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• Comparative genomics research for Chinese colorectal cancer

  In order to clarify the mechanisms of genesis and development of colorectal carcinoma, in this study, we performed comparative genomics research in Chinese colorectal cancer at various clinical stage via whole exon sequencing or whole genome sequencing. Comparative bioinformatical analysis was perform to find the difference on somatic single-nucleotide variants, short insert/deletion variants, genomic structural variants, and copy number variants between cancer tissue and normal tissue, or among cancer tissues at various clinical stages.

  Study EGAS00001002576

• Prime editing for functional repair in patient-derived disease models

  Prime editing is a novel genome editing technology using fusion proteins of Cas9-nickase and reverse transcriptase, that holds promise to correct a wide variety of genetic defects. We succeeded in efficient prime editing and functional recovery of disease-causing mutations in patient-derived liver and intestinal stem cell organoids. Whole genome sequencing of did not detect off-target mutations or a mutational signature induced by prime editing.

  Study EGAS00001004611

• Comparative genomics research for Chinese colorectal cancer

  In order to clarify the mechanisms of genesis and development of colorectal carcinoma, in this study, we performed comparative genomics research in Chinese colorectal cancer at various clinical stage via whole exon sequencing or whole genome sequencing. Comparative bioinformatical analysis was perform to find the difference on somatic single-nucleotide variants, short insert/deletion variants, genomic structural variants, and copy number variants between cancer tissue and normal tissue, or among cancer tissues at various clinical stages.

  Study EGAS00001002607

• Single-cell analysis for metastatic gastric adenocarcinoma

  Intra-tumoral heterogeneity (ITH) is the fundamental property of cancer, however, the origins of ITH remain poorly understood. Here we performed single-cell RNA sequencing of peritoneal carcinomatosis (PC) from patients with advanced gastric adenocarcinoma (GAC), constructed a transcriptome map of 45,048 PC cells, profiled the transcriptome states of diverse tumor cell lineages and types, incisively explored ITH of PC tumor cells, and identified significant correlates with patient survival.

  Study EGAS00001004443

• GCAT Hereditary Cancer Panel Sequencing Dataset

  This dataset comprises targeted sequencing data from 191 participants of the GCAT cohort generated using the I2HCP custom hereditary cancer panel. The panel is based on a custom bait library covering 122 genes associated with hereditary cancer syndromes, including genes related to hereditary colorectal cancer, hereditary breast and ovarian cancer, neurofibromatosis, and related disorders. Target enrichment was performed using a clinically validated design optimized for diagnostic accuracy. The panel and associated bioinformatic pipeline were previously validated, achieving analytical sensitivity and specificity greater than 99%. Sequencing data were processed using standardized alignment and variant calling procedures against the human reference genome. The dataset includes raw sequencing reads (FASTQ format) and processed variant call files (VCF format). All samples were pseudonymized prior to submission in accordance with ethical approvals and participant consent. Data are available under controlled access.

  Dataset EGAD50000002415

• WGS of T-cell and NK-cell lymphoma for ICGC (NKTL-SG)

  WGS of T-cell and NK-cell lymphoma The tumor samples were sequenced with Illumina HiSeq 2500 platform and the resulting FASTq files have been uploaded.

  Dataset EGAD00001003271

• RNA-seq of human iPSC-derived neurons to explore cellular phenotypes associated with schizophrenia.

  This dataset contains high-throughput RNA-sequencing of 12 samples, each sample comprising neural precursor cells derived from human induced pluripotent stem cells from individuals with and without the 16p13.11 microduplication (a copy number variant associated with a range of neurodevelopmental disorders). 4 samples derive from patients carrying the 16p13.11 microduplication, and 8 derive from unaffected family controls. RNA samples were processed to deplete rRNA using the TruSeq Stranded Total RNA with Ribo-Gold kit. Libraries were then sequenced using the NextSeq 500/550 High-Output v2 Kit on the Illumina NextSeq 550 platform, to produce 75 base pair paired-end sequencing reads at an average depth of around 100 million reads per sample. Raw sequencing reads data are stored in two FASTQ files per sample for these paired-end reads.

  Dataset EGAD00001004064

• SNV and indel calls from 8086 individuals in the British Autozygosity Populations BioResource dataset

  This includes variant calls (single nucleotide variants and small insertions/deletions) from 8086 (mostly British Pakistani/British Bangladeshi) individuals from the following studies: 1. 3781 British Pakistani/British Bangladeshi adults from East London Genes and Health 2. 2791 British South Asian mothers from Born in Bradford 3. 1428 British South Asian adults from Birmingham 4. 86 individuals (mixed ancestries) from families with rare diseases, from Queen Mary University London All of the Birmingham and most of the Born in Bradford samples were previously sequenced as part of PMID: 26940866. Mapping was done with bwa-mem and variant calling was carried out with GATK HaplotypeCaller. We removed variant sites for which the following was true: SNPs: "QD < 2.0 || FS > 30 || MQ < 40.0 || MQRankSum < -12.5 || ReadPosRankSum < -8.0" Indels: "QD < 2.0 || FS > 30 || ReadPosRankSum < -20.0"

  Dataset EGAD00001004581

• The Natural History of Human Prostate Cancer

  Whole genome DNA sequencing was used to decrypt the phylogeny of multiple samples from distinct areas of cancer and morphologically normal tissue taken from the prostates of 3 men. For each of three different prostates, multiple tumour samples (4, 5, and 3 depending on the case) and one normal tissue sample were whole genome sequenced with a matched blood sample using the Illumiuna HiSeq platform. Tumour samples were sequenced to a target depth of 50X and normals and blood to a target depth of 30X. As of September 2020, some of the studies using these data include: Cooper et al, Nature Genetics 2015 (PMID: 25730763) Wedge et al, Nature Genetics 2018 (PMID: 29662167) Pan-Cancer Analysis of Whole Genomes, Nature 2020 (PMID: 32025007)

  Dataset EGAD00001000689

• Acquisition of additional mutations drives accelerated progression of NPM1 positive CMML to AML

  This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/ We performed exome sequencing on serial samples from a patient with CMML who progressed to AML. The exome sequencing suggests that NPM1, TET2 and DNMT3a mutations were present in the dominant clone in the CMML sample and that NRAS is a new subclonal mutation in the AML sample. Diagnostic data shows the presence of a FLT3-ITD mutation in the AML sample, which is likely to have driven progression. Here we are performing re-sequencing of the putative driver and some passenger mutations which appear to be in the same clone to validate these mutations and to verify the relative quantification of these abnormalities .

  Dataset EGAD00001002194

• MultiOMICS study of a pair of infant monozygotic twins with concordant B-cell ALL (WGS)

  B-cell acute lymphoblastic leukemia (B-cell ALL) is the most common cancer in childhood. Studying identical twins with B-cell ALL provides a unique and tractable model for deciphering the developmental timing of pre- and post-natal mutations contributing to clonal evolution. To date, this has mainly focused on major cytogenetic subgroups of childhood B-cell ALL, including MLL fusions, ETV6-RUNX1, hyperdiploidy, and BCR-ABL1. However, formal demonstration of the prenatal origin and “backtracking” the natural history of the leukemia remains understudied in “B-other”/Normal Karyotype (NK) B-cell ALL. To characterize the genetic landscape of this particular leukemia subtype, we performed whole genome DNA- and B-cell receptor (BCR)- on a pair of 8-month-old monozygotic twins diagnosed with concordant “B-other”/NK B-cell ALL.

  Dataset EGAD00001005017

• MultiOMICS study of a pair of infant monozygotic twins with concordant B-cell ALL (WGBS)

  B-cell acute lymphoblastic leukemia (B-cell ALL) is the most common cancer in childhood. Studying identical twins with B-cell ALL provides a unique and tractable model for deciphering the developmental timing of pre- and post-natal mutations contributing to clonal evolution. To date, this has mainly focused on major cytogenetic subgroups of childhood B-cell ALL, including MLL fusions, ETV6-RUNX1, hyperdiploidy, and BCR-ABL1. However, formal demonstration of the prenatal origin and “backtracking” the natural history of the leukemia remains understudied in “B-other”/Normal Karyotype (NK) B-cell ALL. To characterize the epigenetic landscape of this particular leukemia subtype, we performed DNA bisulfite-sequencing on a pair of 8-month-old monozygotic twins diagnosed with concordant “B-other”/NK B-cell ALL.

  Dataset EGAD00001005018

• RNA-seq study of longitudinal blood cell samples drawn from children at risk of type 1 diabetes

  The appearance of type 1 diabetes (T1D)-associated autoantibodies is the first and only measurable parameter to predict progression toward T1D in genetically susceptible individuals. However, autoantibodies indicate an active autoimmune reaction, wherein the immune tolerance is already broken. Therefore, there is a clear and urgent need for new biomarkers that predict the onset of the autoimmune reaction preceding auto-antibody positivity or reflect progressive b-cell destruction. Here we report the mRNA sequencing-based analysis of 306 samples including fractionated samples of CD4+ and CD8+ T cells as well as CD4, CD8 cell fractions and unfractionated PBMC samples longitudinally collected from seven children who developed beta-cell autoimmunity (case subjects) at a young age and matched control subjects.

  Dataset EGAD00001005767

• Somatic mutation and clonal evolution in the human bladder_WGS (2020-05-05)

  In this study, we aim to characterise the landscape of mutation and clonal selection in the human bladder. The data in this study will be generated by whole-genome sequencing of laser-dissected microbiopsies from the bladder. The samples utilised in this study will include urothelium from transplant donors with no history of bladder cancer and cystectomy specimens from patients with bladder cancer. . This dataset contains all the data available for this study on 2020-05-05.

  Dataset EGAD00001006113

• Somatic mutation and clonal evolution in the human bladder Novaseq (2020-05-05)

  In this study, we aim to characterise the landscape of mutation and clonal selection in the human bladder. The data in this study will be generated by whole-genome sequencing of laser-dissected microbiopsies from the bladder. The samples utilised in this study will include urothelium from transplant donors with no history of bladder cancer and cystectomy specimens from patients with bladder cancer. . This dataset contains all the data available for this study on 2020-05-05.

  Dataset EGAD00001006116

• Somatic mutation and clonal evolution in the human bladder_WES (2020-05-05)

  In this study, we aim to characterise the landscape of mutation and clonal selection in the human bladder. The data in this study will be generated by whole-exome sequencing of laser-dissected microbiopsies from the bladder. The samples utilised in this study will include urothelium from transplant donors with no history of bladder cancer and cystectomy specimens from patients with bladder cancer. . This dataset contains all the data available for this study on 2020-05-05.

  Dataset EGAD00001006115

• RNA-seq of non-LPS treated (N), non-tolerized (NT), and tolerized (T) IFNg-primed macrophages pretreated with or without HDAC3i

  In this experiment we investigated the effect of HDAC3 inhibition on the transcriptome of IFNg-primed macrophages under different tolerization conditions. Peripheral blood mononuclear cells (PBMCs) were isolated from 3 healthy donors. PBMCs were isolated from whole blood of healthy donors using Ficoll gradient (Invitrogen). Monocytes (CD14+ cells) were positively selected from PBMCs using CD14 Microbeads according to the manufacturer’s instructions (Miltenyi Biotec). Monocytes were subsequently treated with or without 500 nM HDAC3i (ITF3100) for 30 minutes prior to overnight IFNg priming (50 ng/mL). Cells were then kept without LPS (non-LPS; N), treated with 10 ng/mL LPS once (non-tolerized; NT), or treated with LPS twice (tolerized; T; second LPS concentration: 100 ng/mL). In total, there were 18 samples included.

  Dataset EGAD00001005959

• MutWP5: CRUK Mutographs of Cancer: Lung: PD37920 (WG) (2020-02-20)

  Sequencing of LCM-derived microbiopsies from explanted lung from COPD patient. Goal to assess the mutational burden, spectrum, and clonal dynamics within the tissue. Deep sampling throughout multiple regions of the lung will determine whether there are differences in smoking-related mutation burden in different portions of the lung. Whole-genome sequencing will be conducted on samples identified as promising from the initial targeted data. Results from this portion of the study will be compared to other individuals with smoking-related diseases (COPD, pulmonary fibrosis, lung cancer), and normal, non-smoking lungs. . This dataset contains all the data available for this study on 2020-02-20.

  Dataset EGAD00001005990

• MutWP5: CRUK Mutographs of Cancer: Lung: PD38234 (WG) (2020-02-20)

  Sequencing of LCM-derived microbiopsies from explanted lung from pulmonary fibrosis patient. Goal to assess the mutational burden, spectrum, and clonal dynamics within the tissue. Deep sampling throughout multiple regions of the lung will determine whether there are differences in smoking-related mutation burden in different portions of the lung. Whole-genome sequencing will be conducted on samples identified as promising from the initial targeted data. Results from this portion of the study will be compared to other individuals with smoking-related diseases (COPD, pulmonary fibrosis, lung cancer), and normal, non-smoking lungs. . This dataset contains all the data available for this study on 2020-02-20.

  Dataset EGAD00001005991

• Peripheral immunoprofiling of stratifies COVID-19 patients based on disease-specific neutrophil signatures

  The SARS-CoV-2 pandemic has led to increasing numbers of COVID-19 patients all over the world. Aetiopathologies range from no symptoms, mild flu-like to severe cases succumbing to respiratory failure. Reports on a dysregulated immune system in the severe cases, showing similarities to cytokine release syndrome, calls for better characterization and understanding of the changes in the immune system as well as their variance across COVID-19 patients in order to be able to design according to host-directed therapies. Here, we profiled blood transcriptomes of 39 COVID-19 patients and 10 control donors. Enriched granulocyte signatures in whole blood samples were verified in granulocyte samples from 49 COVID-19 patients in a second cohort.

  Dataset EGAD00001006326

• Combinatorial CRISPR screen identifies fitness effects of paralogues FAM50A and FAM50B

  Genetic redundancy has evolved as a way for human cells to survive the loss of genes that are single copy and essential in other organisms, but also allows tumours to survive despite having highly rearranged genomes. In this study we CRISPR screen 1,191 gene pairs, including paralogues and known and predicted synthetic lethal interactions to identify 105 gene combinations whose co-disruption results in a loss of cellular fitness. 27 pairs influence fitness across multiple cell lines including the paralogues FAM50A/FAM50B, two genes of unknown function. Silencing of FAM50B occurs across a range of tumour types and in this context disruption of FAM50A reduces cellular fitness whilst promoting micronucleus formation and extensive perturbation of transcriptional programmes. This dataset includes CRISPR screening of cancer cell lines, RNA sequencing studies of cancer cell lines and also data from the sequencing of tumour xenografts collected from mice.

  Dataset EGAD00001006648

• DKFZ-St.Jude Medulloblastoma - 8 MB cases, exome/WGS data

  Tumor/Control pairs for 8 medulloblastoma cases, MB cohort, mixed exome and whole genome data, sequenced on Illumina machines from the paper "Germline Elongator mutations in Sonic Hedgehog medulloblastoma" (Waszak et al. 2020 Nature).

  Dataset EGAD00001006660

• Peripheral immunoprofiling of stratifies COVID-19 patients based on disease-specific neutrophil signatures

  The SARS-CoV-2 pandemic has led to increasing numbers of COVID-19 patients all over the world. Aetiopathologies range from no symptoms, mild flu-like to severe cases succumbing to respiratory failure. Reports on a dysregulated immune system in the severe cases, showing similarities to cytokine release syndrome, calls for better characterization and understanding of the changes in the immune system as well as their variance across COVID-19 patients in order to be able to design according to host-directed therapies. Here, we profiled blood transcriptomes of 39 COVID-19 patients and 10 control donors. Enriched granulocyte signatures in whole blood samples were verified in granulocyte samples from 49 COVID-19 patients in a second cohort.

  Dataset EGAD00001006671

• Novel de novo pathogenic variant in the GNAI1 as a cause of severe disorders of intellectual development with autistic features

  Our probands A and B are boys-monozygotic twins with the clinical diagnosis of severe intellectual impairment, developmental stagnation, and dysphasia. They were diagnosed at the Department of Medical Genetics and Genomics (University Hospital Brno). Parents provided written informed consent, which was approved by the Research Ethics Committee of Masaryk University and Ethics Committee of University Hospital Brno. Peripheral blood samples were collected in sterile heparinized tubes for cytogenetic analysis. Genomic DNA samples were obtained from 1 ml peripheral blood in EDTA, according to the standard DNA isolation process using the MagNaPure system (Roche Diagnostics, Basel, Switzerland). Quality and quantity were checked using a DeNovix DS-11 Spectrophotometer (DeNovix Inc., Wilmington, DE, USA) and Qubit® 2.0 (Thermo Fisher Scientific, Inc., Waltham, MA, USA).

  Dataset EGAD00001007736

• Single cell RNA-seq and ATAC-seq of human fetal forebrain tissue, weeks 8 to 11.

  Single cell RNA-seq from 6 and single nuclei ATAC-seq from 3 human fetal tissue samples. Samples from 8 to 11 weeks. Includes a 8.5 weeks samples with matching both ATAC-seq and RNA-seq runs. Data was sequenced using 10X Genomics chromium technology, for scRNA-seq samples belong to v2 and v3.

  Dataset EGAD00001008653

• Interval whole-genome sequence (WGS) data

  Whole-genome sequencing (WGS) genotype data generated as part of the Interval project. The data are reported, separately per chromosome, in variant call format (VCF). The genotypes are denoted in diploid format (for chrY the genotype 1 denoted as 1/1 and 0 denoted as 0/0). Note that multi-allelic variants are present in the data, but encoded to appear on separate, consecutive lines. The data are reported in following versions - unphased, phased, phased with imputation, sites only. Note: the unphased version has additional genotype information, while the phased versions only contain the genotypes.

  Dataset EGAD00001008661

• Dataset for Ewing_sarcoma_PNET-WHOLE_GENOME

  Rare cancer sequencing data of 18 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008874

• Dataset for colorectal_cancer-WHOLE_GENOME

  Rare cancer sequencing data of 54 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008872

• Dataset for Ewing_sarcoma_PNET-EXON

  Rare cancer sequencing data of 30 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008873

• Dataset for gynecologic_cancer-EXON

  Rare cancer sequencing data of 55 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008877

• Dataset for liposarcoma-EXON

  Rare cancer sequencing data of 29 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008886

• Dataset for NSCLC-EXON

  Rare cancer sequencing data of 42 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008892

• Dataset for synovial_sarcoma-EXON

  Rare cancer sequencing data of 34 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008898

• Full blood mRNA sequencing of myotonic dystrophy type 1 patients after cognitive behavioural therapy

  This dataset consists of 60 mRNA sequencing runs from full blood of 31 myotonic dystrophy type 1 patients, of which for 27 patients reliable data is available before and after 10 months of cognitive behavioural therapy. >30 million 150 bp paired end reads were obtained with UMI-labeled adapters to facilitate filtering of PCR duplicates. Via UMI-analysis we found samples with the aliases sample_01 and sample_02 to contain a very high number of PCR duplicates and recommend the use of these samples only with highest caution or not at all.

  Dataset EGAD00001008383

• Myeloma Genome Project Targeted Sequencing Panel - Oxford Data

  This dataset comprises the BAM files from targeted genome sequencing of CD138+ selected myeloma tumour samples from 21 individuals. In 5 cases there is only one tumour sample, but in the other 16 cases there are sequential samples, spanning treatment relapses. There are denoted Tumor A, B C etc. Therefore there are a total of 48 myeloma tumour samples. For each individual there is also a germline control samples, obtained either from peripheral blood or from CD138-selected bone marrow cells.

  Dataset EGAD00001008735

• Dataset for upper_gastrointestinal_tumor-RNA

  Rare cancer sequencing data of 44 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008853

• Fecal WMS UC sequencing data

  Fecal WMS data from NCT02749630 ulcerative colitis patients. Stool samples were collected at screening as well as on day 64 and prepared for whole metagenomic sequencing.

  Dataset EGAD00001008817

• Paired WES and low coverage WGS of osteosarcoma

  Longitudinal genome-sequencing analysis of 12 patients with metastatic or refractory osteosarcoma. The study was approved at the University Hospital Basel, following the approval of the ethical committee for mutational analysis of anonymized samples (“Ethikkommission beider Basel” ref. 274/12). Informed consent was obtained from all 12 patients. All tumor samples were evaluated by an experienced bone pathologist to confirm the diagnosis. WES and low coverage WGS are aligned against the reference genome GRCh37. More details in the associated publication.

  Dataset EGAD00001007509

• Sequence Data from the phase 2 PrE0505 trial of Durvalumab with First Line Platinum-Pemetrexed for Unresectable Pleural Mesothelioma

  Dataset comprises of 84 bam files from exome sequencing data, including 40 tumor-normal pairs and 4 normal files. Each sample is numbered by the patient case ID such as 135, 156 and so on. The filenames are suffixed with "_tumor" and "_normal" to indicate tumor and normal bam files respectively.

  Dataset EGAD00001008016

• Identification of immune mechanisms associated with the high rate of relapse in patient with visceral leishmaniasis and HIV co-infection

  RNA-seq transcriptomics of whole blood samples from longitudinal follow-up of a cohort of visceral leishmaniasis (VL) patients with and without HIV coinfection, from active disease through apparent cure and potential relapse. Analysis will identify potential correlates of relapse to identify immune mechanisms underlying the high rate of relapse in HIV/VL coinfection. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/ .

  Dataset EGAD00001008361

• Anthropological dataset 2 for The admixture histories of Cabo Verde

  Datasets used in the article "The genetic and linguistic admixture histories of the islands of Cabo Verde" by Laurent R et al. eLife 2023 (DOI: https://doi.org/10.7554/eLife.79827 - URL: https://elifesciences.org/articles/79827) File name "eGAdeposit_233CaboVerde_GEOcoordFULL_FINAL_01062022.txt" Column 1 corresponds to individual alphanumeric codes as in the "eGAdeposit_233CaboVerde_GenotypeFile_FINAL_01062022.vcf" genotype file Column 2-3 corresponds to X-Y GPS coordinates of individual's interview location in Cabo Verde Column 4-5 corresponds to X-Y GPS coordinates of individual's self-reported residence location at the time of the interview Column 6-7 corresponds to X-Y GPS coordinates of individual's self-reported birth-place location Column 8-9 corresponds to X-Y GPS coordinates of individual's self-reported paternal birth-place location Column 10-11 corresponds to X-Y GPS coordinates of individual's self-reported maternal birth-place location

  Dataset EGAD00001008977

• Separation of chronic myeloid leukemia stem cells from normal hematopoietic stem cells at single-cell resolution

  We provide whole exome DNA sequening data in fastq format for 23 clinical samples of chronic myeloid leukemia stem cells (CML-SC) plus two buccal swipes derived normal samples. CML samples are comprised of 4 to 8 replicates from two patients, at diagnosis and after treatment. Single CML stem cells before treatment and single non-transformed hematopoietic stem cells (HSC) at remission were selected from bone marrow samples by FACS, according to newly identified genetic markers CD33+CD26+ at diagnosis and CD33+CD26-/CD33-CD26- at remission. WES libraries of colony forming assays derived CML-SC and HSC populations were prepared using Agilent SureSelect Human All Exon V6 kit and sequenced running 150 cycles (2x 75bp paired-end) on an Illumina NextSeq 500 platform.

  Dataset EGAD00001009847

• RNA stability controlled by m6A methylation contributes to X-to-autosome dosage compensation in mamals

  In mammals, X-chromosomal genes are expressed from a single copy since males (XY) possess a single X chromosome, while females (XX) undergo X inactivation. To compensate for this reduction in dosage compared to two active copies of autosomes, it has been proposed that genes from the active X chromosome exhibit dosage compensation. However, the existence and mechanism of X-to-autosome dosage compensation are still under debate. Here, we show that X-chromosomal transcripts are reduced in m6A modifications and more stable compared to their autosomal counterparts. Acute depletion of m6A selectively stabilises autosomal transcripts, resulting in perturbed dosage compensation in mouse embryonic stem cells. We propose that higher stability of X-chromosomal transcripts is directed by lower levels of m6A, indicating that mammalian dosage compensation is partly regulated by epitranscriptomic RNA modifications.

  Dataset EGAD00001010194

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0187_001A

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0187_001A for Follicular lymphoma patient sample TFRIPAIR10_FL

  Dataset EGAD00001010222

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0170_003

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0170_003 for Follicular lymphoma patient sample TFRI_Cont_5

  Dataset EGAD00001010207

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0167_003

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0167_003 for Follicular lymphoma patient sample TFRI_Cont_2

  Dataset EGAD00001010204

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0168_003

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0168_003 for Follicular lymphoma patient sample TFRI_Cont_3

  Dataset EGAD00001010205

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0169_003

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0169_003 for Follicular lymphoma patient sample TFRI_Cont_4

  Dataset EGAD00001010206

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0171_003A

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0171_003A for Follicular lymphoma patient sample TFRI_Cont_6

  Dataset EGAD00001010208

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0178_003A

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0178_003A for Follicular lymphoma patient sample TFRI_Cont_8

  Dataset EGAD00001010209

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0183_003A

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0183_003A for Follicular lymphoma patient sample TFRI_Cont_9

  Dataset EGAD00001010210

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0187_003A

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0187_003A for Follicular lymphoma patient sample TFRI_Cont_10

  Dataset EGAD00001010211

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0188_003A

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0188_003A for Follicular lymphoma patient sample TFRI_Cont_11

  Dataset EGAD00001010212

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0197_003A

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0197_003A for Follicular lymphoma patient sample TFRI_Cont_12

  Dataset EGAD00001010213

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0198_003A

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0198_003A for Follicular lymphoma patient sample TFRI_Cont_13

  Dataset EGAD00001010214

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0167_001

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0167_001 for Follicular lymphoma patient sample TFRIPAIR2_FL

  Dataset EGAD00001010215

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0168_001

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0168_001 for Follicular lymphoma patient sample TFRIPAIR3_FL

  Dataset EGAD00001010216

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0169_001

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0169_001 for Follicular lymphoma patient sample TFRIPAIR4_FL

  Dataset EGAD00001010217

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0170_001A

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0170_001A for Follicular lymphoma patient sample TFRIPAIR5_FL

  Dataset EGAD00001010218

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0171_001A

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0171_001A for Follicular lymphoma patient sample TFRI_Pair_6_FL

  Dataset EGAD00001010219

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0178_001A

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0178_001A for Follicular lymphoma patient sample TFRIPAIR8_FL

  Dataset EGAD00001010220

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0183_001A

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0183_001A for Follicular lymphoma patient sample TFRIPAIR9_FL

  Dataset EGAD00001010221

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0188_001A

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0188_001A for Follicular lymphoma patient sample TFRIPAIR11_FL

  Dataset EGAD00001010223

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0197_001A

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0197_001A for Follicular lymphoma patient sample TFRIPAIR12_FL

  Dataset EGAD00001010224

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0198_001A

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0198_001A for Follicular lymphoma patient sample TFRI_Pair_13_FL

  Dataset EGAD00001010225

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0167_002

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0167_002 for Diffuse large B-cell lymphoma patient sample TFRIPAIR2_DLBCL

  Dataset EGAD00001010226

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0168_002

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0168_002 for Diffuse large B-cell lymphoma patient sample TFRIPAIR3_DLBCL

  Dataset EGAD00001010227

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0169_002

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0169_002 for Diffuse large B-cell lymphoma patient sample TFRIPAIR4_DLBCL

  Dataset EGAD00001010228

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0170_002A

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0170_002A for Diffuse large B-cell lymphoma patient sample TFRIPAIR5_DLBCL

  Dataset EGAD00001010229

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0171_002A

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0171_002A for Diffuse large B-cell lymphoma patient sample TFRI_Pair_6_DLBCL

  Dataset EGAD00001010230

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0178_002A

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0178_002A for Diffuse large B-cell lymphoma patient sample TFRIPAIR8_DLBCL

  Dataset EGAD00001010231

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0183_002A

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0183_002A for Diffuse large B-cell lymphoma patient sample TFRI_Pair_9_DLBCL

  Dataset EGAD00001010232

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0187_002A

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0187_002A for Diffuse large B-cell lymphoma patient sample TFRIPAIR10_DLBCL

  Dataset EGAD00001010233

• Dataset for Sarcoma-WES linked from study EGAS00001004813

  Sequencing data of 144 sarcoma tumor and control runs, which were uploaded to EGAS00001004813. The sequencing was always paired.

  Dataset EGAD00001010258

• Estimating transcription factor variability in PC-9 cells using scRNA-seq

  PC-9 cells were barcoded following protocol from Chang et al. Nature Biotech. 2021, which does not affect downstream analysis. PC-9 cells were trypsinized into single-cell suspensions and processed using Chromium Single Cell Gene Expression 3’ Library and Gel Bead Kit V2.0 following the manufacturer’s instructions (10X Genomics). Cells were counted and checked for viability using Vi-CELL XR cell counter (Beckman Coulter), and then injected into microfluidic chips to form Gel Beads-in-Emulsion (GEMs) in the 10X Chromium instrument. Reverse transcription was performed on the GEMs, and RT products were purified and amplified. Expression libraries were made from the cDNA and profiled using the Bioanalyzer High Sensitivity DNA kit (Agilent Technologies) and quantified with Kapa Library Quantification Kit (Kapa Biosystems). Illumina HiSeq2500 (Illumina) was used to sequence the libraries.

  Dataset EGAD00001011041

• Sequencing data from a phase II study of nivolumab and ipilimumab in recurrent or refractory cancer of unknown primary (CheCUP trial)

  Sequencing data from a phase II study of nivolumab and ipilimumab in recurrent or refractory cancer of unknown primary (CheCUP trial). Panel sequencing data from baseline FFPE biopsies were used to perform a comprehensive genomic profiling of CUP metastases. Combined targeted next-generation sequencing of patient-specific hotsport mutations and shallow whole genome sequencing of baseline and follow-up liquid biopsy samples were used to analyze ctDNA and to evaluate response to immunei checkpoint inhibitor treatment. In some cases, whole exome sequencing of peripheral blood mononuclear cells was performed to screen for potential CHIP and germline mutations.

  Dataset EGAD00001011130

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0063_000

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0063_000 for Triple negative breast cancer patient-derived xenograft SA609X3XB01584

  Dataset EGAD00001011209

• 10x Single Cell Gene Expression Library TENX068

  10x Single Cell Gene Expression library TENX068 for Triple negative breast cancer patient-derived xenograft SA609X4XB03080

  Dataset EGAD00001011210

• 10x Single Cell Gene Expression Library TENX069

  10x Single Cell Gene Expression library TENX069 for Triple negative breast cancer patient-derived xenograft SA609X4XB03083

  Dataset EGAD00001011211

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0150_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0150_001 for Triple negative breast cancer sample SA609X5XB03230

  Dataset EGAD00001011212

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0150_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0150_002 for Triple negative breast cancer sample SA609X5XB03231

  Dataset EGAD00001011213

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0146_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0146_002 for Triple negative breast cancer patient-derived xenograft SA609X5XB03223

  Dataset EGAD00001011214

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0152_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0152_001 for Triple negative breast cancer patient-derived xenograft SA609X6XB03401

  Dataset EGAD00001011215

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0152_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0152_002 for Triple negative breast cancer sample SA609X6XB03404

  Dataset EGAD00001011216

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0172_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0172_001 for Triple negative breast cancer sample SA609X6XB03447

  Dataset EGAD00001011217

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0163_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0163_002 for Triple negative breast cancer sample SA609X7XB03510

  Dataset EGAD00001011218

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0163_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0163_001 for Triple negative breast cancer patient-derived xenograft SA609X7XB03505

  Dataset EGAD00001011219

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0172_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0172_002 for Triple negative breast cancer sample SA609X7XB03554

  Dataset EGAD00001011220

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0148_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0148_001 for Triple negative breast cancer patient-derived xenograft SA535X4XB02498

  Dataset EGAD00001011221

• 10x Single Cell Gene Expression Library TENX048

  10x Single Cell Gene Expression library TENX048 for Triple negative breast cancer sample SA535X5XB02895

  Dataset EGAD00001011222

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0146_006

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0146_006 for Triple negative breast cancer sample SA535X6XB03099

  Dataset EGAD00001011223

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0175_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0175_001 for Triple negative breast cancer patient-derived xenograft SA535X6XB03101

  Dataset EGAD00001011224

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0182_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0182_001 for Triple negative breast cancer patient-derived xenograft SA535X7XB03304

  Dataset EGAD00001011225

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0189_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0189_001 for Triple negative breast cancer patient-derived xenograft SA535X7XB03448

  Dataset EGAD00001011226

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0184_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0184_001 for Triple negative breast cancer sample SA535X7XB03305

  Dataset EGAD00001011227

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0146_004

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0146_004 for Triple negative breast cancer sample SA535X7XB03305

  Dataset EGAD00001011228

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0189_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0189_002 for Triple negative breast cancer patient-derived xenograft SA535X8XB03663

  Dataset EGAD00001011229

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0182_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0182_002 for Triple negative breast cancer sample SA535X8XB03431

  Dataset EGAD00001011230

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0184_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0184_002 for Triple negative breast cancer patient-derived xenograft SA535X8XB03434

  Dataset EGAD00001011231

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0173_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0173_002 for Triple negative breast cancer patient-derived xenograft SA535X9XB03617

  Dataset EGAD00001011232