15130 results for "cancer studies using rna-seq"

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• Evolutionary analysis of pancreatic cancer and coexistent precursor lesions using whole exome sequencing data

  Most adult carcinomas develop from noninvasive precursor lesions, a progression that is supported by genetic analysis. Further, the evolution of these lesions contextualizes the dynamics of advanced stage disease. We analyzed the somatic variants of co-existing pancreatic cancers and precursor lesions sampled from distinct regions of the same pancreas. After inferring evolutionary relationships, we found that the ancestral cell had initiated and clonally expanded to form one or more lesions, and that subsequent driver gene mutations eventually led to a pancreatic cancer. We also found that this multi-step progression generally spans many years. Our data suggest that independent, high-grade pancreatic lesions observed in a histologic cross section often represent a single neoplasm that can spread and colonize the ductal system, accumulating spatial and genetic divergence over time.

  Study EGAS00001002778

• We evaluate the PGD/PGS including 129 couples with NGS test and 266 couples with SNP-array test for the detection of embryonic chromosomal abnormalities.

  A total of 395 couples were subjected to IVF-PGD treatment, including 129 couples with NGS test and 266 couples with SNP-array test for the detection of embryonic chromosomal abnormalities. The NGS test was performed using low coverage whole genome sequencing with HiSeq 2000 platform. And the SNP array test was using Affymetrix Gene Chip Mapping Nsp I 262K.

  Study EGAS00001000981

• Gut metagenome/FINRISK 2002 (Salosensaari et al. Nature Comms 2021)

  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 EGAS00001005020

• Pacbio_methylation_controls

  The PacBio control dataset for differential methylation analysis included 39 control samples without methylation disorders. All samples were sequenced using PacBio Revio® HiFi reads at a matched coverage of 15X, and methylation was called using pb-cpg-tools on aligned BAM files.

  Dataset EGAD00010002806

• Aplastic anemia

  Clonal hematopoiesis was investigated in patients with aplastic anemia using next-generation sequencing and single-nucleotide polymorphism (SNP) array-based karyotyping.

  Study EGAS00001001153

• Analysis of somatic mutations in normal blood, AML and MDS samples

  Targeted pulldown of genes known to be recurrently mutated in AML & MDS from patient and normal samples using Agilent Sureselect and for some cases also using Illumina Truseq technology.

  Dataset EGAD00001002180

• The evolutionary steps from primary to metastatic prostate cancer are largely uncharted, and the ability to use DNA present in body fluids as correlates of aggregate metastatic status is under-examined. We reconstructed phylogenies in ten prostate cancer patients with fatal disease using deep targeted sequencing of the prostate, adjacent and distant organs, as well as plasma, serum, and cerebrospinal fluid at various time points. A total of 163 samples are studied.

  The evolutionary steps from primary tumor to metastasis in prostate cancer are largely uncharted, and the ability to use serum, plasma, and cerebrospinal fluid as a correlate of aggregate metastatic tumor genomic status has not been tested. We used deep targeted sequencing to reconstruct tumor evolution in ten prostate cancer patients with fatal disease encompassing examination of the prostate and adjacent and distant organs, as well as plasma, serum, and cerebrospinal fluid at various time points. We show that there is substantial evolution from a common ancestor within the prostate that results in branching to multiple lineages which form an intermixed multi-clonal primary tumor mass. After the occurrence of key driver aberrations, one of these lineages will metastasize to multiple sites in a sequential fashion. These metastatic sites are then susceptible to being populated by cells from other intra-prostatic lineages or from other metastases. Genomic representation of metastases in body fluids is not uniform. Cerebrospinal fluid analysis can detect lineages not detected in circulating DNA, suggesting possible clinical utility.

  Study EGAS00001003848

• Detection of cancers three years prior to diagnosis using plasma cell-free DNA

  A major goal of early cancer detection is to identify subclinical disease when the tumor burden is low, so that treatments are more effective. But how early can cancers be detected prior to clinical signs or symptoms? This question can be answered only through the evaluation of participants whose clinical course has not been altered by the study itself. We here describe such an evaluation, performed on prospectively collected plasma samples from the Atherosclerosis Risk in Communities (ARIC) study, including 26 participants diagnosed with cancer and 26 matched controls. At the index time point, eight of these 52 participants scored positively with a multicancer early detection (MCED) blood test. All eight of these participants were diagnosed with cancer within 4 months after blood collection. In six of these 8 participants, we were able to assess an earlier plasma sample collected 3.1 to 3.5 years prior to clinical diagnosis. In four of these six participants, the same mutations detected by the MCED test could be identified, but at 8.6 to 79-fold lower levels. These results demonstrate that it is possible to detect circulating tumor DNA (ctDNA) more than three years prior to clinical diagnosis, and provide benchmark sensitivities required for the success of ctDNA-based tests for this purpose.

  Study EGAS00001008068

• Combined – whole blood and skin fibroblasts - transcriptomic analysis in Psoriatic arthritis.

  Whole blood RNA sequencing was performed longitudinally in 27 subjects with PsA at the beginning, one month and six months after treatment and response to therapy was defined at 6 months. As control groups 7 healthy individuals and 9 subjects with rheumatoid arthritis were recruited combined with public RNA-sequencing datasets from patients with psoriasis and systemic lupus erythematous. Differential expression analysis and weighted gene co-expression network analysis were performed to identify gene expression signatures, while deconvolution and flow cytometry to characterize the peripheral blood immune cell profile. RNA sequencing of skin fibroblasts was performed in a subset of affected (n=3) and healthy individuals (n=3) combined with CellChat to identify the blood-skin fibroblasts interaction network.

  Study EGAS00001006288

• 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

• A Multimodal Atlas of Human Brain Cell Types 2021 Data

  Human neocortical neurons display diverse morphology, physiology and gene expression profiles. We use a method called Patch-seq, where these three data modalities are assayed simultaneously from the same neuron, to understand how the genetic profile of human neocortical neuron types corresponds to their morphological and electrophysiological phenotypes. This dataset consists of transcriptome samples collected during Patch-seq recordings from layer 5 pyramidal neurons in the human middle temporal gyrus.

  Study phs002697

• Drug Perturbation of Primary Lymphoma Patient Samples and RNA Sequencing

  This submission contains 3'-end-based shallow-depth RNA sequencing data from 108 patients with chronic lymphocytic leukemia and 8 patients with lymphoma. Each primary patient sample was treated in vitro with DMSO as a control and with up to ten small-molecule drugs (ibrutinib, duvelisib, everolimus, trametinib, nutlin-3a, I-BET762, MK2206, selinexor, compound 26, and a combination of ibrutinib and compound 26). The samples were sequenced after 48 hours. The total data set consists of 1476 RNA sequencing samples from 6 batches (pilot, batch 1, batch 1 replicate, batch 2, batch 3, batch 4).

  Study EGAS50000001500

• bulk RNA sequencing, single cell RNA sequencing and nanopore sequencing of T-ALL patients with TCF7-SI1 fusion

  This dataset contains all sequencing data of the publication "Oncogenic cooperation between the TCF7-SPI1 fusion and NRAS(G12D) requires β-catenin activity to drive T-cell acute lymphoblastic leukemia." This is bulk RNA sequencing of 4 T-ALL patients (X09, XB37, XB41 and XB47) of which X09 has a TCF7-SPI1 fusion, single cell RNA sequencing of these 4 patients toghether with a PDX model of the X09 patient and two patients from another cohort (SJTALL030263 and SJTALL031201) which also have a TCF7-SPI1 fusion, and nanopore sequencing of all patients with the TCF7-SPI1 fusion. Moreover these patient samples with the fusion where treated with PKF 118-310, and bulk RNA sequencing was performed in triplicate to determine the differentially expressed genes.

  Dataset EGAD00001007010

• ATAC-seq in LCLs derived from constitutional MLH1 epimutation carriers and non-carrier relatives

  ATAC-seq performed in LCLs derived from constitutional MLH1 epimutation carriers and non-carrier relatives to profile alterations in chromatin accessibility associated with constitutional MLH1 epimutation.

  Dataset EGAD50000000714

• McGill EMC Release 4 for assay "ATAC-seq"

  McGill EMC Release 4 for assay "ATAC-seq": Sequencing of transposase-accessible chromatin as described by Buenrostro et al. (Nature Methods 10, 1213?1218 (2013) doi:10.1038/nmeth.2688)

  Dataset EGAD00001001300

• BLUEPRINT: WGBS-seq for monocytes and neutrophils

  WGBS-seq for monocytes and neutrophils

  Dataset EGAD00001000673

• scDNA-seq

  Single cell DNA-seq data (CLL gene panel - Tapestri single-cell DNA CLL panel, Mission Bio)

  Dataset EGAD00001008960

• scRNA-seq of monocultures and co-cultures of PDAC-PDOs and CAFs

  scRNA-seq of monocultures and co-cultures of patient-derived PDAC organoids and matched CAFs. 3 sample sets per patient.

  Dataset EGAD00001009655

• Distinct immune cell infiltration patterns in pancreatic ductal adenocarcinoma (PDAC) exhibit divergent immune cell selection and immunosuppressive mechanisms

  Pancreatic ductal adenocarcinoma has a dismal prognosis. A comprehensive analysis of single-cell multi-omic data from matched tumour-infiltrated CD45+ cells and peripheral blood in 12 patients, and two published datasets, reveals a complex immune infiltrate. Patients have either a myeloid-enriched or adaptive-enriched tumour microenvironment. Lymphocyte enrichment is intrinsically linked with highly distinct B and T cell clonal selection, diversification, and differentiation. Using TCR data, we see the largest clonal expansions in CD8 effector memory, senescent cells, and highly activated regulatory T cells which are induced within the tumour from naïve cells. We identify pathways that potentially lead to a suppressive microenvironement, including targets of immunotherapy and checkpoints including TIGIT/PVR and SIRPA/CD47. PDAC patient tumors from the APACT clinical trial revealed that patients with myeloid enrichment had the shorter overall survival compared to those adaptive cell enrichment. We identify multiple therapeutic targets that should form the basis for rational design including boosting of B cell responses, targeting immunosuppressive macrophages, and specific Treg depletion approaches. The dataset is scRNA-seq data from PDAC patients.

  Dac EGAC50000000319

• DNA methylation changes upon growth hormone treatment in children with idiopathic short stature

  This dataset includes DNA methylation profiles before and after GH treatment (with a duration of ~18 months in average) on 47 healthy children using customized methylC-seq capture sequencing. It includes 360 fastq files (i.e. 180 paired-end fastq files) where 40 fastq files were generated with HiSeq and 320 fastq files were generated with NovaSeq.

  Dataset EGAD00001008641

• Fecal microbiome of T2D patients undergoing semaglutide or empagliflozin treatment

  The gut microbiome has been shown to be affected by the use of many human-targeted medications, and the interaction can be bidirectional. This has been clearly demonstrated for type 2 diabetes medications that have been in clinical use for several decades. However, the bidirectional effects of novel type 2 diabetes drugs semaglutide, empagliflozin, and the gut microbiome have yet to be clearly described. We investigated the effect of semaglutide and empagliflozin initiation on the gut microbiome of type 2 diabetes patients. In addition, we analyzed whether the pre-treatment gut microbiome can predict the treatment efficacy. Twenty subjects (10 women and 10 men) were enrolled in the study between November 2019 and January 2023 from the University of Tartu Hospital, Tartu, Estonia. All participants had been diagnosed with type 2 diabetes. The study group taking semaglutide included 6 women and 4 men, and the group prescribed empagliflozin included 4 women and 6 men. Gut microbiome fecal samples donated at four timepoints (Baseline, Month 1, Month 3; Month 12) were studied using 16S ribosomal RNA gene sequencing and analysis.

  Dataset EGAD50000000756

• Cryopreservation of human cancers conserves tumour heterogeneity for single-cell multi-omics analysis

  Single-cell RNA-Sequencing of three primary breast cancers, two primary prostate cancers, and a metastatic melanoma sample from Wu et al. (2021) Genome Medicine study. Each tumour was sequenced across different cryopreservation conditions including Fresh Tissue (FT), cryopreserved single-cell suspensions (CCS), cryopreserved solid tissue fragments (CT) and a cryopreserved after overnight cold storage (CO). Data was generated using the Chromium controller (10X Genomics) and sequenced on the NextSeq platform.

  Dataset EGAD00001007030

• Multi-omics characterization of KMT2A-rearranged infant acute lymphoblastic leukemia

  KMT2A gene rearranged (KMT2A-r) infant acute lymphoblastic leukemia (ALL) is the most refractory subgroup of childhood leukemia, whose molecular heterogeneity remains largely unknown. To better understand the molecular pathogenesis of KMT2A-r infant ALL, we comprehensively analyzed the RNA sequencing, methylation array, whole exome sequencing, single-cell RNA sequencing data of diagnostic samples of KMT2A-r infant ALL. We also performed ChIP sequencing of infant ALL-derived cell lines.

  Study JGAS000385

• Effect of inflammation on human hematopoietic stem cells in a xenograft model

  Transcriptional heterogeneity in cord blood HSCs was investigated by 10X single-cell Multiome (RNA+ATAC). To investigate the effect of inflammatory stress on human HSC, 10X single-cell Multiome profiling of cells from xenografted mice recovered from repeated inflammatory challenge was performed. To investigate transmission of inflammatory stress-related molecular programs from HSC to their progeny, 10X single-cell RNA sequencing was performed on committed progenitor and myeloid cells from the same mice.

  Study EGAS50000001624

• Sequencing data for oesophageal and related samples - Ganguli et al (RNA)

  Data supporting: "TBC" Ganguli et al (RNA for 394 samples)

  Dataset EGAD00001011190

• BLUEPRINT September 2016, ChIP-Seq for conventional dendritic cell from cord blood, on Genome GRCh38

  ChIP-Seq data for 1 sample(s) for conventional dendritic cell from cord blood, on Genome GRCh38. 1 run(s), 1 experiment(s), 1 alignment(s). Part of BLUEPRINT (September 2016).Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_chipseq_analysis_ebi_20140811

  Dataset EGAD00001002948

• BLUEPRINT September 2016, ChIP-Seq Acute Lymphocytic Leukemia for precursor B cell from venous blood, on Genome GRCh38

  ChIP-Seq data for 1 sample(s) Acute Lymphocytic Leukemia for precursor B cell from venous blood, on Genome GRCh38. 1 run(s), 1 experiment(s), 1 alignment(s). Part of BLUEPRINT (September 2016).Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_chipseq_analysis_ebi_20140811

  Dataset EGAD00001002949

• BLUEPRINT September 2016, ChIP-Seq for memory B cell from venous blood, on Genome GRCh38

  ChIP-Seq data for 1 sample(s) for memory B cell from venous blood, on Genome GRCh38. 1 run(s), 1 experiment(s), 1 alignment(s). Part of BLUEPRINT (September 2016).Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_chipseq_analysis_ebi_20140811

  Dataset EGAD00001002950

• BLUEPRINT September 2016, ChIP-Seq T-cell Acute Lymphocytic Leukemia from capillary blood, on Genome GRCh38

  ChIP-Seq data for 4 sample(s) T-cell Acute Lymphocytic Leukemia from capillary blood, on Genome GRCh38. 7 run(s), 7 experiment(s), 7 alignment(s). Part of BLUEPRINT (September 2016).Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_chipseq_analysis_ebi_20140811

  Dataset EGAD00001002952

• TARGET-seq+ single-cell genotyping

  Single-cell genotyping data for bone marrow samples from 9 cases with clonal hematopoiesis and 1 control sample. The TARGET-seq+ protocol was used to generate plate-based 3' transcriptome data. For details on cell sorting and the TARGET-seq+ protocol see the methods section of the manuscript. One FASTQ file is provided per cell. Cells are named with their plate and well IDs and the subject ID. Empty wells (no-cell controls) are named "blank". Corresponding transcriptome files use the same naming with the "_transcriptome" suffix.

  Dataset EGAD00001011150

• A Single Cell Atlas of Gene Regulatory Elements in the Human Heart

  Background: Cis-regulatory elements such as enhancers and promoters are crucial for directing gene expression in the human heart. Dysregulation of these elements can result in many cardiovascular diseases that are major leading causes of morbidity and mortality worldwide. In addition, genetic variants associated with cardiovascular disease risk are enriched within cis-regulatory elements. However, the location and activity of these cis-regulatory elements in individual cardiac cell types remains to be fully defined. Methods: We performed single nucleus ATAC-seq and single nucleus RNA-seq to define a comprehensive catalogue of candidate cis-regulatory elements (cCREs) and gene expression patterns for the distinct cell types comprising each chamber of four non-failing human hearts. We used this catalogue to computationally deconvolute dynamic enhancers in failing hearts and to assign cardiovascular disease risk variants to cCREs in individual cardiac cell types. Finally, we applied reporter assays, genome editing, and electrophysiogical measurements in in vitro differentiated human cardiomyocytes to validate the molecular mechanisms of cardiovascular disease risk variants. Results: We defined >287,000 candidate cis-regulatory elements (cCREs) in human hearts at single-cell resolution, which notably revealed gene regulatory programs controlling specific cell types in a cardiac region/structure-dependent manner and during heart failure. We further report enrichment of cardiovascular disease risk variants in cCREs of distinct cardiac cell types, including a strong enrichment of atrial fibrillation variants in cardiomyocyte cCREs, and reveal 38 candidate causal atrial fibrillation variants localized to cardiomyocyte cCREs. Two such risk variants residing within a cardiomyocyte-specific cCRE at the KCNH2/HERG locus resulted in reduced enhancer activity compared to the non-risk allele. Finally, we found that deletion of the cCRE containing these variants decreased KCNH2 expression and prolonged action potential repolarization in an enhancer dosage-dependent manner. Conclusion: This comprehensive atlas of human cardiac cCREs provides the foundation for not only illuminating cell type-specific gene regulatory programs controlling human hearts during health and disease, but also interpreting genetic risk loci for a wide spectrum of cardiovascular diseases.

  Study phs002204

• Rheumatoid Arthritis Finger Stick RNA Sequence Data

  This is a longitudinal study of 355 blood samples collected weekly from 5 patients with Rheumatoid Arthritis with varying disease activity over the course of one to five years. Blood samples were self collected from finger sticks and mailed from home. RNA was extracted, purified and sequenced.

  Study phs003179