TanglabCircularRNAPipeline

TanglabCircularRNAPipeline processes raw fastq sequencing data to identify and characterize circular RNAs (circRNAs), polyA+ mRNAs, polyA- RNAs, and novel transcripts for transcriptomic analysis of human pre-implantation embryos and comparative studies with mouse.

Key Features:

• Comprehensive Transcriptome Analysis: Systematic analysis of polyA+ messenger RNAs (mRNAs) and polyA- RNAs in human pre-implantation embryos, including oocytes and early developmental stages.
• De Novo Identification of circRNAs: De novo identification of circRNAs using sequencing approaches such as SUPeR-seq, reporting 10,032 circRNAs from 2,974 hosting genes in the cited study.
• Developmental Stage-Specific Analysis: Identification of circRNAs with dynamic, developmental stage-specific expression patterns during oogenesis and early embryogenesis.
• Comparative Genomics: Comparative analysis of circRNA profiles between human and mouse embryos to reveal conserved and species-specific elements.
• Intronic Length Analysis: Assessment of flanking intron lengths of circRNAs, including observation of significantly longer flanking introns in humans versus mice.
• Novel Transcript Identification: RNA de novo assembly to identify novel transcript units, including candidate long non-coding RNAs (lncRNAs).

Scientific Applications:

• Regulatory Function Analysis: Identification of maternally expressed and stage-specific circRNAs to investigate potential regulatory roles during early human development.
• Species-Specific Studies: Comparative circRNA profiling across human and mouse to study evolutionary conservation and species-specific gene regulation.
• Transcriptome Resource Creation: Generation of detailed datasets of polyA+ mRNAs and polyA- RNAs for resources supporting studies of regulatory mechanisms in pre-implantation development.

Methodology:

Processes raw fastq sequencing data (including SUPeR-seq), employs algorithms for RNA de novo assembly and circRNA identification, and integrates comparative genomics approaches for cross-species analysis.