Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell...

Area of Science:

• Transcriptomics
• Bioinformatics
• Molecular Biology

Background:

• High-throughput mRNA sequencing (RNA-Seq) enables transcript discovery and abundance estimation.
• Existing algorithms often rely on prior gene annotations and struggle with alternative transcription and splicing.
• A need exists for annotation-agnostic algorithms to fully capture transcriptome complexity.

Purpose of the Study:

• To introduce novel algorithms for transcript discovery and abundance estimation without prior gene annotations.
• To present Cufflinks, an open-source software implementing these algorithms.
• To analyze transcriptome dynamics during mouse myoblast differentiation using RNA-Seq.

Main Methods:

• Development of annotation-agnostic algorithms for RNA-Seq data analysis.
• Implementation of these algorithms into the open-source Cufflinks software.
• Sequencing and analysis of over 430 million paired 75-bp RNA-Seq reads from mouse myoblasts during differentiation.

Main Results:

• Detection of 13,692 known and 3,724 previously unannotated transcripts.
• Validation of 62% of novel transcripts through independent expression data or homology.
• Observation of 330 genes with complete switches in transcription start sites or splice isoforms, and shifts in 1,304 others over the time series.

Conclusions:

• Cufflinks effectively identifies novel transcripts and reveals extensive regulatory flexibility in transcript usage.
• The software improves transcriptome-based genome annotation, particularly for complex biological processes.
• Findings highlight the dynamic nature of gene regulation even in well-studied systems like muscle development.