Unbalanced X-autosome translocations: LRS and RNA-seq datasets for autosomal silencing analysis (PhD - FAPESP:2020/16422-5)

Patients Patient (control identifier) Karyotype Patient 1 - pt1 (control 1 for pt2 and pt3) 46,XY,psu dic(15;X)(p13;q11.1)mat Patient 2 - pt2 (control 2 for pt1 and pt3) 45,X,psu dic(X;21)(q21.33;p13)dn Patient 3 - pt3 (control 3 for pt1 and pt2) 45,X,psu dic(X;13)(p22.12;q33.3)dn Long-read sequencing (LRS) – .cov files Raw LRS data generated from a Revio run (30x) were processed using the PacBio WGS pipeline for alignment (hg38), variant calling, phasing, and methylation calling with pb-cpg-tools. This pipeline produced .bed files containing methylation calls for each haplotype (hap) for both patients and controls. We extracted columns 1, 2, 3, 9, 7, and 8 (in this order) from the .bed files and filtered them for the autosome involved in each translocation. This procedure generated the .cov files, which were subsequently used as input for methylKit. The .cov files contain methylation call information, including the columns “chr”, “start”, “end”, “%raw_meth”, “methylated”, and “unmethylated” for each chromosome of interest in the studied translocations. RNA-seq files - count matrix RNA-seq was performed using Illumina’s stranded mRNA kit for a mean of 33 million reads/replicate. Reads were aligned to the reference genome and GTF file (hg19) using STAR with default parameters. Gene-level counts were obtained with htseq-count (parameters: -r pos, -s reverse), generating count matrices. Each count matrix includes a column with gene names (as specified in the GTF file) and a column with raw counts. These matrices were used for differential expression analysis with DESeq2. Funding This work was supported by the Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP), PhD fellowship to B.P.F. #2020/16422-5 and grant to M.I.M. #2019/21644-0.