A workflow for simplified analysis of ATAC-cap-seq data in R

Bibliometric review of ATAC-Seq and its application in gene expression

With recent advances in high-throughput next-generation sequencing, it is possible to describe the regulation and expression of genes at multiple levels. An assay for transposase-accessible chromatin using sequencing (ATAC-seq), which uses Tn5 transposase to sequence protein-free binding regions of the genome, can be combined with chromatin immunoprecipitation coupled with deep sequencing (ChIP-seq) and ribonucleic acid sequencing (RNA-seq) to provide a detailed description of gene expression. Here, we reviewed the literature on ATAC-seq and described the characteristics of ATAC-seq publications. We then briefly introduced the principles of RNA-seq, ChIP-seq and ATAC-seq, focusing on the main features of the techniques. We built a phylogenetic tree from species that had been previously studied by using ATAC-seq. Studies of Mus musculus and Homo sapiens account for approximately 90% of the total ATAC-seq data, while other species are still in the process of accumulating data. We summarized the findings from human diseases and other species, illustrating the cutting-edge discoveries and the role of multi-omics data analysis in current research. Moreover, we collected and compared ATAC-seq analysis pipelines, which allowed biological researchers who lack programming skills to better analyze and explore ATAC-seq data. Through this review, it is clear that multi-omics analysis and single-cell sequencing technology will become the mainstream approach in future research.

High-resolution expression profiling of selected gene sets during plant immune activation

The plant immune system involves detection of pathogens via both cell-surface and intracellular receptors. Both receptor classes can induce transcriptional reprogramming that elevates disease resistance. To assess differential gene expression during plant immunity, we developed and deployed quantitative sequence capture (CAP-I). We designed and synthesized biotinylated single-strand RNA bait libraries targeted to a subset of defense genes, and generated sequence capture data from 99 RNA-seq libraries. We built a data processing pipeline to quantify the RNA-CAP-I-seq data, and visualize differential gene expression. Sequence capture in combination with quantitative RNA-seq enabled cost-effective assessment of the expression profile of a specified subset of genes. Quantitative sequence capture is not limited to RNA-seq or any specific organism and can potentially be incorporated into automated platforms for high-throughput sequencing.

Single-nucleus chromatin accessibility reveals intratumoral epigenetic heterogeneity in IDH1 mutant gliomas

The presence of genome-wide DNA hypermethylation is a hallmark of lower grade gliomas (LGG) with isocitrate dehydrogenase (IDH) mutations. Further molecular classification of IDH mutant gliomas is defined by the presence (IDHmut-codel) or absence (IDHmut-noncodel) of hemizygous codeletion of chromosome arms 1p and 19q. Despite the DNA hypermethylation seen in bulk tumors, intra-tumoral heterogeneity at the epigenetic level has not been thoroughly analyzed. To address this question, we performed the first epigenetic profiling of single cells in a cohort of 5 gliomas with IDH1 mutation using single nucleus Assay for Transposase-Accessible Chromatin with high-throughput sequencing (snATAC-seq). Using the Fluidigm HT IFC microfluidics platform, we generated chromatin accessibility maps from 336 individual nuclei, and identified variable promoter accessibility of non-coding RNAs in LGGs. Interestingly, local chromatin structures of several non-coding RNAs are significant factors that contribute to heterogeneity, and show increased promoter accessibility in IDHmut-noncodel samples. As an example for clinical significance of this result, we identify CYTOR as a poor prognosis factor in gliomas with IDH mutation. Open chromatin assay points to differential accessibility of non-coding RNAs as an important source of epigenetic heterogeneity within individual tumors and between molecular subgroups. Rare populations of nuclei that resemble either IDH mutant molecular group co-exist within IDHmut-noncodel and IDHmut-codel groups, and along with non-coding RNAs may be an important issue to consider for future studies, as they may help guide predict treatment response and relapse.

A web-based explorer for the data is available at shiny.turcanlab.org.

A workflow for simplified analysis of ATAC-cap-seq data in R

Background

Assay for Transposase-Accessible Chromatin (ATAC)-cap-seq is a high-throughput sequencing method that combines ATAC-seq with targeted nucleic acid enrichment of precipitated DNA fragments. There are increased analytical difficulties arising from working with a set of regions of interest that may be small in number and biologically dependent. Common statistical pipelines for RNA sequencing might be assumed to apply but can give misleading results on ATAC-cap-seq data. A tool is needed to allow a nonspecialist user to quickly and easily summarize data and apply sensible and effective normalization and analysis.

Results

We developed atacR to allow a user to easily analyze their ATAC enrichment experiment. It provides comprehensive summary functions and diagnostic plots for studying enriched tag abundance. Application of between-sample normalization is made straightforward. Functions for normalizing based on user-defined control regions, whole library size, and regions selected from the least variable regions in a dataset are provided. Three methods for detecting differential abundance of tags from enriched methods are provided, including bootstrap t, Bayes factor, and a wrapped version of the standard exact test in the edgeR package. We compared the precision, recall, and F-score of each detection method on resampled datasets at varying replicate, significance threshold, and genes changed and found that the Bayes factor method had the greatest overall detection power, though edgeR was slightly stronger in simulations with lower numbers of genes changed.

Conclusions

Our package allows a nonspecialist user to easily and effectively apply methods appropriate to the analysis of ATAC-cap-seq in a reproducible manner. The package is implemented in pure R and is fully interoperable with common workflows in Bioconductor.