Assembly of the haddock genome

Annotation and identifying orthologous genes

An iterative automatic annotation with MAKER [46, 47] using an Illumina based transcriptome of haddock created from reads sequenced by Sørhus et al. [48], and proteins from UniProt/SwissProt [49], annotated 96,576 gene models. InterProScan [50] was run on the predicted proteins of these, and gene names were allocated based on match with proteins in UniProt/SwissProt. We created a filtered set where all genes had an Annotation Edit Distance (AED) [51] of less than 0.5 (where 0.0 indicates perfect concordance between the gene model and evidence (mRNA and/or protein alignments), and 1.0 no concordance). This resulted in 27,437 gene models.

We used OrthoFinder [52] to create a catalogue of orthologous genes, inferring them based on the predicted proteins of different species. We included the following species from Ensembl r81: Amazon molly (Poecilia formosa), cave fish (Astyanax mexicanus), Atlantic cod (Gadus morhua; gadMor1), fugu (Takifugu rubripes), medaka (Oryzias latipes), platyfish (Xiphophorus maculatus), spotted gar (Lepisosteus oculatus), stickleback (Gasterosteus aculeatus), tetraodon (Tetraodon nigroviridis), tilapia (Oreochromis niloticus) and zebrafish (Danio rerio), in addition to haddock and the most recent Atlantic cod genome assembly (gadMor2). For each gene, only the longest protein isoform was used. 281,838 proteins were placed into 17,519 orthogroups, with 20,661 proteins without a match. Cod and haddock have 11,500 groups in common (at least one protein from each species). See Additional file 1: Table S1 for the number of orthogroups shared between the other species-pairs.

Genetic variation and historic effective population size

While we have investigated only one individual per species, in general there is a correlation between nucleotide diversity of one individual and effective population size [54]. We used Pairwise Sequentially Markovian Coalescent (PSMC) [55] to infer the historic effective population size for the two species (Fig. 1). We used a generation time of 10 years for cod and 6 years for haddock [56] with mutation rates derived from the phylogeny used in Malmstrøm et al. (2016) [11]. From this we found that haddock has an approximately 2.5 times larger historic effective population size than cod (Fig. 1).

The TLR repertoire

Thirty-six full-length TLRs were identified for cod, whereas 28 were identified for haddock (Table 3). For both species, TLRs 1/6, 2, 4, 5, 21beta and 26 were not present. The gene numbers for most of the TLRs (TLR 3, 7, 9, 14, 21, 22, 23 and 25) were similar between both species. In contrast, cod had a significantly higher number of TLR22 (10) than haddock (5).

The MHCI repertoire

The number of MHCI loci has previously been characterized in cod, using both qPCR and read-depth comparisons, with 80–100 and ~ 70 copies were estimated, respectively [11, 12]. By using read-depth comparisons for haddock, ~ 30 copies were calculated for this species [11]. Only two copies of MHCI were found in the first version of the cod genome assembly (gadMor1) [12]. We used the new assemblies of cod and haddock to investigate the number of copies of MHCI.

Celera Assembler, the assembler used for assembling melAeg and GM_CA454PB, outputs so-called unitigs in addition to outputting contigs and scaffolds. Unitigs are sequences that are either unique in the genome or are collapsed repeated sequence. These are incorporated into contigs based on different rules (e.g., likelihood of being a repeat). Often, the contigs only contain a subset of the unitigs, and therefore could contain fewer genes. We translated the unitigs assemblies of melAeg and GM_CA454PB into all six reading frames with transeq [57] and searched these with the MHCI PFAM [58] domain PF00129, consisting of alpha 1 and 2, using HMMER [59]. For cod and haddock, the domain spans two exons, thus we counted occurrences of the first and last part of the profile found in the assemblies (Table 4). We found 27 copies of the first part of the domain and 30 copies of the last part in haddock and 69 and 70, respectively, in cod, approximately the same as in Malmstrøm et al. (2016) [11]. It is likely that some of these are collapsed because of the repeated nature of MHCI genes.

Expansion of NLRs in teleosts

The zebrafish has a lineage-specific expansion of the NLRs [60], but it is unclear how many copies are found in other teleost genome assemblies. We investigated the NLRs with several approaches. First, we ran InterProScan [50] on the longest protein per gene to annotate protein domains. We parsed the output and counted occurrences of the PFAM [58] domains PF05729 (NACHT domain) and PF14484 (Fish-specific NACHT associated domain, FISNA) (Fig. 2). Second, we translated the assemblies into all six reading frames with transeq [57] and used these to search for the NACHT and FISNA domains using HMMER [59]. For all species, the number of domains identified was substantially elevated when scrutinizing the assemblies compared to the predicted proteins (Fig. 2). For example, in platyfish the number of NACHT domains increased from 29 to 120. The reported numbers show a large variation in copy number between the different species (Fig. 2), with large difference between relatively closely related species, such as tetraodon and fugu, or cod and haddock, where there are three times as many copies in cod compared to haddock.

For the species with contigs/scaffolds placed into either linkage groups or chromosomes (cod, stickleback, zebrafish, spotted gar, medaka and tetraodon) we counted the number of genes where the relevant domains were found in either placed (i.e. in the linkage map) or unplaced sequences (Fig. 2, Additional file 1: Tables S5-S10). We found that many of the sequences with these kinds of domains are unplaced, as previously reported [20, 60]. While zebrafish has a majority of domains in placed sequences, most sequences in stickleback with FISNA and NACHT domains are not placed. About half the sequences are placed in cod, while most sequences are placed in the other species.

There are multiple reasons for a genome to not assemble properly, but repeated sequence is one of the most influential [23]. Genes occurring in multiple copies such as NLRs are indistinguishable from any other repeated sequence for the assembler. One consequence of this is that some of these unplaced contigs/scaffolds would have higher coverage in reads than average since they basically are collapsed repeats. For haddock and cod we have sequencing read data available, and we estimated and plotted the average coverage for all sequences with the FISNA domain (Fig. 3). Many of the sequences shorter than 100,000 bp show a higher than average coverage. This is especially the case for those sequences around 10,000 bp, and indicates that these contain multiple copies of the FISNA domain, i.e. these contain collapsed copies.

Investigating the STR content of the haddock genome assembly

We investigated the amount of short tandem repeats (STRs) in the haddock genome assembly, compared to cod and other ray-finned fishes. We used Phobos [61] to annotate all STRs with an unit size of 1–10 bp. Haddock has an even higher density of STRs in its genome assembly compared to cod, 96,364 bp/Mbp in haddock and 80,706 bp/Mbp in cod (Fig. 4a). The amino acid coding parts of the genome also contain a high proportion of STRs, 25,639 bp/Mbp in haddock and 16,501 bp/Mbp in cod. This mostly consists of dinucleotide repeats, but both cod and haddock have approximately 6000 bp/Mbp of trinucleotide STRs in protein coding regions, compared to 530 bp/Mbp in medaka, and up to 934 bp/Mbp in zebrafish with the other fishes harboring intermediate amounts (Fig. 4b). Cod and haddock also have higher frequencies (loci/Mbp) of STRs in the assemblies (Fig. 4c and Additional file 1: Table S2), and in the protein coding regions (Fig. 4d). By using the overlap between annotated STRs and genes, we also report the number of genes with one or more STR for these species (Additional file 1: Table S3).

For haddock and cod, we were also able to find indels (called by FreeBayes) and STRs in protein-coding regions, and where these structural variants overlap. We found STRs of all unit sizes in the protein coding regions (Fig. 4d), but those STRs with unit sizes that do not create frame shifts, such as tri-, hexa- and enneanucleotides, are most interesting from a functional perspective. Of these, the vast majority are trinucleotides, and we restricted our analysis to these. We found 581 genes with an indel of size 3 in a trinucleotide repeat in haddock (2.1%) and 660 genes in cod (2.9%), i.e. these are heterozygous in these two individuals.

Between-species comparisons of STR enrichment in genes

Cod and haddock have a much larger proportion of their protein coding sequence in dinucleotide and trinucleotide STRs compared to other species (Fig. 4). In the process of annotating a genome, many genes are assigned a gene ontology term (GO term), describing the processes the protein encoded by that gene is involved in. We wanted to investigate if genes with STRs are randomly spread across different GO groups, or if some GO groups in some species are enriched for genes with STRs. Fisher’s exact test was used to perform pairwise comparisons of the number of genes with STRs and the number of genes without STRs between each species (Fig. 5 for examples, Additional file 2: Figure S1 and Additional file 1: Table S4 for details). Of the 2748 GO terms in the dataset, there are significant differences between species in 74 GO groups after correcting for multiple testing (false discovery rate with Benjamini/Yekutieli). For many of these, haddock and cod differ significantly from all other species, but not from each other (Additional file 1: Table S4). These include protein kinase activity (GO:0004672), G-protein coupled receptor activity (GO:0004930), signal transduction (GO:0007165), metabolic process (GO:0008152) and transmembrane transport (GO:0055085).

Within-species comparisons of STR enrichment within genes

To investigate enrichment and purification (under-representation) of STRs in GO terms, we used goatools.

[62] (Fig. 6, Additional file 3: Figure S2). We corrected for multiple testing. For some terms, both cod and haddock are enriched, whereas this is not the case in the other species. These are cation channel activity (GO:0005261), regulation of signal transduction (GO:0009966), regulation of cell communication (GO:0010646), regulation of signaling (GO:0023051), regulation of Rho protein signal transduction (GO:0035023), regulation of Ras protein signal transduction (GO:0046578), regulation of response to stimulus (GO:0048583), regulation of small GTPase mediated signal transduction (GO:0051056), regulation of intracellular signal transduction (GO:1902531). These are mainly in the hierarchy above regulation of Rho protein signal transduction (GO:0035023), as well as cation channel activity (GO:0005261).

A highly contiguous genome assembly for haddock

Here we have taken advantage of long and short read technologies to produce an annotated and highly contiguous assembly of the haddock genome, with comparable gene content and assembly statistics to the recently released Atlantic cod genome assembly [28] (Table 1). The genic completeness of the assembly is high, as seen by the BUSCO score, where > 90% of the 4584 genes are found complete (Table 1). PacBio reads span more repeated regions than Illumina reads, and the contig N50 is therefore longer for the haddock assembly than other fishes sequenced with only Illumina reads, for instance the Asian arowana [63] and the seahorse [5]. With the increased affordability, availability and usage of such long-read sequencing technologies as PacBio [64] and Oxford Nanopore [65] reads, more complete assemblies for diverse species are likely to arrive in near future.

Increased number of tandem repeats in codfishes

Several studies have shown the Atlantic cod genome has a high STR content [66–68]. The first version of the cod genome assembly [12] was fragmented, and STRs have recently been identified as the main factor causing this fragmentation [28]. Since STRs have a high mutation rate, their presence in genes might disrupt normal gene product function, as seen for the multitude of human diseases due to large expansions in STRs [69]. Surprisingly, while both cod and haddock have a high density and frequency of STRs in the assembly overall, they also have a substantial amount of STRs in protein coding regions compared to other ray-finned fish (Fig. 4). STRs shrink and expand by DNA polymerase slippage or recombination [29], but a repeated motif has to be present for this to happen. A short tandem repeat might be created by a mutation (changing ATAG to ATAT), or as the result of transposable element activity [70]. Further work is needed to investigate the basis for the high STR content in Atlantic cod and haddock and in codfishes in general.

STRs are present in almost twice as many genes in cod and haddock compared to the other ray-finned fishes (Additional file 1: Table S3). Specifically, in around 8000 genes in codfishes compared to 1500–4000 in the other species. This is almost twice as many as in humans (4500) [31]. In humans, genes connected to processes such as transcriptional regulation, chromatin remodeling, morphogenesis, and neurogenesis have been found enriched for STRs [34, 71]. Similar enrichment has been found in other species, such as yeast [35], fruit fly [36] and plants and algae [37]. In the fish species investigated here, there is enrichment in genes with STRs in functional (Gene Ontology) groups primarily concerned with transcription, similar to previous studies [35–37] (Additional file 3: Figure S2). One example is the transcriptional regulator Ssn6 in yeast, where increased length of a polyglutamine tract (encoded by a STR), was positively correlated with increased expression of some target genes, and negatively correlated with others [72]. Haddock and cod have significantly larger proportions of genes with STRs in GO groups associated with genes encoding proteins involved in signal transduction compared to the other species. These GO groups contain a higher proportion of genes with STRs than expected with comparing GO groups per species. This is also true when comparing GO groups between species. Many of these functional groups are connected to small GTP-binding proteins such as regulation of Rho protein signal transduction (GO:0035023), regulation of Ras protein signal transduction (GO:0046578), and regulation of small GTPase mediated signal transduction (GO:0051056). The small GTP-binding proteins are involved in regulation of processes such as gene expression, cytoskeletal reorganization, intracellular vesicle trafficking and cytokinesis [73, 74]. The regulation of the activity of small GTPases are mainly performed by GTPase-activating proteins (GAPs) and guanine nucleotide-exchange factors (GEFs) by suppression (GAPs) or promotion (GEFs) of the GTPase’ activity [75]. For instance, in humans, 81 GEFs and 67 GAPs [76] regulate the activity of the 22 Rho GTPases [77]. Some of the small GTPases are important for proper immune function [78, 79], by regulating chemotaxis and phagocytosis [80]. In mammals, the GTPase RhoA is important for TLR signaling, specifically for TLR2 and TLR4 [80]. Thus, between two populations of codfishes, adapted to different environments, there may potentially be variation in immune responses based on length variations of STRs in GEFs and GAPs.

Historic effective population size and STRs

Many marine fish with a pelagic life style are characterized by large effective population sizes [81]. Atlantic herring has an estimated effective population size of approximately 1 million and a nucleotide diversity of 0.32% [4], similar to cod with an effective population size around 400,000 and 0.39% nucleotide diversity and haddock at around 1.1 million and 0.54% nucleotide diversity (Table 1). Intriguingly, herring seems to have a high amount of STRs (Supplementary File E in [4]), suggesting that the life history strategies of cod, haddock and herring might facilitate a high density and frequency of STRs. The high effective population sizes in these species would imply low genetic drift and more efficient selection.

With around 760,000 STR loci in haddock and cod (Additional file 1: Table S2), the majority are likely to be highly polymorphic in such large haddock and cod populations. In a study of over 1000 human individuals, most of the 700,000 STR loci sequenced were polymorphic [31], although constraints were apparent for mutations in coding sequences [30]. Haddock and cod (Fig. 1) have at least ten times the historic effective population size of humans [55], and their high fecundity would generate many STR variants for each generation. We find trinucleotide indels in STRs in 2–3% of the genes, i.e., they have different length variants of the STRs in these genes. With such large effective populations and few barriers, genetic drift is weak, and local populations should respond to even weak selection [81]. There are studies suggesting STR loci are under selection in cod [82, 83]. Most tools for genome-wide investigations of selection have focused on SNPs, but methods for selection on STRs have been developed [84]. With high accuracy STR genotyping [85, 86] and resequencing data from different populations or controlled experiments over several generations, we suspect substantial numbers of STRs under selection will be found.

The MHCI and TLR repertoire in haddock and cod

In the first cod genome assembly, only two MHCI classical U-lineage genes were found, despite qPCR indicating around 100 copies [12]. Other investigations have also estimated a large number of MHCI copies in cod [11, 87, 88], but these have either investigated transcriptional data or read depth comparisons between MHCI loci and single-copy genes. Malmstørm et al. (2016) [11] estimated around 30 copies in haddock and 70 in cod. We found similar numbers to those predicted by [11] using our unitig assemblies of the same species; however in contrast a much lower number was found in the final assemblies. In the cod assembly, seven of the in total thirteen MHCI copies with complete alpha domains are located on unplaced contigs/scaffold in the gadMor2 assembly (data not shown). Their numbers are likely to be underestimated because the unplaced contigs/scaffold often have a higher read depth, indicating that these contain multiple, collapsed copies. Using PacBio reads in both the haddock and the cod assemblies likely substantially contributed to the more complete representation of MHCI genes, compared to the previous cod genome assembly. The Asian seabass, another assembly based on PacBio reads, resulted in “a more continuous cluster of MHC-class I genes compared to the well-assembled G. aculeatus [three-spined stickleback] genome” [26], highlighting the importance of long reads for properly capturing these regions of the genome. In contrast, the TLR repertoire is by and large similar between haddock and cod. The only main difference is found within TLR22; with twice as many copies in cod (10 vs. 5). We were unable to perform the domain-based search for TLRs, since they do not have a TLR-specific domain. The TIR domain (PFAM domain PF01582), the most likely candidate, is also found in the large interleukin-1 receptor family [89] and in adaptor proteins such as MyD88 and SARM [90].

The high copy number of NLRs in teleosts

In this study we enumerate genes (putative NLRs) with the NACHT (PFAM domain PF05729) and FISNA (PF14484) domains. These two domains together characterize a family of proteins substantially expanded in zebrafish with around 400 copies [60] and indications of substantial expansions in other teleosts as well [20, 91, 92].

For genome assemblers, identical or highly similar sequences occurring in multiple locations in a genome are indistinguishable from repeated sequence such as for example transposable elements. Depending on the sequencing strategy and assembler, these may introduce gaps into an assembly because the assembler is unable to place them correctly and they might be collapsed as a single contig/scaffold [23]. In general, genome assemblers might treat the large amount of NLR genes in these species as repeated sequence, and thus be unable to place them into scaffolds. For the species with genome assemblies in linkage groups or chromosomes, we looked at the contigs/scaffolds that were placed into these versus those that were not (Fig. 2). Even with the large number of genes (> 400), only 10% of the putative NLRs are unplaced for zebrafish. This is likely due to its sequencing and assembly strategy, with tiling of individually sequenced and assembled bacterial artificial chromosome clones [93]. For Atlantic cod, about 50% the contigs/scaffolds with putative NLRs are unplaced, and for stickleback about 15% are unplaced. The stickleback genome assembly is based on 9× coverage with Sanger sequencing reads [94], which may result in a more fragmented assembly than using PacBio reads (as for cod) or clones (zebrafish) because Sanger sequencing reads are shorter.

The numbers of putative NLRs from Fig. 2 should be interpreted with caution. It is likely that all species have some or several of the gene copies collapsed [20]. For Atlantic cod and haddock, we mapped reads back to the assembly, and investigated the coverage for all sequences (Fig. 3). There are many contigs/scaffolds with more than 5 times coverage compared to the average in the assemblies, and the numbers of putative NLRs are likely underestimated. Even though these two assemblies are highly contiguous and have been created with the use of PacBio reads, multi-copy genes such as NLRs may still be problematic. We also investigated the content of the unitig assemblies for Atlantic cod and haddock, and found similar numbers of NLRs between the two species (Table 5), however, many of these are likely pseudogenes due to stop codons. The difference between the unitig assemblies and the final assemblies are because of differences in assembly processes (Additional file 1: Note S1), where the final haddock assembly contains fewer short contigs. Most likely the NLR content of the two codfishes is highly similar. The numbers of NLRs are likely severely underestimated in most currently investigated ray-finned fish. Assemblies of higher quality are needed to properly investigate this intriguing family of innate immune genes.

It is unclear how such large gene families as the NLRs in zebrafish evolved [95]. In zebrafish, the majority of NLRs are located on one chromosome 4 arm [60] (Additional file 1: Table S6). Although the other assemblies are of lower quality than the zebrafish genome, there are no clear patterns of chromosomal enrichment in NLRs in other ray-finned fishes. Possible exceptions are medaka with 33 FISNA domains found on linkage group 2 (Additional file 1: Table S9) and stickleback with 12 FISNA and NACHT domains found on groupXIII (Additional file 1: Table S7). For Atlantic cod, the NLRs are evenly divided across linkage groups (Additional file 1: Table S5). Further, tetraodon (Additional file 1: Table S10) and spotted gar (Additional file 1: Table S8) have relatively few copies in total.

Sampling and sequencing

The sequenced individual, a wild caught specimen approximately 1.3 kg belonging to the North-East Artic haddock population, was sampled near the Lofoten Islands (N68.04 E13.41), outside of its spawning season (in July 2009). We always aim to limit the effect of our research on populations and individuals. Whenever possible we collaborate with other sources, such as commercial fisheries or aquaculture farms, where samples can be harvested freely in combination with their normal business. This way, no animals need to be euthanized to serve our scientific purpose alone. The specimen used in this study comes from a wild population and was part of a larger haul of commercially fished individuals intended for human consumption. Following capture the fish was immediately stunned with a blunt object, then killed by bleeding, following standard procedure by local fishermen. Sampling in this manner does not fall under any specific legislation in Norway, but it is in accordance with the guidelines set by the ‘Norwegian consensus platform for replacement, reduction and refinement of animal experiments’ (www.norecopa.no). DNA was extracted from the spleen (stored on RNALater) using a standard high salt DNA extraction protocol.

200 bp insert size paired end libraries were constructed with Illumina DNA paired end sample preparation reagents and sequenced at the McGill University and Génome Québec Innovation Centre, both 100 bp long reads, with 322 M read pairs, 64 Gbp of sequence in total and 150 bp reads, with 224 M read pairs and 67 Gbp sequence. The 3 kbp (368 M read pairs, 74 Gbp) and 10 kbp (175 M read pairs, 35 Gbp) insert size libraries were prepared with the Illumina Mate Pair gDNA reagents and sequenced at the McGill University and Génome Québec Innovation Centre with 100 bp reads. All Illumina libraries were sequenced on the HiSeq 2000 using V3 chemistry.

PacBio SMRT sequencing was performed on a PacBio RS II instrument (Pacific Biosciences of California Inc., Menlo Park, CA, USA) at the Norwegian Sequencing Centre (NSC, www.sequencing.uio.no/). Long insert SMRTbell template libraries were prepared at NSC according to PacBio protocols. In total, 24 SMRT-cells were sequenced using P6v2 polymerase binding and C4 sequencing kits with 120 min acquisition. Approximately 16.4 Gbp of library bases were produced from 2.7 M reads with average read length of 5980 bp.

Assembly

Annotation

Finding orthologues

We downloaded all genome assemblies, cDNA and protein fasta files for all fishes at Ensembl release 81 (Amazon molly, cavefish, Atlantic cod (gadMor1), fugu, medaka, platyfish, spotted gar, stickleback, tetraodon, tilapia and zebrafish), and extracted the longest protein using a custom script (get_only_longest_protein_per_gene.py) because some annotations provide multiple proteins per gene. We did an all-against-all BLASTP of the protein sequences of all the Ensembl fishes in addition to the new cod and haddock annotated proteins, following the default options as set by OrthoFinder. The results of this were used as input to OrthoFinder v1.0.6 [52].

Investigating variants in the haddock and cod assemblies

Both haddock and cod were sequenced in the [11] study, and these 150 bp reads were mapped to the respective assemblies using BWA MEM v0.7.12 [40], and sorted using samtools v0.1.19 [107]. Bamtools v2.3.0 and the script ‘coverage_to_regions.py’ from FreeBayes v0.9.14 [53] were used to split the assembly into regions, and FreeBayes was run in parallel. Vcflib from a GitHub snapshot at 20140325 was used to filter the variants, and only variants with more than 20 in quality and 5 in depth were retained.

Estimating historic effective population size

A GitHub snapshot from August25th 2015 of PSMC [55] was used together with samtools v1.1 and bcftools v1.2 on the mapped reads, and historic effective population size was inferred for cod and haddock. The mutation rates were estimated along the branches of the phylogeny reported in [11] and the generation times were set to 10 years for cod and 6 years for haddock [56].

Identification of TLRs

Toll-like receptors (TLRs) are a key component of the innate immune response. The toll interleukine receptor (TIR) is the most conserved domain of the TLRs [108]. To determine candidate regions likely containing TLR genes, we aligned all TIRs protein sequences available on Ensembl and GenBank against the haddock and cod genome assemblies using TBLASTN from the BLAST+ suite [109] with an e-value cutoff of 1e-10. We then extracted 10,000 bp around the regions containing TIR like motifs. We used BLASTN to align coding sequences representative of all the TLRs classes against the candidate regions containing TLR copies. Here we report full-length TLR copies as well as partial copies (≥60% of the coding sequence).

Identification of MHCI

We used the alpha-3 domain of the MHCI complex to identify the candidate regions containing MHCI genes in both haddock and Atlantic cod. We used TBLASTN to align alpha-3 coding sequences from Atlantic cod and zebra fish (Danio rerio) against the haddock and Atlantic cod genome assemblies, with an e-value threshold of 1e-10. We then extracted the region located 10,000 bp around the putative alpha-3 domains. We used BLASTN to align the extracted regions against the non-redundant nucleotide database on NCBI. Regions containing the three alpha domains of MHCI (α1, α2 and α3) were used as a proxy to determine the number of MHCI gene copy number.

To better assess the differences between the unitig assemblies and the final assemblies, we translated the unitigs assemblies of melAeg and GM_CA454PB (both are basis for the final assemblies) into all six reading frames with transeq from Emboss v6.5.7 [57], and used the PFAM v31.0 [58] domain PF00129 (Class I Histocompatibility antigen, domains alpha 1 and 2; MHCI) in HMMER v3.1b2 [59] to search the unitig assemblies for putative MHCI genes.

Identification of NLRs

We ran InterProScan v5.4–47 [50] on the longest protein per gene to annotate protein domains. The default Ensembl annotation of these seemed out of date for several species, and with this procedure we had a more uniform dataset. We counted the occurrences of the PFAM v31.0 [58] domains PF05729 (NACHT domain) and PF14484 (Fish-specific NACHT associated domain, FISNA). In addition we translated the assemblies of all species into all six reading frames with transeq from Emboss v6.5.7 [57], and searched these with the NACHT and FISNA domains with HMMER v3.1b2 [59]. The species relationship in Fig. 2 is derived from [11] and we used ETE3 [110] to plot the dendogram.

We used v1.3.1 of samtools [107] with the ‘depth –a –a’ option to calculate the per base pair coverage of the assemblies, and used awk to calculate average depth per sequence and average for the whole assembly. We extracted all sequences with FISNA domains, and plotted length versus depth for these using ggplot2 [111] in the R environment.

As for MHCI, we searched the unitig assemblies of cod and haddock with the FISNA and NACHT domains.

STRs in the assemblies and coding regions

We used Phobos v3.3.12 [61] to detect all TRs with unit size 1–10 bp in the assemblies. The output was in Phobos native format that was processed with the sat-stat v1.3.12 program, yielding files with different statistics and a gff file. The other settings were as used in [28].

We counted the number of different STRs in genes and number of genes with STRs by using bedtools [112] and overlaps between STRs and genes. For cod and haddock, we also counted the number of overlaps between trinucleotide TRs, indels of size 3 and genes.

Enrichment of STRs in genes

For each gene ontology group we performed pairwise comparisons of the number of genes with STRs and total number of genes between the different species using Fisher’s exact test (implemented in SciPy [113]). We corrected for multiple testing using the Benjamini-Yekutieli [114] procedure of False Discovery Rate as implemented in statsmodels (http://www.statsmodels.org/stable/index.html). Of 2748 gene ontology terms, we found significant differences in 74.

For each gene ontology group we also tested the enrichment or purification of STRs compared to amount of STRs all the genes in a species using goatools, and correcting for multiple testing with Benjamini-Yekutieli procedure of False Discovery Rate [62].