Evolutionary Sparse Learning for Phylogenomics

We introduce a supervised machine learning approach with sparsity constraints for phylogenomics, referred to as evolutionary sparse learning (ESL). ESL builds models with genomic loci—such as genes, proteins, genomic segments, and positions—as parameters. Using the Least Absolute Shrinkage and Selection Operator, ESL selects only the most important genomic loci to explain a given phylogenetic hypothesis or presence/absence of a trait. ESL models do not directly involve conventional parameters such as rates of substitutions between nucleotides, rate variation among positions, and phylogeny branch lengths. Instead, ESL directly employs the concordance of variation across sequences in an alignment with the evolutionary hypothesis of interest. ESL provides a natural way to combine different molecular and nonmolecular data types and incorporate biological and functional annotations of genomic loci in model building. We propose positional, gene, function, and hypothesis sparsity scores, illustrate their use through an example, and suggest several applications of ESL. The ESL framework has the potential to drive the development of a new class of computational methods that will complement traditional approaches in evolutionary genomics, particularly for identifying influential loci and sequences given a phylogeny and building models to test hypotheses. ESL’s fast computational times and small memory footprint will also help democratize big data analytics and improve scientific rigor in phylogenomics.     

Almost 20 years of Neanderthal palaeogenetics: adaptation,admixture, diversity,demography and extinction

Nearly two decades since the first retrieval of Neanderthal DNA, recent advances in next-generation sequencing technologies have allowed the generation of high-coverage genomes from two archaic hominins, a Neanderthal and a Denisovan, as well as a complete mitochondrial genome from remains which probably represent early members of the Neanderthal lineage. This genomic information, coupled with diversity exome data from several Neanderthal specimens is shedding new light on evolutionary processes such as the genetic basis of Neanderthal and modern human-specific adaptations—including morphological and behavioural traits—as well as the extent and nature of the admixture events between them. An emerging picture is that Neanderthals had a long-term small population size, lived in small and isolated groups and probably practised inbreeding at times. Deleterious genetic effects associated with these demographic factors could have played a role in their extinction. The analysis of DNA from further remains making use of new large-scale hybridization-capture-based methods as well as of new approaches to discriminate contaminant DNA sequences will provide genetic information in spatial and temporal scales that could help clarify the Neanderthal''s—and our very own—evolutionary history.     

Computational Prediction of Protein–Protein Interactions

Recently a number of computational approaches have been developed for the prediction of protein–protein interactions. Complete genome sequencing projects have provided the vast amount of information needed for these analyses. These methods utilize the structural, genomic, and biological context of proteins and genes in complete genomes to predict protein interaction networks and functional linkages between proteins. Given that experimental techniques remain expensive, time-consuming, and labor-intensive, these methods represent an important advance in proteomics. Some of these approaches utilize sequence data alone to predict interactions, while others combine multiple computational and experimental datasets to accurately build protein interaction maps for complete genomes. These methods represent a complementary approach to current high-throughput projects whose aim is to delineate protein interaction maps in complete genomes. We will describe a number of computational protocols for protein interaction prediction based on the structural, genomic, and biological context of proteins in complete genomes, and detail methods for protein interaction network visualization and analysis.     

Comparative genomics of cyclic-di-GMP signalling in bacteria: post-translational regulation and catalytic activity

Cyclic-di-GMP is a bacterial second messenger that controls the switch between motile and sessile states. It is synthesized by proteins containing the enzymatic GGDEF domain and degraded by the EAL domain. Many bacterial genomes encode several copies of proteins containing these domains, raising questions on how the activities of parallel c-di-GMP signalling systems are segregated to avoid potentially deleterious cross-talk. Moreover, many ‘hybrid’ proteins contain both GGDEF and EAL domains; the relationship between the two apparently opposing enzymatic activities has been termed a ‘biochemical conundrum’. Here, we present a computational analysis of 11 248 GGDEF- and EAL-containing proteins in 867 prokaryotic genomes to address these two outstanding questions. Over half of these proteins contain a signal for cell-surface localization, and a majority accommodate a signal-sensing partner domain; these indicate widespread prevalence of post-translational regulation that may segregate the activities of proteins that are co-expressed. By examining the conservation of amino acid residues in the GGDEF and EAL catalytic sites, we show that there are predominantly two types of hybrid proteins. In the first, both sites are intact; an additional regulatory partner domain, present in most of these proteins, might determine the balance between the two enzymatic activities. In the second type, only the EAL catalytic site is intact; these—unlike EAL-only proteins—generally contain a signal-sensing partner domain, suggesting distinct modes of regulation for EAL activity under different sequence contexts. Finally, we discuss the role of proteins that have lost GGDEF and EAL catalytic sites as potential c-di-GMP-binding effectors. Our findings will serve as a genomic framework for interpreting ongoing molecular investigations of these proteins.     

Exploring the mycobacteriophage metaproteome: phage genomics as an educational platform

Bacteriophages are the most abundant forms of life in the biosphere and carry genomes characterized by high genetic diversity and mosaic architectures. The complete sequences of 30 mycobacteriophage genomes show them collectively to encode 101 tRNAs, three tmRNAs, and 3,357 proteins belonging to 1,536 “phamilies” of related sequences, and a statistical analysis predicts that these represent approximately 50% of the total number of phamilies in the mycobacteriophage population. These phamilies contain 2.19 proteins on average; more than half (774) of them contain just a single protein sequence. Only six phamilies have representatives in more than half of the 30 genomes, and only three—encoding tape-measure proteins, lysins, and minor tail proteins—are present in all 30 phages, although these phamilies are themselves highly modular, such that no single amino acid sequence element is present in all 30 mycobacteriophage genomes. Of the 1,536 phamilies, only 230 (15%) have amino acid sequence similarity to previously reported proteins, reflecting the enormous genetic diversity of the entire phage population. The abundance and diversity of phages, the simplicity of phage isolation, and the relatively small size of phage genomes support bacteriophage isolation and comparative genomic analysis as a highly suitable platform for discovery-based education.     

MitoNuc: a database of nuclear genes coding for mitochondrial proteins. Update 2002

Mitochondria, besides their central role in energy metabolism, have recently been found to be involved in a number of basic processes of cell life and to contribute to the pathogenesis of many degenerative diseases. All functions of mitochondria depend on the interaction of nuclear and organelle genomes. Mitochondrial genomes have been extensively sequenced and analysed and data have been collected in several specialised databases. In order to collect information on nuclear coded mitochondrial proteins we developed MitoNuc, a database containing detailed information on sequenced nuclear genes coding for mitochondrial proteins in Metazoa. The MitoNuc database can be retrieved through SRS and is available via the web site http://bighost.area.ba.cnr.it/mitochondriome where other mitochondrial databases developed by our group, the complete list of the sequenced mitochondrial genomes, links to other mitochondrial sites and related information, are available. The MitoAln database, related to MitoNuc in the previous release, reporting the multiple alignments of the relevant homologous protein coding regions, is no longer supported in the present release. In order to keep the links among entries in MitoNuc from homologous proteins, a new field in the database has been defined: the cluster identifier, an alpha numeric code used to identify each cluster of homologous proteins. A comment field derived from the corresponding SWISS-PROT entry has been introduced; this reports clinical data related to dysfunction of the protein. The logic scheme of MitoNuc database has been implemented in the ORACLE DBMS. This will allow the end-users to retrieve data through a friendly interface that will be soon implemented.     

Plant genome resources at the national center for biotechnology information

The National Center for Biotechnology Information (NCBI) integrates data from more than 20 biological databases through a flexible search and retrieval system called Entrez. A core Entrez database, Entrez Nucleotide, includes GenBank and is tightly linked to the NCBI Taxonomy database, the Entrez Protein database, and the scientific literature in PubMed. A suite of more specialized databases for genomes, genes, gene families, gene expression, gene variation, and protein domains dovetails with the core databases to make Entrez a powerful system for genomic research. Linked to the full range of Entrez databases is the NCBI Map Viewer, which displays aligned genetic, physical, and sequence maps for eukaryotic genomes including those of many plants. A specialized plant query page allow maps from all plant genomes covered by the Map Viewer to be searched in tandem to produce a display of aligned maps from several species. PlantBLAST searches against the sequences shown in the Map Viewer allow BLAST alignments to be viewed within a genomic context. In addition, precomputed sequence similarities, such as those for proteins offered by BLAST Link, enable fluid navigation from unannotated to annotated sequences, quickening the pace of discovery. NCBI Web pages for plants, such as Plant Genome Central, complete the system by providing centralized access to NCBI's genomic resources as well as links to organism-specific Web pages beyond NCBI.     

Transposases are the most abundant,most ubiquitous genes in nature

Genes, like organisms, struggle for existence, and the most successful genes persist and widely disseminate in nature. The unbiased determination of the most successful genes requires access to sequence data from a wide range of phylogenetic taxa and ecosystems, which has finally become achievable thanks to the deluge of genomic and metagenomic sequences. Here, we analyzed 10 million protein-encoding genes and gene tags in sequenced bacterial, archaeal, eukaryotic and viral genomes and metagenomes, and our analysis demonstrates that genes encoding transposases are the most prevalent genes in nature. The finding that these genes, classically considered as selfish genes, outnumber essential or housekeeping genes suggests that they offer selective advantage to the genomes and ecosystems they inhabit, a hypothesis in agreement with an emerging body of literature. Their mobile nature not only promotes dissemination of transposable elements within and between genomes but also leads to mutations and rearrangements that can accelerate biological diversification and—consequently—evolution. By securing their own replication and dissemination, transposases guarantee to thrive so long as nucleic acid-based life forms exist.     

The Neighborhood of the Spike Gene Is a Hotspot for Modular Intertypic Homologous and Nonhomologous Recombination in Coronavirus Genomes

Coronaviruses (CoVs) have very large RNA viral genomes with a distinct genomic architecture of core and accessory open reading frames (ORFs). It is of utmost importance to understand their patterns and limits of homologous and nonhomologous recombination, because such events may affect the emergence of novel CoV strains, alter their host range, infection rate, tissue tropism pathogenicity, and their ability to escape vaccination programs. Intratypic recombination among closely related CoVs of the same subgenus has often been reported; however, the patterns and limits of genomic exchange between more distantly related CoV lineages (intertypic recombination) need further investigation. Here, we report computational/evolutionary analyses that clearly demonstrate a substantial ability for CoVs of different subgenera to recombine. Furthermore, we show that CoVs can obtain—through nonhomologous recombination—accessory ORFs from core ORFs, exchange accessory ORFs with different CoV genera, with other viruses (i.e., toroviruses, influenza C/D, reoviruses, rotaviruses, astroviruses) and even with hosts. Intriguingly, most of these radical events result from double crossovers surrounding the Spike ORF, thus highlighting both the instability and mobile nature of this genomic region. Although many such events have often occurred during the evolution of various CoVs, the genomic architecture of the relatively young SARS-CoV/SARS-CoV-2 lineage so far appears to be stable.     

A first-principles model of early evolution: emergence of gene families, species, and preferred protein folds

In this work we develop a microscopic physical model of early evolution where phenotype—organism life expectancy—is directly related to genotype—the stability of its proteins in their native conformations—which can be determined exactly in the model. Simulating the model on a computer, we consistently observe the “Big Bang” scenario whereby exponential population growth ensues as soon as favorable sequence–structure combinations (precursors of stable proteins) are discovered. Upon that, random diversity of the structural space abruptly collapses into a small set of preferred proteins. We observe that protein folds remain stable and abundant in the population at timescales much greater than mutation or organism lifetime, and the distribution of the lifetimes of dominant folds in a population approximately follows a power law. The separation of evolutionary timescales between discovery of new folds and generation of new sequences gives rise to emergence of protein families and superfamilies whose sizes are power-law distributed, closely matching the same distributions for real proteins. On the population level we observe emergence of species—subpopulations that carry similar genomes. Further, we present a simple theory that relates stability of evolving proteins to the sizes of emerging genomes. Together, these results provide a microscopic first-principles picture of how first-gene families developed in the course of early evolution.     

Toward Computational Cumulative Biology by Combining Models of Biological Datasets

A main challenge of data-driven sciences is how to make maximal use of the progressively expanding databases of experimental datasets in order to keep research cumulative. We introduce the idea of a modeling-based dataset retrieval engine designed for relating a researcher''s experimental dataset to earlier work in the field. The search is (i) data-driven to enable new findings, going beyond the state of the art of keyword searches in annotations, (ii) modeling-driven, to include both biological knowledge and insights learned from data, and (iii) scalable, as it is accomplished without building one unified grand model of all data. Assuming each dataset has been modeled beforehand, by the researchers or automatically by database managers, we apply a rapidly computable and optimizable combination model to decompose a new dataset into contributions from earlier relevant models. By using the data-driven decomposition, we identify a network of interrelated datasets from a large annotated human gene expression atlas. While tissue type and disease were major driving forces for determining relevant datasets, the found relationships were richer, and the model-based search was more accurate than the keyword search; moreover, it recovered biologically meaningful relationships that are not straightforwardly visible from annotations—for instance, between cells in different developmental stages such as thymocytes and T-cells. Data-driven links and citations matched to a large extent; the data-driven links even uncovered corrections to the publication data, as two of the most linked datasets were not highly cited and turned out to have wrong publication entries in the database.     

Use of artificial genomes in assessing methods for atypical gene detection

Parametric methods for identifying laterally transferred genes exploit the directional mutational biases unique to each genome. Yet the development of new, more robust methods—as well as the evaluation and proper implementation of existing methods—relies on an arbitrary assessment of performance using real genomes, where the evolutionary histories of genes are not known. We have used the framework of a generalized hidden Markov model to create artificial genomes modeled after genuine genomes. To model a genome, “core” genes—those displaying patterns of mutational biases shared among large numbers of genes—are identified by a novel gene clustering approach based on the Akaike information criterion. Gene models derived from multiple “core” gene clusters are used to generate an artificial genome that models the properties of a genuine genome. Chimeric artificial genomes—representing those having experienced lateral gene transfer—were created by combining genes from multiple artificial genomes, and the performance of the parametric methods for identifying “atypical” genes was assessed directly. We found that a hidden Markov model that included multiple gene models, each trained on sets of genes representing the range of genotypic variability within a genome, could produce artificial genomes that mimicked the properties of genuine genomes. Moreover, different methods for detecting foreign genes performed differently—i.e., they had different sets of strengths and weaknesses—when identifying atypical genes within chimeric artificial genomes.     

Genome bias influences amino acid choices: analysis of amino acid substitution and re-compilation of substitution matrices exclusive to an AT-biased genome

The genomic era has seen a remarkable increase in the number of genomes being sequenced and annotated. Nonetheless, annotation remains a serious challenge for compositionally biased genomes. For the preliminary annotation, popular nucleotide and protein comparison methods such as BLAST are widely employed. These methods make use of matrices to score alignments such as the amino acid substitution matrices. Since a nucleotide bias leads to an overall bias in the amino acid composition of proteins, it is possible that a genome with nucleotide bias may have introduced atypical amino acid substitutions in its proteome. Consequently, standard matrices fail to perform well in sequence analysis of these genomes. To address this issue, we examined the amino acid substitution in the AT-rich genome of Plasmodium falciparum, chosen as a reference and reconstituted a substitution matrix in the genome's context. The matrix was used to generate protein sequence alignments for the parasite proteins that improved across the functional regions. We attribute this to the consistency that may have been achieved amid the target and background frequencies calculated exclusively in our study. This study has important implications on annotation of proteins that are of experimental interest but give poor sequence alignments with standard conventional matrices.     

Predicting protein function with hierarchical phylogenetic profiles: the Gene3D Phylo-Tuner method applied to eukaryotic genomes

“Phylogenetic profiling” is based on the hypothesis that during evolution functionally or physically interacting genes are likely to be inherited or eliminated in a codependent manner. Creating presence–absence profiles of orthologous genes is now a common and powerful way of identifying functionally associated genes. In this approach, correctly determining orthology, as a means of identifying functional equivalence between two genes, is a critical and nontrivial step and largely explains why previous work in this area has mainly focused on using presence–absence profiles in prokaryotic species. Here, we demonstrate that eukaryotic genomes have a high proportion of multigene families whose phylogenetic profile distributions are poor in presence–absence information content. This feature makes them prone to orthology mis-assignment and unsuited to standard profile-based prediction methods. Using CATH structural domain assignments from the Gene3D database for 13 complete eukaryotic genomes, we have developed a novel modification of the phylogenetic profiling method that uses genome copy number of each domain superfamily to predict functional relationships. In our approach, superfamilies are subclustered at ten levels of sequence identity—from 30% to 100%—and phylogenetic profiles built at each level. All the profiles are compared using normalised Euclidean distances to identify those with correlated changes in their domain copy number. We demonstrate that two protein families will “auto-tune” with strong co-evolutionary signals when their profiles are compared at the similarity levels that capture their functional relationship. Our method finds functional relationships that are not detectable by the conventional presence–absence profile comparisons, and it does not require a priori any fixed criteria to define orthologous genes.     

GST-PRIME: a genome-wide primer design software for the generation of gene sequence tags

The availability of sequenced genomes has generated a need for experimental approaches that allow the simultaneous analysis of large, or even complete, sets of genes. To facilitate such analyses, we have developed GST-PRIME, a software package for retrieving and assembling gene sequences, even from complex genomes, using the NCBI public database, and then designing sets of primer pairs for use in gene amplification. Primers were designed by the program for the direct amplification of gene sequence tags (GSTs) from either genomic DNA or cDNA. Test runs of GST-PRIME on 2000 randomly selected Arabidopsis and Drosophila genes demonstrate that 93 and 88% of resulting GSTs, respectively, fulfilled imposed length criteria. GST-PRIME primer pairs were tested on a set of 1900 Arabidopsis genes coding for chloroplast-targeted proteins: 95% of the primer pairs used in PCRs with genomic DNA generated the correct amplicons. GST-PRIME can thus be reliably used for large-scale or specific amplification of intron-containing genes of multicellular eukaryotes.     

Comparative and Evolutionary Analysis of the Bacterial Homologous Recombination Systems

Homologous recombination is a housekeeping process involved in the maintenance of chromosome integrity and generation of genetic variability. Although detailed biochemical studies have described the mechanism of action of its components in model organisms, there is no recent extensive assessment of this knowledge, using comparative genomics and taking advantage of available experimental data on recombination. Using comparative genomics, we assessed the diversity of recombination processes among bacteria, and simulations suggest that we missed very few homologs. The work included the identification of orthologs and the analysis of their evolutionary history and genomic context. Some genes, for proteins such as RecA, the resolvases, and RecR, were found to be nearly ubiquitous, suggesting that the large majority of bacterial genomes are capable of homologous recombination. Yet many genomes show incomplete sets of presynaptic systems, with RecFOR being more frequent than RecBCD/AddAB. There is a significant pattern of co-occurrence between these systems and antirecombinant proteins such as the ones of mismatch repair and SbcB, but no significant association with nonhomologous end joining, which seems rare in bacteria. Surprisingly, a large number of genomes in which homologous recombination has been reported lack many of the enzymes involved in the presynaptic systems. The lack of obvious correlation between the presence of characterized presynaptic genes and experimental data on the frequency of recombination suggests the existence of still-unknown presynaptic mechanisms in bacteria. It also indicates that, at the moment, the assessment of the intrinsic stability or recombination isolation of bacteria in most cases cannot be inferred from the identification of known recombination proteins in the genomes.     

Web database of molecular genetic data from fish stocks

Much research at the national and international level has been devoted to the development of several genetic methods for use in the characterization of fish stocks. We have developed a database that collates data from population genetic studies of fish. This database is accessible to researchers and control authorities on the Internet and should serve as a repository for genetic information of commercially important fish species. The prototype database has been developed in such a way that the new information can easily be updated by researchers in the field, who can submit their own data. The site can be found at http://fishgen.jrc.it.     

GeneCruiser: a web service for the annotation of microarray data

SUMMARY: GeneCruiser is a web service allowing users to annotate their genomic data by mapping microarray feature identifiers to gene identifiers from databases, such as UniGene, while providing links to web resources, such as the UCSC Genome Browser. It relies on a regularly updated database that retrieves and indexes the mappings between microarray probes and genomic databases. Genes are identified using the Life Sciences Identifier standard. AVAILABILITY: GeneCruiser is freely available in the following forms: Web service and Web application, http://www.genecruiser.org; GenePattern, GeneCruiser access has been integrated into our microarray analysis platform, GenePattern. http://www.genepattern.org.     

Competition between Primary Nucleation and Autocatalysis in Amyloid Fibril Self-Assembly

Kinetic measurements of the self-assembly of proteins into amyloid fibrils are often used to make inferences about molecular mechanisms. In particular, the lag time—the quiescent period before aggregates are detected—is often found to scale with the protein concentration as a power law, whose exponent has been used to infer the presence or absence of autocatalytic growth processes such as fibril fragmentation. Here we show that experimental data for lag time versus protein concentration can show signs of kinks: clear changes in scaling exponent, indicating changes in the dominant molecular mechanism determining the lag time. Classical models for the kinetics of fibril assembly suggest that at least two mechanisms are at play during the lag time: primary nucleation and autocatalytic growth. Using computer simulations and theoretical calculations, we investigate whether the competition between these two processes can account for the kinks which we observe in our and others’ experimental data. We derive theoretical conditions for the crossover between nucleation-dominated and growth-dominated regimes, and analyze their dependence on system volume and autocatalysis mechanism. Comparing these predictions to the data, we find that the experimentally observed kinks cannot be explained by a simple crossover between nucleation-dominated and autocatalytic growth regimes. Our results show that existing kinetic models fail to explain detailed features of lag time versus concentration curves, suggesting that new mechanistic understanding is needed. More broadly, our work demonstrates that care is needed in interpreting lag-time scaling exponents from protein assembly data.