BINge: Multispecies Ortholog Clustering for Differential Gene Expression Analyses

Area of Science:

• Genomics
• Bioinformatics
• Evolutionary Biology

Background:

• Differential gene expression (DGE) analysis links gene expression to phenotypic traits.
• Accurate gene expression quantification is crucial for DGE studies within and across species.
• Comparing gene expression across species reveals genetic underpinnings of phenotypic differences.

Purpose of the Study:

• To address the challenge of creating accurate references for multispecies DGE analysis.
• To minimize reference bias in cross-species gene expression quantification.
• To introduce BINge, a software tool for improved multispecies DGE reference generation.

Main Methods:

• Developed a novel approach for modeling orthology to create multispecies transcript clusters.
• Ensured transcript clusters accurately reflect locus orthology.
• Evaluated BINge's effectiveness against existing clustering methods.

Main Results:

• BINge produces multispecies transcript clusters that accurately represent locus orthology.
• The novel orthology modeling approach effectively minimizes bias in multispecies DGE.
• Evaluation demonstrated BINge's superiority over existing methods not designed for multispecies DGE.

Conclusions:

• BINge offers an effective solution for generating accurate references for multispecies DGE analysis.
• The software mitigates reference bias, enabling more reliable cross-species gene expression comparisons.
• BINge facilitates the study of genetic mechanisms underlying interspecies phenotypic variation.