Project description:This dataset contains spatial transcriptomics data of four lung neuroendocrine tumours (lung NETs), a rare and understudied type of lung cancer. The dataset consists of raw sequencing data, metadata, and gene expression matrices. It is part of the lungNENomics project. See https://doi.org/10.5281/zenodo.19366762 for processed data for the series, including downstream analyses data for the four samples in this dataset, such as inferred CNVs and aneuploidy status, and spatial domain and cell proportions for each spot. The lungNENomics project also generated other molecular data for a series of more than 200 samples. The raw sequencing data (fastq files for RNA-seq, cram files for WGS, and idat files for methylation arrays) is hosted on the European Genome-Phenome Archive website, study EGAS00001005979. Medical imaging data (Hematoxylin & Eosin stained whole-slide images) from the lungNENomics project is hosted in the EBI bioImage Archive (10.6019/S-BIAD3143).
Project description:Whole exome sequencing of 5 HCLc tumor-germline pairs. Genomic DNA from HCLc tumor cells and T-cells for germline was used. Whole exome enrichment was performed with either Agilent SureSelect (50Mb, samples S3G/T, S5G/T, S9G/T) or Roche Nimblegen (44.1Mb, samples S4G/T and S6G/T). The resulting exome libraries were sequenced on the Illumina HiSeq platform with paired-end 100bp reads to an average depth of 120-134x. Bam files were generated using NovoalignMPI (v3.0) to align the raw fastq files to the reference genome sequence (hg19) and picard tools (v1.34) to flag duplicate reads (optical or pcr), unmapped reads, reads mapping to more than one location, and reads failing vendor QC.
