15020 results for "cancer studies using rna-seq"

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• Genetics of Human Inherited Retinal Diseases (GHIRD)

  The goal of this project is to identify novel mutations and genes involved in human retinal disorders, a stated priority of the National Eye Institute. To accomplish this, additional genes whose mutations cause inherited retinal diseases (IRD) will be identified by combining targeted gene panel sequencing, whole exome sequencing, and whole genome sequencing. We have collected about 1050 patient families around the world. In this proposal, we will identify the underlying mutations in these patients using a combination of targeted gene panel sequencing, whole exome sequencing, whole genome sequencing, bioinformatics, statistics, and functional studies. Genes targeted for gene panel sequencing are: ABCA4; ASIC2; JAG1; ABCC6; ARL3; ATP1B2; BBS1; BBS2; BBS4; OPN1SW; CAPN5; C21orf2; CA4; CACNA1F; CDH3; CHM; ERCC8; CLCN2; CLCN3; CLCN7; TPP1; CLN3; CLN5; CNGB1; CNGA1; CNGA3; COL2A1; COL9A1; COL11A1; CRX; VCAN; CTSD; TIMM8A; DMD; CLN8; ERCC6; EFEMP1; OPN1MW; GNAT1; GNAT2; GNGT1; GRM6; GUCA1A; GUCA1B; GUCY2F; GUCY2D; HARS; CFH; HK1; IDH3B; IMPDH1; IMPG1; KCNJ13; KIF11; LRP5; MAK; MITF; TRPM1; MTTP; MVK; MYO7A; NDP; NEK2; NEUROD1; NPHP1; NRL; OAT; OPA1; OTX2; PAX2; PAX6; PDCL; PCYT1A; PDE6A; PDE6C; PDE6G; PDE6H; PDE6B; PEX1; PEX6; PEX7; PEX10; PEX12; PEX13; PEX14; PFDN5; PGK1; PHYH; PIN1; PLA2G5; PPT1; PRKCZ; PEX19; PEX2; PEX5; RB1; RBP3; RBP4; OPN1LW; RDH5; PRPH2; RGR; RHO; GRK1; RLBP1; ROM1; RP9; RP1; RP2; RPGR; RPE65; RS1; SAG; ATXN7; CCL2; SLC6A6; ELOVL4; TEAD1; NR2F1; TIMP3; NR2E1; TTPA; TUB; TULP1; UCHL3; USH2A; CLRN1; VLDLR; BEST1; WFS1; ALMS1; MKKS; FZD4; OFD1; PEX3; CTSF; GBF1; ADAM9; RGS9; PEX11B; PROM1; UNC119; PRPF4; PRPF3; AIFM1; SLC24A1; LRAT; ASIC3; RAB28; PEX16; ITM2B; SLC4A7; IQCB1; CROCC; IFT140; DHX38; MFN2; NR2E3; USH1C; TOPORS; MERTK; FBLN5; CIB2; PRPF8; SDCCAG8; IFT27; TREX1; RRAS2; CEP164; ATF6; TRIM32; RIMS1; SNRNP200; CLUAP1; EMC1; ZNF423; TTLL5; AGTPBP1; RPGRIP1L; CRB1; TSPAN12; ARL2BP; AIPL1; PRPF6; FSCN2; ABHD12; PRPF31; TCTN3; IFT172; CNNM4; NPHP3; INVS; BBS9; FLVCR1; RDH8; IMPG2; WDPCP; RDH11; TMEM216; TMEM138; LZTFL1; CNGB3; AHI1; MKS1; CLN6; BBS7; POMGNT1; PEX26; SPATA7; KIZ; KLHL7; INPP5E; MDM1; CABP4; RPGRIP1; MCOLN1; CC2D2A; KIAA1549; SLC7A14; WDR19; NYX; CDH23; SEMA4A; MPP5; NMNAT1; TMEM237; PCDH15; C5orf42; GNPTAB; TMEM231; TCTN1; SRD5A3; BBS10; ZNF408; TTC21B; CSPP1; LPCAT1; DHDDS; PDZD7; PANK2; CEP290; OPA3; DNAJC5; PITPNM3; MFRP; HMCN1; ADGRV1; ARL6; FAM161A; TMEM126A; GNPTG; GJA10; RAX2; TUBGCP6; TMEM67; ACBD5; C12orf65; CDHR1; BBIP1; REEP6; CACNA2D4; RP1L1; CEP41; C1QTNF5; NXNL1; TTC8; USH1G; BBS5; ZNF513; RDH12; SLC38A8; C8orf37; VPS13B; NXNL2; BBS12; ADGRA3; LCA5; KCNV2; ADAMTS18; ARL13B; ATOH7; MFSD8; NPHP4; CCDC66; CYP4V2; CRB2; RD3; LRIT3; EYS; KIF7; CERKL; RGS9BP; C2orf71; GDF6; DTHD1; GPR179; CISD2; CCR2; PRCD; WHRN; ADGRA3. The current dbGaP study release makes available all sequencing-derived vcf data of study participants.

  Study phs001517

• Diet, Genetic Factors, and the Gut Microbiome in Crohn's Disease

  We investigate the hypothesis that consistent changes in the human gut microbiome are associated with Crohn's disease, a form of inflammatory bowel disease, and that altered microbiota contributes to pathogenesis. Analysis of this problem is greatly complicated by the fact that multiple factors influence the composition of the gut microbiota, including diet, host genotype, and disease state. For example, data from us and others document a drastic impact of diet on the composition of the gut microbiome. No amount of sequencing will yield a useful picture of the role of the microbiota in disease if samples are confounded with uncontrolled variables. We aim to characterize the composition of the gut microbiome while controlling for diet, host genotype, and disease state. Diet is controlled by analyzing children treated for Crohn's disease by placing them on a standardized elemental diet, and by testing effects of different diets on the gut microbiome composition in adult volunteers. Genotype is analyzed by large scale SNP genotyping, which is already underway and separately funded--team member Hakon Hakonarson is currently genotyping 50 children a week at ~half a million loci each and investigating connections with inflammatory bowel disease. Clinical status is ascertained in the very large IBD practice in the UPenn/CHOP hospital system. Effects of diet, host genotype, and disease state on the gut microbiome are summarized in a multivariate model, allowing connections between microbiome and disease to be assessed free of confounding factors. This project is divided into four sub-studies. In the Fecal Storage Methods (FSM) study, methods of stool storage and DNA extraction are compared to examine their impact on DNA sequence analysis results. The Controlled Feeding Experiment (CaFE) addresses the effects of controlled diets on the gut microbiome. In the Cross-sectional Study of Diet and Stool Microbiome Composition (COMBO), the effects of diet analyzed using surveys and deep sequencing of stool specimens. The fourth study, Pediatric Longitudinal Study of Elemental Diet and Stool Microbiome Composition (PLEASE), examines the effects of an elemental diet treatment on pediatric patients diagnosed with inflammatory bowel disease (IBD), particularly Crohn's disease. Fecal Storage Methods (FSM): Cross-sectional study Controlled Feeding Experiment (CaFE): Controlled trial Cross-sectional Study of Diet and Stool Microbiome Composition (COMBO): Cross-sectional study Pediatric Longitudinal Study of Elemental Diet and Stool Microbiome Composition (PLEASE): Longitudinal cohort study

  Study phs000252

• RNA sequencing of primary B-cells infected with EBV virus

  Study EGAS00001007187

• Total RNA-Sequencing of patient-derived xenograft models of myxoid liposarcoma

  Study EGAS00001004901

• Profiling of childhood neuroblastoma and the immune microenvironment by single-cell sequencing of RNA and TCR

  Neuroblastoma tumor cells and their tumor microenvironment are studied by single-cell RNA sequencing of primary neuroblastoma tumors and neuroblastoma-infiltrated tissues/metastases. Both diagnostic samples, taken pre-treatment and samples taken after induction chemotherapy at surgical resection and biopsies from relapse samples are included.  For a number of patients single-cell RNA sequencing of blood samples (controls) are included and additionally we performed single-cell T cell receptor sequencing (TCR) on the tumor and blood samples. With this study, we will focus on identifying differences in tumor cells and their (immune) microenvironment 1) before and after treatment or at relapse and 2) between primary tumors and infiltrated tissues/metastases. Moreover, with TCR sequencing we aim to unveil immune cell dynamics in patients with neuroblastoma.

  Study EGAS00001006823

• GoNL aligned sequence data in BAM format.

  We mapped the data to the UCSC human reference genome build 37 using BWA 0.5.9-r16. We first mapped each read pair separately using bwa aln. Then we used bwa sampe to map the paired reads together to a BAM9 file. The BAM file was then sorted by genomic position and indexed using PicardTools-1.32 SortSam. To prevent PCR artifacts from influencing the downstream analysis of our data, we used Picard to mark the duplicate reads, which were ignored in downstream analysis. We used GATK IndelRealigner on our data around known indels (from 1KG Pilot). The IndelRealigner creates all possible read alignments using the source and computes the likelihood of the data containing the indel based on the read pileup. Whenever the maximum likelihood contains an indel, the reads are realigned accordingly. Each base is associated with a phred-scaled base quality score. Calibration of Phred scores is crucial as they are used in some of the downstream analysis models. We used GATK to recalibrate the base qualities with respect to (i) the base cycle, (ii) original quality score, and (iii) dinucleotide context. To minimize issues stemming from mapping problems around indels, we decided to undergo a second round of indel realignment using the GATK IndelRealigner by family rather than by individual. For this second round, we considered two sources of possible indels: 1KG Phase 1 indels and indels aligned by BWA in the GoNL data.

  Dataset EGAD00001001038

• Board considering access to van Hijfte snRNA-seq GBM dataset

  Dac EGAC00001003044

• Human LV H3K27ac ChIP-seq Data Access Commitee

  Dac EGAC00001001169

• Bulk ATAC-Seq on sorted cord blood hematopoietic populations

  Study EGAS00001004742

• EGAD00010000600

  Prostate Adenocarcinomas samples using 450K

  Dataset EGAD00010000600

• Breast Cancer - Very young women

  This project will focus on sequencing of breast cancer from young age cancer patients characterized by age at diagnosis under 35. Based on the incidence rate by Korean National Cancer Incidence Database, breast cancer is the most common solid tumors among young adults aged 15-34 excepting thyroid cancer (Cancer statistics in Korea (2012)). This project just started from the mid of 2015. National cancer center in Korea are collecting more than 100 tumor specimens from breast cancer patients aged 20-34. Genetic features of young patients will be investigated with somatic mutation pattern, structural variations, and canonical pathways.

  Study EGAS00001001908

• Whole-genome and transcriptome versus panel sequencing in precision oncology: A translational-clinical comparison

  Precision oncology offers new cancer treatment options. While the use of whole-genome (WGS) and transcriptome (TS) sequencing remains often confined to specific clinical programs, molecular analysis with gene panels is more common clinical practice. Methods: Twenty patients with rare or advanced tumors sequenced by WGS/TS within the DKFZ/NCT/DKTK-MASTER program from 2015 to 2020 (MASTER 1) were additionally sequenced with the TSO500 DNA and TST170 RNA Assay by Illumina. Original Molecular Tumor Board (MTB) therapy recommendations (TR) were juxtaposed with TR from NGS panel data analysis (comparison 1). To mitigate temporal differences between the two sequencings, WGS/TS was bioinformatically reanalyzed (MASTER 2), and new TR were again compared (comparison 2). Results: In comparison 1, 47.1% of the MASTER 1’s TR and 62.8% of the panel’s TR were identical, as were 45.9% of the TR of MASTER 2 and 54.9% of the panel in comparison 2. In both MASTER 1 and MASTER 2, 26.5% and 36.1% of TR, respectively, relied on Biomarkers (BM) which were not covered by the panel. Eight of the ten MTB-derived therapy implementations were also supported in the panel, the two remaining ones being based on BM absent from the panel. Conclusions: About half of the TR in WGS/TS and panel sequencing were identical. Around a third of TR issued by WGS/TS were based on BM which are not covered by the panel, leading to therapy implementations. The presented data underscores the clinical value of comprehensive molecular analyses in individuals with advanced cancers.

  Study EGAS50000000431

• WTCCC case-control study for T1D and RA - combined cases

  WTCCC genome-wide case-control association study using two disease collections together with the 1958 British Birth Cohort and the UK National Blood Service collections as controls.

  Study EGAS00000000013

• SNP_array

  33 ETMR samples were genotyped using Illumina HumanOmni2.5M array. Hybridization and scanning was done according to the manufacturer's instructions (Illumina). Copy number (Log R) and B allele frequency estimates were obtained sing the Genotyping module (v1.9.4) in GenomeStudio v2011.1 (Illumina). Normalized and log2-transformed copy number measurements were imported from genomestudio and analysed using R package CopyNumber to identify segments with similar copy number.

  Dataset EGAD00010001667

• RCC_HTA2.0_Buettner2022

  GeneChip HTA 2.0 data of primary renal cell carcinoma (RCC) related to Buettner et al, Genome Med 2022. Microarrays were normalized individually using the SCAN method from the R package SCAN.UPC (version 2.26.0, R version 3.6.1). Probe sets were summarized on the Entrez GeneID level using the annotation provided by BrainArray (version 23).

  Dataset EGAD00010002353

• PDX_HTA2.0_Guergen2022

  GeneChip HTA 2.0 data of primary renal cell carcinoma (RCC) and RCC metastases related to Guergen et al, Front Oncol 12:2022 889789. Microarrays were normalized individually using the SCAN method from the R package SCAN.UPC (version 2.34.0). Probe sets were summarized on the Entrez GeneID level using the annotation provided by BrainArray (version 25).

  Dataset EGAD00010002392

• Investigating differential diagnostic and prognostic biomarkers in primary cutaneous follicle center lymphoma and follicular lymphoma using low-coverage whole-genome sequencing

  In this study, we analyzed 28 primary cutaneous follicle center lymphoma samples from 20 patients and compared the copy number profiles to a cohort of diagnostic samples of 64 nodal follicular lymphoma patients using low-coverage whole genome sequencing (lcWGS).

  Dataset EGAD50000000199

• WTCCC2 case-control study for Ulcerative Colitis

  WTCCC2 project genome-wide case-control association study for Ulcerative Colitis (UC) using the 1958 British Birth Cohort and the UK National Blood Service collections as controls.

  Study EGAS00000000084

• NanoSeq of buccal swab samples

  16 buccal swab samples were analyzed by Nanoseq. S3 in the file names indicates 28 mm^2 swab size, and S5 indicates 254 mm^2 swab size. We analyzed 28 mm^2 swab samples using paired 254 mm^2 swab samples as controls. The files were generated by the Nanoseq pipeline using GRCh37 as the reference genome.

  Dataset EGAD50000000999