ICGC Benchmarking Exercise
Ontology highlight
ABSTRACT:
PROVIDER: EGAS00001000433 | EGA |
REPOSITORIES: EGA
Similar Datasets
Project description:The main objective of this benchmark is the comparison of the full sequencing pipeline of different ICGC partners, including procedures, methods and performance of library preparation and whole-genome deep-sequencing. A secondary objective will be a follow-up comparison of data analysis pipelines for identification of germline and somatic variants subsequent to the results of the ICGC Somatic Variant Calling Pipeline Benchmark.
| EGAD00001000625 | EGA
Project description:ICGC-TCGA DREAM Somatic Mutation Calling Challenge
| PRJNA248553 | ENA
Project description:Strand-specific massively-parallel cDNA sequencing (RNA-Seq) is a powerful tool for novel transcript discovery, genome annotation, and expression profiling. Despite multiple published methods for strand-specific RNA-Seq, no consensus exists as to how to choose between them. Here, we developed a comprehensive computational pipeline for the comparison of library quality metrics from any RNA-Seq method. Using the well-annotated Saccharomyces cerevisiae transcriptome as a benchmark, we compared seven library construction protocols, including both published and our own novel methods. We found marked differences in complexity, strand-specificity, evenness and continuity of coverage, agreement with known annotations, and accuracy for expression profiling. Weighing each method’s performance and ease, we identify the dUTP second strand marking and the Illumina RNA ligation methods as the leading protocols, with the former benefitting from the availability of paired-end sequencing. Our analysis provides a comprehensive benchmark, and our computational pipeline is applicable for assessment of future protocols in any organism.
2010-08-15 | GSE21739 | GEO
Project description:Benchmark data for Single Cell library preparations and sequencing technology + Spatial
| PRJEB83819 | ENA
Project description:Strand-specific massively-parallel cDNA sequencing (RNA-Seq) is a powerful tool for novel transcript discovery, genome annotation, and expression profiling. Despite multiple published methods for strand-specific RNA-Seq, no consensus exists as to how to choose between them. Here, we developed a comprehensive computational pipeline for the comparison of library quality metrics from any RNA-Seq method. Using the well-annotated Saccharomyces cerevisiae transcriptome as a benchmark, we compared seven library construction protocols, including both published and our own novel methods. We found marked differences in complexity, strand-specificity, evenness and continuity of coverage, agreement with known annotations, and accuracy for expression profiling. Weighing each method’s performance and ease, we identify the dUTP second strand marking and the Illumina RNA ligation methods as the leading protocols, with the former benefitting from the availability of paired-end sequencing. Our analysis provides a comprehensive benchmark, and our computational pipeline is applicable for assessment of future protocols in any organism. Examination of 11 different strand-specific RNA-Seq libraries from 7 distinct methods; also 2 control non-strand-specific RNA-Seq libraries. To assess the performance of each strand-specific library in digital expression profiling, we compared them to reference expression measurements estimated from expression profiles using competitive hybridization of a mid-log RNA sample vs. genomic DNA using Agilent arrays.
2010-08-15 | E-GEOD-21739 | biostudies-arrayexpress
Project description:The Open Problems Perurbation Prediction (OP3) dataset is to-date the largest single-cell small molecule perturbation dataset in primary tissue with multiple donor replicates. Compounds are dosed in cultured peripheral blood mononuclear cells (PBMCs), enabling the robust computation of perturbation effects on gene expression across four distinct cell type populations: T-cells, B-cells, NK cells, and Myeloid cells. This dataset is the basis for the OP3 benchmark, which evaluates machine learning models on the task of extrapolation perturbation effects into unseen cell types.
2024-10-22 | GSE279945 | GEO
Project description:SILAC labelled Hela cell lysates were mixed in defined ratios and analysed using both DDA and DIA methods, to serve as a benchmark for the novel plexDIA features of DIA-NN.
2023-10-19 | PXD039578 | Pride
Project description:We developed a new computational pipeline called Viral-Track that can detect the viruses in a single-cell RNA sequencing sample. To validate this tool, we benchmarked it on several mouse models of infection, including lymph node infection by the Lymphocytic choriomeningitis virus (LCMV) and the Vesicular Stomatitis Virus (VSV). We also used a GFP expressing LCMV strain to infect a mouse and then sequenced the infected (GFP+) cells.
2020-05-08 | GSE149443 | GEO
Project description:Background: Lung cancer is a heterogeneous disease and the primary cause of cancer-related mortality worldwide. Somatic mutations, including large structural variants, are important biomarkers in lung cancer for selecting targeted therapy. Genomic studies in lung cancer have been conducted using short-read sequencing. Emerging long-read sequencing technologies are a promising alternative to study somatic structural variants, however there is no current consensus on how to process data and call somatic events. In this study, we preformed whole genome sequencing of lung cancer and matched non-tumour samples using long and short read sequencing to comprehensively benchmark three sequence aligners and six structural variant callers comprised of four generic callers (Sniffles2, cuteSV, SVIM and DELLY in generic mode) and two somatic callers (nanomonsv and DELLY in somatic modes).
Results: Different combinations of aligners and variant callers influenced somatic structural variant detection. The choice of caller had a significant influence on somatic structural variant detection in terms of variant type, size, sensitivity, and accuracy. The performance of each variant caller was assessed by comparing to somatic structural variants identified by short read sequencing. When compared to somatic structural variants detected with short read sequencing, more events were detected with long read sequencing. The mean recall of somatic variant events identified by short read sequencing was higher for the somatic callers (64%) than generic callers (52%), with nanomonsv (69%) showing the best performance.
Conclusion: Long read sequencing can identify somatic structural variants. The longer reads have the potential to improve our understanding of cancer development and inform personalized cancer treatment.
| EGAS00001007819 | EGA
Project description:Cleavage Under Targets and Release Using Nuclease (CUT&RUN) has rapidly gained prominence as an effective approach for mapping protein-DNA interactions, especially histone modifications, offering substantial improvements over conventional chromatin immunoprecipitation sequencing (ChIP-seq). However, the effectiveness of this technique is contingent upon accurate peak identification, necessitating the use of optimal peak calling methods tailored to the unique characteristics of CUT&RUN data. Here, we benchmark four prominent peak calling tools - MACS2, SEACR, GoPeaks, and LanceOtron - evaluating their performance in identifying peaks from CUT&RUN datasets. Our analysis utilizes in-house data of three histone marks (H3K4me3, H3K27ac, and H3K27me3) from mouse brain tissue, as well as samples from the 4DNucleome database. We systematically assess these tools based on parameters such as the number of peaks called, peak length distribution, signal enrichment, and reproducibility across biological replicates. Our findings reveal substantial variability in peak calling efficacy, with each method demonstrating distinct strengths in sensitivity, precision, and applicability depending on the histone mark in question. These insights provide a comprehensive evaluation that will assist in selecting the most suitable peak caller for high-confidence identification of regions of interest in CUT&RUN experiments, ultimately enhancing the study of chromatin dynamics and transcriptional regulation.
2025-06-02 | GSE282809 | GEO