Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Highly quantitative measurement of differential protein-genome binding with PerCell chromatin sequencing

ABSTRACT: Quantitative comparison of ChIP-seq profiling between experimental conditions or samples remains technically challenging for the epigenetics field. Here, we report a strategy combining the use of well-defined cellular spike-in ratios of orthologous species’ chromatin and a bioinformatic analysis pipeline to facilitate highly quantitative comparisons of 2D chromatin sequencing across experimental conditions. We find that the PerCell methodology results in efficient and consistent levels of spike-in vs. experimental genomic reads. We demonstrate use of the method and pipeline to enable quantitative, internally normalized chromatin sequencing on zebrafish embryos and human cancer cells. Overall, we propose the PerCell method to enable cross-species comparative epigenomics and promote uniformity of data analyses and sharing across labs.

ORGANISM(S): Homo sapiens

PROVIDER: GSE271986 | GEO | 2025/05/19

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

ChIP-seq data processing and relative and quantitative signal normalization for Saccharomyces cerevisiae

Project description:Chromatin immunoprecipitation with high-throughput sequencing (ChIP-seq) is a widely used technique for genome-wide analyses of protein-DNA interactions. This protocol provides a guide to ChIP-seq data processing in Saccharomyces cerevisiae, with a focus on signal normalization to address data biases and enable meaningful comparisons within and between samples. Designed for researchers with minimal bioinformatics experience, it includes practical overviews and refers to scripting examples for key tasks, such as configuring computational environments, trimming and aligning reads, processing alignments, and visualizing signals. This protocol employs the sans-spike-in method for quantitative ChIP-seq (siQ-ChIP) and normalized coverage for absolute and relative comparisons of ChIP-seq data, respectively. While spike-in normalization, which is semiquantitative, is addressed for context, siQ-ChIP and normalized coverage are recommended as mathematically rigorous and reliable alternatives.

2025-02-02 | GSE288548 | GEO

siqRNA-seq is a spike-in-independent technique for quantitative mapping of mRNA landscape

Project description:Normalization of RNA sequencing (RNA-seq) data for gene expression comparison is essential to ensure accurate gene expression quantification. It has been argued that samples with large differences in global expression level cannot be properly normalized without spike-in control RNAs, however, spike-in controls are expensive and not yet widely used. Here, we presented a spike-in independent quantitative RNA sequencing (siqRNA-seq) method, which uses reads from genome DNA as an internal reference to quantify gene expression level. We showed that siqRNA-seq profiles gene expression as traditional RNA-seq, but allows to identify different expression genes between samples with distinct mRNA content. We also showed siqRNA-seq enable us to assess the copy number of mRNA per cell without counting cells and adding spike-ins. Thus, siqRNA-seq provides a convenient and versatile means to quantitatively profile the mRNA landscape in cells.

2024-04-08 | GSE223145 | GEO

S100A4 mRNA-protein relationship uncovered by measurement noise reduction

Project description:Intrinsic biological fluctuation and/or measurement error can obscure the association of gene expression patterns between RNA and protein levels. Appropriate normalisation of reverse-transcription quantitative PCR (RT-qPCR) data can reduce technical noise in transcript measurement, thus uncovering such relationships. The accuracy of gene expression measurement is often challenged in the context of cancer due to its genetic instability and ‘splicing weakness’. Here we sequenced the poly(A) cancer transcriptome of canine osteosarcoma using mRNA-Seq. Expressed sequences were resolved at the level of two consecutive exons to enable the design of exon-border spanning RT-qPCR assays and ranked for stability based on the coefficient of variation (CV). Using the same template type for RT-qPCR validation, i.e. poly(A) RNA, avoided skewing of stability assessment by circular RNAs (circRNAs) and/or rRNA deregulation. The strength of the relationship between mRNA expression of the tumour marker S100A4 and its proportion score of quantitative immunochemistry (qIHC) was introduced as an experimental read-out to fine-tune the normalisation choice. Together with the essential logit transformation of qIHC scores, this approach reduced the noise of measurement as demonstrated by uncovering a highly significant, strong association between mRNA and protein expressions of S100A4 (Spearman's coefficient r = 0.72 (p = 0.006)).

2020-03-26 | GSE147550 | GEO

Global ChIP-seq Normalization Reveals Epigenome Modulations

Project description:Epigenomic profiling by ChIP-seq is a prevailing methodology used to investigate chromatin-based regulation in biological systems, such as human disease, yet the lack of an empirical methodology to normalize amongst experiments has limited the usefulness of this technique. Here we describe a “spike-in” normalization method that allows the quantitative comparison of histone modification status across cell populations using defined quantities of a reference epigenome. We demonstrate the utility of this method in measuring epigenomic changes following chemical perturbations and show how control normalization of ChIP-seq experiments enables discovery of disease-relevant changes in histone modification occupancy. ChIP-Seq of histone modifications H3K79me2 and H3K4me3 in human samples treated with EPZ-5676 with/without reference epigenome spike-in.

2014-10-30 | E-GEOD-60104 | biostudies-arrayexpress

Improved statistical analysis of budding yeast TAG microarrays

Project description:Yeast knockout collection TAG microarrays are an emergent platform for rapid, genome-wide functional characterization of yeast genes. We describe a method for analyzing two-color array data to efficiently represent differential knockout strain representation across two experimental conditions. Using a fully defined spike-in pool, we show that the sensitivity and specificity of this method exceed typical current approaches. Keywords: Saccharomyces cerevisiae, yeast, self_vs_self, spike-in pools

2005-09-02 | GSE2832 | GEO

A Method for High-throughput Production of Sequence-verified DNA Libraries and Strain Collections

Project description:The low costs of array-synthesized oligonucleotide libraries are empowering rapid advances in quantitative and synthetic biology. Unfortunately, high synthesis error rates, uneven representation, and lack of access to individual oligonucleotides limit the true potential of these libraries. We have developed a cost-effective method called Recombinase Directed Indexing (REDI) to address these problems. The method involves integration of a complex library into yeast, site-specific recombination to index (i.e. barcode) library DNA, and then next-generation sequencing to identify clones containing the DNA of interest. We used REDI to generate a molecular probe library (n = ~3,300) that exhibited >96% purity and remarkable uniformity (>95% of probes were within 2-fold relative abundance of the median). Moreover, each sequence-verified probe was readily accessible. We also used REDI to rapidly create an arrayed collection of ~9,000 strains for CRISPR interference in yeast and demonstrate the utility of this collection for highly sensitive phenotypic screening. Our approach will enable a variety of applications requiring accurate, high-quality DNA libraries.

2017-02-14 | GSE81094 | GEO

Global ChIP-seq Normalization Reveals Epigenome Modulations

2014-10-30 | GSE60104 | GEO

Genome wide identification of acetyl-CoA regulated loci

Project description:In order to identify regions of the genome that display quantitative changes in acetyl-CoA, we utilized an established “spike-in” normalization method that allows the quantitative comparison of histone modifications across cell populations using defined quantities of a reference epigenome. We found that some regions of the genome are more sensitive to changes in acetyl-CoA compared to others.

2018-12-20 | GSE109340 | GEO

SNP-ChIP: a versatile and tag-free method to quantify changes in protein binding across the genome

Project description:SNP-ChIP is a novel method leveraging small-scale intra-species genetic polymorphisms, mainly SNPs, to allow quantitative spike-in normalization of ChIP-seq results. SNP-ChIP uses a different strain of the same organism as the spike-in material and can be applied to any organism for which genome assemblies are available for two different strains or individuals with sufficient genetic diversity. This ensures antibody cross-reactivity and thus extends the applicability of the method beyond the small number of highly conserved proteins. It also ensures complete physiological coherence between the test and the spike-in cells. In this work we develop and validate the method using test cases from budding yeast meiosis. We use strains with ~0.7% genomic sequence divergence as test strain background and the spike-in strain, respectively. Sequencing reads are mapped to a hybrid genome, with naturally occurring sequence polymorphisms allowing assignment of most reads to one of the two genomes. By targeting the yeast chromosomal protein Red1, we show that SNP-ChIP reliably identifies previously reported changes in overall protein levels, irrespective of changes in binding distribution. We also show that SNP-ChIP is robust to wide changes in sequencing depth, as well as the amount of spike-in material. SNP-ChIP allowed discovery of novel regulators of global Red1 protein accumulation and is also shown to allow quantitative analysis of the DNA-damage associated histone modification gamma-H2AX. SNP-ChIP is a robust and versatile spike-in normalization method that can be used with any target against which a ChIP-grade antibody is available and for any organisms with sufficient intra-species diversity, including most model organisms as well as human cells. Grant ID: FY16-208 Grant title: Meiotic segregation of small chromosomes Funding source: March of Dimes Grantee name: Andreas Hochwagen, New York University, New York, NY, United States

2018-11-21 | GSE115092 | GEO

Improved cohesin HiChIP protocol and bioinformatic analysis for robust detection of chromatin loops and stripes

Project description:Chromosome Conformation Capture (3 C) methods, including Hi-C (a high-throughput variation of 3 C), detect pairwise interactions between DNA regions, enabling the reconstruction of chromatin architecture in the nucleus. HiChIP is a modification of the Hi-C experiment that includes a chromatin immunoprecipitation (ChIP) step, allowing genome-wide identification of chromatin contacts mediated by a protein of interest. In mammalian cells, cohesin protein complex is one of the major players in the establishment of chromatin loops. We present an improved cohesin HiChIP experimental protocol. Using comprehensive bioinformatic analysis, we show that a dual chromatin fixation method compared to the standard formaldehyde-only method, results in a substantially better signal-to-noise ratio, increased ChIP efficiency and improved detection of chromatin loops and architectural stripes. Additionally, we propose an automated pipeline called nf-HiChIP (https://github.com/SFGLab/hichip-nf-pipeline) for processing HiChIP samples starting from raw sequencing reads data and ending with a set of significant chromatin interactions (loops), which allows efficient and timely analysis of multiple samples in parallel, without requiring additional ChIP-seq experiments. Finally, using advanced approaches for biophysical modelling and stripe calling we generate accurate loop extrusion polymer models for a region of interest and provide a detailed picture of architectural stripes, respectively.

2025-03-14 | GSE266640 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data