Using the the reference lines as an example, the data is organized by genes (rows) and annotation-versions (columns).

To work with either of these files, uncompress it, then open it using Excel or similar spreadsheet program;
or if you have a little familiarity with a Unix terminal, you can extract data in many ways (a few examples):

            cat Glycine.pan5.MKRS.table_ref_lines.tsv | tr '\t' '\n'   # to see the list of headers
            cut -f1,2,8,10 Glycine.pan5.MKRS.table_ref_lines.tsv | head   # to see four selected columns (the first 10 entries)
            grep -f YOUR_LIST_OF_GENES.txt Glycine.pan5.MKRS.table_ref_lines.tsv  # to search a provided list of gene IDs against the file
          

The Pandagma software package (Cannon, Lee, Weeks, Berendzen) was used to identify pangene and gene family sets. The main steps in Pandagma's pangene process are:

• Make pairwise homology comparisons between each annotation set;
• Filter by provided percent identity and coverage parameters;
• Identify synteny blocks among all annotation sets;
• Cluster genes in synteny blocks;
• Add back remaining genes based on homology, constraining by chromosome (e.g., chr1 genes to chr1 clusters)
• Add "extra" annotation sets (those with more fragmentary assemblies or questionable annotation quality) to clusters identified above.

The Pandagma package is available at https://github.com/legumeinfo/pandagma, including the configuration used to calculate the pangene data above.

The pangene collection for Glycine, including data in several formats and descriptions of the fies, is in the "Glycine/GENUS/pangenes" section of the Data Store.

If you have extensive programmatic work and need to translate among arbitrary accessions, the gene_translate.pl utility in pandagma may be helpful.