromatin loops to oncogenes compared with normal blood stem cells. We identified heterogeneity in chromatin interactions at oncogenes, and heterogeneity in super-enhancers that loop to oncogenes, as two key epigenetic mechanisms that underlie MEIS1 and HOXA9 oncogene expression respectively.

cancer biology↗

bioRxiv2019

NanoVar: Accurate Characterization of Patients’ Genomic Structural Variants Using Low-Depth Nanopore Sequencing

Despite the increasing relevance of structural variants (SV) in the development of many human diseases, progress in novel pathological SV discovery remains impeded, partly due to the challenges of accurate and routine SV characterization in patients. The recent advent of third-generation sequencing (3GS) technologies brings promise for better characterization of genomic aberrations by virtue of having longer reads. However, the applications of 3GS are restricted by their high sequencing error rates and low sequencing throughput. To overcome these limitations, we present NanoVar, an accurate, rapid and low-depth (4X) 3GS SV caller utilizing long-reads generated by Oxford Nanopore Technologies. NanoVar employs split-reads and hard-clipped reads for SV detection and utilizes a neural network classifier for true SV enrichment. In simulated data, NanoVar demonstrated the highest SV detection accuracy (F1 score = 0.91) amongst other long-read SV callers using 12 gigabases (4X) of sequencing data. In patient samples, besides the detection of genomic aberrations, NanoVar also uncovered many normal alternative sequences or alleles which were present in healthy individuals. The low sequencing depth requirements of NanoVar enable the use of Nanopore sequencing for accurate SV characterization at a lower sequencing cost, an approach compatible with clinical studies and large-scale SV-association research.

bioinformatics↗