i-ADHoRe 3.0—fast and sensitive detection of genomic homology in extremely large data sets

Abstract

Comparative genomics is a powerful means to gain insight into the evolutionary processes that shape the genomes of related species. As the number of sequenced genomes increases, the development of software to perform accurate cross-species analyses becomes indispensable. However, many implementations that have the ability to compare multiple genomes exhibit unfavorable computational and memory requirements, limiting the number of genomes that can be analyzed in one run. Here, we present a software package to unveil genomic homology based on the identification of conservation of gene content and gene order (collinearity), i-ADHoRe 3.0, and its application to eukaryotic genomes. The use of efficient algorithms and support for parallel computing enable the analysis of large-scale data sets. Unlike other tools, i-ADHoRe can process the Ensembl data set, containing 49 species, in 1 h. Furthermore, the profile search is more sensitive to detect degenerate genomic homology than chaining pairwise collinearity information based on transitive homology. From ultra-conserved collinear regions between mammals and birds, by integrating coexpression information and protein–protein interactions, we identified more than 400 regions in the human genome showing significant functional coherence. The different algorithmical improvements ensure that i-ADHoRe 3.0 will remain a powerful tool to study genome evolution.

The Apostasia genome and the evolution of orchids

“…In brief, paralogous gene pairs located in duplicated segments (anchors) and duplicated pairs lying under the WGD peak (peak-based duplicates) were collected for phylogenetic dating. Anchors, assumed to correspond to the most recent WGD event, were detected using i-ADHoRe (v3.0) 71,72 . Their K S distribution is shown in Extended Data Fig.…”

The genome sequence of the orchid Phalaenopsis equestris

“…Genomic homology was detected using i-ADHoRe 3.0 (ref. 79), included in the PLAZA pipeline, using the following settings: alignment method gg2, gap size 30, tandem gap 30, cluster gap 35, q value 0.85, prob cutoff 0.01, anchor points 5 and multiple hypothesis correction FDR). The output was processed by the pipeline and included in a relational database to which visualization programs can connect and on which additional statistical analysis can be performed.…”

AspWood: High-Spatial-Resolution Transcriptome Profiles Reveal Uncharacterized Modularity of Wood Formation in Populus tremula

“…Genomic homology was detected using i-ADHoRe 3.0 (Proost et al, 2012) using the following settings: alignment method gg4, gap size 30, cluster gap 35, tandem gap 10, q value 0.85, prob cutoff 0.001, anchor points 5, multiple hypothesis correction false discovery rate, and level 2 only true. Two gene clusters with significantly many shared paralog pairs (i.e., with one paralog in each cluster) were found by comparing the clustering to 10,000 randomly generating clusterings.…”