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• Highly complex single-cell mixture of 5 individuals of low cell number

  Mixture of 5 unrelated individuals sequenced by 10x as a scATAC-seq with low cell count. The dataset was then processed by Cell Ranger and deconvoluted to yield each individuals genetic profile using the De-goulash pipeline. The separation file is submitted as the processed file. The bam are submitted as unprocessed files.

  Dataset EGAD50000000479

• A Single Cell Transcriptomic Analysis of Human Neocortical Development

  Defining the number, proportion, or lineage of distinct cell types in the developing human brain is an important goal of modern brain research. We produced single cell transcriptomic profiles for 40,000 cells at mid-gestation to define deep expression profiles corresponding to all known major cell types at this developmental period and compare this with bulk tissue profiles. We identified multiple transcription factors (TFs) and co-factors expressed in specific cell types, including multiple new cell-type-specific relationships, providing an unprecedented resource for understanding human neocortical development and evolution. This includes the first single-cell characterization of human subplate neurons and subtypes of developing glutamatergic and GABAergic neurons. We also used these data to deconvolute single cell regulatory networks that connect regulatory elements and transcriptional drivers to single cell gene expression programs in the developing CNS. We characterized major developmental trajectories that tie cell cycle progression with early cell fate decisions during early neurogenesis. Remarkably, we found that differentiation occurs on a transcriptomic continuum, so that differentiating cells not only express the few key TFs that drive cell fates, but express broad, mixed cell-type transcriptomes prior to telophase. Finally, we mapped neuropsychiatric disease genes to specific cell types, implicating dysregulation of specific cell types in ASD, ID, and epilepsy, as the mechanistic underpinnings of several neurodevelopmental disorders. Together these results provide an extensive catalog of cell types in human neocortex and extend our understanding of early cortical development, human brain evolution and the cellular basis of neuropsychiatric disease.

  Study phs001836

• POU4F3 mutation screening in Japanese hearing loss patients.

  A variant in a transcription factor gene, POU4F3, is responsible for autosomal dominant nonsyndromic hereditary hearing loss, DFNA15. To date, 14 variants, including a whole deletion of POU4F3, have been reported to cause HL in various ethnic groups. In the present study, genetic screening for POU4F3 variants was carried out for a large series of Japanese hearing loss (HL) patients to clarify the prevalence and clinical characteristics of DFNA15 in the Japanese population. Massively parallel DNA sequencing of 68 target candidate genes was utilized in 2,549 unrelated Japanese HL patients (probands) to identify genomic variations responsible for HL. The detailed clinical features in patients with POU4F3 variants were collected from medical charts and analyzed. Novel 12 POU4F3 likely pathogenic variants (six missense variants, three frameshift variants, and three nonsense variants) were successfully identified in 15 probands (2.5%) among 602 families exhibiting autosomal dominant HL, whereas no variants were detected in the other 1,947 probands with autosomal recessive or inheritance pattern unknown HL. To obtain the audiovestibular configuration of the patients harboring POU4F3 variants, we collected audiograms and vestibular symptoms of the probands and their affected family members. Audiovestibular phenotypes in a total of 24 individuals from the 15 families possessing variants were characterized by progressive HL, with a large variation in the onset age and severity with or without vestibular symptoms observed. Pure-tone audiograms indicated the most prevalent configuration as mid-frequency HL type followed by high-frequency HL type, with asymmetry observed in approximately 20% of affected individuals. Analysis of the relationship between age and pure-tone average suggested that individuals with truncating variants showed earlier onset and slower progression of HL than did those with non-truncating variants. The present study showed that variants in POU4F3 were a common cause of autosomal dominant HL.

  Study JGAS000093

• Assessing the impact of low frequency coding variants on disease risk using the Exomechip

  Following several rounds of Genome Wide Association (GWA) scans and subsequent meta-analyses of results multiple risk loci have been identified for many common diseases. However, in most instances the implicated common variants identified so far explain only a modest fraction of the genetic risk. In parallel to our efforts to identify the causative variants in the known loci it is necessary to assess the full spectrum of sequence variants for disease risk and in particular low frequency and rare variants that have not been tested in a comprehensive way so far. To this end an international effort by investigators who have performed whole exome sequencing in circa 12,000 individuals have assembled a set of ~250,000 exonic variants of low frequency – defined as seen in at least two studies and a minimum of three individuals (non-sysnonymous) or two individuals for SNPs altering splice sites / stop codons. The SNP content from the exome studies was complemented with additional interesting SNP sets totalling 25,000 markers (GWA tag SNPs, grid of common variants, HLA, Mitochondrial, Y-chromosome etc) and were used to generate a custom iSELECT array, the exome chip. Adequately powered association studies to test low frequency variants for association to disease risk are still very expensive if conducted by whole exome sequencing. The exome chip provides a cost efficient way to undertake such an experiment and the Wellcome Trust Cases Control Consortium will be applying this approach to eight diseases, type 1 and type 2 diabetes, coronary artery disease, hypertension, multiple sclerosis, rheumatoid arthritis, bipolar, and ankylosing spondylitis. The first objective will be to generate a large set of at least 10,000 common UK controls as analysis of lower frequency variants will require large sample sizes. This includes ~6000 samples from the 1958 Birth Cohort.

  Study EGAS00001000584

• Combined single-cell transcriptomics and T-cell receptor sequencing reveal heterogeneity of mycosis fungoides between and within patients and identify a CD4+ cytotoxic subtype

  Comprehensive analysis of the malignant cells present in the skin lesions of Mycosis fungoides, the most common subtype of cutaneous T-cell lymphoma, is complicated by the fact that malignant from reactive T cells are challenging to distinguish with certainty. To this end, combined single-cell RNA and TCR sequencing allows unambiguous identification of MF cells, thereby revealing a marked heterogeneity between and within patients with unexpected functional phenotypes. It should be further noted, that mRNA expression of established CTCL protein markers in single-cell transcriptomics to identify MF cells should be used with caution; however, our study uncovered an exciting gene expression pattern that serves as a beacon separating malignant from reactive T cells.

  Study EGAS50000000226

• ctDNA monitoring using patient-specific sequencing and integration of variant reads - Lung cohort

  Circulating tumor-derived DNA (ctDNA) can be used to monitor cancer dynamics noninvasively. Detection of ctDNA can be challenging in patients with low-volume or residual disease, where plasma contains very few tumor-derived DNA fragments. We show that sensitivity for ctDNA detection in plasma can be improved by analyzing hundreds to thousands of mutations that are first identified by tumor genotyping. We describe the INtegration of VAriant Reads (INVAR) pipeline, which combines custom error-suppression methods and signal-enrichment approaches based on biological features of ctDNA. With this approach, the detection limit in each sample can be estimated independently based on the number of informative reads sequenced across multiple patient-specific loci. We applied INVAR to custom hybrid-capture sequencing data from 176 plasma samples from 105 patients with melanoma, lung, renal, glioma, and breast cancer across both early and advanced disease. By integrating signal across a median of >105 informative reads, ctDNA was routinely quantified to 1 mutant molecule per 100,000, and in some cases with high tumor mutation burden and/or plasma input material, to individual parts per million. This resulted in median Area Under the Curve (AUC) values of 0.98 in advanced cancers, and 0.80 in early stage and challenging settings for ctDNA detection. We generalized this method to whole-exome and whole-genome sequencing, showing that the INVAR may be applied without requiring personalized sequencing panels, so long as a tumor mutation list is available. As tumor sequencing becomes increasingly performed, such methods for personalized cancer monitoring may enhance the sensitivity of cancer liquid biopsies.

  Study EGAS00001004447

• ctDNA monitoring using patient-specific sequencing and integration of variant reads - Breast cohort

  Circulating tumor-derived DNA (ctDNA) can be used to monitor cancer dynamics noninvasively. Detection of ctDNA can be challenging in patients with low-volume or residual disease, where plasma contains very few tumor-derived DNA fragments. We show that sensitivity for ctDNA detection in plasma can be improved by analyzing hundreds to thousands of mutations that are first identified by tumor genotyping. We describe the INtegration of VAriant Reads (INVAR) pipeline, which combines custom error-suppression methods and signal-enrichment approaches based on biological features of ctDNA. With this approach, the detection limit in each sample can be estimated independently based on the number of informative reads sequenced across multiple patient-specific loci. We applied INVAR to custom hybrid-capture sequencing data from 176 plasma samples from 105 patients with melanoma, lung, renal, glioma, and breast cancer across both early and advanced disease. By integrating signal across a median of >105 informative reads, ctDNA was routinely quantified to 1 mutant molecule per 100,000, and in some cases with high tumor mutation burden and/or plasma input material, to individual parts per million. This resulted in median Area Under the Curve (AUC) values of 0.98 in advanced cancers, and 0.80 in early stage and challenging settings for ctDNA detection. We generalized this method to whole-exome and whole-genome sequencing, showing that the INVAR may be applied without requiring personalized sequencing panels, so long as a tumor mutation list is available. As tumor sequencing becomes increasingly performed, such methods for personalized cancer monitoring may enhance the sensitivity of cancer liquid biopsies.

  Study EGAS00001004446

• Asian Immune Diversity Atlas (AIDA) sQTL

  Current efforts within the Human Cell Atlas (HCA) are largely focused on defining reference human cell types using a relatively small number of samples of predominantly European origin. However, the next phase of the HCA is likely to address the topic of human diversity by comprehensively characterizing variation in cell states across age, sex, population groups, diseases and environments. To spur this next phase, and also expand the geographical scope of the HCA, we propose an HCA-Asia seed network to generate an Asian Immune Diversity Atlas (AIDA). AIDA will generate transcriptome variation datasets from 5 major Asian population groups (Chinese, Japanese, Korean, Indian, Malay), define an atlas of Asian cell types and states, and characterize their variation associated with ethnicity, age and sex. We will also formalize a study design based on common controls that can in principle reduce technical artifacts in a broad range of comparative single cell studies. This study design will be complemented with novel algorithms designed to normalize datasets of interest against the common controls, to increase the robustness of biological inferences. In addition to providing new tools and resources to the HCA, this seed network will serve as a template for future comparative studies in the single cell field.The AIDA dataset consists of scRNA-seq (10x 5′ v2 scRNA-seq) and genotype (Illumina Infinium Global Screening Array v.3) data. In the AIDA splicing quantitative trait loci (sQTL) project, we identified 11,577 independent cis-sQTLs, 607 trans-sQTLs, and 107 dynamic sQTLs. The post-imputation genotype for the Japanese and Korean cohort will be available through dbGaP. The Singaporean (ethnically Chinese, Indian and Malay) genotypes require a data access application to the HELIOS Data Access Committee (helios_science@ntu.edu.sg). European samples are used as control.

  Study phs003848

• Pseudotime ordering of cell cycle state (2020-01-29)

  We will be using G&T method to sequence single cell genome and transcriptome derived from FS13B iPSCs cell line. The cell cycle state of each of the single cells is known. Hence, we will be analysing the genome and transcriptome of single cells from each of the cell cycle state to generate a copy number profile and transcriptome profile per given cell cycle stage: G1, S, G2, S. . This dataset contains all the data available for this study on 2020-01-29.

  Dataset EGAD00001005919

• WGS of off-target analysis of prime editing in organoids

  Five edited and two unedited organoid clones with one clone prior to editing were paired-end whole genome sequenced using Illumina Novaseq 6000 system. The reads were mapped to hg38 genome assembly and data is provided as BAM files.

  Dataset EGAD00001007744