Itay Mayrose

Pan-genomics is an emerging approach for studying the genetic diversity within plant populations. In contrast to common resequencing studies that compare whole genome sequencing data with a single reference genome, the construction of a pan-genome (PG) involves the direct comparison of multiple genomes to one another, thereby enabling the detection of genomic sequences and genes not present in the reference, as well as the analysis of gene content diversity. Although multiple studies describing PGs of various plant species have been published in recent years, a better understanding regarding the effect of the computational procedures used for PG construction could guide researchers in making more informed methodological decisions. Here, we examine the effect of several key methodological factors on the obtained gene pool and on gene presence–absence detections by constructing and comparing multiple PGs of Arabidopsis thaliana and cultivated soybean, as well as conducting a meta-analysis on published PGs. These factors include the construction method, the sequencing depth, and the extent of input data used for gene annotation. We observe substantial differences between PGs constructed using three common procedures (de novo assembly and annotation, map-to-pan, and iterative assembly) and that results are dependent on the extent of the input data. Specifically, we report low agreement between the gene content inferred using different procedures and input data. Our results should increase the awareness of the community to the consequences of methodological decisions made during the process of PG construction and emphasize the need for further investigation of commonly applied methodologies.

The ConSurf web‐sever for the analysis of proteins, RNA, and DNA provides a quick and accurate estimate of the per‐site evolutionary rate among homologues. The analysis reveals functionally important regions, such as catalytic and ligand‐binding sites, which often evolve slowly. Since the last report in 2016, ConSurf has been improved in multiple ways. It now has a user‐friendly interface that makes it easier to perform the analysis and to visualize the results. Evolutionary rates are calculated based on a set of homologous sequences, collected using hidden Markov model‐based search tools, recently embedded in the pipeline. Using these, and following the removal of redundancy, ConSurf assembles a representative set of effective homologues for protein and nucleic acid queries to enable informative analysis of the evolutionary patterns. The analysis is particularly insightful when the evolutionary rates are mapped on the macromolecule structure. In this respect, the availability of AlphaFold model structures of essentially all UniProt proteins makes ConSurf particularly relevant to the research community. The UniProt ID of a query protein with an available AlphaFold model can now be used to start a calculation. Another important improvement is the Python re‐implementation of the entire computational pipeline, making it easier to maintain. This Python pipeline is now available for download as a standalone version. We demonstrate some of ConSurf's key capabilities by the analysis of caveolin‐1, the main protein of membrane invaginations called caveolae.