RNA-Seq: A Versatile Approach to Study Biological Processes
A special issue of BioTech (ISSN 2673-6284).
Deadline for manuscript submissions: closed (31 August 2020) | Viewed by 223
Special Issue Information
Dear Colleagues,
Deep transcriptome sequencing, also termed RNA-seq, has been performed for more than 10 years now. During this period, large progress has been made in data generation and processing leading to simplification and wider use of RNA-seq. Genome-guided transcriptome assembly is generally devoted to annotate genomes by defining transcribed regions and gene models with alternative splicing events, while de novo assembly may generate full-length transcript sequences for any living organism. Many broad range sequencing projects have already been conducted or are still underway to characterize very taxonomically distant species. One major advantage of RNA-seq over microarrays is that raw sequencing data strictly reflect the sample RNA content and are not restricted to a set of probes. Availability of raw sequencing data in public databases clearly allow the integration of previously published datasets in new analyses. Major improvements have also been made to quantify transcript abundance in terms of accuracy, precision, and speed.
In this SI, standard research articles and reviews will highlight progresses made in RNA-seq analysis. Major topics such as transcriptome assembly and quantification will be reviewed, and light will be shed on the possible evolution of RNA-seq. Papers focusing on third-generation read-based RNA-seq will be particularly welcomed. Articles dealing with state-of-the-art approaches to identify splicing events and differentially expressed genes/transcripts/isoforms to study biological networks or to classify samples or transcripts with machine learning-based approaches will also be considered.
Dr. Thomas Duge De Bernonville
Guest Editor
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Keywords
- RNA-seq
- Differential gene expression
- Transcriptome assembly
- Gene expression
- Coexpression network
- Genome annotation
- Alternative splicing
- High-throughput sequencing
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