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      Single-cell RNA-seq analysis of mouse preimplantation embryos by third-generation sequencing

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          Abstract

          The development of next generation sequencing (NGS) platform-based single-cell RNA sequencing (scRNA-seq) techniques has tremendously changed biological researches, while there are still many questions that cannot be addressed by them due to their short read lengths. We developed a novel scRNA-seq technology based on third-generation sequencing (TGS) platform (single-cell amplification and sequencing of full-length RNAs by Nanopore platform, SCAN-seq). SCAN-seq exhibited high sensitivity and accuracy comparable to NGS platform-based scRNA-seq methods. Moreover, we captured thousands of unannotated transcripts of diverse types, with high verification rate by reverse transcription PCR (RT-PCR)–coupled Sanger sequencing in mouse embryonic stem cells (mESCs). Then, we used SCAN-seq to analyze the mouse preimplantation embryos. We could clearly distinguish cells at different developmental stages, and a total of 27,250 unannotated transcripts from 9,338 genes were identified, with many of which showed developmental stage-specific expression patterns. Finally, we showed that SCAN-seq exhibited high accuracy on determining allele-specific gene expression patterns within an individual cell. SCAN-seq makes a major breakthrough for single-cell transcriptome analysis field.

          Abstract

          This study describes a novel single-cell RNA-seq technology called SCAN-seq which can capture the full-length transcripts in single cells based on the third-generation Nanopore sequencing platform, and demonstrates its performance on mouse preimplantation embryos.

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          Most cited references38

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            Fast and accurate short read alignment with Burrows–Wheeler transform

            Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk
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              fastp: an ultra-fast all-in-one FASTQ preprocessor

              Abstract Motivation Quality control and preprocessing of FASTQ files are essential to providing clean data for downstream analysis. Traditionally, a different tool is used for each operation, such as quality control, adapter trimming and quality filtering. These tools are often insufficiently fast as most are developed using high-level programming languages (e.g. Python and Java) and provide limited multi-threading support. Reading and loading data multiple times also renders preprocessing slow and I/O inefficient. Results We developed fastp as an ultra-fast FASTQ preprocessor with useful quality control and data-filtering features. It can perform quality control, adapter trimming, quality filtering, per-read quality pruning and many other operations with a single scan of the FASTQ data. This tool is developed in C++ and has multi-threading support. Based on our evaluation, fastp is 2–5 times faster than other FASTQ preprocessing tools such as Trimmomatic or Cutadapt despite performing far more operations than similar tools. Availability and implementation The open-source code and corresponding instructions are available at https://github.com/OpenGene/fastp.
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                Author and article information

                Contributors
                Role: Data curationRole: MethodologyRole: Project administrationRole: ResourcesRole: ValidationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Formal analysis
                Role: Data curationRole: InvestigationRole: MethodologyRole: ResourcesRole: ValidationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Formal analysis
                Role: Validation
                Role: Data curation
                Role: Data curation
                Role: Resources
                Role: Validation
                Role: Writing – review & editing
                Role: Writing – review & editing
                Role: Writing – review & editing
                Role: SupervisionRole: Writing – review & editing
                Role: Academic Editor
                Journal
                PLoS Biol
                PLoS Biol
                plos
                plosbiol
                PLoS Biology
                Public Library of Science (San Francisco, CA USA )
                1544-9173
                1545-7885
                30 December 2020
                December 2020
                30 December 2020
                : 18
                : 12
                : e3001017
                Affiliations
                [1 ] Beijing Advanced Innovation Center for Genomics, Biomedical Pioneering Innovation Center, School of Life Sciences, Peking University, Beijing, China
                [2 ] Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, China
                [3 ] GrandOmics Biosciences, Beijing, China
                [4 ] Ministry of Education Key Laboratory of Cell Proliferation and Differentiation, Beijing, China
                [5 ] Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
                IMBA, AUSTRIA
                Author notes

                The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0002-9238-0893
                https://orcid.org/0000-0003-3515-3333
                Article
                PBIOLOGY-D-20-01931
                10.1371/journal.pbio.3001017
                7773192
                33378329
                054e3320-f50c-429e-99ad-7085794ede13
                © 2020 Fan et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 28 June 2020
                : 3 December 2020
                Page count
                Figures: 5, Tables: 2, Pages: 19
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 31625018
                Award Recipient :
                Funded by: Beijing Municipal Science & Technology Commission
                Award ID: Z181100001318001
                Award Recipient :
                The work was supported by the National Natural Science Foundation of China (31625018) and Beijing Municipal Science & Technology Commission (Z181100001318001). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Methods and Resources
                Biology and Life Sciences
                Genetics
                Gene Expression
                Biology and life sciences
                Molecular biology
                Molecular biology techniques
                Sequencing techniques
                RNA sequencing
                Research and analysis methods
                Molecular biology techniques
                Sequencing techniques
                RNA sequencing
                Biology and Life Sciences
                Developmental Biology
                Embryology
                Embryos
                Biology and Life Sciences
                Cell Biology
                Cellular Types
                Animal Cells
                Germ Cells
                OVA
                Oocytes
                Biology and life sciences
                Molecular biology
                Molecular biology techniques
                Sequencing techniques
                DNA sequencing
                Next-Generation Sequencing
                Research and analysis methods
                Molecular biology techniques
                Sequencing techniques
                DNA sequencing
                Next-Generation Sequencing
                Biology and Life Sciences
                Computational Biology
                Genome Analysis
                Transcriptome Analysis
                Next-Generation Sequencing
                Biology and Life Sciences
                Genetics
                Genomics
                Genome Analysis
                Transcriptome Analysis
                Next-Generation Sequencing
                Biology and life sciences
                Biochemistry
                Nucleic acids
                RNA
                Non-coding RNA
                Long non-coding RNA
                Biology and Life Sciences
                Genetics
                Genomics
                Animal Genomics
                Mammalian Genomics
                Biology and Life Sciences
                Computational Biology
                Genome Complexity
                Introns
                Biology and Life Sciences
                Genetics
                Genomics
                Genome Complexity
                Introns
                Custom metadata
                All relevant data are available from the Sequence Read Archive (SRA) database (accession number: PRJNA616184).

                Life sciences
                Life sciences

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