Gene homology inference

To predict probable homology relationships between proteomes we used the heuristic predictor InParanoid/MultiParanoid based on the RBH concept [12, 17]. Among various heuristic-based methods for sequence homology detection, OrthoMCL [8] and InParanoid [12] have been shown to exhibit comparable high specificity and sensitivity scores estimated by Latent Class Analysis [9], so in the present study we exploited InParanoid/MultiParanoid v. 4.1 for the purpose of simplicity in computational implementation. InParanoid initially performs bidirectional BLAST hits (BBHs) between two proteomes to detect BBHs in the pairwise manner. For this step, we set default parameters with the BLOSUM62 protein substitution matrix and bit score cutoff of 40 for all-against-all BLAST search. Next, MultiParanoid forms multi-species groups using the notion of a single-linkage. Due to inefficient MultiParanoid clustering algorithm, we had to perform a transitive closure to compile homology clusters for all species together. Transitive closure is an operation performed on a set of related values. Formally, a set S is transitive if the following condition is true: for all values A, B, and C in S, if A is related to B and B is related to C, then A is related to C. Transitive closure takes a set (transitive or non-transitive) and creates all transitive relationships, if they do not already exist. When a set is already transitive, its transitive closure is identical to itself. In the case of the pairwise relationships produced by InParanoid, we constructed orthologous clusters using the notion of transitive closure, where gene identifiers were the values, and homology was the relationship.

For example, our OD_S data set consisted of N = 20 proteomes, so we had to perform N×(N - 1)/2 = 190 pairwise InParanoid queries. A simple transitive closure yielded total 13,998 homology clusters for OD_S. The DROSO data set yielded 20,676, 18,584 and 17,067 homology clusters for 100 %, 50 % and 10 % respectively. Then putative homologous genes were aligned to form individual MSA homology clusters for the subsequent analyses using MAFFT v. 6.864b [18] with the “-auto” flag that enabled detection of the best alignment strategy between accuracy- and speed-oriented methods.

Additionally, we utilized HaMStR v. 13.2.3 [10] under default parameters to delineate putative orthologous sequences in the OD_S proteome sets. 5,332 core 1-to-1ortholog clusters of 5 arthropod species (Ixodes scapularis, Daphnia pulex, Rhodnius prolixus, Apis mellifera and Heliconius melpomene) for training pHMM were retrieved from the latest version of OrthoDB [19]. We used Rhodnius prolixus (triatomid bug) as the reference core proteome because this is the closest phylogenetically related species and publically available proteome to the Ephemeroptera/Odonata lineage [20]. As previously described, each core ortholog cluster was aligned to create MSA using MAFFT and converted into HMM profile using HMMER v. 3.0 [21]. BBHs against the reference proteome were derived using reciprocal BLAST.