cuffdiff

cuffdiff analyzes high-throughput mRNA sequencing (RNA-Seq) data to identify differential transcript expression, alternative splicing, and promoter (TSS) usage for quantifying transcript abundance and regulatory changes.

Key Features:

• Transcript discovery and abundance estimation: Detects both annotated and novel transcripts and quantifies their expression, demonstrated on a dataset of over 430 million paired 75-bp RNA-Seq reads from a mouse myoblast cell line that identified 13,692 known transcripts and 3,724 novel transcripts, with 62% of novel findings supported by independent expression data or homologous genes.
• Regulatory flexibility analysis: Identifies changes in transcription start sites (TSS) and splice isoforms, including complete switches (330 genes) and subtler regulatory shifts (1,304 genes) observed during muscle development studies.
• Annotation-independent algorithms: Employs algorithms not constrained by prior gene annotations to detect alternative transcription, splicing events, and promoter usage.
• Part of the Cufflinks suite: Integrates computationally with the Cufflinks suite for transcriptome assembly and downstream differential analysis.

Scientific Applications:

• Cellular differentiation studies: Quantifies transcriptome dynamics and isoform-level regulation during processes such as muscle development (mouse myoblast studies).
• Disease progression and treatment response: Detects differential expression and splicing changes relevant to disease states and responses to interventions.
• Genome annotation from transcriptomic data: Supports discovery of novel transcripts and refinement of gene models based on RNA-Seq evidence.
• Transcriptional regulation and splicing variability: Characterizes promoter usage and alternative splicing patterns to elucidate regulatory mechanisms.

Methodology:

Analyzes RNA-Seq reads using algorithms for transcript discovery and abundance estimation and for detecting alternative transcription, splicing, and promoter (TSS) usage without requiring pre-existing gene annotations.