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Co-assembly of stranded and unstranded RNA-seq data improves coding and noncoding transcriptome maps

ABSTRACT: We want to develop transcriptome assembly pipeline that significantly improves the quality of the assemblies constructed using stranded and/or unstranded RNA-seq data.

ORGANISM(S): Mus musculus

PROVIDER: GSE84946 | GEO | 2016/07/29

SECONDARY ACCESSION(S): PRJNA335726

REPOSITORIES: GEO

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Co-assembly of stranded and unstranded RNA-seq data improves coding and noncoding transcriptome maps

Project description:We want to develop transcriptome assembly pipeline that significantly improves the quality of the assemblies constructed using stranded and/or unstranded RNA-seq data. Transcriptome of mouse embryonic stem cells (mESC) were assembled using stranded and unstranded library generated by Illumina HiSeq 2000

2016-07-29 | E-GEOD-84946 | biostudies-arrayexpress

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Project description:The advent of high-throughput RNA sequencing (RNA-seq) has led to the discovery of unprecedentedly immense transcriptomes encoded by eukaryotic genomes. However, the transcriptome maps are still incomplete partly because they were mostly reconstructed based on RNA-seq reads that lack their orientations (known as unstranded reads) and certain boundary information. Methods to expand the usability of unstranded RNA-seq data by predetermining the orientation of the reads and precisely determining the boundaries of assembled transcripts could significantly benefit the quality of the resulting transcriptome maps. Here, we present a high-performing transcriptome assembly pipeline, called CAFE, that significantly improves the original assemblies, respectively assembled with stranded and/or unstranded RNA-seq data, by orienting unstranded reads using the maximum likelihood estimation and by integrating information about transcription start sites and cleavage and polyadenylation sites. Applying large-scale transcriptomic data comprising 230 billion RNA-seq reads from the ENCODE, Human BodyMap Projects, The Cancer Genome Atlas, and GTEx, CAFE enabled us to predict the directions of about 220 billion unstranded reads, which led to the construction of more accurate transcriptome maps, comparable to the manually curated map, and a comprehensive lncRNA catalogue that includes thousands of novel lncRNAs. Our pipeline should not only help to build comprehensive, precise transcriptome maps from complex genomes but also to expand the universe of non-coding genomes.

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Merging short and stranded long reads improves transcript assembly

Project description:New tools for improved long-read transcript assembly and coalescence with its short-read counterpart are required. Using our short- and long-read measurements from different cell lines with spiked-in standards, we systematically compared key parameters and biases in the read alignment and assembly of transcripts. We report a cDNA synthesis artifact in long-read datasets that impacts the identity and quantitation of assembled transcripts. We developed a computational pipeline to strand long-read cDNA libraries that markedly improves assembly of transcripts from long-reads. Incorporating stranded long-reads in a new hybrid assembly approach, we demonstrate its efficacy for improved characterization of challenging lncRNA transcripts. Our workflow can be applied to a wide range of transcriptomics datasets for superior demarcation of transcript ends and refined isoform structure, which can enable better differential gene expression analyses and molecular manipulations of transcripts.

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Unstranded RNA-seq profiling of cerebral cortices from Taf8-deleted and Taf8-intact mouse embryos

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