Ultra-high molecular weight (UHMW) DNA was extracted following manufacturer’s guidelines (Bionano Genomics Inc, San Diego, CA, USA) from flash frozen brain regions (superior frontal gyrus and primary visual cortex) as well as pelleted frozen Peripheral Blood Mononuclear Cells (PBMCs). Briefly, a total of 15-20mg of brain tissue or 1.5-2 million PBMCs were homogenized in cell buffer and digested with Proteinase K. DNA was precipitated with isopropanol and bound with nanobind magnetic disk. Bound UHMW DNA was resuspended in the elution buffer and quantified with Qubit dsDNA assay kits (ThermoFisher Scientific, Waltham, MA, USA). Total RNA was extracted using Qiagen RNeasy Kit following manufacturer’s guidelines (Qiagen, Hilden, Germany). RNA sequencing was performed at Novogene Inc, mRNA was selected from total RNA using poly-T oligo-attached magnetic beads and sequenced on an Illumina short-read instrument with 85 million reads (Novogene, Bejing, China).
DNA labeling was performed following manufacturer’s protocols (Bionano Genomics Inc). Direct Labeling Enzyme 1 (DLE-1) reactions were carried out using 750 ng of purified UHMW DNA. Labeled DNA was loaded on Saphyr chips for imaging. The fluorescently labeled DNA molecules were imaged sequentially across nanochannel arrays (Saphyr chip) on a Saphyr instrument (Bionano Genomics Inc). Effective genome coverage of greater than 500X was achieved for all samples. All samples also met the following quality control metrics: labelling density of ~15/100 kbp; filtered (>15kbp) N50 > 230 kbp; map rate > 70%.
Optical genome mapping analysis
Genome analysis was performed using software solutions provided by Bionano Genomics Inc. Automated, OGM specific, pipelines – Bionano Access and Solve (versions 1.7 and 3.7, respectively), were used for data processing and variant calling. De novo assembly was performed using Bionano’s custom assembler software program based on the Overlap-Layout-Consensus paradigm. Pairwise comparison of all DNA molecules was done to generate the initial consensus genome maps (*.cmap). Genome maps were further refined and extended with best matching molecules. Structural Variants (SVs) were identified based on the alignment profiles between the de novo assembled genome maps and the Human Genome Reference Consortium GRCh38 assembly. If the assembled map did not align contiguously to the reference, but instead were punctuated by internal alignment gaps (outlier) or end alignment gaps (endoutlier), then a putative SV was identified. Rare variant analyses were performed to performed to capture mosaic SVs occurring at low allelic fractions. Molecules of a given sample dataset were first aligned against GRCh38 assembly. SVs were identified based on discrepant alignment between sample molecules and reference genome, with no assumption about ploidy. Consensus genome maps (*.cmaps) were then assembled from clustered sets of molecules that identify the same variant. Finally, the cmaps were realigned to GRCh38, with SV data confirmed by consensus forming final SV calls. Fractional copy number analyses were performed from alignment of molecules and labels against GRCh38 (alignmolvrefsv). A sample’s raw label coverage was normalized against relative coverage from normal human controls, segmented, and baseline copy number state estimated from calculating mode of coverage of all labels. If chromosome Y molecules were present, baseline coverage in sex chromosomes was halved. With a baseline estimated, copy number states of segmented genomic intervals were assessed for significant increase/decrease from the baseline. Corresponding copy number gains and losses were exported. Certain SV and copy number calls were masked, if occurring in GRC38 regions found to be in high variance (gaps, segmental duplications, etc.)
Bionano Access (Bionano Genomics Inc) was used for SV annotation and filtering. Variants were filtered in access and nanotatoR 10 based on the following criteria: for de novo, rare variant and copy number variant pipelines, SVs were filtered based on Bionano Genomics recommended size and confidence cutoff values (e.g., >500bp/5kbp size cutoff for de novo assembly and rare variant pipelines respectively for insertions and deletions (INDELs)). Rare SVs were selected by filtering out common variants with population frequency of >1% using Bionano Genomics’ database of SVs containing >300 healthy individuals. To select for potential clinically significant aberrations a gene list overlapping SVs was used.
Genome sequence analysis
FASTQ reads were aligned to GRCh38 reference genome using, BWA-MEM 11, followed by processing of the aligned bam (for variant calling) using SAMtools 12 and Picard (Broad Institute). Next, for small nucleotide variant (SNV) and small INDEL calling we use Mutect2 13, followed by annotation using ANNOVAR 14. Due to absence of a non-tumor tissues from the same individual, we used a 1,000-genome panel of normal variant call file from Broad Institute, as a proxy. Quality filtration for SNV/INDEL was performed using the Mutect2 function. FilterMutectCalls. For larger structural variant calls, we used Manta 15, followed by annotation using AnnotSV 16. For the SV visualization Integrative Genome Viewer was used.
RNA-seq data was aligned to GRCh38 reference genome, followed by fusion calling using STAR-Fusion 17. Visualization of the fusion was performed using Clinker 18 and Integrative Genome Viewer.
EPIC methylation chip analysis
An input of 300 ng of DNA was bisulfite-converted using the DNA Methylation-Lightning kit (Zymo Research, Irvine, CA, USA). After whole-genome amplification and enzymatic fragmentation, samples were hybridized to BeadChip arrays using the Infinium Methylation EPIC BeadChip kit according to the manufacturer’s protocol (Illumina, SanDiego, CA, USA). Intensity values at the over 850,000 methylation sites on the BeadChips were measured across the genome at single-nucleotide resolution using iScan (Illumina). For classifying the tumors, CNS tumor classification tool hosted at molecularneuropathology.org, was used on the methylation signal files19.