A Sequence-Pair-Classification-Based Method For Detecting And Correcting Under-Clustered Gene Families

Abstract

Gene families are groups of genes that have descended from a common ancestral gene present in the species under study. Current, widely used gene family building algorithms can produce family clusters that may be fragmented or missing true family sequences (under-clustering). Here we present a classification method based on sequence pairs that, first, inspects given families for under-clustering and then predicts the missing sequences for the families using family-specific alignment score cutoffs. We have tested this method on a set of curated, gold-standard ("true") families from the Yeast Gene Order Browser (YGOB) database, including 20 yeast species, as well as a test set of intentionally under-clustered ("deficient") families derived from the YGOB families. For 83% of the modified yeast families, our pair-classification method was able to reliably detect under-clustering in "deficient" families that were missing 20% of sequences relative to the full/" true" families. We also attempted to predict back the missing sequences using the family-specific alignment score cutoffs obtained during the detection phase. In the case of "pure" under-clustered families (under-clustered families with no "wrong"/unrelated sequences), for 78% of families the prediction precision and recall was [≥]0.75, with mean precision = 0.928 and mean recall = 0.859. For "impure" under-clustered families, (under-clustered families containing closest sequences from outside the family, in addition to missing true family sequences), the prediction precision and recall was [≥]0.75 for 63% of families with mean precision = 0.790 and mean recall = 0.869. To check if our method can detect and correct incomplete families obtained using existing family building methods, we attempted to correct 374 under-clustered yeast families produced using the OrthoFinder tool. We were able to predict missing sequences for at least 19 yeast families with mean precision of 0.9 and mean recall of 0.65. We also analyzed 14,663 legume families built using the OrthoFinder program, with 14 legume species. We were able to identify 1,665 OrthoFinder families that were missing one or more sequences - sequences which were previously un-clustered or clustered into unusually small families. Further, using a simple merging strategy, we were able to merge 2,216 small families into 933 under-clustered families using the predicted missing sequences. Out of the 933 merged families, we could confirm correct mergings in at least 534 families using the maximum-likelihood phylogenies of the merged families. We also provide recommendations on different types of family-specific alignment score cutoffs that can be used for predicting the missing sequences based on the "purity" of under-clustered families and the chosen precision and recall for prediction. Finally, we provide the containerized version of the pair-classification method that can be applied on any given set of gene families.