2965 results for "愛の戦士完全破壊2-re-imagination-version-鞭首絞め・電撃・ヘソか"

in 10.52 milliseconds.

• Somatic mutation and selection at epidemiological scale - TwinsUK_ExomeNanoSeq_Buccal

  Exome NanoSeq in buccal swab samples obtained from patients with a wide range of clinical phenotypes. 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 EGAD00001015620

• RNA sequencing data for primary and recurrent ovarian granulosa cell tumors

  RNA sequencing dataset for primary and recurrent ovarian granulosa cell tumors consists of 24 .bam files with aligned reads, including 8 primary and 16 recurrent tumors. Total RNA was extracted from cryopreserved tissue of adult-type granulosa cell tumor samples. Libraries were prepared from cDNA using the NuGEN Ovation Ultralow Library System V2 (San Carlos, CA). Paired-end bulk RNA sequencing was performed on the Illumina HiSeq 2000 platform. RNA sequencing reads were aligned to the hg19/GRCh37 reference human genome using the STAR software (version 2.6.0b) with default parameters. Samples description: GCT001 Adult-type Granulosa Cell Tumor: recurrent tumor GCT002 Adult-type Granulosa Cell Tumor: primary tumor GCT003 Adult-type Granulosa Cell Tumor: recurrent tumor GCT004 Adult-type Granulosa Cell Tumor: primary tumor GCT005 Adult-type Granulosa Cell Tumor: recurrent tumor GCT006 Adult-type Granulosa Cell Tumor: recurrent tumor GCT007 Adult-type Granulosa Cell Tumor: primary tumor GCT008 Adult-type Granulosa Cell Tumor: recurrent tumor GCT009 Adult-type Granulosa Cell Tumor: recurrent tumor GCT010 Adult-type Granulosa Cell Tumor: recurrent tumor GCT011 Adult-type Granulosa Cell Tumor: primary tumor GCT012 Adult-type Granulosa Cell Tumor: recurrent tumor GCT013 Adult-type Granulosa Cell Tumor: recurrent tumor GCT014 Adult-type Granulosa Cell Tumor: recurrent tumor GCT015 Adult-type Granulosa Cell Tumor: recurrent tumor GCT016 Adult-type Granulosa Cell Tumor: recurrent tumor GCT017 Adult-type Granulosa Cell Tumor: recurrent tumor GCT018 Adult-type Granulosa Cell Tumor: primary tumor GCT019 Adult-type Granulosa Cell Tumor: recurrent tumor GCT020 Adult-type Granulosa Cell Tumor: primary tumor GCT021 Adult-type Granulosa Cell Tumor: primary tumor GCT022 Adult-type Granulosa Cell Tumor: recurrent tumor GCT023 Adult-type Granulosa Cell Tumor: recurrent tumor GCT024 Adult-type Granulosa Cell Tumor: primary tumor

  Dataset EGAD00001009108

• 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

• Somatic mutation and selection at epidemiological scale - TwinsUK_TargetedEMSeq_Buccal

  Development of a targeted methylation assay to determine the cell-type composition of a sample. 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 EGAD00001015622

• Somatic mutation and selection at epidemiological scale - TwinsUK_TargetedEMSeq_Blood

  Development of a targeted methylation assay to determine the cell-type composition of a sample. 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 EGAD00001015623

• Genome Instability in Mammary Cells of Pathogenic BRCA1/2 Mutation Carriers

  It remains unknown if inherited germline mutations in the BRCA1 or BRCA2 gene increase the risk of breast and ovarian cancer by elevating mutation rate in mammary cells. Here we show, using a single-cell whole genome sequencing approach, that mammary luminal epithelial cells isolated from tissues obtained from preventive mastectomy surgeries in women with a pathogenic BRCA1/2 germline mutation, exhibit a statistically significant, increased mutation frequency as compared to age-matched controls without genetic risk of breast cancer.

  Study phs002411

• VitGene Generalized Vitiligo Genetics Study-Phase 2

  Generalized vitiligo is an autoimmune disease in which melanocyte loss results in patchy depigmentation of skin and hair. For this third GWAS of generalized vitiligo, SNPs were genotyped in 1,059 generalized vitiligo cases of European-derived white (EUR) ancestry using the Illumina Human OmniExpress BeadChip array. The data were combined with those of Phase 1 and Phase 2 to provide at least 85% power to detect associations with OR > 1.22 at genome-wide significance (P = 5 x 10-8) for MAF > 0.25.

  Study phs000224

• Single-cell RNA sequencing of sorted regulatory T cells

  Single-cell RNA sequencing of flow cytometry enriched CD4+ CD127- CD25+ regulatory T cells from 8 systemic lupus erythematosus patients (SLE_001 to SLE_007, and SLE_009) collected at baseline (day 0, before interleukin-2 immunotherpay) and day 5 (after 1 treatment cycle of interleukin-2 immunotherapy). Sorted immune cells were hash tagged and then stained with TotalSeq Human Universal Cocktail (Biolegend) before further processing for single-cell RNA sequencing using the 10X-Genomics platform (incl. generation of TCR libraries). Samples from patients SLE_001, SLE_002, SLE_004, SLE_006 were collected and processed on the first day of experiment (Day 1), and samples from patients SLE_003, SLE_005, SLE_007, SLE_009 on the second day of experiment (Day 2). On each day, samples were pooled and distributed to four lanes on the chromium controller. Run ID 245269 and ID 245270 identify samples sequenced on separate flow cells. Provided are raw sequencing files.

  Dataset EGAD50000000663

• Epigenetic memory of SARS-CoV-2 mRNA vaccination in monocyte-derived macrophages

  Immune memory is key to effective antimicrobial responses, but the impact of mRNA vaccines on this process is not fully understood. Our research shows that SARS-CoV-2 mRNA vaccines alter the epigenetic profile of human macrophages, specifically enhancing histone acetylation, which is linked to immune training. Significant epigenetic changes, along with increased cytokine release, require two vaccine doses. However, these effects diminish over time but can be restored with a booster dose six months later, maintaining a strong pro-inflammatory response.

  Study EGAS50000000341

• Longitudinal Transcriptomic Profiling of Endothelial Progenitor Cells in Post-COVID-19 Patients: Insights at 3 and 6-Months Post-SARS-CoV-2 Infection

  Bulk RNA-sequencing analysis of endothelial cells isolated from post-COVID patients at 3 and 6 months post-infection, alongside healthy controls. This study aims to identify differentially expressed genes in endothelial cells from post-COVID patients—at both 3- and 6-month time points—compared to those from healthy individuals. The objective is to uncover upregulated and downregulated pathways potentially associated with long-term consequences of SARS-CoV-2 infection.

  Study EGAS50000000993

• Variability in immune response genes and prediction of severe SARS-CoV-2 infection (INMUNGEN-Cov2 project)

  Genetic factors may contribute to explain the variable clinical response to SARS-CoV-2 infection. To this end we sequenced the DNA of 392 COVID-19 patients from Spain. Whole Exome Sequencing (WES) was performed at two different sequencing centers. Half of the samples were sequenced at a commercial service, NIMGenetics, whereas the other half were sequenced by the EASI-Genomics consortium. Finally, Variant Call Format (VCF) files for each sample was also obtained.

  Study EGAS50000000066

• Mutational spectrum of highly differentiated, fusion-negative rhabdomyosarcoma (set 2).

  Pediatric PAX-FOXO1 fusion-negative rhabdomyosarcoma represents a diverse spectrum of tumors. In this study, the mutational spectrum of 22 fusion-negative rhabdomyosarcomas with high, rhabdomyoma-like levels of differentiation was evaluated using OncoPanel v.2 (OPv2), an Agilent SureSelect custom designed bait set consisting of the coding regions of 504 cancer-relevant genes. Corresponding normal tissue was not available. Discarded normal human skeletal muscle, obtained from 7 donors without tumor history, were also included in the sequencing run.

  Study EGAS00001002771

• Tumorigenic role of Musashi-2 in aggressive mantle cell lymphoma

  SOX11 is overexpressed in the highly aggressive conventional mantle cell lymphoma (MCL) but rarely in the indolent non-nodal MCL subtype. We performed RNA-seq of SOX11+ and SOX11- MCLs to search for SOX11-regulated genes that contributed to MCL clinical evolution, identifying Musashi-2 (MSI2) stem cell-related gene. By performing RNA-seq upon MSI2 knockdown or inhibition in MCL, we identified MSI2 as a potential target to inhibit drug resistance in aggressive MCLs.

  Study EGAS00001006613

• Patient tumour data (RNAseq, WGBS, ChIPseq, WGS)

  Content: 60 GB patient tumours and 4 normal brain samples combined in pairs by region (x2=8 total input samples). RNAseq: 1 lane per sample, total strand-specific rRNA-depleted (normal samples were combined = 2 lanes/samples per brain region). WGBS: 2 lanes per sample (normal samples were combined = 2 lanes/samples per brain region). ChIPseq (histone mark): a subset of 20 GB samples were profiled. For the same modification were multiplexed and sequenced on 4 lanes each (H3K27ac, H3K4me1) or a single lane (all others). WGS: used as matching input control for the 20 ChIPseq samples. Data type and technology: RNA-seq: PE 100bp sequenced on HiSeq2000. WGBS: PE 100bp sequenced on HiSeq2000/4000. ChIPseq: SE 50bp sequenced on HiSeq2000/4000. WGS: PE 150bp sequenced on HiSeq X.

  Dataset EGAD00001005492

• Smart-seq 2 single cell sequencing of CD4 and CD8 T cells from the blood and synovial fluid of 4 psoriatic arthritis patients.

  This dataset was used to characterise T cell gene expression and clonality at sites of active inflammation within the joints of psoriatic arthritis (PsA) patients, and to compare these results with T cells from the peripheral blood of those same patients. Freshly sorted CD45RA negative CD3+CD4+ and CD3+CD8+ single cells from four patients were individually flow sorted into 96-well full-skirted plates (Eppendorf) containing 10µL of a 2% Dithiothreitol (DTT, 2M Sigma-Aldrich), RTL lysis buffer (Qiagen) solution. Cell lysates were sealed, mixed and spun down before storing at -80 ºC. Paired-end multiplexed sequencing libraries were prepared following the Smart-seq 2 protocol using the Nextera XT DNA library prep kit (Illumina). A pool of barcoded libraries from four different plates were sequenced across two lanes on the Illumina HiSeq 2500.

  Dataset EGAD00001006342

• scEC&T-seq manuscript data

  This dataset includes all data produced in the study describing "scEC&T-seq", a method for parallel sequencing of extrachromosomal circular DNA and transcriptome in single cells. This dataset includes: - Illumina scEC&T-seq Circle-seq data (scCircle-seq) for a total of 626 single cells / nuclei - bam files - Illumina scEC&T-seq RNA-seq data (scRNA-seq-Illumina) for the same single cells / nuclei - bam files - Nanopore scCircle-seq data for 18 single cells - bam files - Nanopore bulk WGS for 2 cell lines and 2 primary tumor samples - bam files - Illumina bulk WGS for 2 cell lines - bam files - Illumina bulk Circle-seq data from 1 cell line - bam file - Illumina ChIP-seq H3K27me3 data from 1 cell line - fasta files + peaks bed file + coverage bw file

  Dataset EGAD00001010071

• S3-EGA-s234-raw

  S3 genotype data wave 2-4 (all SNPs)

  Dataset EGAD00010002624

• S3-EGA-s234-qc1

  S3 genotype data wave 2-4 (QC+ SNPs)

  Dataset EGAD00010002628

• Spatial Transcriptomic

  Spatial Transcriptomic

  Dataset EGAD50000000136

• Exome

  Exome Seq for Study EGAS00001001844

  Dataset EGAD00001002159

• Exome

  Exome Seq from EGAS00001001846

  Dataset EGAD00001002162

• PyEGA3 download client

  PyEGA3 Checks prior to download - Is the dataset available for download? Most datasets are available using the PyEGA3 Download client, except for specific legacy datasets. For these ones, refer to Live Outbox distribution, an alternative way to download these datasets.If the dataset you've been granted access to begins with the ID EGAD5XXXXXXX, please refer to the Live Outbox distribution. If the dataset you have been granted access to contains encrypted data (filename ending in .gpg), contact EGA Helpdesk for detailed information on how to download these files.General Information The PyEGA3 download client is available at its GitHub repository. In its README you will find a step by step guide on how to use it to download files you have been granted access to. PyEGA3 implements the GA4GH-compliant HTSGET protocol supporting requests over genomic ranges. This enables the download of specific regions of interest rather than the entire file. HTSGET is not possible for all the files held by EGA: if the dataset does not contain index files (e.g. filename ending in *.crai), then genomic range requests cannot be performed. The maximum number of connections allowed is 30. If more than 30 connections are used, the download client will encounter 500 errors. The recommended number of connections for maximum output is one. Check out our video tutorial! Frequently Asked Questions General questions How do I download the datasets to which I have been granted access? After setting up your EGA download account, you can proceed to download using the EGA download client – PyEGA3. The pyEGA3 download client is a python-based tool for viewing and downloading files from authorised EGA datasets. This download client is continuously being developed for more user-friendly download experiences. Can I download the dataset’s metadata via the download client? No, the download client cannot be used to download metadata. Registered EGA users can download metadata of an authorised dataset by logging into the EGA webpage and navigating to the dataset of choice. Approximately two thirds down the page you will find the option to download the metadata as a zip file. How do I download large datasets as quickly as possible? The main EGA download client is pyEGA3 download client, a python-based tool for viewing and downloading files from authorised EGA datasets.We could exceptionally consider the option of an Aspera download for users experiencing very slow download rates or facing particular issues.Contact the EGA Helpdesk for further information. Pre-requirement and Installation What are the major requirements for the PyEGA3 download client installation and usage? The PyEGA3 client is compatible with any OS with Python 3.6+ installed. The client requires a connection to the internet, sufficient space on the destination drive, and the EGA download account credentials. Where can I find the PyEGA3 download client installation and download instructions? We strongly recommend checking our video tutorial demonstrating the usage of PyEGA3 from installation to file download. More detailed information is available at PyEGA3 GitHub. What ports are required for the PyEGA3 download client to function? PyEGA3 makes HTTPS calls to the EGA Data API (https://ega.ebi.ac.uk:8443). This port 8443 must be reachable from the location where PyEGA3 is executed to avoid timeouts. PyEGA3 download client How to update the PyEGA3 download client to be on the latest version? Updating the client to the latest version can be achieved by running the following command: pip3 install pyega3 --upgrade I do not have an active EGA account. Can I test the download with the PyEGA3 download client? An EGA download test account (ega-test-data@ebi.ac.uk) has been created for troubleshooting and training purposes. The test account does not require an EGA username and password because it contains publicly accessible files from the 1000 Genomes Project. More information about the use of this account and log in details are available at pyEGA3. I lost my connection while the download was in progress. Does this mean that I have to download the file again from the start? The PyEGA3 download client supports automatic resumption of downloads. To enable quicker download speeds, the API breaks files into up to four segments and downloads them in parallel. With the resume feature, downloads will automatically resume if you encounter any errors or if the connection is interrupted. How can I improve the overall download speed of the PyEGA3 download client? Download speeds can be optimised using the --connections parameter which will parallelise download at the file level. If the --connections parameter is provided, all files >100Mb will be downloaded using the specified number of parallel connections. It is important to note that files are still downloaded sequentially, so using multiple connections does not mean downloading multiple files in parallel. The maximum number of connections allowed is 30. If more than 30 connections are used, the download client will encounter 500 errors. The recommended number of connections for maximum output is one. Why is my file taking a long time to be saved? Please note that when a file is being saved, it goes through two processes. First, the downloaded file "chunks" are pieced back together to reconstruct the original file. Secondly, PyEGA3 calculates the checksum of the file to confirm that the file was downloaded successfully. Larger files will take more time to reconstruct and validate the checksum. Why is the HTSGET protocol not working with my dataset of interest? PyEGA3 implements the GA4GH-compliant htsget protocol for supporting requests over genomic ranges. This exciting new feature means that for data files with accompanying index files (e.g. .crai for CRAM files) users can download specific regions of interest rather than the entire file, saving both time and storage space. Please note that in order for the genomic range requests to work for a BAM, CRAM or VCF file there must be an associated BAI, CRAI or TBI file, respectively. If the dataset does not contain these index files then genomic range requests cannot be performed. After download Do I need to decrypt data files downloaded through the pyEGA3 download client? Files are transferred over secure HTTPS connections and received unencrypted, thus there is no need for decryption after data download. What is the purpose of an MD5 file? After the download completes, file integrity is verified using checksums. The PyEGA3 download client automatically verifies each file’s unencrypted md5 after download to ensure that the file was downloaded correctly from the EGA. Errors Why do I get a “400 Client Error” while trying to access the download client? Ensure that your credentials are formatted correctly. Please contact the EGA Helpdesk if you are unsure about your account’s username (i.e. your email address). Why do I get a “500 Server Error”? It is important to note that files are still downloaded sequentially, so using multiple connections does not mean downloading multiple files in parallel. The maximum number of connections allowed is 30. If more than 30 connections are used, the download client will encounter 500 errors. The recommended number of connections for maximum output is one. Otherwise, 500 server errors are internal server errors from EGA’s end. Retry after some time and, if the 500 error persists: check our homepage or Twitter for any outage information or report it to EGA Helpdesk. Why do I get a “slice error”? This type of error occurs when one slice download was prematurely interrupted. Please re-start the same download. The download client will pick up any partial download and download any missing slices. Why do I get the error “Dataset' EGADXXXXXXXXXXX' is not in the list of your authorised datasets”? Please note that the data access was given to the email (username) provided to the Data Access Committee (DAC) in your official request. Therefore, you shall check that the account that you are using is the same one that you sent to the DAC. Why do I get “ERROR:root:Failed to obtain IP address”? This might mean that the connection ports are blocked. Check that the port 8443 is reachable.In case the port is reachable and the error persists, please try to modify the file pyega3.py on line 44 in the python module installation: Replace endpoint = 'https://ipinfo.io/json' with endpoint = 'https://api.myip.com' Why do I get the error “Invalid username, password or secret key - please check and retry”? Make sure that you are using the correct credentials (username and password) and there is no typo. Contact EGA Helpdesk if the problem persists.

  Documentation access/download/files/pyega3

• Therapeutic Targeting of ATR Yields Durable Regressions in High Replication Stress Tumors

  Patients with relapsed or recurrent small cell lung cancer or extra-pulmonary small cell cancers were enrolled on an investigator-initiated, single-arm, phase 2 trial (NCT02487095) combining an ATR inhibitor M6620 (VX-970, berzosertib) administered at 210 mg/m2 intravenously over 60 minutes on day 2 and day 5, and topotecan 1.25 mg/m2 intravenously over 30 minutes every 23 hours on days 1 through 5, with a support of pegfilgrastim 6mg subcutaneously on day 6 in 21-day cycles. Tumor samples were collected either just before starting the treatment or before starting treatment prior to be enrolled in the trial. Whole exome and RNA were sequenced to evaluate genomic and transcriptomic characteristics associated with the response to the combination treatment.

  Study phs002327

• Whole exome sequencing in multiplex cleft families from a consortium

  Building upon our previous linkage and association studies, we will use whole exome sequencing studies of second and third degree affected relatives drawn from multiplex families. Our specific aims are: 1) To conduct whole exome sequencing on affected members (2° and 3° relatives) drawn from multiplex cleft families from a consortium to identify novel genes causing non-syndromic oral clefts; 2) To confirm and test the role of rare variants in these novel genes through confirmatory Sanger sequencing, plus linkage information using additional members available in these multiplex families. This whole exome sequencing approach will combine evidence from linkage studies and large scale sequencing to identify novel genes causing oral clefts in multiplex cleft families.

  Study phs000459

• Multidimensional and Longitudinal Immune Profiling of Sepsis in Uganda

  We conducted a prospective, observational cohort study of adults (RESERVE-U-2-TOR) admitted to Tororo General Hospital in eastern Uganda with severe, undifferentiated infection (i.e., suspected sepsis). Briefly, whole-blood samples were collected in PAXgene blood RNA tubes (PreAnalytiX, Qiagen, Germantown, MD, USA), from which RNA was extracted using PAXgene Blood RNA Kits (cat. no. 762164) (Qiagen). Sequencing libraries were constructed with the NEBNext Ultra II RNA Library Preparation Kit for Illumina, after which libraries were multiplexed and sequenced on the Illumina NovaSeq X Plus platform (Illumina, Inc, San Diego, CA). Data from this study available through dbGaP include raw RNA-Seq data generated from whole-blood samples collected upon RESERVE-U-2-TOR study enrollment (N=274).

  Study phs003914