"The Wild West of Spike-in Normalization": Benchmarking the ChIP-seq spike-in normalization method
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1144087 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Spike-in normalization is a powerful approach to assess global changes in data obtained from genomic mapping of DNA-associated proteins by methods such as ChIP-sequencing (ChIP-seq) or CUT&RUN. While multiple spike-in methods provide detailed documentation, the implementation of these approaches often omit critical quality control steps and veer from the established procedures. Here, we show that proper application of spike-in normalization can increase quantification accuracy across a spectrum of conditions. Next, we outline how misuse of spike-in approaches can create erroneous biological interpretations and provide guidelines to minimize pitfalls when applying this approach to normalize data from protein-DNA interaction results.
2024-08-05 | GSE273915 | GEO
Project description:Normalization of high-throughput small RNA sequencing (sRNA-Seq) data is required to compare sRNA levels across different samples. Commonly used relative normalization approaches can cause erroneous conclusions due to fluctuating small RNA populations between tissues. We developed a set of sRNA spike-in oligonucleotides (sRNA spike-ins) that enable absolute normalization of sRNA-Seq data across independent experiments, as well as the genome-wide estimation of sRNA:mRNA stoichiometries when used together with mRNA spike-in oligonucleotides.
2017-07-19 | GSE98553 | GEO
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.
2014-10-30 | GSE60104 | GEO
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
Project description:This study aims to assess the reliability of spike-in normalization for analyzing single-cell RNA sequencing data. This is done by performing mixture experiments where two different sets of spike-in RNA (ERCC and SIRV) are added separately to a single mouse cell (416B or trophoblast stem cells (TSCs)), followed by generation of sequencing libraries using a modified version of the Smart-seq2 protocol. The aim is to measure the variance of the log-ratio of the total counts between the two spike-in sets. This will quantify how precisely the spike-in RNA was added to each well. As a control, addition was also performed with a premixed solution of both spike-ins, to quantify the variability in the log-ratios due to the experimental protocol. The same data can also be used to measure the well-to-well variability in the differences in behaviour between the two spike-in sets. The data contain four batches of libraries (block), using different batches of cells that were processed and sequenced separately. Each batch contains libraries with all three types of spike-in addition (ERCC+SIRV, SIRV+ERCC or Premixed). The 416B cells also contain a CBFB-MYH11 oncogene, which is expressed in half of the cells (Induced) and silent in the other half (Control).
2017-05-31 | E-MTAB-5522 | biostudies-arrayexpress
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
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
Project description:Assessing the reliability of spike-in normalization for analyses of single-cell RNA sequencing data
| PRJEB20128 | ENA
Project description:Epitranscriptome analysis of NAD-capped RNA by spike-in-based normalization and prediction of chronological age
| PRJNA940858 | ENA
Project description:Profiling miRNA levels in cells with miRNA-microarrays is becoming a widely used technique. Although normalization methods for mRNA gene expression arrays are well established, miRNA array normalization has so far not been investigated in detail. In this study we investigate the impact of normalization on data generated with the Agilent miRNA array platform. Here, we developed a method to select non-changing miRNAs (“invariants”) and used them to compute linear regression normalization coefficients or Variance Stabilizing Normalization (VSN) parameters. We compared the invariant normalizations to normalization by, scaling, quantile and VSN with default parameters as well as to no normalization using samples with strong differential expression of miRNAs (heart-brain comparison) and samples where only few miRNAs are affected (p53 overexpression in SCC13 cells versus GFP vector control transfected cells). All normalization methods performed better than no normalization. Normalizations procedures based on the set of invariants and quantile were the most robust over all experimental conditions tested. Our method of invariant selection and normalization is not limited to Agilent miRNA arrays and can be applied to other datasets from one color miRNA microarray platforms, focused gene expression arrays and gene expression analysis using quantitative PCR. Keywords: miRNA profiling
2009-02-17 | GSE12085 | GEO