14989 results for "cancer studies using rna-seq"

in 19.98 milliseconds.

• Single-cell RNA sequencing of CML patients

  This dataset contains 15 single-cell RNA sequencing samples from 6 CML patients before and after TKI-cessation. The raw data is available as fastq files.

  Dataset EGAD00001012842

• Patient TSO500 RNA

  Original patient tumours from which PDX models were derived. TruSight Oncology RNA panel for 2 samples, sequenced over 4 lanes each, gave 8 pairs of fastq files

  Dataset EGAD00001009659

• The Extracellular RNA Quality Control (exRNAQC) study (phase 1)

  mRNA capture sequencing and small RNA sequencing data (FASTQ files) of the exRNAQC study phase 1.

  Dataset EGAD00001007697

• PrevANZ RNAseq dataset

  Whole blood samples collected at baseline and week 12 in PAXgene Blood RNA tubes. RNA sequencing on the Illumina NovaSeq 6000 System generated 150 base pair length paired end reads.

  Dataset EGAD00001011069

• RNA sequencing data of child-mother pairs, maternal smoking.

  RNA sequencing data of whole blood samples from smoking and non-smoking mothers and their children at gestation/birth and follow-up years.

  Dataset EGAD00001002011

• RNA sequencing study for 8 pairs of primary NSCLCs and distant metastases

  RNA sequencing study for 8 pairs of primary NSCLCs and distant metastases. This dataset includes a total of 29 samples.

  Dataset EGAD00001005765

• Aspirin and Inflammation: Mutations, Genes, Pathways and Prevention in Barrett's Esophagus

  Background: Use of aspirin and other non-steroidal anti-inflammatory drugs (NSAIDs) has been shown to protect against tetraploidy, aneuploidy, and chromosomal alterations in the metaplastic condition Barrett's esophagus (BE) and to lower the incidence and mortality of esophageal adenocarcinoma (EA). The esophagus is exposed to both intrinsic and extrinsic mutagens resulting from gastric reflux, chronic inflammation and exposure to environmental carcinogens such as those found in cigarettes. Here we test the hypothesis that NSAID use inhibits accumulation of point mutations/indels during somatic genomic evolution in BE. Methods: Whole exome sequences were generated from 82 purified epithelial biopsies and paired blood samples from a cross-sectional study of 41 NSAID users and 41 nonusers matched by sex, age, smoking, and continuous time using or not using NSAIDs. Results: NSAID use reduced overall frequency of point mutations across the spectrum of mutation types, lowered the frequency of mutations even when adjusted for both TP53 mutation and smoking status, and decreased the prevalence of clones with high variant allele frequency. Never smokers who consistently used NSAIDs had fewer point mutations in signature 17, which is commonly found in EA. NSAID users had on average a 50% reduction in functional gene mutations in nine cancer-associated pathways and also had less diversity in pathway mutational burden compared to nonusers. Conclusions: These results indicate NSAID use functions to limit overall mutations on which selection can act and supports a model in which specific mutant cell populations survive or expand better in the absence of NSAIDs. Galipeau PC, et al. Genome Med. 2018 Feb 27;10(1):17. PMID: 29486792.

  Study phs001654

• Analysis of error profiles in deep next-generation sequencing data

  BackgroundSequencing errors are key confounding factors for detecting low-frequency genetic variants that are important for cancer molecular diagnosis, treatment, and surveillance using deep next-generation sequencing (NGS). However, there is a lack of comprehensive understanding of errors introduced at various steps of a conventional NGS workflow, such as sample handling, library preparation, PCR enrichment, and sequencing. In this study, we use current NGS technology to systematically investigate these questions.ResultsBy evaluating read-specific error distributions, we discover that the substitution error rate can be computationally suppressed to 10-5 to 10-4, which is 10- to 100-fold lower than generally considered achievable (10-3) in the current literature. We then quantify substitution errors attributable to sample handling, library preparation, enrichment PCR, and sequencing by using multiple deep sequencing datasets. We find that error rates differ by nucleotide substitution types, ranging from 10-5 for A>C/T>G, C>A/G>T, and C>G/G>C changes to 10-4 for A>G/T>C changes. Furthermore, C>T/G>A errors exhibit strong sequence context dependency, sample-specific effects dominate elevated C>A/G>T errors, and target enrichment PCR led to aprox 6-fold increase of overall error rate. We also find that more than 70% of hotspot variants can be detected at 0.1% - 0.01% frequency with the current NGS technology by applying in-silico error suppression.ConclusionsWe present the first comprehensive analysis of sequencing error sources in conventional NGS workflows. The error profiles revealed by our study highlight new directions for further improving NGS analysis accuracy both experimentally and computationally, ultimately enhancing the precision of deep sequencing.

  Study EGAS00001003444

• Phased whole genome sequencing of 10 melanoma samples

  We have sequenced 10 melanoma samples using 10X linked reads technology to obtain phased whole genome sequence data. Using this data, we created diploid personalized genomes for each sample and aligned functional genomics data obtained from the same samples in order to find allele specific events (such as allele-specific binding and allele-specific chromatin accessibility).

  Study EGAS00001004136

• Abnormal_foetal_development_exome_trios

  This project is to explore the contribution of de novo mutations to severe structural malformations diagnosed prenatally using ultrasound. These malformations include heart, CNS, renal and GI abnormalities. In this pilot project we aim to exome sequence 30 parent-foetus trios to ~50X mean coverage and identify de novo functional variants using an algorithm developed in the Hurles group

  Study EGAS00001000167

• Profiling of H3K27ac landscape in immune cells from rheumatoid arthritis patients and healthy controls

  We collected peripheral blood mononuclear cells (PBMC) from 6 RA patients and 4 healthy controls, as well as synovial fluid from the same RA patients. We then sorted B cells, CD4+ and CD8+ T cells, regulatory T cells and monocytes using flow cytometry and profiled regions marked with H3K27ac using CUT&Tag.

  Study EGAS00001005085

• Epi2Diag

  Genomic DNA was extracted from whole blood and bisulfite converted using the EZ DNA Methylation Lightning Kit (Zymo research). DNA methylation profile was then derived using Illumina’s Infinium EPIC array v2.0. in accordance with the manufacturer’s protocol at the ASGARD-Rouen genomic platform (Inserm U1245, University of Rouen and Rouen University Hospital, Rouen France) on an Illumina NextSeq550 scanner.

  Study EGAS00001008070

• Gut metagenome/FR 2002

  The main goal of the project is the study the associations between the gut metagenome and human health. The dataset contains data for n=7211 FINRISK 2002 participants who underwent fecal sampling. Demultiplexed shallow shotgun metagenomic sequences were quality filtered and adapter trimmed using Atropos (Didion et al., 2017), and human filtered using Bowtie2 (Langmead and Salzberg, 2012).

  Study EGAS00001005038

• Whole exome sequencing of atypical 3q26 samples

  The Genomic DNA Clean & Concentrator kit (ZYMO Research) was used to remove EDTA from the DNA samples. Sample libraries were prepared using 100 ng of input according to the KAPA HyperPlus Kit (Roche) using Unique Dual Index adapters (Integrated DNA Technologies, Inc.). Exomes were captured using the SeqCap EZ MedExome (Roche Nimblegen) according to SeqCap EZ HyperCap Library v1.0 Guide (Roche) with the xGen Universal blockers – TS Mix (Integrated DNA Technologies, Inc.). The amplified captured sample libraries were paired-end sequenced (2x100 bp) on the Novaseq 6000 platform (Illumina) and aligned to the hg19 reference genome using the Burrows-Wheeler Aligner (BWA).

  Dataset EGAD00001006102

• Heterogeneity of IKZF1 genomic alterations and risk of relapse in childhood B-cell precursor acute lymphoblastic leukemia

  Purpose Genomic alterations of IKZF1 are common and associated with adverse clinical features in B-ALL, but the relationship between the type of IKZF1 alteration, disease subtype and outcome are incompletely understood. Patients and Methods We used transcriptome and genomic sequencing, and SNP microarray data to determine leukemia subtype and genomic alterations for 688 pediatric patients with B-ALL enrolled into St. Jude Total Therapy 15 and 16 studies. Results IKZF1 alterations were identified in 115 (16.7%) patients, commonly in BCR::ABL1 (78%) and CRLF2-rearranged, BCR::ABL1-like B-ALL (70%), and were associated with five-year cumulative incidence of relapse (CIR) of 14.8 ± 3.3% compared to 5.0 ± 0.9% for patients without any IKZF1 alteration (P < 0.0001). IKZF1 deletions of exon 4-7 (P = 0.0002), genomic IKZF1plus with any IKZF1 deletion (P = 0.006) or with focal IKZF1 deletion (P = 0.0007), and unfavorable genomic subtypes (P < 0.005) were independently adversely prognostic. Patients with both IKZF1 exon 4-7 deletion and unfavorable genomic subtype had a significantly increased risk of relapse (HR = 58.3; 95% CI, 11.9 – 285.4, P < 0.0001) whereas IKZF1 sequence mutations were not independently predictive of outcome. Conclusions Genomic IKZF1plus with any IKZF1 deletion, IKZF1 deletion of exon 4-7, and unfavorable subtype confer increased risk of relapse. The combination of unfavorable genomic subtype and IKZF1 deletion of exon 4-7 identified patients at greatest risk of relapse despite MRD-directed therapy. The type of IKZF1 alteration together with subtype are informative for risk stratification and predict response in patients with B-ALL.

  Study EGAS50000000106

• Study of Tumor Recurrence Related to the Expression of the PAX3-FOXO1 Oncogenic Transcription Factor in Fusion-Positive Rhabdomyosarcoma

  Alveolar rhabdomyosarcoma (ARMS) is a highly aggressive pediatric soft tissue cancer characterized by a recurrent 2;13 chromosome translocation. This translocation generates a PAX3-FOXO1 (P3F) fusion gene, which encodes a chimeric transcription factor with oncogenic activity. Using a human myoblast system that models targeted therapy directed against P3F, we demonstrated that the expression of the fibroblast growth factor 8 (FGF8), a direct transcriptional target of P3F, is necessary and sufficient for PAX3-FOXO1-independent tumor recurrence in addition to be required for the tumorigenic activity of primary tumors expressing P3F. In addition, we showed that FGF8 is expressed in human rhabdomyosarcoma cell lines that express P3F. We also demonstrated that FGF8 contributes to their proliferation and transformation in vitro, and is sufficient to maintain their oncogenicity after the loss of P3F expression. We suggest that targeting the expression of the PAX3-FOXO1 transcription factor can generate resistance and recommend that additional anticipated therapeutic approaches, such as targeting FGF8 pathway, may avoid this resistance mechanism.

  Study phs002344

• Pharmacokinetics and Pharmacogenomics of Ribociclib in Race-Based Cohorts (LEANORA)

  Underrepresented populations' participation in clinical trials remains limited, and the potential impact of genomic variants on drug metabolism remains elusive. This study aimed to assess the pharmacokinetics (PK) and pharmacogenomics (PGx) of ribociclib in self-identified Black women with hormone receptor positive (HR+)/human epidermal growth factor receptor 2-negative (HER2) advanced breast cancer. LEANORA (NCT04657679) was a prospective, observational, multicenter cohort study involving 14 Black women. PK and PGx were evaluated using tandem mass spectrometry and PharmacoScan microarray. CYP3A5*3, CYP3A5*6, and CYP3A5*7 variants were genotyped and CYP3A5 phenotypes were constructed from these data. We observed 7 poor metabolizers (PM), 6 intermediate metabolizers (IM), and one normal metabolizer (NM). The area under the curve of plasma concentration versus time curve (AUC) of ribociclib did not differ between PMs (39,230 hr*ng/mL) and IM/NMs (43,546 hr*ng/mL; P=0.38). The incidence of adverse events (AEs) was also similar. We found no association between CYP3A5 genotype and ribociclib exposure. Continued efforts are needed to include diverse populations in clinical trials to ensure equitable treatment outcomes.

  Study phs003770

• Scalable whole-genome single-cell library preparation without pre-amplification

  Single-cell genomics is critical for understanding cellular heterogeneity in cancer, but existing library preparation methods are expensive, require sample preamplification and introduce coverage bias. Here we describe direct library preparation (DLP), a robust, scalable, and high-fidelity method that uses nanoliter-volume transposition reactions for single-cell whole-genome library preparation without preamplification. We examined 782 cells from cell lines and triple-negative breast xenograft tumors. Low-depth sequencing, compared with existing methods, revealed greater coverage uniformity and more reliable detection of copy-number alterations. Using phylogenetic analysis, we found minor xenograft subpopulations that were undetectable by bulk sequencing, as well as dynamic clonal expansion and diversification between passages. Merging single-cell genomes in silico, we generated "bulk-equivalent" genomes with high depth and uniform coverage. Thus, low-depth sequencing of DLP libraries may provide an attractive replacement for conventional bulk sequencing methods, permitting analysis of copy number at the cell level and of other genomic variants at the population level.

  Study EGAS00001002170

• How to use EGA Webin?

  EGA Webin EGA Webin serves as a platform for registering metadata for array based submissions, large scale sequence submission as well as for legacy EGA submission accounts (ega-box-XXXX). For large scale submitters of sequence data you have also the option to submit metadata via XMLS programmatic submission, while new submitters are advised to utilise the Submitter Portal for their submissions. You can request a legacy EGA submission account (ega-box-XXXX) by populating this form. Please, allow two business days for our Helpdesk team to contact you after populating this form. WEBIN actions: Register metadata for a sequence submission Register study, samples, experiments, runs, DAC, policy and dataset/s after file upload. Register components for your array-based metadata submission Register study, samples, DAC or policy before uploading files. Edit existing submission metadata Change or update previously submitted metadata. Register metadata for a sequence submission Ensure that all sequence files have been encrypted before uploading them to your submission account using the EgaCryptor. Go to the EGA Webin and log in using your submission account name (ega-box-XXX) and password. Register components of your metadata submission Study Samples Data Access Committee (DAC) Data access policy Dataset For array-base submissions: Study, Samples, Data Access Committee (DAC) and Data access policy may all be registered BEFORE file upload and dataset registration through the array-base template. Register your Study Go to the “Studies (Projects)” box Click on “Register Study” and fill in the information related to your study. Click on “Submit”, this will save the information and generate an EGA study ID. To use the study accession number in a publication, the study has to be previously released on the EGA website, we suggest the following format: "Sequence data has been deposited at the European Genome-phenome Archive (EGA), which is hosted by the EBI and the CRG, under accession number EGASXXXXXXXXXXX.Further information about EGA can be found on https://ega-archive.org and “The European Genome-phenome Archive in 202 "(10.1093/nar/gkab1059)" Register your Samples Go to the “Samples” box Click on “Register Samples” Select “Download spreadsheet to register samples” and customise your template, there is a default EGA template (EGA default checklist) but more attributes can be added if required. For the EGA default checklist, there are mandatory,recommended and optional attributes. As well custom fields which can be added if required. Mandatory attributes Field Name Description tax_id Taxonomy ID of the organism as in the NCBI Taxonomy database. Entries in the NCBI Taxonomy database have integer taxon IDs. See our tips for sample taxonomy here scientific_name Scientific name of the organism as in the NCBI Taxonomy database. Scientific names typically follow the binomial nomenclature. For example, the scientific name for humans is Homo sapiens. sample_alias Unique name of the sample. If not selected system will auto generate an unique alias sample_title Title of the sample sample_description Description of the sample phenotype *** Where possible, please use the Experimental Factor Ontology (EFO) to describe your phenotypes. Recommended attributes Field Name Description subject_id Identifier for the subject where the sample has been derived from gender * Sex Optional attributes Field Name Description sex sex of the organism from which the sample was obtained disease_site Affected organ sample type Affected organ donor_id ** Identifier of the donor where the sample has been derived from *Gender should be described as 'male', 'female' or 'unknown'. If 'unknown' due to a known sex chromosome aneuploidy, please create a user defined attribute called 'Sex chromosome karyotype' and add the appropriate value, for example, 'XXY'. **Donor id (Subject id) should be a de-identified subject handle. If unknown, please add 'unknown' to the field. ***Phenotypes should, where possible, be an Experimental Factor Ontology accession. If a term cannot be found to describe your phenotype please use free text. All sample phenotypes considered important for further analysis of the data should be provided (for example, tumour type), additional phenotype attributes can be created by defining your own attributes; use the notion 'phenotype2', 'phenotype3', etc. After you have customised the fields for the sample submission, download the template and fill in the information. Example of the sample template: Finally upload the sample template to get the EGA accession IDs for the samples. Register your Data Access Committee (DAC) Further information on the role of your DAC Go to the “Data Access” box Click on “Register Dacs” Input the information about the DAC and register at least one main DAC contact. Register your Data Access Policy Your Data Access Policy provides the terms and conditions of data use, this is also referred to as the Data Access Agreement (DAA). Completion of a DAA by the applicant/s should form part of the application process to the Data Access Committee (DAC). Go to the “Data Access” box Click on “Register Policies” Select the DAC to which this policy will be linked to and fill in the policy information. Submitting your Runs and Analyses This section is only for sequence data submission, for array-based submission it can be skipped. Please refer to our Submitting array based metadata Runs Registration Go to the “Raw Reads (Experiments and Runs)” box Click on “Submit Reads” Select “Download spreadsheet template for Read submission” Select the template corresponding to your submission type For the templates you have the option to customise the optional fields. To check their description click on “Show Description “ Download the template and fill in the required information. Example of the runs template: We recommend that Fastq, BAM, and CRAM read files are submitted using Webin-CLI When using this interface instead of Webin-CLI, raw sequences must be uploaded in one of the supported data formats before they can be submitted. The files can be uploaded using FTP or Aspera. The study and the sequenced samples must be pre-registered before the raw reads are submitted. Please note that each individual study and sample should be registered only once. You will be asked to provide information about the sequencing libraries and instruments. Submitting your Dataset This section is only for sequence data submissions, for array based submissions it can be skipped. Please refer to our Submitting array based metadata The dataset describes the data files, defined by the run (EGARXXXXXXXXXXX) and analysis (EGAZXXXXXXXXXXX) accessions that make up the dataset and links the collection of data files to a specified Data Access Committee and Data Access Policy. As a result, you must have registered your Reads and experiments, Data Access Committee (DAC) and Data access policy before submitting your Dataset. Please consider the number of datasets that your submission consists of, for example, a case control study is likely to consist of at least two datasets. In addition, we suggest that multiple datasets should be described for studies using the same samples but different sequence technologies. Please contact EGA Helpdesk for further assistance. Go to the “Data Access” box Click on “Register Dataset” Select the Data Access Committee (DAC) and Data access policy Register your dataset After submitting your dataset you should contact the EGA Helpdesk to provide a release date for your dataset. Datasets are automatically held (i.e. not released) unless they are affiliated to a study that has already been released. Edit/update existing submission metadata Go to the “Report” section of the object you would like to edit. Locate the object and click on the arrow under action. An option menu will be displayed. Objects can be edited through their XML or with the WEBIN menu. After an object has been edited, changes would be available on the website until the submission is released again. Please contact the EGA Helpdesk if you require further assistance.

  Documentation submission/metadata/submission/EGA_webin

• scRNA-seq and scTCR-seq from 7 MF patients

  Dataset containing scRNA and scTCR sequencing of 7 patients with cutaneous T cell lymphoma. Sequencing was performed on Illumina NextSeq 550, HiSeq 4000 and NovaSeq 6000. The sequencing was always paired.

  Dataset EGAD50000000332

• Spontaneously differentiatiated iPSCs to EBs

  SUM-seq data (single-cell chromatin accessibility and gene expression) for spontaneously differentiatiated iPSCs. This data is the representative data in the Nature Protocols publication.

  Study EGAS50000001094

• BLUEPRINT September 2016, ATAC-seq Chronic Lymphocytic Leukemia from venous blood, on Genome GRCh38

  ATAC-seq data for 106 sample(s) Chronic Lymphocytic Leukemia from venous blood, on Genome GRCh38. 111 run(s), 109 experiment(s), 109 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002916

• BLUEPRINT September 2016, ATAC-seq Mantle Cell Lymphoma from venous blood, on Genome GRCh38

  ATAC-seq data for 5 sample(s) Mantle Cell Lymphoma from venous blood, on Genome GRCh38. 5 run(s), 5 experiment(s), 5 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002918

• BLUEPRINT September 2016, ATAC-seq for tonsil, on Genome GRCh38

  ATAC-seq data for 7 sample(s) from tonsil, on Genome GRCh38. 7 run(s), 7 experiment(s), 7 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002708

• Transcription factor binding in human monocyte differentiation

  This dataset consists of ChIP-seq data data from human monocytes, monocyte-derived dendritic cells as well as monocyte-derived cells that were subjected to mRNA transfection for PU.1, IRF4, and EGR2. In total, it the data set includes 18 samples.

  Dataset EGAD00001006602

• scRNA-seq dataset for AD and Pso patients

  This dataset contains scRNA-seq fastq files to the paper entitled "Single-cell profiles reveal distinctive immune response in atopic dermatitis in contrast to psoriasis". The details of experiment setup was described in the paper.

  Dataset EGAD00001010106

• NKI-AvL OpACIN DNA-seq of stage III melanoma patients

  The dataset “NKI-AvL OpACIN DNA-seq of stage III melanoma patients" includes 2 x 18 normal and 2 x 18 tumor FASTQ files from paired-end whole exome sequencing on Illumina HiSeq2500 for 18 stage III melanoma patients.

  Dataset EGAD00001004217

• siRNA knockdown of Allelic Imbalance target TFs followed by mRNA-seq

  siRNA knockdown of 43 Allelic Imbalance target TFs followed by mRNA-seq done in triplicates in three (GP5D, LoVo, COLO320DM) different cell colorectal adenocarcinoma cell lines.

  Dataset EGAD00001004098

• COVID-19 Postmortem Lung snRNA-seq

  A deeper understanding of the pathological mechanisms of SARS-CoV-2 infection is required to combat COVID-19. Through this dataset, we analyze postmortem lung cells from patients that are infected/uninfected with SARS-CoV-2 with snRNA-seq.

  Dataset EGAD00001006584

• WGS data of paediatric TCF3::PBX1 acute lymphoblastic leukemia (set2)

  The dataset includes FASTQ and BAM files from diagnostic and matched remission (control) samples of one TCF3::PBX1 ALL patient (ALLT-376). Sequencing library was made using PCR free-based WGS library preparation (PCR-), using NEB Next® Ultra™ DNA Library Prep Kit. Both diagnostic (90x) and remission (30x) samples were sequenced using HiSeq X.

  Dataset EGAD50000001795