The secondary structure profiles were computed using the CROSS (Computational Recognition of Secondary Structure) algorithm. CROSS is a neural network-based machine learning approach trained on experimental data, able to quickly profile large and complex molecules such as viral genomes without length restrictions. CROSS was already used to profile the complete HIV genome, also showing an AUC of 0.75 with experimental 'Selective 2′ Hydroxyl Acylation analyzed by Primer Extension' (SHAPE) data 21 . For a comprehensive analysis, we used the Global Score model, considering nucleotides with a score >0 as double-stranded, and <0 as single-stranded. For the purpose of computing the structural content of a complete genome (i.e., % double-stranded nucleotides), the total number of nucleotides with a score >0 was averaged for the total length of each genome. For the plots showing the complete secondary structure of dengue regions, we used the MFE structure computed using RNAfold 22 .
To analyse the protein binding motifs in the viral genomes, we selected 5-mer motifs from the table S3 of Dominguez et al. 23 . All of the 520 possible redundant motifs (270 nonredundant) were selected for further analysis. We scanned for the motifs on the complete genomes of the different strains, selecting only perfect matches. The number of motifs normalized by the average genome length of the different species of viruses was used to define a score for the number of potential interactions with proteins, according to the following formula:
where m is the number of exact motifs found in a genome, and avg(n) is the average length of the genome of a specific species.
We also computed high-throughput predictions against the human proteome using the catRAPID Omics algorithm 24 , which estimates the binding propensity between proteins and RNA by combining secondary structure, hydrogen bonding and van der Waals contributions.
The algorithms computed more than 2 millions interactions between viral genomes and human proteins. The Discriminative Power (DP) was used to progressively filtering for strong interactions. The Discriminative Power (DP) ranges from 0 to 1, where DP values above 0.5
indicate that the interaction is likely to take place.
The structural content and the averaged number of binding domains were employed to build dendrograms. To this end, we computed the Euclidean distance between the values associated with each virus using statistical software R. We then used the hclust function based on a ward.D2 module to build the dendrograms according to the hierarchical clustering.
We used hundreds of profiles generated using CROSS to build secondary structure consensus profiles for Zika and Chikungunya. To build the consensus profiles we selected non-overlapping windows of 50 nucleotides across all the genomes of each species, and we averaged CROSS propensity scores for each window in all the genomes available. To avoid problems due to the different lengths of the genomes, we limited the sliding window till the average length of the genomes of that specific species (Table 1) . Regions with a score >0 are double-stranded regions in agreement in all the genomes of a species, while <0 for single-stranded consensus regions.
Here, we analysed, for the first time to our knowledge, the secondary structure profiles of the complete genomes of more than 7500 ss-RNA viruses ( To analyse Dengue secondary structure, we selected one strain for each serotype, focusing on strains that were widely used in previous publications 25 . In general, the four serotypes show significant differences in sequence, with around 65-70% sequence similarity 5 . Their secondary structure also shows notable differences ( Figure 1 ). For example, the 3'
UTR of DENV-1 shows a peculiar structural valley, compared to the others. Interestingly, DENV-1 and DENV-2 share the highest structural peak around 6000 nucleotides, while DENV-3 and DENV-4 also have the highest structural peak in common, but at position 4000.
We further expanded our analysis to cover the 4 different serotypes of the dengue virus, comprising ~4000 genomes ( Figure 2 ; Table 1 ). The analysis revealed that DENV-2 and DENV-3 are less structured than DENV-1 and DENV-4. It is well known that a lower amount of secondary structure can improve the translation efficiency 26 , suggesting that DENV-2 and DENV-3 could be more efficiently translated in the host cell.
To validate our approach, we compared our predictions with SHAPE experiments performed on Dengue genomes 16 . Using the Area Under the ROC curve (AUC) to distinguish ranked SHAPE reactivities, we obtained an AUC ranging from 0.75 to 0.85 for DENV-2 and DENV-1 ( Supplementary Figure 1a, b) . This further proves the power of our in silico approach, which can generate thousands of secondary structure profiles with high performances on experimental data.
Interestingly, dengue serotypes tend to be less structured than other flaviviruses, such as West Nile Fever, Yellow Fever, Tick-borne Encephalitis, and Japanese Encephalitis ( Figure   2 ). Even if not properly a VHF, we also used as comparison Zika, due to the similarities with
Dengue not only in the vector (Aedes aegypti), but also in terms of secondary structure domains 16 . Interestingly, while Tick-borne Encephalitis and Zika genomes are more structured (average double-stranded nucleotides > 56%), Nile Fever and Japanese
Encephalitis have a similar structural distribution, especially since they are also close in the species tree 27 . To further compare and classify the secondary structure of other VHFs outside of flavivirus, we also included >500 genomes of Ebola and Chikungunya ( Figure 2 , Table 1 ). The analysis revealed that the other viruses are significantly more structured than Dengue (mean structural content for Flaviviruses and Dengue serotypes is 0.55, 0.51, respectively; Kolmogorov-Smirnov < 2.2e-16), with the exception of Ebola, which is not only the most deadly virus in our dataset (50% mortality rate according to WHO; 28 ), but also one of the less structured (mean structural content 0.50).
To further study the secondary structure content for the >7500 viral genomes, we also analysed the 5' and 3' UTRs (first 1000 nt considered 5' UTR; last 1000 nt considered 3' UTR; Figure 3a , b). DENV-3 is the only serotype with both UTRs more structured than the entire genome (5' UTR= 0.55 and 3' UTR=0.53; Figure 2 ), while DENV-1 has a more structured 5' UTR (structural content = 0.53). This result is in line with the experimental Parallel Analysis of RNA Structure (PARS) data coming from human RNAs, where the UTRs were more structured than the CDS 29 . This suggests that some viruses tend to mimic the secondary structure of human mRNAs to be efficiently translated by the cellular machinery 30 . This is also further supported in Dengue, where a complex structure at the 3' UTR was shown to mimic the absent polyA, to enhance translation 31 Figure 3a ). Finally, Ebola, DENV-1, and DENV-3 exhibit a more structured 5' UTR, especially when compared with DENV-2, DENV-4 and Japanese Encephalitis, which tend to be more structured ( Figure   3c ).
The overall similarities and differences in structure are an additional feature that could be employed to characterise the different viruses. For the next step, the structural content (mean of the % of double-stranded nucleotides for all the viral genomes) in a specific species was used to hierarchically group the 11 different viruses (Methods: Hierarchical clustering; Table 1 The structural content revealed interesting clustering of the viruses. For example, while having a different genome sequence, Dengue viruses also cluster together with Ebola, since they share a less structured genome. Interestingly, DENV-2, the most virulent and spread serotype 7, 8 , is similar in structural content to Ebola, probably the most lethal viral hemorrhagic fever 28 . According to its structural content, Zika is also part of the sub-cluster, together with West Nile Fever and Japanese Encephalitis. The mosquito-transmitted Yellow Fever and Chikungunya form a cluster, indicating that their structural content is similar. This is partially in agreement with the VHFs tree 27 . Interestingly, since Tick-borne Encephalitis is more structured than any of these viruses, it forms an outlier. This is not a surprise, since it was previously shown that the secondary structure of mosquito-and tick-borne flaviviruses s are more different, especially in the 3' UTR 32 . To conclude, these results indicate that the level of secondary structure inside a viral genome can be used as a metric to build a tree of similarities, which could be further employed to classify viruses.
Interaction between viral genomes and human host proteins can be used to classify showing the highest number of motifs, followed by DENV-4 (Supplementary Figure 1) .
Similarly to the structural content analysis above, we used the number of protein binding domains to classify the viruses. To further understand how the connection between structure and interaction with proteins can classify viruses, we compared the resulting trees ( Figure 4) . Interestingly, the Dengue cluster is almost perfectly maintained, except that, for the number of protein interactions, DENV-2 is more similar to Chikungunya than Ebola, which in turn is more related to Yellow Fever. Furthermore, clustering of West Nile Fever, Japanese Encephalitis and Zika is partially maintained when using structure and interaction with proteins. To conclude, by analyzing thousands of different viral genomes, we identified specific clusters, conserved both in terms of secondary structure content and the potential number of interactions with proteins.
Since both the structural content and the number of binding motifs could be used to classify the viruses, we hypothesized that there is a correlation between these two features. We found an overall high anti-correlation (r=-0.74; p-value < 2.2e-16) between the number of protein-binding motifs and the structural content in Dengue, meaning that less structured
Dengue viruses tend to bind more proteins (Figure 5a) . Interestingly, the different dengue serotypes cluster together according to their structure and the interaction with proteins Zika consensus profile and one of the most highly-contacted by proteins is the one encoding for the nonstructural protein 3 (NS3), a helicase essential for viral replication 35 . We speculate that this region needs a very specific structure in order to be highly regulated by proteins. Conversely, the most contacted regions for Chikungunya consensus secondary structure profile fall into highly unstructured regions (Figure 6c ). The least structured region of Chikungunya consensus secondary structure profile, and one of the most regulated by proteins, encode for the structural protein E3 36 .
To extend and further validate this result, we checked the interactions between the most structured Zika and Chikungunya viruses and >1000 human RBPs. After selecting the 10 most and least structural genomes of Zika and Chikungunya, we used the catRAPID algorithm 24 to predict > 4x10 6 protein interactions between the genomes and human proteins (Methods: Protein-RNA interactions). Interestingly, the highly-structured Zika viruses have stronger and more frequent interactions with proteins, reaching x10 more strong interactions with proteins, compared to Chikungunya (Supplementary Table 1 ). This result supports our hypothesis that Zika interacts with human proteins mainly using doublestranded regions when compared to Chikungunya.
We also studied the geographical connection between Dengue serotypes and the secondary structure content. We found that the African DENV-1 is predicted to be more structured than the other serotypes, even when compared with the Asian strains (Supplementary Figure 23 Kolmogorov-Smirnov = 0.001), while on the contrary, Asian DENV-3 is more structured than the African strains (Kolmogorov-Smirnov = 0.07; Supplementary Figure 3 ).
The correlation between binding motifs and secondary structure at the geographical level portrays a quite complex scenario (Figure 7) . The clearest clusters are identifiable for DENV-3, where the South-American strains are not only less structured, but also highly interacting with proteins. DENV-3 from South-America is also more distant from the Asian strains (Euclidean distance centroids x1000=5), compared, for example, with DENV-1 (Euclidean distance centroids x1000=1).
The genomes of viral hemorrhagic fevers show different level of secondary structure, especially structured in the UTR regions 14, 16 . This secondary structure is thought to be needed for different viral mechanisms, such as packaging and egression [11] [12] [13] . However, a comprehensive secondary structural landscape for their genomes was lacking. In our work, we computationally profiled and analysed the secondary structure profiles of more than 7500 complete viral genomes, including almost 4000 dengue samples, and 3500 other viral hemorrhagic fever-causing viruses. By studying the structural profiles, we observed that Dengue viruses are predicted to be less structured compared to viruses such as Zika, Yellow Fever, and West Nile Fever ( Figure 2 ). Conversely, Dengue serotypes still tend to retain structured UTRs, probably to be efficiently translated by the cellular machinery, similarly to human mRNAs 26, 29 . Single-stranded regions could be necessary to confer flexibility to the viral genomes, since flaviviruses need a high level of structural plasticity to undergo conformational changes during their life cycle, including circularization 37 .
We also identified a correlation between the secondary structure and the number of protein binding domains, implying that the secondary structure is employed to regulate potential binding with proteins, as observed for human RNAs 38 . For viruses, the situation is more complex, as we observed a significantly positive (Tick-borne Encephalitis and Zika) and negative (Chikungunya, DENV-3, and DENV-4) relationship between secondary structure and the potential interaction with proteins. We speculate that the opposite behaviors of the viruses are caused by the fundamental differences between viruses. For example, Zika (positive RNA genome, positive correlation between secondary structure and interaction with proteins) and Chikungunya (negative genome, negative correlation) belong to different viral families (Flavivirus and Togavirus respectively), and have a completely different capsid. Also while Zika shows a sequence similarity of 56% with Yellow Fever and Japanese encephalitis, it shows only 1.3% sequence identity with Chikungunya 39 .
We also analysed the secondary structure at a geographical level, showing that DENV-3 strains from South-America and Asia have different patterns in their structure and the potential interaction with proteins, especially when compared with DENV-1 and DENV-2 ( Figure 7) . Interestingly, this is in line with DENV-3 being the youngest serotype and the only one with a proposed origin not in Asia but in America 40 . This could explain the niched behaviour of DENV-3 in terms of structure and protein interactions, as well as supporting a possible independent origin of Asia and American DENV-3. Interestingly, DENV-4 shows a similar trend, but there are too few samples available to explain its evolution.
To our knowledge, this is the first study that employed secondary structural content and the presence of protein-binding domains to build similarity trees between VHFs. The secondary structure and interaction with proteins can be used to cluster the viruses in agreement with previous phylogenetic trees, such as Dengue serotypes, and Japanese
Encephalitis with West Nile Fever. Conversely, some relationships are surprising, as for example, DENV-2 is closer to Ebola when the secondary structure is used to establish similarity, but not when using the interaction with proteins. This result suggests how different measures, especially the secondary structure content, could be used to classify further and characterise different classes of viruses.
Our massive computational analysis provided novel results regarding the secondary structure and the interaction with human proteins, not only for Dengue serotypes but also for other viral hemorrhagic fevers. We envision that these approaches can be used by the scientific community to classify further and characterise these complex viruses. 