Transcript assembly improves expression quantification of transposable elements in single-cell RNA-seq data

AbstractTransposable elements (TEs) are an integral part of the host transcriptome. TE-containing noncoding RNAs (ncRNAs) exhibit considerable tissue specificity and play crucial roles during development, including stem cell maintenance and cell differentiation. Recent advances in single cell RNA-seq (scRNA-seq) revolutionized cell-type specific gene expression analysis. However, scRNA-seq quantification tools tailored for TEs are lacking, limiting our ability to dissect TE expression dynamics at single cell resolution. To address this issue, we established a TE expression quantification pipeline that is compatible with scRNA-seq data generated across multiple technology platforms. We constructed TE containing ncRNA references using bulk RNA-seq data and demonstrated that quantifying TE expression at the transcript level effectively reduces noise. As proof of principle, we applied this strategy to mouse embryonic stem cells and successfully captured the expression profile of endogenous retroviruses in single cells. We further expanded our analysis to scRNA-seq data from early stages of mouse embryogenesis. Our results illustrated the dynamic TE expression at pre-implantation stages and revealed 137 TE-containing ncRNA transcripts with substantial tissue specificity during gastrulation and early organogenesis.

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Exploratory bioinformatics analysis reveals importance of “junk” DNA in early embryo development

10.1101/079921 ◽

2016 ◽

Author(s):

Steven Xijin Ge

Keyword(s):

Gene Expression ◽

Stem Cells ◽

Transposable Elements ◽

Single Cell ◽

Exploratory Analysis ◽

List Type ◽

Dependent Manner ◽

Rna Seq ◽

Long Terminal Repeats ◽

Terminal Repeats

AbstractBackgroundInstead of testing predefined hypotheses, the goal of exploratory data analysis (EDA) is to find what data can tell us. Following this strategy, we re-analyzed a large body of genomic data to investigate how the early mouse embryos develop from fertilized eggs through a complex, poorly understood process.ResultsStarting with a single-cell RNA-seq dataset of 259 mouse embryonic cells from zygote to blastocyst stages, we reconstructed the temporal and spatial dynamics of gene expression. Our analyses revealed similarities in the expression patterns of regular genes and those of retrotransposons, and the enrichment of transposable elements in the promoters of corresponding genes. Long Terminal Repeats (LTRs) are associated with transient, strong induction of many nearby genes at the 2-4 cell stages, probably by providing binding sites for Obox and other homeobox factors. The presence of B1 and B2 SINEs (Short Interspersed Nuclear Elements) in promoters is highly correlated with broad upregulation of intracellular genes in a dosage-and distance-dependent manner. Such enhancer-like effects are also found for human Alu and bovine tRNA SINEs. Promoters for genes specifically expressed in embryonic stem cells (ESCs) are rich in B1 and B2 SINEs, but low in CpG islands.ConclusionsOur results provide evidence that transposable elements may play a significant role in establishing the expression landscape in early embryos and stem cells. This study also demonstrates that open-ended, exploratory analysis aimed at a broad understanding of a complex process can pinpoint specific mechanisms for further study.Major findingSingle-cell RNA-seq data enables estimation of retrotransposon expression during PDSimilar expression dynamics of retrotransposons and regular genes during PDLong terminal repeats may be essential for the 1st wave of gene expressionObox homeobox factors are possible regulators of PD, upstream of Zscan4SINE repeats predict expression of nearby genes in murine, human and bovine embryosExploratory analysis of large single-cell data pinpoints developmental pathways

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Cloud accelerated alignment and assembly of full-length single-cell RNA-seq data using Falco

BMC Genomics ◽

10.1186/s12864-019-6341-6 ◽

2019 ◽

Vol 20 (S10) ◽

Author(s):

Andrian Yang ◽

Abhinav Kishore ◽

Benjamin Phipps ◽

Joshua W. K. Ho

Keyword(s):

Single Cell ◽

Full Length ◽

Rna Seq ◽

Computing Environment ◽

Parallel And Distributed Computing ◽

Read Alignment ◽

Speed Up ◽

Alignment Process ◽

Transcript Assembly ◽

Processing Framework

Abstract Background Read alignment and transcript assembly are the core of RNA-seq analysis for transcript isoform discovery. Nonetheless, current tools are not designed to be scalable for analysis of full-length bulk or single cell RNA-seq (scRNA-seq) data. The previous version of our cloud-based tool Falco only focuses on RNA-seq read counting, but does not allow for more flexible steps such as alignment and read assembly. Results The Falco framework can harness the parallel and distributed computing environment in modern cloud platforms to accelerate read alignment and transcript assembly of full-length bulk RNA-seq and scRNA-seq data. There are two new modes in Falco: alignment-only and transcript assembly. In the alignment-only mode, Falco can speed up the alignment process by 2.5–16.4x based on two public scRNA-seq datasets when compared to alignment on a highly optimised standalone computer. Furthermore, it also provides a 10x average speed-up compared to alignment using published cloud-enabled tool for read alignment, Rail-RNA. In the transcript assembly mode, Falco can speed up the transcript assembly process by 1.7–16.5x compared to performing transcript assembly on a highly optimised computer. Conclusion Falco is a significantly updated open source big data processing framework that enables scalable and accelerated alignment and assembly of full-length scRNA-seq data on the cloud. The source code can be found at https://github.com/VCCRI/Falco.

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scRNAss: a single-cell RNA-seq assembler via imputing dropouts and combing junctions

Bioinformatics ◽

10.1093/bioinformatics/btz240 ◽

2019 ◽

Vol 35 (21) ◽

pp. 4264-4271

Author(s):

Juntao Liu ◽

Xiangyu Liu ◽

Xianwen Ren ◽

Guojun Li

Keyword(s):

Single Cell ◽

Open Source Software ◽

State Of The Art ◽

Supplementary Information ◽

Rna Seq ◽

Transcript Reconstruction ◽

Full Length Transcript ◽

Free Open Source ◽

Transcript Assembly ◽

Novel Isoforms

Abstract Motivation Full-length transcript reconstruction is essential for single-cell RNA-seq data analysis, but dropout events, which can cause transcripts discarded completely or broken into pieces, pose great challenges for transcript assembly. Currently available RNA-seq assemblers are generally designed for bulk RNA sequencing. To fill the gap, we introduce single-cell RNA-seq assembler, a method that applies explicit strategies to impute lost information caused by dropout events and a combing strategy to infer transcripts using scRNA-seq. Results Extensive evaluations on both simulated and biological datasets demonstrated its superiority over the state-of-the-art RNA-seq assemblers including StringTie, Cufflinks and CLASS2. In particular, it showed a remarkable capability of recovering unknown ‘novel’ isoforms and highly computational efficiency compared to other tools. Availability and implementation scRNAss is free, open-source software available from https://sourceforge.net/projects/single-cell-rna-seq-assembly/files/. Supplementary information Supplementary data are available at Bioinformatics online.

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