ATAC-seq with unique molecular identifiers improves quantification and footprinting

Zhu, Tao; Liao, Keyan; Zhou, Rongfang; Xia, Chen; Xie, Weibo

doi:10.1038/s42003-020-01403-4

Cited by 25 publications

(23 citation statements)

References 39 publications

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“…The accuracy of datasets with a low abundance of RNA was significantly improved ( Yu F. et al, 2018 ). UMI-ATAC-seq technique was integrated into standard ATAC-seq procedures, which can rescue about 20% of reads that are mistaken as PCR duplicates in standard ATAC-seq ( Zhu T. et al, 2020 ). In this study, we counted molecules by the total number of distinct UMIs to quantitatively assess miRNA-seq data, which provided accurate expression patterns of miRNA response to salt stress in cotton.…”

Section: Discussionmentioning

confidence: 99%

“…The UMI method begins with a reverse-transcription reaction using a primer designed with an anchored polyT and a unique barcode, which enable sequencing reads to be assigned to individual transcript molecules and thus the removal of amplification noise and biases. The quantitative precision of this method is thus improved, especially at low molecule counts ( Islam et al, 2011 ), and UMI method was widely used for high-accuracy ( Zhu T. et al, 2020 ; Karst et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

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Integration of mRNA and miRNA Analysis Reveals the Molecular Mechanism of Cotton Response to Salt Stress

et al. 2021

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To identify the regulatory network of known and novel microRNAs (miRNAs) and their targets responding to salt stress, a combined analysis of mRNA libraries, small RNA libraries, and degradome libraries were performed. In this study, we used unique molecular identifiers (UMIs), which are more sensitive, accurate, and reproducible than traditional methods of sequencing, to quantify the number of molecules and correct for amplification bias. We identified a total of 312 cotton miRNAs using seedlings at 0, 1, 3, and 6 h after NaCl treatment, including 80 known ghr-miRNAs and 232 novel miRNAs and found 155 miRNAs that displayed significant differential expression under salt stress. Among them, fifty-nine differentially expressed miRNAs were simultaneously induced in two or three tissues, while 66, 11, and 19 were specifically expressed in the roots, leaves, and stems, respectively. It is indicated there were different populations of miRNAs against salt stress in roots, leaves and stems. 399 candidate targets of salt-induced miRNAs showed significant differential expression before and after salt treatment, and 72 targets of 25 miRNAs were verified by degradome sequencing data. Furthermore, the regulatory relationship of miRNA-target gene was validated experimentally via 5′RLM-RACE, proving our data reliability. Gene ontology and KEGG pathway analysis found that salt-responsive miRNA targets among the differentially expressed genes were significantly enriched, and mainly involved in response to the stimulus process and the plant hormone signal transduction pathway. Furthermore, the expression levels of newly identified miRNA mir1 and known miRNAs miR390 and miR393 gradually decreased when subjected to continuous salt stress, while overexpression of these miRNAs both increased sensitivity to salt stress. Those newly identified miRNAs and mRNA pairs were conducive to genetic engineering and better understanding the mechanisms responding to salt stress in cotton.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Integration of mRNA and miRNA Analysis Reveals the Molecular Mechanism of Cotton Response to Salt Stress

et al. 2021

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“…Minghui63 ( O. sativa-MH ), O. sativa cv. Zhenshan97 ( O. sativa-ZS ), Setaria italica , Sorghum bicolor and Zea mays were generated from our previously established protocol ( 25 ) and deposited in NCBI with SRA accession SRP308654.…”

Section: Methodsmentioning

confidence: 99%

PlantDeepSEA, a deep learning-based web service to predict the regulatory effects of genomic variants in plants

Zhao

Liu

et al. 2021

Nucleic Acids Research

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Characterizing regulatory effects of genomic variants in plants remains a challenge. Although several tools based on deep-learning models and large-scale chromatin-profiling data have been available to predict regulatory elements and variant effects, no dedicated tools or web services have been reported in plants. Here, we present PlantDeepSEA as a deep learning-based web service to predict regulatory effects of genomic variants in multiple tissues of six plant species (including four crops). PlantDeepSEA provides two main functions. One is called Variant Effector, which aims to predict the effects of sequence variants on chromatin accessibility. Another is Sequence Profiler, a utility that performs ‘in silico saturated mutagenesis’ analysis to discover high-impact sites (e.g., cis-regulatory elements) within a sequence. When validated on independent test sets, the area under receiver operating characteristic curve of deep learning models in PlantDeepSEA ranges from 0.93 to 0.99. We demonstrate the usability of the web service with two examples. PlantDeepSEA could help to prioritize regulatory causal variants and might improve our understanding of their mechanisms of action in different tissues in plants. PlantDeepSEA is available at http://plantdeepsea.ncpgr.cn/.

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“…However, this can induce further biases. By making use of UMIs, one study was able to quantify how many Tn5 insertions may independently occur at the same position [48]. This showed that up to 20% of the reads discarded based on their alignment positions, a standard method step in many protocols, were done so erroneously and actually arose due to independent Tn5 insertion events.…”

Section: Scatac-seqmentioning

confidence: 99%

Discovering Cellular Mitochondrial Heteroplasmy Heterogeneity with Single Cell RNA and ATAC Sequencing

Marshall

Jones

2021

Biology

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Next-generation sequencing technologies have revolutionised the study of biological systems by enabling the examination of a broad range of tissues. Its application to single-cell genomics has generated a dynamic and evolving field with a vast amount of research highlighting heterogeneity in transcriptional, genetic and epigenomic state between cells. However, compared to these aspects of cellular heterogeneity, relatively little has been gleaned from single-cell datasets regarding cellular mitochondrial heterogeneity. Single-cell sequencing techniques can provide coverage of the mitochondrial genome which allows researchers to probe heteroplasmies at the level of the single cell, and observe interactions with cellular function. In this review, we give an overview of two popular single-cell modalities—single-cell RNA sequencing and single-cell ATAC sequencing—whose throughput and widespread usage offers researchers the chance to probe heteroplasmy combined with cell state in detailed resolution across thousands of cells. After summarising these technologies in the context of mitochondrial research, we give an overview of recent methods which have used these approaches for discovering mitochondrial heterogeneity. We conclude by highlighting current limitations of these approaches and open problems for future consideration.

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ATAC-seq with unique molecular identifiers improves quantification and footprinting

Cited by 25 publications

References 39 publications

Integration of mRNA and miRNA Analysis Reveals the Molecular Mechanism of Cotton Response to Salt Stress

Integration of mRNA and miRNA Analysis Reveals the Molecular Mechanism of Cotton Response to Salt Stress

PlantDeepSEA, a deep learning-based web service to predict the regulatory effects of genomic variants in plants

Discovering Cellular Mitochondrial Heteroplasmy Heterogeneity with Single Cell RNA and ATAC Sequencing

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