Expressed Sequence Tags

We obtained the ENCODE region sequences, GENCODE annotations, and the various EGASP annotation datasets from the EGASP ftp site [54 ]. We encountered some difficulties working with the downloaded data files because of inconsistent file formats, inconsistent annotation of stop codons, and annotation features extending out of the sequence range. We therefore converted each data file over to a more strict GTF format, clipping annotations at the bounds of the ENCODE regions and adding stop codons where they were obviously lacking. Prediction accuracies of the EGASP datasets were recomputed (Additional data file 1 [Figure S4]) and were found to agree with the previously reported values; small differences between our recomputed values and previously published values are likely because of the slight differences in our stated implementation of our accuracy evaluation software and those differences resulting from our file conversions. Our refined versions of the EGASP datasets are available from the EVM software website [41 ].
Additional evidence compiled for the GENCODE annotations included homologies to nonhuman proteins using AAT-nap and GeneWise, alignments to assembled animal ESTs downloaded from the Gene Index using AAT-gap2, and PASA alignment assemblies. This additional evidence is also available from the EVM software site [41 ].

The ab initio gene prediction programs Fgenesh [5 (link)], GeneMark.hmm [6 (link)], and GlimmerHMM [4 (link)] were applied to the rice genome sequences. Fgenesh and GlimmerHMM were applied to repeat-masked genome sequences. Repeats were masked using RepeatMasker [50 ] and the rice repeat library [51 (link)]. GeneMark.hmm was applied to the unmasked genome sequence; software problems prevented us from running GeneMark.hmm on all repeat-masked genome sequences, and so we chose instead to use the unmasked genome in this case. The AAT software [12 (link)] was used to generate spliced protein and transcript alignments. For generating spliced protein alignments, AAT was used to search a comprehensive and nonredundant protein database that was first filtered from rice protein sequences. A database of other plant transcript sequences was compiled by downloading and joining all plant gene indices provided by The Gene Index at the Dana Farber Cancer Institute [52 ], excepting the rice gene indices. Rice ESTs and FL-cDNAs were aligned to the rice genome and assembled into gene structures as described previously [53 (link)], with the exception being that the high quality single-exon transcript alignments were included here along with spliced alignments.

Repeats in the genome assembly of P. sorghi were defined with RepeatModeler v1.73 (Smit and Hubley 2008 ) and masked with RepeatMasker v4.0.9 (Smit et al. 2013 ). The same library was used to identify repeats in the transcriptome assembly. Gene models were annotated in the genome assembly using MAKER (Cantarel et al. 2008 (link)), with additional putative effectors identified using hidden Markov models (HMM) with HMMER (Eddy 2011 (link))and regular expression string searches of ORFs (Fletcher and Michelmore 2018 ). The MAKER pipeline was provided with the RepeatModeler profile as well as assembled transcripts and translated ORFs from the transcriptome of P. sorghi, all described above, plus ESTs (option: altest) and protein sequences of other oomycete species available from NCBI. MAKER was initially run without a SNAP HMM, inferring genes using est2genome and protein2genome. These predictions were used to train a SNAP HMM (Korf 2004 (link)) that was used for a subsequent run of MAKER with both est2genome and protein2genome set to 0. The predicted proteins were again used to train a new SNAP HMM (Campbell et al. 2014 (link)). This process was repeated twice to generate three SNAP HMMs, which were used sequentially in three independent runs of MAKER. The annotations produced were evaluated as previously described (Fletcher et al. 2018 (link)) to select a single optimal run. This involved contrasting the number of gene models predicted, mean protein length, BLASTp hits to other oomycete annotations, and Pfam domains annotated by InterProScan (Finn et al. 2014 (link); Jones et al. 2014 (link)). Annotation of genes encoding putative effectors was performed as previously described (Fletcher et al. 2018 (link)). Briefly, the entire genome was translated into ORFs. These ORFs were surveyed for secretion signals using SignalP3.1 and SignalP4.0, and crinkler (CRN) motifs of LWY domains using HMMs. For peptides with secretion signals, the 60 residues beyond the predicted cleavage site were surveyed for an RXLR or RXLR-like motif and subsequently for a downstream EER or EER-like motif. ORFs encoding peptides that were predicted to be secreted and contained an (L)WY domain or a CRN motif were considered high-confidence putative effectors (HCPEs). ORFs encoding peptides that were predicted to be secreted and encoded an RXLR and EER domain, but did contain an (L)WY domain, or encoding peptides not predicted to be secreted, but contained an (L)WY domain, or a CRN motif were considered low-confidence putative effectors (LCPEs). The putative effectors and MAKER annotations were reconciled so that annotations did not overlap on the same strand. This was performed so that (1) any HQE or LQE annotations that did not overlap a MAKER annotation were added to the master annotation; for P. sorghi this was 12 HCPEs and 122 LCPEs. (2) HCPEs that overlapped MAKER annotations with the same start coordinates but earlier stop coordinates were discarded; for P. sorghi this was six peptides. (3) HCPEs that overlapped MAKER annotations with the same start coordinates but later stop coordinates replaced the model proposed by MAKER if they had a higher BLASTp score to the NCBI nr database than the overlapping MAKER model; for P. sorghi this was six peptides. (4) HCPEs that overlapped MAKER annotations but had different start coordinates and later or identical stop coordinates were retained over proposed MAKER models; for P. sorghi this was 27 peptides. (5) HCPEs that overlapped MAKER annotations but had different start coordinates and earlier stop coordinates were investigated to determine if the MAKER model should have a modified start coordinate; for P. sorghi this was six peptides. (6) Any LCPEs that overlapped MAKER annotations were discarded; for P. sorghi this was 142 ORFs. The same effector prediction workflow was then applied to the reconciled annotation set to determine the reported effector counts. Tracks for repeats, transcript coverage, annotation, and effector annotations were generated in 100 kb windows along each chromosome using Bedtools v2.29.2 and plotted using Circos (Krzywinski et al. 2009 (link)).

The nucleotide and amino acid sequences for tobacco (Nicotiana tabacum) KED [6 (link)] were used as a starting point for the initial search using BLASTN and BLASTP tools against the GenBank and OneKP database including non-redundant nucleotide and protein sequences, whole-genome shot gun, expressed sequence tags, high throughput genomic sequences, UniProtKB, transcriptome shotgun assembly proteins and protein data bank. Initial search using both the coding nucleotide sequences and the amino acid sequences identified 32 eudicots and at least one monocot (Elaeis guineensis). Subsequent searches were performed against the E. guineensis amino acid sequence through Liliopsida (monocotyledons) database to identify matching sequences of monocots. Likewise, using retrieved sequences to systematically search databases of the same orders and families of eudicotyledons yielded more species of possible matching sequences. Similar strategy was used to identified KED sequences from gymnosperms. Further searches were done sequentially by narrowing organism groups to find matches from more closely related species. However, it must be pointed out that the database search was aimed at surveying broadly the possible taxonomic presence of the KED gene and the retrieved sequences are not by no means an exhaustive outcome due to genomic sequence availability and the annotation quality of the public databases.
To search for possible KED-rich sequences in charophytes, bacteria and animals, KED protein and nucleotide sequences from plants were first repeatedly blasted through each of the intended organism groups in the databases. Then each match was further examined by retrieving its sequence from the database. Translation tool was used to generate open reading frames, followed by amino acid composition analysis, specifically for K+E+D content, to score the putative KED candidates. Once a KED sequence was identified from one taxon group (for example, charophyte), this sequence was used to search the entire available entries from this group. This way, sequences predicting KED-rich open reading frames in genomes of several charophyte, bacterial and animal species were identified.
During the course of searching animal KED candidates, a 6,229-amino acid microtubule-associated protein futsch from honeybee (Apis cerana) was found to contain an internal KED-rich region, whereas its N- and C-terminus portions have normal K, E and D contents. To illustrate examples of the presence of KED sequences in animal species, this 750-amino acid internal KED-rich region was arbitrarily taken out for demonstration in this study.
All retrieved sequences of possible matches were manually reviewed and verified for proper open reading frames and translated sequences. Wherever applicable, both genomic sequences and mRNA sequences were matched to verify the correct coding sequences. The full-length, translated sequences with considerable sequence identity and a high percentage of KED (K+E+D% greater than 30%) were designated as a candidate match.
Only partial KED sequences were available for two plants: cedar (Cryptomeria japonica, a gymnosperm; without C-terminus) and barley (Hordeum vulgare, a monocot, angiosperm; without N-terminus). However, they both still possessed the conserved domain (see “Results” below), therefore were included in sequence comparison analysis. But because their KED protein lengths were unknown and would distort the analysis parameters, they were excluded from the dataset for phylogenetic analysis described below.