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Improved spike-in normalization clarifies the relationship between active histone modifications and transcription

ABSTRACT: Improved spike-in normalization clarifies the relationship between active histone modifications and transcription

PROVIDER: PRJNA1217867 | ENA |

REPOSITORIES: ENA

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Improved spike-in normalization clarifies the relationship between active histone modifications and transcription

Project description:Spike-in normalization enables quantitative analysis of ChIP-sequencing (ChIP-seq) signal. Here we introduce a novel robust dual-spike-in normalization approach for ChIP-seq (ChIP-wrangler) and revisit recent claims that active histone marks are dependent on transcription. Concerned that previous studies improperly used spike-in normalization to arrive at their conclusions, we used ChIP-wrangler to show that acute depletion of RNA polymerase II has only a modest impact on the levels of H3K4me3 and H3K27ac. In line with other studies, our results provide proof that the maintenance of histone acetylation is not merely a consequence of ongoing transcription. Our innovative ChIP-seq normalization approach provides increased rigor and “guardrails” for successful spike-in normalization, and as applied here refines the understanding of the intricate crosstalk between RNA polymerase II activity and histone marks associated with transcription

2025-02-05 | GSE286875 | GEO

"The Wild West of Spike-in Normalization": Benchmarking the ChIP-seq spike-in normalization method

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

"The Wild West of Spike-in Normalization": Benchmarking the ChIP-seq spike-in normalization method

Project description:"The Wild West of Spike-in Normalization": Benchmarking the ChIP-seq spike-in normalization method

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Novel small RNA spike-in oligonucleotides enable absolute normalization of small RNA-Seq data

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

Assessing the reliability of spike-in normalization for analyses of single-cell RNA sequencing data

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

Assessing the reliability of spike-in normalization for analyses of single-cell RNA sequencing data

Project description:Assessing the reliability of spike-in normalization for analyses of single-cell RNA sequencing data

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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.

2014-10-30 | GSE60104 | GEO

Global ChIP-seq Normalization Reveals Epigenome Modulations

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

Epitranscriptome analysis of NAD-capped RNA by spike-in-based normalization and prediction of chronological age

Project description:Epitranscriptome analysis of NAD-capped RNA by spike-in-based normalization and prediction of chronological age

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

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2025-02-02 | GSE288548 | GEO

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