EV-sorting small RNA in normal human plasma-EV compared with EV-depleted plasma
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE278414 | GEO | 2025/05/09
REPOSITORIES: GEO
Similar Datasets
Project description:EV-sorting small RNA in normal human plasma-EV compared with EV-depleted plasma
| PRJNA1167218 | ENA
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:High-resolution methods such as 4C and Capture-C enable the study of chromatin loops such as those formed between promoters and enhancers or CTCF/cohesin binding sites. An important aspect of 4C/CapC analyses is the identification of robust peaks in the data for the identification of chromatin loops. Here we present an R package for the analysis of 4C/CapC data. We generated 4C data for 10 viewpoints in 2 tissues in triplicate to test our methods. We developed a non-parametric peak caller based on rank-products. Sampling analysis shows that not read depth but template quality is the most important determinant of success in 4C experiments. By performing peak calling on single experiments we show that the peak calling results are similar to the replicate experiments, but that false positive rates are significantly reduced by performing replicates.
2018-05-14 | GSE105177 | GEO
Project description:Invasive nature and pain caused to patients inhibit the routine use of tissue biopsy-based procedures for cancer diagnosis and surveillance. The analysis of extracellular vesicles (EVs) from biofluids have recently gained significant traction in the liquid biopsy field. EVs offer an essential “snapshot” of their precursor cells in real time and contain information-rich collection of nucleic acids, proteins, lipids, etc. The analysis of protein phosphorylation, as a direct marker of cellular signaling and disease progression, could be an important stepstone to successful liquid biopsy applications. Here, we introduce a rapid EV isolation method based on chemical affinity called EVtrap (Extracellular Vesicles Total Recovery and Purification) for EV phosphoproteomics analysis of human plasma. Incorporating EVtrap with high performance mass spectrometry (MS), we were able to identify over 16,000 unique peptides representing 2,238 unique EV proteins from just 5 μL plasma sample, including most known EV markers, with substantially higher recovery levels compared to ultracentrifugation. Most importantly, more than 5,500 unique phosphopeptides representing almost 1,600 phosphoproteins in EVs were identified using only 1 mL of plasma. Finally, we carried out quantitative EV phosphoproteomics analysis of plasma samples from patients diagnosed with chronic kidney disease or kidney cancer (RCC), identifying dozens of phosphoproteins capable of distinguishing disease states from healthy controls. The study demonstrates the potential feasibility of our robust analytical pipeline for cancer signaling monitoring by tracking plasma EV phosphorylation.
2020-06-15 | PXD017994 | Pride
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
Project description:Illumina 1M Omni Quad arrays were used to test mutation calling accuracy of qSNP tool (a mutation caller) Ilumina array genotypes with GenCal (GC score)>0.70 were used in the comparison of genotype calls using next generation sequencing data and qSNP (mutation caller)
2014-06-13 | GSE47904 | GEO
Project description:26 breast cancer patient serum EV derived RNA and 4 control serum EV derived RNA was sequenced to explore the diagnostic potential of EV. EV RNA was isolated by EXOQUICK® Exosome isolation and RNA purification kit for serum/plasma (System Biosciences,CA) and was reverse transcribed to make amplified cDNA by SMART seq v4 ultra-low input RNA kit (Takara Bio Inc, USA). A sequencing library was made using 1 ng of sheared cDNA using The Low Input Library Prep Kit v2 (Takara Bio Inc, USA). DNA unique Dual index kit was used to combine libraries for sequencing (Takara Bio Inc, USA). NOVASEQ 6000 was used for sequencing to generate 25 million paired end reads per sample.
2025-01-27 | GSE256523 | GEO
Project description:Illumina 1M Omni Quad arrays were used to test mutation calling accuracy of qSNP tool (a mutation caller) Ilumina array genotypes with GenCal (GC score)>0.70 were used in the comparison of genotype calls using next generation sequencing data and qSNP (mutation caller) 2 samples (control cell line and Melanoma cell line). This is the data for a validation step. contributor: Australian Pancreatic Cancer Genome Initiative
2014-06-13 | E-GEOD-47904 | biostudies-arrayexpress
Project description:Populus deltoides EV transcriptome - EV B3 leaf
| PRJNA520081 | ENA
Project description:Populus deltoides EV transcriptome - EV C1 leaf
| PRJNA520082 | ENA