5e2f464b-f475-437f-9b5e-a10199aa4585 - samples
Ontology highlight
ABSTRACT:
PROVIDER: EGAD00001009678 | EGA |
REPOSITORIES: EGA
Similar Datasets
Project description:Single-cell sequencing methodologies such as scRNA-seq and scATAC-seq have become widespread and effective tools to interrogate tissue composition. Increasingly, variant callers are being applied to these methodologies to resolve the genetic heterogeneity of a sample, especially in the case of detecting the clonal architecture of a tumor. Typically, traditional bulk DNA variant callers are applied to the pooled reads of a single-cell library to detect candidate mutations. Recently, multiple studies have applied such callers on reads from individual cells, with some citing the ability to detect rare variants with higher sensitivity. Many studies apply these two approaches to the Chromium (10x Genomics) scRNA-seq and scATAC-seq methodologies. However, Chromium-based libraries may offer additional challenges to variant calling compared to existing single-cell methodologies, raising questions for the validity of variants obtained from such a workflow. To determine the merits and challenges of various variant-calling approaches on Chromium scRNA-seq and scATAC-seq libraries, we use sample libraries with matched bulk whole-genome-sequencing to evaluate the performance of callers. We review caller performance, finding that bulk callers applied on pooled reads significantly outperform individual-cell approaches. We also evaluate variants unique to scRNA-seq and scATAC-seq methodologies, finding patterns of noise but also potential capture of RNA-editing events. Finally, we review the notion that variant calling at the single-cell level can detect rare somatic variants, providing empirical results that suggest resolving such variants is infeasible in single-cell Chromium libraries.
2024-08-05 | GSE213503 | GEO
Project description:Single-cell sequencing methodologies such as scRNA-seq and scATAC-seq have become widespread and effective tools to interrogate tissue composition. Increasingly, variant callers are being applied to these methodologies to resolve the genetic heterogeneity of a sample, especially in the case of detecting the clonal architecture of a tumor. Typically, traditional bulk DNA variant callers are applied to the pooled reads of a single-cell library to detect candidate mutations. Recently, multiple studies have applied such callers on reads from individual cells, with some citing the ability to detect rare variants with higher sensitivity. Many studies apply these two approaches to the Chromium (10x Genomics) scRNA-seq and scATAC-seq methodologies. However, Chromium-based libraries may offer additional challenges to variant calling compared to existing single-cell methodologies, raising questions for the validity of variants obtained from such a workflow. To determine the merits and challenges of various variant-calling approaches on Chromium scRNA-seq and scATAC-seq libraries, we use sample libraries with matched bulk whole-genome-sequencing to evaluate the performance of callers. We review caller performance, finding that bulk callers applied on pooled reads significantly outperform individual-cell approaches. We also evaluate variants unique to scRNA-seq and scATAC-seq methodologies, finding patterns of noise but also potential capture of RNA-editing events. Finally, we review the notion that variant calling at the single-cell level can detect rare somatic variants, providing empirical results that suggest resolving such variants is infeasible in single-cell Chromium libraries.
2024-08-05 | GSE213338 | GEO
Project description:This data set includes bam files (aligned to hg38) from the germline of parents whose children have CHEK2 germline mutations.
| EGAD00001009516 | EGA
Project description:Somatic mutations of RUNX1, which encodes the myeloid and lymphoid transcriptional factor RUNX1, are common in both B- and T- acute lymphoid leukemia (ALL) and are associated with poor prognosis of T-ALL. However, there has been no comprehensive investigation of the pattern or prevalence of RUNX1 germline mutation in both B- and T-ALL. Here we report germline RUNX1 variants in 1.23% of B-ALL and 2.11% of T-ALL, identifying 31 unique variants in 62 B-ALL and 18 unique variants in 26 T-ALL children. The majority of frameshift and nonsense variants affected RUNX1 function in transcriptional regulation, hematopoiesis, and cellular proliferation. We identified JAK3 as the most frequent somatic mutation in T-ALL with RUNX1 variants. These results not only identify RUNX1 as a leukemia predisposition gene but also further underline the importance of germline genetic variants to the development of ALL
2021-12-31 | GSE178238 | GEO
Project description:Somatic mutations of RUNX1, which encodes the myeloid and lymphoid transcriptional factor RUNX1, are common in both B- and T- acute lymphoid leukemia (ALL) and are associated with poor prognosis of T-ALL. However, there has been no comprehensive investigation of the pattern or prevalence of RUNX1 germline mutation in both B- and T-ALL. Here we report germline RUNX1 variants in 1.23% of B-ALL and 2.11% of T-ALL, identifying 31 unique variants in 62 B-ALL and 18 unique variants in 26 T-ALL children. The majority of frameshift and nonsense variants affected RUNX1 function in transcriptional regulation, hematopoiesis, and cellular proliferation. We identified JAK3 as the most frequent somatic mutation in T-ALL with RUNX1 variants. These results not only identify RUNX1 as a leukemia predisposition gene but also further underline the importance of germline genetic variants to the development of ALL
2021-12-31 | GSE178237 | GEO
Project description:ChIP-Seq is a technique used to analyse protein-DNA interactions. The protein-DNA complex is pulled down using a protein antibody, after which sequencing and analysis of the bound DNA fragments is performed. A key bioinformatics analysis step is “peak” calling - identifying regions of enrichment. Benchmarking studies have consistently shown that no optimal peak caller exists. Peak callers have distinct selectivity and specificity characteristics which are often not additive and seldom completely overlap in many scenarios. In the absence of a universal peak caller, we rationalized one ought to utilize multiple peak-callers to 1) gauge peak confidence as determined through detection by multiple algorithms, and 2) more thoroughly survey the protein-bound landscape by capturing peaks not detected by individual peak callers owing to algorithmic limitations and biases. We therefore developed an integrated ChIP-Seq Analysis Pipeline (ChIP AP) which performs all analysis steps from raw fastq files to final result, and utilizes four commonly used peak callers to more thoroughly and comprehensively analyse datasets. Results are integrated and presented in a single file enabling users to apply selectivity and sensitivity thresholds to select the consensus peak set, the union peak set, or any sub-set in-between to more confidently and comprehensively explore the protein bound landscape. (https://github.com/JSuryatenggara/ChIP-AP).
2022-01-03 | GSE172355 | GEO
Project description:Analysis of gene expression in cholangiocarcinoma patients. Please download the supplementary file GSE89748_CCA_batch02_illumina_Gene_expression_noNorm_noBKGD.txt.gz (link below) to obtain gene expression matrix with sample IDs used in the publication.
2017-07-03 | GSE89748 | GEO
Project description:Analysis of gene expression in cholangiocarcinoma patients. Please download the supplementary file GSE89747_CCA_batch01_illumina_Gene_expression_noNorm_noBKGD.txt.gz (link below) to obtain gene expression matrix with sample IDs used in the publication.
2017-07-03 | GSE89747 | GEO
Project description:We have benchmarked the performance of cancer CNV calling by six most recent software tools on their detection accuracy, sensitivity, and reproducibility. We also explored the consistency of CNV calling across different orthogonal technologies, including optical mapping and microarrays. Using consensus results from six CNV callers and confirmation from three orthogonal methods, we established a high-confidence CNV call set for the reference sample.
2023-08-25 | GSE212051 | GEO
Project description:Cyclin D1 is an important cell cycle regulator but in cancer its overexpression also increases cellular migration mediated by p27KIP1 stabilization and RhoA inhibition. Recently, a common polymorphism at the exon 4-intron 4 boundary of the human cyclin D1 gene within a splice donor region was associated with an altered risk of developing cancer. Altered RNA splicing caused by this polymorphism gives rise to a variant cyclin D1 isoform termed cyclin D1b, which has the same N-terminus as the canonical cyclin D1a isoform but a distinct C-terminus. Analysis was performed of mouse cyclin D1 knockout 3T3 cells infected with splice variants of cyclin D1. 3T3 cells transduced with retroviral vectors expressing each cyclin D1 isoform were processed for expression analysis. Experiment Overall Design: Three Mouse Embryonic Fibroblasts cell lines obtained from littermate cyclin D1 knockout E14 embyos were serial passaged to obtain cyclin D1 knockout (D1KO) 3T3 cells. Each of the D1KO 3T3 cell lines were infected using the Murine Stem Cell Virus (MSCV) expressing splice variants of cyclin D1; cyclin D1a/ cyclin D1b or GFP control to give triplicate sample sets. 7 days post infection total RNA from each sample was extracted using Trizol and further purified using Qiagenâs RNeasy Kit. Preparation of biotinylated cRNA and hybridization to oligonucleotide arrays (Affymetrix mouse genome genechip 430 2.0) were performed in conjuction with Pestell lab and the Nucleic Acid Core Facility at Thomas Jefferson University. Mouse 430 2.0 genechip contains 39,000 transcripts. Gene chips were scanned and analyzed using Robust Multi-array Average (RMA) algorithm.
2008-06-16 | E-GEOD-9161 | biostudies-arrayexpress