Cancer is a genetic disorder, meaning that a plethora of different mutations, whether somatic or germ line, underlie the etiology of the 'Emperor of Maladies'. Point mutations, chromosomal rearrangements and copy number changes, whether they have occurred spontaneously in predisposed individuals or have been induced by intrinsic or extrinsic (environmental) mutagens, lead to the activation of oncogenes and inactivation of tumor suppressor genes, thereby promoting malignancy. This scenario has now been recognized and experimentally confirmed in a wide range of different contexts. Over the past decade, a surge in available sequencing technologies has allowed the sequencing of whole genomes from liquid malignancies and solid tumors belonging to different types and stages of cancer, giving birth to the new field of cancer genomics. One of the most striking discoveries has been that cancer genomes are highly enriched with mutations of specific kinds. It has been suggested that these mutations can be classified into 'families' based on their mutational signatures. A mutational signature may be regarded as a type of base substitution (e.g. C:G to T:A) within a particular context of neighboring nucleotide sequence (the bases upstream and/or downstream of the mutation). These mutational signatures, supplemented by mutable motifs (a wider mutational context), promise to help us to understand the nature of the mutational processes that operate during tumor evolution because they represent the footprints of interactions between DNA, mutagens and the enzymes of the repair/replication/modification pathways.

We compare the sets of experimentally validated long intergenic non-coding (linc)RNAs from human and mouse and apply a maximum likelihood approach to estimate the total number of lincRNA genes as well as the size of the conserved part of the lincRNome. Under the assumption that the sets of experimentally validated lincRNAs are random samples of the lincRNomes of the corresponding species, we estimate the total lincRNome size at approximately 40,000 to 50,000 species, at least twice the number of protein-coding genes. We further estimate that the fraction of the human and mouse euchromatic genomes encoding lincRNAs is more than twofold greater than the fraction of protein-coding sequences. Although the sequences of most lincRNAs are much less strongly conserved than protein sequences, the extent of orthology between the lincRNomes is unexpectedly high, with 60 to 70% of the lincRNA genes shared between human and mouse. The orthologous mammalian lincRNAs can be predicted to perform equivalent functions; accordingly, it appears likely that thousands of evolutionarily conserved functional roles of lincRNAs remain to be characterized.

Protein-coding genes in eukaryotes are interrupted by introns, but intron densities widely differ between eukaryotic lineages. Vertebrates, some invertebrates and green plants have intron-rich genes, with 6-7 introns per kilobase of coding sequence, whereas most of the other eukaryotes have intron-poor genes. We reconstructed the history of intron gain and loss using a probabilistic Markov model (Markov Chain Monte Carlo, MCMC) on 245 orthologous genes from 99 genomes representing the three of the five supergroups of eukaryotes for which multiple genome sequences are available. Intron-rich ancestors are confidently reconstructed for each major group, with 53 to 74% of the human intron density inferred with 95% confidence for the Last Eukaryotic Common Ancestor (LECA). The results of the MCMC reconstruction are compared with the reconstructions obtained using Maximum Likelihood (ML) and Dollo parsimony methods. An excellent agreement between the MCMC and ML inferences is demonstrated whereas Dollo parsimony introduces a noticeable bias in the estimations, typically yielding lower ancestral intron densities than MCMC and ML. Evolution of eukaryotic genes was dominated by intron loss, with substantial gain only at the bases of several major branches including plants and animals. The highest intron density, 120 to 130% of the human value, is inferred for the last common ancestor of animals. The reconstruction shows that the entire line of descent from LECA to mammals was intron-rich, a state conducive to the evolution of alternative splicing.

A substantial fraction of eukaryotic proteins contains multiple domains, some of which show a tendency to occur in diverse domain architectures and can be considered mobile (or 'promiscuous'). These promiscuous domains are typically involved in protein-protein interactions and play crucial roles in interaction networks, particularly those contributing to signal transduction. They also play a major role in creating diversity of protein domain architecture in the proteome. It is now apparent that promiscuity is a volatile and relatively fast-changing feature in evolution, and that only a few domains retain their promiscuity status throughout evolution. Many such domains attained their promiscuity status independently in different lineages. Only recently, we have begun to understand the diversity of protein domain architectures and the role the promiscuous domains play in evolution of this diversity. However, many of the biological mechanisms of protein domain mobility remain shrouded in mystery. In this review, we discuss our present understanding of protein domain promiscuity, its evolution and its role in cellular function.

The availability of multiple, complete eukaryotic genome sequences allows one to address many fundamental evolutionary questions on genome scale. One such important, long-standing problem is evolution of exon-intron structure of eukaryotic genes. Analysis of orthologous genes from completely sequenced genomes revealed numerous shared intron positions in orthologous genes from animals and plants and even between animals, plants and protists. The data on shared and lineage-specific intron positions were used as the starting point for evolutionary reconstruction with parsimony and maximum-likelihood approaches. Parsimony methods produce reconstructions with intron-rich ancestors but also infer lineage-specific, in many cases, high levels of intron loss and gain. Different probabilistic models gave opposite results, apparently depending on model parameters and assumptions, from domination of intron loss, with extremely intron-rich ancestors, to dramatic excess of gains, to the point of denying any true conservation of intron positions among deep eukaryotic lineages. Development of models with adequate, realistic parameters and assumptions seems to be crucial for obtaining more definitive estimates of intron gain and loss in different eukaryotic lineages. Many shared intron positions were detected in ancestral eukaryotic paralogues which evolved by duplication prior to the divergence of extant eukaryotic lineages. These findings indicate that numerous introns were present in eukaryotic genes already at the earliest stages of evolution of eukaryotes and are compatible with the hypothesis that the original, catastrophic intron invasion accompanied the emergence of the eukaryotic cells. Comparison of various features of old and younger introns starts shedding light on probable mechanisms of intron insertion, indicating that propagation of old introns is unlikely to be a major mechanism for origin of new ones. The existence and structure of ancestral protosplice sites were addressed by examining the context of introns inserted within codons that encode amino acids conserved in all eukaryotes and, accordingly, are not subject to selection for splicing efficiency. It was shown that introns indeed predominantly insert into or are fixed in specific protosplice sites which have the consensus sequence (A/C)AG|Gt.

Gene order in prokaryotes is conserved to a much lesser extent than protein sequences. Only some operons, primarily those that encode physically interacting proteins, are conserved in all or most of the bacterial and archaeal genomes. Nevertheless, even the limited conservation of operon organisation that is observed provides valuable evolutionary and functional clues through multiple genome comparisons. With the rapid growth in the number and diversity of sequenced prokaryotic genomes, functional inferences for uncharacterized genes located in the same conserved gene neighborhood with well-studied genes are becoming increasingly important. In this review, we discuss various computational approaches for identification of conserved gene strings and construction of local alignments of gene orders in prokaryotic genomes.

Mutation frequencies vary along a nucleotide sequence, and nucleotide positions with an exceptionally high mutation frequency are called hotspots. Mutation hotspots in DNA often reflect intrinsic properties of the mutation process, such as the specificity with which mutagens interact with nucleic acids and the sequence-specificity of DNA repair/replication enzymes. They might also reflect structural and functional features of target protein or RNA sequences in which they occur. The determinants of mutation frequency and specificity are complex and there are many analytical methods for their study. This paper discusses computational approaches to analysing mutation spectra (distribution of mutations along the target genes) that include many detectable (mutable) positions. The following methods are reviewed: mutation hotspot prediction; pairwise and multiple comparisons of mutation spectra; derivation of a consensus sequence; and analysis of correlation between nucleotide sequence features and mutation spectra. Spectra of spontaneous and induced mutations are used for illustration of the complexities and pitfalls of such analyses. In general, the DNA sequence context of mutation hotspots is a fingerprint of interactions between DNA and DNA repair/replication/modification enzymes, and the analysis of hotspot context provides evidence of such interactions.

MOTIVATION: The context of the start codon (typically, AUG) and the features of the 5' Untranslated Regions (5' UTRs) are important for understanding translation regulation in eukaryotic mRNAs and for accurate prediction of the coding region in genomic and cDNA sequences. The presence of AUG triplets in 5' UTRs (upstream AUGs) might effect the initiation rate and, in the context of gene prediction, could reduce the accuracy of the identification of the authentic start. To reveal potential connections between the presence of upstream AUGs and other features of 5' UTRs, such as their length and the start codon context, we undertook a systematic analysis of the available eukaryotic 5' UTR sequences. RESULTS: We show that a large fraction of 5' UTRs in the available cDNA sequences, 15-53% depending on the organism, contain upstream ATGs. A negative correlation was observed between the information content of the translation start signal and the length of the 5' UTR. Similarly, a negative correlation exists between the 'strength' of the start context and the number of upstream ATGs. Typically, cDNAs containing long 5' UTRs with multiple upstream ATGs have a 'weak' start context, and in contrast, cDNAs containing short 5' UTRs without ATGs have 'strong' starts. These counter-intuitive results may be interpreted in terms of upstream AUGs having an important role in the regulation of translation efficiency by ensuring low basal translation level via double negative control and creating the potential for additional regulatory mechanisms. One of such mechanisms, supported by experimental studies of some mRNAs, includes removal of the AUG-containing portion of the 5' UTR by alternative splicing. AVAILABILITY: An ATG_ EVALUATOR program is available upon request or at www.itba.mi.cnr.it/webgene. CONTACT: [email protected], [email protected].

Attachment regions of the eukaryotic chromosomal DNA to the nuclear scaffold/matrix (S/MARs) participate in various important cellular processes. However, no obvious characteristics common for these nucleotide sequences have been revealed, except that S/MARs are non-coding sites containing putative regulatory elements and binding sites of DNA-topoisomerase II. Heterogeneity among S/MARs can be caused by a variety of biological factors. In this paper, the accuracy of two S/MARs prediction programs, MAR-Finder (Singh, Kramer and Krawetz, 1997) and ChrClass (Glazkov, Rogozin and Glazko, 1998) are compared and it is concluded that both programs can be recommended for analysis of eukaryotic genomes. However, results of their prediction should be interpreted with caution since estimation of prediction accuracy of both programs needs further analysis. Problems of S/MARs prediction are illustrated on several examples of human protein-coding genes, repeated elements and the beta-globin locus from different mammalian species. Results of our analysis suggest that the proportion of missed S/MARs is lower for ChrClass, whereas the proportion of wrong S/MARs is lower for MAR-Finder (a default set of parameters).

The presence of repetitive elements can create serious problems for sequence analysis, especially in the case of homology searches in nucleotide sequence databases. Repetitive elements should be treated carefully by using special programs and databases. In this paper, various aspects of SINE (short interspersed repetitive element) identification, analysis and evolution are discussed.

MOTIVATION: The study and comparison of mutational spectra is an important problem in molecular biology, because these spectra often reveal important features of the action of various mutagens and the functioning of repair/replication enzymes. As is known, mutability varies significantly along nucleotide sequences: mutations often concentrate at certain positions in a sequence, otherwise termed 'hotspots'. RESULTS: Herein, we propose a regression analysis method based on the use of regression trees in order to analyse the influence of nucleotide context on the occurrence of such hotspots. The REGRT program developed has been tested on simulated and real mutational spectra. For the G:C-->T:A mutational spectra induced by Sn1 alkylating agents (nine spectra), the prediction accuracy was 0. 99. AVAILABILITY: The REGRT program is available upon request from V.Berikov.

MOTIVATION: Prediction of gene structure in newly sequenced DNA becomes very important in large genome sequencing projects. This problem is complicated due to the exon-intron structure of eukaryotic genes and because gene expression is regulated by many different short nucleotide domains. In order to be able to analyse the full gene structure in different organisms, it is necessary to combine information about potential functional signals (promoter region, splice sites, start and stop codons, 3' untranslated region) together with the statistical properties of coding sequences (coding potential), information about homologous proteins, ESTs and repeated elements. RESULTS: We have developed the GeneBuilder system which is based on prediction of functional signals and coding regions by different approaches in combination with similarity searches in proteins and EST databases. The potential gene structure models are obtained by using a dynamic programming method. The program permits the use of several parameters for gene structure prediction and refinement. During gene model construction, selecting different exon homology levels with a protein sequence selected from a list of homologous proteins can improve the accuracy of the gene structure prediction. In the case of low homology, GeneBuilder is still able to predict the gene structure. The GeneBuilder system has been tested by using the standard set (Burset and Guigo, Genomics, 34, 353-367, 1996) and the performances are: 0.89 sensitivity and 0.91 specificity at the nucleotide level. The total correlation coefficient is 0.88. AVAILABILITY: The GeneBuilder system is implemented as a part of the WebGene a the URL: http://www.itba.mi. cnr.it/webgene and TRADAT (TRAncription Database and Analysis Tools) launcher URL: http://www.itba.mi.cnr.it/tradat.

In this paper a new approach for the prediction of protein coding gene structures is described. The principal scheme of prediction is as follows: first, the exons with the best potential are predicted in a sequence with unknown functions and a list of potential amino acid fragments coded by these exons is formed. Second, testing the homology between each amino acid fragment from the list and proteins from the SWISS-PROT database of amino acid sequences. One protein with the best homology is chosen out of all the homologous sequences. Third, reconstruction of the exon-intron structure, basing it on its homology with the chosen protein sequences. The method was tested on an independent control set (20 genes). The results were as follows: 21% of real exons were lost and 3% of non-real exons were found. This system can be used to refine the results of gene prediction systems, especially if highly homologous proteins are found in the amino acid sequence database.