cloudrnaSPAdes

cloudrnaSPAdes assembles full-length isoforms from barcoded RNA sequencing (RNA-seq) linked-read data in a reference-free manner to reconstruct transcriptomes and enable novel isoform discovery.


Key Features:

  • Barcoded linked-reads: Utilizes barcoded short-read RNA data where each barcode links reads originating from the same RNA molecule to enable reconstruction of full-length transcripts.
  • Reference-free assembly: Operates without a reference genome or transcriptome, supporting novel isoform discovery and analyses in poorly annotated organisms.
  • Handling coverage gaps: Manages coverage gaps within molecules by assembling isoforms per barcode to maintain accurate reconstruction despite uneven read distribution.
  • High isoform diversity: Capable of reconstructing complex transcriptomes and handling genes with high isoform diversity, as demonstrated on simulated and real human data sets.
  • SPAdes-based framework: Builds upon the SPAdes assembler framework with RNA-seq-specific enhancements to process barcoded linked-reads.

Scientific Applications:

  • Novel isoform discovery: Enables discovery of previously unannotated transcript isoforms by linking reads from the same molecule to achieve effective full-length assembly.
  • Reference-free transcriptomic studies: Supports investigation of gene expression patterns and alternative splicing without relying on reference-based methods.

Methodology:

cloudrnaSPAdes builds upon the SPAdes assembler framework and processes barcoded linked-reads to determine expressed isoforms per barcode, incorporating RNA-seq-specific enhancements.

Topics

Details

Cost:
Free of charge
Tool Type:
workflow
Programming Languages:
C++
Added:
5/24/2024
Last Updated:
11/24/2024

Operations

Publications

Meleshko D, Prjbelski AD, Raiko M, Tomescu AI, Tilgner H, Hajirasouliha I. <scp>cloudrna</scp> SP <scp>Ades</scp> : isoform assembly using bulk barcoded RNA sequencing data. Bioinformatics. 2024;40(2). doi:10.1093/bioinformatics/btad781. PMID:38262343. PMCID:PMC10868327.

PMID: 38262343
Funding: - NIGMS Maximizing Investigators’ Research Award: R35 GM138152