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The UK Biobank (UKB), a large-scale biomedical database that includes demographic and electronic health record data for more than half a million ethnically diverse participants, is a potentially valuable resource for the study of health disparities. However, publicly accessible databases that catalog health disparities in the UKB do not exist. We developed the UKB Health Disparities Browser with the aims of (i) facilitating the exploration of the landscape of health disparities in the UK and (ii) directing the attention to areas of disparities research that might have the greatest public health impact. Health disparities were characterized for UKB participant groups defined by age, country of residence, ethnic group, sex and socioeconomic deprivation. We defined disease cohorts for UKB participants by mapping participant International Classification of Diseases, Tenth Revision (ICD-10) diagnosis codes to phenotype codes (phecodes). For each of the population attributes used to define population groups, disease percent prevalence values were computed for all groups from phecode case–control cohorts, and the magnitude of the disparities was calculated by both the difference and ratio of the range of disease prevalence values among groups to identify high- and low-prevalence disparities. We identified numerous diseases and health conditions with disparate prevalence values across population attributes, and we deployed an interactive web browser to visualize the results of our analysis: https://ukbatlas.health-disparities.org. The interactive browser includes overall and group-specific prevalence data for 1513 diseases based on a cohort of >500 000 participants from the UKB. Researchers can browse and sort by disease prevalence and prevalence differences to visualize health disparities for each of the five population attributes, and users can search for diseases of interest by disease names or codes. Database URL https://ukbatlas.health-disparities.org/

Objective The goal of this study was to investigate the relationship between comorbidities and ethnic health disparities in a diverse, cosmopolitan population. Materials and Methods We used the UK Biobank (UKB), a large progressive cohort study of the UK population. Study participants self-identified with 1 of 5 ethnic groups and participant comorbidities were characterized using the 31 disease categories captured by the Elixhauser Comorbidity Index. Ethnic disparities in comorbidities were quantified as the extent to which disease prevalence within categories varies across ethnic groups and the extent to which pairs of comorbidities co-occur within ethnic groups. Disease-risk factor comorbidity pairs were identified where one comorbidity is known to be a risk factor for a co-occurring comorbidity. Results The Asian ethnic group shows the greatest average number of comorbidities, followed by the Black and then White groups. The Chinese group shows the lowest average number of comorbidities. Comorbidity prevalence varies significantly among the ethnic groups for almost all disease categories, with diabetes and hypertension showing the largest differences across groups. Diabetes and hypertension both show ethnic-specific comorbidities that may contribute to the observed disease prevalence disparities. Discussion These results underscore the extent to which comorbidities vary among ethnic groups and reveal group-specific disease comorbidities that may underlie ethnic health disparities. Conclusion The study of comorbidity distributions across ethnic groups can be used to inform targeted group-specific interventions to reduce ethnic health disparities.

Genome-enabled approaches to molecular epidemiology have become essential to public health agencies and the microbial research community. We developed the algorithm STing to provide turn-key solutions for molecular typing and gene detection directly from next generation sequence data of microbial pathogens. Our implementation of STing uses an innovative k-mer search strategy that eliminates the computational overhead associated with the time-consuming steps of quality control, assembly, and alignment, required by more traditional methods. We compared STing to six of the most widely used programs for genome-based molecular typing and demonstrate its ease of use, accuracy, speed and efficiency. STing shows superior accuracy and performance for standard multilocus sequence typing schemes, along with larger genome-scale typing schemes, and it enables rapid automated detection of antimicrobial resistance and virulence factor genes. STing determines the sequence type of traditional 7-gene MLST with 100% accuracy in less than 10 seconds per isolate. We hope that the adoption of STing will help to democratize microbial genomics and thereby maximize its benefit for public health.

Summary Next-generation sequencing (NGS) enables reliable detection of resistance mutations in minority variants of human immunodeficiency virus type 1 (HIV-1). There is paucity of evidence for the association of minority resistance to treatment failure, and this requires evaluation. However, the tools for analyzing HIV-1 drug resistance (HIVDR) testing data are mostly web-based which requires uploading data to webservers. This is a challenge for laboratories with internet connectivity issues and instances with restricted data transfer across networks. We present QuasiFlow, a pipeline for reproducible analysis of NGS-based HIVDR testing data across different computing environments. Since QuasiFlow entirely depends on command-line tools and a local copy of the reference database, it eliminates challenges associated with uploading HIV-1 NGS data onto webservers. The pipeline takes raw sequence reads in FASTQ format as input and generates a user-friendly report in PDF/HTML format. The drug resistance scores obtained using QuasiFlow were 100% and 99.12% identical to those obtained using web-based HIVdb program and HyDRA web respectively at a mutation detection threshold of 20%. Availability and implementation QuasiFlow and corresponding documentation are publicly available at https://github.com/AlfredUg/QuasiFlow. The pipeline is implemented in Nextflow and requires regular updating of the Stanford HIV drug resistance interpretation algorithm. Supplementary information Supplementary data are available at Bioinformatics Advances online.

Biobank projects around the world are generating genomic data for many thousands and even millions of individuals. Computational methods are needed to handle these massive data sets, including tools for genetic ancestry (GA) inference. Current methods for GA inference are generally accurate, but they are slow and do not scale to biobank-size genomic datasets. Here we present Rye – a new algorithm for GA inference at biobank scale. We compare the accuracy and runtime performance of Rye to the widely used RFMix and ADMIXTURE programs, and we apply it to a dataset of 488,221 genome-wide variant samples from the UK Biobank. Rye infers GA based on principal component analysis (PCA) of genomic variant samples from ancestral reference populations and query individuals. The algorithm’s accuracy is powered by Metropolis-Hastings optimization and its speed is provided by non-negative least squares (NNLS) regression. Rye produces highly accurate GA estimates for three-way admixed populations – African, European, and Native American – compared to RFMix and ADMIXTURE (R2 = 0.998 – 1.00), and shows 50x runtime improvement compared to ADMIXTURE on the UK Biobank dataset. Rye analysis of UK Biobank samples demonstrates how it can be used to infer GA at different levels of relatedness. We discuss user consideration and options for the use of Rye; the program and its documentation are distributed on the GitHub repository: https://github.com/healthdisparities/rye.

Human populations from around the world show striking phenotypic variation across a wide variety of traits. Genome-wide association studies (GWAS) are used to uncover genetic variants that influence the expression of heritable human traits; accordingly, population-specific distributions of GWAS-implicated variants may shed light on the genetic basis of human phenotypic diversity. With this in mind, we developed the GlobAl Distribution of GEnetic Traits web server (GADGET http://gadget.biosci.gatech.edu). The GADGET web server provides users with a dynamic visual platform for exploring the relationship between worldwide genetic diversity and the genetic architecture underlying numerous human phenotypes. GADGET integrates trait-implicated single nucleotide polymorphisms (SNPs) from GWAS, with population genetic data from the 1000 Genomes Project, to calculate genome-wide polygenic trait scores (PTS) for 818 phenotypes in 2504 individual genomes. Population-specific distributions of PTS are shown for 26 human populations across 5 continental population groups, with traits ordered based on the extent of variation observed among populations. Users of GADGET can also upload custom trait SNP sets to visualize global PTS distributions for their own traits of interest.

Receptor Activity Modifying Proteins (RAMPs) serve as accessory proteins that modulate the signaling activities of G-Protein Coupled Receptors (GPCRs). RAMPs function by interacting with the N-termini and transmembrane domains of GPCRs, and the receptor phenotypes of the resulting complexes are determined by the specific isoform of the interacting RAMPs. RAMPs were discovered in 1998, and since that time the number of known RAMP-GPCR interactions has steadily increased; RAMPs are now known to interact with nearly every member of the class ‘B’, Secretin receptor family of peptide-binding GPCRs as well as some members of the class ‘A’ and ‘C’ peptide-binding GPCRs. Given the steadily increasing number of known RAMP–GPCR interactions, phenotypes and functions, there is a pressing need for a central resource dedicated to their storage, prediction and dissemination. We have developed a web application and database—RampDB—with the goal of addressing this need. RampDB consists of a custom RAMP–GPCR–ligand database integrated with a search utility, which together facilitate the exploration and analysis of RAMP interactions. The RampDB search utility allows users to explore known RAMP interactions, or to predict novel interactions, via either protein sequence (bioinformatic) or ligand (chemoinformatic) queries. The underlying architecture of RampDB was designed using best database practices in order to enable rapid retrieval of search results, automated updates and the seamless incorporation of additional features. Database URL: http://rampdb.biology.gatech.edu

We report on the development of an unsupervised algorithm for the genome-wide discovery and analysis of chromatin signatures. Our Chromatin-profile Alignment followed by Tree-clustering algorithm (ChAT) employs dynamic programming of combinatorial histone modification profiles to identify locally similar chromatin sub-regions and provides complementary utility with respect to existing methods. We applied ChAT to genomic maps of 39 histone modifications in human CD4(+) T cells to identify both known and novel chromatin signatures. ChAT was able to detect chromatin signatures previously associated with transcription start sites and enhancers as well as novel signatures associated with a variety of regulatory elements. Promoter-associated signatures discovered with ChAT indicate that complex chromatin signatures, made up of numerous co-located histone modifications, facilitate cell-type specific gene expression. The discovery of novel L1 retrotransposon-associated bivalent chromatin signatures suggests that these elements influence the mono-allelic expression of human genes by shaping the chromatin environment of imprinted genomic regions. Analysis of long gene-associated chromatin signatures point to a role for the H4K20me1 and H3K79me3 histone modifications in transcriptional pause release. The novel chromatin signatures and functional associations uncovered by ChAT underscore the ability of the algorithm to yield novel insight on chromatin-based regulatory mechanisms.

Mammalian genomes encode numerous cis-natural antisense transcripts (cis-NATs). The extent to which these cis-NATs are actively regulated and ultimately functionally relevant, as opposed to transcriptional noise, remains a matter of debate. To address this issue, we analyzed the chromatin environment and RNA Pol II binding properties of human cis-NAT promoters genome-wide. Cap analysis of gene expression data were used to identify thousands of cis-NAT promoters, and profiles of nine histone modifications and RNA Pol II binding for these promoters in ENCODE cell types were analyzed using chromatin immunoprecipitation followed by sequencing (ChIP-seq) data. Active cis-NAT promoters are enriched with activating histone modifications and occupied by RNA Pol II, whereas weak cis-NAT promoters are depleted for both activating modifications and RNA Pol II. The enrichment levels of activating histone modifications and RNA Pol II binding show peaks centered around cis-NAT transcriptional start sites, and the levels of activating histone modifications at cis-NAT promoters are positively correlated with cis-NAT expression levels. Cis-NAT promoters also show highly tissue-specific patterns of expression. These results suggest that human cis-NATs are actively transcribed by the RNA Pol II and that their expression is epigenetically regulated, prerequisites for a functional potential for many of these non-coding RNAs.

Boundary elements partition eukaryotic chromatin into active and repressive domains, and can also block regulatory interactions between domains. Boundary elements act via diverse mechanisms making accurate feature-based computational predictions difficult. Therefore, we developed an unbiased algorithm that predicts the locations of human boundary elements based on the genomic distributions of chromatin and transcriptional states, as opposed to any intrinsic characteristics that they may possess. Application of our algorithm to ChIP-seq data for histone modifications and RNA Pol II-binding data in human CD4(+) T cells resulted in the prediction of 2542 putative chromatin boundary elements genome wide. Predicted boundary elements display two distinct features: first, position-specific open chromatin and histone acetylation that is coincident with the recruitment of sequence-specific DNA-binding factors such as CTCF, EVI1 and YYI, and second, a directional and gradual increase in histone lysine methylation across predicted boundaries coincident with a gain of expression of non-coding RNAs, including examples of boundaries encoded by tRNA and other non-coding RNA genes. Accordingly, a number of the predicted human boundaries may function via the synergistic action of sequence-specific recruitment of transcription factors leading to non-coding RNA transcriptional interference and the blocking of facultative heterochromatin propagation by transcription-associated chromatin remodeling complexes.

Independent lines of investigation have documented effects of both transposable elements (TEs) and gene length (GL) on gene expression. However, TE gene fractions are highly correlated with GL, suggesting that they cannot be considered independently. We evaluated the TE environment of human genes and GL jointly in an attempt to tease apart their relative effects. TE gene fractions and GL were compared with the overall level of gene expression and the breadth of expression across tissues. GL is strongly correlated with overall expression level but weakly correlated with the breadth of expression, confirming the selection hypothesis that attributes the compactness of highly expressed genes to selection for economy of transcription. However, TE gene fractions overall, and for the L1 family in particular, show stronger anticorrelations with expression level than GL, indicating that GL may not be the most important target of selection for transcriptional economy. These results suggest a specific mechanism, removal of TEs, by which highly expressed genes are selectively tuned for efficiency. MIR elements are the only family of TEs with gene fractions that show a positive correlation with tissue-specific expression, suggesting that they may provide regulatory sequences that help to control human gene expression. Consistent with this notion, MIR fractions are relatively enriched close to transcription start sites and associated with coexpression in specific sets of related tissues. Our results confirm the overall relevance of the TE environment to gene expression and point to distinct mechanisms by which different TE families may contribute to gene regulation.

Motivation: Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) is widely used in biological research. ChIP-seq experiments yield many ambiguous tags that can be mapped with equal probability to multiple genomic sites. Such ambiguous tags are typically eliminated from consideration resulting in a potential loss of important biological information. Results: We have developed a Gibbs sampling-based algorithm for the genomic mapping of ambiguous sequence tags. Our algorithm relies on the local genomic tag context to guide the mapping of ambiguous tags. The Gibbs sampling procedure we use simultaneously maps ambiguous tags and updates the probabilities used to infer correct tag map positions. We show that our algorithm is able to correctly map more ambiguous tags than existing mapping methods. Our approach is also able to uncover mapped genomic sites from highly repetitive sequences that can not be detected based on unique tags alone, including transposable elements, segmental duplications and peri-centromeric regions. This mapping approach should prove to be useful for increasing biological knowledge on the too often neglected repetitive genomic regions. Availability: http://esbg.gatech.edu/jordan/software/map Contact: king.jordan@biology.gatech.edu Supplementary Information: Supplementary data are available at Bioinformatics online.

Motivation: New sequencing technologies have accelerated research on prokaryotic genomes and have made genome sequencing operations outside major genome sequencing centers routine. However, no off-the-shelf solution exists for the combined assembly, gene prediction, genome annotation and data presentation necessary to interpret sequencing data. The resulting requirement to invest significant resources into custom informatics support for genome sequencing projects remains a major impediment to the accessibility of high-throughput sequence data. Results: We present a self-contained, automated high-throughput open source genome sequencing and computational genomics pipeline suitable for prokaryotic sequencing projects. The pipeline has been used at the Georgia Institute of Technology and the Centers for Disease Control and Prevention for the analysis of Neisseria meningitidis and Bordetella bronchiseptica genomes. The pipeline is capable of enhanced or manually assisted reference-based assembly using multiple assemblers and modes; gene predictor combining; and functional annotation of genes and gene products. Because every component of the pipeline is executed on a local machine with no need to access resources over the Internet, the pipeline is suitable for projects of a sensitive nature. Annotation of virulence-related features makes the pipeline particularly useful for projects working with pathogenic prokaryotes. Availability and implementation: The pipeline is licensed under the open-source GNU General Public License and available at the Georgia Tech Neisseria Base (http://nbase.biology.gatech.edu/). The pipeline is implemented with a combination of Perl, Bourne Shell and MySQL and is compatible with Linux and other Unix systems. Contact: king.jordan@biology.gatech.edu Supplementary information: Supplementary data are available at Bioinformatics online.

The Meningococcus Genome Informatics Platform (MGIP) is a suite of computational tools for the analysis of multilocus sequence typing (MLST) data, at http://mgip.biology.gatech.edu. MLST is used to generate allelic profiles to characterize strains of Neisseria meningitidis, a major cause of bacterial meningitis worldwide. Neisseria meningitidis strains are characterized with MLST as specific sequence types (ST) and clonal complexes (CC) based on the DNA sequences at defined loci. These data are vital to molecular epidemiology studies of N. meningitidis, including outbreak investigations and population biology. MGIP analyzes DNA sequence trace files, returns individual allele calls and characterizes the STs and CCs. MGIP represents a substantial advance over existing software in several respects: (i) ease of use--MGIP is user friendly, intuitive and thoroughly documented; (ii) flexibility--because MGIP is a website, it is compatible with any computer with an internet connection, can be used from any geographic location, and there is no installation; (iii) speed--MGIP takes just over one minute to process a set of 96 trace files; and (iv) expandability--MGIP has the potential to expand to more loci than those used in MLST and even to other bacterial species.

Motivation: Endogenous retrovirus (ERV) elements have been shown to contribute promoter sequences that can initiate transcription of adjacent human genes. However, the extent to which retroviral sequences initiate transcription within the human genome is currently unknown. We analyzed genome sequence and high-throughput expression data to systematically evaluate the presence of retroviral promoters in the human genome. Results: We report the existence of 51 197 ERV-derived promoter sequences that initiate transcription within the human genome, including 1743 cases where transcription is initiated from ERV sequences that are located in gene proximal promoter or 5′ untranslated regions (UTRs). A total of 114 of the ERV-derived transcription start sites can be demonstrated to drive transcription of 97 human genes, producing chimeric transcripts that are initiated within ERV long terminal repeat (LTR) sequences and read-through into known gene sequences. ERV promoters drive tissue-specific and lineage-specific patterns of gene expression and contribute to expression divergence between paralogs. These data illustrate the potential of retroviral sequences to regulate human transcription on a large scale consistent with a substantial effect of ERVs on the function and evolution of the human genome. Contact: king.jordan@biology.gatech.edu Supplementary information: Supplementary data are available at Bioinformatics online.

We sought to evaluate the extent of the contribution of transposable elements (TEs) to human microRNA (miRNA) genes along with the evolutionary dynamics of TE-derived human miRNAs. We found 55 experimentally characterized human miRNA genes that are derived from TEs, and these TE-derived miRNAs have the potential to regulate thousands of human genes. Sequence comparisons revealed that TE-derived human miRNAs are less conserved, on average, than non-TE-derived miRNAs. However, there are 18 TE-derived miRNAs that are relatively conserved, and 14 of these are related to the ancient L2 and MIR families. Comparison of miRNA vs. mRNA expression patterns for TE-derived miRNAs and their putative target genes showed numerous cases of anti-correlated expression that are consistent with regulation via mRNA degradation. In addition to the known human miRNAs that we show to be derived from TE sequences, we predict an additional 85 novel TE-derived miRNA genes. TE sequences are typically disregarded in genomic surveys for miRNA genes and target sites; this is a mistake. Our results indicate that TEs provide a natural mechanism for the origination miRNAs that can contribute to regulatory divergence between species as well as a rich source for the discovery of as yet unknown miRNA genes.

The role of natural selection in biology is well appreciated. Recently, however, a critical role for physical principles of network self-organization in biological systems has been revealed. Here, we employ a systems level view of genome-scale sequence and expression data to examine the interplay between these two sources of order, natural selection and physical self-organization, in the evolution of human gene regulation. The topology of a human gene coexpression network, derived from tissue-specific expression profiles, shows scale-free properties that imply evolutionary self-organization via preferential node attachment. Genes with numerous coexpressed partners (the hubs of the coexpression network) evolve more slowly on average than genes with fewer coexpressed partners, and genes that are coexpressed show similar rates of evolution. Thus, the strength of selective constraints on gene sequences is affected by the topology of the gene coexpression network. This connection is strong for the coding regions and 3' untranslated regions (UTRs), but the 5' UTRs appear to evolve under a different regime. Surprisingly, we found no connection between the rate of gene sequence divergence and the extent of gene expression profile divergence between human and mouse. This suggests that distinct modes of natural selection might govern sequence versus expression divergence, and we propose a model, based on rapid, adaptation-driven divergence and convergent evolution of gene expression patterns, for how natural selection could influence gene expression divergence.

Dasheng is one of the highest copy number long terminal repeat elements and one of the most recent elements to amplify in the rice (Oryza sativa) genome. However, the absence of any significant coding capacity for retroviral proteins, including gag and pol, suggests that Dasheng is a nonautonomous element. Here, we have exploited the availability of 360 Mb of rice genomic sequence to identify a candidate autonomous element. RIRE2 is a previously described gypsy-like long terminal repeat retrotransposon with significant sequence similarity toDasheng in the regions where putative cis factors for retrotransposition are thought to be located. Dashengand RIRE2 elements have similar chromosomal distribution patterns and similar target site sequences, suggesting that they use the same transposition machinery. In addition, the presence of severalRIRE2-Dasheng element chimeras in the genome is consistent with the copackaging of element mRNAs in the same virus-like particle. Finally, both families have recently amplified members, suggesting that they could have been co-expressed, a necessary prerequisite for RIRE2 to serve as the source of transposition machinery for Dasheng. Consistent with this hypothesis, transcripts from both elements were found in the same expressed sequence tag library.

Motivation: Class I α-mannosidases comprise a homologous and functionally diverse family of glycoside hydrolases. Phylogenetic analysis based on an amino acid sequence alignment of the catalytic domain of class I α-mannosidases reveals four well-supported phylogenetic groups within this family. These groups include a number of paralogous members generated by gene duplications that occurred as far back as the initial divergence of the crown-group of eukaryotes. Three of the four phylogenetic groups consist of enzymes that have group-specific biochemical specificity and/or sites of activity. An attempt has been made to uncover the role that natural selection played in the sequence and structural divergence between the phylogenetically and functionally distinct Endoplasmic Reticulum (ER) and Golgi apparatus groups. Results: Comparison of site-specific amino acid variability profiles for the ER and Golgi groups revealed statistically significant evidence for functional diversification at the sequence level and indicated a number of residues that are most likely to have played a role in the functional divergence between the two groups. The majority of these sites appear to contain residues that have been fixed within one organelle-specific group by positive selection. Somewhat surprisingly these selected residues map to the periphery of the α-mannosidase catalytic domain tertiary structure. Changes in these peripherally located residues would not seem to have a gross effect on protein function. Thus diversifying selection between the two groups may have acted in a gradual manner consistent with the Darwinian model of natural selection. Contact: bishogr@millsaps.edu * To whom correspondence should be addressed.

alpha-Mannosidase enzymes comprise a class of gylcoside hydrolases involved in the maturation and degradaton of glycoprotein-linked oligosaccharides. Various alpha-mannosidase enzymatic activities are encoded by an ancient and ubiquitous gene superfamily. A comparative sequence analysis was employed here to characterize the evolutionary relationships and dynamics of the alpha-mannosidase superfamily. A series of lineage-specific BLAST searches recovered the first ever recognized archaean and eubacterial alpha-mannosidase sequences, in addition to numerous eukaryotic sequences. Motif-based alignment and subsequent phylogenetic analysis of the entire superfamily revealed the presence of three well-supported monophyletic clades that represent discrete alpha-mannosidase families. The comparative method was used to evaluate the phylogenetic distribution of alpha-mannosidase functional variants within families. Results of this analysis demonstrate a pattern of functional diversification of alpha-mannosidase paralogs followed by conservation of function among orthologs. Nucleotide polymorphism among the most closely related pair of duplicated genes was analyzed to evaluate the role of natural selection in the functional diversification of alpha-mannosidase paralogs. Ratios of nonsynonymous and synonymous variation show an increase in the rate of nonsynonymous change after duplication and a relative excess of fixed nonsynonymous changes between the two groups of paralogs. These data point to a possible role for positive Darwinian selection in the evolution of alpha-mannosidase functional diversification following gene duplication.

Presented here is an analysis of the molecular evolutionary dynamics of the P gene among 76 representative sequences of the Paramyxoviridae and Rhabdoviridae RNA virus families. In a number of Paramyxoviridae taxa, as well as in vesicular stomatitis viruses of the Rhabdoviridae, the P gene encodes multiple proteins from a single genomic RNA sequence. These products include the phosphoprotein (P), as well as the C and V proteins. The complexity of the P gene makes it an intriguing locus to study from an evolutionary perspective. Amino acid sequence alignments of the proteins encoded at the P and N loci were used in independent phylogenetic reconstructions of the Paramyxoviridae and Rhabdoviridae families. P-gene-coding capacities were mapped onto the Paramyxoviridae phylogeny, and the most parsimonious path of multiple-coding-capacity evolution was determined. Levels of amino acid variation for Paramyxoviridae and Rhabdoviridae P-gene-encoded products were also analyzed. Proteins encoded in overlapping reading frames from the same nucleotides have different levels of amino acid variation. The nucleotide architecture that underlies the amino acid variation was determined in order to evaluate the role of selection in the evolution of the P gene overlapping reading frames. In every case, the evolution of one of the proteins encoded in the overlapping reading frames has been constrained by negative selection while the other has evolved more rapidly. The integrity of the overlapping reading frame that represents a derived state is generally maintained at the expense of the ancestral reading frame encoded by the same nucleotides. The evolution of such multicoding sequences is likely a response by RNA viruses to selective pressure to maximize genomic information content while maintaining small genome size. The ability to evolve such a complex genomic strategy is intimately related to the dynamics of the viral quasispecies, which allow enhanced exploration of the adaptive landscape.