ReadZS detects cell type-specific and developmentally regulated RNA processing programs in single-cell RNA-seq

Meyer, Elisabeth; Chaung, Kaitlin; Dehghannasiri, Roozbeh; Salzman, Julia

doi:10.1186/s13059-022-02795-8

Cited by 7 publications

(7 citation statements)

References 62 publications

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“…We utilize a similar procedure to that for generating the initial p-values on the target x sample count matrices, selecting 50 pairs of random c and f, and taking the minimum p-value across these random c and f after applying Bonferroni correction. The analysis techniques are identical to those used in the original NOMAD paper (Meyer et al 2022). We additionally utilize alternating maximization-based c and f, where p-values are derived using a sample splitting approach, recently derived in (Bharav et al, 2022).…”

Section: Methodsmentioning

confidence: 99%

Unsupervised reference-free inference reveals unrecognized regulated transcriptomic complexity in human single cells

Dehghannasiri

Henderson

Bierman

et al. 2022

Preprint

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Myriad mechanisms diversify the sequence content of eukaryotic transcripts at the DNA and RNA level with profound functional consequences. Examples include diversity generated by RNA splicing and V(D)J recombination. Today, these and other events are detected with fragmented bioinformatic tools that require predefining a form of transcript diversification; moreover, they rely on alignment to a necessarily incomplete reference genome, filtering out unaligned sequences which can be among the most interesting. Each of these steps introduces blindspots for discovery. Here, we develop NOMAD+, a new analytic method that performs unified, reference-free statistical inference directly on raw sequencing reads, extending the core NOMAD algorithm to include a micro-assembly and interpretation framework. NOMAD+ discovers broad and new examples of transcript diversification in single cells, bypassing genome alignment and without requiring cell type metadata and impossible with current algorithms. In 10,326 primary human single cells in 19 tissues profiled with SmartSeq2, NOMAD+ discovers a set of splicing and histone regulators with highly conserved intronic regions that are themselves targets of complex splicing regulation and unreported transcript diversity in the heat shock protein HSP90AA1. NOMAD+ simultaneously discovers diversification in centromeric RNA expression, V(D)J recombination, RNA editing, and repeat expansions missed by or impossible to measure with existing bioinformatic methods. NOMAD+ is a unified, highly efficient algorithm enabling unbiased discovery of an unprecedented breadth of RNA regulation and diversification in single cells through a new paradigm to analyze the transcriptome.

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Section: Methodsmentioning

confidence: 99%

Unsupervised reference-free inference reveals unrecognized regulated transcriptomic complexity in human single cells

Dehghannasiri

Henderson

Bierman

et al. 2022

Preprint

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“…We applied the SpliZ and ReadZS, recently-developed methods to identify splicing differences and UTR length differences in single-cell data respectively (Meyer et al, 2022; Olivieri et al, 2022), to identify spatially-regulated RNA processing changes in the mouse brain and kidney. Intuitively, the ReadZS quantifies the average location of read build-up in discrete genomic windows (5000 bp-length continuous regions of the genome) in each spatial cell, while the SpliZ quantifies the deviation of the ranked length of introns in a given cell from the population average for that gene (lower values indicate shorter introns, higher values indicate longer introns).…”

Section: Resultsmentioning

confidence: 99%

Section: Data Availabilitymentioning

confidence: 99%

“…The ReadZS pipeline (https://github.com/salzman-lab/ReadZS) was run separately on each dataset with ontologyCols = "pixquant" (so differential RNA processing is determined based on quantiled pixel value of the image, though these differential RNA processing calls are not used), and otherwise default arguments (Meyer et al, 2022). This treats each Visium spot as a "separate cell" and otherwise follows the logic of the original ReadZS paper (Meyer et al, 2022). An example config file is available here: https://github.com/juliaolivieri/visium_analysis/blob/main/nextflow_inputs/visium_readzs.config, an example sample sheet is available here: https://github.com/juliaolivieri/visium_analysis/blob/main/nextflow_inputs/samplesheet_readzs.c sv, and an example bash script is available here: https://github.com/juliaolivieri/visium_analysis/blob/main/nextflow_inputs/run_readzs.sh.…”

Section: Data Availabilitymentioning

confidence: 99%

“…In this work we apply the SpliZ (Olivieri et al, 2022) and ReadZS (Meyer et al, 2022) methods, developed to analyze alternative splicing and 3’ UTR usage, in scRNA-seq data, to study spatial regulation of RNA processing directly from ST data for the first time. Because both droplet-based scRNA-seq and ST technologies suffer from similar challenges due to sparsity and 3’ bias, these methods are directly applicable to ST data.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of RNA processing directly from spatial transcriptomics data reveals previously unknown regulation

Olivieri¹,

Salzman

2023

Preprint

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Technical advances have led to an explosion in the amount of biological data available in recent years, especially in the field of RNA sequencing. Specifically, spatial transcriptomics (ST) datasets, which allow each RNA molecule to be mapped to the 2D location it originated from within a tissue, have become readily available. Due to computational challenges, ST data has rarely been used to study RNA processing such as splicing or differential UTR usage. We apply the ReadZS and the SpliZ, methods developed to analyze RNA process in scRNA-seq data, to analyze spatial localization of RNA processing directly from ST data for the first time. Using Moran's I metric for spatial autocorrelation, we identify genes with spatially regulated RNA processing in the mouse brain and kidney, re-discovering known spatial regulation in Myl6 and identifying previously-unknown spatial regulation in genes such as Rps24, Gng13, Slc8a1, Gpm6a, Gpx3, ActB, Rps8, and S100A9. The rich set of discoveries made here from commonly used reference datasets provides a small taste of what can be learned by applying this technique more broadly to the large quantity of Visium data currently being created.

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A Survey on Methods for Predicting Polyadenylation Sites from DNA Sequences, Bulk RNA-Seq, and Single-Cell RNA-Seq

Fraser

Lian

et al. 2022

Genomics, Proteomics &Amp; Bioinformatics

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Alternative polyadenylation (APA) plays important roles in modulating mRNA stability, translation, and subcellular localization, and contributes extensively to shaping eukaryotic transcriptome complexity and proteome diversity. Identification of poly(A) sites (pAs) on a genome-wide scale is a critical step toward understanding the underlying mechanism of APA-mediated gene regulation. A number of established computational tools have been proposed to predict pAs from diverse genomic data. Here we provided an exhaustive overview of computational approaches for predicting pAs from DNA sequences, bulk RNA sequencing (RNA-seq) data, and single-cell RNA sequencing (scRNA-seq) data. Particularly, we examined several representative tools using bulk RNA-seq and scRNA-seq data from peripheral blood mononuclear cells and put forward operable suggestions on how to assess the reliability of pAs predicted by different tools. We also proposed practical guidelines on choosing appropriate methods applicable to diverse scenarios. Moreover, we discussed in depth the challenges in improving the performance of pA prediction and benchmarking different methods. Additionally, we highlighted outstanding challenges and opportunities using new machine learning and integrative multi-omics techniques, and provided our perspective on how computational methodologies might evolve in the future for non-3′ untranslated region, tissue-specific, cross-species, and single-cell pA prediction.

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ReadZS detects cell type-specific and developmentally regulated RNA processing programs in single-cell RNA-seq

Cited by 7 publications

References 62 publications

Unsupervised reference-free inference reveals unrecognized regulated transcriptomic complexity in human single cells

Unsupervised reference-free inference reveals unrecognized regulated transcriptomic complexity in human single cells

Analysis of RNA processing directly from spatial transcriptomics data reveals previously unknown regulation

A Survey on Methods for Predicting Polyadenylation Sites from DNA Sequences, Bulk RNA-Seq, and Single-Cell RNA-Seq

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