Ornaments for efficient allele-specific expression estimation with bias correction.

Area of Science:

• Genomics
• Computational Biology
• Molecular Biology

Background:

• Allele-specific expression (ASE) is vital for understanding genomic imprinting and cis-regulatory variants.
• Current RNA-sequencing (RNA-seq) quantification methods struggle with allele-specific read mapping biases, impacting accuracy.
• Reference bias and ambiguous mapping bias are key challenges in ASE analysis.

Purpose of the Study:

• To introduce Ornaments, a computational tool for accurate and efficient estimation of allele-specific transcript expression.
• To correct for allele-specific read mapping biases in RNA-seq data.
• To improve the detection of genomic imprinting and gene expression variations.

Main Methods:

• Ornaments corrects reference bias by mapping reads to a personalized transcriptome.
• Ambiguous mapping bias is addressed by probabilistically assigning reads to multiple transcripts and variant loci.
• Ornaments is an extension of kallisto, enhancing WASP's bias correction capabilities.

Main Results:

• Ornaments significantly improves the accuracy of allele-specific expression quantification compared to existing tools.
• The tool demonstrates high efficiency, being nearly as fast as kallisto and much faster than WASP.
• Ornaments shows increased sensitivity in detecting imprinted transcripts at the transcript level.

Conclusions:

• Ornaments provides a rapid and accurate solution for estimating allele-specific expression from RNA-seq data.
• The tool effectively corrects for critical read mapping biases, enhancing biological insights.
• Ornaments offers improved sensitivity for detecting imprinted transcripts, advancing genomic imprinting research.