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• WGS of high grade serous ovarian tumours and matched blood normals

  FASTQ reads for 81 matched tumour-normal WGS pairs for high grade serous ovarian cancer patients. Scottish HGSOC samples were collected via local Bioresource facilities at Edinburgh, Glasgow, Dundee and Aberdeen and stored in liquid Nitrogen until required. HGSOC patients were determined from pathology records and were included in the study where there was matched tumour and whole blood samples. Tumour samples were divided into two for DNA and RNA extraction and slivers of tissue were taken, fixed in formalin and embedded in paraffin wax (FFPE). Samples were only included if they were confirmed as HGSOC and there was greater than 40% tumour cellularity throughout the tumour, determined using H&E staining of the FFPE sections and pathology review. Somatic DNA was extracted from the tumour and germline DNA was extracted from whole blood. Somatic DNA was extracted using the Qiagen DNeasy Blood and tissue kit (cat no 69504). The tissue was initially homogenised using a Qiagen Bioruptor, followed by the manufacturers recommended protocol (including RNase digestion step). Germline DNA was extracted from 1-3ml whole blood using the Qiagen FlexiGene kit (cat no 51206) following the manufacturers recommended protocol. The resulting DNA underwent quality control as follows: firstly, A260 and A280nm were measured on a Denovix DS-11 Fx to qualitatively illustrate A260/280nm and A260/230nm ratios as surrogate measures of DNA purity. A260/280 had to be 1.8 or greater and A260/230 had to be 2.0 or greater. Then, DNA was quantified using LifeTechnologies Qubit dsDNA BR kit (cat no Q32850) and we required a minimum of 50ul at 25ng/ul for WGS. Thirdly, DNA was diluted to 25ng/ul and a representative sample was loaded onto a 0.8% TAE gel, ran at 100v for 60mins and then imaged using a BioRad ChemiDoc imaging system to visualise the DNA quality.

  Dataset EGAD00001009049

• WGS of high grade serous ovarian tumours and matched blood normals

  FASTQ reads for 34 matched tumour-normal WGS pairs for high grade serous ovarian cancer patients. Scottish HGSOC samples were collected via local Bioresource facilities at Edinburgh, Glasgow, Dundee and Aberdeen and stored in liquid Nitrogen until required. HGSOC patients were determined from pathology records and were included in the study where there was matched tumour and whole blood samples. Tumour samples were divided into two for DNA and RNA extraction and slivers of tissue were taken, fixed in formalin and embedded in paraffin wax (FFPE). Samples were only included if they were confirmed as HGSOC and there was greater than 40% tumour cellularity throughout the tumour, determined using H&sE staining of the FFPE sections and pathology review. Somatic DNA was extracted from the tumour and germline DNA was extracted from whole blood. Somatic DNA was extracted using the Qiagen DNeasy Blood and tissue kit (cat no 69504). The tissue was initially homogenised using a Qiagen Bioruptor, followed by the manufacturers recommended protocol (including RNase digestion step). Germline DNA was extracted from 1-3ml whole blood using the Qiagen FlexiGene kit (cat no 51206) following the manufacturers recommended protocol. The resulting DNA underwent quality control as follows: firstly, A260 and A280nm were measured on a Denovix DS-11 Fx to qualitatively illustrate A260/280nm and A260/230nm ratios as surrogate measures of DNA purity. A260/280 had to be 1.8 or greater and A260/230 had to be 2.0 or greater. Then, DNA was quantified using LifeTechnologies Qubit dsDNA BR kit (cat no Q32850) and we required a minimum of 50ul at 25ng/ul for WGS. Thirdly, DNA was diluted to 25ng/ul and a representative sample was loaded onto a 0.8% TAE gel, ran at 100v for 60mins and then imaged using a BioRad ChemiDoc imaging system to visualise the DNA quality.

  Dataset EGAD00001010135

• Somatic mutation and selection at epidemiological scale - TwinsUK_TargetedNanoSeq_Blood

  Utilising Targeted NanoSeq, we hope to explore the driver landscape in unprecedented detail in donors from TwinsUK. These donors have paired buccal swabs and data from this will help contextualise that work. As we age, many tissues become colonised by microscopic clones carrying somatic driver mutations. Some of these clones represent a first step towards cancer whereas others may contribute to ageing and other diseases. However, our understanding of the clonal landscapes of human tissues, and their impact on cancer risk, ageing and disease, remains limited due to the challenge of detecting somatic mutations present in small numbers of cells. Here, we introduce a new version of nanorate sequencing (NanoSeq), a duplex sequencing method with error rates of less than 5 per billion base pairs, which is compatible with whole-exome and targeted gene sequencing. Deep sequencing of polyclonal samples with single-molecule sensitivity enables the simultaneous detection of mutations in large numbers of clones, yielding accurate somatic mutation rates, mutational signatures and driver mutation frequencies in any tissue. Applying targeted NanoSeq to 1,042 non-invasive samples of oral epithelium and 371 samples of blood from a twin cohort, we found an unprecedentedly rich landscape of selection, with 46 genes under positive selection driving clonal expansions in the oral epithelium, over 62,000 driver mutations, and evidence of negative selection in some genes. The high number of positively selected mutations in multiple genes provides high-resolution maps of selection across coding and non-coding sites, a form of in vivo saturation mutagenesis. Multivariate regression models enable mutational epidemiology studies on how carcinogenic exposures and cancer risk factors, such as age, tobacco or alcohol, alter the acquisition and selection of somatic mutations. Accurate single-molecule sequencing has the potential to unveil the polyclonal landscape of any tissue, providing a powerful tool to study early carcinogenesis, cancer prevention and the role of somatic mutations in ageing and disease.

  Dataset EGAD00001015619

• Molecular diagnosis of albinism (2018-03-14)

  Albinism is genetically heterogeneous rare genetic condition affecting 1:17000 in the Western world (but more frequent in Africa) whose main feature is a profound visual impairment, characterised by foveal hypoplasia, abnormal chiasmatic connections, nystagmus and photofobia. All these features result in severly altered visual acuity (<0,1), absent depth perception and poor night vision. People with albinism are primarily visually handicapped. In addition, for some types of albinism, the visual phenotype can be presented with partial or total hypopigmentation, hence resulting in a secondary phenotype which can lead to skin cancer if skin is not adequately protected. Recently a new syndrome has been described, FHONDA, with the same visual abnormalities of albinism but without pigment alteration. The traditional classification differentiates Oculoculatenous albinism (OCA), where hypopigmentation involves hair, skin and eyes versus Ocular Albinism (OA), where hypopigmentation only affects the eyes. These are non-sydrimic types of albinism. Some syndromic forms (Hermansky-Pudlak=HPS, Chediak-Higashi=CHS) affect cells beyond pigment cells, present in the lungs, immune system, platelets and intestines, resulting in more severe phenotypes that can be fatal. Mutations in at least 19 genes are assocaited with the corresponding types of albinism. Most hospitals will only diagnose the most frequent cases using traditional Sanger, MLPA approaches. Some will use CGH arrays. We aim to diagnose all cases of albinism through the Albinochip proposal, which combines a Sequenom first step of known mutations combined with subsequent NGS approaches. In some cases we fail to find a second mutation, these are good candidates for further full exome analyses. 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/ . This dataset contains all the data available for this study on 2018-03-14.

  Dataset EGAD00001004039

• Single Cell RNA Transcriptomics of Mantle Cell Lymphoma Reveals the Presence of Treatment-Resistant Subclones at the Time of Diagnosis

  Mantle cell lymphoma is a chronically relapsing B cell neoplasm characterized by clonal heterogeneity. We implemented single cell RNA sequencing of paired tumor samples from four patients with an aggressive nodal MCL at diagnosis and after failure of standard immunochemotherapy. Clustering the cells based on the inferred CNVs showed small subclones present at diagnosis with a CNV profile similar to the major subclone at relapse. Comparison of these subclones to major subclones present at diagnosis revealed 119 upregulated pathways common for the four patient with an aggressive MCL, including NF-kappaB and PTEN signaling pathways. Additionally, we sequenced a patient with an indolent to blastoid MCL transformation with intestinal tissue involvement. In contrast to other compartments at relapse, the cells in the intestine showed strikingly different CNV profile and presence of new driver mutations including NOTCH1. In conclusion, our study demonstrates that scRNAseq can potentially detect therapy resistant subclones at diagnosis.

  Study EGAS50000000825

• DAC for A Karadimitris Lab

  Dac EGAC00001001003

• Cellular indexing of transcriptomes and epitopes sequencing of peritoneal fluid from patients with achalasia

  The data published here contains cellular indexing of transcriptomes and epitopes sequencing (CITEseq) using the oligo-tagged TotalSeq™-B Human Universal Cocktail, V1.0 from BioLegend on peritoneal fluid (PF) from patients with achalasia. Sequencing was performed in a paired-ended fashion on the NovaSeq6000. Below an overview of the epitopes to oligotags as obtained from BioLegend: DNA_ID Description clone Sequence Ensembl ID B0006 anti-human CD86 IT2.2 GTCTTTGTCAGTGCA ENSG00000114013 B0007 anti-human CD274 (B7-H1, PD-L1) 29E.2A3 GTTGTCCGACAATAC ENSG00000120217 B0020 anti-human CD270 (HVEM, TR2) 122 TGATAGAAACAGACC ENSG00000157873 B0023 anti-human CD155 (PVR) SKII.4 ATCACATCGTTGCCA ENSG00000073008 B0024 anti-human CD112 (Nectin-2) TX31 AACCTTCCGTCTAAG ENSG00000130202 B0026 anti-human CD47 CC2C6 GCATTCTGTCACCTA ENSG00000196776 B0029 anti-human CD48 BJ40 CTACGACGTAGAAGA ENSG00000117091 B0031 anti-human CD40 5C3 CTCAGATGGAGTATG ENSG00000101017 B0032 anti-human CD154 24-31 GCTAGATAGATGCAA ENSG00000102245 B0033 anti-human CD52 HI186 CTTTGTACGAGCAAA ENSG00000169442 B0034 anti-human CD3 UCHT1 CTCATTGTAACTCCT ENSG00000167286 B0046 anti-human CD8 SK1 GCGCAACTTGATGAT ENSG00000153563 B0047 anti-human CD56 (NCAM) 5.1H11 TCCTTTCCTGATAGG ENSG00000149294 B0050 anti-human CD19 HIB19 CTGGGCAATTACTCG ENSG00000177455 B0052 anti-human CD33 P67.6 TAACTCAGGGCCTAT ENSG00000105383 B0053 anti-human CD11c S-HCL-3 TACGCCTATAACTTG ENSG00000140678 B0058 anti-human HLA-A,B,C W6/32 TATGCGAGGCTTATC ENSG00000206503 B0063 anti-human CD45RA HI100 TCAATCCTTCCGCTT ENSG00000081237 B0064 anti-human CD123 6H6 CTTCACTCTGTCAGG ENSG00000185291 B0066 anti-human CD7 CD7-6B7 TGGATTCCCGGACTT ENSG00000173762 B0068 anti-human CD105 43A3 ATCGTCGAGAGCTAG ENSG00000106991 B0070 anti-human/mouse CD49f GoH3 TTCCGAGGATGATCT ENSG00000091409 B0071 anti-human CD194 (CCR4) L291H4 AGCTTACCTGCACGA ENSG00000183813 B0072 anti-human CD4 RPA-T4 TGTTCCCGCTCAACT ENSG00000010610 B0073 anti-mouse/human CD44 IM7 TGGCTTCAGGTCCTA ENSG00000026508 B0081 anti-human CD14 M5E2 TCTCAGACCTCCGTA ENSG00000170458 B0083 anti-human CD16 3G8 AAGTTCACTCTTTGC ENSG00000203747 B0085 anti-human CD25 BC96 TTTGTCCTGTACGCC ENSG00000134460 B0087 anti-human CD45RO UCHL1 CTCCGAATCATGTTG ENSG00000081237 B0088 anti-human CD279 (PD-1) EH12.2H7 ACAGCGCCGTATTTA ENSG00000188389 B0089 anti-human TIGIT (VSTM3) A15153G TTGCTTACCGCCAGA ENSG00000181847 B0090 Mouse IgG1, κ isotype MOPC-21 GCCGGACGACATTAA B0091 Mouse IgG2a, κ isotype MOPC-173 CTCCTACCTAAACTG B0092 Mouse IgG2b, κ isotype MPC-11 ATATGTATCACGCGA B0095 Rat IgG2b, κ isotype RTK4530 GATTCTTGACGACCT B0100 anti-human CD20 2H7 TTCTGGGTCCCTAGA ENSG00000156738 B0101 anti-human CD335 (NKp46) 9E2 ACAATTTGAACAGCG ENSG00000189430 B0124 anti-human CD31 WM59 ACCTTTATGCCACGG ENSG00000261371 B0127 anti-Human Podoplanin NC-08 GGTTACTCGTTGTGT ENSG00000162493 B0134 anti-human CD146 P1H12 CCTTGGATAACATCA ENSG00000076706 B0136 anti-human IgM MHM-88 TAGCGAGCCCGTATA ENSG00000211899 B0138 anti-human CD5 UCHT2 CATTAACGGGATGCC ENSG00000110448 B0141 anti-human CD195 (CCR5) J418F1 CCAAAGTAAGAGCCA ENSG00000160791 B0142 anti-human CD32 FUN-2 GCTTCCGAATTACCG ENSG00000143226 B0143 anti-human CD196 (CCR6) G034E3 GATCCCTTTGTCACT ENSG00000112486 B0144 anti-human CD185 (CXCR5) J252D4 AATTCAACCGTCGCC ENSG00000160683 B0145 anti-human CD103 (Integrin αE) Ber-ACT8 GACCTCATTGTGAAT ENSG00000083457 B0146 anti-human CD69 FN50 GTCTCTTGGCTTAAA ENSG00000110848 B0147 anti-human CD62L DREG-56 GTCCCTGCAACTTGA ENSG00000188404 B0149 anti-human CD161 HP-3G10 GTACGCAGTCCTTCT ENSG00000111796 B0151 anti-human CD152 (CTLA-4) BNI3 ATGGTTCACGTAATC ENSG00000163599 B0152 anti-human CD223 (LAG-3) 11C3C65 CATTTGTCTGCCGGT ENSG00000089692 B0153 anti-human KLRG1 (MAFA) SA231A2 CTTATTTCCTGCCCT ENSG00000139187 B0154 anti-human CD27 O323 GCACTCCTGCATGTA ENSG00000139193 B0155 anti-human CD107a (LAMP-1) H4A3 CAGCCCACTGCAATA ENSG00000185896 B0156 anti-human CD95 (Fas) DX2 CCAGCTCATTAGAGC ENSG00000026103 B0158 anti-human CD134 (OX40) Ber-ACT35 (ACT35) AACCCACCGTTGTTA ENSG00000186827 B0159 anti-human HLA-DR L243 AATAGCGAGCAAGTA ENSG00000204287 B0160 anti-human CD1c L161 GAGCTACTTCACTCG ENSG00000158481 B0161 anti-human CD11b ICRF44 GACAAGTGATCTGCA ENSG00000169896 B0162 anti-human CD64 10.1 AAGTATGCCCTACGA ENSG00000150337 B0163 anti-human CD141 (Thrombomodulin) M80 GGATAACCGCGCTTT ENSG00000178726 B0164 anti-human CD1d 51.1 TCGAGTCGCTTATCA ENSG00000158473 B0165 anti-human CD314 (NKG2D) 1D11 CGTGTTTGTTCCTCA ENSG00000213809 B0167 anti-human CD35 E11 ACTTCCGTCGATCTT ENSG00000203710 B0168 anti-human CD57 Recombinant QA17A04 AACTCCCTATGGAGG ENSG00000109956 B0170 anti-human CD272 (BTLA) MIH26 GTTATTGGACTAAGG ENSG00000186265 B0171 anti-human/mouse/rat CD278 (ICOS) C398.4A CGCGCACCCATTAAA ENSG00000163600 B0174 anti-human CD58 (LFA-3) TS2/9 GTTCCTATGGACGAC ENSG00000116815 B0176 anti-human CD39 A1 TTACCTGGTATCCGT ENSG00000138185 B0179 anti-human CX3CR1 K0124E1 AGTATCGTCTCTGGG ENSG00000168329 B0180 anti-human CD24 ML5 AGATTCCTTCGTGTT ENSG00000272398 B0181 anti-human CD21 Bu32 AACCTAGTAGTTCGG ENSG00000117322 B0185 anti-human CD11a TS2/4 TATATCCTTGTGAGC ENSG00000005844 B0187 anti-human CD79b (Igβ) CB3-1 ATTCTTCAACCGAAG ENSG00000007312 B0189 anti-human CD244 (2B4) C1.7 TCGCTTGGATGGTAG ENSG00000122223 B0206 anti-human CD169 7-239 TACTCAGCGTGTTTG ENSG00000088827 B0214 anti-human/mouse integrin β7 FIB504 TCCTTGGATGTACCG ENSG00000139626 B0215 anti-human CD268 (BAFF-R) 11C1 CGAAGTCGATCCGTA ENSG00000159958 B0216 anti-human CD42b HIP1 TCCTAGTACCGAAGT ENSG00000203618 B0217 anti-human CD54 HA58 CTGATAGACTTGAGT ENSG00000090339 B0218 anti-human CD62P (P-Selectin) AK4 CCTTCCGTATCCCTT ENSG00000174175 B0219 anti-human CD119 (IFN-γ R α chain) GIR-208 TGTGTATTCCCTTGT ENSG00000027697 B0224 anti-human TCR α/β IP26 CGTAACGTAGAGCGA B0236 Rat IgG1, κ isotype RTK2071 ATCAGATGCCCTCAT B0238 Rat IgG2a, κ Isotype RTK2758 AAGTCAGGTTCGTTT B0242 anti-human CD192 (CCR2) K036C2 GAGTTCCCTTACCTG ENSG00000121807 B0246 anti-human CD122 (IL-2Rβ) TU27 TCATTTCCTCCGATT ENSG00000100385 B0352 anti-human FcεRIα AER-37 (CRA-1) CTCGTTTCCGTATCG ENSG00000179639 B0353 anti-human CD41 HIP8 ACGTTGTGGCCTTGT ENSG00000005961 B0355 anti-human CD137 (4-1BB) 4B4-1 CAGTAAGTTCGGGAC ENSG00000049249 B0358 anti-human CD163 GHI/61 GCTTCTCCTTCCTTA ENSG00000177575 B0359 anti-human CD83 HB15e CCACTCATTTCCGGT ENSG00000112149 B0363 anti-human CD124 (IL-4Rα) G077F6 CCGTCCTGATAGATG ENSG00000077238 B0364 anti-human CD13 WM15 TTTCAACGCCCTTTC ENSG00000166825 B0367 anti-human CD2 TS1/8 TACGATTTGTCAGGG ENSG00000116824 B0368 anti-human CD226 (DNAM-1) 11A8 TCTCAGTGTTTGTGG ENSG00000150637 B0369 anti-human CD29 TS2/16 GTATTCCCTCAGTCA ENSG00000150093 B0370 anti-human CD303 (BDCA-2) 201A GAGATGTCCGAATTT ENSG00000198178 B0371 anti-human CD49b P1E6-C5 GCTTTCTTCAGTATG ENSG00000164171 B0373 anti-human CD81 (TAPA-1) 5A6 GTATCCTTCCTTGGC ENSG00000110651 B0384 anti-human IgD IA6-2 CAGTCTCCGTAGAGT ENSG00000211898 B0385 anti-human CD18 TS1/18 TATTGGGACACTTCT ENSG00000160255 B0386 anti-human CD28 CD28.2 TGAGAACGACCCTAA ENSG00000178562 B0389 anti-human CD38 HIT2 TGTACCCGCTTGTGA ENSG00000004468 B0390 anti-human CD127 (IL-7Rα) A019D5 GTGTGTTGTCCTATG ENSG00000168685 B0391 anti-human CD45 HI30 TGCAATTACCCGGAT ENSG00000081237 B0393 anti-human CD22 S-HCL-1 GGGTTGTTGTCTTTG ENSG00000012124 B0394 anti-human CD71 CY1G4 CCGTGTTCCTCATTA ENSG00000072274 B0396 anti-human CD26 BA5b GGTGGCTAGATAATG ENSG00000197635 B0398 anti-human CD115 (CSF-1R) 9-4D2-1E4 AATCACGGTCCTTGT ENSG00000182578 B0404 anti-human CD63 H5C6 GAGATGTCTGCAACT ENSG00000135404 B0406 anti-human CD304 (Neuropilin-1) 12C2 GGACTAAGTTTCGTT ENSG00000099250 B0407 anti-human CD36 5-271 TTCTTTGCCTTGCCA ENSG00000135218 B0408 anti-human CD172a (SIRPα) 15-414 CGTGTTTAACTTGAG ENSG00000198053 B0419 anti-human CD72 3F3 CAGTCGTGGTAGATA ENSG00000137101 B0420 anti-human CD158 (KIR2DL1/S1/S3/S5) HP-MA4 TATCAACCAACGCTT ENSG00000125498 B0446 anti-human CD93 VIMD2 GCGCTACTTCCTTGA ENSG00000125810 B0575 anti-human CD49a TS2/7 ACTGATGGACTCAGA ENSG00000213949 B0576 anti-human CD49d 9F10 CCATTCAACTTCCGG ENSG00000115232 B0577 anti-human CD73 (Ecto-5'-nucleotidase) AD2 CAGTTCCTCAGTTCG ENSG00000135318 B0579 anti-human CD9 HI9a GAGTCACCAATCTGC ENSG00000010278 B0581 anti-human TCR Vα7.2 3C10 TACGAGCAGTATTCA B0582 anti-human TCR Vδ2 B6 TCAGTCAGATGGTAT B0591 anti-human LOX-1 15C4 ACCCTTTACCGAATA ENSG00000173391 B0592 anti-human CD158b (KIR2DL2/L3, NKAT2) DX27 GACCCGTAGTTTGAT ENSG00000243772 B0599 anti-human CD158e1 (KIR3DL1, NKB1) DX9 GGACGCTTTCCTTGA ENSG00000167633 B0822 anti-human CD142 NY2 CACTGCCGTCGATTA ENSG00000117525 B0830 anti-human CD319 (CRACC) 162.1 AGTATGCCATGTCTT ENSG00000026751 B0864 anti-human CD352 (NTB-A) NT-7 AGTTTCCACTCAGGC ENSG00000162739 B0867 anti-human CD94 DX22 CTTTCCGGTCCTACA ENSG00000134539 B0871 anti-human CD162 KPL-1 ATATGTCAGAGCACC ENSG00000110876 B0896 anti-human CD85j (ILT2) GHI/75 CCTTGTGAGGCTATG ENSG00000104972 B0897 anti-human CD23 EBVCS-5 TCTGTATAACCGTCT ENSG00000104921 B0902 anti-human CD328 (Siglec-7) 6-434 CTTAGCATTTCACTG ENSG00000168995 B0918 anti-human HLA-E 3D12 GAGTCGAGAAATCAT ENSG00000204592 B0920 anti-human CD82 ASL-24 TCCCACTTCCGCTTT ENSG00000085117 B0944 anti-human CD101 (BB27) BB27 CTACTTCCCTGTCAA ENSG00000134256 B1046 anti-human CD88 (C5aR) S5/1 GCCGCATGAGAAACA ENSG00000197405 B1052 anti-human CD224 KF29 CTGATGAGATGTCAG ENSG00000100031

  Dataset EGAD50000000252

• Single-cell transcriptomics of hepatoblastoma

  10X Genomics single-cell transcriptomics of hepatoblastoma tissues using Chromium Next GEM Single-Cell 3’ Reagent Kits v3.1. Single-cell transcriptomics data (bam files) of PT9, post-chemotherapy tumor sample, and PT13, treatment naive tumor sample. Single-cell solutions were obtained from viably frozen tissue samples using enzymatic digestion.

  Dataset EGAD50000000794

• Genomic WES plasma control samples

  This datasets consists os genomic WES of paired-end raw data (FASTQ R1, R2 and UMI sequence) obtained from plasma and HapMap control samples. Specifically, it consists in two Hapmap samples (NA12877, NA12878) and 2 plasma standarts (HD780 and HD816) with VAF mutations at 0.0%, 0.1%, 1.0%, and 5.0%, in total 21 plasma samples.

  Dataset EGAD50000000805

• LuCaP cell line RNA-seq

  RNA-seq of LuCaP 189.4 cell line. Treatment with DCC DMSO (n=3 replicates) or 1nm R1881 (n=3 replicates). All 6 samples were sequenced using cDNA oligo-dT using paired-end reads on Illumina NovaSeq 6000. Each sample has 2 fastq files, one for the forward and reverse read, respectively.

  Dataset EGAD50000001344

• WGS data of paediatric B-other B cell acute lymphoblastic leukemia (set6)

  The dataset includes FASTQ and CRAM files from diagnostic and matched remission (control) samples of one B-other ALL patients (ALLK-138). Sequencing library was made using Novogene NGS DNA Library Prep Set. Both diagnostic (90x) and remission (30x) samples were sequenced using NovaSeqX Plus.

  Dataset EGAD50000002161

• Exome sequencing of tumor DNA samples from patients with Waldenstrom macroglobulinemia

  DNA extracted from sorted CD19+ tumor cells (18 samples - 16 patients) was used for exome capture with the SureSelect All Exon Kit following the standard protocols. Paired-end sequencing (2 x 100 bp) was performed using HiSeq2000 sequencing instruments. The files are in FASTQ format.

  Dataset EGAD00001005322

• ChIP-seq fastq and alignment files

  ChIP-seq experiments: fastq files; both ChIP and Input for each sample. Illumina HiSeq 2500. ChIP-seq alignment files for trimmed, mapping q20 and nonredundant reads; both ChIP and Input for each sample. Software: Trim Galore v0.3.7; Bowtie 2 v2.1.0; samtools v1.7

  Dataset EGAD00001006279

• Single Cell Genome Sequence for DLP+ library A96171A

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA501 passage 2, patient-derived xenograft SA611 passage 3 on DLP+ library A96171A

  Dataset EGAD00001009458

• 4C-seq in endometrial healthy and tumor tissues

  4C-seq performed on 10 slices (30um thick) of fresh frozen endometrial tissues. These tissue include 2 healthy tissues and 4 tumor tisseus (post-menopausal patients) in replicate. The library has been performed using DpnII (primary) and NlaIII (secondary) restriction enzymes. Raw single-end fastq.gz files are provided.

  Dataset EGAD00001010897

• Dataset of whole-exome sequencing of intravascular large B-cell lymphoma

  This dataset includes 44 bam files derived of 21 patients with IVL. Tumor samples are derived from cfDNA (n = 18), PDX (n = 4) and bone marrow (n = 2). Normal samples are derived from peripheral blood.

  Dataset EGAD00001005445

• Illumina whole genome sequencing data for two patients with congenital disease

  BAM files with sequencing reads derived from Illumina whole genome sequencing of two DNA samples from lymphoblastoid cell lines from two patients with congenital disease. Whole genome sequencing was performed using Illumina HiSeq X Ten and samples were prepared using TruSeq library prep.

  Dataset EGAD00001003510

• 2015 AML-ETO WGS additional analysis result

  This data is belong to additional 2015 AML-ETO patients' genome data which is aligned to human reference(human_g1k_v37.fasta). There are 2 paired tumor/normal samples from SNUH. All samples has passed QC and recalibration steps while aligning to reference.

  Dataset EGAD00001004012

• Highly differentiated, fusion-negative rhabdomyosarcoma.

  Sequencing was performed using OncoPanel v.2 (OPv2), an Agilent SureSelect custom designed bait set consisting of the coding regions of 504 genes, previously linked to human cancer. Sequencing wa sperformed on an Illumina HiSeq 2500. 8 highly differentiated, fusion-negative rhabdomyosarcoma tumor samples were sequenced. BAM files are available for download.

  Dataset EGAD00001003887

• V2 panel bait design test

  This study comprises of three different datasets. 1) 57 samples from the 1243 canapps cell line study,2) 91 FFPE normal samples and 3) 87 samples from the SCORT WS2 dataset. The aim is to sequence these 235 samples in order to test the new V2 Colorectal bait design.

  Dataset EGAD00001003242

• A Single-Cell and Spatial Atlas of Early Human Olfactory Development

  The human nasal region is a complex anatomical structure comprising neural crest- and placode-derived lineages, yet its developmental dynamics in humans remain poorly understood due to limited access to fetal tissue and challenges in resolving its spatial and cellular complexity. We built a single-cell and spatial transcriptomic atlas of the human fetal nasal region from 10 fetuses spanning post-conceptional weeks 7 to 12. Using snRNA-seq, we identify 34 cell types in the fetal nasal region and reconstruct lineage trajectories in the olfactory epithelium (OE). Spatial analyses uncover regionalized OE organization, dorsoventral gradients of olfactory receptors (ORs) and high-resolution OR expression. Combining snRNAseq and MERFISH, we reveal early onset of OR gene expression, with most olfactory sensory neurons (OSNs) following the one neuron-one receptor rule already during the first trimester. This work provides the first integrated molecular and spatial framework of early human nasal region and it provides an unprecedented molecular blueprint of the human olfactory system development. In addition, this study offers a foundational resource for embryologists and developmental biologists investigating sensory neurogenesis, epithelial patterning, and congenital diseases.

  Study EGAS50000001203

• The mission of the BIOS Consortium is to create a large-scale data infrastructure and to bring together BBMRI researchers focusing on integrative omics studies in Dutch Biobanks.

  The advent of the genome-wide association study (GWAS) led to the successful identification of thousands of variants that are robustly associated with complex disease phenotypes. Dutch biobanks played a substantial role in these discoveries. For most of these variants, however, the mechanisms through which they contribute to these phenotypes remain unknown. The BIOS Consortium applies a functional genomics approach that integrates genome-wide genetic data with data on the epigenome and transcriptome to elucidate these mechanisms. Over 4000 samples from BBMRI-NL biobanks with in-depth information on disease phenotypes and GWAS data are being enriched with RNA-sequencing and genome-wide DNA methylation data. The same is true for samples with whole-genome sequencing data from GoNL. This unique data infrastructure provides a powerful platform to evaluate key questions in integrative omics from establishing comprehensive eQTL and meQTL catalogues to linking molecular pathways across omics levels to phenotypic outcomes.

  Study EGAS00001001077

• The Egyptian Collaborative Cardiac Genomics (ECCO-GEN) Project: Defining a Healthy Volunteer Cohort

  The integration of comprehensive genomic and phenotypic data from diverse ethnic populations offers unprecedented opportunities towards advancements in precision medicine and novel diagnostic technologies. Current reference genomic databases are not representative of the global human population, making variant interpretation challenging, especially in underrepresented populations such as the North African population. To address this, the Egyptian Collaborative Cardiac Genomics (ECCO-GEN) Project launched a study comprising 1000 individuals free of cardiovascular disease (CVD). Here, we present the first 391 Egyptian healthy volunteers (EHVols) recruited to establish a pilot phenotyped control cohort. All individuals underwent detailed clinical investigation, including cardiac MRI, and were sequenced using a targeted panel of 174 genes with reported roles in inherited cardiac conditions (ICC). We identified 1,262 variants in 27 cardiomyopathy genes of which 15.1% were not captured in current global and regional genetic reference databases (here: gnomAD and Great Middle Eastern (GME) Variome). The ECCO-GEN project aims at defining the genetic landscape of an understudied population and providing individual-level genetic and phenotypic data to support future studies in CVD and population genetics.

  Study EGAS00001004434

• MGP Panel: a comprehensive targeted genomics panel for molecular profiling of multiple myeloma patients

  We designed a comprehensive multiple myeloma (MM) targeted sequencing panel to identify common genomic abnormalities in a single assay and validated it against known standards. The panel comprised 228 genes/exons for mutations, 6 regions for translocations, and 56 regions for copy number abnormalities (CNAs). Toward panel validation, targeted sequencing was conducted on 233 patient samples and further validated using clinical fluorescence in situ hybridization (FISH) (translocations), multiplex ligation probe analysis (MLPA) (CNAs), whole genome sequencing (WGS) (CNAs, mutations, translocations) or droplet digital PCR (ddPCR) of known standards (mutations). Canonical IgH translocations were detected in 43.2% of patients by sequencing, and aligned with FISH except for one patient. CNAs determined by sequencing and MLPA for 22 regions were comparable in 103 samples and concordance between platforms was R2=0.969. VAFs for 74 mutations were compared between sequencing and ddPCR with concordance of R2=0.9849. In summary, we have developed a targeted sequencing panel that is as robust or superior to FISH and WGS. This molecular panel is cost effective, comprehensive, clinically actionable and can be routinely deployed to assist risk stratification at diagnosis or post-treatment to guide sequencing of therapies.

  Study EGAS00001006222

• Submitters and requesters Statistics

  EGA Statistics Bibliography Growth Community Archive Distribution Catalogue Here, we show the amount and global distribution of submitters and requesters Requester accounts created by country The below figure represents the number of requester accounts by country. In most of the cases cases, country was inferred from the requester email domain. Those requesters whose email domain could not be assigned to a country such as @gmail.com, are not included. Note that a requester is a user that has had a granted permission at some point in time. Requester Accounts Created by Country Download as PNG Download as JPG Download as WEBP Download as CSV map pie mapChart('requester-country', 'https://stats.ega-archive.org/requester/country') pieChart('requester-country-pie-chart', 'https://stats.ega-archive.org/requester/country','Requester accounts created by country') Requester accounts created by year The below figure represents the number of requester accounts by year. Note that a requester is a user that has had a granted permission at some point in time. Requester Accounts Created by Year Download as PNG Download as JPG Download as WEBP Download as CSV barChart('requester-year', 'https://stats.ega-archive.org/requester/year', ['Requester Accounts', 'Years'], true) Industry requester accounts The below figure represents the top 15 industry requesting companies. Industry Requester Accounts Download as PNG Download as JPG Download as WEBP Download as CSV pieChart('industry-requester-accounts', 'https://stats.ega-archive.org/industry/requester/accounts', 'Industry Requester Accounts') Submitter accounts created by country The below figure represents the number of submitter accounts by country. Location was infered from the requester email domain. Submitter Accounts Created by Country Download as PNG Download as JPG Download as WEBP Download as CSV map pie mapChart('submitter-accounts', 'https://stats.ega-archive.org/submitter/accounts/country') pieChart('submitter-accounts-pie-chart', 'https://stats.ega-archive.org/submitter/accounts/country','Submitter accounts created by country') Submitter accounts created by year The below figure represents the number of submitter accounts by year. Submitter Accounts Created by Year Download as PNG Download as JPG Download as WEBP Download as CSV barChart('submitter-users-year', 'https://stats.ega-archive.org/submitter/users/year', ['Submitter Accounts', 'Years'], true) DAC Members by Country The below figure represents the number of DAC members by country. DAC Members by Country Download as PNG Download as JPG Download as WEBP Download as CSV map pie mapChart('dac-members-by-country', 'https://stats.ega-archive.org/dac_member/country') pieChart('dac-members-by-country-pie-chart', 'https://stats.ega-archive.org/dac_member/country','DAC members by country')

  Documentation about/statistics/community