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Aim: This study investigates factors influencing pandemic mortality rates across U.S. states during different waves of SARS-CoV-2 infection from February 2020 to April 2023, given that over one million people died from COVID-19 in the country., Methods: We performed statistical analyses and used linear regression models to estimate age-adjusted and unadjusted excess mortality as functions of life expectancy, vaccination rates, and GDP per capita in U.S. states., Results and Discussion: States with lower life expectancy and lower GDP per capita experienced significantly higher mortality rates during the pandemic, underscoring the critical role of underlying health conditions and healthcare infrastructure, as reflected in these factors. When categorizing states by vaccination rates, significant differences in GDP per capita and pre-pandemic life expectancy emerged between states with lower and higher vaccination rates, likely explaining mortality disparities before mass vaccination. During the Delta and Omicron BA.1 waves, when vaccines were widely available, the mortality gap widened, and states with lower vaccination rates experienced nearly double the mortality compared to states with higher vaccination rates (Odds Ratio 1.8, 95% CI 1.7-1.9, p < 0.01). This disparity disappeared during the later Omicron variants, likely because the levels of combined immunity from vaccination and widespread infection across state populations became comparable. We showed that vaccination rates were the only significant factor influencing age-adjusted mortality, highlighting the substantial impact of age-specific demographics on both life expectancy and GDP across states., Conclusion: The study underscores the critical role of high vaccination rates in reducing excess deaths across all states, regardless of economic status. Vaccination rates proved more decisive than GDP per capita in reducing excess deaths. Additionally, states with lower pre-pandemic life expectancy faced greater challenges, reflecting the combined effects of healthcare quality, demographic variations, and social determinants of health. These findings call for comprehensive public health strategies that address both immediate interventions, like vaccination, and long-term improvements in healthcare infrastructure and social conditions.

Ribosomal DNA (rDNA) repeat units are organized into tandem clusters in eukaryotic cells. In mice, these clusters are located on at least eight chromosomes and show extensive variation in the number of repeats between mouse genomes. To analyze intra- and inter-genomic variation of mouse rDNA repeats, we selectively isolated 25 individual rDNA units using Transformation-Associated Recombination (TAR) cloning. Long-read sequencing and subsequent comparative sequence analysis revealed that each full-length unit comprises an intergenic spacer (IGS) and a ∼13.4 kb long transcribed region encoding the three rRNAs, but with substantial variability in rDNA unit size, ranging from ∼35 to ∼46 kb. Within the transcribed regions of rDNA units, we found 209 variants, 70 of which are in external transcribed spacers (ETSs); but the rDNA size differences are driven primarily by IGS size heterogeneity, due to indels containing repetitive elements and some functional signals such as enhancers. Further evolutionary analysis categorized rDNA units into distinct clusters with characteristic IGS lengths; numbers of enhancers; and presence/absence of two common SNPs in promoter regions, one of which is located within promoter (p)RNA and may influence pRNA folding stability. These characteristic features of IGSs also correlated significantly with 5'ETS variant patterns described previously and associated with differential expression of rDNA units. Our results suggest that variant rDNA units are differentially regulated and open a route to investigate the role of rDNA variation on nucleolar formation and possible associations with pathology., (Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics 2024.)

In recent years, several deep learning-based methods have been proposed for predicting peptide fragment intensities. This study aims to provide a comprehensive assessment of six such methods, namely Prosit, DeepMass:Prism, pDeep3, AlphaPeptDeep, Prosit Transformer, and the method proposed by Guan et al. To this end, we evaluated the accuracy of the predicted intensity profiles for close to 1.7 million precursors (including both tryptic and HLA peptides) corresponding to more than 18 million experimental spectra procured from 40 independent submissions to the PRIDE repository that were acquired for different species using a variety of instruments and different dissociation types/energies. Specifically, for each method, distributions of similarity (measured by Pearson's correlation and normalized angle) between the predicted and the corresponding experimental b and y fragment intensities were generated. These distributions were used to ascertain the prediction accuracy and rank the prediction methods for particular types of experimental conditions. The effect of variables like precursor charge, length, and collision energy on the prediction accuracy was also investigated. In addition to prediction accuracy, the methods were evaluated in terms of prediction speed. The systematic assessment of these six methods may help in choosing the right method for MS/MS spectra prediction for particular needs.

Having fast, accurate, and broad spectrum methods for the identification of microorganisms is of paramount importance to public health, research, and safety. Bottom-up mass spectrometer-based proteomics has emerged as an effective tool for the accurate identification of microorganisms from microbial isolates. However, one major hurdle that limits the deployment of this tool for routine clinical diagnosis, and other areas of research such as culturomics, is the instrument time required for the mass spectrometer to analyze a single sample, which can take ∼1 h per sample, when using mass spectrometers that are presently used in most institutes. To address this issue, in this study, we employed, for the first time, tandem mass tags (TMTs) in multiplex identifications of microorganisms from multiple TMT-labeled samples in one MS/MS experiment. A difficulty encountered when using TMT labeling is the presence of interference in the measured intensities of TMT reporter ions. To correct for interference, we employed in the proposed method a modified version of the expectation maximization (EM) algorithm that redistributes the signal from ion interference back to the correct TMT-labeled samples. We have evaluated the sensitivity and specificity of the proposed method using 94 MS/MS experiments (covering a broad range of protein concentration ratios across TMT-labeled channels and experimental parameters), containing a total of 1931 true positive TMT-labeled channels and 317 true negative TMT-labeled channels. The results of the evaluation show that the proposed method has an identification sensitivity of 93-97% and a specificity of 100% at the species level. Furthermore, as a proof of concept, using an in-house-generated data set composed of some of the most common urinary tract pathogens, we demonstrated that by using the proposed method the mass spectrometer time required per sample, using a 1 h LC-MS/MS run, can be reduced to 10 and 6 min when samples are labeled with TMT-6 and TMT-10, respectively. The proposed method can also be used along with Orbitrap mass spectrometers that have faster MS/MS acquisition rates, like the recently released Orbitrap Astral mass spectrometer, to further reduce the mass spectrometer time required per sample.

The choice of guide RNA (gRNA) for CRISPR-based gene targeting is an essential step in gene editing applications, but the prediction of gRNA specificity remains challenging. Lack of transparency and focus on point estimates of efficiency disregarding the information on possible error sources in the model limit the power of existing Deep Learning-based methods. To overcome these problems, we present a new approach, a hybrid of Capsule Networks and Gaussian Processes. Our method predicts the cleavage efficiency of a gRNA with a corresponding confidence interval, which allows the user to incorporate information regarding possible model errors into the experimental design. We provide the first utilization of uncertainty estimation in computational gRNA design, which is a critical step toward accurate decision-making for future CRISPR applications. The proposed solution demonstrates acceptable confidence intervals for most test sets and shows regression quality similar to existing models. We introduce a set of criteria for gRNA selection based on off-target cleavage efficiency and its variance and present a collection of pre-computed gRNAs for human chromosome 22. Using Neural Network Interpretation methods, we show that our model rediscovers an established biological factor underlying cleavage efficiency, the importance of the seed region in gRNA., (© The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research.)

The rDNA clusters and flanking sequences on human chromosomes 13, 14, 15, 21 and 22 represent large gaps in the current genomic assembly. The organization and the degree of divergence of the human rDNA units within an individual nucleolar organizer region (NOR) are only partially known. To address this lacuna, we previously applied transformation-associated recombination (TAR) cloning to isolate individual rDNA units from chromosome 21. That approach revealed an unexpectedly high level of heterogeneity in human rDNA, raising the possibility of corresponding variations in ribosome dynamics. We have now applied the same strategy to analyze an entire rDNA array end-to-end from a copy of chromosome 22. Sequencing of TAR isolates provided the entire NOR sequence, including proximal and distal junctions that may be involved in nucleolar function. Comparison of the newly sequenced rDNAs to reference sequence for chromosomes 22 and 21 revealed variants that are shared in human rDNA in individuals from different ethnic groups, many of them at high frequency. Analysis infers comparable intra- and inter-individual divergence of rDNA units on the same and different chromosomes, supporting the concerted evolution of rDNA units. The results provide a route to investigate further the role of rDNA variation in nucleolar formation and in the empirical associations of nucleoli with pathology.

Mass spectrometry-based proteomics starts with identifications of peptides and proteins, which provide the bases for forming the next-level hypotheses whose "validations" are often employed for forming even higher level hypotheses and so forth. Scientifically meaningful conclusions are thus attainable only if the number of falsely identified peptides/proteins is accurately controlled. For this reason, RAId continued to be developed in the past decade. RAId employs rigorous statistics for peptides/proteins identification, hence assigning accurate P-values/E-values that can be used confidently to control the number of falsely identified peptides and proteins. The RAId web service is a versatile tool built to identify peptides and proteins from tandem mass spectrometry data. Not only recognizing various spectra file formats, the web service also allows four peptide scoring functions and choice of three statistical methods for assigning P-values/E-values to identified peptides. Users may upload their own protein database or use one of the available knowledge integrated organismal databases that contain annotated information such as single amino acid polymorphisms, post-translational modifications, and their disease associations. The web service also provides a friendly interface to display, sort using different criteria, and download the identified peptides and proteins. RAId web service is freely available at https://www.ncbi.nlm.nih.gov/CBBresearch/Yu/raid., (Published 2019. This article is a U.S. Government work and is in the public domain in the USA. Proteomics Published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Rapid and accurate identification and classification of microorganisms is of paramount importance to public health and safety. With the advance of mass spectrometry (MS) technology, the speed of identification can be greatly improved. However, the increasing number of microbes sequenced is complicating correct microbial identification even in a simple sample due to the large number of candidates present. To properly untwine candidate microbes in samples containing one or more microbes, one needs to go beyond apparent morphology or simple "fingerprinting"; to correctly prioritize the candidate microbes, one needs to have accurate statistical significance in microbial identification. We meet these challenges by using peptide-centric representations of microbes to better separate them and by augmenting our earlier analysis method that yields accurate statistical significance. Here, we present an updated analysis workflow that uses tandem MS (MS/MS) spectra for microbial identification or classification. We have demonstrated, using 226 MS/MS publicly available data files (each containing from 2500 to nearly 100,000 MS/MS spectra) and 4000 additional MS/MS data files, that the updated workflow can correctly identify multiple microbes at the genus and often the species level for samples containing more than one microbe. We have also shown that the proposed workflow computes accurate statistical significances, i.e., E values for identified peptides and unified E values for identified microbes. Our updated analysis workflow MiCId, a freely available software for Microorganism Classification and Identification, is available for download at https://www.ncbi.nlm.nih.gov/CBBresearch/Yu/downloads.html . Graphical Abstract ᅟ.

Despite the key role of the human ribosome in protein biosynthesis, little is known about the extent of sequence variation in ribosomal DNA (rDNA) or its pre-rRNA and rRNA products. We recovered ribosomal DNA segments from a single human chromosome 21 using transformation-associated recombination (TAR) cloning in yeast. Accurate long-read sequencing of 13 isolates covering ∼0.82 Mb of the chromosome 21 rDNA complement revealed substantial variation among tandem repeat rDNA copies, several palindromic structures and potential errors in the previous reference sequence. These clones revealed 101 variant positions in the 45S transcription unit and 235 in the intergenic spacer sequence. Approximately 60% of the 45S variants were confirmed in independent whole-genome or RNA-seq data, with 47 of these further observed in mature 18S/28S rRNA sequences. TAR cloning and long-read sequencing enabled the accurate reconstruction of multiple rDNA units and a new, high-quality 44 838 bp rDNA reference sequence, which we have annotated with variants detected from chromosome 21 of a single individual. The large number of variants observed reveal heterogeneity in human rDNA, opening up the possibility of corresponding variations in ribosome dynamics.

Off-target oligoprobe's interaction with partially complementary nucleotide sequences represents a problem for many bio-techniques. The goal of the study was to identify oligoprobe sequence characteristics that control the ratio between on-target and off-target hybridization. To understand the complex interplay between specific and genome-wide off-target (cross-hybridization) signals, we analyzed a database derived from genomic comparison hybridization experiments performed with an Affymetrix tiling array. The database included two types of probes with signals derived from (i) a combination of specific signal and cross-hybridization and (ii) genomic cross-hybridization only. All probes from the database were grouped into bins according to their sequence characteristics, where both hybridization signals were averaged separately. For selection of specific probes, we analyzed the following sequence characteristics: vulnerability to self-folding, nucleotide composition bias, numbers of G nucleotides and GGG-blocks, and occurrence of probe's k-mers in the human genome. Increases in bin ranges for these characteristics are simultaneously accompanied by a decrease in hybridization specificity-the ratio between specific and cross-hybridization signals. However, both averaged hybridization signals exhibit growing trends along with an increase of probes' binding energy, where the hybridization specific signal increases significantly faster in comparison to the cross-hybridization. The same trend is evident for the S function, which serves as a combined evaluation of probe binding energy and occurrence of probe's k-mers in the genome. Application of S allows extracting a larger number of specific probes, as compared to using only binding energy. Thus, we showed that high values of specific and cross-hybridization signals are not mutually exclusive for probes with high values of binding energy and S. In this study, the application of a new set of sequence characteristics allows detection of probes that are highly specific to their targets for array design and other bio-techniques that require selection of specific probes., Competing Interests: The commercial affiliation Biopolymer Design LLC does not alter our adherence to PLOS ONE policies on sharing data and materials.

Comparison of mRNA and protein structures shows that highly structured mRNAs typically encode compact protein domains suggesting that mRNA structure controls protein folding. This function is apparently performed by distinct structural elements in the mRNA, which implies 'fine tuning' of mRNA structure under selection for optimal protein folding. We find that, during evolution, changes in the mRNA folding energy follow amino acid replacements, reinforcing the notion of an intimate connection between the structures of a mRNA and the protein it encodes, and the double encoding of protein sequence and folding in the mRNA.

Specific structures in mRNA modulate translation rate and thus can affect protein folding. Using the protein structures from two eukaryotes and three prokaryotes, we explore the connections between the protein compactness, inferred from solvent accessibility, and mRNA structure, inferred from mRNA folding energy (ΔG). In both prokaryotes and eukaryotes, the ΔG value of the most stable 30 nucleotide segment of the mRNA (ΔGmin) strongly, positively correlates with protein solvent accessibility. Thus, mRNAs containing exceptionally stable secondary structure elements typically encode compact proteins. The correlations between ΔG and protein compactness are much more pronounced in predicted ordered parts of proteins compared to the predicted disordered parts, indicative of an important role of mRNA secondary structure elements in the control of protein folding. Additionally, ΔG correlates with the mRNA length and the evolutionary rate of synonymous positions. The correlations are partially independent and were used to construct multiple regression models which explain about half of the variance of protein solvent accessibility. These findings suggest a model in which the mRNA structure, particularly exceptionally stable RNA structural elements, act as gauges of protein co-translational folding by reducing ribosome speed when the nascent peptide needs time to form and optimize the core structure., (Published by Oxford University Press on behalf of Nucleic Acids Research 2016. This work is written by (a) US Government employee(s) and is in the public domain in the US.)

Motivation: Target-specific hybridization depends on oligo-probe characteristics that improve hybridization specificity and minimize genome-wide cross-hybridization. Interplay between specific hybridization and genome-wide cross-hybridization has been insufficiently studied, despite its crucial role in efficient probe design and in data analysis., Results: In this study, we defined hybridization specificity as a ratio between oligo target-specific hybridization and oligo genome-wide cross-hybridization. A microarray database, derived from the Genomic Comparison Hybridization (GCH) experiment and performed using the Affymetrix platform, contains two different types of probes. The first type of oligo-probes does not have a specific target on the genome and their hybridization signals are derived from genome-wide cross-hybridization alone. The second type includes oligonucleotides that have a specific target on the genomic DNA and their signals are derived from specific and cross-hybridization components combined together in a total signal. A comparative analysis of hybridization specificity of oligo-probes, as well as their nucleotide sequences and thermodynamic features was performed on the database. The comparison has revealed that hybridization specificity was negatively affected by low stability of the fully-paired oligo-target duplex, stable probe self-folding, G-rich content, including GGG motifs, low sequence complexity and nucleotide composition symmetry., Conclusion: Filtering out the probes with defined 'negative' characteristics significantly increases specific hybridization and dramatically decreasing genome-wide cross-hybridization. Selected oligo-probes have two times higher hybridization specificity on average, compared to the probes that were filtered from the analysis by applying suggested cutoff thresholds to the described parameters. A new approach for efficient oligo-probe design is described in our study., Contact: shabalin@ncbi.nlm.nih.gov or olga.matveeva@gmail.com, Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

Correct and rapid identification of microorganisms is the key to the success of many important applications in health and safety, including, but not limited to, infection treatment, food safety, and biodefense. With the advance of mass spectrometry (MS) technology, the speed of identification can be greatly improved. However, the increasing number of microbes sequenced is challenging correct microbial identification because of the large number of choices present. To properly disentangle candidate microbes, one needs to go beyond apparent morphology or simple 'fingerprinting'; to correctly prioritize the candidate microbes, one needs to have accurate statistical significance in microbial identification. We meet these challenges by using peptidome profiles of microbes to better separate them and by designing an analysis method that yields accurate statistical significance. Here, we present an analysis pipeline that uses tandem MS (MS/MS) spectra for microbial identification or classification. We have demonstrated, using MS/MS data of 81 samples, each composed of a single known microorganism, that the proposed pipeline can correctly identify microorganisms at least at the genus and species levels. We have also shown that the proposed pipeline computes accurate statistical significances, i.e., E-values for identified peptides and unified E-values for identified microorganisms. The proposed analysis pipeline has been implemented in MiCId, a freely available software for Microorganism Classification and Identification. MiCId is available for download at http://www.ncbi.nlm.nih.gov/CBBresearch/Yu/downloads.html . Graphical Abstract ᅟ.

We calculate the polarization portion of electrostatic interactions at the atomic scale using quantum mechanical methods such as density functional theories (DFT) and the coupled cluster approach, and using classical methods such as a surface charge method and a polarizable force field. The agreement among various methods is investigated. Using the coupled clusters method CCSD(T) with large basis sets as the reference, we find that for systems comprising two to six atoms and ions in S-states the classical surface charge method performs much better than commonly used DFT methods with moderate basis sets such as B3LYP/6-31G(d,p). The remarkable performance of the classical approach comes as a surprise. The present results indicate that the use of a rigorous formalism of classical electrostatics can be better justified for determining molecular interactions at intermediate distances than some of the widely used methods of quantum chemistry. PACS numbers: 41.20.Cv,32.10.Dk, 87.10.Tf.

Alternative splicing (AS), alternative transcription initiation (ATI) and alternative transcription termination (ATT) create the extraordinary complexity of transcriptomes and make key contributions to the structural and functional diversity of mammalian proteomes. Analysis of mammalian genomic and transcriptomic data shows that contrary to the traditional view, the joint contribution of ATI and ATT to the transcriptome and proteome diversity is quantitatively greater than the contribution of AS. Although the mean numbers of protein-coding constitutive and alternative nucleotides in gene loci are nearly identical, their distribution along the transcripts is highly non-uniform. On average, coding exons in the variable 5' and 3' transcript ends that are created by ATI and ATT contain approximately four times more alternative nucleotides than core protein-coding regions that diversify exclusively via AS. Short upstream exons that encompass alternative 5'-untranslated regions and N-termini of proteins evolve under strong nucleotide-level selection whereas in 3'-terminal exons that encode protein C-termini, protein-level selection is significantly stronger. The groups of genes that are subject to ATI and ATT show major differences in biological roles, expression and selection patterns., (Published by Oxford University Press on behalf of Nucleic Acids Research 2014. This work is written by (a) US Government employee(s) and is in the public domain in the US.)

In this paper, we present Molecular Isotopic Distribution Analysis (MIDAs), a new software tool designed to compute molecular isotopic distributions with adjustable accuracies. MIDAs offers two algorithms, one polynomial-based and one Fourier-transform-based, both of which compute molecular isotopic distributions accurately and efficiently. The polynomial-based algorithm contains few novel aspects, whereas the Fourier-transform-based algorithm consists mainly of improvements to other existing Fourier-transform-based algorithms. We have benchmarked the performance of the two algorithms implemented in MIDAs with that of eight software packages (BRAIN, Emass, Mercury, Mercury5, NeutronCluster, Qmass, JFC, IC) using a consensus set of benchmark molecules. Under the proposed evaluation criteria, MIDAs's algorithms, JFC, and Emass compute with comparable accuracy the coarse-grained (low-resolution) isotopic distributions and are more accurate than the other software packages. For fine-grained isotopic distributions, we compared IC, MIDAs's polynomial algorithm, and MIDAs's Fourier transform algorithm. Among the three, IC and MIDAs's polynomial algorithm compute isotopic distributions that better resemble their corresponding exact fine-grained (high-resolution) isotopic distributions. MIDAs can be accessed freely through a user-friendly web-interface at http://www.ncbi.nlm.nih.gov/CBBresearch/Yu/midas/index.html.

Small hairpin RNAs (shRNAs) became an important research tool in cell biology. Reliable design of these molecules is essential for the needs of large functional genomics projects. To optimize the design of efficient shRNAs, we performed comparative, thermodynamic, and correlation analyses of ~18,000 miR30-based shRNAs with known functional efficiencies, derived from the Sensor Assay project (Fellmann et al., 2011). We identified features of the shRNA guide strand that significantly correlate with the silencing efficiency and performed multiple regression analysis, using 4/5 of the data for training purposes and 1/5 for cross validation. A model that included the position-dependent nucleotide preferences was predictive in the cross-validation data subset (R = 0.39). However, a model, which in addition to the nucleotide preferences included thermodynamic shRNA features such as a thermodynamic duplex stability and position-dependent thermodynamic profile (dinucleotide free energy) was performing better (R = 0.53). Software "miR&#95;Scan" was developed based upon the optimized models. Calculated mRNA target secondary structure stability showed correlation with shRNA silencing efficiency but failed to improve the model. Correlation analysis demonstrates that our algorithm for identification of efficient miR30-based shRNA molecules performs better than approaches that were developed for design of chemically synthesized siRNAs (R(max) = 0.36).

Querying MS/MS spectra against a database containing only proteotypic peptides reduces data analysis time due to reduction of database size. Despite the speed advantage, this search strategy is challenged by issues of statistical significance and coverage. The former requires separating systematically significant identifications from less confident identifications, while the latter arises when the underlying peptide is not present, due to single amino acid polymorphisms (SAPs) or post-translational modifications (PTMs), in the proteotypic peptide libraries searched. To address both issues simultaneously, we have extended RAId's knowledge database to include proteotypic information, utilized RAId's statistical strategy to assign statistical significance to proteotypic peptides, and modified RAId's programs to allow for consideration of proteotypic information during database searches. The extended database alleviates the coverage problem since all annotated modifications, even those that occurred within proteotypic peptides, may be considered. Taking into account the likelihoods of observation, the statistical strategy of RAId provides accurate E-value assignments regardless whether a candidate peptide is proteotypic or not. The advantage of including proteotypic information is evidenced by its superior retrieval performance when compared to regular database searches., (Published by Elsevier B.V.)

Statistically meaningful comparison/combination of peptide identification results from various search methods is impeded by the lack of a universal statistical standard. Providing an E-value calibration protocol, we demonstrated earlier the feasibility of translating either the score or heuristic E-value reported by any method into the textbook-defined E-value, which may serve as the universal statistical standard. This protocol, although robust, may lose spectrum-specific statistics and might require a new calibration when changes in experimental setup occur. To mitigate these issues, we developed a new MS/MS search tool, RAId&#95;aPS, that is able to provide spectrum-specific-values for additive scoring functions. Given a selection of scoring functions out of RAId score, K-score, Hyperscore and XCorr, RAId&#95;aPS generates the corresponding score histograms of all possible peptides using dynamic programming. Using these score histograms to assign E-values enables a calibration-free protocol for accurate significance assignment for each scoring function. RAId&#95;aPS features four different modes: (i) compute the total number of possible peptides for a given molecular mass range, (ii) generate the score histogram given a MS/MS spectrum and a scoring function, (iii) reassign E-values for a list of candidate peptides given a MS/MS spectrum and the scoring functions chosen, and (iv) perform database searches using selected scoring functions. In modes (iii) and (iv), RAId&#95;aPS is also capable of combining results from different scoring functions using spectrum-specific statistics. The web link is http://www.ncbi.nlm.nih.gov/CBBresearch/Yu/raid&#95;aps/index.html. Relevant binaries for Linux, Windows, and Mac OS X are available from the same page.

Prediction of efficient oligonucleotides for RNA interference presents a serious challenge, especially for the development of genome-wide RNAi libraries which encounter difficulties and limitations due to ambiguities in the results and the requirement for significant computational resources. Here we present a fast and practical algorithm for shRNA design based on the thermodynamic parameters. In order to identify shRNA and siRNA features universally associated with high silencing efficiency, we analyzed structure-activity relationships in thousands of individual RNAi experiments from publicly available databases (ftp://ftp.ncbi.nlm.nih.gov/pub/shabalin/siRNA/si&#95;shRNA&#95;selector/). Using this statistical analysis, we found free energy ranges for the terminal duplex asymmetry and for fully paired duplex stability, such that shRNAs or siRNAs falling in both ranges have a high probability of being efficient. When combined, these two parameters yield a approximately 72% success rate on shRNAs from the siRecords database, with the target RNA levels reduced to below 20% of the control. Two other parameters correlate well with silencing efficiency: the stability of target RNA and the antisense strand secondary structure. Both parameters also correlate with the short RNA duplex stability; as a consequence, adding these parameters to our prediction scheme did not substantially improve classification accuracy. To test the validity of our predictions, we designed 83 shRNAs with optimal terminal asymmetry, and experimentally verified that small shifts in duplex stability strongly affected silencing efficiency. We showed that shRNAs with short fully paired stems could be successfully selected by optimizing only two parameters: terminal duplex asymmetry and duplex stability of the hypothetical cleavage product, which also relates to the specificity of mRNA target recognition. Our approach performs at the level of the best currently utilized algorithms that take into account prediction of the secondary structure of the target and antisense RNAs, but at significantly lower computational costs. Based on this study, we created the si-shRNA Selector program that predicts both highly efficient shRNAs and functional siRNAs (ftp://ftp.ncbi.nlm.nih.gov/pub/shabalin/siRNA/si&#95;shRNA&#95;selector/).

Background: Alternative splicing (AS) in protein-coding sequences has emerged as an important mechanism of regulation and diversification of animal gene function. By contrast, the extent and roles of alternative events including AS and alternative transcription initiation (ATI) within the 5'-untranslated regions (5'UTRs) of mammalian genes are not well characterized., Results: We evaluated the abundance, conservation and evolution of putative regulatory control elements, namely, upstream start codons (uAUGs) and open reading frames (uORFs), in the 5'UTRs of human and mouse genes impacted by alternative events. For genes with alternative 5'UTRs, the fraction of alternative sequences (those present in a subset of the transcripts) is much greater than that in the corresponding coding sequence, conceivably, because 5'UTRs are not bound by constraints on protein structure that limit AS in coding regions. Alternative regions of mammalian 5'UTRs evolve faster and are subject to a weaker purifying selection than constitutive portions. This relatively weak selection results in over-abundance of uAUGs and uORFs in the alternative regions of 5'UTRs compared to constitutive regions. Nevertheless, even in alternative regions, uORFs evolve under a stronger selection than the rest of the sequences, indicating that some of the uORFs are conserved regulatory elements; some of the non-conserved uORFs could be involved in species-specific regulation., Conclusion: The findings on the evolution and selection in alternative and constitutive regions presented here are consistent with the hypothesis that alternative events, namely, AS and ATI, in 5'UTRs of mammalian genes are likely to contribute to the regulation of translation.

The mu-opioid receptor (OPRM1) is the principal receptor target for both endogenous and exogenous opioid analgesics. There are substantial individual differences in human responses to painful stimuli and to opiate drugs that are attributed to genetic variations in OPRM1. In searching for new functional variants, we employed comparative genome analysis and obtained evidence for the existence of an expanded human OPRM1 gene locus with new promoters, alternative exons and regulatory elements. Examination of polymorphisms within the human OPRM1 gene locus identified strong association between single nucleotide polymorphism (SNP) rs563649 and individual variations in pain perception. SNP rs563649 is located within a structurally conserved internal ribosome entry site (IRES) in the 5'-UTR of a novel exon 13-containing OPRM1 isoforms (MOR-1K) and affects both mRNA levels and translation efficiency of these variants. Furthermore, rs563649 exhibits very strong linkage disequilibrium throughout the entire OPRM1 gene locus and thus affects the functional contribution of the corresponding haplotype that includes other functional OPRM1 SNPs. Our results provide evidence for an essential role for MOR-1K isoforms in nociceptive signaling and suggest that genetic variations in alternative OPRM1 isoforms may contribute to individual differences in opiate responses.

Background: Existing scientific literature is a rich source of biological information such as disease markers. Integration of this information with data analysis may help researchers to identify possible controversies and to form useful hypotheses for further validations. In the context of proteomics studies, individualized proteomics era may be approached through consideration of amino acid substitutions/modifications as well as information from disease studies. Integration of such information with peptide searches facilitates speedy, dynamic information retrieval that may significantly benefit clinical laboratory studies., Description: We have integrated from various sources annotated single amino acid polymorphisms, post-translational modifications, and their documented disease associations (if they exist) into one enhanced database per organism. We have also augmented our peptide identification software RAId&#95;DbS to take into account this information while analyzing a tandem mass spectrum. In principle, one may choose to respect or ignore the correlation of amino acid polymorphisms/modifications within each protein. The former leads to targeted searches and avoids scoring of unnecessary polymorphism/modification combinations; the latter explores possible polymorphisms in a controlled fashion. To facilitate new discoveries, RAId&#95;DbS also allows users to conduct searches permitting novel polymorphisms as well as to search a knowledge database created by the users., Conclusion: We have finished constructing enhanced databases for 17 organisms. The web link to RAId&#95;DbS and the enhanced databases is http://www.ncbi.nlm.nih.gov/CBBResearch/qmbp/RAId&#95;DbS/index.html. The relevant databases and binaries of RAId&#95;DbS for Linux, Windows, and Mac OS X are available for download from the same web page.

Background: Current experimental techniques, especially those applying liquid chromatography mass spectrometry, have made high-throughput proteomic studies possible. The increase in throughput however also raises concerns on the accuracy of identification or quantification. Most experimental procedures select in a given MS scan only a few relatively most intense parent ions, each to be fragmented (MS2) separately, and most other minor co-eluted peptides that have similar chromatographic retention times are ignored and their information lost., Results: We have computationally investigated the possibility of enhancing the information retrieval during a given LC/MS experiment by selecting the two or three most intense parent ions for simultaneous fragmentation. A set of spectra is created via superimposing a number of MS2 spectra, each can be identified by all search methods tested with high confidence, to mimick the spectra of co-eluted peptides. The generated convoluted spectra were used to evaluate the capability of several database search methods - SEQUEST, Mascot, X!Tandem, OMSSA, and RAId&#95;DbS - in identifying true peptides from superimposed spectra of co-eluted peptides. We show that using these simulated spectra, all the database search methods will gain eventually in the number of true peptides identified by using the compound spectra of co-eluted peptides., Open Peer Review: Reviewed by Vlad Petyuk (nominated by Arcady Mushegian), King Jordan and Shamil Sunyaev. For the full reviews, please go to the Reviewers' comments section.

Background: Protein kinase (PK) genes comprise the third largest superfamily that occupy approximately 2% of the human genome. They encode regulatory enzymes that control a vast variety of cellular processes through phosphorylation of their protein substrates. Expression of PK genes is subject to complex transcriptional regulation which is not fully understood., Principal Findings: Our comparative analysis demonstrates that genomic organization of regulatory PK genes differs from organization of other protein coding genes. PK genes occupy larger genomic loci, have longer introns, spacer regions, and encode larger proteins. The primary transcript length of PK genes, similar to other protein coding genes, inversely correlates with gene expression level and expression breadth, which is likely due to the necessity to reduce metabolic costs of transcription for abundant messages. On average, PK genes evolve slower than other protein coding genes. Breadth of PK expression negatively correlates with rate of non-synonymous substitutions in protein coding regions. This rate is lower for high expression and ubiquitous PKs, relative to low expression PKs, and correlates with divergence in untranslated regions. Conversely, rate of silent mutations is uniform in different PK groups, indicating that differing rates of non-synonymous substitutions reflect variations in selective pressure. Brain and testis employ a considerable number of tissue-specific PKs, indicating high complexity of phosphorylation-dependent regulatory network in these organs. There are considerable differences in genomic organization between PKs up-regulated in the testis and brain. PK genes up-regulated in the highly proliferative testicular tissue are fast evolving and small, with short introns and transcribed regions. In contrast, genes up-regulated in the minimally proliferative nervous tissue carry long introns, extended transcribed regions, and evolve slowly., Conclusions/significance: PK genomic architecture, the size of gene functional domains and evolutionary rates correlate with the pattern of gene expression. Structure and evolutionary divergence of tissue-specific PK genes is related to the proliferative activity of the tissue where these genes are predominantly expressed. Our data provide evidence that physiological requirements for transcription intensity, ubiquitous expression, and tissue-specific regulation shape gene structure and affect rates of evolution.

Background: The key to mass-spectrometry-based proteomics is peptide identification, which relies on software analysis of tandem mass spectra. Although each search engine has its strength, combining the strengths of various search engines is not yet realizable largely due to the lack of a unified statistical framework that is applicable to any method., Results: We have developed a universal scheme for statistical calibration of peptide identifications. The protocol can be used for both de novo approaches as well as database search methods. We demonstrate the protocol using only the database search methods. Among seven methods -SEQUEST (v27 rev12), ProbID (v1.0), InsPecT (v20060505), Mascot (v2.1), X!Tandem (v1.0), OMSSA (v2.0) and RAId&#95;DbS - calibrated, except for X!Tandem and RAId&#95;DbS most methods require a rescaling according to the database size searched. We demonstrate that our calibration protocol indeed produces unified statistics both in terms of average number of false positives and in terms of the probability for a peptide hit to be a true positive. Although both the protocols for calibration and the statistics thus calibrated are universal, the calibration formulas obtained from one laboratory with data collected using either centroid or profile format may not be directly usable by the other laboratories. Thus each laboratory is encouraged to calibrate the search methods it intends to use. We also address the importance of using spectrum-specific statistics and possible improvement on the current calibration protocol. The spectra used for statistical (E-value) calibration are freely available upon request.

Background: The key to mass-spectrometry-based proteomics is peptide identification. A major challenge in peptide identification is to obtain realistic E-values when assigning statistical significance to candidate peptides., Results: Using a simple scoring scheme, we propose a database search method with theoretically characterized statistics. Taking into account possible skewness in the random variable distribution and the effect of finite sampling, we provide a theoretical derivation for the tail of the score distribution. For every experimental spectrum examined, we collect the scores of peptides in the database, and find good agreement between the collected score statistics and our theoretical distribution. Using Student's t-tests, we quantify the degree of agreement between the theoretical distribution and the score statistics collected. The T-tests may be used to measure the reliability of reported statistics. When combined with reported P-value for a peptide hit using a score distribution model, this new measure prevents exaggerated statistics. Another feature of RAId&#95;DbS is its capability of detecting multiple co-eluted peptides. The peptide identification performance and statistical accuracy of RAId&#95;DbS are assessed and compared with several other search tools. The executables and data related to RAId&#95;DbS are freely available upon request.

Evolution of protein sequences is largely governed by purifying selection, with a small fraction of proteins evolving under positive selection. The evolution at synonymous positions in protein-coding genes is not nearly as well understood, with the extent and types of selection remaining, largely, unclear. A statistical test to identify purifying and positive selection at synonymous sites in protein-coding genes was developed. The method compares the rate of evolution at synonymous sites (Ks) to that in intron sequences of the same gene after sampling the aligned intron sequences to mimic the statistical properties of coding sequences. We detected purifying selection at synonymous sites in approximately 28% of the 1,562 analyzed orthologous genes from mouse and rat, and positive selection in approximately 12% of the genes. Thus, the fraction of genes with readily detectable positive selection at synonymous sites is much greater than the fraction of genes with comparable positive selection at nonsynonymous sites, i.e., at the level of the protein sequence. Unlike other genes, the genes with positive selection at synonymous sites showed no correlation between Ks and the rate of evolution in nonsynonymous sites (Ka), indicating that evolution of synonymous sites under positive selection is decoupled from protein evolution. The genes with purifying selection at synonymous sites showed significant anticorrelation between Ks and expression level and breadth, indicating that highly expressed genes evolve slowly. The genes with positive selection at synonymous sites showed the opposite trend, i.e., highly expressed genes had, on average, higher Ks. For the genes with positive selection at synonymous sites, a significantly lower mRNA stability is predicted compared to the genes with negative selection. Thus, mRNA destabilization could be an important factor driving positive selection in nonsynonymous sites, probably, through regulation of expression at the level of mRNA degradation and, possibly, also translation rate. So, unexpectedly, we found that positive selection at synonymous sites of mammalian genes is substantially more common than positive selection at the level of protein sequences. Positive selection at synonymous sites might act through mRNA destabilization affecting mRNA levels and translation.

Current literature describes several methods for the design of efficient siRNAs with 19 perfectly matched base pairs and 2 nt overhangs. Using four independent databases totaling 3336 experimentally verified siRNAs, we compared how well several of these methods predict siRNA cleavage efficiency. According to receiver operating characteristics (ROC) and correlation analyses, the best programs were BioPredsi, ThermoComposition and DSIR. We also studied individual parameters that significantly and consistently correlated with siRNA efficacy in different databases. As a result of this work we developed a new method which utilizes linear regression fitting with local duplex stability, nucleotide position-dependent preferences and total G/C content of siRNA duplexes as input parameters. The new method's discrimination ability of efficient and inefficient siRNAs is comparable with that of the best methods identified, but its parameters are more obviously related to the mechanisms of siRNA action in comparison with BioPredsi. This permits insight to the underlying physical features and relative importance of the parameters. The new method of predicting siRNA efficiency is faster than that of ThermoComposition because it does not employ time-consuming RNA secondary structure calculations and has much less parameters than DSIR. It is available as a web tool called 'siRNA scales'.

The transcriptional regulation of genes is a complex process, particularly for genes exhibiting a tissue-specific pattern of expression. We studied 28 genes that are expressed primarily in endothelial cells, another 28 genes that are expressed highly, but not exclusively, in cultured endothelial cells, and three control sets, consisting of genes not expressed in endothelium, genes expressed in neural tissues and housekeeping genes. For each gene, we identified conserved non-coding sequences (CNSs) of lengths 50 to >1000 nucleotides, located within the upstream intergenic region (from 500 to as far as 200 000 nucleotides upstream from the transcription start) or within the first intron. As a functional test, we assayed the CNSs from the set of endothelial cell-specific genes (EC-CNSs) for DNase hypersensitivity. Among 262 distant EC-CNSs, 33% are hypersensitive (HS) in endothelial cells, whereas only 16% are HS in control fibroblasts. A search for short sequence patterns revealed a number of motifs which are over-represented in EC-CNSs relative to CNSs from the control gene sets. In particular, the motif SAGGAAR is strongly and consistently over-represented among EC-CNSs, and is more over-represented in HS CNSs than in non-HS CNSs. CNSs which contain this motif are no closer to the promoter than an average CNS. This motif contains the core element of binding sites from the Ets family of transcription factors. Thus, one or several factors from this family may play a key role in the regulation of endothelial gene expression.

The impact of synonymous nucleotide substitutions on fitness in mammals remains controversial. Despite some indications of selective constraint, synonymous sites are often assumed to be neutral, and the rate of their evolution is used as a proxy for mutation rate. We subdivide all sites into four classes in terms of the mutable CpG context, nonCpG, postC, preG, and postCpreG, and compare four-fold synonymous sites and intron sites residing outside transposable elements. The distribution of the rate of evolution across all synonymous sites is trimodal. Rate of evolution at nonCpG synonymous sites, not preceded by C and not followed by G, is approximately 10% below that at such intron sites. In contrast, rate of evolution at postCpreG synonymous sites is approximately 30% above that at such intron sites. Finally, synonymous and intron postC and preG sites evolve at similar rates. The relationship between the levels of polymorphism at the corresponding synonymous and intron sites is very similar to that between their rates of evolution. Within every class, synonymous sites are occupied by G or C much more often than intron sites, whose nucleotide composition is consistent with neutral mutation-drift equilibrium. These patterns suggest that synonymous sites are under weak selection in favor of G and C, with the average coefficient s approximately 0.25/Ne approximately 10(-5), where Ne is the effective population size. Such selection decelerates evolution and reduces variability at sites with symmetric mutation, but has the opposite effects at sites where the favored nucleotides are more mutable. The amino-acid composition of proteins dictates that many synonymous sites are CpGprone, which causes them, on average, to evolve faster and to be more polymorphic than intron sites. An average genotype carries approximately 10(7) suboptimal nucleotides at synonymous sites, implying synergistic epistasis in selection against them.

Motivation: Evaluating all possible internal loops is one of the key steps in predicting the optimal secondary structure of an RNA molecule. The best algorithm available runs in time O(L(3)), L is the length of the RNA., Results: We propose a new algorithm for evaluating internal loops, its run-time is O(M(*)log(2)L), M < L(2) is a number of possible nucleotide pairings. We created a software tool Afold which predicts the optimal secondary structure of RNA molecules of lengths up to 28 000 nt, using a computer with 2 Gb RAM. We also propose algorithms constructing sets of conditionally optimal multi-branch loop free (MLF) structures, e.g. the set that for every possible pairing (x, y) contains an optimal MLF structure in which nucleotides x and y form a pair. All the algorithms have run-time O(M(*)log(2)L).

Single-stranded mRNA molecules form secondary structures through complementary self-interactions. Several hypotheses have been proposed on the relationship between the nucleotide sequence, encoded amino acid sequence and mRNA secondary structure. We performed the first transcriptome-wide in silico analysis of the human and mouse mRNA foldings and found a pronounced periodic pattern of nucleotide involvement in mRNA secondary structure. We show that this pattern is created by the structure of the genetic code, and the dinucleotide relative abundances are important for the maintenance of mRNA secondary structure. Although synonymous codon usage contributes to this pattern, it is intrinsic to the structure of the genetic code and manifests itself even in the absence of synonymous codon usage bias at the 4-fold degenerate sites. While all codon sites are important for the maintenance of mRNA secondary structure, degeneracy of the code allows regulation of stability and periodicity of mRNA secondary structure. We demonstrate that the third degenerate codon sites contribute most strongly to mRNA stability. These results convincingly support the hypothesis that redundancies in the genetic code allow transcripts to satisfy requirements for both protein structure and RNA structure. Our data show that selection may be operating on synonymous codons to maintain a more stable and ordered mRNA secondary structure, which is likely to be important for transcript stability and translation. We also demonstrate that functional domains of the mRNA [5'-untranslated region (5'-UTR), CDS and 3'-UTR] preferentially fold onto themselves, while the start codon and stop codon regions are characterized by relaxed secondary structures, which may facilitate initiation and termination of translation.

Background: Small interfering RNAs (siRNAs) have become an important tool in cell and molecular biology. Reliable design of siRNA molecules is essential for the needs of large functional genomics projects., Results: To improve the design of efficient siRNA molecules, we performed a comparative, thermodynamic and correlation analysis on a heterogeneous set of 653 siRNAs collected from the literature. We used this training set to select siRNA features and optimize computational models. We identified 18 parameters that correlate significantly with silencing efficiency. Some of these parameters characterize only the siRNA sequence, while others involve the whole mRNA. Most importantly, we derived an siRNA position-dependent consensus, and optimized the free-energy difference of the 5' and 3' terminal dinucleotides of the siRNA antisense strand. The position-dependent consensus is based on correlation and t-test analyses of the training set, and accounts for both significantly preferred and avoided nucleotides in all sequence positions. On the training set, the two parameters' correlation with silencing efficiency was 0.5 and 0.36, respectively. Among other features, a dinucleotide content index and the frequency of potential targets for siRNA in the mRNA added predictive power to our model (R = 0.55). We showed that our model is effective for predicting the efficiency of siRNAs at different concentrations. We optimized a neural network model on our training set using three parameters characterizing the siRNA sequence, and predicted efficiencies for the test siRNA dataset recently published by Novartis. On this validation set, the correlation coefficient between predicted and observed efficiency was 0.75. Using the same model, we performed a transcriptome-wide analysis of optimal siRNA targets for 22,600 human mRNAs., Conclusion: We demonstrated that the properties of the siRNAs themselves are essential for efficient RNA interference. The 5' ends of antisense strands of efficient siRNAs are U-rich and possess a content similarity to the pyrimidine-rich oligonucleotides interacting with the polypurine RNA tracks that are recognized by RNase H. The advantage of our method over similar methods is the small number of parameters. As a result, our method requires a much smaller training set to produce consistent results. Other mRNA features, though expensive to compute, can slightly improve our model.

We propose a method for estimating the evolutionary distance between DNA sequences in terms of insertions and deletions (indels), defined as the per site number of indels accumulated in the course of divergence of the two sequences. We derive a maximal likelihood estimate of this distance from differences between lengths of orthologous introns or other segments of sequences delimited by conservative markers. When indels accumulate, lengths of orthologous introns diverge only slightly slower than linearly, because long indels occur with substantial frequencies. Thus, saturation is not a major obstacle for estimating indel-based evolutionary distance. For introns of medium lengths, our method recovers the known evolutionary distance between rat and mouse, 0.014 indels per site, with good precision. We estimate that mouse-human divergence exceeds rat-mouse divergence by a factor of 4, so that mouse-human evolutionary distance in terms of selectively neutral indels is 0.056. Because in mammals, indels are approximately 14 times less frequent than nucleotide substitutions, mouse-human evolutionary distance in terms of selectively neutral substitutions is approximately 0.8., (Copyright 2004 Cold Spring Harbor Laboratory Press ISSN)

New alleles become fixed owing to random drift of nearly neutral mutations or to positive selection of substantially advantageous mutations. After decades of debate, the fraction of fixations driven by selection remains uncertain. Within 9,390 genes, we analysed 28,196 codons at which rat and mouse differ from each other at two nucleotide sites and 1,982 codons with three differences. At codons where rat-mouse divergence involved two non-synonymous substitutions, both of them occurred in the same lineage, either rat or mouse, in 64% of cases; however, independent substitutions would occur in the same lineage with a probability of only 50%. All three non-synonymous substitutions occurred in the same lineage for 46% of codons, instead of the 25% expected. Furthermore, comparison of 12 pairs of prokaryotic genomes also shows clumping of multiple non-synonymous substitutions in the same lineage. This pattern cannot be explained by correlated mutation or episodes of relaxed negative selection, but instead indicates that positive selection acts at many sites of rapid, successive amino acid replacement.

Background: Computer programs for the generation of multiple sequence alignments such as "Clustal W" allow detection of regions that are most conserved among many sequence variants. However, even for regions that are equally conserved, their potential utility as hybridization targets varies. Mismatches in sequence variants are more disruptive in some duplexes than in others. Additionally, the propensity for self-interactions amongst oligonucleotides targeting conserved regions differs and the structure of target regions themselves can also influence hybridization efficiency. There is a need to develop software that will employ thermodynamic selection criteria for finding optimal hybridization targets in related sequences., Results: A new scheme and new software for optimal detection of oligonucleotide hybridization targets common to families of aligned sequences is suggested and applied to aligned sequence variants of the complete HIV-1 genome. The scheme employs sequential filtering procedures with experimentally determined thermodynamic cut off points: 1) creation of a consensus sequence of RNA or DNA from aligned sequence variants with specification of the lengths of fragments to be used as oligonucleotide targets in the analyses; 2) selection of DNA oligonucleotides that have pairing potential, greater than a defined threshold, with all variants of aligned RNA sequences; 3) elimination of DNA oligonucleotides that have self-pairing potentials for intra- and inter-molecular interactions greater than defined thresholds. This scheme has been applied to the HIV-1 genome with experimentally determined thermodynamic cut off points. Theoretically optimal RNA target regions for consensus oligonucleotides were found. They can be further used for improvement of oligo-probe based HIV detection techniques., Conclusions: A selection scheme with thermodynamic thresholds and software is presented in this study. The package can be used for any purpose where there is a need to design optimal consensus oligonucleotides capable of interacting efficiently with hybridization targets common to families of aligned RNA or DNA sequences. Our thermodynamic approach can be helpful in designing consensus oligonucleotides with consistently high affinity to target variants in evolutionary related genes or genomes.

Sequencing of multiple, nearly complete eukaryotic genomes creates opportunities for detecting previously unnoticed, subtle functional signals in non-coding regions. A genome-wide comparative analysis of orthologous sets of mammalian and yeast mRNAs revealed distinct patterns of evolutionary conservation at the boundaries of the untranslated regions (UTRs) and the coding region (CDS). Elevated sequence conservation was detected in approximately 30 nt regions around the start codon. There seems to be a complementary relationship between sequence conservation in the approximately 30 nt regions of the 5'-UTR immediately upstream of the start codon and that in the synonymous positions of the 5'-terminal 30 nt of the CDS: in mammalian mRNAs, the 5'-UTR shows a greater conservation than the CDS, whereas the opposite trend holds for yeast mRNAs. Unexpectedly, a approximately 30 nt region downstream of the stop codon shows a substantially lower level of sequence conservation than the downstream portions of the 3'-UTRs. However, the sequence in this poorly conserved 30 nt portion of the 3'-UTR is non-random in that it has a higher GC content than the rest of the UTR. It is hypothesized that the elevated sequence conservation in the region immediately upstream of the start codon is related to the requirement for initiation factor binding during pre-initiation ribosomal scanning. In contrast, the poorly conserved region downstream of the stop codon could be involved in the post- termination scanning and dissociation of the ribosomes from the mRNA, which requires only the mRNA-ribosome interaction. Additionally, it was found that the choice of the stop codon in mammals, but not in yeasts, and the context in the immediate vicinity of the stop codons in both mammals and yeasts are subject to strong selection. Thus, genome-wide analysis of orthologous gene sets allows detection of previously unrecognized patterns of sequence conservation, which are likely to reflect hidden functional signals, such as ribosomal filters that could regulate translation by modulating the interaction between the mRNA and ribosomes.

Only a fraction of eukaryotic genes affect the phenotype drastically. We compared 18 parameters in 1273 human morbid genes, known to cause diseases, and in the remaining 16 580 unambiguous human genes. Morbid genes evolve more slowly, have wider phylogenetic distributions, are more similar to essential genes of Drosophila melanogaster, code for longer proteins containing more alanine and glycine and less histidine, lysine and methionine, possess larger numbers of longer introns with more accurate splicing signals and have higher and broader expressions. These differences make it possible to classify as non-morbid 34% of human genes with unknown morbidity, when only 5% of known morbid genes are incorrectly classified as non-morbid. This classification can help to identify disease-causing genes among multiple candidates.

Post-transcriptional regulation and the formation of mRNA 3' ends are crucial for gene expression in eukaryotes. Interspecies conservation of many sequences within 3'UTRs reveals selective constraint due to similar function. To study the pattern of conservation within 3'UTRs, we compiled and aligned 50 sets of complete orthologous 3'UTRs from four orders of mammals. We observed a mosaic pattern of conservation, with alternating regions of high (phylogenetic footprints) and low similarity. Conservation in 3'UTRs correlates with their base composition and also with the synonymous substitution rate in corresponding coding regions. The non-uniform distribution of conservation is more pronounced for 3'UTRs with a moderate or low level of overall conservation, where invariant nucleotides are more numerous, and their runs of lengths 4-7 occur more frequently than if conservation were random. Many runs of invariant nucleotides are AU-rich or pyrimidine-rich. Some of these runs coincide with known functional cis- elements of eukaryotic mRNAs, such as the U-rich upstream element, polyadenylation signal and DICE regulatory signal. More divergent regions of multiple alignments of 3'UTRs are often more G- and/or C-rich. Our results provide evidence on the importance of moderately conserved regions in 3'UTRs and suggest that regulatory functions of 3'UTRs might utilize gene-specific information in these regions.

Motivation: As a first approximation, similarity between two long orthologous regions of genomes can be represented by a chain of local similarities. Within such a chain, pairs of successive similarities are collinear (non-conflicting), i.e. segments involved in the nth similarity precede in both sequences segments involved in the (n+1)th similarity. However, when all similarities between two long sequences are considered, usually there are many conflicts between them. Although some conflicts can be avoided by masking transposons or low-complexity sequences, selecting only those similarities that reflect orthology and, thus, belong to the evolutionarily true chain is not trivial., Results: We propose a simple, hierarchical algorithm of finding the true chain of local similarities. Starting from similarities with low P-values, we resolve each pairwise conflict by deleting a similarity with a higher P-value. This greedy approach constructs a chain of similarities faster than when a chain optimal with respect to some global criterion is sought, and makes more sense biologically.

OWEN is an interactive tool for aligning two long DNA sequences that represents similarity between them by a chain of collinear local similarities. OWEN employs several methods for constructing and editing local similarities and for resolving conflicts between them. Alignments of sequences of lengths over 10(6) can often be produced in minutes. OWEN requires memory below 20 L, where L is the sum of lengths of the compared sequences.

Patterns of similarity between genomes of related species reflect the distribution of selective constraint within DNA. We analyzed alignments of 142 orthologous intergenic regions of Caenorhabditis elegans and Caenorhabditis briggsae and found a mosaic pattern with regions of high similarity (phylogenetic footprints) interspersed with non-alignable sequences. Footprints cover approximately 20% of intergenic regions, often occur in clumps and are rare within 5' UTRs but common within 3' UTRs. The footprints have a higher ratio of transitions to transversions than expected at random and a higher GC content than the rest of the intergenic region. The number of footprints and the GC content of footprints within an intergenic region are higher when genes are oriented so that their 5' ends form the boundaries of the intergenic region. Overall, the patterns and characteristics identified here, along with other comparative and experimental studies, suggest that many footprints have a regulatory function, although other types of function are also possible. These conclusions may be quite general across eukaryotes, and the characteristics of conserved regulatory elements determined from genomic comparisons can be useful in prediction of regulation sites within individual DNA sequences.

We aligned and analyzed 100 pairs of complete, orthologous intergenic regions from the human and mouse genomes (average length approximately 12 000 nucleotides). The alignments alternate between highly similar segments and dissimilar segments, indicating a wide variation of selective constraint. The average number of selectively constrained nucleotides within a mammalian intergenic region is at least 2000. This is threefold higher than within a nematode intergenic region and at least twofold higher than the number of selectively constrained nucleotides coding for an average protein. Because mammals possess only two- to threefold more proteins than Caenorhabditis elegans, the higher complexity of mammals might be primarily because of the functioning of intergenic DNA.