Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.11.20.390930v1?rss=1 Authors: Das Roy, R., Hallikas, O., Christensen, M. M., Renvoise, E., Jernvall, J. Abstract: Exploration of genetically modified organisms, developmental processes, diseases or responses to various treatments require accurate measurement of changes in gene expression. This can be done for thousands of genes using high throughput technologies such as microarray and RNAseq. However, identification of differentially expressed (DE) genes poses technical challenges due to limited sample size, few replicates, or simply very small changes in expression levels. Consequently, several methods have been developed to determine DE genes, such as Limma, RankProd, SAM, and DeSeq2. These methods identify DE genes based on the expression levels alone. As genomic co-localization of genes is generally not linked to co-expression, we deduced that DE genes could be detected with the help of genes from chromosomal neighbourhood. Here, we present a new method, DELocal, which identifies DE genes by comparing their expression changes to changes in adjacent genes in their chromosomal regions. Our results show that DELocal provides distinct benefits in the identification of DE genes. Furthermore, our comparative analysis of the dispersal of genes with related functions suggests that DELocal is applicable to a wide range of developmental systems. With increasing availability of genomic data, gene neighbourhood can become a powerful tool to detect differential expression. Copy rights belong to original authors. Visit the link for more info