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• Complex-I stratification of Parkinson's disease in the prefrontal cortex - bulk RNA seq

  Dataset corresponding to the bulk RNA seq dataset from prefrontal cortex for a total of N=98 samples. Tissue samples correspond to patients with idiopathic Parkison's disease (PD) with varying levels of Complex-I activity (N=79) and neurologically healthy controls (N=19). RNA seq was carried out using ribosomal depletion. A subset of these samples was additionally sequenced using single nucleus RNA seq and is available in the same Study as an additional Dataset.

  Dataset EGAD50000000433

• RNA-sequencing on thyroid samples from fetuses with Down syndrome and fetuses with no genetic/developmental abnormality

  The current data pertains RNA-sequencing reads obtained from thyroid samples acquired from fetuses with Down syndrome and fetuses with no genetic/developmental abnormality. Total RNA was isolated from left lobe from thyroid samples using a hand-held homogenizer and the Promega ReliaPrep RNA Miniprep System (Thermo Fisher Scientific). RNA yield was determined with the NanoDrop Microvolume Spectrophotometer (Thermo Fisher Scientific). Fragmentation and mRNA library preparation was performed using the Kapa mRNA Hyperprep Kit (Roche, Basel, Switzerland). Libraries were equimolar pooled and quality was checked on a TapeStation system using the DNA1000 ScreenTape (Agilent Technologies, Santa Clara, CA, USA). Libraries were sequenced with poly(A) selection to sequence all messenger RNA for gene expression analysis on the NovaSeq6000 PE150 (Illumina, San Diego, CA, USA), producing at least 40M 150-bp paired-end reads per library.

  Dataset EGAD50000000387

• EGAD00010000807

  Illumina HumanCoreExome genotyping data from the British Society for Surgery of the Hand Genetics of Dupuytren’s Disease consortium (BSSH-GODD consortium) collection

  Dataset EGAD00010000807

• EGAD00010000768

  Replication data for HipSci normal samples using both HumanCoreExome-12_v1 and HumanOmni2.5-8 BeadChips

  Dataset EGAD00010000768

• Policy Documentation

  Policy Documentation The following policy documentation is required to be prepared and submitted to the EGA, together with your data files and associated metadata. Data Access Agreement (DAA) The Data Access Agreement is a contract made between Data User and Data Access Committee. The agreement should be drafted by the DAC and includes, but is not limited to, details of data use, publication embargoes and storage. Completion of a DAA by the applicant/s should form part of the application process to the DAC. NOTICE The data access agreement template below is provided for guidance only and should be adapted as you see fit to suit your own purpose. In the interest of promoting data sharing, we suggest that if an agreement cannot be met around clause 19 in this example that both parties should agree to remain silent, and that the clause should be removed from the agreement. Example DAA template Alternate (harmonised) DAA template

  Documentation access/data-access-committee/policy-documentation

• 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

• 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