Single-cell Transcriptome Study as Big Data
| Autor: | Wei Lin, Pingjian Yu |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
0301 basic medicine
Big data RNA-Seq Review Biology Biochemistry Transcriptome 03 medical and health sciences Single cell transcriptome Databases Genetic Genetics Animals Humans Single cell lcsh:QH301-705.5 Molecular Biology business.industry Sequence Analysis RNA SIGNAL (programming language) Computational Biology High-Throughput Nucleotide Sequencing Data science Computational Mathematics 030104 developmental biology Workflow lcsh:Biology (General) Signal normalization Transcriptional heterogeneity Computer data storage RNA Single-Cell Analysis RNA-seq business |
| Zdroj: | Genomics, Proteomics & Bioinformatics Genomics, Proteomics & Bioinformatics, Vol 14, Iss 1, Pp 21-30 (2016) |
| ISSN: | 2210-3244 |
| Popis: | The rapid growth of single-cell RNA-seq studies (scRNA-seq) demands efficient data storage, processing, and analysis. Big-data technology provides a framework that facilitates the comprehensive discovery of biological signals from inter-institutional scRNA-seq datasets. The strategies to solve the stochastic and heterogeneous single-cell transcriptome signal are discussed in this article. After extensively reviewing the available big-data applications of next-generation sequencing (NGS)-based studies, we propose a workflow that accounts for the unique characteristics of scRNA-seq data and primary objectives of single-cell studies. |
| Databáze: | OpenAIRE |
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