NanoPARE: parallel analysis of RNA 5′ ends from low-input RNA

Abstract

Diverse RNA 5′ ends are generated through both transcriptional and post-transcriptional processes. These important modes of gene regulation often vary across cell types and can contribute to the diversification of transcriptomes and thus cellular differentiation. Therefore, the identification of primary and processed 5′ ends of RNAs is important for their functional characterization. Methods have been developed to profile either RNA 5′ ends from primary transcripts or the products of RNA degradation genome-wide. However, these approaches either require high amounts of starting RNA or are performed in the absence of paired gene-body mRNA-seq data. This limits current efforts in RNA 5′ end annotation to whole tissues and can prevent accurate RNA 5′ end classification due to biases in the data sets. To enable the accurate identification and precise classification of RNA 5′ ends from standard and low-input RNA, we developed a next-generation sequencing-based method called nanoPARE and associated software. By integrating RNA 5′ end information from nanoPARE with gene-body mRNA-seq data from the same RNA sample, our method enables the identification of transcription start sites at single-nucleotide resolution from single-cell levels of total RNA, as well as small RNA-mediated cleavage events from at least 10,000-fold less total RNA compared to conventional approaches. NanoPARE can therefore be used to accurately profile transcription start sites, noncapped RNA 5′ ends, and small RNA targeting events from individual tissue types. As a proof-of-principle, we utilized nanoPARE to improve Arabidopsis thaliana RNA 5′ end annotations and quantify microRNA-mediated cleavage events across five different flower tissues.

Characterization of Arabidopsis thaliana promoter bidirectionality and antisense RNAs by depletion of nuclear RNA decay enzymes

“…Overall, these results indicate that CAGE, PEAT-seq and nanoPARE are the most accurate TSS sets, where PEAT-seq likely has lower sensitivity, followed by annotated TAIR10 TSSs. In contrast, ARAPORT11 TSSs are misplaced by 128 bp upstream on average, possibly due to the overestimation of transcript lengths, as suggested previously (Schon et al, 2018) . Due to the higher agreement of CAGE, and other 5'-end sets, with TAIR10 annotated TSSs vs.…”

“…Because we found a discrepancy between ARAPORT11 and TAIR10 TSS annotations, it was important to assess which of these TSS annotations is the most accurate, in particular since our CAGE data were more supportive of the TAIR10 annotation ( Figure 1E-F ). To do this, we also considered two previous genome-wide TSS data sets: paired-end analysis of transcription sequencing (PEAT-seq) using roots (Morton et al, 2014) , and parallel analysis of RNA 5'-ends (nanoPARE) using flower buds (Schon et al, 2018) . The majority (62%) of TCs/TSSs was supported by at least two methods, where nanoPARE had the highest fraction of called TCs/TSSs without support from the other methods (25%), compared to CAGE (12%) and PEAT (1%) ( Figure 3A , see Methods).…”

MicroRNA Dynamics and Functions DuringArabidopsisEmbryogenesis

“…We previously developed a method called nanoPARE to enable the confident identification of miRNA-mediated target RNA cleavage products from low-input RNA (Schon et al, 2018) . To identify embryonic miRNA targets, we therefore generated nanoPARE libraries from the same eight stages of embryogenesis used for miRNA profiling in biological triplicates.…”

“…The nanoPARE datasets and target predictions for 164 miRNAs detected ≥1 RPM in ≥1 embryonic stage were used as input for the EndCut software (Schon et al, 2018) and 115 significant individual target transcript cleavage sites in developing embryos were identified in ≥1 library (Benjamini-Hochberg adjusted P -values < 0.05) (Table S3). These 115 target sites included 59 sites that were identified in ≥2 biological replicates from ≥1 developmental stage (i.e.…”

“…Therefore, abnormal cell division patterns in early Arabidopsis embryos can be screened for upon disrupting miRNA functions in order to test whether the miRNA activities are required for morphogenesis, and thus yield insights into the molecular basis of the corresponding patterning events. In this study, we optimized a low-input small RNA sequencing (sRNA-seq) method to generate profiles of hundreds of miRNAs and used the recently developed nanoPARE approach (Schon et al, 2018) to identify corresponding target transcripts throughout embryogenesis. We found that miRNAs dynamically cleave and repress at least 59 transcripts including 30 encoding transcription factors belonging to eight different families.…”