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: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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).

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.

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.

Abstract Summary: 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 106 can often be produced in minutes. OWEN requires memory below 20 L, where L is the sum of lengths of the compared sequences. Availability: Pre-compiled versions of OWEN for Windows, Linux for PC, SunOS for Sun, and Irix for SGI, user manual, and examples of alignments are freely available atftp://ftp.ncbi.nih.gov/pub/kondrashov/owen Contact: [email protected] * To whom correspondence should be addressed.

Abstract 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. Availability: A software tool OWEN based on the proposed approach is described in the accompanying note and is freely available at ftp://ftp.ncbi.nih.gov/pub/kondrashov/owen Contact: [email protected] Supplementary information: Algorithm Chain and examples of chains of local similarities are available at ftp://ftp.ncbi.nih.gov/pub/kondrashov/owen/extra * To whom correspondence should be addressed.

The SAMSON package is a tool for advanced analysis of primary DNA, RNA and protein structures. The package consists of 16 programs performing statistical analysis and comparison of biopolymer sequences, search for homologies, translation of DNA and RNA sequences into amino acid sequences, splicing of RNA sequences and restriction map construction, recognition of functionally related sites in biopolymer molecules, textual analysis of DNA and RNA regulatory sites and prediction of intermolecular hybridization sites in DNA and RNA molecules.