RNA-Seq: a revolutionary tool for transcriptomics
TLDR
The RNA-Seq approach to transcriptome profiling that uses deep-sequencing technologies provides a far more precise measurement of levels of transcripts and their isoforms than other methods.Abstract:
RNA-Seq is a recently developed approach to transcriptome profiling that uses deep-sequencing technologies. Studies using this method have already altered our view of the extent and complexity of eukaryotic transcriptomes. RNA-Seq also provides a far more precise measurement of levels of transcripts and their isoforms than other methods. This article describes the RNA-Seq approach, the challenges associated with its application, and the advances made so far in characterizing several eukaryote transcriptomes.read more
Citations
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A single-molecule long-read survey of the human transcriptome.
TL;DR: The results show the feasibility of deep sequencing full-length RNA from complex eukaryotic transcriptomes on a single-molecule level and high-confidence mappings are consistent with GENCODE annotations.
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Library construction for next-generation sequencing: Overviews and challenges
Steven R. Head,H. Kiyomi Komori,Sarah A. LaMere,Thomas Whisenant,Filip Van Nieuwerburgh,Daniel R. Salomon,Phillip Ordoukhanian +6 more
TL;DR: The critical role of sequencing library quality is examined and important challenges when preparing NGS libraries from DNA and RNA sources are considered, including the quantity and physical characteristics of the RNA or DNA source material.
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TL;DR: It is concluded that microarrays remain useful and accurate tools for measuring expression levels, and RNA-Seq complements and extends microarray measurements.
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Classification of low quality cells from single-cell RNA-seq data
Tomislav Ilicic,Tomislav Ilicic,Jong Kyoung Kim,Aleksandra A. Kolodziejczyk,Aleksandra A. Kolodziejczyk,Frederik Otzen Bagger,Frederik Otzen Bagger,Frederik Otzen Bagger,Davis J. McCarthy,Davis J. McCarthy,John C. Marioni,John C. Marioni,John C. Marioni,Sarah A. Teichmann,Sarah A. Teichmann,Sarah A. Teichmann +15 more
TL;DR: This work presents a generic approach for processing scRNA-seq data and detecting low quality cells, using a curated set of over 20 biological and technical features, which improves classification accuracy by over 30 % compared to traditional methods.
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