Whole genome sequencing data and de novo draft assemblies for 66 teleost species

Abstract

Teleost fishes comprise more than half of all vertebrate species, yet genomic data are only available for 0.2% of their diversity. Here, we present whole genome sequencing data for 66 new species of teleosts, vastly expanding the availability of genomic data for this important vertebrate group. We report on de novo assemblies based on low-coverage (9–39×) sequencing and present detailed methodology for all analyses. To facilitate further utilization of this data set, we present statistical analyses of the gene space completeness and verify the expected phylogenetic position of the sequenced genomes in a large mitogenomic context. We further present a nuclear marker set used for phylogenetic inference and evaluate each gene tree in relation to the species tree to test for homogeneity in the phylogenetic signal. Collectively, these analyses illustrate the robustness of this highly diverse data set and enable extensive reuse of the selected phylogenetic markers and the genomic data in general. This data set covers all major teleost lineages and provides unprecedented opportunities for comparative studies of teleosts.

Background & Summary

Fueled by recent advances in comparative genomics, teleost fishes are becoming increasingly important research objects in several scientific disciplines, ranging from ecology, physiology and evolution to medicine, cancer research and aquaculture^1–7. Genome information from non-model organisms is highly important in these comparative genomic analyses as they represent specific phenotypes that aid in disentangling the common parts of gene sets from those that have evolved as adaptations to specific ecosystems. In a quest to identify the evolutionary origin of the MHC II pathway loss first observed in the Atlantic cod (Gadus morhua)^8,9, we applied a single sequencing library procedure to cost-efficiently produce draft assemblies for 66 teleost species, representing all major lineages within teleost fishes¹⁰. Since the alternative immune system, characterized by both the lack of MHC II and an expansion of MHC I, has so far only been identified in the Atlantic cod, we sampled the cod-like fishes of the order Gadiformes more densely than other groups, including 27 species of this order. Based on these genome sequence data, we were able to reconstruct the evolutionary history of the sampled lineages, to pinpoint the loss of the MHC II pathway to the common ancestor of all Gadiformes, and to identify several independent expansions in MHC I copy number within and outside the order Gadiformes. While these analyses and results are reported in a companion paper (Malmstrøm et al.¹¹), we here present in greater detail the underlying data sets used for these analyses, including samples, sequencing reads (Data Citation 1), draft assemblies (Data Citation 2), and both mitochondrial and nuclear phylogenetic markers. By providing these data and the applied methodology in a coherent manner we aim to supply the scientific community with a highly diverse, reliable, and easy-to-use genomic resource for future comparative studies.

Our sequencing strategy was chosen on the basis of several pseudo-replicates of the budgerigar (Melopsittacus undulatus) genome¹² (Data Citation 3), comprising different combinations of read lengths and coverages to determine the most cost-effective manner to produce genome data of sufficient quality for a reliable determination of gene presence or absence. These budgerigar data sets were furthermore assembled with two of the most used assemblers, the de Bruijn graph based SOAPdenovo¹³ and the Overlap-Layout-Consensus based Celera Assembler¹⁴ to investigate which assembly algorithm performed best on the various data replicates. On the basis of these in silico experiments, all species were sequenced on the Illumina HiSeq2000 platform, aiming for ~15× coverage. The sequenced reads were then quality controlled, error corrected and trimmed before performing assembly with Celera Assembler. The continuity of the assemblies was subsequently assessed through N50 statistics and the assembly quality was evaluated on the basis of gene space completeness of highly conserved genes. The assemblies were further used to identify mitochondrial genome sequences, which we used in combination with previously available sequences of related teleosts to verify the phylogenetic positions of sampled taxa (Data Citations 4 to 124). By recovering all taxa in their expected positions, clustering with conspecific or congeneric sequences where such were available, our phylogenetic analysis corroborates the correct identification of all sampled taxa and the absence of DNA contamination.

Figure 1 illustrates the total workflow, and detailed information for each analysis step is further provided in the Methods section and in Tables 1–7 (available online only). The data sets presented here contain sequencing reads and assembled draft genomes for non-model species adapted to a wide variety of habitats, ranging from the deep sea and tropical coral reefs, to rivers and freshwater lakes. These data sets can be used individually or collectively, as resources for studies such as gene family evolution, adaptation to different habitats, phylogenetic inference of teleost orders, transposons and repeat content evolution as well as many other applications regarding gene and genome evolution in a comparative or model organism framework.

Figure 1. Flowchart illustrating the processes involved in creating and validating sequence data for 66 teleost species.

(1) A full overview of species, sample supplier and tissue used for DNA extraction is provided in Table 1 (available online only). (2) The DNA extraction method is also found in Table 1 (available online only). (3) All sequencing libraries were created using the Illumina TruSeq Sample Prep v2 Low-Throughput Protocol. Adaptor indexes are provided in Table 2 (available online only). (4) Sequencing statistics and insert sizes for all species are also listed in Table 2 (available online only). (5) FastQC and SGA PreQC analyses were performed for all read sets prior to assembly. (6) Estimated genome sizes, coverages and assembly statistics for all species are presented in Table 3 (available online only), and accession links are provided in Table 4 (available online only). (7) CEGMA and BUSCO statistics are reported in Table 5 (available online only). (8) GenBank accession numbers and UTG IDs for all mitochondrial genomes used in phylogenetic analyses are provided in Tables 6 and 7 (available online only). (9) The maximum-likelihood phylogeny based on mitochondrial genomes is presented in Fig. 3.

Methods

Sample acquisition and DNA extraction

The majority of samples were taken from validated species (mostly voucher specimens) and were provided by museums or university collections. Some samples were obtained from wild caught specimens, in collaboration with local fishermen. All samples were stored on either 96% ethanol or RNA-later (Ambion). The extraction of genomic DNA was carried out using either EZNA Tissue DNA Kit (Omega Bio-Tek), following the manufacturer’s instructions, or using the ‘High salt DNA extraction’ method as described by Phill Watts (https://www.liverpool.ac.uk/~kempsj/IsolationofDNA.pdf). Detailed information about all samples, including origin, voucher specimen ID and DNA extraction method is provided in Table 1 (available online only).

Table 1. Sample information for all species in the reported data set.

Order	Species	Tissue	Sample ID	Voucher ID
ZSCM numbers are vouchers from Zoological State Collection Munich
CFM numbers are vouchers from Chicago Field Museum collection
ZMUC number refers to voucher from Zoological Museum University of Copenhagen collection
KUI number refer to voucher from University of Kansas Biodiversity Institute Icthyology collection
UAIC number refers to voucher from University of Alabama Ichtyology collection
SAIAB number refers to voucher from South African Institute for Aquatic Biodiversity collection
MCZ number refer to voucher from Museum of Comparative Zoology, Harvard University collection
NSMT-P number refer to voucher from National Museum of Nature and Science, Tsukuba, Japan
1Kjartan Østbye (University of Oslo, Norway), 2Jan Yde Poulsen (Greenland Institute of Natural Resources, Greenland), 3Reinhold Hanel (Thünen-Institute of Fisheries Ecology, Germany), 4Masaki Miya (Natural History Museum & Institute in Chiba, Japan), 5Andrew Bentley (University of Kansas Biodiversity Institute, USA), 6Martin Malmstrøm (University of Oslo, Norway), 7Christophe Pampoulie (Marine Research Institute of Iceland, Iceland), 8Irvin Kilde (NorwegianUniversity of Science and Technology in Trondheim, Norway), 9Walter Salzburger (University of Basel, Switzerland), 10Ian Bradbury (Memorial university, Canada), 11Lukas Rüber (Natural History Museum in Bern, Switzerland), 12Fabio Cortesi (University of Queensland, Australia)
Osmeriformes	Osmerus eperlanus1	Fin*	Osep_1_#2	NA
Stomiatiformes	Borostomias antarcticus2	Muscle†	JYP 598	ZMUC 8046
Aulopiformes	Parasudis fraserbrunneri3	Muscle*	A430	CFM 117870
Ateleopodiformes	Guentherus altivela3	Muscle*	B375	NA
Myctophiformes	Benthosema glaciale2	Muscle†	JYP 403	ZMUC 8477
Polymyxiformes	Polymixia japonica4	Muscle*	NSMT-P 79586.1	NSMTNAP 79586
Percopsiformes	Percopsis transmontana5	Muscle*	KU:KUIT:1890	KU:KUI:29775
Percopsiformes	Typhlichthys subterraneus5	Muscle*	KU:KUIT:8754	UAIC 14148.01
Zeiformes	Zeus faber3	Muscle*	B11	ZSCM 32795
Zeiformes	Cyttopsis roseus3	Muscle†	B361	ZSCM 32479
Stylephoriformes	Stylephorus chordatus5	Muscle*	KU:KUIT:8138	MCZ 165920
Gadiformes	Bregmaceros cantori5	Muscle†	KU:KUIT:5133	KU:KUI:30244
Gadiformes	Merluccius polli3	Muscle†	B116	ZSCM 40336
Gadiformes	Merluccius merluccius6	Thymus*	Meme(Ly)_IOF_1_#2	NA
Gadiformes	Merluccius capensis3	Muscle†	B16	ZSCM 32773
Gadiformes	Melanonus zugmayeri3	Muscle*	B304	ZSCM 32519
Gadiformes	Muraenolepis marmoratus3	Muscle*	#95	NA
Gadiformes	Trachyrincus scabrus3	Muscle*	A35	CFM 117888
Gadiformes	Trachyrincus murrayi7	Fin*	A9-2012-420-171-1	NA
Gadiformes	Mora moro8	Muscle*	Momo(Dy)_Sula_4_#1	NA
Gadiformes	Laemonema laureysi3	Muscle†	B43	ZSCM 32710
Gadiformes	Bathygadus melanobranchus3	Muscle*	B365	ZSCM 40344
Gadiformes	Macrourus berglax6	Muscle*	Mabe_1_#1	NA
Gadiformes	Malacocephalus occidentalis3	Muscle*	A25	CFM 117884
Gadiformes	Phycis blennoides7	Fin*	A9-2012-418-76-1	NA
Gadiformes	Phycis phycis3	Muscle†	Phph_X5	NA
Gadiformes	Lota lota3	Muscle*	Lolo_X10	NA
Gadiformes	Molva molva6	Thymus*	Momo(Br)_IOF_1_#2	NA
Gadiformes	Brosme brosme6	Spleen*	Brbr_LO_1_#2	NA
Gadiformes	Trisopterus minutus6	Spleen†	Trmi_IOF_1_#1	NA
Gadiformes	Gadiculus argenteus6	Spleen†	Gaar_IOF_1_#2	NA
Gadiformes	Pollachius virens6	Spleen†	Povi_LO_1_#1	NA
Gadiformes	Melanogrammus aeglefinus6	Spleen†	Meae_LO_1_#2	NA
Gadiformes	Merlangius merlangus6	Thymus*	Meme(Hy)_OOF_1_#2	NA
Gadiformes	Arctogadus glacialis9	Fin†	0A-08-045_#3	NA
Gadiformes	Boreogadus saida7	Fin*	B3-2012-189-6_#1	NA
Gadiformes	Theragra chalcogramma10	Fin†	HS-08.010_#1	NA
Gadiformes	Gadus morhua6	Blood‡	NEAC_001	NA
Lampriformes	Regalecus glesne3	Muscle*	Regl_X3	NA
Lampriformes	Lampris guttatus3	Muscle*	Lagu_X8	NA
Beryciformes	Monocentris japonica4	Muscle*	NSMT-P 75883.1	NSMTNAP 75883
Holocentriformes	Myripristis jacobus3	Blood*	KV124	NA
Holocentriformes	Holocentrus rufus3	Blood†	X2	NA
Holocentriformes	Neoniphon sammara5	Muscle*	KU:KUIT:6925	SAIAB 77852
Beryciformes	Beryx splendens3	Muscle†	A252	NA
Beryciformes	Rondeletia loricata5	Muscle*	KU:KUIT:8426	MCZ 167869
Beryciformes	Acanthochaenus luetkenii5	Muscle†	KU:KUIT:8430	MCZ 167873
Ophidiiformes	Brotula barbata3	Muscle*	B392	ZSCM 32626
Ophidiiformes	Lamprogrammus exutus3	Muscle†	A69	CFM 118100
Ophidiiformes	Carapus acus3	Muscle†	B358	ZSCM 32503
Batrachoidiformes	Chatrabus melanurus3	Muscle†	B25	ZSCM 32594
Scombriformes	Thunnus albacares3	Muscle†	Sp569	NA
Gobiiformes	Lesueurigobius cf. sanzi3	Muscle*	B265	NA
Perciformes	Perca fluviatilis3	Muscle†	Pefl_NEG_1_#1	NA
Perciformes	Myoxocephalus scorpius3	Muscle*	Seescorpion_1	NA
Perciformes	Sebastes norvegicus6	Spleen†	Seno_LO_1_#1	NA
Perciformes	Chaenocephalus aceratus9	Muscle†	ANT-XXVII/3,#299	NA
Labriformes	Symphodus melops6	Muscle*	Kg47	NA
Spariformes	Spondyliosoma cantharus3	Muscle*	Sp28	NA
Lophiiformes	Antennarius striatus3	Muscle†	B133	ZSCM 32591
Carangiformes	Selene dorsalis3	Muscle†	B97	NA
Anabantiformes	Helostoma temminckii11	Muscle*	LR10256	NA
Anabantiformes	Anabas testudineus11	Muscle†	LR11643	NA
Blenniiformes	Parablennius parvicornis11	Muscle*	LR00424	NA
Ovalentariae incertae sedis	Chromis chromis3	Muscle*	Sp51	NA
Ovalentariae incertae sedis	Pseudochromis fuscus12	Muscle*	F5-C4_1	NA

*Isolated with EZNA spin columns

^†Isolated with high salt method

^‡Isolated with blood plug protocol (See Star et al.)8

Fragmentation and library preparation

Genomic DNA samples were diluted to 120 μl (50 ng μl⁻¹) with Qiagen Elution Buffer (Qiagen) if necessary and fragmented to lengths of ~400 bp by sonication using a Covaris S220 (Life Technologies) with the following settings: 200 cycles for 90 s with ω-peak at 105. All sequencing libraries were constructed following the Illumina TruSeq Sample Prep v2 Low-Throughput Protocol.

Sequencing and quality control

All sequencing was performed on an Illumina HiSeq 2000 platform with additional chemicals added to extend the number of cycles, yielding paired reads of 150 bp each. The read quality was then assessed using FastQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Prior to assembly we used SGA PreQC¹⁵ to estimate coverage, per-base error rates, level of heterozygosity, repeat content and genome size in order to assess whether more sequencing would be needed. Some samples were then subjected to a second round of sequencing of the same library. Sequencing statistics are presented in Table 2 (available online only).

Table 2. Sequencing information for all species in the reported data set (Data Citation 1).

Species	N reads*	N bases	Bases used†	Insert size‡	Adaptor index
Osmerus eperlanus	84.7	12,709	74%	366	AD023
Borostomias antarcticus	131.9	19,789	83%	439	AD002
Parasudis fraserbrunneri	149.6	22,436	82%	366	AD022
Guentherus altivela	101.7	15,256	97%	380	AD020
Benthosema glaciale	194.4	29,154	87%	453	AD002
Polymixia japonica	90.2	13,525	85%	367	AD016
Percopsis transmontana	147.8	22,168	82%	350	AD001
Typhlichthys subterraneus	142.7	21,398	81%	336	AD003
Zeus faber	151.5	22,722	74%	335	AD008
Cyttopsis roseus	161.0	24,156	75%	340	AD013
Stylephorus chordatus	171.7	25,752	87%	428	AD014
Bregmaceros cantori	310.3	46,537	84%	343	AD013
Merluccius polli	112.9	16,931	80%	353	AD015
Merluccius merluccius	146.5	21,968	82%	359	AD013
Merluccius capensis	107.0	16,047	81%	353	AD014
Melanonus zugmayeri	179.2	26,884	81%	351	AD016
Muraenolepis marmoratus	127.3	19,101	80%	345	AD009
Trachyrincus scabrus	179.5	26,928	82%	358	AD008
Trachyrincus murrayi	66.8	20,050	89%	519	AD014
Mora moro	153.0	22,945	85%	345	AD014
Laemonema laureysi	105.7	15,862	80%	348	AD015
Bathygadus melanobranchus	122.4	18,362	83%	330	AD008
Macrourus berglax	119.7	17,948	82%	356	AD001
Malacocephalus occidentalis	140.8	21,117	78%	352	AD003
Phycis blennoides	80.6	24,191	87%	505	AD015
Phycis phycis	130.9	19,631	76%	328	AD005
Lota lota	125.1	18,758	83%	341	AD007
Molva molva	152.3	22,852	79%	329	AD006
Brosme brosme	122.5	18,373	80%	342	AD012
Trisopterus minutus	151.6	22,737	76%	325	AD005
Gadiculus argenteus	138.1	20,709	79%	320	AD004
Pollachius virens	112.4	16,854	79%	328	AD006
Melanogrammus aeglefinus	110.9	16,639	82%	324	AD007
Merlangius merlangus	165.2	24,787	85%	339	AD012
Arctogadus glacialis	165.2	22,006	81%	323	AD002
Boreogadus saida	155.1	25,243	77%	316	AD004
Theragra chalcogramma	155.1	23,268	84%	322	AD002
Gadus morhua	62.8	18,847	92%	531	AD019
Regalecus glesne	183.9	27,578	83%	344	AD027
Lampris guttatus	162.7	24,407	82%	353	AD010
Monocentris japonica	90.5	13,580	87%	468	AD012
Myripristis jacobus	64.3	19,274	84%	353	AD001
Holocentrus rufus	61.5	18,463	83%	354	AD003
Neoniphon sammara	90.9	13,631	87%	474	AD005
Beryx splendens	90.1	13,509	97%	465	AD005
Rondeletia loricata	127.8	19,167	89%	467	AD004
Acanthochaenus luetkenii	129.4	19,411	90%	430	AD014
Brotula barbata	131.8	19,769	81%	352	AD015
Lamprogrammus exutus	63.3	9,500	93%	348	AD014
Carapus acus	102.7	15,410	79%	349	AD016
Chatrabus melanurus	333.0	49,954	82%	353	AD020
Thunnus albacares	120.5	18,075	86%	461	AD015
Lesueurigobius cf. sanzi	66.2	19,846	92%	529	AD018
Perca fluviatilis	96.9	14,532	78%	364	AD025
Myoxocephalus scorpius	105.7	15,847	80%	423	AD019
Sebastes norvegicus	131.0	19,653	80%	349	AD027
Chaenocephalus aceratus	301.2	45,178	83%	355	AD022
Symphodus melops	86.4	12,694	86%	440	AD019
Spondyliosoma cantharus	97.6	14,642	90%	443	AD014
Antennarius striatus	71.6	10,742	71%	363	AD022
Selene dorsalis	107.9	16,179	86%	458	AD016
Helostoma temminckii	85.3	12,795	87%	464	AD016
Anabas testudineus	96.8	14,520	88%	459	AD016
Parablennius parvicornis	141.9	21,288	88%	459	AD019
Chromis chromis	166.1	24,917	87%	341	AD018
Pseudochromis fuscus	54.7	16,404	87%	418	AD018

*In millions

^†Percentage of total bases used in Celera assembly

^‡After merging with FLASH (bp)

Draft genome assembly

The methods used for genome assembly are also described in the Supplementary Note of Malmstrøm et al.¹¹. We expand on these methods here, describing the different parameters and settings in greater detail in order to present a complete overview of our analyses.

All draft genomes were created using Celera Assembler, and the version used was downloaded from the CVS (Concurrent Version System, http://wgs-assembler.sourceforge.net/) repository on January 12th 2013. The program meryl, included in the Celera Assembler package was used to create a database of k-mers from the pairs of sequencing reads. Lower k-mer sizes might not resolve repetitive regions, while higher k-mer sizes might not overlap, leading to a loss of information required to correct the reads. Thus, an intermediate k-mer size of 22 was used for all assemblies. Meryl was run with the following options, where the sequences from the reads were concatenated into a file named ‘reads.fa’:

meryl -B -v -m 22 -memory 55000 -threads 16 -C -s reads.fa -o reads

In this command, –B specifies that a k-mer database should be created, and that this should be done using the verbose setting (-v). The –m option denotes the ‘merSize’, while –C specifies that canonical reads (both strands) should be used for creating the k-mer database.

The options –threads and –memory specify the computational resources that meryl can utilize and only influence run-time.

Most of the computational time used by Celera Assembler is required to identify overlap between reads. To reduce analysis time and generate longer input sequences, overlapping paired reads were merged with the software FLASH v1.2 (ref. 16), executed with the following command, where –d denotes the path to the output directory (with the prefix given with the –o option), –r is the read length, –f is the insert size, and –s is the standard deviation of the insert size:

flash input_1.fastq input_2.fastq -d. -r 150 -f 290 -s 50 –o output_prefix

Celera Assembler’s merTrim program (see Tørresen et al.¹⁷) was used to trim, error correct and remove adapters of all reads. The merTrim program estimates the coverage of the sequencing library by analysing the abundance of k-mers versus the number of k-mers at that abundance. By default, k-mers occurring at a frequency corresponding to at least one fourth of the coverage peak can be used to correct reads with k-mers that occur with a frequency of at most one third of the coverage peak. Reads were trimmed to the largest region containing k-mers with a frequency of more than one third of the coverage peak. The trimming of reads removes sequences not supported by other reads and reduces the possible fragmentation of the assembly. Adaptor sequences are not part of the genome and could lead to assembly fragmentation in the same way as repeated regions would. To remove adaptor sequences and other unsupported sequences from the read data, merTrim was executed with the following command:

merTrim -F reads.fastq -m 22 -mc meryl_db -mCillumina -t 16 -o out.fastq

In this command, –F specifies the reads, –m the k-mer size, –mc the database of trusted k-mers, and –mCillumina specifies that Illumina type adapters should be removed. The –t option defines the number of threads and thus only influences run time.

Following correction and trimming, the files in frg format were created with the following commands, as implemented in Celera Assembler:

fastqToCA -technology illumina -insertsize 500 50 -libraryname lib_name -mates read1_clean.fastq,read2_clean.fastq>paired_reads.frg

fastqToCA -technology illumina-long -insertsize 500 50 -libraryname lib_name -reads merged_reads.fastq>merged_reads.frg

The frg files contain information about the sequencing data, such as the expected insert size, location of the fastq files and the prefix for determining the species. Providing this information in the form of frg files is a prerequisite for Celera Assembler. Celera Assembler was then used to assemble the sequencing reads, with the following command specifying the prefix (–p) and the directory for the output (–d):

runCA -p prefix -d CA -s spec_file

The ‘spec_file’ contains a list of settings and run-options for Celera Assembler. Some of the settings and options are specific to the computing system used for the assembly (such as the number of parallel overlap processes, ‘ovlConcurrency’), but as mentioned above, k-mer size as specified with the option –m (‘merSize’) can have effects on the contiguity of the assembly. The option ‘doFragmentCorrection’ was set to 0 because the reads were corrected with merTrim. The content of this file was:

ovlConcurrency=4
ovlThreads=8
cnsConcurrency=32
merSize=22
merylMemory=50000
merylThreads=32
merThreshold=5000
doOBT=0
overlapper=ovl
ovlRefBlockSize=6000000
ovlHashBits=24
ovlHashBlockLength=800000000
doFragmentCorrection=0
unitigger=bogart
batMemory=55
batThreads=32
doExtendClearRanges=0
doToggle=0
paired_reads.frg
merged_reads.frg

The output of Celera Assembler consists of a set of three fasta files with increasing continuity that contain unitigs, contigs and scaffolds, respectively. Unitigs are either a unique DNA sequence found in a genome or a repeat, and unique unitigs are used as seeds to create contigs and scaffolds. In cases where Celera Assembler was not able to place a unitig confidently in the assembly, this unitig was not included in the contigs and scaffolds, but output separately. As a result of this, some additional sequence information is available in the assembled unitig fasta file compared to the assembled scaffolds. These additional sequences can include repeated sequences like transposable elements and tandem repeats, but also repeated gene fragments, conserved gene family domains, and other sequences that conflict with the biological assumptions of the assembler. As multiple copies of the mitochondrial genome are present in each cell, it is sequenced to a much higher coverage than the nuclear genome, and may therefore also be excluded from contigs due to false classification as a repetitive region. For these reasons, unitigs instead of contigs were used for both the identification of fragmented genes (see Malmstrøm et al.¹¹) and for the mitochondrial phylogeny analysis described below. Assembly statistics for all draft genomes are provided in Table 3 (available online only).

Table 3. Assembly statistics for all species in the reported data set (Data Citation 2).

Species	Genome size* (Mb)	Coverage†	N50 contigs length (bp)	N50 scaffold length (bp)	Total span of scaffolds (Mb)	Recovered‡
Osmerus eperlanus	489	19.16	4,524	6,798	342	70%
Borostomias antarcticus	865	18.91	3,928	5,352	429	50%
Parasudis fraserbrunneri	935	19.55	4,177	6,366	706	76%
Guentherus altivela	1,701	8.73	2,928	3,199	538	32%
Benthosema glaciale	1,304	19.54	4,393	6,091	674	52%
Polymixia japonica	635	18.09	5,803	9,534	553	87%
Percopsis transmontana	509	35.57	8,161	15,134	457	90%
Typhlichthys subterraneus	759	22.85	7,314	9,640	555	73%
Zeus faber	732	22.92	4,642	6,313	609	83%
Cyttopsis roseus	640	28.14	4,843	7,060	545	85%
Stylephorus chordatus	971	23.04	3,373	4,661	487	50%
Bregmaceros cantori	1,650	23.60	4,452	5,909	1,142	69%
Merluccius polli	609	22.35	3,471	4,468	400	66%
Merluccius merluccius	611	29.65	3,670	5,094	400	66%
Merluccius capensis	653	19.89	3,792	4,760	413	63%
Melanonus zugmayeri	589	37.09	4,562	7,599	432	73%
Muraenolepis marmoratus	840	18.19	3,126	3,549	415	49%
Trachyrincus scabrus	579	37.96	3,900	6,346	369	64%
Trachyrincus murrayi	678	26.47	6,231	19,931	450	66%
Mora moro	499	39.10	3,267	4,412	344	69%
Laemonema laureysi	524	24.28	3,431	4,696	305	58%
Bathygadus melanobranchus	577	26.32	4,956	6,466	430	75%
Macrourus berglax	693	21.18	3,353	4,278	399	58%
Malacocephalus occidentalis	504	32.68	3,697	4,907	349	69%
Phycis blennoides	674	31.24	4,532	10,570	414	61%
Phycis phycis	468	32.04	3,458	4,486	345	74%
Lota lota	512	30.51	3,803	4,876	397	78%
Molva molva	539	33.68	4,136	5,251	437	81%
Brosme brosme	551	26.78	3,682	4,636	412	75%
Trisopterus minutus	517	33.50	3,248	3,962	334	65%
Gadiculus argenteus	567	29.03	3,379	3,942	396	70%
Pollachius virens	513	25.84	3,457	4,331	394	77%
Melanogrammus aeglefinus	543	25.17	3,215	3,690	374	69%
Merlangius merlangus	566	37.16	3,538	4,430	423	75%
Arctogadus glacialis	646	27.54	3,282	3,696	429	66%
Boreogadus saida	641	30.48	3,221	3,566	412	64%
Theragra chalcogramma	661	29.43	3,603	4,323	448	68%
Gadus morhua	674	25.86	5,765	16,731	492	73%
Regalecus glesne	750	30.64	6,781	9,753	654	87%
Lampris guttatus	1,405	14.20	4,051	5,212	847	60%
Monocentris japonica	706	16.71	8,046	18,610	554	79%
Myripristis jacobus	778	20.75	9,816	21,260	719	92%
Holocentrus rufus	735	20.72	9,243	21,323	648	88%
Neoniphon sammara	696	16.96	8,761	21,687	657	94%
Beryx splendens	897	14.64	4,286	5,972	532	59%
Rondeletia loricata	1,049	16.21	5,112	7,444	567	54%
Acanthochaenus luetkenii	825	21.10	5,636	8,398	544	66%
Brotula barbata	519	30.96	17,578	45,713	484	93%
Lamprogrammus exutus	901	9.80	4,213	5,459	492	55%
Carapus acus	448	27.21	9,554	16,897	387	86%
Chatrabus melanurus	1,965	20.79	4,581	5,906	1,126	57%
Thunnus albacares	836	18.60	16,808	46,871	726	87%
Lesueurigobius cf. sanzi	1,349	13.57	6,729	11,439	808	60%
Perca fluviatilis	903	12.51	4,140	5,951	629	70%
Myoxocephalus scorpius	759	16.64	5,716	9,443	518	68%
Sebastes norvegicus	782	20.04	9,467	16,530	716	92%
Chaenocephalus aceratus	1,050	35.91	5,460	7,309	623	59%
Symphodus melops	628	17.80	9,362	21,217	533	85%
Spondyliosoma cantharus	767	17.19	11,633	28,109	679	89%
Antennarius striatus	552	13.78	6,086	9,743	441	80%
Selene dorsalis	576	24.13	11,209	32,351	527	92%
Helostoma temminckii	686	16.14	17,055	71,662	599	87%
Anabas testudineus	576	22.08	18,817	50,098	524	91%
Parablennius parvicornis	623	30.04	7,343	16,734	598	96%
Chromis chromis	907	23.88	8,509	14,185	832	92%
Pseudochromis fuscus	740	19.39	12,029	24,629	656	89%

*Estimated by Celera Assembler

^†Based on Celera Assembler genome estimation

^‡Span of scaffolds divided by estimated genome size

Code availability

The most crucial commands are implemented in the Methods section, while additional scripts (used in phylogenetic analyses) are available on the code repository on GitHub (https://github.com/uio-cees/teleost_genomes_data_descriptor).

Data Records

All raw sequencing reads have been deposited in the European Nucleotide Archive (ENA) with study accession number PRJEB12469 (Data Citation 1). Table 4 (available online only) list the sample identifiers for each species. Each read file is available as a compressed file in fastq format (with extension fastq.gz). For some of the species, more than one read set is available as these were sequenced in two rounds, aiming to increase coverage. Two versions of all assembled genomes, unitigs (utg) and scaffolds (scf), are deposited in the Dryad repository under digital object identifier (DOI): doi:10.5061/dryad.326r8. (Data Citation 2). See Table 4 (available online only) for specific DOI for each species and assembly type.

Table 4. Individual identifiers for samples (read sets) in ENA and genome assemblies in the Dryad repository (Data Citation 2).

Species	ENA sample accession	DOI for scaffold assembly	Size (Mb)	DOI for unitig assembly	Size (Mb)
Osmerus eperlanus	SAMEA4028764	doi:10.5061/dryad.326r8/81.	104.5	doi:10.5061/dryad.326r8/82.	207.1
Borostomias antarcticus	SAMEA4028765	doi:10.5061/dryad.326r8/98.	128.6	doi:10.5061/dryad.326r8/90.	460.2
Parasudis fraserbrunneri	SAMEA4028766	doi:10.5061/dryad.326r8/71.	213.6	doi:10.5061/dryad.326r8/72.	463.8
Guentherus altivela	SAMEA4028767	doi:10.5061/dryad.326r8/77.	165.8	doi:10.5061/dryad.326r8/78.	858.8
Benthosema glaciale	SAMEA4028768	doi:10.5061/dryad.326r8/91.	204.8	doi:10.5061/dryad.326r8/92.	684.3
Polymixia japonica	SAMEA4028769	doi:10.5061/dryad.326r8/47.	167.5	doi:10.5061/dryad.326r8/48.	282.7
Percopsis transmontana	SAMEA4028770	doi:10.5061/dryad.326r8/49.	140.0	doi:10.5061/dryad.326r8/50.	194.1
Typhlichthys subterraneus	SAMEA4028771	doi:10.5061/dryad.326r8/51.	169.5	doi:10.5061/dryad.326r8/52.	286.6
Zeus faber	SAMEA4028772	doi:10.5061/dryad.326r8/53.	186.1	doi:10.5061/dryad.326r8/54.	362.3
Cyttopsis roseus	SAMEA4028773	doi:10.5061/dryad.326r8/55.	166.8	doi:10.5061/dryad.326r8/56.	294.8
Stylephorus chordatus	SAMEA4028774	doi:10.5061/dryad.326r8/103.	147.4	doi:10.5061/dryad.326r8/104.	455.1
Bregmaceros cantori	SAMEA4028775	doi:10.5061/dryad.326r8/41.	341.4	doi:10.5061/dryad.326r8/42.	879.0
Merluccius polli	SAMEA4028776	doi:10.5061/dryad.326r8/29.	121.2	doi:10.5061/dryad.326r8/30.	295.7
Merluccius merluccius	SAMEA4028777	doi:10.5061/dryad.326r8/25.	121.2	doi:10.5061/dryad.326r8/26.	328.6
Merluccius capensis	SAMEA4028778	doi:10.5061/dryad.326r8/27.	125.1	doi:10.5061/dryad.326r8/28.	351.8
Melanonus zugmayeri	SAMEA4028779	doi:10.5061/dryad.326r8/31.	130.3	doi:10.5061/dryad.326r8/32.	331.4
Muraenolepis marmoratus	SAMEA4028780	doi:10.5061/dryad.326r8/39.	123.4	doi:10.5061/dryad.326r8/40.	398.6
Trachyrincus scabrus	SAMEA4028781	doi:10.5061/dryad.326r8/67.	110.6	doi:10.5061/dryad.326r8/68.	316.0
Trachyrincus murrayi	SAMEA4028782	doi:10.5061/dryad.326r8/125.	132.6	doi:10.5061/dryad.326r8/126.	326.5
Mora moro	SAMEA4028783	doi:10.5061/dryad.326r8/43.	103.2	doi:10.5061/dryad.326r8/44.	292.2
Laemonema laureysi	SAMEA4028784	doi:10.5061/dryad.326r8/45.	92.3	doi:10.5061/dryad.326r8/46.	266.4
Bathygadus melanobranchus	SAMEA4028785	doi:10.5061/dryad.326r8/37.	129.8	doi:10.5061/dryad.326r8/38.	285.2
Macrourus berglax	SAMEA4028786	doi:10.5061/dryad.326r8/33.	120.9	doi:10.5061/dryad.326r8/34.	312.6
Malacocephalus occidentalis	SAMEA4028787	doi:10.5061/dryad.326r8/35.	106.0	doi:10.5061/dryad.326r8/36.	230.5
Phycis blennoides	SAMEA4028788	doi:10.5061/dryad.326r8/127.	121.7	doi:10.5061/dryad.326r8/128.	455.0
Phycis phycis	SAMEA4028789	doi:10.5061/dryad.326r8/17.	104.6	doi:10.5061/dryad.326r8/18.	240.8
Lota lota	SAMEA4028790	doi:10.5061/dryad.326r8/21.	119.9	doi:10.5061/dryad.326r8/22.	254.5
Molva molva	SAMEA4028791	doi:10.5061/dryad.326r8/19.	131.6	doi:10.5061/dryad.326r8/20.	243.0
Brosme brosme	SAMEA4028792	doi:10.5061/dryad.326r8/23.	125.0	doi:10.5061/dryad.326r8/24.	251.4
Trisopterus minutus	SAMEA4028793	doi:10.5061/dryad.326r8/5.	101.5	doi:10.5061/dryad.326r8/6.	269.0
Gadiculus argenteus	SAMEA4028794	doi:10.5061/dryad.326r8/15.	119.2	doi:10.5061/dryad.326r8/16.	291.8
Pollachius virens	SAMEA4028795	doi:10.5061/dryad.326r8/7.	120.2	doi:10.5061/dryad.326r8/8.	230.0
Melanogrammus aeglefinus	SAMEA4028796	doi:10.5061/dryad.326r8/9.	114.6	doi:10.5061/dryad.326r8/10.	256.7
Merlangius merlangus	SAMEA4028797	doi:10.5061/dryad.326r8/11.	128.6	doi:10.5061/dryad.326r8/12.	284.2
Arctogadus glacialis	SAMEA4028798	doi:10.5061/dryad.326r8/1.	130.1	doi:10.5061/dryad.326r8/2.	286.9
Boreogadus saida	SAMEA4028799	doi:10.5061/dryad.326r8/3.	124.9	doi:10.5061/dryad.326r8/4.	290.5
Theragra chalcogramma	SAMEA4028800	doi:10.5061/dryad.326r8/13.	135.8	doi:10.5061/dryad.326r8/14.	304.1
Gadus morhua	SAMEA4028801	doi:10.5061/dryad.326r8/131.	146.9	doi:10.5061/dryad.326r8/132.	336.6
Regalecus glesne	SAMEA4028802	doi:10.5061/dryad.326r8/73.	200.3	doi:10.5061/dryad.326r8/74.	312.1
Lampris guttatus	SAMEA4028803	doi:10.5061/dryad.326r8/75.	259.5	doi:10.5061/dryad.326r8/76.	671.2
Monocentris japonica	SAMEA4028804	doi:10.5061/dryad.326r8/99.	169.7	doi:10.5061/dryad.326r8/100.	283.4
Myripristis jacobus	SAMEA4028805	doi:10.5061/dryad.326r8/63.	220.3	doi:10.5061/dryad.326r8/64.	319.3
Holocentrus rufus	SAMEA4028806	doi:10.5061/dryad.326r8/65.	198.9	doi:10.5061/dryad.326r8/66.	307.2
Neoniphon sammara	SAMEA4028807	doi:10.5061/dryad.326r8/97.	201.5	doi:10.5061/dryad.326r8/98.	278.7
Beryx splendens	SAMEA4028808	doi:10.5061/dryad.326r8/95.	163.5	doi:10.5061/dryad.326r8/96.	572.3
Rondeletia loricata	SAMEA4028809	doi:10.5061/dryad.326r8/93.	173.6	doi:10.5061/dryad.326r8/94.	450.5
Acanthochaenus luetkenii	SAMEA4028810	doi:10.5061/dryad.326r8/101.	167.4	doi:10.5061/dryad.326r8/102.	350.9
Brotula barbata	SAMEA4028811	doi:10.5061/dryad.326r8/59.	148.2	doi:10.5061/dryad.326r8/60.	210.3
Lamprogrammus exutus	SAMEA4028812	doi:10.5061/dryad.326r8/57.	151.5	doi:10.5061/dryad.326r8/58.	482.8
Carapus acus	SAMEA4028813	doi:10.5061/dryad.326r8/61.	118.5	doi:10.5061/dryad.326r8/62.	200.3
Chatrabus melanurus	SAMEA4028814	doi:10.5061/dryad.326r8/69.	347.3	doi:10.5061/dryad.326r8/70.	1001.0
Thunnus albacares	SAMEA4028815	doi:10.5061/dryad.326r8/107.	222.2	doi:10.5061/dryad.326r8/108.	363.4
Lesueurigobius cf. sanzoi	SAMEA4028816	doi:10.5061/dryad.326r8/129.	244.7	doi:10.5061/dryad.326r8/120.	683.5
Perca fluviatilis	SAMEA4028817	doi:10.5061/dryad.326r8/83.	193.8	doi:10.5061/dryad.326r8/84.	382.4
Myoxocephalus scorpius	SAMEA4028818	doi:10.5061/dryad.326r8/123.	158.7	doi:10.5061/dryad.326r8/124.	375.4
Sebastes norvegicus	SAMEA4028819	doi:10.5061/dryad.326r8/85.	219.4	doi:10.5061/dryad.326r8/86.	339.5
Chaenocephalus aceratus	SAMEA4028820	doi:10.5061/dryad.326r8/87.	190.5	doi:10.5061/dryad.326r8/88.	573.8
Symphodus melops	SAMEA4028821	doi:10.5061/dryad.326r8/119.	162.9	doi:10.5061/dryad.326r8/120.	238.1
Spondyliosoma cantharus	SAMEA4028822	doi:10.5061/dryad.326r8/105.	209.0	doi:10.5061/dryad.326r8/106.	281.5
Antennarius striatus	SAMEA4028823	doi:10.5061/dryad.326r8/79.	135.7	doi:10.5061/dryad.326r8/80.	280.1
Selene dorsalis	SAMEA4028824	doi:10.5061/dryad.326r8/113.	161.7	doi:10.5061/dryad.326r8/114.	235.8
Helostoma temminckii	SAMEA4028825	doi:10.5061/dryad.326r8/109.	183.5	doi:10.5061/dryad.326r8/110.	241.8
Anabas testudineus	SAMEA4028826	doi:10.5061/dryad.326r8/111.	160.6	doi:10.5061/dryad.326r8/112.	206.3
Parablennius parvicornis	SAMEA4028827	doi:10.5061/dryad.326r8/117.	182.7	doi:10.5061/dryad.326r8/118.	257.3
Chromis chromis	SAMEA4028828	doi:10.5061/dryad.326r8/115.	253.3	doi:10.5061/dryad.326r8/116.	397.6
Pseudochromis fuscus	SAMEA4028829	doi:10.5061/dryad.326r8/121.	199.4	doi:10.5061/dryad.326r8/122.	284.4

Technical Validation

Both genome coverage and N50 lengths of contigs and scaffolds are considered important attributes for assessing a genome assembly. Assembly statistics for all species are reported in Table 2 (available online only). Another, and perhaps more crucial attribute, is the completeness of gene space, which is particularly important for the investigation of gene presence or absence. We used two different programs, CEGMA¹⁸ (Core Eukaryotic Genes Mapping Approach) v. 2.4.010312 and BUSCO¹⁹ (Benchmarking Universal Single-Copy Orthologs) v. 1.1b, to assess the gene-space completeness of our draft genome assemblies. CEGMA generates a list of ‘partial’ and ‘complete’ gene hits for the 248 most conserved genes, which were used as a validation of the assembly quality. BUSCO can be executed with several different reference data sets, optimized for different taxonomic groups. We used the ‘actinopterygii’ data set consisting of 3,698 highly conserved genes in acanthopterygian species (this specific data set is not publicly available yet—as of September 9th, 2016—but was provided by the developers of BUSCO upon request). BUSCO identifies and classifies these genes in the target genomes as either ‘Complete’, ‘Complete and duplicated’, ‘Fragmented’ or ‘Missing’. Table 5 (available online only) lists the CEGMA and BUSCO results for all assembled draft genomes, while Fig. 2a,b show the proportions of these conserved genes found (as partial hits) in relation to the read coverage and N50 scaffold length of all assemblies. In line with the results of our initial investigation of the budgerigar genome, we find no improvement in CEGMA or BUSCO gene set recovery when assembly coverage exceeds ~15× for the genomes included in this data set (linear regression of BUSCO versus coverage (>15×): R²=0.038, P=0.07; CEGMA versus coverage (>15×): R²=0.002, P=0.30) (Fig. 2a). When comparing the fractions of partial CEGMA and BUSCO genes recovered in each assembly with the N50 scaffold lengths of these assemblies, an initial steep increase is evident, clearly illustrating the sensitivity of these methods in relation to continuity (linear regression of BUSCO versus N50 scaffold length: R²=0.55, P<10^–12; CEGMA versus N50 scaffold length: R²=0.30, P<10^–5) (Fig. 2b). Finally, we find that the N50 scaffold length is largely uncorrelated with coverage (linear regression: R²=0.015, P=0.17), indicating that the specific sequencing strategy (insert size and read length) and the properties of the sequenced genomes (repeat content etc.) are more likely the limiting factors for N50 scaffold length (Fig. 2c). The observed lack of a correlation across all assemblies seems to be influenced by generally low N50 scaffold lengths for species of the order Gadiformes despite relatively high coverage for these genomes (mean coverage: 28×, mean N50 scaffold length: 6 kbp) compared to all other genomes (mean coverage: 20×, mean 50 scaffold length: 16 kbp). Thus, the species of the order Gadiformes appear more difficult to assemble which is likely explained by their high proportion of repetitive regions (see Tørresen et al.¹⁷). Collectively, these analyses illustrate that most of the variation in the recovery rate of the highly conserved genes is not due to low coverage, but rather reflects lineage-specific genomic features such as the amount and identity of repetitive elements that hamper the assembly of long continuous sequences.

Table 5. Gene space completeness metrics for all draft assemblies in this data set (Data Citation 2).

Species	CEGMA complete*	CEGMA partial*	BUSCO complete†	BUSCO duplicated†	BUSCO fragmented†	BUSCO missing†
Osmerus eperlanus	175	220	2,071	71	760	867
Borostomias antarcticus	116	180	1,101	37	869	1,728
Parasudis fraserbrunneri	131	205	1,625	63	880	1,193
Guentherus altivela	44	119	402	11	809	2,487
Benthosema glaciale	171	214	1,478	128	768	1,452
Polymixia japonica	188	230	2,474	80	663	561
Percopsis transmontana	185	233	2,411	71	639	648
Typhlichthys subterraneus	167	222	2,024	55	763	911
Zeus faber	155	219	1,758	48	917	1,023
Cyttopsis roseus	185	233	1,969	62	867	862
Stylephorus chordatus	125	193	1,189	39	926	1,583
Bregmaceros cantori	85	189	867	32	942	1,889
Merluccius polli	133	204	1,188	44	1,019	1,491
Merluccius merluccius	147	210	1,257	36	959	1,482
Merluccius capensis	144	209	1,363	38	986	1,349
Melanonus zugmayeri	163	218	1,803	56	895	1,000
Muraenolepis marmoratus	143	209	1,258	37	1,040	1,400
Trachyrincus scabrus	190	225	1,957	45	872	869
Trachyrincus murrayi	218	235	2,806	69	464	428
Mora moro	155	215	1,582	40	948	1,168
Laemonema laureysi	163	223	1,814	41	844	1,040
Bathygadus melanobranchus	179	223	2,026	60	831	841
Macrourus berglax	147	206	1,172	61	927	1,599
Malacocephalus occidentalis	147	210	1,419	40	996	1,283
Phycis blennoides	190	231	2,155	44	717	826
Phycis phycis	144	213	1,461	36	966	1,271
Lota lota	169	213	1,740	49	902	1,056
Molva molva	172	218	1,739	61	939	1,020
Brosme brosme	167	215	1,712	36	907	1,079
Trisopterus minutus	138	189	1,199	39	917	1,582
Gadiculus argenteus	119	195	1,193	26	958	1,547
Pollachius virens	147	208	1,405	35	968	1,325
Melanogrammus aeglefinus	145	201	1,356	41	996	1,346
Merlangius merlangus	136	195	1,429	39	946	1,323
Arctogadus glacialis	141	199	1,177	34	1,036	1,485
Boreogadus saida	137	205	1,261	43	955	1,482
Theragra chalcogramma	153	210	1,486	36	971	1,241
Gadus morhua	202	235	2,455	43	608	635
Regalecus glesne	184	234	2,228	70	675	795
Lampris guttatus	129	201	1,467	47	953	1,278
Monocentris japonica	199	235	2,709	74	545	444
Myripristis jacobus	200	230	2,844	102	461	393
Holocentrus rufus	202	233	2,944	97	441	313
Neoniphon sammara	195	228	2,742	81	505	451
Beryx splendens	152	197	1,638	58	941	1,119
Rondeletia loricata	140	204	1,828	62	921	949
Acanthochaenus luetkenii	138	219	1,736	59	936	1,026
Brotula barbata	230	243	3,278	97	247	173
Lamprogrammus exutus	146	211	1,692	77	1,011	995
Carapus acus	215	240	2,666	52	505	527
Chatrabus melanurus	92	188	1,142	36	1,091	1,465
Thunnus albacares	209	236	3,147	99	351	200
Lesueurigobius cf. sanzi	163	206	2,130	58	625	943
Perca fluviatilis	122	189	1,673	55	1,027	998
Myoxocephalus scorpius	164	217	2,016	69	803	879
Sebastes norvegicus	190	233	2,458	69	698	542
Chaenocephalus aceratus	146	199	1,918	63	876	904
Symphodus melops	199	229	2,755	76	537	406
Spondyliosoma cantharus	215	240	3,001	78	426	271
Antennarius striatus	195	226	2,312	73	656	730
Selene dorsalis	215	231	2,968	80	427	303
Helostoma temminckii	225	236	3,387	100	204	107
Anabas testudineus	225	240	3,314	110	245	139
Parablennius parvicornis	186	224	2,336	63	650	712
Chromis chromis	180	230	2,451	75	670	577
Pseudochromis fuscus	210	231	2,837	90	475	386

*Out of 248 highly conserved eukaryotic genes.

^†Out of 3,698 highly conserved acanthopterygian genes.

Figure 2. Correlation between gene space completeness, coverage, and N50 scaffold length for the 66 teleost genomes.

(a) Scatterplot illustrating the correlation of gene space completeness (evaluated on the basis of BUSCO and CEGMA partially complete genes detected) and the read coverage (linear regression of BUSCO versus coverage (>15×): R²=0.038, P=0.07; CEGMA versus coverage (>15×): R²=0.002, P=0.30). (b) Scatterplot showing the correlation of BUSCO / CEGMA scores and N50 scaffold length (linear regression of BUSCO versus N50 scaffold length: R²=0.55, P<10^–12 and CEGMA versus N50 scaffold length: R²=0.30, P<10^–5) for all genome presented in the data set. (c) Scatterplot illustrating the correlation of coverage and N50 scaffold length (linear regression: R²=0.015, P=0.17). Species within the order Gadiformes are represented by triangles in all three plots. The lines shown are smooth LOESS curves, also referred to as local regressions, and the gray shaded areas represent 95% confidence interval in all three plots.

Phylogenetic analyses using mitochondrial genomes

To verify the correct identification of sampled species and the absence of contamination, we performed phylogenetic analyses of mitochondrial genomes extracted from all assemblies, in combination with previously available mitochondrial sequence data for sampled taxa and their close relatives. Mitochondrial genomes are particularly suitable for this comparison as the coverage of mitochondrial sequences is usually extremely high owing to the multiple copies of mitochondrial DNA (mtDNA) present in each mitochondrion and the large number of mitochondria per cell²⁰. Furthermore, mitochondrial genomes are useful phylogenetic markers due to the very low frequency of recombination in animal mtDNA²¹ and the large number of mitochondrial genome sequences already available in GenBank²² (Data Citations 5 to 124).

We downloaded mitochondrial genome sequences for 120 species of which 14 species (Lampris guttatus, Polymixia japonica, Percopsis transmontana, Zeus faber, Stylephorus chordatus, Lota lota, Gadus morhua, Monocentris japonicus, Rondeletia loricata, Beryx splendens, Antennarius striatus, Anabas testudineus, Helostoma temminkii, and Perca fluviatilis) were also included in our set of 66 new teleost genome assemblies and an additional 8 species (Osmerus mordax, Polymixia lowei, Bregmaceros nectabanus, Beryx decadactylus, Myripristis berndti, Lamprogrammus niger, Carapus bermudensis, and Thunnus thynnus) were represented by a congener. GenBank accession numbers for the 120 downloaded genome sequences are given in Table 6 (available online only) (Data Citations 5 to 124). Protein-coding sequences for all mitochondrial genes except mt-ND6 (see Miya et al.²³) were extracted from the 120 mitochondrial genomes, aligned with the software MAFFT²⁴, v7.213 and translated to amino-acid sequences using AliView²⁵ v.1.16.

Table 6. GenBank accession numbers for 120 previously published mitochondrial genomes (Data Citation 5 – 124).

Species	GenBank accession	Species	GenBank accession
Abudefduf vaigiensis	NC_009064	Kareius bicoloratus	NC_003176
Allocyttus niger	NC_004398	Labracinus cyclophthalmus	NC_009054
Anabas testudineus	NC_024752	Lampris guttatus	NC_003165
Anomalops katoptron	NC_008128	Lamprogrammus niger	NC_004378
Anoplogaster cornuta	NC_004391	Lophiomus setigerus	NC_008125
Antennarius striatus	AB282828	Lophius americanus	NC_004380
Antigonia capros	NC_004391	Lota lota	NC_004379
Aphredoderus sayanus	NC_004372	Lycodes toyamensis	NC_004409
Aptocyclus ventricosus	NC_008129	Mastacembelus favus	NC_003193
Arcos sp KU 149	NC_004413	Melanocetus murrayi	NC_004384
Aspasma minima	NC_008130	Melanotaenia lacustris	NC_004385
Ateleopus japonicus	NC_003178	Monocentris japonicus	NC_004392
Aulopus japonicus	NC_002674	Monopterus albus	NC_003192
Bassozetus zenkevitchi	NC_004374	Mugil cephalus	NC_003182
Batrachomoeus trispinosus	AP006738	Myctophum affine	NC_003163
Beryx decadactylus	NC_004393	Myripristis berndti	NC_003189
Beryx splendens	NC_003188	Neocyttus rhomboidalis	NC_004399
Bregmaceros nectabanus	NC_008124	Neolamprologus brichardi	NC_009062
Carangoides armatus	NC_004405	Neoscopelus microchir	NC_003180
Caranx melampygus	NC_004406	Odax cyanomelas	NC_009061
Carapus bermudensis	NC_004373	Oncorhynchus mykiss	NC_001717
Cataetyx rubrirostris	NC_004375	Oryzias latipes	NC_004387
Caulophryne jordani	NC_004383	Osmerus mordax	NC_015246
Cetostoma regani	NC_004389	Ostichthys japonicus	NC_004394
Champsocephalus gunnari	NC_018340	Paralichthys olivaceus	NC_002386
Chauliodus sloani	NC_003159	Parazen pacificus	NC_004396
Chaunax abei	NC_004381	Perca fluviatilis	NC_026313
Chaunax tosaensis	NC_004382	Percopsis transmontana	NC_003168
Chlorophthalmus agassizi	NC_003160	Petroscirtes breviceps	NC_004411
Coelorinchus kishinouyei	NC_003169	Pholis crassispina	NC_004410
Cololabis saira	NC_003183	Physiculus japonicus	NC_004377
Coregonus lavaretus	NC_002646	Polymixia japonica	NC_002648
Cottus reinii	NC_004404	Polymixia lowei	NC_003181
Crossostoma lacustre	NC_001727	Porichthys myriaster	NC_006920
Cyprinus carpio	NC_001606	Poromitra oscitans	NC_003172
Dactyloptena peterseni	NC_003194	Pterocaesio tile	NC_004408
Dactyloptena tiltoni	NC_004402	Rhyacichthys aspro	NC_004414
Danacetichthys galathenus	NC_003185	Rondeletia loricata	NC_003186
Diaphus splendidus	NC_003164	Salarias fasciatus	AP004451
Diplacanthopoma brachysoma	NC_004376	Sardinops melanostictus	NC_002616
Diplophos sp MM1999	AB034825	Sargocentron rubrum	NC_004395
Diretmoides veriginae	NC_008126	Satyrichthys amiscus	NC_004403
Diretmus argenteus	NC_008127	Saurida undosquamis	NC_003162
Eleotris acanthopoma	NC_004415	Scarus schlegeli	NC_011936
Emmelichthys struhsakeri	NC_004407	Scomber japonicus	NC_013723
Etheostoma radiosum	NC_005254	Scopelogadus mizolepis	NC_003171
Exocoetus volitans	NC_003184	Sigmops gracilis	NC_002574
Gadus morhua	NC_002081	Sirembo imberbis	NC_008123
Gambusia affinis	NC_004388	Sparus aurata	NC_024236
Gasterosteus aculeatus	AP002944	Stephanolepis cirrhifer	NC_003177
Halieutaea stellata	AP005977	Stylephorus chordatus	NC_009948
Harpadon microchir	NC_003161	Sufflamen fraenatum	NC_004416
Helicolenus hilgendorfi	NC_003195	Synbranchus marmoratus	AP004439
Helostoma temminkii	NC_022728	Thunnus thynnus	NC_014052
Histrio histrio	AB282829	Trachipterus trachipterus	NC_003166
Hoplostethus japonicus	NC_003187	Zalieutes elater	AB282835
Hypoatherina tsurugae	NC_004386	Zenion japonicum	NC_004397
Hypoptychus dybowskii	NC_004400	Zenopsis nebulosus	NC_003173
Ijimaia dofleini	NC_003179	Zeus faber	NC_003190
Indostomus paradoxus	NC_004401	Zu cristatus	NC_003167

To extract mitochondrial genomes from the 66 new unitig assemblies, we generated nucleotide BLAST databases for a subset of each assembly, consisting of all unitigs matched by at least 1,000 reads. This threshold was selected based on observed coverage distributions and the assumption that mitochondrial unitigs have particularly high coverage due to the relatively higher abundance of mitochondrial compared to nuclear DNA within each cell. The use of this threshold does not imply that all unitigs with higher coverage are mitochondrial, only that unitigs with lower coverage were ignored when mining for mitochondrial orthologs. For each mitochondrial gene, all 120 aligned amino-acid sequences were used as queries in searches with TBLASTN²⁶ v.2.2.29 to identify unitigs with orthologous sequences in each of the 66 BLAST databases. For comparison, we also performed TBLASTN searches with the same queries against 10 additional BLAST databases generated for genome assemblies downloaded from ENSEMBL²⁷ v.78 (Danio rerio, Astyanax mexicanus, Gadus morhua, Gasterosteus aculeatus, Oreochromis niloticus, Oryzias latipes, Takifugu rubripes, Tetraodon nigroviridis and Xiphophorus maculatus) and GenBank (Salmo salar; NCBI accession number AGKD00000000.3). For each of the 76 BLAST databases, the overall best TBLASTN hit for each mitochondrial gene was recorded and accepted as a homologous sequence if its e-value was below 1e^–15. In cases where different unitigs matched different regions of the same gene (each with e-values below the threshold), these unitigs were jointly recorded as a single hit. Unitig identifiers for all hits are given in Table 7 (available online only). All hits were subsequently added to the untranslated mitochondrial gene alignments and realigned on the basis of amino-acid translations using TranslatorX²⁸. Alignments were further analyzed with the software BMGE²⁹ v.1.0 to determine unreliably aligned regions, and we excluded all codons that included sites with a gap rate above 0.2 or a smoothed entropy-like score (see Criscuolo & Gribaldo²⁹) above 0.5. Finally, we concatenated the alignments of all mitochondrial genes, excluding two taxa (Parasudis fraserbrunneri and Acanthochaenus luetkenii) for which no homologs could be identified for eight or more genes. The final alignment used for phylogenetic inference included 9,303 bp.

Table 7. ID of all unitigs containing mitochondrial data for all species included in the data set.

Species	UTG IDs
Acanthochaenus luetkenii	utg7180003914080, utg7180003914081
Anabas testudineus	utg7180000074085
Antennarius striatus	utg7180002097916, utg7180002097917, utg7180002097918, utg7180002097919
Arctogadus glacialis	utg7180001210258
Bathygadus melanobranchus	utg7180000000032
Benthosema glaciale	utg7180006223522, utg7180007030062, utg7180007030067, utg7180007030068, utg7180007152696, utg7180007434654, utg7180007609485, utg7180007660002, utg7180007660003, utg7180007673377, utg7180007673378
Beryx splendens	utg7180000469666, utg7180000771701, utg7180004939694, utg7180005165554, utg7180005165555, utg7180005341444, utg7180005341445, utg7180005366167, utg7180005385228, utg7180005385229, utg7180005385230, utg7180005385231, utg7180005476828, utg7180005476831, utg7180005476832, utg7180005569875, utg7180005569876, utg7180005673983, utg7180005673984, utg7180005673985, utg7180005691644, utg7180005691645, utg7180005705333, utg7180005869836, utg7180006066164, utg7180006120309, utg7180006133924
Boreogadus saida	utg7180001220567, utg7180001220611
Borostomias antarcticus	utg7180001274025, utg7180003691481, utg7180003691544, utg7180003691567, utg7180003754703, utg7180003754717, utg7180003754718, utg7180003754733, utg7180003811941, utg7180004025360, utg7180004025368, utg7180004025384, utg7180004102570, utg7180004469635, utg7180004469636, utg7180004492307, utg7180004584377, utg7180004605154, utg7180004605155, utg7180004625717
Bregmaceros cantori	utg7180000000000, utg7180000000028, utg7180000003257
Brosme brosme	utg7180001047115, utg7180001047140
Brotula barbata	utg7180000000018
Carapus acus	utg7180000000000
Chaenocephalus aceratus	utg7180002324592
Chatrabus melanurus	utg7180004205210, utg7180004208747, utg7180004208748, utg7180004208749, utg7180004208753, utg7180004208754, utg7180004208755, utg7180004208756, utg7180004212026, utg7180004212030, utg7180004212034, utg7180004212035, utg7180004212036, utg7180004215531, utg7180004215533,
Chromis chromis	utg7180001202954, utg7180001209662, utg7180001209663, utg7180001266383, utg7180001266389, utg7180001322771, utg7180001335484, utg7180001335485, utg7180001335486, utg7180001335489, utg7180001415702, utg7180001623875, utg7180001623880, utg7180001660412
Cyttopsis roseus	utg7180001278658, utg7180001278979
Gadiculus argenteus	utg7180001379789, utg7180001379798, utg7180001387987
Guentherus altivela	utg7180000271871, utg7180000494472, utg7180000503520, utg7180001132842, utg7180001368068, utg7180001512825, utg7180001890828, utg7180002011637, utg7180002196109, utg7180002773145, utg7180007337091, utg7180008012272, utg7180008012273, utg7180008012274, utg7180008012275, utg7180008692799, utg7180008692802, utg7180008940047, utg7180008940048, utg7180008940049, utg7180009466012
Helostoma temminckii	utg7180000715927, utg7180000715930
Holocentrus rufus	utg7180000000000
Laemonema laureysi	utg7180001371677
Lampris guttatus	utg7180002509326, utg7180002509328, utg7180002509329, utg7180002509330, utg7180002509333, utg7180002509335, utg7180002509336, utg7180002509337, utg7180002509339, utg7180002509340, utg7180002509341, utg7180002509342, utg7180002511349, utg7180002511350, utg7180002511351, utg7180002512148, utg7180002817224
Lamprogrammus exutus	utg7180004205210, utg7180004208747, utg7180004208748, utg7180004208749, utg7180004208753, utg7180004208754, utg7180004208755, utg7180004208756, utg7180004212026, utg7180004212030, utg7180004212034, utg7180004212035, utg7180004212036, utg7180004215531, utg7180004215533,
Lesueurigobius cf. sanzoi	utg7180000000879
Lota lota	utg7180000000000
Macrourus berglax	utg7180000034506, utg7180001621271, utg7180001623489, utg7180001624139
Malacocephalus occidentalis	utg7180000000000
Melanogrammus aeglefinus	utg7180000000000
Melanonus zugmayeri	utg7180000000010, utg7180001692953
Merlangius merlangus	utg7180000000000
Merluccius capensis	utg7180001513810
Merluccius merluccius	utg7180001887176, utg7180001917684, utg7180001972531, utg7180001981428, utg7180002025406, utg7180002025422, utg7180002097733, utg7180002097734, utg7180002097738, utg7180002079715, utg7180002146127, utg7180002218855, utg7180002218856, utg7180002307512
Merluccius polli	utg7180001442827
Molva molva	utg7180000000000
Monocentris japonica	utg7180000342919, utg7180000463143, utg7180000514479, utg7180000538369, utg7180001377029, utg7180001377031, utg7180001377032, utg7180001434412, utg7180001434417, utg7180001434418, utg7180001434429, utg7180001434430, utg7180001434446, utg7180001434447, utg7180001434448, utg7180001469573, utg7180001469581, utg7180001469582, utg7180001469586, utg7180001469587, utg7180001469589, utg7180001486715, utg7180001516364, utg7180001516372, utg7180001516373, utg7180001516374, utg7180001524523, utg7180001524552, utg7180001550906, utg7180001550907, utg7180001550908, utg7180001692212, utg7180001692214, utg7180001820920
Mora moro	utg7180000000000
Muraenolepis marmoratus	utg7180000000000, utg7180001973851, utg7180001973898
Myoxocephalus scorpius	utg7180002464675, utg7180002464676, utg7180002464677, utg7180002464678, utg7180002504481, utg7180002504482, utg7180002533598, utg7180002533599, utg7180002533605, utg7180002533606
Myripristis jacobus	utg7180000000000
Neoniphon sammara	utg7180000064300
Osmerus eperlanus	utg7180000000726
Parablennius parvicornis	utg7180000020269
Parasudis fraserbrunneri	utg7180003294189, utg7180003426264, utg7180003433528
Perca fluviatilis	utg7180001412776, utg7180001412933
Percopsis transmontana	utg7180000622724, utg7180000622787, utg7180000622789, utg7180000630769, utg7180000630770, utg7180000634249, utg7180000640180, utg7180000671918, utg7180000671919, utg7180000681531, utg7180000690201, utg7180000690202, utg7180000695306, utg7180000695308, utg7180000716419, utg7180000757684, utg7180000782025
Phycis blennoides	utg7180003799308
Phycis phycis	utg7180001189424
Pollachius virens	utg7180000000000
Polymixia japonica	utg7180001067565, utg7180001067570, utg7180001067565
Pseudochromis fuscus	utg7180001142570, utg7180001142583, utg7180001142600, utg7180001145451, utg7180001145455
Regalecus glesne	utg7180000000000
Rondeletia loricata	utg7180000491946, utg7180000516073, utg7180000842519, utg7180000847838, utg7180000928011, utg7180000966600, utg7180001048288, utg7180001149138, utg7180001161638, utg7180001206941, utg7180001478759, utg7180001623954, utg7180002730734, utg7180002730736, utg7180002730737, utg7180002814675, utg7180002976817, utg7180002976819, utg7180003297619, utg7180003297620, utg7180003394061, utg7180003394062, utg7180003438009, utg7180003438010, utg7180003438011, utg7180003438012, utg7180003438013, utg7180003936305, utg7180003936306, utg7180003936728, utg7180004045904, utg7180004045909, utg7180004317827, utg7180004338610, utg7180004338611, utg7180004601252, utg7180004621852, utg7180004746867, utg7180004746868
Sebastes norvegicus	utg7180001468849
Selene dorsalis	utg7180001234455
Spondyliosoma cantharus	utg7180001069401, utg7180001069405
Stylephorus chordatus	utg7180003402356, utg7180003402376, utg7180003402383, utg7180003402389, utg7180003428557, utg7180003428577, utg7180003428590, utg7180003428591, utg7180003428594, utg7180003428603, utg7180003428622, utg7180003444599, utg7180003444601, utg7180003444608, utg7180003456661, utg7180003456662, utg7180003456664, utg7180003456684, utg7180003456692, utg7180003514255, utg7180003514256, utg7180003560601, utg7180003560603, utg7180003560604, utg7180003560606, utg7180003560623, utg7180003727526, utg7180003727530, utg7180003727534, utg7180003727536, utg7180003727543, utg7180003727550, utg7180003727562, utg7180003917108, utg7180003917109, utg7180003917110, utg7180003917115, utg7180003917116, utg7180003917712, utg7180003917713, utg7180004070138, utg7180004070139
Symphodus melops	utg7180000868836, utg7180000889427, utg7180000868836
Theragra chalcogramma	utg7180000000000
Thunnus albacares	utg7180001817200, utg7180001817201, utg7180001817221, utg7180001817255, utg7180001817266, utg7180001817279, utg7180001817286, utg7180001817289, utg7180001817293, utg7180001958519, utg7180001958520, utg7180002005352, utg7180002005353, utg7180002005358, utg7180002030942, utg7180002030943, utg7180002031021, utg7180002031022, utg7180002163410, utg7180002163441
Trachyrincus murrayi	utg7180002283202, utg7180002334643
Trachyrincus scabrus	utg7180000000000
Trisopterus minutus	utg7180000000029
Typhlichthys subterraneus	utg7180000000573, utg7180000802674
Zeus faber	utg7180000003061, utg7180001638560

Maximum-likelihood phylogenetic inference was performed with the software RAxML³⁰ v.8.1.12, applying separate instances of the GTRCAT substitution model³¹ to three partitions corresponding to all first, second, and third codon positions. To assess the impact of potentially saturated third codon positions in the phylogenetic inference, we conducted two additional analyses in which these positions were either completely ignored or coded as ‘R’ and ‘Y’ so that only transversions would be counted as state changes. Phylogenetic node support was estimated through bootstrapping with an automatically determined number of bootstrap replicates (RAxML option ‘autoMRE’).

Topologies of the three resulting maximum-likelihood phylogenies based on different usage of third codon positions were highly congruent, however, basal branches appeared to be best resolved in the analysis based on the alignment with three equally coded partitions. This maximum-likelihood phylogeny (Fig. 3) also received the highest mean bootstrap support (81.6, compared to 76.7 and 80.1 for the analyses in which third codon positions were ignored or coded as ‘R’ and ‘Y’, respectively). All taxa sampled for new genome assemblies had phylogenetic positions according to the expectations; for the 14 species for which we included both a GenBank sequence and a mitochondrial genome extracted from new assembly data, the two sequences clustered monophyletically in each case and were connected by short branches (see e.g., Polymixia japonica; Fig. 3). In other cases, mitochondrial genomes extracted from new assemblies clustered monophyletically with their congeneric counterparts downloaded from GenBank (see e.g., the mitochondrial genomes of Osmerus eperlanus and Osmerus mordax; Fig. 3).

Figure 3. Maximum-likelihood phylogeny of teleost mitochondrial genome sequences.

Sequences extracted from the new assemblies are marked in bold, all other mitochondrial genome sequences were previously available from the GenBank or ENSEMBL (where noted) databases. Black circles on nodes are sized proportional to bootstrap support, and the circle size corresponding to support values of 50, 75, and 100 are shown. Clade labels indicate taxonomic orders of all species as well as (with smaller font size) the (sub)family of gadiform species, following Betancur-R. et al.¹⁰ and Nelson⁴⁸. Note that the orders Tetraodontiformes, Beloniformes, and Lophiiformes appear as non-monophyletic (marked with asterisks). For comparability, color code is identical to Fig. 1 in Malmstrøm et al.¹¹. The tree file in Newick format has been deposited on Figshare under DOI: doi:10.6084/m9.figshare.4224234 (Data Citation 4).

It should be noted that basal phylogenetic nodes generally received relatively weak bootstrap support values, indicating that mitochondrial sequence data may not be sufficient to reliably resolve these ancient divergence events. Furthermore, three orders (Tetraodontiformes, Beloniformes, and Lophiiformes) appeared non-monophyletic, however, in all of these cases only weakly supported nodes separated two subgroups of the order. Thus, our results do not contradict the monophyly of these orders, which has been strongly supported in previous studies^10,32,33. Most importantly, despite the not unexpected lower support values of basal nodes, our mitochondrial phylogeny corroborates the correct species identification and the absence of DNA contamination in the 66 new assemblies.

Phylogenetic analyses using nuclear markers

To reliably reconstruct the evolutionary history of the 66 sequenced teleost species, we further extracted a set of carefully selected phylogenetic markers from the nuclear genomes. Based on a strict filtering procedure (see Malmstrøm et al.¹¹), we selected one-to-one orthologs for 567 exons of 111 genes from the 66 draft assemblies and from 10 genome assemblies available in the ENSEMBL database (Danio rerio, Astyanax mexicanus, Gasterosteus aculeatus, Oreochromis niloticus, Oryzias latipes, Takifugu rubripes, Tetraodon nigroviridis, Poecilia formosa and Xiphophorus maculatus) or GenBank (Salmo salar). The 111 selected genes were characterized by clock-like evolution, homogeneity in GC content among species, and no or only weak signals of selection and were therefore particularly well suited for the reconstruction of time-calibrated phylogenies. The 111 genes were distributed across all chromosomes of the zebrafish genome and included between 3 and 14 exons that were used in our analyses. Per gene, we concatenated sequences of these exons into a single alignment, which then included between 300 and 1,888 (mean: 643.4) bp, between 47 and 777 (mean: 240.5) variable sites and between 33 and 490 (mean: 157.5) parsimony-informative sites. As orthologous sequences for the 111 genes could be detected in almost all assemblies, the resulting 111 alignments contained only between 1.4 and 11.9% (mean: 7.3%) missing data (Table 8 (available online only)).

Table 8. Nuclear markers used in phylogenetic analyses.

ENSEMBL Gene ID	Gene name	Chr. in D. rerio	Num. exons	Alignment length	Num. variable sites	Num. PI sites*	Proportion of missing data	Mean node support	SH P-value†	K-score
ENSDARG00000003189	psmd1	chr 22	6	730	116	75	0.025	0.62	0.013	439.5
ENSDARG00000003495	madd	chr 7	5	594	140	86	0.091	0.502	0.007	419.9
ENSDARG00000003984	LTN1	chr 10	4	472	183	120	0.115	0.518	0.006	352.7
ENSDARG00000004302	slc45a1	chr 11	4	524	122	82	0.047	0.55	0.025	425.8
ENSDARG00000005058	ncapd2	chr 2	7	820	357	221	0.079	0.683	0.008	360.2
ENSDARG00000005236	srcap	chr 12	8	910	261	177	0.059	0.791	0.002	361.5
ENSDARG00000006169	lrrk2	chr 25	3	330	140	83	0.105	0.502	0.006	450.5
ENSDARG00000007092	xab2	chr 3	4	466	135	84	0.042	0.52	0.015	410.9
ENSDARG00000007744	tsr1	chr 15	4	578	320	220	0.118	0.703	0.003	323.5
ENSDARG00000009953	med14	chr 9	4	444	126	72	0.096	0.628	0.001	NA
ENSDARG00000009965	mag	chr 15	4	792	309	207	0.038	0.744	0.002	286.9
ENSDARG00000011764	asun	chr 4	3	376	121	80	0.104	0.492	0.013	434.1
ENSDARG00000012403	ERCC6L2	chr 8	4	456	239	176	0.081	0.684	0.002	296.2
ENSDARG00000013150	dhx16	chr 15	5	606	184	123	0.111	0.611	0.002	387.9
ENSDARG00000013240	zgc172271	chr 6	5	534	282	193	0.082	0.567	0.003	308.8
ENSDARG00000016177	eif4enif1	chr 6	4	488	194	140	0.06	0.658	0.006	363.3
ENSDARG00000016415	dhtkd1	chr 25	4	516	237	161	0.084	0.658	0.013	311.4
ENSDARG00000016443	eif3c	chr 12	6	724	133	92	0.095	0.523	0.012	395.8
ENSDARG00000016775	aqr	chr 17	5	624	156	101	0.083	0.646	0.001	411.4
ENSDARG00000016936	hmcn1	chr 20	5	574	304	197	0.085	0.797	0.003	321.4
ENSDARG00000017034	sqrdl	chr 25	5	602	245	157	0.014	0.676	0.001	268
ENSDARG00000017696	diexf	chr 13	5	688	349	247	0.081	0.772	0.002	NA
ENSDARG00000018296	rev1	chr 9	3	350	173	110	0.065	0.562	0.037	NA
ENSDARG00000019000	smc3	chr 22	6	748	154	87	0.073	0.625	0.002	461.2
ENSDARG00000019300	ints7	chr 20	3	394	130	82	0.058	0.512	0.006	408.6
ENSDARG00000019834	EDRF1	chr 17	8	968	394	249	0.068	0.815	0.02	307.3
ENSDARG00000022730	aasdh	chr 20	4	462	251	186	0.076	0.607	0.016	289.8
ENSDARG00000025011	synj1	chr 10	8	998	243	172	0.076	0.79	0.001	338.7
ENSDARG00000025269	pdcd6ip	chr 19	3	372	171	121	0.119	0.437	0.006	NA
ENSDARG00000026180	prpf8	chr 15	14	1,874	173	128	0.018	0.791	0.007	388
ENSDARG00000027353	zmym2	chr 9	3	366	131	99	0.096	0.498	0.003	396.9
ENSDARG00000027689	pold1	chr 3	3	348	97	68	0.069	0.558	0.005	452.6
ENSDARG00000029556	kansl3	chr 8	4	444	164	110	0.034	0.67	0.069	330.4
ENSDARG00000030945	si:ch211-259g3.4	chr 15	11	1,888	777	490	0.104	0.874	0.003	317.6
ENSDARG00000031886	ift140	chr 24	4	474	265	176	0.097	0.659	0.007	363.3
ENSDARG00000032459	med24	chr 12	3	332	126	77	0.072	0.5	0.002	394.2
ENSDARG00000032704	qrsl1	chr 17	4	470	228	154	0.034	0.723	0.008	347.6
ENSDARG00000034178	cpsf1	chr 19	4	458	119	72	0.09	0.479	0.003	460.6
ENSDARG00000035330	taf1	chr 5	5	588	162	114	0.097	0.634	0.01	398.9
ENSDARG00000035761	mcm7	chr 14	4	426	140	98	0.059	0.609	0.005	397.7
ENSDARG00000035978	ube3c	chr 7	4	526	154	83	0.095	0.574	0.001	418.3
ENSDARG00000036338	vps11	chr 10	10	1,238	345	216	0.054	0.719	0.002	269.3
ENSDARG00000036755	prmt10	chr 1	5	604	248	158	0.038	0.65	0.003	344.6
ENSDARG00000037017	ube4b	chr 23	4	532	98	60	0.073	0.534	0.003	476.2
ENSDARG00000037898	ppl	chr 3	4	438	255	181	0.08	0.579	0.003	310.8
ENSDARG00000038882	smc4	chr 15	10	1,260	445	261	0.083	0.813	0.035	293.4
ENSDARG00000039134	MTR	chr 12	5	524	202	147	0.091	0.703	0.006	384.2
ENSDARG00000041895	cad	chr 20	6	790	200	124	0.088	0.582	0.005	355.1
ENSDARG00000042530	nup205	chr 18	13	1,536	599	377	0.051	0.844	0.028	246.1
ENSDARG00000042728	plaa	chr 7	5	582	249	164	0.074	0.609	0.014	NA
ENSDARG00000043019	exoc1	chr 20	4	528	137	83	0.017	0.606	0	351.6
ENSDARG00000045626	nek8	chr 15	3	428	78	54	0.032	0.49	0.002	478.6
ENSDARG00000045900	agbl5	chr 4	3	300	131	83	0.058	0.592	0.014	413.5
ENSDARG00000051889	dhodh	chr 7	5	566	233	171	0.072	0.669	0.012	287.7
ENSDARG00000053087	mthfr	chr 8	3	360	121	79	0.071	0.484	0.001	NA
ENSDARG00000053200	dis3l	chr 7	3	316	174	116	0.101	0.672	0.01	390.5
ENSDARG00000053303	map3k4	chr 13	4	458	170	100	0.084	0.481	0.003	388.6
ENSDARG00000054154	bms1l	chr 12	6	700	285	199	0.057	0.663	0.001	NA
ENSDARG00000056037	itih6	chr 23	3	372	183	113	0.066	0.554	0.21	397.1
ENSDARG00000056160	hspd1	chr 9	3	388	121	85	0.073	0.543	0	391.5
ENSDARG00000056318	kansl2	chr 23	4	428	172	112	0.056	0.65	0.004	346.3
ENSDARG00000056530	CPAMD8	chr 22	6	820	293	176	0.089	0.577	0.028	318.7
ENSDARG00000056932	tfip11	chr 23	3	390	131	91	0.097	0.448	0.006	NA
ENSDARG00000057508	nbeal2	chr 16	3	482	276	193	0.107	0.54	0.006	345.1
ENSDARG00000057997	tmf1	chr 11	3	308	151	105	0.053	0.583	0.009	NA
ENSDARG00000058533	pole	chr 5	7	848	234	147	0.076	0.663	0.018	NA
ENSDARG00000059553	SYMPK	chr 5	7	900	308	196	0.075	0.626	0.01	355.6
ENSDARG00000059631	ints1	chr 3	11	1,360	405	288	0.076	0.825	0.004	270.1
ENSDARG00000059711	nol6	chr 5	6	624	330	226	0.076	0.665	0.009	NA
ENSDARG00000059760	wdtc1	chr 16	3	386	132	85	0.025	0.662	0.004	354
ENSDARG00000059846	EPG5	chr 5	7	930	515	354	0.086	0.728	0.009	310.1
ENSDARG00000059925	usp24	chr 20	5	590	182	112	0.071	0.707	0	413.7
ENSDARG00000060089	btaf1	chr 13	7	764	300	196	0.078	0.781	0.022	235.2
ENSDARG00000061013	ankfy1	chr 5	6	726	203	127	0.068	0.653	0.013	340.2
ENSDARG00000061394	baz1b	chr 18	3	1,034	509	329	0.113	0.656	0.009	339.5
ENSDARG00000061789	gnl1	chr 16	3	322	162	109	0.048	0.577	0.091	356.2
ENSDARG00000062198	pcm1	chr 1	6	790	328	185	0.076	0.754	0.005	378.6
ENSDARG00000062632	duox	chr 25	11	1270	602	387	0.053	0.867	0.001	217.5
ENSDARG00000062868	eea1	chr 18	3	362	142	94	0.071	0.576	0.008	413.8
ENSDARG00000063558	ARHGEF17	chr 18	3	386	144	87	0.089	0.525	0.006	381.5
ENSDARG00000063626	ddx21	chr 13	4	424	177	120	0.033	0.713	0.001	359.9
ENSDARG00000067805	ggcx	chr 10	5	516	173	119	0.059	0.619	0	343
ENSDARG00000069274	ighmbp2	chr 18	8	1,108	561	398	0.039	0.783	0.05	268.8
ENSDARG00000070109	ncapg	chr 1	3	326	135	94	0.092	0.523	0.003	NA
ENSDARG00000071294	TONSL	chr 17	3	332	184	137	0.068	0.607	0.021	321.2
ENSDARG00000073862	ptpn13	chr 21	5	572	227	141	0.109	0.64	0.018	353.5
ENSDARG00000074137	C2CD3	chr 15	5	620	340	233	0.094	0.638	0.012	NA
ENSDARG00000074314	TTC37	chr 5	3	398	209	148	0.1	0.482	0.017	NA
ENSDARG00000074410	brip1	chr 15	3	334	146	106	0.097	0.571	0.002	NA
ENSDARG00000074424	ibtk	chr 16	4	548	249	163	0.097	0.564	0.017	327.2
ENSDARG00000074524	CNTNAP1	chr 3	9	1,186	439	309	0.039	0.862	0.001	292.3
ENSDARG00000074571	GPAA1	chr 2	5	586	183	120	0.105	0.515	0.011	302.9
ENSDARG00000074675	pan2	chr 23	5	758	230	137	0.064	0.761	0.002	382.5
ENSDARG00000074749	abca12	chr 9	4	560	274	192	0.096	0.737	0.009	270.1
ENSDARG00000074759	ccar1	chr 13	5	600	150	90	0.091	0.659	0.002	NA
ENSDARG00000075108	tmco3	chr 1	4	460	194	113	0.071	0.64	0.002	444.1
ENSDARG00000075672	pms2	chr 12	4	450	186	117	0.046	0.711	0.001	NA
ENSDARG00000075798	USP38	chr 1	3	674	332	208	0.05	0.572	0.047	NA
ENSDARG00000075826	msh4	chr 17	5	550	221	122	0.016	0.731	0.009	356.3
ENSDARG00000076920	ZNF335	chr 8	5	538	191	121	0.083	0.556	0.002	NA
ENSDARG00000076994	gpr124	chr 8	5	844	454	279	0.098	0.78	0.012	313.6
ENSDARG00000077139	col6a3	chr 9	3	994	577	407	0.115	0.668	0.003	318.3
ENSDARG00000077469	polr1b	chr 13	6	708	331	223	0.09	0.786	0.001	336.1
ENSDARG00000077536	snrnp200	chr 8	10	1,134	223	140	0.052	0.773	0.004	398.3
ENSDARG00000077860	ankhd1	chr 21	3	398	47	33	0.039	0.43	0.007	NA
ENSDARG00000077891	npc1l1	chr 2	4	486	206	148	0.097	0.729	0	384.1
ENSDARG00000078135	MRC2	chr 3	3	318	177	110	0.086	0.681	0.006	419.7
ENSDARG00000078890	wdfy3	chr 5	13	1,528	490	291	0.097	0.706	0.005	365.9
ENSDARG00000079702	wdr81	chr 15	3	342	132	75	0.066	0.545	0.002	370.6
ENSDARG00000079751	megf8	chr 16	12	1,498	571	336	0.097	0.703	0.028	291.5
ENSDARG00000090858	AREL1	chr 17	5	654	190	112	0.028	0.627	0.037	375.9

*The number of parsimony-informative sites.

^†The P-values obtained from the Shimodaira-Hasegawa tests. Genes with non-significant P-value from the Shimodaira-Hasegawa tests are in bold.

These alignments were used for an extensive set of phylogenetic analyses to reconstruct the species tree as well as individual gene trees, using both maximum-likelihood and Bayesian inference. Detailed descriptions of these analyses and a discussion of the resulting species tree can be found in Malmstrøm et al.¹¹ In addition, we here present analyses of gene tree discordance in relation to the 66 new assemblies, as a heterogeneous phylogenetic signal among gene trees could, among other causes (e.g. Fontaine et al.³⁴; Gante et al.³⁵), result from assembly issues such as contamination. To quantify gene tree discordance, we compared each gene tree to the species tree based on their K-scores³⁶ and using the Shimodaira-Hasegawa (SH) test³⁷ implemented in PAUP* v.4.0a150 (http://paup.csit.fsu.edu). All gene trees used in this comparison were maximum-clade-credibility (MCC) trees inferred with the software BEAST³⁸ v.2.2.0 for each of the 111 alignments. Similarly, we considered the MCC tree inferred with BEAST for a single concatenated alignment of all genes as the species tree (Fig. 1 in Malmstrøm et al.¹¹) used in this comparisons. According to results of the SH test, all but four gene trees were significantly different (P<0.05) from the species tree (Table 8 (available online only)). K-scores were calculated for 91 of the 111 gene trees, but could not be calculated for the remaining 20 gene trees due to negative branch lengths. The resulting K-scores ranged from 217.5 to 478.6, indicating considerable gene tree discordance in agreement with the results of the SH test (even though individual K-scores and SH test P-values did not correlate; P=0.64). However, such tree discordance does not necessarily indicate assembly issues but can arise from multiple factors including incomplete lineage sorting³⁹ or a lack of phylogenetic signal⁴⁰. While high levels of incomplete lineage sorting have been shown to affect phylogenomic inference of rapidly radiating lineages like Neoavian birds⁴¹ or cichlid fishes^42,43, its effect is expected to be limited in the analysis of ancient clades with long internode distances⁴⁴ such as the teleost species tree inferred from our set of 111 nuclear markers¹¹. We investigated the presence of incomplete lineage sorting in this species tree in Malmstrøm et al.¹¹ by testing for a correlation of indel hemiplasy and branch length⁴⁵. However, since no such correlation could be detected in our data set, we concluded that incomplete lineage sorting was weak or absent in the teleost species tree reported in Malmstrøm et al.¹¹. In addition, we now tested whether instead of incomplete lineage sorting, a lack of phylogenetic signal in individual marker alignments could explain the observed gene tree discordance. To this end, we calculated the mean Bayesian posterior probability (BPP) of each gene tree as a measure of its phylogenetic signal and compared it to the K-score between this gene tree and the species tree. We find a highly significant negative correlation between the two measures (linear regression: R²=0.34, P<10^–15), which is illustrated in Fig. 4. Furthermore, we also detected a highly significant correlation between the number of parsimony-informative sites per marker and the respective K-score (linear regression: R²=0.49, P<10^–13) (Fig. 4). These tests show that low phylogenetic signal in individual marker alignments, rather than contamination in the assemblies, is responsible for the observed gene tree discordance. This lack of signal in individual alignments, however, is not exclusive to our phylogenomic data set, but is a feature that is commonly observed in nuclear markers^40,44. As demonstrated by Malmstrøm et al.¹¹ as well as other phylogenomic studies^45–47 the combination of such stringently filtered exonic markers nevertheless allows an extremely reliable inference of ancient species trees that could not be achieved with faster-evolving sequence such as mitochondrial genomes, intronic regions, or genes under selection. We therefore recommend the reuse of the marker set presented here as a highly suitable resource for future analyses of the teleost species tree with extended taxon sets.

Figure 4. Distances between tree topologies compared to phylogenetic signal.

Topological distances are measured by the K-score between the gene trees and the species trees, and phylogenetic signal of the gene trees is measured as mean Bayesian posterior probability (BPP). Dots are colored according to the number of parsimonious-informative (PI) sites. The black line represents the linear regression (R²=0.34, P<10^–15).

Usage Notes

Sequencing reads from all species can be downloaded from the European Nucleotide Archive (ENA), under the sample identifiers ERS1199874—ERS1199939. Unitig and scaffold level assemblies are available for download from the Dryad repository with individual assemblies found under DOI: doi:10.5061/dryad.326r8/1.—dryad.326r8/132. See Table 7 (available online only) for individual identifiers for both the raw sequencing read sets and the two assembly versions. The mitochondrial phylogeny (Fig. 3) can be downloaded as a tree file in Newick format from Figshare under DOI: doi:10.6084/m9.figshare.4224234 (Data Citation 4).

Additional information

How to cite: Malmstrøm, M. et al. Whole genome sequencing data and de novo draft assemblies for 66 teleost species. Sci. Data 4:160132 doi: doi:10.1038/sdata.2016.132 (2017).

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