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Normalization benchmark of ATAC-seq datasets shows the importance of accounting for GC-content effects.

ABSTRACT: The assay for transposase-accessible chromatin using sequencing (ATAC-seq) allows the study of epigenetic regulation of gene expression by assessing chromatin configuration for an entire genome. Despite its popularity, there have been limited studies investigating the analytical challenges related to ATAC-seq data, with most studies leveraging tools developed for bulk transcriptome sequencing. Here, we show that GC-content effects are omnipresent in ATAC-seq datasets. Since the GC-content effects are sample specific, they can bias downstream analyses such as clustering and differential accessibility analysis. We introduce a normalization method based on smooth-quantile normalization within GC-content bins and evaluate it together with 11 different normalization procedures on 8 public ATAC-seq datasets. Accounting for GC-content effects in the normalization is crucial for common downstream ATAC-seq data analyses, improving accuracy and interpretability. Through case studies, we show that exploratory data analysis is essential to guide the choice of an appropriate normalization method for a given dataset.

SUBMITTER: Van den Berge K

PROVIDER: S-EPMC9701614 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

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Normalization benchmark of ATAC-seq datasets shows the importance of accounting for GC-content effects.

Van den Berge Koen K Chou Hsin-Jung HJ Roux de Bézieux Hector H Street Kelly K Risso Davide D Ngai John J Dudoit Sandrine S

Cell reports methods 20221101 11

The assay for transposase-accessible chromatin using sequencing (ATAC-seq) allows the study of epigenetic regulation of gene expression by assessing chromatin configuration for an entire genome. Despite its popularity, there have been limited studies investigating the analytical challenges related to ATAC-seq data, with most studies leveraging tools developed for bulk transcriptome sequencing. Here, we show that GC-content effects are omnipresent in ATAC-seq datasets. Since the GC-content effect ...[more]

PMID: 36452861

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Project description:Meat quality is one of the most important economic traits in pig breeding and production, and intramuscular fat (IMF) content is the major factor in improving meat quality. The IMF deposition in pigs is influenced by transcriptional regulation, which is dependent on chromatin accessibility. However, how chromatin accessibility plays a regulatory role in IMF deposition in pigs has not been reported. Xidu black is a composite pig breed with excellent meat quality, which is an ideal research object of this study. In this study, we used the assay for transposase-accessible chromatin using sequencing (ATAC-seq) and RNA sequencing (RNA-seq) analysis to identify the accessible chromatin regions and key genes affecting IMF content in Xidu black pig breed with extremely high and low IMF content. First, we identified 21,960 differential accessible chromatin peaks and 297 differentially expressed genes. The motif analysis of differential peaks revealed several potential cis-regulatory elements containing binding sites for transcription factors with potential roles in fat deposition, including Mef2c, CEBP, Fra1, and AP-1. Then, by integrating the ATAC-seq and RNA-seq analysis results, we found 47 genes in the extremely high IMF (IMF_H) group compared with the extremely low IMF (IMF_L) group. For these genes, we observed a significant positive correlation between the differential gene expression and differential ATAC-seq signal (r 2 = 0.42). This suggests a causative relationship between chromatin remodeling and the resulting gene expression. We identified several candidate genes (PVALB, THRSP, HOXA9, EEPD1, HOXA10, and PDE4B) that might be associated with fat deposition. Through the PPI analysis, we found that PVALB gene was the top hub gene. In addition, some pathways that might regulate fat cell differentiation and lipid metabolism, such as the PI3K-Akt signaling pathway, MAPK signaling pathway, and calcium signaling pathway, were significantly enriched in the ATAC-seq and RNA-seq analysis. To the best of our knowledge, our study is the first to use ATAC-seq and RNA-seq to examine the mechanism of IMF deposition from a new perspective. Our results provide valuable information for understanding the regulation mechanism of IMF deposition and an important foundation for improving the quality of pork.

Project description:Genome-scale metabolic models (GEMs) cover the entire list of metabolic genes in an organism and associated reactions, in a tissue/condition non-specific manner. RNA-seq provides crucial information to make the GEMs condition-specific. Integrative Metabolic Analysis Tool (iMAT) and Integrative Network Inference for Tissues (INIT) are the two most popular algorithms to create condition-specific GEMs from human transcriptome data. The normalization method of choice for raw RNA-seq count data affects the model content produced by these algorithms and their predictive accuracy. However, a benchmark of the RNA-seq normalization methods on the performance of iMAT and INIT algorithms is missing in the literature. Another important phenomenon is covariates such as age and gender in a dataset, and they can affect the predictivity of analysis. In this study, we aimed to compare five different RNA-seq data normalization methods (TPM, FPKM, TMM, GeTMM, and RLE) and covariate adjusted versions of the normalized data by mapping them on a human GEM using the iMAT and INIT algorithms to generate personalized metabolic models. We used RNA-seq data for Alzheimer's disease (AD) and lung adenocarcinoma (LUAD) patients. The results demonstrated that RNA-seq data normalized by the RLE, TMM, or GeTMM methods enabled the production of condition-specific metabolic models with considerably low variability in terms of the number of active reactions compared to the within-sample normalization methods (FPKM, TPM). Using these models, we could more accurately capture the disease-associated genes (average accuracy of ~0.80 for AD and ~0.67 for LUAD) for the RLE, TMM, and GeTMM normalization methods. An increase in the accuracies was observed for all the methods when covariate adjustment was applied. We found a similar accuracy trend when we compared the metabolites of perturbed reactions to metabolome data for AD. Together, our benchmark study shows that the between-sample RNA-seq normalization methods reduce false positive predictions at the expense of missing some true positive genes when mapped on GEMs.

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Normalization benchmark of ATAC-seq datasets shows the importance of accounting for GC-content effects.

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Normalization benchmark of ATAC-seq datasets shows the importance of accounting for GC-content effects.

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