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The pangenome of a species is the sum of the genomes of its individuals. As coding sequences often represent only a small fraction of each genome, analyzing the pangene set can be a cost-effective strategy for plants with large genomes or highly heterozygous species. Here, we describe a step-by-step protocol to analyze plant pangene sets with the software GET_HOMOLOGUES-EST . After a short introduction, where the main concepts are illustrated, the remaining sections cover the installation and typical operations required to analyze and annotate pantranscriptomes and gene sets of plants. The recipes include instructions on how to call core and accessory genes, how to compute a presence-absence pangenome matrix, and how to identify and analyze private genes, present only in some genotypes. Downstream phylogenetic analyses are also discussed.

Ensembl Plants ( http://plants.ensembl.org ) offers genome-scale information for plants, with four releases per year. As of release 47 (April 2020) it features 79 species and includes genome sequence, gene models, and functional annotation. Comparative analyses help reconstruct the evolutionary history of gene families, genomes, and components of polyploid genomes. Some species have gene expression baseline reports or variation across genotypes. While the data can be accessed through the Ensembl genome browser, here we review specifically how our plant genomes can be interrogated programmatically and the data downloaded in bulk. These access routes are generally consistent across Ensembl for other non-plant species, including plant pathogens, pests, and pollinators.

The plant-dedicated mirror of the Regulatory Sequence Analysis Tools (RSAT, http://plants.rsat.eu ) offers specialized options for researchers dealing with plant transcriptional regulation. The website contains whole-sequenced genomes from species regularly updated from Ensembl Plants and other sources (currently 40), and supports an array of tasks frequently required for the analysis of regulatory sequences, such as retrieving upstream sequences, motif discovery, motif comparison, and pattern matching. RSAT::Plants also integrates the footprintDB collection of DNA motifs. This protocol explains step-by-step how to discover DNA motifs in regulatory regions of clusters of co-expressed genes in plants. It also explains how to empirically control the significance of the result, and how to associate the discovered motifs with putative binding factors.

FootprintDB is a database and search engine that compiles regulatory sequences from open access libraries of curated DNA cis-elements and motifs, and their associated transcription factors (TFs). It systematically annotates the binding interfaces of the TFs by exploiting protein-DNA complexes deposited in the Protein Data Bank. Each entry in footprintDB is thus a DNA motif linked to the protein sequence of the TF(s) known to recognize it, and in most cases, the set of predicted interface residues involved in specific recognition. This chapter explains step-by-step how to search for DNA motifs and protein sequences in footprintDB and how to focus the search to a particular organism. Two real-world examples are shown where this software was used to analyze transcriptional regulation in plants. Results are described with the aim of guiding users on their interpretation, and special attention is given to the choices users might face when performing similar analyses.

In this protocol, we explain how to run ab initio motif discovery in order to gather putative transcription factor binding motifs (TFBMs) from sets of genomic regions returned by ChIP-seq experiments. The protocol starts from a set of peak coordinates (genomic regions) which can be either downloaded from ChIP-seq databases, or produced by a peak-calling software tool. We provide a concise description of the successive steps to discover motifs, cluster the motifs returned by different motif discovery algorithms, and compare them with reference motif databases. The protocol is documented with detailed notes explaining the rationale underlying the choice of options. The interpretation of the results is illustrated with an example from the model plant Arabidopsis thaliana.

GET_HOMOLOGUES is an open-source software package written in Perl and R to define robust core- and pan-genomes by computing consensus clusters of orthologous gene families from whole-genome sequences using the bidirectional best-hit, COGtriangles, and OrthoMCL clustering algorithms. The granularity of the clusters can be fine-tuned by a user-configurable filtering strategy based on a combination of blastp pairwise alignment parameters, hmmscan-based scanning of Pfam domain composition of the proteins in each cluster, and a partial synteny criterion. We present detailed protocols to fit exponential and binomial mixture models to estimate core- and pan-genome sizes, compute pan-genome trees from the pan-genome matrix using a parsimony criterion, analyze and graphically represent the pan-genome structure, and identify lineage-specific gene families for the 12 complete pIncA/C plasmids currently available in NCBI's RefSeq. The software package, license, and detailed user manual can be downloaded for free for academic use from two mirrors: http://www.eead.csic.es/compbio/soft/gethoms.php and http://maya.ccg.unam.mx/soft/gethoms.php.