Chromosome-scale genome assembly of the European common cuttlefish Sepia officinalis

Coleoid cephalopods (octopus, squid, cuttlefish) are a highly derived group of mollusks, characterized by the largest nervous systems among all invertebrates (ca. 500 million neurons in adult octopus of which 200 million are in the central brain Giuditta et al., 1971; Young, 1963a, compared to ca. 140,000 in the fruit fly Dorkenwald et al., 2024 or 70 million in the mouse Herculano-Houzel et al., 2006) and specializations with a great historical importance for neuroscience (e.g. ‘giant axons’ Hodgkin et al., 1952 and ‘giant synapses’ Llinás et al., 1981a; Llinás et al., 1981b; Llinas, 1984). These animals exhibit very sophisticated behaviors, such as dynamic camouflage (Hanlon and Messenger, 2018; Josef et al., 2016; Osorio et al., 2022; Marshall and Messenger, 1996; Zylinski et al., 2009a; Zylinski et al., 2012; Zylinski et al., 2009b), learning (Jozet-Alves et al., 2013; Schnell et al., 2021; Boycott and Young, 1955), social communication (Mather, 2006; Hall and Hanlon, 2002; Norman et al., 1999) and hunting (Sampaio et al., 2021; Sampaio et al., 2024; Feord et al., 2020) as well as two-stage sleep (Medeiros et al., 2021; Iglesias et al., 2019; Pophale et al., 2023). Because of these characteristics, cephalopods have been the focus of many fundamental studies by biologists, biophysicists, and physiologists over the past century (Young, 1974; Young, 1962a; Young, 1960; Young, 1991; Young, 1963b; Young, 1962b; Nixon and Young, 2003; Case et al., 1972; Dilly et al., 1963; Stephens and Young, 1969). Thanks to advances in sequencing technologies, coleoid cephalopods are now also emerging as animal models in molecular neurobiology, evolution, and genomics (Baden et al., 2023). Recent studies examined cephalopod biology from the perspectives of single-cell gene expression (Styfhals et al., 2022; Songco-Casey et al., 2022; Duruz et al., 2023; Gavriouchkina et al., 2025), genome topology and gene regulation (Schmidbaur et al., 2022; Rouressol et al., 2023).

Despite recent technical progress in genomics, the genomes of cephalopods remain challenging to assemble because of their large sizes and high repeat fractions. While the genomes of Octopodiformes (Octopus, Eledone, Argonauta) are either smaller than (1.1 Gigabases or Gb Yoshida et al., 2022) or comparable in size to that of humans (around 3 Gb Albertin et al., 2015; Destanović et al., 2023), the typical genomes of Decapodiformes (squids and cuttlefish) often reach 6 Gb (Belcaid et al., 2019; Albertin et al., 2022). The biggest contributing factors to this genome expansion are transposable elements (TEs), with different TE classes differentiating squid and cuttlefish genomes from those of octopods (Marino et al., 2022). Besides such differences in genome sizes, karyotypes in coleoid cephalopods are also unusual when compared to those of other invertebrates, including other mollusks: for example, haploid chromosome numbers are around 30 in octopuses and around 46 in squids and cuttlefish (Albertin et al., 2022; Simakov et al., 2022), while they are between 8 and 19 in non-cephalopod mollusks (Zou et al., 2024; Chen et al., 2025; Zeng et al., 2021; Sun et al., 2024; Männer et al., 2024; Ma et al., 2023; Liu et al., 2023; Peñaloza et al., 2021). Karyotype reconstruction and validation techniques in animals with such large genomes are also not as developed as they are for more familiar invertebrate and vertebrate species.

The common cuttlefish Sepia officinalis has been used as a model organism for studies in neural control and development (Arias-Montecino et al., 2024; O’Brien et al., 2017), behavior (Hanlon and Messenger, 2018; Schnell et al., 2021), evolution (Bellingham et al., 1998), environmental studies (Chemello et al., 2022; Court et al., 2024), and biomechanics (Gladman and Askew, 2023; Yang et al., 2020). Found in the Mediterranean, the English Channel, and the European coastal Atlantic, it can produce large egg clutches containing hundreds of embryos, several times throughout its semelparous reproductive cycle (Boletzky, 1987; Laptikhovsky et al., 2003). More recently, this species has emerged as an important model to study the neural basis of cephalopod camouflage (Reiter et al., 2018; Woo et al., 2023) building on extensive behavioral studies (Hanlon and Messenger, 2018; Josef et al., 2016; Osorio et al., 2022; Marshall and Messenger, 1996; Zylinski et al., 2009a; Zylinski et al., 2012; Zylinski et al., 2009b) and neuronal cell type characterization, evolution, and development (Imarazene et al., 2017; Cocci et al., 2023; Andouche et al., 2013).

Despite a widespread interest in this animal, including the recent sequencing of several cephalopod species as part of the Aquatic Symbiosis Genomics project (McKenna et al., 2021), a detailed, annotated genomic resource for this species is still lacking. We describe here the sequencing, assembly, and annotation of the Sepia officinalis genome.

Our data provided initial evidence for the existence of 1n=47 chromosomes. We compared them with available genome assemblies (i) from another S. officinalis individual by the Darwin Tree of Life project (DToL) (Blaxter et al., 2022), itself suggesting 49 chromosomes, and (ii) from other decapod species, such as Euprymna scolopes, Doryteuthis pealeii, and Acanthosepion esculentum, each with estimates of 46 chromosomes (Albertin et al., 2022). When compared with the assemblies generated for the other species, both S. officinalis assemblies contained additional and independent chromosome splits. We thus investigated the repeat content and the alignment of raw data at the discrepant chromosome junctions to assess whether these differences were of a technical or biological nature.

We sequenced the genome of a 6-month-old Sepia officinalis male individual, reared from eggs collected in the Portuguese Atlantic coast. The DNA was extracted from brain tissue and sequenced using long-read (PacBio HiFi) and chromatin conformation (Hi-C) methods (Figure 1A and B). The sex of the animal was confirmed by qPCR following a recently described protocol (Rubino et al., 2025).

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Comparison with another chromosome-scale Sepia officinalis assembly

A chromosome-scale assembly for Sepia officinalis was released recently by the Wellcome Sanger Institute’s Darwin Tree of Life project (Blaxter et al., 2022) (DToL, GCA_964300435.1). That genome was assembled from a male individual using high coverage PacBio Sequel II (~51 x) and Arima2 Hi-C (~80 x) data, with a final assembly size of 5.8 Gb. The haploid chromosome number was estimated to be 49. To compare both S. officinalis datasets directly, we downloaded the DToL data and created two new assemblies using the pipeline described above (hifiasm using PacBio HiFi and Hi-C data). The resulting assemblies were overall very similar, with the DToL assembly having a slightly higher contiguity (N50 length, see Table 1) and BUSCO completeness (Figure 2—figure supplement 1A and B) due to their higher sequencing coverage.

Table 1

	MPIBR reassembly			DToL reassembly
version	p_ctg	hap1	hap2	p_ctg	hap1	hap2
number of contigs	8.289	10.651	10.425	8.783	11.026	11.089
raw length [bp]	6.049.669.443	5.675.386.986	5.662.586.038	6.053.996.452	5.721.157.269	5.950.565.264
N50 length [bp]	1.723.203	1.032.632	1.010.375	1.810.137	1.165.578	1.182.649
average contig length [bp]	729.843	532.850	543.173	689.285	518.878	536.618

After scaffolding with YAHS, both datasets reached the previously identified chromosome numbers (1n=47 for MPIBR and 1n=49 for DToL, Figure 2A and B). To further investigate this surprising discrepancy, we aligned both assemblies using Winnowmap (Jain et al., 2022; Jain et al., 2020) to locate the differences between them (Figure 2C). We observed four ‘breakpoints’ (BP) of chromosome scaffolds: one in the MPIBR assembly compared to DToL (BP1: DToL_5=MPIBR_40+44) and three in the DToL assembly compared to MPIBR (BP2: DToL_31+40 = MPIBR_2, BP3: DToL_41+46 = MPIBR_6, BP4: DToL_44+45 = MPIBR_7). We also aligned the assemblies to the chromosome-scale genome of another cuttlefish Acanthosepion esculentum (1n=46, GCA_964036315.1). In this alignment, all four breakpoints were collinear with single A. esculentum chromosomes (Figure 2D).

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To better understand the potential cause of these divergent chromosome numbers, we analyzed the Hi-C and HiFi coverage in the breakpoint regions (Figure 2—figure supplement 2A). First, we aligned the Hi-Fi reads to the scaffolds and extracted all alignments along the 200 kb terminal scaffold windows to find any notable drops in coverage, or reads spanning any of the scaffold junctions. We detected no spanning reads. This is not surprising given that no contigs were assembled at these sites, resulting in the observed scaffold junctions. More interestingly, we noted a~5 fold decrease in HiFi coverage along the DToL scaffold_40 (part of BP2) relative to its flanking regions, indicating a highly repetitive, low-mappability region at this boundary.

Next, we realigned the Hi-C data to the scaffolded assemblies using bwa-mem2 (Vasimuddin et al., 2019) and extracted all trans HiC pairs (between-scaffold contacts) using pairtools (Abdennur et al., 2023). We normalized trans HiC contacts to the scaffold length and compared contact rates between breakpoint scaffolds to the baseline contact rate (computed from pairs of scaffolds with a clear 1-to-1 match between assemblies), and the contact rate within scaffolds (intra-scaffold pairs) (Figure 2—figure supplement 2B and C). The contact rates within breakpoints were consistently lower than within scaffolds, likely falling below the threshold to be merged during assembly. However, the contact rates at three of four breakpoints (BP1, BP3, BP4) were significantly elevated above the genome-wide background distribution (empirical p=0.010, 0.005, 0.005, respectively), suggesting that they may represent intra-chromosomal contacts disrupted by a misassembly. Notably, BP2 was not significant (empirical p=0.170), likely due to the low coverage and mappability around the DToL scaffold_40 boundary. Considered jointly, the three DToL breakpoint scaffold pairs showed significantly higher trans contact rates than the background (Wilcoxon rank-sum, one-tailed, U=1771, p=0.004).

Lastly, we analyzed the repeat landscape around the 200 kb scaffold ends using RepeatMasker (Smit et al., 2025) and the custom repeat library that we had generated for Sepia officinalis (described further below). Compared to control scaffolds of the same assembly, we observed consistently elevated repeat content at the breakpoint junctions (mean 71.5% vs 67.6% masked bases), with an enrichment of unclassified repeats (32.1% vs 30.0%), which could explain a repeat-driven assembly fragmentation or scaffolding failure (Figure 2—figure supplement 2D). The BP2 DToL scaffold_40 junction window was 99.99% masked (99.2% unclassified repeats), providing a likely mechanistic explanation for both the HiFi coverage drop and the absence of a significant trans Hi-C signal at this breakpoint. Taken together, these analyses suggest that the different chromosome numbers across the two S. officinalis assemblies are due to technical reasons, caused by repeat-rich scaffold boundaries that impair HiFi and Hi-C read alignment and in turn, correct assembly in these regions.

Comparisons with other chromosome-scale Decapodiformes genomes

To further investigate why our initial estimate of Sepia officinalis’s chromosome numbers differred from those in other studies, we compared our assembly with the chromosome-scale genomes of two other decapod cephalopods, Euprymna scolopes (Schmidbaur et al., 2022) (a sepiolid) and Doryteuthis pealeii (Albertin et al., 2022) (a loliginid), both described as having 46 chromosomes (Figure 3A). Using orthogroups to perform linkage analysis, we could detect a clear chromosome homology between Doryteuthis and Sepia, and to a lesser extent between Euprymna and Sepia (Figure 3B). We observed some small-scale rearrangements between Euprymna and Doryteuthis, such as a fusion of chromosomes 24 and 40 from Doryteuthis in chromosome 2 of Euprymna, which has also been observed in other Sepiolids (Gavriouchkina et al., 2025; Sanchez et al., 2026). Dotplots showing detailed pairwise synteny comparisons are shown in Figure 3—figure supplement 1 (Sepia to Doryteuthis) and Figure 3—figure supplement 2 (Sepia to Euprymna). The inferred species tree as part of this analysis places Euprymna as sister to Sepia and Doryteuthis, matching recent phylogenetic analyses (Chen et al., 2025; López-Córdova et al., 2022) (note, however, that this tree was constructed without the inclusion of outgroup taxa, and therefore lacks a reliable root).

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This comparison revealed that chromosomes 40 and 43 in Sepia are merged into one in Euprymna and in Dorytheutis (Figure 3C), as they were in the DToL Sepia officinalis assembly (Figure 2C). Together with the whole-genome alignments between the cuttlefish assemblies described earlier (Figure 2D), these results lead to the following parsimonious conclusion: that the karyotype for S. officinalis is 1n=46 chromosomes, as it is for other Decapodiformes.

We also performed a synteny comparison, including the cuttlefish A. esculentum that was recently annotated by genome liftover from Acanthosepion pharaonis (Coffing et al., 2024). That assembly, constructed from a female individual, showed a low read coverage for a sex chromosome in a ZZ/Z0 sex determination system (Coffing et al., 2025). Our syntenic comparison indicated a strong homology between the inferred Z chromosome of A. esculentum and chromosome 46 of S. officinalis (Figure 3—figure supplement 3A). As indicated above, we determined the sex of our S. officinalis specimen by replicating the analysis used to identify the Z chromosome in A. esculentum (Coffing et al., 2025). For this, we aligned short-reads (Illumina) from the same S. officinalis individual to the assembly and examined the normalized read coverage for each chromosome (Figure 3—figure supplement 3B). In contrast to the low coverage observed in the female A. esculentum assembly (Figure 3—figure supplement 3C), we observed no significant decrease in read coverage for chromosome 46, suggesting that our material came from a male animal. Additionally, we used a recently published genotyping protocol for cephalopods (Rubino et al., 2025) and performed qPCR on extracted genomic DNA from tissue samples, confirming the male sex of our sequenced individual.

Genome repeat landscape

After creating a custom repeat library for Sepia officinalis using RepeatModeler (Flynn et al., 2020), we masked the genome using RepeatMasker (Smit et al., 2025), resulting in 71.17% masked bases. The categories of repeats are shown in Figure 4A. Most repeats were not characterized (39.65% of total bp) and presumably represent ancient repeats that diverged beyond recognition (Feschotte, 2008). Retroelements constituted the largest characterized repeat category (17.32%) followed by DNA transposons (5.92%); 5.83% were annotated as simple repeats. As observed in other Decapodiformes, the Sepia genome contained almost no short interspersed nuclear elements (SINEs), supporting the hypothesis that the SINE expansion observed in octopuses occurred independently in their lineage (Marino et al., 2022).

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Tissue-specific expression of expanded gene families

To place the identified gene families in a functional context, we profiled their expression in the bulk RNA-seq data (taken from multiple tissues of S. officinalis) used originally for gene modeling (Figure 5A). Principal component analysis (PCA) revealed the largest axis of variation in gene expression to separate brain tissues from peripheral tissues, with skin being the most transcriptomically distinct (Figure 5A), consistent with the high number of tissue-specific differentially expressed (DE) genes identified in non-neural tissues (Figure 5B). We identified the genes belonging to expanded families that were differentially expressed across tissues and enriched gene ontology (Aleksander et al., 2026; Ashburner et al., 2000) (GO) terms for them to gain additional insight. The large families excluded from CAFE5 modelling and the significantly expanded families identified by CAFE5 were analyzed separately.

Figure 5

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Eleven of the largest gene families were expressed in our data (Figure 5C) and five had enriched GO terms (Figure 5D and E). Among them, the cadherin family showed brain-restricted expression and GO terms related to cell–cell adhesion and calcium binding, consistent with their role in neuronal connectivity and circuit formation (Albertin et al., 2015; Redies, 1997). Two C2H2 zinc finger gene families were expressed in the optic and vertical/subvertical lobes of the brain and in the skin, with GO terms related to DNA-binding, transcriptional regulation, or development. The RING-type zinc finger family was expressed specifically in the skin, with GO terms, including zinc binding and ubiquitin protein ligase activity, the canonical function of RING-domain E3 ligases (Joazeiro et al., 1999). Genes of the HPGDS/S-crystallin family were expressed in the brain (basal and optic lobes and posterior subesophageal mass) and skin, with GO terms related to glutathione metabolism, matching their described enzymatic function. We did not find expression in the retina, which is expected given that S-crystallins are expressed in lentigenic cells of the eye (Gavriouchkina et al., 2025; Ryu et al., 2023) and these cells were not included during sampling.

Among the 30 most significantly expanded families examined (out of 1813 total), expression was widespread (20/30) and tissue-specific differential expression was common (17/30), suggesting that a substantial proportion of expanded paralogs represent functional coding sequences with specialized spatial deployment (Figure 4—figure supplement 1B). Ten of the retrotransposon-associated families were differentially expressed in the brain (optic and vertical/subvertical lobes) and skin, arguing against these loci being inactive repeat fragments and supporting their inclusion as transcribed gene models. Two significantly expanded families showed both differential expression and enriched GO terms (Figure 4—figure supplement 1C). The first was the UGT2A1-related family, which had the largest number of differentially expressed genes overall, with expression concentrated in the skin, retina, and posterior subesophageal mass of the brain. Enriched GO terms matched the described enzymatic function for this family, namely UDP-glycosyltransferase activity. The second gene family was the homeodomain-like family with enrichment for DNA binding terms consistent with their role as transcription factors, and was preferentially expressed in the vertical and subvertical brain lobes with weaker expression in other areas.

Collectively, many differentially expressed genes from expanded families were restricted to specific tissues or brain subregions (Figure 5F, Figure 4—figure supplement 1D), indicating that paralogs within an expanded family have adopted distinct spatial expression domains and possibly, specialized functions.

This study presents a detailed, chromosome-scale genome assembly and annotation for the common cuttlefish Sepia officinalis, providing an additional important resource for comparative genomics, neurobiology, and evolutionary studies within cephalopods and mollusks. The assembly was derived from a combination of PacBio HiFi long reads, Dovetail Omni-C chromatin conformation capture, and multiple rounds of scaffolding and manual curation. The resulting assembly has a haploid genome size of 5.68 Gb, and contains 47 chromosome-like clusters. This number prompted us to perform detailed analyses of chromosome structure and homology across various decapod genomes, leading to a likely consensus of 46 chromosomes. This resource should be valuable for future efforts in cephalopod genomics, especially within the Decapodiformes, where large and repetitive genomes have hindered previous efforts.

The genome assembly and raw data can be found at the BioProject PRJNA1091451 on NCBI. Raw sequencing reads are deposited at SRA (study accession SRP570862). The code for the genome assembly and annotation is available at https://gitlab.mpcdf.mpg.de/mpibr/laur/cuttlefishomics/soffgenome (copy archived at Tushev, 2026). Genome annotation files are deposited at https://doi.org/10.17617/3.CGO7QG.

1. 1. Rencken S
   2. Tushev G
   3. Hain D
   4. Ciirdaeva E
   5. Simakov O
   6. Laurent G

   (2025) NCBI BioProject

   ID PRJNA1091451. Sepia officinalis isolate:GLC-03058 (common cuttlefish).

   https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1091451
2. 1. Rencken S
   2. Tushev G
   3. Hain D
   4. Ciirdaeva E
   5. Simakov O
   6. Laurent G

   (2025) Edmond - the Open Research Data Repository of the Max Planck Society

   Chromosome-scale genome assembly of the European common cuttlefish Sepia officinalis.

   https://doi.org/10.17617/3.CGO7QG

1. Preprint

   1. Abdennur N
   2. Fudenberg G
   3. Flyamer IM
   4. Galitsyna AA
   5. Goloborodko A
   6. Imakaev M
   7. Venev SV
   8. Open2C

   (2023) Pairtools: from sequencing data to chromosome contacts

   bioRxiv.

   https://doi.org/10.1101/2023.02.13.528389

   • Google Scholar
2. 1. Aceituno-Valenzuela U
   2. Micol-Ponce R
   3. Ponce MR

   (2020) Genome-wide analysis of CCHC-type zinc finger (ZCCHC) proteins in yeast, Arabidopsis, and humans

   Cellular and Molecular Life Sciences 77:3991–4014.

   https://doi.org/10.1007/s00018-020-03518-7

   • PubMed
   • Google Scholar
3. 1. Adachi K
   2. Ohnishi K
   3. Kuramochi T
   4. Yoshinaga T
   5. Okumura SI

   (2014) Molecular cytogenetic study in Octopus (Amphioctopus) areolatus from Japan

   Fisheries Science 80:445–450.

   https://doi.org/10.1007/s12562-014-0703-4

   • Google Scholar
4. 1. Ahmed S
   2. Stanley D
   3. Kim Y

   (2018) An insect prostaglandin E₂ synthase acts in immunity and reproduction

   Frontiers in Physiology 9:1231.

   https://doi.org/10.3389/fphys.2018.01231

   • PubMed
   • Google Scholar
5. 1. Ahn SJ
   2. Marygold SJ

   (2021) The UDP-Glycosyltransferase family in Drosophila melanogaster: Nomenclature update, gene expression and phylogenetic analysis

   Frontiers in Physiology 12:648481.

   https://doi.org/10.3389/fphys.2021.648481

   • PubMed
   • Google Scholar
6. 1. Akasaki T
   2. Nikaido M
   3. Tsuchiya K
   4. Segawa S
   5. Hasegawa M
   6. Okada N

   (2006) Extensive mitochondrial gene arrangements in coleoid Cephalopoda and their phylogenetic implications

   Molecular Phylogenetics and Evolution 38:648–658.

   https://doi.org/10.1016/j.ympev.2005.10.018

   • PubMed
   • Google Scholar
7. 1. Aken BL
   2. Ayling S
   3. Barrell D
   4. Clarke L
   5. Curwen V
   6. Fairley S
   7. Fernandez Banet J
   8. Billis K
   9. García Girón C
   10. Hourlier T
   11. Howe K
   12. Kähäri A
   13. Kokocinski F
   14. Martin FJ
   15. Murphy DN
   16. Nag R
   17. Ruffier M
   18. Schuster M
   19. Tang YA
   20. Vogel JH
   21. White S
   22. Zadissa A
   23. Flicek P
   24. Searle SMJ

   (2016) The Ensembl gene annotation system

   Database 2016:baw093.

   https://doi.org/10.1093/database/baw093

   • PubMed
   • Google Scholar
8. 1. Albertin CB
   2. Simakov O
   3. Mitros T
   4. Wang ZY
   5. Pungor JR
   6. Edsinger-Gonzales E
   7. Brenner S
   8. Ragsdale CW
   9. Rokhsar DS

   (2015) The octopus genome and the evolution of cephalopod neural and morphological novelties

   Nature 524:220–224.

   https://doi.org/10.1038/nature14668

   • PubMed
   • Google Scholar
9. 1. Albertin CB
   2. Medina-Ruiz S
   3. Mitros T
   4. Schmidbaur H
   5. Sanchez G
   6. Wang ZY
   7. Grimwood J
   8. Rosenthal JJC
   9. Ragsdale CW
   10. Simakov O
   11. Rokhsar DS

   (2022) Genome and transcriptome mechanisms driving cephalopod evolution

   Nature Communications 13:2427.

   https://doi.org/10.1038/s41467-022-29748-w

   • PubMed
   • Google Scholar
10. 1. Aleksander SA
    2. Balhoff JP
    3. Carbon S
    4. Cherry JM
    5. Ebert D
    6. Feuermann M
    7. Gaudet P
    8. Harris NL
    9. Hill DP
    10. Kalita P
    11. Lee R
    12. Mi H
    13. Moxon S
    14. Mungall CJ
    15. Muruganujan A
    16. Mushayahama T
    17. Sternberg PW
    18. Thomas PD
    19. Van Auken K
    20. Wong ED
    21. Wood V
    22. Ramsey J
    23. Siegele DA
    24. Chisholm RL
    25. Dodson R
    26. Fey P
    27. Aspromonte MC
    28. Nugnes MV
    29. Naser XAC
    30. Tosatto SCE
    31. Giglio M
    32. Nadendla S
    33. Antonazzo G
    34. Attrill H
    35. Brown NH
    36. dos Santos G
    37. Marygold S
    38. Röper K
    39. Strelets V
    40. Tabone CJ
    41. Thurmond J
    42. Zhou P
    43. Zaru R
    44. Lovering RC
    45. Logie C
    46. Chen D
    47. Naba A
    48. Christie K
    49. Corbani L
    50. Ni L
    51. Sitnikov D
    52. Smith C
    53. Seager J
    54. Cooper L
    55. Elser J
    56. Jaiswal P
    57. Gupta P
    58. Naithani S
    59. Carme P
    60. Rutherford K
    61. De Pons JL
    62. Dwinell MR
    63. Hayman GT
    64. Kaldunski ML
    65. Kwitek AE
    66. Laulederkind SJF
    67. Tutaj MA
    68. Vedi M
    69. Wang SJ
    70. D’Eustachio P
    71. Aimo L
    72. Axelsen K
    73. Bridge A
    74. Hyka-Nouspikel N
    75. Morgat A
    76. Goldbold G
    77. Engel SR
    78. Miyasato SR
    79. Nash RS
    80. Sherlock G
    81. Weng S
    82. Bakker E
    83. Berardini TZ
    84. Reiser L
    85. Auchincloss A
    86. Argoud-Puy G
    87. Blatter MC
    88. Boutet E
    89. Breuza L
    90. Casals-Casas C
    91. Coudert E
    92. Estreicher A
    93. Famiglietti ML
    94. Gos A
    95. Gruaz-Gumowski N
    96. Hulo C
    97. Jungo F
    98. Mercier PL
    99. Lieberherr D
    100. Masson P
    101. Pedruzzi I
    102. Pourcel L
    103. Poux S
    104. Rivoire C
    105. Sundaram S
    106. Bateman A
    107. Adesina A
    108. Bowler-Barnett E
    109. Carpentier D
    110. Denny P
    111. Ignatchenko A
    112. Ishtiaq R
    113. Lock A
    114. Lussi Y
    115. Magrane M
    116. Martin MJ
    117. Orchard S
    118. Raposo P
    119. Speretta E
    120. Tyagi N
    121. Urakova N
    122. Warner K
    123. Yu CWH
    124. Chan J
    125. Diamantakis S
    126. Quinton-Tulloch M
    127. Raciti D
    128. Fisher M
    129. James-Zorn C
    130. Ponferrada V
    131. Zorn A
    132. Howe D
    133. Ramachandran S
    134. Ruzicka L
    135. Westerfield M
    136. The Gene Ontology Consortium

    (2026) The gene ontology knowledgebase in 2026

    Nucleic Acids Research 54:D1779–D1792.

    https://doi.org/10.1093/nar/gkaf1292

    • Google Scholar
11. 1. Amaral P
    2. Carbonell-Sala S
    3. De La Vega FM
    4. Faial T
    5. Frankish A
    6. Gingeras T
    7. Guigo R
    8. Harrow JL
    9. Hatzigeorgiou AG
    10. Johnson R
    11. Murphy TD
    12. Pertea M
    13. Pruitt KD
    14. Pujar S
    15. Takahashi H
    16. Ulitsky I
    17. Varabyou A
    18. Wells CA
    19. Yandell M
    20. Carninci P
    21. Salzberg SL

    (2023) The status of the human gene catalogue

    Nature 622:41–47.

    https://doi.org/10.1038/s41586-023-06490-x

    • PubMed
    • Google Scholar
12. 1. Anderson FE
    2. Lindgren AR

    (2021) Phylogenomic analyses recover a clade of large-bodied decapodiform cephalopods

    Molecular Phylogenetics and Evolution 156:107038.

    https://doi.org/10.1016/j.ympev.2020.107038

    • PubMed
    • Google Scholar
13. 1. Andouche A
    2. Bassaglia Y
    3. Baratte S
    4. Bonnaud L

    (2013) Reflectin genes and development of iridophore patterns in Sepia officinalis embryos (Mollusca, Cephalopoda)

    Developmental Dynamics 242:560–571.

    https://doi.org/10.1002/dvdy.23938

    • PubMed
    • Google Scholar
14. 1. Andrews PLR
    2. Darmaillacq AS
    3. Dennison N
    4. Gleadall IG
    5. Hawkins P
    6. Messenger JB
    7. Osorio D
    8. Smith VJ
    9. Smith JA

    (2013) The identification and management of pain, suffering and distress in cephalopods, including anaesthesia, analgesia and humane killing

    Journal of Experimental Marine Biology and Ecology 447:46–64.

    https://doi.org/10.1016/j.jembe.2013.02.010

    • Google Scholar
15. 1. Arias-Montecino A
    2. Sykes A
    3. Álvarez-Hernán G
    4. de Mera-Rodríguez JA
    5. Calle-Guisado V
    6. Martín-Partido G
    7. Rodríguez-León J
    8. Francisco-Morcillo J

    (2024) Histological and scanning electron microscope observations on the developing retina of the cuttlefish (Sepia officinalis Linnaeus, 1758)

    Tissue & Cell 88:102417.

    https://doi.org/10.1016/j.tice.2024.102417

    • PubMed
    • Google Scholar
16. 1. Ashburner M
    2. Ball CA
    3. Blake JA
    4. Botstein D
    5. Butler H
    6. Cherry JM
    7. Davis AP
    8. Dolinski K
    9. Dwight SS
    10. Eppig JT
    11. Harris MA
    12. Hill DP
    13. Issel-Tarver L
    14. Kasarskis A
    15. Lewis S
    16. Matese JC
    17. Richardson JE
    18. Ringwald M
    19. Rubin GM
    20. Sherlock G

    (2000) Gene ontology: tool for the unification of biology

    Nature Genetics 25:25–29.

    https://doi.org/10.1038/75556

    • PubMed
    • Google Scholar
17. 1. Baden T
    2. Briseño J
    3. Coffing G
    4. Cohen-Bodénès S
    5. Courtney A
    6. Dickerson D
    7. Dölen G
    8. Fiorito G
    9. Gestal C
    10. Gustafson T
    11. Heath-Heckman E
    12. Hua Q
    13. Imperadore P
    14. Kimbara R
    15. Król M
    16. Lajbner Z
    17. Lichilín N
    18. Macchi F
    19. McCoy MJ
    20. Nishiguchi MK
    21. Nyholm SV
    22. Otjacques E
    23. Pérez-Ferrer PA
    24. Ponte G
    25. Pungor JR
    26. Rogers TF
    27. Rosenthal JJC
    28. Rouressol L
    29. Rubas N
    30. Sanchez G
    31. Santos CP
    32. Schultz DT
    33. Seuntjens E
    34. Songco-Casey JO
    35. Stewart IE
    36. Styfhals R
    37. Tuanapaya S
    38. Vijayan N
    39. Weissenbacher A
    40. Zifcakova L
    41. Schulz G
    42. Weertman W
    43. Simakov O
    44. Albertin CB

    (2023) Cephalopod-omics: emerging fields and technologies in cephalopod biology

    Integrative and Comparative Biology 63:1226–1239.

    https://doi.org/10.1093/icb/icad087

    • PubMed
    • Google Scholar
18. 1. Belcaid M
    2. Casaburi G
    3. McAnulty SJ
    4. Schmidbaur H
    5. Suria AM
    6. Moriano-Gutierrez S
    7. Pankey MS
    8. Oakley TH
    9. Kremer N
    10. Koch EJ
    11. Collins AJ
    12. Nguyen H
    13. Lek S
    14. Goncharenko-Foster I
    15. Minx P
    16. Sodergren E
    17. Weinstock G
    18. Rokhsar DS
    19. McFall-Ngai M
    20. Simakov O
    21. Foster JS
    22. Nyholm SV

    (2019) Symbiotic organs shaped by distinct modes of genome evolution in cephalopods

    PNAS 116:3030–3035.

    https://doi.org/10.1073/pnas.1817322116

    • Google Scholar
19. 1. Bellingham J
    2. Morris AG
    3. Hunt DM

    (1998) The rhodopsin gene of the cuttlefish Sepia officinalis: sequence and spectral tuning

    The Journal of Experimental Biology 201:2299–2306.

    https://doi.org/10.1242/jeb.201.15.2299

    • PubMed
    • Google Scholar
20. 1. Blaxter M
    2. Mieszkowska N
    3. Di Palma F
    4. Holland P
    5. Durbin R
    6. Richards T
    7. Berriman M
    8. Kersey P
    9. Hollingsworth P
    10. Wilson W
    11. Twyford A
    12. Gaya E
    13. Lawniczak M
    14. Lewis O
    15. Broad G
    16. Howe K
    17. Hart M
    18. Flicek P
    19. Barnes I
    20. The Darwin Tree of Life Project Consortium

    (2022) Sequence locally, think globally: the darwin tree of life project

    PNAS 119:e2115642118.

    https://doi.org/10.1073/pnas.2115642118

    • Google Scholar
21. 1. Blum M
    2. Andreeva A
    3. Florentino LC
    4. Chuguransky SR
    5. Grego T
    6. Hobbs E
    7. Pinto BL
    8. Orr A
    9. Paysan-Lafosse T
    10. Ponamareva I
    11. Salazar GA
    12. Bordin N
    13. Bork P
    14. Bridge A
    15. Colwell L
    16. Gough J
    17. Haft DH
    18. Letunic I
    19. Llinares-López F
    20. Marchler-Bauer A
    21. Meng-Papaxanthos L
    22. Mi H
    23. Natale DA
    24. Orengo CA
    25. Pandurangan AP
    26. Piovesan D
    27. Rivoire C
    28. Sigrist CJA
    29. Thanki N
    30. Thibaud-Nissen F
    31. Thomas PD
    32. Tosatto SCE
    33. Wu CH
    34. Bateman A

    (2025) InterPro: the protein sequence classification resource in 2025

    Nucleic Acids Research 53:D444–D456.

    https://doi.org/10.1093/nar/gkae1082

    • PubMed
    • Google Scholar
22. 1. Boletzky S

    (1987)

    Fecundity variation in relation to intermittent or chronic spawning in the cuttlefish, Sepia officinalis L. (Mollusca, Cephalopoda)

    Bulletin of Marine Science 40:382–388.

    • Google Scholar
23. 1. Boycott BB
    2. Young JZ

    (1955) A memory system in Octopus vulgaris Lamarck

    PNAS 143:449–480.

    https://doi.org/10.1098/rspb.1955.0024

    • PubMed
    • Google Scholar
24. 1. Brůna T
    2. Hoff KJ
    3. Lomsadze A
    4. Stanke M
    5. Borodovsky M

    (2021) BRAKER2: automatic eukaryotic genome annotation with GeneMark-EP+ and AUGUSTUS supported by a protein database

    NAR Genomics and Bioinformatics 3:lqaa108.

    https://doi.org/10.1093/nargab/lqaa108

    • PubMed
    • Google Scholar
25. Preprint

    1. Brůna T
    2. Gabriel L
    3. Hoff KJ

    (2024a) Navigating eukaryotic genome annotation pipelines: a route map to BRAKER, Galba, and TSEBRA

    arXiv.

    https://doi.org/10.48550/arXiv.2403.19416

    • Google Scholar
26. 1. Brůna T
    2. Lomsadze A
    3. Borodovsky M

    (2024b) GeneMark-ETP significantly improves the accuracy of automatic annotation of large eukaryotic genomes

    Genome Research 34:757–768.

    https://doi.org/10.1101/gr.278373.123

    • PubMed
    • Google Scholar
27. 1. Buchfink B
    2. Xie C
    3. Huson DH

    (2015) Fast and sensitive protein alignment using DIAMOND

    Nature Methods 12:59–60.

    https://doi.org/10.1038/nmeth.3176

    • PubMed
    • Google Scholar
28. 1. Cantalapiedra CP
    2. Hernández-Plaza A
    3. Letunic I
    4. Bork P
    5. Huerta-Cepas J

    (2021) eggNOG-mapper v2: functional annotation, orthology assignments, and domain prediction at the metagenomic scale

    Molecular Biology and Evolution 38:5825–5829.

    https://doi.org/10.1093/molbev/msab293

    • PubMed
    • Google Scholar
29. 1. Cartolano M
    2. Huettel B
    3. Hartwig B
    4. Reinhardt R
    5. Schneeberger K

    (2016) cDNA library enrichment of full length transcripts for SMRT long read sequencing

    PLOS ONE 11:e0157779.

    https://doi.org/10.1371/journal.pone.0157779

    • PubMed
    • Google Scholar
30. 1. Case NM
    2. Gray EG
    3. Young JZ

    (1972) Ultrastructure and synaptic relations in the optic lobe of the brain of Eledone and Octopus

    Journal of Ultrastructure Research 39:115–123.

    https://doi.org/10.1016/s0022-5320(72)80012-1

    • PubMed
    • Google Scholar
31. 1. Challis R
    2. Richards E
    3. Rajan J
    4. Cochrane G
    5. Blaxter M

    (2020) BlobToolKit – interactive quality assessment of genome assemblies

    G3: Genes, Genomes, Genetics 10:1361–1374.

    https://doi.org/10.1534/g3.119.400908

    • Google Scholar
32. 1. Chemello G
    2. Faraoni V
    3. Notarstefano V
    4. Maradonna F
    5. Carnevali O
    6. Gioacchini G

    (2022) First evidence of microplastics in the yolk and embryos of common cuttlefish (Sepia officinalis) from the central adriatic sea: evaluation of embryo and hatchling structural integrity and development

    Animals 13:95.

    https://doi.org/10.3390/ani13010095

    • PubMed
    • Google Scholar
33. 1. Chen Z
    2. Baeza JA
    3. Chen C
    4. Gonzalez MT
    5. González VL
    6. Greve C
    7. Kocot KM
    8. Arbizu PM
    9. Moles J
    10. Schell T
    11. Schwabe E
    12. Sun J
    13. Wong NLWS
    14. Yap-Chiongco M
    15. Sigwart JD

    (2025) A genome-based phylogeny for Mollusca is concordant with fossils and morphology

    Science 387:1001–1007.

    https://doi.org/10.1126/science.ads0215

    • Google Scholar
34. 1. Cheng H
    2. Concepcion GT
    3. Feng X
    4. Zhang H
    5. Li H

    (2021) Haplotype-resolved de novo assembly using phased assembly graphs with hifiasm

    Nature Methods 18:170–175.

    https://doi.org/10.1038/s41592-020-01056-5

    • PubMed
    • Google Scholar
35. 1. Cocci P
    2. Mosconi G
    3. Palermo FA

    (2023) Effect of polycyclic aromatic hydrocarbons on homeobox gene expression during embryonic development of cuttlefish, Sepia officinalis

    Chemosphere 325:138315.

    https://doi.org/10.1016/j.chemosphere.2023.138315

    • PubMed
    • Google Scholar
36. Data

    1. Coffing GC
    2. Tittes S
    3. Small ST
    4. Songco-Casey JO
    5. Piscopo DM
    6. Pungor JR
    7. Miller AC
    8. Niell CM
    9. Kern AD

    (authors) (2024) Cephalopod sex determination and its ancient evolutionary origin

    Zenodo.

    https://doi.org/10.5281/zenodo.13278523
37. 1. Coffing GC
    2. Tittes S
    3. Small ST
    4. Songco-Casey JO
    5. Piscopo DM
    6. Pungor JR
    7. Miller AC
    8. Niell CM
    9. Kern AD

    (2025) Cephalopod sex determination and its ancient evolutionary origin

    Current Biology 35:931–939.

    https://doi.org/10.1016/j.cub.2025.01.005

    • PubMed
    • Google Scholar
38. 1. Court M
    2. Macau M
    3. Ranucci M
    4. Marquês T
    5. Repolho T
    6. Lopes VM
    7. Rosa R
    8. Paula JR

    (2024) Oxygen loss compromises growth and cognition of cuttlefish newborns

    Proceedings. Biological Sciences 291:20241291.

    https://doi.org/10.1098/rspb.2024.1291

    • PubMed
    • Google Scholar
39. 1. Destanović D
    2. Schultz DT
    3. Styfhals R
    4. Cruz F
    5. Gómez-Garrido J
    6. Gut M
    7. Gut I
    8. Fiorito G
    9. Simakov O
    10. Alioto TS
    11. Ponte G
    12. Seuntjens E

    (2023) A chromosome-level reference genome for the common octopus, Octopus vulgaris (Cuvier, 1797)

    G3 13:jkad220.

    https://doi.org/10.1093/g3journal/jkad220

    • PubMed
    • Google Scholar
40. 1. Dilly PN
    2. Gray EG
    3. Young JZ

    (1963) Electron microscopy of optic nerves and optic lobes of octopus and eledone

    Proceedings of the Royal Society of London. Series B, Biological Sciences 158:446–456.

    https://doi.org/10.1098/rspb.1963.0057

    • PubMed
    • Google Scholar
41. 1. Dobin A
    2. Davis CA
    3. Schlesinger F
    4. Drenkow J
    5. Zaleski C
    6. Jha S
    7. Batut P
    8. Chaisson M
    9. Gingeras TR

    (2013) STAR: ultrafast universal RNA-seq aligner

    Bioinformatics 29:15–21.

    https://doi.org/10.1093/bioinformatics/bts635

    • PubMed
    • Google Scholar
42. 1. Dorkenwald S
    2. Matsliah A
    3. Sterling AR
    4. Schlegel P
    5. Yu S-C
    6. McKellar CE
    7. Lin A
    8. Costa M
    9. Eichler K
    10. Yin Y
    11. Silversmith W
    12. Schneider-Mizell C
    13. Jordan CS
    14. Brittain D
    15. Halageri A
    16. Kuehner K
    17. Ogedengbe O
    18. Morey R
    19. Gager J
    20. Kruk K
    21. Perlman E
    22. Yang R
    23. Deutsch D
    24. Bland D
    25. Sorek M
    26. Lu R
    27. Macrina T
    28. Lee K
    29. Bae JA
    30. Mu S
    31. Nehoran B
    32. Mitchell E
    33. Popovych S
    34. Wu J
    35. Jia Z
    36. Castro MA
    37. Kemnitz N
    38. Ih D
    39. Bates AS
    40. Eckstein N
    41. Funke J
    42. Collman F
    43. Bock DD
    44. Jefferis GSXE
    45. Seung HS
    46. Murthy M
    47. FlyWire Consortium

    (2024) Neuronal wiring diagram of an adult brain

    Nature 634:124–138.

    https://doi.org/10.1038/s41586-024-07558-y

    • PubMed
    • Google Scholar
43. Preprint

    1. Dudchenko O
    2. Shamim MS
    3. Batra SS
    4. Durand NC
    5. Musial NT
    6. Mostofa R
    7. Pham M
    8. Glenn St Hilaire B
    9. Yao W
    10. Stamenova E
    11. Hoeger M
    12. Nyquist SK
    13. Korchina V
    14. Pletch K
    15. Flanagan JP
    16. Tomaszewicz A
    17. McAloose D
    18. Pérez Estrada C
    19. Novak BJ
    20. Omer AD
    21. Aiden EL

    (2018) The juicebox assembly tools module facilitates de Novo assembly of mammalian genomes with chromosome-length scaffolds for under $1000

    bioRxiv.

    https://doi.org/10.1101/254797

    • Google Scholar
44. 1. Duruz J
    2. Sprecher M
    3. Kaldun JC
    4. Al-Soudy A-S
    5. Lischer HEL
    6. van Geest G
    7. Nicholson P
    8. Bruggmann R
    9. Sprecher SG

    (2023) Molecular characterization of cell types in the squid Loligo vulgaris

    eLife 12:e80670.

    https://doi.org/10.7554/eLife.80670

    • PubMed
    • Google Scholar
45. 1. Ebrahimi Pour M

    (2009) The study of persian gulf cuttlefish (Sepia pharaonis) chromosome via incubation of blood cells

    Applied Biology 22:1–8.

    https://doi.org/10.22051/jab.2009.3307

    • Google Scholar
46. 1. Emms DM
    2. Kelly S

    (2017) STRIDE: species tree root inference from gene duplication events

    Molecular Biology and Evolution 34:3267–3278.

    https://doi.org/10.1093/molbev/msx259

    • PubMed
    • Google Scholar
47. Preprint

    1. Emms DM
    2. Kelly S

    (2018) STAG: species tree inference from all genes

    bioRxiv.

    https://doi.org/10.1101/267914

    • Google Scholar
48. 1. Emms DM
    2. Kelly S

    (2019) OrthoFinder: phylogenetic orthology inference for comparative genomics

    Genome Biology 20:238.

    https://doi.org/10.1186/s13059-019-1832-y

    • PubMed
    • Google Scholar
49. Preprint

    1. Emms DM
    2. Liu Y
    3. Belcher L
    4. Holmes J
    5. Kelly S

    (2025) OrthoFinder: Scalable phylogenetic orthology inference for comparative genomics

    bioRxiv.

    https://doi.org/10.1101/2025.07.15.664860

    • Google Scholar
50. 1. Feord RC
    2. Sumner ME
    3. Pusdekar S
    4. Kalra L
    5. Gonzalez-Bellido PT
    6. Wardill TJ

    (2020) Cuttlefish use stereopsis to strike at prey

    Science Advances 6:eaay6036.

    https://doi.org/10.1126/sciadv.aay6036

    • PubMed
    • Google Scholar
51. 1. Feschotte C

    (2008) Transposable elements and the evolution of regulatory networks

    Nature Reviews. Genetics 9:397–405.

    https://doi.org/10.1038/nrg2337

    • PubMed
    • Google Scholar
52. 1. Fiorito G
    2. Affuso A
    3. Basil J
    4. Cole A
    5. de Girolamo P
    6. D’Angelo L
    7. Dickel L
    8. Gestal C
    9. Grasso F
    10. Kuba M
    11. Mark F
    12. Melillo D
    13. Osorio D
    14. Perkins K
    15. Ponte G
    16. Shashar N
    17. Smith D
    18. Smith J
    19. Andrews PLR

    (2015) Guidelines for the care and welfare of cephalopods in research -A consensus based on an initiative by CephRes, FELASA and the Boyd Group

    Laboratory Animals 49:1–90.

    https://doi.org/10.1177/0023677215580006

    • PubMed
    • Google Scholar
53. 1. Flynn JM
    2. Hubley R
    3. Goubert C
    4. Rosen J
    5. Clark AG
    6. Feschotte C
    7. Smit AF

    (2020) RepeatModeler2 for automated genomic discovery of transposable element families

    PNAS 117:9451–9457.

    https://doi.org/10.1073/pnas.1921046117

    • Google Scholar
54. 1. Focareta L
    2. Sesso S
    3. Cole AG

    (2014) Characterization of homeobox genes reveals sophisticated regionalization of the central nervous system in the European cuttlefish Sepia officinalis

    PLOS ONE 9:e109627.

    https://doi.org/10.1371/journal.pone.0109627

    • PubMed
    • Google Scholar
55. 1. Force A
    2. Lynch M
    3. Pickett FB
    4. Amores A
    5. Yan YL
    6. Postlethwait J

    (1999) Preservation of duplicate genes by complementary, degenerative mutations

    Genetics 151:1531–1545.

    https://doi.org/10.1093/genetics/151.4.1531

    • PubMed
    • Google Scholar
56. 1. Formenti G
    2. Rhie A
    3. Walenz BP
    4. Thibaud-Nissen F
    5. Shafin K
    6. Koren S
    7. Myers EW
    8. Jarvis ED
    9. Phillippy AM

    (2022) Merfin: improved variant filtering, assembly evaluation and polishing via k-mer validation

    Nature Methods 19:696–704.

    https://doi.org/10.1038/s41592-022-01445-y

    • PubMed
    • Google Scholar
57. 1. Frankish A
    2. Carbonell-Sala S
    3. Diekhans M
    4. Jungreis I
    5. Loveland JE
    6. Mudge JM
    7. Sisu C
    8. Wright JC
    9. Arnan C
    10. Barnes I
    11. Banerjee A
    12. Bennett R
    13. Berry A
    14. Bignell A
    15. Boix C
    16. Calvet F
    17. Cerdán-Vélez D
    18. Cunningham F
    19. Davidson C
    20. Donaldson S
    21. Dursun C
    22. Fatima R
    23. Giorgetti S
    24. Giron CG
    25. Gonzalez JM
    26. Hardy M
    27. Harrison PW
    28. Hourlier T
    29. Hollis Z
    30. Hunt T
    31. James B
    32. Jiang Y
    33. Johnson R
    34. Kay M
    35. Lagarde J
    36. Martin FJ
    37. Gómez LM
    38. Nair S
    39. Ni P
    40. Pozo F
    41. Ramalingam V
    42. Ruffier M
    43. Schmitt BM
    44. Schreiber JM
    45. Steed E
    46. Suner M-M
    47. Sumathipala D
    48. Sycheva I
    49. Uszczynska-Ratajczak B
    50. Wass E
    51. Yang YT
    52. Yates A
    53. Zafrulla Z
    54. Choudhary JS
    55. Gerstein M
    56. Guigo R
    57. Hubbard TJP
    58. Kellis M
    59. Kundaje A
    60. Paten B
    61. Tress ML
    62. Flicek P

    (2023) GENCODE: reference annotation for the human and mouse genomes in 2023

    Nucleic Acids Research 51:D942–D949.

    https://doi.org/10.1093/nar/gkac1071

    • PubMed
    • Google Scholar
58. 1. Freemont PS

    (2000) Ubiquitination: RING for destruction?

    Current Biology 10:R84–R87.

    https://doi.org/10.1016/S0960-9822(00)00287-6

    • Google Scholar
59. 1. Gabriel L
    2. Hoff KJ
    3. Brůna T
    4. Borodovsky M
    5. Stanke M

    (2021) TSEBRA: transcript selector for BRAKER

    BMC Bioinformatics 22:566.

    https://doi.org/10.1186/s12859-021-04482-0

    • PubMed
    • Google Scholar
60. 1. Gabriel L
    2. Brůna T
    3. Hoff KJ
    4. Ebel M
    5. Lomsadze A
    6. Borodovsky M
    7. Stanke M

    (2024) BRAKER3: fully automated genome annotation using RNA-seq and protein evidence with GeneMark-ETP, AUGUSTUS, and TSEBRA

    Genome Research 34:769–777.

    https://doi.org/10.1101/gr.278090.123

    • PubMed
    • Google Scholar
61. 1. Gamsjaeger R
    2. Liew CK
    3. Loughlin FE
    4. Crossley M
    5. Mackay JP

    (2007) Sticky fingers: zinc-fingers as protein-recognition motifs

    Trends in Biochemical Sciences 32:63–70.

    https://doi.org/10.1016/j.tibs.2006.12.007

    • PubMed
    • Google Scholar
62. 1. Gao YM
    2. Natsukari Y

    (1990)

    Karyological studies on seven cephalopods

    Venus 49:126–145.

    • Google Scholar
63. 1. Gavriouchkina D
    2. Tan Y
    3. Parey E
    4. Ziadi-Künzli F
    5. Hasegawa Y
    6. Piovani L
    7. Zhang L
    8. Sugimoto C
    9. Luscombe N
    10. Marlétaz F
    11. Rokhsar DS

    (2025) A single-cell atlas of the bobtail squid visual and nervous system highlights molecular principles of convergent evolution

    Nature Ecology & Evolution 9:1245–1262.

    https://doi.org/10.1038/s41559-025-02720-9

    • PubMed
    • Google Scholar
64. 1. Giuditta A
    2. Libonati M
    3. Packard A
    4. Prozzo N

    (1971) Nuclear counts in the brain lobes of Octopus vulgaris as a function of body size

    Brain Research 25:55–62.

    https://doi.org/10.1016/0006-8993(71)90566-x

    • PubMed
    • Google Scholar
65. 1. Gladman NW
    2. Askew GN

    (2023) The hydrodynamics of jet propulsion swimming in hatchling and juvenile European common cuttlefish, Sepia officinalis

    The Journal of Experimental Biology 226:jeb246225.

    https://doi.org/10.1242/jeb.246225

    • PubMed
    • Google Scholar
66. 1. Goldfarb T
    2. Kodali VK
    3. Pujar S
    4. Brover V
    5. Robbertse B
    6. Farrell CM
    7. Oh DH
    8. Astashyn A
    9. Ermolaeva O
    10. Haddad D
    11. Hlavina W
    12. Hoffman J
    13. Jackson JD
    14. Joardar VS
    15. Kristensen D
    16. Masterson P
    17. McGarvey KM
    18. McVeigh R
    19. Mozes E
    20. Murphy MR
    21. Schafer SS
    22. Souvorov A
    23. Spurrier B
    24. Strope PK
    25. Sun H
    26. Vatsan AR
    27. Wallin C
    28. Webb D
    29. Brister JR
    30. Hatcher E
    31. Kimchi A
    32. Klimke W
    33. Marchler-Bauer A
    34. Pruitt KD
    35. Thibaud-Nissen F
    36. Murphy TD

    (2025) NCBI RefSeq: reference sequence standards through 25 years of curation and annotation

    Nucleic Acids Research 53:D243–D257.

    https://doi.org/10.1093/nar/gkae1038

    • PubMed
    • Google Scholar
67. 1. Gotoh O

    (2008) A space-efficient and accurate method for mapping and aligning cDNA sequences onto genomic sequence

    Nucleic Acids Research 36:2630–2638.

    https://doi.org/10.1093/nar/gkn105

    • PubMed
    • Google Scholar
68. Software

    1. Haas BJ

    (2026) TransDecoder, version 66d4712

    Github.

    https://github.com/TransDecoder/TransDecoder
69. 1. Hahsler M
    2. Piekenbrock M
    3. Doran D

    (2019) dbscan: fast density-based clustering with R

    Journal of Statistical Software 91:1–30.

    https://doi.org/10.18637/jss.v091.i01

    • Google Scholar
70. 1. Hall K
    2. Hanlon R

    (2002) Principal features of the mating system of a large spawning aggregation of the giant Australian cuttlefish Sepia apama (Mollusca: Cephalopoda)

    Marine Biology 140:533–545.

    https://doi.org/10.1007/s00227-001-0718-0

    • Google Scholar
71. Book

    1. Hanlon RT
    2. Messenger JB

    (2018) Cephalopod Behaviour

    Cambridge University Press.

    https://doi.org/10.1017/9780511843600

    • Google Scholar
72. 1. Herculano-Houzel S
    2. Mota B
    3. Lent R

    (2006) Cellular scaling rules for rodent brains

    PNAS 103:12138–12143.

    https://doi.org/10.1073/pnas.0604911103

    • Google Scholar
73. 1. Hjelmen CE

    (2024) Genome size and chromosome number are critical metrics for accurate genome assembly assessment in Eukaryota

    Genetics 227:iyae099.

    https://doi.org/10.1093/genetics/iyae099

    • PubMed
    • Google Scholar
74. 1. Hodgkin AL
    2. Huxley AF
    3. Katz B

    (1952) Measurement of current‐voltage relations in the membrane of the giant axon of Loligo

    The Journal of Physiology 116:424–448.

    https://doi.org/10.1113/jphysiol.1952.sp004716

    • Google Scholar
75. 1. Hoff KJ
    2. Lange S
    3. Lomsadze A
    4. Borodovsky M
    5. Stanke M

    (2016) BRAKER1: unsupervised RNA-Seq-Based genome annotation with GeneMark-ET and AUGUSTUS

    Bioinformatics 32:767–769.

    https://doi.org/10.1093/bioinformatics/btv661

    • PubMed
    • Google Scholar
76. 1. Hoff KJ
    2. Lomsadze A
    3. Borodovsky M
    4. Stanke M

    (2019) Whole-genome annotation with BRAKER

    Methods in Molecular Biology 1962:65–95.

    https://doi.org/10.1007/978-1-4939-9173-0_5

    • PubMed
    • Google Scholar
77. 1. Huang N
    2. Li H

    (2023) compleasm: a faster and more accurate reimplementation of BUSCO

    Bioinformatics 39:btad595.

    https://doi.org/10.1093/bioinformatics/btad595

    • PubMed
    • Google Scholar
78. 1. Huerta-Cepas J
    2. Forslund K
    3. Coelho LP
    4. Szklarczyk D
    5. Jensen LJ
    6. von Mering C
    7. Bork P

    (2017) Fast genome-wide functional annotation through orthology assignment by eggNOG-Mapper

    Molecular Biology and Evolution 34:2115–2122.

    https://doi.org/10.1093/molbev/msx148

    • PubMed
    • Google Scholar
79. 1. Iglesias TL
    2. Boal JG
    3. Frank MG
    4. Zeil J
    5. Hanlon RT

    (2019) Cyclic nature of the REM sleep-like state in the cuttlefish Sepia officinalis

    The Journal of Experimental Biology 222:jeb174862.

    https://doi.org/10.1242/jeb.174862

    • PubMed
    • Google Scholar
80. 1. Imarazene B
    2. Andouche A
    3. Bassaglia Y
    4. Lopez PJ
    5. Bonnaud-Ponticelli L

    (2017) Eye development in Sepia officinalis embryo: what the uncommon gene expression profiles tell us about eye evolution

    Frontiers in Physiology 8:613.

    https://doi.org/10.3389/fphys.2017.00613

    • PubMed
    • Google Scholar
81. 1. Iwata H
    2. Gotoh O

    (2012) Benchmarking spliced alignment programs including Spaln2, an extended version of Spaln that incorporates additional species-specific features

    Nucleic Acids Research 40:e161.

    https://doi.org/10.1093/nar/gks708

    • PubMed
    • Google Scholar
82. 1. Jain C
    2. Rhie A
    3. Zhang H
    4. Chu C
    5. Walenz BP
    6. Koren S
    7. Phillippy AM

    (2020) Weighted minimizer sampling improves long read mapping

    Bioinformatics 36:i111–i118.

    https://doi.org/10.1093/bioinformatics/btaa435

    • PubMed
    • Google Scholar
83. 1. Jain C
    2. Rhie A
    3. Hansen NF
    4. Koren S
    5. Phillippy AM

    (2022) Long-read mapping to repetitive reference sequences using Winnowmap2

    Nature Methods 19:705–710.

    https://doi.org/10.1038/s41592-022-01457-8

    • Google Scholar
84. 1. Jazayeri A
    2. Papan F
    3. Motamedi H
    4. Mahmoudi S

    (2011)

    Karyological investigation of Persian Gulf cuttle fish (sepia arabica) in the coasts of Khuzestan province

    Life Science Journal 8:849–852.

    • Google Scholar
85. 1. Joazeiro CAP
    2. Wing SS
    3. Huang H
    4. Leverson JD
    5. Hunter T
    6. Liu YC

    (1999) The tyrosine kinase negative regulator c-Cbl as a RING-type, E2-dependent ubiquitin-protein ligase

    Science 286:309–312.

    https://doi.org/10.1126/science.286.5438.309

    • PubMed
    • Google Scholar
86. 1. Josef N
    2. Berenshtein I
    3. Rousseau M
    4. Scata G
    5. Fiorito G
    6. Shashar N

    (2016) Size matters: observed and modeled camouflage response of European Cuttlefish (Sepia officinalis) to different substrate patch sizes during movement

    Frontiers in Physiology 7:671.

    https://doi.org/10.3389/fphys.2016.00671

    • PubMed
    • Google Scholar
87. 1. Jozet-Alves C
    2. Bertin M
    3. Clayton NS

    (2013) Evidence of episodic-like memory in cuttlefish

    Current Biology 23:R1033–R1035.

    https://doi.org/10.1016/j.cub.2013.10.021

    • PubMed
    • Google Scholar
88. 1. Kanaoka Y
    2. Urade Y

    (2003) Hematopoietic prostaglandin D synthase

    Prostaglandins, Leukotrienes, and Essential Fatty Acids 69:163–167.

    https://doi.org/10.1016/s0952-3278(03)00077-2

    • PubMed
    • Google Scholar
89. 1. Kovaka S
    2. Zimin AV
    3. Pertea GM
    4. Razaghi R
    5. Salzberg SL
    6. Pertea M

    (2019) Transcriptome assembly from long-read RNA-seq alignments with StringTie2

    Genome Biology 20:278.

    https://doi.org/10.1186/s13059-019-1910-1

    • PubMed
    • Google Scholar
90. 1. Kröger B
    2. Vinther J
    3. Fuchs D

    (2011) Cephalopod origin and evolution: a congruent picture emerging from fossils, development and molecules

    BioEssays 33:602–613.

    https://doi.org/10.1002/bies.201100001

    • PubMed
    • Google Scholar
91. 1. Lam ET
    2. Hastie A
    3. Lin C
    4. Ehrlich D
    5. Das SK
    6. Austin MD
    7. Deshpande P
    8. Cao H
    9. Nagarajan N
    10. Xiao M
    11. Kwok PY

    (2012) Genome mapping on nanochannel arrays for structural variation analysis and sequence assembly

    Nature Biotechnology 30:771–776.

    https://doi.org/10.1038/nbt.2303

    • PubMed
    • Google Scholar
92. 1. Laptikhovsky V
    2. Salman A
    3. Önsoy B
    4. Katagan T

    (2003) Fecundity of the common cuttlefish, Sepia officinalis L. (Cephalopoda, Sepiida): a new look at the old problem

    Scientia Marina 67:279–284.

    https://doi.org/10.3989/scimar.2003.67n3279

    • Google Scholar
93. Software

    1. Li H

    (2013) Seqtk: a fast and lightweight tool for processing FASTA or FASTQ sequences, version 94e7070

    Github.

    https://github.com/lh3/seqtk
94. 1. Li H

    (2018) Minimap2: pairwise alignment for nucleotide sequences

    Bioinformatics 34:3094–3100.

    https://doi.org/10.1093/bioinformatics/bty191

    • PubMed
    • Google Scholar
95. 1. Li H

    (2023) Protein-to-genome alignment with miniprot

    Bioinformatics 39:btad014.

    https://doi.org/10.1093/bioinformatics/btad014

    • Google Scholar
96. 1. Li H
    2. Durbin R

    (2024) Genome assembly in the telomere-to-telomere era

    Nature Reviews. Genetics 25:658–670.

    https://doi.org/10.1038/s41576-024-00718-w

    • PubMed
    • Google Scholar
97. 1. Liao Y
    2. Smyth GK
    3. Shi W

    (2014) featureCounts: an efficient general purpose program for assigning sequence reads to genomic features

    Bioinformatics 30:923–930.

    https://doi.org/10.1093/bioinformatics/btt656

    • PubMed
    • Google Scholar
98. 1. Lindgren AR
    2. Pankey MS
    3. Hochberg FG
    4. Oakley TH

    (2012) A multi-gene phylogeny of Cephalopoda supports convergent morphological evolution in association with multiple habitat shifts in the marine environment

    BMC Evolutionary Biology 12:129.

    https://doi.org/10.1186/1471-2148-12-129

    • PubMed
    • Google Scholar
99. 1. Liu Z
    2. Huang Y
    3. Chen H
    4. Liu C
    5. Wang M
    6. Bian C
    7. Wang L
    8. Song L

    (2023) Chromosome-level genome assembly of the deep-sea snail Phymorhynchus buccinoides provides insights into the adaptation to the cold seep habitat

    BMC Genomics 24:679.

    https://doi.org/10.1186/s12864-023-09760-0

    • Google Scholar
100. 1. Llinás R
     2. Steinberg IZ
     3. Walton K

     (1981a) Presynaptic calcium currents in squid giant synapse

     Biophysical Journal 33:289–321.

     https://doi.org/10.1016/S0006-3495(81)84898-9

     • PubMed
     • Google Scholar
101. 1. Llinás R
     2. Steinberg IZ
     3. Walton K

     (1981b) Relationship between presynaptic calcium current and postsynaptic potential in squid giant synapse

     Biophysical Journal 33:323–351.

     https://doi.org/10.1016/S0006-3495(81)84899-0

     • PubMed
     • Google Scholar
102. Book

     1. Llinas RR

     (1984) The squid giant synapse

     In: Kleinzeller A, editors. Current Topics in Membranes and Transport The Squid Axon. Academic Press. pp. 519–546.

     https://doi.org/10.1016/S0070-2161(08)60483-9

     • Google Scholar
103. 1. López-Córdova DA
     2. Avaria-Llautureo J
     3. Ulloa PM
     4. Braid HE
     5. Revell LJ
     6. Fuchs D
     7. Ibáñez CM

     (2022) Mesozoic origin of coleoid cephalopods and their abrupt shifts of diversification patterns

     Molecular Phylogenetics and Evolution 166:107331.

     https://doi.org/10.1016/j.ympev.2021.107331

     • PubMed
     • Google Scholar
104. 1. Love MI
     2. Huber W
     3. Anders S

     (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

     Genome Biology 15:550.

     https://doi.org/10.1186/s13059-014-0550-8

     • PubMed
     • Google Scholar
105. 1. Lovell JT
     2. Sreedasyam A
     3. Schranz ME
     4. Wilson M
     5. Carlson JW
     6. Harkess A
     7. Emms D
     8. Goodstein DM
     9. Schmutz J

     (2022) GENESPACE tracks regions of interest and gene copy number variation across multiple genomes

     eLife 11:e78526.

     https://doi.org/10.7554/eLife.78526

     • PubMed
     • Google Scholar
106. 1. Lukhtanov VA
     2. Dincă V
     3. Talavera G
     4. Vila R

     (2011) Unprecedented within-species chromosome number cline in the Wood White butterfly Leptidea sinapis and its significance for karyotype evolution and speciation

     BMC Evolutionary Biology 11:109.

     https://doi.org/10.1186/1471-2148-11-109

     • PubMed
     • Google Scholar
107. 1. Lukhtanov VA
     2. Dincă V
     3. Friberg M
     4. Šíchová J
     5. Olofsson M
     6. Vila R
     7. Marec F
     8. Wiklund C

     (2018) Versatility of multivalent orientation, inverted meiosis, and rescued fitness in holocentric chromosomal hybrids

     PNAS 115:E9610–E9619.

     https://doi.org/10.1073/pnas.1802610115

     • Google Scholar
108. 1. Lyčka M
     2. Bubeník M
     3. Závodník M
     4. Peska V
     5. Fajkus P
     6. Demko M
     7. Fajkus J
     8. Fojtová M

     (2024) TeloBase: a community-curated database of telomere sequences across the tree of life

     Nucleic Acids Research 52:D311–D321.

     https://doi.org/10.1093/nar/gkad672

     • PubMed
     • Google Scholar
109. 1. Lynch M
     2. Force A

     (2000) The probability of duplicate gene preservation by subfunctionalization

     Genetics 154:459–473.

     https://doi.org/10.1093/genetics/154.1.459

     • PubMed
     • Google Scholar
110. 1. Ma B
     2. Jin W
     3. Fu H
     4. Sun B
     5. Yang S
     6. Ma X
     7. Wen H
     8. Wu X
     9. Wang H
     10. Cao X

     (2023) A high-quality chromosome-level genome assembly of a snail Cipangopaludina cathayensis (Gastropoda: Viviparidae)

     Genes 14:1365.

     https://doi.org/10.3390/genes14071365

     • PubMed
     • Google Scholar
111. 1. Männer L
     2. Schell T
     3. Spies J
     4. Galià-Camps C
     5. Baranski D
     6. Ben Hamadou A
     7. Gerheim C
     8. Neveling K
     9. Helfrich EJN
     10. Greve C

     (2024) Chromosome-level genome assembly of the sacoglossan sea slug Elysia timida (Risso, 1818)

     BMC Genomics 25:941.

     https://doi.org/10.1186/s12864-024-10829-7

     • PubMed
     • Google Scholar
112. 1. Marino A
     2. Kizenko A
     3. Wong WY
     4. Ghiselli F
     5. Simakov O

     (2022) Repeat age decomposition informs an ancient set of repeats associated with coleoid cephalopod divergence

     Frontiers in Genetics 13:793734.

     https://doi.org/10.3389/fgene.2022.793734

     • PubMed
     • Google Scholar
113. 1. Marshall NJ
     2. Messenger JB

     (1996) Colour-blind camouflage

     Nature 382:408–409.

     https://doi.org/10.1038/382408b0

     • Google Scholar
114. 1. Mather JA

     (2006) Behaviour development: a cephalopod perspective

     International Journal of Comparative Psychology 19:19.01.02.

     https://doi.org/10.46867/IJCP.2006.19.01.02

     • Google Scholar
115. 1. McKenna V
     2. Archibald JM
     3. Beinart R
     4. Dawson MN
     5. Hentschel U
     6. Keeling PJ
     7. Lopez JV
     8. Martín-Durán JM
     9. Petersen JM
     10. Sigwart JD
     11. Simakov O
     12. Sutherland KR
     13. Sweet M
     14. Talbot NJ
     15. Thompson AW
     16. Bender S
     17. Harrison PW
     18. Rajan J
     19. Cochrane G
     20. Berriman M
     21. Lawniczak MKN
     22. Blaxter M

     (2021) The Aquatic Symbiosis Genomics Project: probing the evolution of symbiosis across the Tree of Life

     Wellcome Open Research 6:254.

     https://doi.org/10.12688/wellcomeopenres.17222.2

     • PubMed
     • Google Scholar
116. 1. Medeiros SDS
     2. Paiva MD
     3. Lopes PH
     4. Blanco W
     5. Lima FD
     6. Oliveira JD
     7. Medeiros IG
     8. Sequerra EB
     9. de Souza S
     10. Leite TS
     11. Ribeiro S

     (2021) Cyclic alternation of quiet and active sleep states in the octopus

     iScience 24:102223.

     https://doi.org/10.1016/j.isci.2021.102223

     • Google Scholar
117. 1. Mendes FK
     2. Vanderpool D
     3. Fulton B
     4. Hahn MW

     (2021) CAFE 5 models variation in evolutionary rates among gene families

     Bioinformatics 36:5516–5518.

     https://doi.org/10.1093/bioinformatics/btaa1022

     • PubMed
     • Google Scholar
118. Preprint

     1. Nevers Y
     2. Rossier V
     3. Train CM
     4. Altenhoff A
     5. Dessimoz C
     6. Glover N

     (2022) Multifaceted quality assessment of gene repertoire annotation with OMArk

     bioRxiv.

     https://doi.org/10.1101/2022.11.25.517970

     • Google Scholar
119. Book

     1. Nixon M
     2. Young JZ

     (2003)

     The Brains and Lives of Cephalopods

     Oxford University Press.

     • Google Scholar
120. 1. Norman MD
     2. Finn J
     3. Tregenza T

     (1999) Female impersonation as an alternative reproductive strategy in giant cuttlefish

     Proceedings of the Royal Society of London. Series B 266:1347–1349.

     https://doi.org/10.1098/rspb.1999.0786

     • Google Scholar
121. 1. O’Brien CE
     2. Mezrai N
     3. Darmaillacq A-S
     4. Dickel L

     (2017) Behavioral development in embryonic and early juvenile cuttlefish (Sepia officinalis)

     Developmental Psychobiology 59:145–160.

     https://doi.org/10.1002/dev.21476

     • PubMed
     • Google Scholar
122. 1. Osorio D
     2. Ménager F
     3. Tyler CW
     4. Darmaillacq AS

     (2022) Multi-level control of adaptive camouflage by European cuttlefish

     Current Biology 32:2556–2562.

     https://doi.org/10.1016/j.cub.2022.04.030

     • PubMed
     • Google Scholar
123. 1. Paradis E
     2. Schliep K

     (2019) ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R

     Bioinformatics 35:526–528.

     https://doi.org/10.1093/bioinformatics/bty633

     • PubMed
     • Google Scholar
124. 1. Pedersen BS
     2. Quinlan AR

     (2018) Mosdepth: quick coverage calculation for genomes and exomes

     Bioinformatics 34:867–868.

     https://doi.org/10.1093/bioinformatics/btx699

     • PubMed
     • Google Scholar
125. 1. Peñaloza C
     2. Gutierrez AP
     3. Eöry L
     4. Wang S
     5. Guo X
     6. Archibald AL
     7. Bean TP
     8. Houston RD

     (2021) A chromosome-level genome assembly for the Pacific oyster Crassostrea gigas

     GigaScience 10:giab020.

     https://doi.org/10.1093/gigascience/giab020

     • PubMed
     • Google Scholar
126. 1. Pertea G
     2. Pertea M

     (2020) GFF utilities: GffRead and GffCompare

     F1000Research 9:304.

     https://doi.org/10.12688/f1000research.23297.2

     • PubMed
     • Google Scholar
127. 1. Ponte G
     2. Roumbedakis K
     3. Galligioni V
     4. Dickel L
     5. Bellanger C
     6. Pereira J
     7. Vidal EA
     8. Grigoriou P
     9. Alleva E
     10. Santucci D
     11. Gili C
     12. Botta G
     13. Imperadore P
     14. Tarallo A
     15. Juergens L
     16. Northrup E
     17. Anderson D
     18. Aricò A
     19. De Luca M
     20. Pieroni EM
     21. Fiorito G
     22. COST Action FA1301

     (2023) General and species-specific recommendations for minimal requirements for the use of cephalopods in scientific research

     Laboratory Animals 57:26–39.

     https://doi.org/10.1177/00236772221111261

     • PubMed
     • Google Scholar
128. 1. Pophale A
     2. Shimizu K
     3. Mano T
     4. Iglesias TL
     5. Martin K
     6. Hiroi M
     7. Asada K
     8. Andaluz PG
     9. Van Dinh TT
     10. Meshulam L
     11. Reiter S

     (2023) Wake-like skin patterning and neural activity during octopus sleep

     Nature 619:129–134.

     https://doi.org/10.1038/s41586-023-06203-4

     • PubMed
     • Google Scholar
129. Software

     1. pysam-developers

     (2026) Pysam, version 3fcfcf2

     Github.

     https://github.com/pysam-developers/pysam
130. 1. Qi R
     2. Li P
     3. Wang Q
     4. Miao J
     5. Li Z
     6. Pan L

     (2026) Integrated analysis of detoxification pathways and hepatotoxicity of butylated hydroxytoluene (BHT) in the Manila clam (Ruditapes philippinarum)

     Ecotoxicology and Environmental Safety 309:119677.

     https://doi.org/10.1016/j.ecoenv.2026.119677

     • PubMed
     • Google Scholar
131. 1. Quinlan AR
     2. Hall IM

     (2010) BEDTools: a flexible suite of utilities for comparing genomic features

     Bioinformatics 26:841–842.

     https://doi.org/10.1093/bioinformatics/btq033

     • PubMed
     • Google Scholar
132. 1. Ranallo-Benavidez TR
     2. Jaron KS
     3. Schatz MC

     (2020) GenomeScope 2.0 and Smudgeplot for reference-free profiling of polyploid genomes

     Nature Communications 11:1432.

     https://doi.org/10.1038/s41467-020-14998-3

     • PubMed
     • Google Scholar
133. Software

     1. R Development Core Team

     (2024) R: a language and environment for statistical computing

     R Foundation for Statistical Computing, Vienna, Austria.

     http://www.r-project.org
134. 1. Redies C

     (1997) Cadherins and the formation of neural circuitry in the vertebrate CNS

     Cell and Tissue Research 290:405–413.

     https://doi.org/10.1007/s004410050947

     • PubMed
     • Google Scholar
135. 1. Reiter S
     2. Hülsdunk P
     3. Woo T
     4. Lauterbach MA
     5. Eberle JS
     6. Akay LA
     7. Longo A
     8. Meier-Credo J
     9. Kretschmer F
     10. Langer JD
     11. Kaschube M
     12. Laurent G

     (2018) Elucidating the control and development of skin patterning in cuttlefish

     Nature 562:361–366.

     https://doi.org/10.1038/s41586-018-0591-3

     • PubMed
     • Google Scholar
136. 1. Rhie A
     2. Walenz BP
     3. Koren S
     4. Phillippy AM

     (2020) Merqury: reference-free quality, completeness, and phasing assessment for genome assemblies

     Genome Biology 21:245.

     https://doi.org/10.1186/s13059-020-02134-9

     • PubMed
     • Google Scholar
137. 1. Rogers TF
     2. Yalçın G
     3. Briseno J
     4. Vijayan N
     5. Nyholm SV
     6. Simakov O

     (2024) Gene modelling and annotation for the Hawaiian bobtail squid, Euprymna scolopes

     Scientific Data 11:40.

     https://doi.org/10.1038/s41597-023-02903-8

     • PubMed
     • Google Scholar
138. Software

     1. Rogers TF

     (2025) E.scolopes-V2.2-BRAKER2-gene-annotation, version e2103c8

     Github.

     https://github.com/TheaFrances/E.scolopes-V2.2-BRAKER2-gene-annotation
139. Preprint

     1. Rogers TF
     2. Stock J
     3. Schulz NG
     4. Yalçin G
     5. Rencken S
     6. Weissenbacher A
     7. Clarence T
     8. Schultz DT
     9. Ragsdale CW
     10. Albertin CB
     11. Simakov O

     (2025) Genome reorganisation and expansion shape 3D genome architecture and define a distinct regulatory landscape in coleoid cephalopods

     bioRxiv.

     https://doi.org/10.1101/2025.08.29.672809

     • Google Scholar
140. 1. Rouressol L
     2. Briseno J
     3. Vijayan N
     4. Chen GY
     5. Ritschard EA
     6. Sanchez G
     7. Nyholm SV
     8. McFall-Ngai MJ
     9. Simakov O

     (2023) Emergence of novel genomic regulatory regions associated with light-organ development in the bobtail squid

     iScience 26:107091.

     https://doi.org/10.1016/j.isci.2023.107091

     • PubMed
     • Google Scholar
141. Preprint

     1. Rubino FA
     2. Coffing GC
     3. Gibbons CJ
     4. Small ST
     5. Desvignes T
     6. Pessutti J
     7. Petersen AM
     8. Arkhipkin A
     9. Shcherbich Z
     10. Postlethwait JH
     11. Kern AD
     12. Montague TG

     (2025) A non-invasive method to genotype cephalopod sex by quantitative PCR

     bioRxiv.

     https://doi.org/10.1101/2025.10.28.685099

     • Google Scholar
142. 1. Ryu KB
     2. Jo GH
     3. Gil YC
     4. Jeon D
     5. Choi NR
     6. Jung SH
     7. Jo S
     8. An HS
     9. Lee HY
     10. Eyun S
     11. Cho SJ

     (2023) Eye development and developmental expression of crystallin genes in the long arm octopus, Octopus minor

     Frontiers in Marine Science 10:1136602.

     https://doi.org/10.3389/fmars.2023.1136602

     • Google Scholar
143. 1. Sampaio E
     2. Seco MC
     3. Rosa R
     4. Gingins S

     (2021) Octopuses punch fishes during collaborative interspecific hunting events

     Ecology 102:e03266.

     https://doi.org/10.1002/ecy.3266

     • PubMed
     • Google Scholar
144. 1. Sampaio E
     2. Sridhar VH
     3. Francisco FA
     4. Nagy M
     5. Sacchi A
     6. Strandburg-Peshkin A
     7. Nührenberg P
     8. Rosa R
     9. Couzin ID
     10. Gingins S

     (2024) Multidimensional social influence drives leadership and composition-dependent success in octopus–fish hunting groups

     Nature Ecology & Evolution 8:2072–2084.

     https://doi.org/10.1038/s41559-024-02525-2

     • Google Scholar
145. 1. Sanchez G
     2. Setiamarga DHE
     3. Tuanapaya S
     4. Tongtherm K
     5. Winkelmann IE
     6. Schmidbaur H
     7. Umino T
     8. Albertin C
     9. Allcock L
     10. Perales-Raya C
     11. Gleadall I
     12. Strugnell JM
     13. Simakov O
     14. Nabhitabhata J

     (2018) Genus-level phylogeny of cephalopods using molecular markers: current status and problematic areas

     PeerJ 6:e4331.

     https://doi.org/10.7717/peerj.4331

     • PubMed
     • Google Scholar
146. 1. Sanchez G
     2. Fernández-Álvarez FÁ
     3. Bernal A
     4. Heath-Heckman E
     5. Lami R
     6. McFall-Ngai M
     7. Nishiguchi M
     8. Nyholm S
     9. Simakov O
     10. Allcock AL
     11. Rokhsar DS

     (2026) Rapid mid-Cretaceous diversification of squid and cuttlefish preceded radiation into coastal niches

     Nature Ecology & Evolution 10:662–676.

     https://doi.org/10.1038/s41559-026-03009-1

     • Google Scholar
147. 1. Schmidbaur H
     2. Kawaguchi A
     3. Clarence T
     4. Fu X
     5. Hoang OP
     6. Zimmermann B
     7. Ritschard EA
     8. Weissenbacher A
     9. Foster JS
     10. Nyholm SV
     11. Bates PA
     12. Albertin CB
     13. Tanaka E
     14. Simakov O

     (2022) Emergence of novel cephalopod gene regulation and expression through large-scale genome reorganization

     Nature Communications 13:2172.

     https://doi.org/10.1038/s41467-022-29694-7

     • PubMed
     • Google Scholar
148. 1. Schnell AK
     2. Boeckle M
     3. Rivera M
     4. Clayton NS
     5. Hanlon RT

     (2021) Cuttlefish exert self-control in a delay of gratification task

     Proceedings. Biological Sciences 288:20203161.

     https://doi.org/10.1098/rspb.2020.3161

     • PubMed
     • Google Scholar
149. 1. Schultz DT
     2. Haddock SHD
     3. Bredeson JV
     4. Green RE
     5. Simakov O
     6. Rokhsar DS

     (2023) Ancient gene linkages support ctenophores as sister to other animals

     Nature 618:110–117.

     https://doi.org/10.1038/s41586-023-05936-6

     • PubMed
     • Google Scholar
150. 1. Shumate A
     2. Wong B
     3. Pertea G
     4. Pertea M

     (2022) Improved transcriptome assembly using a hybrid of long and short reads with StringTie

     PLOS Computational Biology 18:e1009730.

     https://doi.org/10.1371/journal.pcbi.1009730

     • PubMed
     • Google Scholar
151. 1. Simakov O
     2. Bredeson J
     3. Berkoff K
     4. Marletaz F
     5. Mitros T
     6. Schultz DT
     7. O’Connell BL
     8. Dear P
     9. Martinez DE
     10. Steele RE
     11. Green RE
     12. David CN
     13. Rokhsar DS

     (2022) Deeply conserved synteny and the evolution of metazoan chromosomes

     Science Advances 8:eabi5884.

     https://doi.org/10.1126/sciadv.abi5884

     • PubMed
     • Google Scholar
152. 1. Simão FA
     2. Waterhouse RM
     3. Ioannidis P
     4. Kriventseva EV
     5. Zdobnov EM

     (2015) BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs

     Bioinformatics 31:3210–3212.

     https://doi.org/10.1093/bioinformatics/btv351

     • PubMed
     • Google Scholar
153. Website

     1. Smit A
     2. Hubley R
     3. Green P

     (2025) RepeatMasker

     Accessed December 17, 2025.

     http://www.repeatmasker.org
154. 1. Song W
     2. Li R
     3. Zhao Y
     4. Migaud H
     5. Wang C
     6. Bekaert M

     (2021) Pharaoh cuttlefish, sepia pharaonis, genome reveals unique reflectin camouflage gene set

     Frontiers in Marine Science 8:639670.

     https://doi.org/10.3389/fmars.2021.639670

     • Google Scholar
155. 1. Songco-Casey JO
     2. Coffing GC
     3. Piscopo DM
     4. Pungor JR
     5. Kern AD
     6. Miller AC
     7. Niell CM

     (2022) Cell types and molecular architecture of the Octopus bimaculoides visual system

     Current Biology 32:5031–5044.

     https://doi.org/10.1016/j.cub.2022.10.015

     • PubMed
     • Google Scholar
156. 1. Stanke M
     2. Schöffmann O
     3. Morgenstern B
     4. Waack S

     (2006) Gene prediction in eukaryotes with a generalized hidden Markov model that uses hints from external sources

     BMC Bioinformatics 7:62.

     https://doi.org/10.1186/1471-2105-7-62

     • PubMed
     • Google Scholar
157. 1. Stanke M
     2. Diekhans M
     3. Baertsch R
     4. Haussler D

     (2008) Using native and syntenically mapped cDNA alignments to improve de novo gene finding

     Bioinformatics 24:637–644.

     https://doi.org/10.1093/bioinformatics/btn013

     • PubMed
     • Google Scholar
158. 1. Stephens PR
     2. Young JZ

     (1969) The glio-vascular system of cephalopods

     Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 255:1–12.

     https://doi.org/10.1098/rstb.1969.0001

     • Google Scholar
159. 1. Styfhals R
     2. Seuntjens E
     3. Simakov O
     4. Sanges R
     5. Fiorito G

     (2019) In silico identification and expression of protocadherin gene family in Octopus vulgaris

     Frontiers in Physiology 9:01905.

     https://doi.org/10.3389/fphys.2018.01905

     • Google Scholar
160. 1. Styfhals R
     2. Zolotarov G
     3. Hulselmans G
     4. Spanier KI
     5. Poovathingal S
     6. Elagoz AM
     7. De Winter S
     8. Deryckere A
     9. Rajewsky N
     10. Ponte G
     11. Fiorito G
     12. Aerts S
     13. Seuntjens E

     (2022) Cell type diversity in a developing octopus brain

     Nature Communications 13:7392.

     https://doi.org/10.1038/s41467-022-35198-1

     • PubMed
     • Google Scholar
161. 1. Summers MF
     2. Henderson LE
     3. Chance MR
     4. Bess JW
     5. South TL
     6. Blake PR
     7. Sagi I
     8. Perez-Alvarado G
     9. Sowder RC
     10. Hare DR

     (1992) Nucleocapsid zinc fingers detected in retroviruses: EXAFS studies of intact viruses and the solution-state structure of the nucleocapsid protein from HIV-1

     Protein Science 1:563–574.

     https://doi.org/10.1002/pro.5560010502

     • PubMed
     • Google Scholar
162. 1. Sun S
     2. Han X
     3. Han Z
     4. Liu Q

     (2024) Chromosomal-scale genome assembly and annotation of the land slug (Meghimatium bilineatum)

     Scientific Data 11:35.

     https://doi.org/10.1038/s41597-023-02893-7

     • PubMed
     • Google Scholar
163. 1. Tan WH
     2. Cheng SC
     3. Liu YT
     4. Wu CG
     5. Lin MH
     6. Chen CC
     7. Lin CH
     8. Chou CY

     (2016) Structure of a highly active cephalopod S-crystallin mutant: new molecular evidence for evolution from an active enzyme into lens-refractive protein

     Scientific Reports 6:31176.

     https://doi.org/10.1038/srep31176

     • PubMed
     • Google Scholar
164. 1. Tanner AR
     2. Fuchs D
     3. Winkelmann IE
     4. Gilbert MTP
     5. Pankey MS
     6. Ribeiro ÂM
     7. Kocot KM
     8. Halanych KM
     9. Oakley TH
     10. da Fonseca RR
     11. Pisani D
     12. Vinther J

     (2017) Molecular clocks indicate turnover and diversification of modern coleoid cephalopods during the Mesozoic Marine Revolution

     Proceedings. Biological Sciences 284:20162818.

     https://doi.org/10.1098/rspb.2016.2818

     • PubMed
     • Google Scholar
165. 1. Tomarev SI
     2. Chung S
     3. Piatigorsky J

     (1995) Glutathione S-transferase and S-crystallins of cephalopods: evolution from active enzyme to lens-refractive proteins

     Journal of Molecular Evolution 41:1048–1056.

     https://doi.org/10.1007/BF00173186

     • PubMed
     • Google Scholar
166. Software

     1. Tushev G

     (2026) Soffgenome, version swh:1:rev:efe218e0bf3c8318c5c729cbab94478dbf5b5502

     Software Heritage.

     https://archive.softwareheritage.org/swh:1:dir:55d0af83f088bd734c66ca681fe48855e35eae83;origin=https://gitlab.mpcdf.mpg.de/mpibr/laur/cuttlefishomics/soffgenome;visit=swh:1:snp:ef236282e660b36085bf722edd6916303f50ce33;anchor=swh:1:rev:efe218e0bf3c8318c5c729cbab94478dbf5b5502
167. Conference

     1. Vasimuddin M
     2. Misra S
     3. Li H
     4. Aluru S

     (2019) Efficient architecture-aware acceleration of BWA-MEM for multicore systems

     2019 IEEE International Parallel and Distributed Processing Symposium (IPDPS). pp. 314–324.

     https://doi.org/10.1109/IPDPS.2019.00041

     • Google Scholar
168. Software

     1. Veenstra GJC

     (2025) Pita_genes_v0.3: doryteuthis pealeii gene models, version Version 0.3

     Zenodo.

     https://doi.org/10.5281/zenodo.15222686
169. 1. Vitturi R
     2. Rasotto MB
     3. Farinella-Ferruzza N

     (1982) The chromosomes of 16 molluscan species

     Bolletino Di Zoologia 49:61–71.

     https://doi.org/10.1080/11250008209439373

     • Google Scholar
170. 1. Vurture GW
     2. Sedlazeck FJ
     3. Nattestad M
     4. Underwood CJ
     5. Fang H
     6. Gurtowski J
     7. Schatz MC

     (2017) GenomeScope: fast reference-free genome profiling from short reads

     Bioinformatics 33:2202–2204.

     https://doi.org/10.1093/bioinformatics/btx153

     • PubMed
     • Google Scholar
171. 1. Wang Y
     2. Tang H
     3. Debarry JD
     4. Tan X
     5. Li J
     6. Wang X
     7. Lee T
     8. Jin H
     9. Marler B
     10. Guo H
     11. Kissinger JC
     12. Paterson AH

     (2012) MCScanX: a toolkit for detection and evolutionary analysis of gene synteny and collinearity

     Nucleic Acids Research 40:e49.

     https://doi.org/10.1093/nar/gkr1293

     • PubMed
     • Google Scholar
172. 1. Wang J-H
     2. Zheng X-D

     (2017) Comparison of the genetic relationship between nine Cephalopod species based on cluster analysis of karyotype evolutionary distance

     Comparative Cytogenetics 11:477–494.

     https://doi.org/10.3897/CompCytogen.v11i3.12752

     • PubMed
     • Google Scholar
173. 1. Wang Y
     2. Yu Y
     3. Pang Y
     4. Yu H
     5. Zhang W
     6. Zhao X
     7. Yu J

     (2021) The distinct roles of zinc finger CCHC-type (ZCCHC) superfamily proteins in the regulation of RNA metabolism

     RNA Biology 18:2107–2126.

     https://doi.org/10.1080/15476286.2021.1909320

     • PubMed
     • Google Scholar
174. 1. Woo T
     2. Liang X
     3. Evans DA
     4. Fernandez O
     5. Kretschmer F
     6. Reiter S
     7. Laurent G

     (2023) The dynamics of pattern matching in camouflaging cuttlefish

     Nature 619:122–128.

     https://doi.org/10.1038/s41586-023-06259-2

     • PubMed
     • Google Scholar
175. 1. Xu S
     2. Hu E
     3. Cai Y
     4. Xie Z
     5. Luo X
     6. Zhan L
     7. Tang W
     8. Wang Q
     9. Liu B
     10. Wang R
     11. Xie W
     12. Wu T
     13. Xie L
     14. Yu G

     (2024) Using clusterProfiler to characterize multiomics data

     Nature Protocols 19:3292–3320.

     https://doi.org/10.1038/s41596-024-01020-z

     • PubMed
     • Google Scholar
176. 1. Yang T
     2. Jia Z
     3. Chen H
     4. Deng Z
     5. Liu W
     6. Chen L
     7. Li L

     (2020) Mechanical design of the highly porous cuttlebone: A bioceramic hard buoyancy tank for cuttlefish

     PNAS 117:23450–23459.

     https://doi.org/10.1073/pnas.2009531117

     • Google Scholar
177. 1. Yoshida M-A
     2. Hirota K
     3. Imoto J
     4. Okuno M
     5. Tanaka H
     6. Kajitani R
     7. Toyoda A
     8. Itoh T
     9. Ikeo K
     10. Sasaki T
     11. Setiamarga DHE

     (2022) Gene recruitments and dismissals in the argonaut genome provide insights into pelagic lifestyle adaptation and shell-like eggcase reacquisition

     Genome Biology and Evolution 14:evac140.

     https://doi.org/10.1093/gbe/evac140

     • PubMed
     • Google Scholar
178. 1. Yoshida M
     2. Tóth E
     3. Kon-Nanjo K
     4. Kon T
     5. Hirota K
     6. Toyoda A
     7. Toh H
     8. Miyazawa H
     9. Terauchi M
     10. Noguchi H
     11. Setiamarga DH
     12. Simakov O

     (2025) Giant genome of the vampire squid reveals the derived state of modern octopod karyotypes

     iScience 28:113832.

     https://doi.org/10.1016/j.isci.2025.113832

     • PubMed
     • Google Scholar
179. 1. Young JZ

     (1960) Regularities in the retina and optic lobes of octopus in relation to form discrimination

     Nature 186:836–839.

     https://doi.org/10.1038/186836a0

     • PubMed
     • Google Scholar
180. 1. Young JZ

     (1962a)

     The optic lobes of Octopus vulgaris

     Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 245:19–58.

     • Google Scholar
181. 1. Young JZ

     (1962b) The retina of cephalopods and its degeneration after optic nerve section

     Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 245:1–18.

     https://doi.org/10.1098/rstb.1962.0004

     • Google Scholar
182. 1. Young JZ

     (1963a) The number and sizes of nerve cells in octopus

     Proceedings of the Zoological Society of London 140:229–254.

     https://doi.org/10.1111/j.1469-7998.1963.tb01862.x

     • Google Scholar
183. 1. Young JZ

     (1963b) Light‐and dark‐adaptation in the eyes of some cephalopods

     Proceedings of the Zoological Society of London 140:255–272.

     https://doi.org/10.1111/j.1469-7998.1963.tb01863.x

     • Google Scholar
184. 1. Young JZ

     (1974) The central nervous system of Loligo. I. The optic lobe

     Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 267:263–302.

     https://doi.org/10.1098/rstb.1974.0002

     • PubMed
     • Google Scholar
185. 1. Young JZ

     (1991) Computation in the learning system of cephalopods

     The Biological Bulletin 180:200–208.

     https://doi.org/10.2307/1542389

     • PubMed
     • Google Scholar
186. 1. Yu G
     2. Wang LG
     3. Han Y
     4. He QY

     (2012) clusterProfiler: an R package for comparing biological themes among gene clusters

     OMICS 16:284–287.

     https://doi.org/10.1089/omi.2011.0118

     • PubMed
     • Google Scholar
187. 1. Zeng Q
     2. Liu J
     3. Wang C
     4. Wang H
     5. Zhang L
     6. Hu J
     7. Bao L
     8. Wang S

     (2021) High-quality reannotation of the king scallop genome reveals no “gene-rich” feature and evolution of toxin resistance

     Computational and Structural Biotechnology Journal 19:4954–4960.

     https://doi.org/10.1016/j.csbj.2021.08.038

     • PubMed
     • Google Scholar
188. 1. Zeng X
     2. Yi Z
     3. Zhang X
     4. Du Y
     5. Li Y
     6. Zhou Z
     7. Chen S
     8. Zhao H
     9. Yang S
     10. Wang Y
     11. Chen G

     (2024) Chromosome-level scaffolding of haplotype-resolved assemblies using Hi-C data without reference genomes

     Nature Plants 10:1184–1200.

     https://doi.org/10.1038/s41477-024-01755-3

     • PubMed
     • Google Scholar
189. 1. Zhang Y
     2. Mao F
     3. Mu H
     4. Huang M
     5. Bao Y
     6. Wang L
     7. Wong NK
     8. Xiao S
     9. Dai H
     10. Xiang Z
     11. Ma M
     12. Xiong Y
     13. Zhang Z
     14. Zhang L
     15. Song X
     16. Wang F
     17. Mu X
     18. Li J
     19. Ma H
     20. Zhang Y
     21. Zheng H
     22. Simakov O
     23. Yu Z

     (2021) The genome of Nautilus pompilius illuminates eye evolution and biomineralization

     Nature Ecology & Evolution 5:927–938.

     https://doi.org/10.1038/s41559-021-01448-6

     • Google Scholar
190. 1. Zhou C
     2. McCarthy SA
     3. Durbin R

     (2023) YaHS: yet another Hi-C scaffolding tool

     Bioinformatics 39:btac808.

     https://doi.org/10.1093/bioinformatics/btac808

     • PubMed
     • Google Scholar
191. 1. Zhu A
     2. Ibrahim JG
     3. Love MI

     (2019) Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences

     Bioinformatics 35:2084–2092.

     https://doi.org/10.1093/bioinformatics/bty895

     • PubMed
     • Google Scholar
192. 1. Zou Y
     2. Fu J
     3. Liang Y
     4. Luo X
     5. Shen M
     6. Huang M
     7. Chen Y
     8. You W
     9. Ke C

     (2024) Chromosome-level genome assembly of the ivory shell Babylonia areolata

     Scientific Data 11:1201.

     https://doi.org/10.1038/s41597-024-04001-9

     • PubMed
     • Google Scholar
193. 1. Zylinski S
     2. Osorio D
     3. Shohet AJ

     (2009a) Perception of edges and visual texture in the camouflage of the common cuttlefish, Sepia officinalis

     Philosophical Transactions of the Royal Society B 364:439–448.

     https://doi.org/10.1098/rstb.2008.0264

     • Google Scholar
194. 1. Zylinski S
     2. Osorio D
     3. Shohet AJ

     (2009b) Cuttlefish camouflage: context-dependent body pattern use during motion

     Proceedings. Biological Sciences 276:3963–3969.

     https://doi.org/10.1098/rspb.2009.1083

     • PubMed
     • Google Scholar
195. 1. Zylinski S
     2. Darmaillacq AS
     3. Shashar N

     (2012) Visual interpolation for contour completion by the European cuttlefish (Sepia officinalis) and its use in dynamic camouflage

     Proceedings. Biological Sciences 279:2386–2390.

     https://doi.org/10.1098/rspb.2012.0026

     • PubMed
     • Google Scholar

Author details

1. Simone Daniela Rencken

   1. Max Planck Institute for Brain Research, Frankfurt am Main, Germany
   2. Radboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, Netherlands

   Contribution

   Conceptualization, Resources, Data curation, Formal analysis, Validation, Investigation, Visualization, Writing – original draft, Project administration, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0009-0003-9341-2898

2. Georgi Tushev

   Max Planck Institute for Brain Research, Frankfurt am Main, Germany

   Contribution

   Data curation, Software, Formal analysis, Validation, Visualization, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3340-9422

3. David Hain

   1. Max Planck Institute for Brain Research, Frankfurt am Main, Germany
   2. Faculty of Biological Sciences, Goethe University, Frankfurt am Main, Germany

   Contribution

   Resources, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8979-7938

4. Elena Ciirdaeva

   Max Planck Institute for Brain Research, Frankfurt am Main, Germany

   Contribution

   Resources, Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

5. Oleg Simakov

   Department of Neuroscience and Developmental Biology, University of Vienna, Vienna, Austria

   Contribution

   Conceptualization, Formal analysis, Supervision, Validation, Writing – original draft, Project administration, Writing – review and editing

   Competing interests

   No competing interests declared

6. Gilles Laurent

   Max Planck Institute for Brain Research, Frankfurt am Main, Germany

   Contribution

   Conceptualization, Supervision, Funding acquisition, Writing – original draft, Project administration, Writing – review and editing

   For correspondence

   g.laurent@brain.mpg.de

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2296-114X