The Singapore Nanopore Expression Data Set

Description

The Singapore Nanopore Expression (SG-NEx) project is an international collaboration to generate reference transcriptomes and a comprehensive benchmark data set for long read Nanopore RNA-Seq. Transcriptome profiling is done using PCR-cDNA sequencing (PCR-cDNA), amplification-free cDNA sequencing (direct cDNA), direct sequencing of native RNA (direct RNA), and short read RNA-Seq. The SG-NEx core data includes 5 of the most commonly used cell lines and it is extended with additional cell lines and samples that cover a broad range of human tissues. All core samples are sequenced with at least 3 high quality replicates. For a subset of samples spike-in RNAs are used and matched m6A profiling data is available.

Update Frequency

Datasets will be updated periodically as additional data are generated.

License

CC BY-NC 4.0

Documentation

https://github.com/GoekeLab/sg-nex-data

Managed By

The Genome Institute of Singapore (https://www.a-star.edu.sg/gis)

See all datasets managed by The Genome Institute of Singapore (https://www.a-star.edu.sg/gis).

Contact

SG-NEx team

How to Cite

The Singapore Nanopore Expression Data Set was accessed on DATE from https://registry.opendata.aws/sgnex. In addition, please cite Chen et al. A systematic benchmark of Nanopore long read RNA sequencing for transcript level analysis in human cell lines. bioRxiv (2021). doi: https://doi.org/10.1101/2021.04.21.440736 when referencing the SG-NEx data in publications.

Usage Examples

Tutorials

• Accessing the SG-NEx dataset on AWS by Ying Chen
  
• Basecalling and analysing SG-NEx samples in S/BLOW5 format by Hasindu Gamaarachchi
  
• Differential RNA modification analysis tutorial with xPore by Yu Song Chuah
  
• Performing transcript discovery and quantification with Bambu by Min Hao Ling
  
• RNA modification analysis tutorial with m6Anet by Yu Song Chuah
  

Tools & Applications

• Bambu: Transcript discovery and quantification using long read RNA-Seq data by Ying Chen, Andre Sim et al.
  
• JAFFAL: Detection of fusion genes from long read RNA-Seq data by Nadia M Davidson et al.
  
• m6Anet: Identification of m6A from a single direct RNA-Seq sample by Christopher Hendra et al.
  
• nf-core/nanoseq: A nanopore DNA and RNA-Seq demultiplexing, QC, alignment and analysis pipeline by Chelsea Sawyer et al.
  
• xPore: Identification of differential RNA modification from direct RNA-Seq data by Ploy Pratanwanich et al.
  

Publications

• A systematic benchmark of Nanopore long read RNA sequencing for transcript level analysis in human cell lines. by Ying Chen et al.
  
• Beyond sequencing: machine learning algorithms extract biology hidden in Nanopore signal data. by Yuk Kei Wan et al.
  
• Detection of m6A from direct RNA sequencing using a Multiple Instance Learning framework. by Christopher Hendra et al.
  
• Identification of differential RNA modifications from nanopore direct RNA sequencing with xPore. by Ploy N Pratanwanich et al.
  
• JAFFAL: Detecting fusion genes with long read transcriptome sequencing. by Nadia M Davidson et al.