Ontology highlight
ABSTRACT:
PROVIDER: PRJNA590866 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:We evaluated whether targeted next-generation sequencing (NGS) using the Ion Torrent Personal Genome Sequencer of cfDNA could identify prognostic or predictive factors for overall survival (OS) or progression free survival (PFS) within a large cohort of patients with advanced lung adenocarcinoma enrolled in the GALAXY-1 trial.
2018-01-10 | E-MTAB-5749 | biostudies-arrayexpress
Project description:Using miRDeep2 and a custom NGS data analysis workflow to annotate and quantify isomiRs in normal and neoplastic colorectal tissues.
2018-05-10 | GSE89974 | GEO
Project description:Mitogenomes generated using Galaxy
| PRJEB80875 | ENA
Project description:ASS1 Variant Calling
| PRJEB91359 | ENA
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:We sequenced and analyzed the genome of a highly inbred miniature Chinese pig strain, the Banna Minipig Inbred Line (BMI). we conducted whole genome screening using next generation sequencing (NGS) technology and performed SNP calling using Sus Scrofa genome assembly Sscrofa11.1.
2020-12-31 | GSE157688 | GEO
Project description:Metaproteomics characterizes proteins expressed by microorganism communities (microbiome) present in environmental samples or a host organism (e.g. human), revealing insights into the molecular functions conferred by these communities. Compared to conventional proteomics, metaproteomics presents unique data analysis challenges, including the use large protein databases derived from hundreds of organisms, as well as numerous processing steps to ensure data quality. This data analysis complexity limits the use of metaproteomics for many researchers. In response, we have developed an accessible and flexible metaproteomics workflow within the Galaxy bioinformatics framework. Via analysis of human oral tissue exudate samples, we have established a modular Galaxy-based workflow that automates a reduction method for searching large sequence databases, enabling comprehensive identification of host proteins (human) as well as meta-proteins from the non-host organisms. Downstream, automated processing steps enable BLASTP analysis and evaluation/visualization of peptide sequence match quality, maximizing confidence in results. Outputted results are compatible with tools for taxonomic and functional characterization (e.g. Unipept, MEGAN5). Galaxy also allows for the sharing of complete workflows with others, promoting reproducibility and also providing a template for further modification and improvement. Our results provide a blueprint for establishing Galaxy as a solution for metaproteomic data analysis.
2015-07-02 | PXD001655 | Pride