Skip to main content
Download PDF

Exploring objective feature sets in constructing the evolution relationship of animal genome sequences

BMC Genomics volume 24, Article number: 634 (2023) Cite this article

Abstract

Background

Exploring evolution regularities of genome sequences and constructing more objective species evolution relationships at the genomic level are high-profile topics. Based on the evolution mechanism of genome sequences proposed in our previous research, we found that only the 8-mers containing CG or TA dinucleotides correlate directly with the evolution of genome sequences, and the relative frequency rather than the actual frequency of these 8-mers is more suitable to characterize the evolution of genome sequences.

Result

Therefore, two types of feature sets were obtained, they are the relative frequency sets of CG1 + CG2 8-mers and TA1 + TA2 8-mers. The evolution relationships of mammals and reptiles were constructed by the relative frequency set of CG1 + CG2 8-mers, and two types of evolution relationships of insects were constructed by the relative frequency sets of CG1 + CG2 8-mers and TA1 + TA2 8-mers respectively. Through comparison and analysis, we found that evolution relationships are consistent with the known conclusions. According to the evolution mechanism, we considered that the evolution relationship constructed by CG1 + CG2 8-mers reflects the evolution state of genome sequences in current time, and the evolution relationship constructed by TA1 + TA2 8-mers reflects the evolution state in the early stage.

Conclusion

Our study provides objective feature sets in constructing evolution relationships at the genomic level.

Peer Review reports

Background

The genome sequence contains information on sequence composition and evolution. The occurrence frequency of k-mers in the genome sequence is considered an ideal feature set for revealing genome information. Based on the non-random feature of k-mer frequency in DNA sequences, functional fragments and functional regions in DNA sequences were analyzed and predicted. Chan and Kibler used 6-mers to predict cis-regulatory motifs [1]. Li and Lin used k-mers (2 ≤ k ≤ 9) to predict promoter region [2, 3]. Zhang et al. used k-mers to predict the binding site of locust DNA [4]. Hariharan found that different k-mers were closely related to the diversity of functional fragments [5]. Guo used k-mers to study nucleosome localization [6]. Other studies have mainly focused on probabilistic models of k-mer distribution, rare k-mers, and rich k-mer fragments and their distribution in chromosomes [5, 7,8,9,10,11,12,13,14,15], and good results have been obtained in searching of some gene regulatory fragments using k-mers [16,17,18,19].

Some studies have attempted to study evolution relationships using the non-random features of k-mer usage to overcome the theoretical defects encountered in constructing evolution relationships by employing the sequence alignment method. The study of evolution relationships among species has generally undergone three stages. Initially, evolution relationships were constructed based on single DNA, RNA, or protein sequence; for example, the Fitch used the cytochrome C protein sequence to constructed evolution relationships [20]. Subsequently, many researchers have used the small subunit rRNA sequence [21], elongation factor Tu [22], heat-shock protein (HSP60) gene [23], the largest subunit of RNA polymerase II (RPB1) [24], and aminoacyl-tRNA synthetases [25]. However, owing to the differences in the selected functional sequences, the inconsistency problem of these evolution relationships cannot be solved. In addition, it is not appropriate to use the information of a single segment to represent the evolutionary information of the whole genome sequence. Later, researchers tried to construct evolution relationships by using a set of functional sequences. For instance, Snel used a common gene set [26, 27], Wolf used a conserved cluster of homologous genes [28], and Qi uses all protein-coding sequences or all protein sequences [29,30,31]. Although this method improves the reliability of evolution relationships, it still cannot resolve the inconsistency problem because there is no uniform standard to select a consistent gene/protein set and to determine the number of selected sets. Furthermore, some genes were not found in some of the analyzed species. The selected sequence set could not represent the whole genome sequence. Some researchers have attempted to construct evolution relationships by aligning whole genome sequences [32, 33]. However, this method is only applicable to mitochondrial genome sequences or very small genome sequences. Currently, there is no method for sequence alignment of larger genomes.

A growing amount of attention has been paid to the information on k-mers frequency in whole genome sequences to construct evolution relationships. Previously, Nussinov explored the compositional heterogeneity of prokaryotic and eukaryotic DNA sequences based on 2-mer frequency [34]. Karlin analyzed and compared the compositional differences in phage, bacterial, and some eukaryotic genome sequences based on k-mers (k = 2, 3, 4) frequency [35, 36]. Chapus proposed k-mers frequency difference analysis of whole genome sequences to construct evolution relationships and discussed the optimum range of k values [37]. Qi used the k-mer feature set of genome sequences to construct evolution relationships in prokaryotes [29]. However, some insurmountable problems still exist with this method. If all k-mers are selected, the evolution relationships are very poor, particularly in eukaryotes. If the k-mer set is filtered or screened, it is difficult to determine the uniform number of k-mers, and the arbitrariness problem in the selection of the k-mer number cannot be solved. In addition, the selection of k-mer features is also difficult. Evidently, the actual frequency of 8-mers is not a suitable feature. The enormous frequency difference of k-mers seriously undermines the equality of their contributions to evolution. Determining the number and features of k-mers associated with the evolution of genome sequences is currently the most significant challenge.

Several studies have focused on the k-mer spectra of genome sequences and the correlations between k-mer spectra and the evolution of genome sequences. The k-mer spectrum is a specific label for a genome sequence. Chen found that the 6-mer spectra differed for different genome sequences [38]. Subsequently, Chor found that the k-mer spectra of prokaryotes, fungi and some lower animals had single-peak distributions, while the k-mer spectra of higher animals, such as tetrapod mammals, showed a triple-peaked distribution, and speculated that the divergence of k-mer spectra is caused by the interaction between CpG inhibition and G + C content constraint [39].

Our group has studied the spectral features of 48 XYi (i = 0, 1, 2) 8-mer subsets in 920 eukaryotic and prokaryotic genome sequences. We found that there are the CG- and TA-independent selection modes in the genome sequences, which indicates that CGi 8-mers and TAi 8-mers play key roles in genome constitution and evolution [40,41,42]. Finally, we proposed an evolution mechanism of genome sequences, in which the evolution state of a genome sequence is determined by the CG- and TA-independent selections intensities and the mutual inhibition relationship between them. Based on the evolution mechanism, we evaluated the contributions of CGi 8-mers and TAi 8-mers in the evolution of genome sequences, explored the 8-mer subsets and their features that are directly related to the evolution of genome sequences, and determined a more objective theoretical method to construct evolution relationships. We used this method to construct evolution relationships of mammals, reptiles and insects. Finally, we verified the reliability of our theoretical method using whole-genome sequences.

Results

Selection of 8-mer sets related evolution

The evolution mechanism of genome sequences shows that there are two evolution modes in genome sequences, called the CG- and TA-independent selection modes. The intensity of the CG-independent selection mode can be depicted by the separability values of CGi 8-mer spectra (δCGi), and the intensity of the TA-independent selection mode can be depicted by the separability values of TAi 8-mer spectra (δTAi). Herein, we obtained the intensity distributions of CG- and TA-independent selection for animal genome sequences. The schematic diagram is shown in Fig. 1, the distribution features of invertebrates are represented by insects and the distribution features of vertebrates are represented by primates. We found that: (1) In all of the animals, the intensity of CG1/CG2-independent selection correlated positively with the evolution levels of genome sequences. The intensity of TA1/TA2-independent selection correlated negatively with the evolution levels of genome sequences. The mutual inhibition relationship exists obviously in invertebrate genome sequences. In vertebrate genome sequences, the intensity of TA1/TA2-independent selection falls gradually into the background (δ = 1) as the evolution levels of species increase, which implies that the phenomenon of TA-independent selection and the mutual inhibition relationship disappeared gradually [42]. (2) The intensity distributions of the CG0 or TA0 8-mer subset is exactly opposite to those of CG1/CG2 or TA1/TA2 8-mer subsets (Fig. 1). This indicates that the CG0 or TA0 8-mer spectral features are also correlated with the evolution of genome sequences.

Fig. 1
figure 1

Separability distributions of CGi and TAi 8-mer spectra in primate and insect genome sequences. A The separability distributions of CG0 and TA0 8-mer spectra. B The separability distributions of CG1 and TA1 8-mer spectra. C The separability distributions of CG2 and TA2 8-mer spectra

Full size image

Based on the above conclusion, we considered that not all 8-mers correlated directly with the evolution of genome sequences. Hence, determining the directly correlated 8-mer set is a key problem. When analyzing the CGi 8-mer spectral distributions in the analyzed animals, we found that CG1 and CG2 8-mer spectra distribute far from the random center and locate at the low-frequency end. The CG0 8-mer spectra distribute around the random center. The distributions of CG1 and CG2 8-mer spectra are more conservative than that of the CG0 8-mer spectra. If an 8-mer spectral distribution is far from the random center and is highly conservative, the occurrence frequency of these 8-mers must be the result of non-random or directed evolution, and these 8-mers must have special biological functions. The results indicated that CG1 and CG2 8-mers correlate directly with the evolution of genome sequences. The same properties were observed for the spectral distributions in the TAi 8-mer subsets.

Although the distribution characteristics of CG0 and TA0 8-mers showed a correlation with the evolution of genome sequences, after verification, we found that the correlation is caused by non-random sampling. According to statistical theory, when 8-mers with low frequency are extracted from the total 8-mers population, the average frequency of the remaining 8-mers must increase, and vice versa. This implies that the independent selection intensity must have a negative correlation between CG1/CG2 and CG0 8-mer spectra and between TA1/TA2 and TA0 8-mer spectra. For CG0 or TA0 8-mers, the correlations with the evolution of genome sequences are caused by 8-mers containing CG or TA dinucleotides. In a word, the 8-mers containing CG or TA dinucleotides correlate directly with the evolution of genome sequences and CG0 or TA0 8-mers correlate indirectly with the evolution of genome sequences.

Therefore, we selected CG1 + CG2 8-mers and TA1 + TA2 8-mers as two sets to characterize the evolution state of genome sequences. The number of 8-mers are 24,991 in the two sets which is selected from a total of 65,536 8-mers.

Selection of evolutionary feature

Determining the feature of the selected 8-mers is another key problem. Generally, the frequency of k-mers was used to characterize the evolution relationship. However, the results were usually unsatisfactory. We considered that the actual frequency of k-mers is not a suitable feature for constructing evolution relationships. Since the spectral distributions of 8-mer subsets are not normal distribution, but more like Poisson distribution, the frequency of more 8-mers is much larger. Using 8-mers with extremely high or extremely low frequency to characterize the evolution of genome sequences is bound to exaggerate or weaken the contribution of these 8-mers. We believed that the suitable feature of each selected 8-mer not only reflect its preference, but also highlight its equality. So, we proposed the relative frequency as the 8-mer feature. The relative frequency is defined as follows: the 8-mers in their subset are arranged in order of their actual frequencies from small to large, and the numerical order is defined as the relative frequency of these 8-mers. The feature not only reflect its preference, but also highlight its equality, and partly eliminates the unequal weight status of the 8-mers in contributing to the evolution of genome sequences. We considered that the number of the obtained 8-mers are objective and have not interfered by man-made, the feature of the relative frequency is more suitable for characterizing the evolution of genome sequences.

Construction of evolution relationships

Evolution relationships of primate genome sequences

In order to verify the reliability of the feature set we selected, we took the actual frequency and the relative frequency as the features to construct evolution relationships based on total 8-mers. According to ‘Materials and Methods’, the distance matrices are established and the evolution relationships are constructed using Mega7 software between primate genome sequences, the results are shown in Fig. 2.

Fig. 2
figure 2

Evolution relationships of primates constructed by total 8-mers. A The evolution relationship constructed by the actual frequency set of total 8-mers. B The evolution relationship constructed by the relative frequency set of total 8-mers. The blue background represents the old world monkeys, the red background represents the anthropoid, the green background represents the new world monkeys, and the apricot background represents the prosimian

Full size image

Based on the actual frequency of total 8-mers, the result showed that only prosimian species are clustered on a main clade, but the other three class species, old world monkeys, anthropoids, new world monkeys, do not form independent clades, they are interlaced with each other (Fig. 2A). The evolution relationship is not consistent obviously with the known conclusion. It means that the actual frequency is not suitable feature. Based on the relative frequency set of total 8-mers, the four class species are clustered basically into four clades (Fig. 2B). But the species in new world monkey (green background) are not clustered into one main clade, and Nasalis larvatus is not clustered one clade with old world monkeys (blue background). The results indicated that the relative frequency of 8-mers is suitable feature. Although the evolution relationship is obviously better than that constructed by the actual frequency of total 8-mers, the results also have unacceptable differences compared with the known conclusion. We thought that the differences are caused by the selected total 8-mers.

Because the TA-independent selection mode has disappeared and only the CG-independent selection mode exists in primate genome sequences, we used the features of CGi 8-mers to construct the evolution relationships of primates. In order to verify the reliability of our selected feature sets, total 8-mers were divided into two subsets: CG1 + CG2 8-mers (N1 = 24,991) and CG0 8-mers (N0 = 40,545). Subsequently, the actual frequency of 8-mers in each set was transformed into relative frequency. Based on the two feature subsets, the evolution relationships of 59 primates were constructed respectively, the results are shown in Fig. 3.

Fig. 3
figure 3

Evolution relationships of primates constructed by CGi 8-mers. A The evolution relationship constructed by the relative frequency set of CG1 + CG2 8-mers. B The evolution relationship constructed by the relative frequency set of CG0 8-mers. The blue background represents the old world monkeys, the red background represents the anthropoid, the green background represents the new world monkeys, and the apricot background represents the prosimian

Full size image

Based on the relative frequency set of CG1 + CG2 8-mers (Fig. 3A), the evolution relationship is consistent with the known conclusions about the evolution relationships of primates [43,44,45,46]. Anthropoid, old world monkeys, new world monkeys and prosimian were clearly divided into four main clades. In anthropoid (red background), our results showed that Homo sapiens (human) and Pongo abelii have the closest evolution relationship, they also formed a clade with Pan troglodytes, Pan paniscus and Gorilla gorilla. Nomascus leucogenys and Hylobates moloch have the closest evolution relationship. In old world monkeys (blue background), among Macaca species, Macaca mulatta and Macaca fascicularis have the closest evolution relationship and Macaca fuscata is adjacent to this clade, but Macaca nemestrina is distant from this clade. We also found that Rhinopithecus roxellana is closer from Pygathrix nemaeus, and is distant from Rhinopithecus bieti. In new world monkeys (green background), four Cebidae species were clustered into a clade with Aotus nancymaae, while the other two Cebidae species, Saguinus imperator and Callithrix jacchus, were clustered into another clade with Pitheciidae (blue lines) and Atelidae (red lines) species. The evolution relationship is close between the two Cebidae species and Pitheciidae species. In prosimian (apricot background), Lemuridae (green lines) and Cheirogaleidae (blue lines) species were clustered into two clades and they are adjacent. We also found that the evolution relationship is distant between Propithecus coquereli and Indre indre.

Based on the relative frequency set of CG0 8-mers (Fig. 3B), the evolution relationship is basically consistent with that of CG1 + CG2 8-mers. However, there are also some differences between them. But these differences are not obvious in the four main clades. Although the two evolution relationships based on CG1 + CG2 8-mers and CG0 8-mers are similar, according to the evolution mechanism of genome sequences, the 8-mers containing CG dinucleotides correlate directly with the evolution of genome sequences, and CG0 8-mers correlate indirectly with the evolution of the genome sequences. The evolution information contained by CG0 8-mers are caused by non-random sampling. Hence, the features of CG0 8-mers did not provide new evolution information. If we used the CG2 + CG1 + CG0 (total 8-mers) to construct the evolution relationship, it must lead to information redundancy phenomenon, and the information of CG0 8-mers would generate background noise, which would inevitably affect the accuracy of evolution relationship. According to above analysis, we considered that the relative frequency set of CG1 + CG2 8-mers is the best feature parameters to construct evolution relationships.

Evolutionary relationships of other vertebrate genome sequences

Our results shown that the evolution mode of vertebrate genome sequences is dominated by CG-independent selection mode and the TA-independent selection mode disappears basically. In order to verify the universality of the feature set we selected across different species, we constructed evolution relationships based on the relative frequency set of CG1 + CG2 8-mers in other mammals and reptiles. The results are shown in Figs. 4 and 5.

Fig. 4
figure 4

Evolution relationships of other mammal species constructed by the relative frequency set of CG1 + CG2 8-mers. A The evolution relationship of Rodentia species. The blue background represents Myomorpha, the red background represents Hystricomorpha and the green background represents Sciuromorpha. B The evolution relationship of Chiroptera species. The blue background represents Vespertilionidae, the red background represents Phyllostomidae, the green background represents Rhinolophidae and the apricot background represents Pteropodidae. C The evolution relationship of Carnivora species. The blue background represents Caniformia and the red background represents Feliformia. D The evolution relationship of Perissodactyla and Artiodactyla species. The blue background represents Perissodactyla and the red background represents Artiodactyla

Full size image

In Rodentia species (Fig. 4A), the result showed that Myomorpha, Hystricomorpha and Sciuromorpha are clearly divided into three main clades. Within the main clade of Myomorpha (blue background), Muridae (black lines) is clearly separated from Cricetidae. And Cricetidae consists of three single clades, Cricetinae (red lines), Arvicolinae (green lines) and Neotominae (apricot lines). Within the main clade of Hystricomorpha (red background), there are two single clades, Octodontidae (black lines) and Caviidae (blue lines). Within the main clade of Sciuromorpha (green background), the only family is Sciuridae (black lines). In Chiroptera species (Fig. 4B), the result showed that Vespertilionidae, Phyllostomidae, Rhinolophidae and Pteropodidae are clearly divided into four main clades. Among them, Rhinolophidae (green background) consists of two single clades, Rhinolophinae (red lines) and Hipposiderinae (black lines). In Chiroptera species (Fig. 4C), the result showed that Otariidae (black lines) and Odobenidae (apricot lines) have the closest evolution relationship, they formed the Pinnipedia clade with Phocidae (red lines). And Pinnipedia, Mustelidae (green lines) and Ursidae (brown lines) formed the main clade of Caniformia (blue background). However, the ingle clade of Canidae (blue lines) is not clustered into the main clade of Caniformia. Three single clades of Felidae (black lines), Herpestidae (green lines) and Hyaenidae (blue lines) are clustered into a main clade of Feliformia (red background). In Fig. 4D, Perissodactyla and Artiodactyla are clearly divided into two main clades. In Perissodactyla (blue background), Equidae (red lines) and Rhinocerotidae (black lines) are clearly divided into two single clades. In Artiodactyla (red background), Camelidae (black lines), Suidae (green lines), Moschidae (blue lines), Cervidae (brown lines), Giraffidae (apricot lines) and Bovidae (cyan lines) all form single clades.

Fig. 5
figure 5

Evolution relationships of reptile species constructed by the relative frequency set of CG1 + CG2 8-mers. A The evolution relationship of Serpentiformes species. The blue background represents Colubridae, the red background represents Viperidae and the green background represents Elapidae. B The evolution relationship of Tesudines species. The blue background represents Testudinoidea, the red background represents Chelonioidea, the green background represents Chelidae and the apricot background represents Trionychia

Full size image

In Serpentiformes species (Fig. 5A), the result showed that Colubridae, Viperidae and Elapidae are clearly divided into three main clades. And within Colubridae clade (blue background), Natricinae (black lines) is clearly separated from Dipsadinae (red lines). In Testudines species (Fig. 5B), Testudinoidea, Chelonioidea, Chelidae and Trionychia are clearly divided into four main clades. Testudinoidea (blue background) consists of three single clades, Emydidae (black lines), Geoemydidae (red lines) and Testudinidae (green lines). Chelonioidea (red background) consists of two single clades, Cheloniidae (black lines) and Dermochelyidae (blue lines). Trionychia (apricot background) consists of two single clades, Trionychidae (black lines) and Carettochelyidae (green lines).

For the four kinds of other mammals and two kinds of other vertebrates, the evolution relationships are consistent with the known conclusions [47,48,49,50,51,52,53,54,55] and the effect of classifications is satisfactory. Therefore, we believed that our method for constructing evolution relationships can be applied to other animal species, and that the feature set is universal across different species.

Evolution relationships of insect genome sequences

In insect genome sequences, both the CG- and TA-independent selection mode exist. Based on the conclusion in primate genome sequences, we are no longer considering the effects of CG0 and TA0 8-mers. The 8-mers were divided into two sets: CG1 + CG2 8-mers and TA1 + TA2 8-mers. The actual frequency of 8-mers is converted to the relative frequency in the two 8-mer sets. According to the method provided, the two evolution relationships of 79 insect genome sequences were obtained respectively, the results are shown in Fig. 6. For convenience, the evolution relationship constructed by the relative frequency set of CG1 + CG2 8-mers is called CG map, and the evolution relationship constructed by the relative frequency set of TA1 + TA2 8-mers is called TA map. The CG map is shown in Fig. 6A and the TA map is shown in Fig. 6B.

Fig. 6
figure 6

Evolution relationships of insects. A The evolution relationship constructed by the relative frequency set of CG1 + CG2 8-mers. B The evolution relationship constructed by the relative frequency set of TA1 + TA2 8-mers. The green background represents Hymenoptera, the red background represents Hemiptera, the blue background represents Lepidoptrea and the apricot background represents Diptera

Full size image

According to the evolution mechanism of genome sequences and the analysis on primate genome sequences, we believed that the relative frequency sets of CG1 + CG2 8-mers and TA1 + TA2 8-mers are the best feature sets to construct evolution relationships of genome sequences. Based on the idea, we analyzed the two kinds of evolution relationships of insect genome sequences. In CG map, we found that Hymenoptera, Hemiptera, Lepidoptera and Diptera are clearly divided into four main clades. In main clade of Hymenoptera (green background), Formicidae (red lines), Braconidae (blue lines) and Apidae (black lines) are clearly divided into three single clades. In main clade of Diptera (apricot background), Drosophilidae (blue lines), Culicidae (black lines) and Tephritidae (red lines) are divided into three clades. In main clade of Hemiptera (red background), Aphididae family is clustered into a single clade. In main clade of Lepidoptera (blue background), the cluster relations are scattered. The species in four families are divided into eight clades. In a word, our results are consistent with the known evolution relationships of insects [56,57,58,59]. In TA map, we found that the evolution relationship is similar to that of CG map. However, there are some clear differences between them. First, seven species of Culicidae are not clustered in the main clade of Diptera and they formed a single clade. Second, five species of Braconidae are divided into two clades. Third, fifteen species of Lepidoptera were clustered into a main clade.

We considered that the differences are not caused by the two selected feature sets, it reflects the information differences between them. Combining previous study [42] and our analysis, the mutual inhibition relationship between CG- and TA-independent selection modes originated from the evolution pressure of an aerobic environment. In bacteria, archaea, fungi, plants and invertebrates, these species have to adopt the mutual inhibition mode to adapt to the aerobic environment. With the increase of evolution levels of animals and plants, the CG-independent selection intensity increases and the TA-independent selection intensity decreases. This indicates that the aerobic environment is the direct cause of the enhancement of CG-independent selection intensity. It means that the CG-independent selection mode is the driving force, and the TA-independent selection mode is changed passively under the mutual inhibition restraints. We considered that the CG 8-mers that represent active evolution must reflect the latest evolution information of genome sequences, and the TA 8-mers that represent passive evolution must contain the legacy evolution information of genome sequences in the early stage. Thus, there should be a time-lag effect between the two feature sets. The information of CG1 + CG2 8-mers characterizes the current evolution state of genome sequences, whereas the information of TA1 + TA2 8-mers characterizes the evolution state in the early stage.

According to our conclusion, CG map characterizes the current evolution state and TA map characterizes the past evolution state of insect genome sequences. Thus, the three differences between CG map and TA map reflect more intensive evolution information.

We can see that seven species of Culicidae are not clustered into their main clade of Diptera in TA map (Fig. 6B, apricot background and black lines). But they are clustered into their main clade of Diptera in CG map. In addition, five species of Braconidae are divided into two clades in TA map (Fig. 6B, green background and blue lines), and they clustered into their main clade of Hymenoptera in CG map. The results indicated that, in the early stage, the evolutionary environment of the seven species of Culicidae differ obviously from the other species of Diptera, it causes the evolution separation between them. Similarly, the difference of evolutionary environment in the early stage causes the evolution direction of the five species of Braconidae that differ obviously from the other species of Hymenoptera. As the evolutionary environment became more and more homogeneous, the evolution direction of these species was similar, then they were clustered into their main clade in current time. In addition, fifteen species are clustered into one main clade of Lepidoptera in TA map (Fig. 6B, blue background). But the fifteen species are divided into eight single clades in CG map (Fig. 6A, blue background). It indicated that the evolutionary environment of the fifteen species of Lepidoptera was similar and they were clustered in one clade in the early stage. Due to the evolutionary environment has changed, their evolution directions appeared difference. It causes that the cluster relations are scattered in main clade of Lepidoptera in current time.

According to the evolution mechanism of genome sequences, we proposed two types of feature sets. The information of CG1 + CG2 8-mers reflects the current evolution state, whereas the information of TA1 + TA2 8-mers reflects the evolution state in the early stage or reflects the legacy information in the early stage. It is interesting that there are two types of feature sets in genome sequences to characterize the evolution state in different periods of time.

Conclusion and discussion

Our previous study revealed the evolution mechanisms of genome sequences [42]. We analyzed the evolution mechanism further based on mammal, reptile and insect genome sequences. We found that the 8-mers containing CG or TA dinucleotide correlated directly, while the CG0 and TA0 8-mers correlate indirectly with the evolution of genome sequences. Therefore, we selected CG1 + CG2 8-mers and TA1 + TA2 8-mers as the feature sets to characterize the evolution relationships of genome sequences. Considering the equality and the preference of the 8-mers to contribute to the evolution of genome sequences, we proposed the relative frequency as the feature of the 8-mers. Therefore, we obtained two types of feature sets to characterize the evolution relationships. We found that the evolution mode of vertebrate genome sequences is dominated by CG-independent selection mode, the TA-independent selection mode disappears basically. Therefore, we constructed evolution relationships based on the relative frequency set of CG1 + CG2 8-mers in vertebrates, including mammals and reptiles, the results are consistent with the known conclusions. It indicated that the feature sets we select is universal across different species. Because the CG- and TA-independent selection modes are obvious in invertebrate genome sequences, two types of evolution relationships of insects were constructed by the relative frequency sets of both TA1 + TA2 8-mers and CG1 + CG2 8-mers. The two types of evolution relationships are similar. However, there are some differences between them. According to the active and passive evolution relations between the CG- and TA-independent selection modes, we considered that the feature of TA1 + TA2 8-mers reflects the evolution information of genome sequences in the early stages, and the feature of CG1 + CG2 8-mers reflects the evolution information of genome sequences in current time. We conjectured that the differences between the two types of evolution relationships could reflect the variation of evolution environment on the earth, the variation information should be reflected in the composition and structure of genome sequences. The evolution time interval displayed by the two feature sets and the evolution process in the time interval should be researched further.

We proposed a theoretical method for constructing evolution relationships based on the evolution mechanism of genome sequences. Our theoretical method involves two novel ideas. (1) The definite 8-mers were identified. In the case of the 8-mers, two sets of 24,991 8-mers were determined, which correlated directly with the evolution of genome sequences, and 40,545 8-mers that correlated indirectly with the evolution of genome sequences were eliminated from the total 65,536 8-mers. Our selection method is objective and solves the puzzling problem of arbitrariness in k-mers screening. (2) A more reliable evolution feature of 8-mers was proposed. The feature of the relative frequency reflects both the equality and the preference for the 8-mers to contribute to the evolution of genome sequences.

The evolution mechanism of genome sequences has species universality. The selected feature sets can be used to construct the evolution relationships of any species. We will further validate the reliability of the selected feature sets for all species from eukaryotes to prokaryotes. We also hope that researchers will participate in this validation work and establish standardized and universal methods to construct more objective evolution relationships at the genomic level.

Materials and methods

Dataset

The whole genome sequences and annotation information for 373 species were obtained from NCBI (https://www.ncbi.nlm.nih.gov/). Sex chromosomes were not included in this dataset. The species genome taxonomy were shown in Table 1, see Supplementary Tables S1, S2 and S3 for detailed information.

Table 1 Number of specie genome sequences
Full size table

Selection of k value in k-mers

For the k-mer spectral analysis, we chose k = 8. This choice was based on the following considerations. First, when k > 6, the statistical results showed that the distributions of the k-mer spectra of the genome sequences tended to be stable. Second, statistical significance should be ensured for the frequency of k-mer occurrences in the genome sequences. If k value is too large, the number of k-mer motifs (N = 4k) is too high, and the frequency of some k-mers is zero, resulting in the loss of statistical information. Beny Chor proposed a formula for minimum k value estimation: k = 0.7log4L, where L is the length of DNA sequence [39]. The k ≥ 8 for eukaryotic genome sequences and k ≥ 6 for prokaryotic genome sequences. Accordingly, we chose k = 8 for our study.

8-mer spectrum

For a given genome sequence, the frequency of each 8-mer was obtained using a window of 8 bp and step size of 1 bp. If the number of 8-mers that occurs i times is Ni, the 8-mer relative motif number (RMN) is defined as:

$$RMN=\frac{{N}_{i}}{{4}^{8}}$$

The distribution of RMN with 8-mers frequency is called the 8-mer spectrum of the genome sequence.

Classification method of 8-mers

After obtaining the frequency of each 8-mer in a genome sequence, we classified the 8-mer set into different subsets according to the compositional features of 8-mers. The 8-mers containing zero XY (X, Y = A, T, C, G) dinucleotide were classified into the XY0 subset, those containing one XY dinucleotide were classified into the XY1 subset, and those containing two or more XY dinucleotides were classified into the XY2 subset. Theoretically, there were 48=65,536 8-mers. When X ≠ Y, there were 40,545 8-mers in the XY0, 21,468 8-mer in the XY1, and 3523 8-mer in the XY2. When X = Y, the numbers of 8-mers in the three subsets were 44,631, 14,931 and 5974, respectively. This is known as the XY dinucleotide classification method. Thus, total 8-mers were divided into 16 XY classes. In each XY class, there were three XY 8-mer subsets called XYi (i = 0,1,2). Finally, we obtained the 8-mer spectra of 48 XYi subsets in each genome sequence.

Separability of 8-mer subset spectrum

The average of the spectral distribution is used to represent its distribution characteristics. In order to eliminate the influence of different genome sizes and show the relative position difference of 8-mer spectra in different subsets, the separability values (δXYi) were defined.

$${ \delta }_{\text{X}\text{Y}i}=\frac{\stackrel{-}{x}}{{\stackrel{-}{x}}_{\text{X}\text{Y}i}}$$

\(\stackrel{-}{x}\) is the average frequency of total 8-mers, called the random center. \({\stackrel{-}{x}}_{\text{X}\text{Y}i}\) is the average frequency of XYi 8-mers. δXYi represents the separability for the distribution position of XYi 8-mer spectrum relative to the random center. δXYi > 1 indicates that XYi 8-mer spectrum is located at the low-frequency end and is away from the random center. δXYi = 1 indicates that the location of the XYi 8-mer spectrum is the same as that of the random center.

In this definition, the separability value is independent of genome size and the absolute position of the subset spectrum; additionally, this parameter can compare not only the distribution difference of different 8-mer subsets within a genome sequence but also the distribution difference of 8-mer subsets among genome sequences.

Evolution mechanism of genome sequences

Our previous study analyzed the intrinsic regularity of the 8-mer spectra of 48 XYi subsets in 920 genome sequences that include bacteria, archaea, fungi, plants and animals [42]. We found the CG and TA independent selection modes in the genome sequences. The two independent selection modes had five properties. (1) Evolutionary independence: only CGi and TAi 8-mer spectra formed independent single-peak distributions. (2) Evolutionary separability: the distributions of CGi and TAi 8-mer spectra show distinct phenomena around the random center. This implies that the separability values were not equal to 1. (3) Evolutionary correlations: the separability values of CGi and TAi 8-mer spectra correlated closely with the evolution levels of genome sequences in animals and plants. We defined the characteristic quantity of separability as the independent selection intensity. (4) Evolutionary homoplasty: there was a positive correlation between the CG1 and CG2 independent selection intensities and between the TA1 and TA2 independent selection intensities; this indicates that their ways of evolution are similar. (5) There is a mutual inhibition relationship between the CG- and TA-independent selection modes (Fig. 1). Therefore, we proposed an evolution mechanism of genome sequences. The CG- and TA-independent selection intensities and the mutual inhibition relationship between them determine the evolution state of genome sequences.

Construction of evolution relationship

To construct the evolution relationship, the feature difference of the selected 8-mers was used to construct the distance of genome sequences. The distance (Dab) is defined as:

$${D}_{ab}=\sqrt{\frac{\sum _{i=1}^{N}{({x}_{ai}-{x}_{bi})}^{2}}{N}}$$

where a and b represent two genome sequences, xa.i. is the i-th 8-mer feature in sequence a, and xbi is the i-th 8-mer feature in sequence b. N is the number of selected 8-mers.

Matrix element Dab was used to construct the distance matrix for a given genome sequence group. Mega7 (http://www.megasoftware.net) was used to construct evolution relationships by employing the neighbor-joining method.

Availability of data and materials

All genome sequences and the corresponding annotation information were obtained from NCBI (https://www.ncbi.nlm.nih.dov/). See Supplementary Tables S1, S2 and S3 for detailed information.

References

  1. Chan BY, Kibler D. Using hexamers to predict cis-regulatory motifs in Drosophila. BMC Bioinform. 2005;6:262.

    Article  Google Scholar 

  2. Li QZ, Lin H. The recognition and prediction of sigma70 promoters in Escherichia coli K-12. J Theor Biol. 2006;242:135–41.

    Article  CAS  PubMed  Google Scholar 

  3. Lin H, Li QZ. Eukaryotic and prokaryotic promoter prediction using hybrid approach. Theor Biosci. 2011;130:91–100.

    Article  Google Scholar 

  4. Zhang Y, Wang XH, Kang L. A k-mer scheme to predict piRNAs and characterize locust piRNAs. Bioinformatics. 2011;27:771–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Hariharan R, Simon R, Pillai MR, Taylor TD. Comparative analysis of DNA word abundances in four yeast genomes using a novel statistical background model. PLoS One. 2013;8(3):e58038. https://doi.org/10.1371/journal.pone.0058038.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Guo SH, Deng EZ, Xu LQ, Ding H, Lin H, Chen W, Chou KC. iNuc-PseKNC: a sequence-based predictor for predicting nucleosome positioning in genomes with pseudo k-tuple nucleotide composition. Bioinformatics. 2014;30(11):1522–9. https://doi.org/10.1093/bioinformatics/btu083.

    Article  CAS  PubMed  Google Scholar 

  7. Csűrös M, Noé L, Kucherov G. Reconsidering the significance of genomic word frequencies. Trends Genet. 2007;23(11):543–6. https://doi.org/10.1016/j.tig.2007.07.008.

    Article  CAS  PubMed  Google Scholar 

  8. Tuller T, Chor B, Nelson N. Forbidden penta-peptides. Protein Sci. 2007;16:2251–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Hao B, Lee HC, Zhang SY. Fractals related to long DNA sequences and complete genomes. Chaos Solit Fractals. 2000;11:825–36.

    Article  CAS  Google Scholar 

  10. Subirana JA, Messeguer X. The most frequent short sequences in non-coding DNA. Nucleic Acids Res. 2009;38:1172–81.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Hampikian G, Andersen T. Absent sequences: nullomers and primes. Pac Symp Biocomput. 2007;12:355–66.

    Google Scholar 

  12. Yu HJ. Segmented K-mer and its application on similarity analysis of mitochondrial genome sequences. Gene. 2013;518(2):419–24. https://doi.org/10.1016/j.gene.2012.12.079.

    Article  CAS  PubMed  Google Scholar 

  13. Chae H, Park J, Lee SW, Nephew KP, Kim S. Comparative analysis using K-mer and K-flank patterns provides evidence for CpG island sequence evolution in mammalian genomes. Nucleic Acids Res. 2013;41(9):4783–91. https://doi.org/10.1093/nar/gkt144.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Yang Y, Nephew K, Kim S. A novel K-mer mixture logistic regression for methylation susceptibility modeling of CpG dinucleotides in human gene promoters. BMC Bioinform. 2012;13:S15.

    Article  CAS  Google Scholar 

  15. Chikhi R, Medvedev P. Informed and automated k-mer size selection for genome assembly. Bioinformatics. 2014;30(1):31–7. https://doi.org/10.1093/bioinformatics/btt310.

    Article  CAS  PubMed  Google Scholar 

  16. D’Haeseleer P. What are DNA sequence motifs? Nat Biotechnol. 2006;24:423–5.

    Article  PubMed  Google Scholar 

  17. Bina M, Wyss P, Lazarus SA, Shah SR, Ren W, Szpankowski W, Crawford GE, Park SP, Song XC. Discovering sequences with potential regulatory characteristics. Genomics. 2009;93(4):314–22. https://doi.org/10.1016/j.ygeno.2008.11.008.

    Article  CAS  PubMed  Google Scholar 

  18. Bina M, Wyss P, Ren W, Szpankowski W, Thomas E, Randhawa R, Reddy S, John PM, Pares-Matos EI, Stein A, Xu H, Lazarus SA. Exploring the characteristics of sequence elements in proximal promoters of human genes. Genomics. 2004;84(6):929–40. https://doi.org/10.1016/j.ygeno.2004.08.013.

    Article  CAS  PubMed  Google Scholar 

  19. Xie X, Lu J, Kulbokas EJ, Golub TR, Mootha V, Lindblad-Toh K, Lander ES, Kellis M. Systematic discovery of regulatory motifs in human promoters and 3’ UTRs by comparison of several mammals. Nature. 2005;434(7031):338–45. https://doi.org/10.1038/nature03441.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Fitch WM, Margoliash E. Construction of phylogenetic trees. Science. 1967;155:279–84.

    Article  CAS  PubMed  Google Scholar 

  21. Woese CR, Fox GE. Phylogenetic structure of the prokaryotic domain: the primary kingdoms. Proc Natl Acad Sci U S A. 1977;74(11):5088–90. https://doi.org/10.1073/pnas.74.11.5088.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Kamla V, Henrich B, Hadding U. Phylogeny based on elongation factor Tu reflects the phenotypic features of mycoplasmas better than that based on 16S rRNA. Gene. 1996;171(1):83–7. https://doi.org/10.1016/0378-1119(95)00884-5.

    Article  CAS  PubMed  Google Scholar 

  23. Kwok AY, Su SC, Reynolds RP, Bay SJ, Av-Gay Y, Dovichi NJ, Chow AW. Species identification and phylogenetic relationships based on partial HSP60 gene sequences within the genus Staphylococcus. Int J Jyst Bacteriol. 1999;49:1181–92.

    CAS  Google Scholar 

  24. Hirt RP, Logsdon JM, Healy B, Dorey MW, Doolittle WF, Embley TM. Microsporidia are related to Fungi: evidence from the largest subunit of RNA polymerase II and other proteins. Proc Natl Acad Sci U S A. 1999;96(2):580–5. https://doi.org/10.1073/pnas.96.2.580.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Woese CR, Olsen GJ, Ibba M, Söll D. Aminoacyl-tRNA synthetases, the genetic code, and the evolutionary process. Microbiol Mol Biol Rev. 2000;64(1):202–36. https://doi.org/10.1128/MMBR.64.1.202-236.2000.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Snel B, Bork P, Huynen MA. Genome phylogeny based on gene content. Nat Genet. 1999;21(1):108–10. https://doi.org/10.1038/5052.

    Article  CAS  PubMed  Google Scholar 

  27. Huynen M, Snel B, Bork P, Stiller J, Hall B, Gupta R, Soltys B, Doolittle W. Lateral gene transfer, genome surveys, and the phylogeny of Prokaryotes. Science. 1999;286:1443a.

    Article  Google Scholar 

  28. Wolf YI, Rogozin IB, Grishin NV, Tatusov RL, Koonin EV. Genome trees constructed using five different approaches suggest new major bacterial clades. BMC Evol Biol. 2001;1(1): 8. https://doi.org/10.1186/1471-2148-1-8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Qi J, Wang B, Hao BL. Whole proteome prokaryote phylogeny without sequence alignment: a K-string composition approach. J Mol Evol. 2004;58:1–11.

    Article  CAS  PubMed  Google Scholar 

  30. Wei H, Qi J, Hao B. Prokaryote phylogeny based on ribosomal proteins and aminoacyl tRNA synthetases by using the compositional distance approach. Sci China C Life Sci. 2004;47:313–21.

    Article  CAS  PubMed  Google Scholar 

  31. Qi J, Luo H, Hao B. CVTree: a phylogenetic tree reconstruction tool based on whole genomes. Nucleic Acids Res. 2004;32:W45-47.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Li M, Badger JH, Chen X, Kwong S, Kearney P, Zhang H. An information-based sequence distance and its application to whole mitochondrial genome phylogeny. Bioinformatics. 2001;17(2):149–54. https://doi.org/10.1093/bioinformatics/17.2.149.

    Article  CAS  PubMed  Google Scholar 

  33. Li W, Fang W, Ling L, Wang J, Xuan Z, Chen R. Phylogeny based on whole genome as inferred from Complete Information Set Analysis. J Biol Phys. 2002;28(3):439–47. https://doi.org/10.1023/A:1020316706928.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Nussinov R. Doublet frequencies in evolutionary distinct groups. Nucleic Acids Res. 1984;12(3):1749–63. https://doi.org/10.1093/nar/12.3.1749.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Karlin S, Cardon LR. Computational DNA sequence analysis. Annu Rev Microbiol. 1994;48(1):619–54. https://doi.org/10.1146/annurev.mi.48.100194.003155.

    Article  CAS  PubMed  Google Scholar 

  36. Gentles AJ, Karlin S. Genome-scale compositional comparisons in eukaryotes. Genome Res. 2001;11(4):540–6. https://doi.org/10.1101/gr.163101.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Chapus C, Dufraigne C, Edwards S, Giron A, Fertil B, Deschavanne P. Exploration of phylogenetic data using a global sequence analysis method. BMC Evol Biol. 2005;5(1):63. https://doi.org/10.1186/1471-2148-5-63.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Chen YH, Nyeo SL, Yeh CY. Model for the distributions of k-mers in DNA sequences. Phys Rev E. 2005;72(1):011908. https://doi.org/10.1103/PhysRevE.72.011908.

    Article  CAS  Google Scholar 

  39. Chor B, Horn D, Goldman N, Levy Y, Massingham T. Genomic DNA k-mer spectra: models and modalities. Genome Biol. 2009;10(10):R108. https://doi.org/10.1186/gb-2009-10-10-r108.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Bao T, Li H, Zhao XQ, Liu GQ. Predicting nucleosome binding motif set and analyzing their distributions around functional sites of human genes. Chromosome Res. 2012;20:685–98.

    Article  CAS  PubMed  Google Scholar 

  41. Zhou DL, Li H, Yang XX. Distributions of 8-mer frequency of appearence and the evolution diversity of 8-mer usage in DNA sequences of human chromosome 1. Acta Biophys Sin. 2015;31:63–4.

    CAS  Google Scholar 

  42. Yang ZH, Li H, Jia Y, Zheng Y, Meng H, Bao T, Li XL, Luo LF. Intrinsic laws of k-Mer spectra of genome sequences and evolution mechanism of genomes. BMC Evol Biol. 2020;20:157.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Matsui A, Rakotondraparany F, Munechika I, Hasegawa M, Horai S. Molecular phylogeny and evolution of prosimians based on complete sequences of mitochondrial DNAs. Gene. 2009;441:53–66.

    Article  CAS  PubMed  Google Scholar 

  44. Perelman P, Johnson WE, Roos C, Seuánez HN, Horvath JE, Moreira MA, Kessing B, Pontius J, Roelke M, Rumpler Y, et al. A molecular phylogeny of living primates. PLoS Genet. 2011;7:e1001342.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Finstermeier K, Zinner D, Brameier M, Meyer M, Kreuz E, Hofreiter M, Roos C. A mitogenomic phylogeny of living primates. PLoS One. 2013;8(7):e69504. https://doi.org/10.1371/journal.pone.0069504.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Pozzi L, Hodgson JA, Burrell AS, Sterner KN, Raaum RL, Disotell TR. Primate phylogenetic relationships and divergence dates inferred from complete mitochondrial genomes. Mol Phylogenet Evol. 2014;75:165–83. https://doi.org/10.1016/j.ympev.2014.02.023.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Montgelard C, Forty E, Arnal V, Matthee CA. Suprafamilial relationships among Rodentia and the phylogenetic effect of removing fast-evolving nucleotides in mitochondrial, exon and intron fragments. BMC Evol Biol. 2008;8(1):321. https://doi.org/10.1186/1471-2148-8-321.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Romanenko SA, Perelman PL, Trifonov VA, Graphodatsky AS. Chromosomal evolution in Rodentia. Heredity. 2012;108(1):4–16. https://doi.org/10.1038/hdy.2011.110.

    Article  CAS  PubMed  Google Scholar 

  49. Jones K, Purvis A, Maclarnon A, Bininda-Emonds ORP, Simmons NB. A phylogenetic Supertree of the bats (Mammalia: chiroptera). Bio Rev. 2002;77:223–59.

    Article  Google Scholar 

  50. Yu L, Li QW, Ryder OA, Zhang YP. Phylogenetic relationships within mammalian order Carnivora indicated by sequences of two nuclear DNA genes. Mol Phylogenet Evol. 2004;33(3):694–705. https://doi.org/10.1016/j.ympev.2004.08.001.

    Article  CAS  PubMed  Google Scholar 

  51. Yuan Y, Zhang YL, Zhang PJ, Liu C, Wang JH, Gao HY, Hoelzel AR, Seim I, Lv MQ, Lin ML, et al. Comparative genomics provides insights into the aquatic adaptations of mammals. Proc Natl Acad Sci U S A. 2021;118:e2106080118.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Spaulding M, O’Leary MA, Gatesy J. Relationships of Cetacea (artiodactyla) among mammals: increased taxon sampling alters interpretations of key fossils and character evolution. PLoS On. 2009;4:e7062.

    Article  Google Scholar 

  53. Li A, Wang JJ, Sun K, Wang SC, Zhao X, Wang TF, Xiong LY, Xu WH, Qiu L, Shang Y, et al. Two reference-quality sea snake genomes reveal their divergent evolution of adaptive traits and Venom systems. Mol Biol Evol. 2021;38:4867–83.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Sterli J. Phylogenetic relationships among extinct and extant turtles: the position of Pleurodira and the effects of the fossils on rooting crown-group turtles. Contrib to Zool. 2010;79:93–106.

    Article  Google Scholar 

  55. Ren YD, Zhang Q, Yan XH, Hou DM, Huang HW, Li CH, Rao DQ, Li YX. Genomic insights into the evolution of the critically endangered soft-shelled turtle Rafetus swinhoei. Mol Ecol Resour. 2022;22:1972–85.

    Article  CAS  PubMed  Google Scholar 

  56. Meusemann K, Reumont BMV, Simon S, Roeding F, Strauss S, Kück P, Ebersberger I, Walzl M, Pass G, Breuers S, et al. A phylogenomic approach to resolve the arthropod tree of life. Mol Biol Evol. 2010;27:2451–64.

    Article  CAS  PubMed  Google Scholar 

  57. Behura SK. Insect phylogenomics. Insect Mol Biol. 2015;24:403–11.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Song F, Li H, Jiang P, Zhou X, Liu J, Sun C, Vogler AP, Cai W. Capturing the phylogeny of Holometabola with mitochondrial genome data and Bayesian site-heterogeneous mixture models. Genome Biol Evol. 2016;8(5):1411–26. https://doi.org/10.1093/gbe/evw086.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Chesters D. The phylogeny of insects in the data-driven era. Syst Entomol. 2020;45:540–51.

    Article  Google Scholar 

Download references

Acknowledgements

We are very grateful for the computational resources provided by the Theoretical Biophysics Laboratory of Inner Mongolia University.

Funding

This work was supported by the National Natural Science Foundation of China (31860304). The funding agency played no role in research design, data collection, analysis and interpretation, and manuscript writing.

Author information

Authors and Affiliations

  1. Laboratory of Theoretical Biophysics, School of Physical Science and Technology, Inner Mongolia University, Hohhot, 010021, China

    Xiaolong Li, Hong Li, Yuan Wu & Mengchuan Zhang

  2. School of Economics and Management, Inner Mongolia University of Science and Technology, Baotou, 014010, China

    Zhenhua Yang

Authors
  1. Xiaolong Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hong Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhenhua Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yuan Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mengchuan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

L.H and L.X.L conceived and designed the study and critically revised the manuscript. L.X.L. and Y.Z.H. analyzed the data and drafted the manuscript. W.Y. and Z.M.C. helped in the study design. All authors contributed to the article and approved the submitted version.

Corresponding author

Correspondence to Hong Li.

Ethics declarations

Ethics approval and consent to participate

Not applicable to this study.

Consent for publication

Not applicable to this study.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1:  Supplementary Table S1.

 The information of Mammalia species and genomes used in this study.

Additional file 2:  Supplementary Table S2.

 The information of Reptilia species and genomes used in this study.

Additional file 3:  Supplementary Table S3.

 The information of Insecta species and genomes used in this study.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, X., Li, H., Yang, Z. et al. Exploring objective feature sets in constructing the evolution relationship of animal genome sequences. BMC Genomics 24, 634 (2023). https://doi.org/10.1186/s12864-023-09747-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12864-023-09747-x

Keywords

BMC Genomics

ISSN: 1471-2164

Contact us