Indel seeds for homology search

We are interested in detecting homologous genomic DNA sequences with the goal of locating approximate inverted, interspersed, and tandem repeats. Standard search techniques start by detecting small matching parts, called seeds, between a query sequence and database sequences. Contiguous seed models have existed for many years. Recently, spaced seeds were shown to be more sensitive than contiguous seeds without increasing the random hit rate. To determine the superiority of one seed model over another, a model of homologous sequence alignment must be chosen. Previous studies evaluating spaced and contiguous seeds have assumed that matches and mismatches occur within these alignments, but not insertions and deletions (indels). This is perhaps appropriate when searching for protein coding sequences (<5% of the human genome), but is inappropriate when looking for repeats in the majority of genomic sequence where indels are common. In this paper, we assume a model of homologous sequence alignment which includes indels and we describe a new seed model, called indel seeds, which explicitly allows indels. We present a waiting time formula for computing the sensitivity of an indel seed and show that indel seeds significantly outperform contiguous and spaced seeds when homologies include indels. We discuss the practical aspect of using indel seeds and finally we present results from a search for inverted repeats in the dog genome using both indel and spaced seeds.     

Choosing the best heuristic for seeded alignment of DNA sequences

Background  

Seeded alignment is an important component of algorithms for fast, large-scale DNA similarity search. A good seed matching heuristic can reduce the execution time of genomic-scale sequence comparison without degrading sensitivity. Recently, many types of seed have been proposed to improve on the performance of traditional contiguous seeds as used in, e.g., NCBI BLASTN. Choosing among these seed types, particularly those that use information besides the presence or absence of matching residue pairs, requires practical guidance based on a rigorous comparison, including assessment of sensitivity, specificity, and computational efficiency. This work performs such a comparison, focusing on alignments in DNA outside widely studied coding regions.     

Good spaced seeds for homology search

MOTIVATION: Filtration is an important technique used to speed up local alignment as exemplified in the BLAST programs. Recently, Ma et al. discovered that better filtering can be achieved by spacing out the matching positions according to a certain pattern, instead of contiguous positions to trigger a local alignment in their PatternHunter program. Such a match pattern is called a spaced seed. RESULTS: Our numerical computation shows that the ranks of spaced seeds (based on sensitivity) change with the sequences similarity. Since homologous sequences may have diverse similarity, we assess the sensitivity of spaced seeds over a range of similarity levels and present a list of good spaced seeds for facilitating homology search in DNA genomic sequences. We validate that the listed spaced seeds are indeed more sensitive using three arbitrarily chosen pairs of DNA genomic sequences.     

Designing sensitive and specific spaced seeds for cross-species mRNA-to-genome alignment.

As the demand for accurately aligning gene sequences to the genome of a related species grows with the sequencing of new genomes, spaced seeds emerge as a promising vehicle for increasing alignment sensitivity. We extend the existing {0, 1} match-mismatch models for sensitivity evaluation to take into account the compositional structure of coding sequences and ultimately produce seeds better suited to this particular application. Designing seeds for alignment programs, however, needs to balance sensitivity and specificity.We assess the effects of seed variations on both sensitivity and specificity in an extended model that incorporates transitions and differentiates among the three codon positions, and show that spaced seeds with transitions offer a better sensitivity-specificity tradeoff. Furthermore, we propose a theoretical formulation for rigorously assessing seed specificity, starting from Bernoulli and Markov models of the mRNA and genomic sequences. Within this framework, we perform the first comprehensive analysis of seeds to serve as a blueprint for selecting sensitive and specific seeds for practical applications. Our analyses show that specificity is relatively constant for seeds of a given weight, while sensitivity varies widely, with the highest values attained by seeds allowing a small (2-6) number of transitions.A strategy for designing seeds, therefore, is to first select the weight of the seed by identifying the desired sensitivity-specificity tradeoff, then choose the most sensitive seed(s) within that weight group. We illustrate our methods with the alignment of chicken coding sequences against the human genome assembly version HG17.     

Multiple spaced seeds for homology search

MOTIVATION: Homology search finds similar segments between two biological sequences, such as DNA or protein sequences. The introduction of optimal spaced seeds in PatternHunter has increased both the sensitivity and the speed of homology search, and it has been adopted by many alignment programs such as BLAST. With the further improvement provided by multiple spaced seeds in PatternHunterII, Smith-Waterman sensitivity is approached at BLASTn speed. However, computing optimal multiple spaced seeds was proved to be NP-hard and current heuristic algorithms are all very slow (exponential). RESULTS: We give a simple algorithm which computes good multiple seeds in polynomial time. Due to a completely different approach, the difference with respect to the previous methods is dramatic. The multiple spaced seed of PatternHunterII, with 16 weight 11 seeds, was computed in 12 days. It takes us 17 s to find a better one. Our approach changes the way of looking at multiple spaced seeds.     

Sim4cc: a cross-species spliced alignment program

Advances in sequencing technologies have accelerated the sequencing of new genomes, far outpacing the generation of gene and protein resources needed to annotate them. Direct comparison and alignment of existing cDNA sequences from a related species is an effective and readily available means to determine genes in the new genomes. Current spliced alignment programs are inadequate for comparing sequences between different species, owing to their low sensitivity and splice junction accuracy. A new spliced alignment tool, sim4cc, overcomes problems in the earlier tools by incorporating three new features: universal spaced seeds, to increase sensitivity and allow comparisons between species at various evolutionary distances, and powerful splice signal models and evolutionarily-aware alignment techniques, to improve the accuracy of gene models. When tested on vertebrate comparisons at diverse evolutionary distances, sim4cc had significantly higher sensitivity compared to existing alignment programs, more than 10% higher than the closest competitor for some comparisons, while being comparable in speed to its predecessor, sim4. Sim4cc can be used in one-to-one or one-to-many comparisons of genomic and cDNA sequences, and can also be effectively incorporated into a high-throughput annotation engine, as demonstrated by the mapping of 64 000 Fagus grandifolia 454 ESTs and unigenes to the poplar genome.     

Quick, practical selection of effective seeds for homology search.

It has been observed that in homology search gapped seeds have better sensitivity than ungapped ones for the same cost (weight). In this paper, we propose a probability leakage model (a dissipative Markov system) to elucidate the mechanism that confers power to spaced seeds. Based on this model, we identify desirable features of gapped search seeds and formulate an extremely efficient procedure for seed design: it samples from the set of spaced seed exhibiting those features, evaluates their sensitivity, and then selects the best. The sensitivity of the constructed seeds is negligibly less than that of the corresponding known optimal seeds. While the challenging mathematical question of characterizing optimal search seeds remains open, we believe that our eminently efficient and effective approach represents a satisfactory solution from a practitioner's viewpoint.     

Sequence analysis and structural prediction of the severe acute respiratory syndrome coronavirus nsp5

The non-structural proteins (nsp or replicase proteins) of coronaviruses are relatively conserved and can be effective targets for drugs. Few studies have been conducted into the function of the severe acute respiratory syndrome coronavirus (SARS-CoV) nsp5. In this study, bioinformatics methods were employed to predict the secondary structure and construct 3-D models of the SARS-CoV GD strain nsp5. Sequencing and sequential comparison was performed to analyze the mutation trend of the polymerase nsp5 gene during the epidemic process using a nucleotide-nucleotide basic local alignment search tool (BLASTN) and a protein-protein basic local alignment search tool (BLASTP). The results indicated that the nsp5 gene was steady during the epidemic process and the protein was homologous with other coronavirus nsp5 proteins. The protein encoded by the nsp5 gene was expressed in COS-7 cells and analyzed by sodium dodecylsulfate-polyacrylamide gel electrophoresis (SDS-PAGE). This study provided the foundation for further exploration of the protein‘s biological function, and contributed to the search for anti-SARS-CoV drugs.     

Analysis of Schistosoma mansoni genes using the expressed sequence tag approach

Expressed sequence tags (ESTs) are partial cDNA sequences read from both ends of random expressed gene fragments used for discovering new genes. DNA libraries from four different developmental stages of Schistosoma mansoni used in this study generated 141 ESTs representing about 2.5% of S. mansoni sequences in dbEST. Sequencing was done by the dideoxy chain termination method. The sequences were submitted to GenBank for homology searching in nonredundant databases using Basic Local Alignment Search Tool for DNA (BLASTN) alignment and for protein (BLASTX) alignment at the National Center for Biotechnology Information (NCBI). Among submitted ESTs, 29 were derived from lambdagt11 sporocyst library, 70 from lambdaZap adult worm library, 31 from lambdaZap cercarial library, and 11 from lambdaZap female B worm library. Homology search revealed that eight (5.6%) ESTs shared homology to previously identified S.mansoni genes in dbEST, 15 (10.6%) are homologous to known genes in other organisms, 116 (81.7%) showed no significant sequence homology in the databases, and the remaining sequences (2.1%) showed low homologies to rRNA or mitochondrial DNA sequences. Thus, among the 141 ESTs studied, 116 sequences are derived from noval, uncharactarized S. mansoni genes. Those 116 ESTs are important for identification of coding regions in the sequences, helping in mapping of schistosome genome, and identifying genes of immunological and pharmacological significance.     

Comparison of sequence and structure alignments for protein domains

Profile search methods based on protein domain alignments have proven to be useful tools in comparative sequence analysis. Domain alignments used by currently available search methods have been computed by sequence comparison. With the growth of the protein structure database, however, alignments of many domain pairs have also been computed by structure comparison. Here, we examine the extent to which information from these two sources agrees. We measure agreement with respect to identification of homologous regions in each protein, that is, with respect to the location of domain boundaries. We also measure agreement with respect to identification of homologous residue sites by comparing alignments and assessing the accuracy of the molecular models they predict. We find that domain alignments in publicly available collections based on sequence and structure comparison are largely consistent. However, the homologous regions identified by sequence comparison are often shorter than those identified by 3D structure comparison. In addition, when overall sequence similarity is low alignments from sequence comparison produce less accurate molecular models, suggesting that they less accurately identify homologous sites. These observations suggest that structure comparison results might be used to improve the overall accuracy of domain alignment collections and the performance of profile search methods based on them.     

A mostly traditional approach improves alignment of bisulfite-converted DNA

Cytosines in genomic DNA are sometimes methylated. This affects many biological processes and diseases. The standard way of measuring methylation is to use bisulfite, which converts unmethylated cytosines to thymines, then sequence the DNA and compare it to a reference genome sequence. We describe a method for the critical step of aligning the DNA reads to the correct genomic locations. Our method builds on classic alignment techniques, including likelihood-ratio scores and spaced seeds. In a realistic benchmark, our method has a better combination of sensitivity, specificity and speed than nine other high-throughput bisulfite aligners. This study enables more accurate and rational analysis of DNA methylation. It also illustrates how to adapt general-purpose alignment methods to a special case with distorted base patterns: this should be informative for other special cases such as ancient DNA and AT-rich genomes.     

Superiority of spaced seeds for homology search

In homology search, good spaced seeds have higher sensitivity for the same cost (weight). However, elucidating the mechanism that confers power to spaced seeds and characterizing optimal spaced seeds still remain unsolved. This paper investigates these two important open questions by formally analyzing the average number of non-overlapping hits and the hit probability of a spaced seed in the Bernoulli sequence model. We prove that when the length of a non-uniformly spaced seed is bounded above by an exponential function of the seed weight, the seed outperforms strictly the traditional consecutive seed of the same weight in both 1) the average number of non-overlapping hits and 2) the asymptotic hit probability. This clearly answers the first problem mentioned above in the Bernoulli sequence model. The theoretical study in this paper also gives a new solution to finding long optimal seeds.     

Extensive sequence homology between the mycobacterium leprae LSR (12 kDa) antigen and its Mycobacterium tuberculosis counterpart

The Mycobacterium leprae LSR (12 kDa) protein antigen has been reported to mimic whole cell M. leprae in T cell responses across the leprosy spectrum. In addition, B cell responses to specific sequences within the LSR antigen have been shown to be associated with immunopathological responses in leprosy patients with erythema nodosum leprosum. We have in the present study applied the M. leprae LSR DNA sequence as query to search for the presence of homologous genes within the recently completed Mycobacterium tuberculosis genome database (Sanger Centre, UK). By using the BLASTN search tool, a homologous M. tuberculosis open reading frame (336 bp), encoding a protein antigen of 12.1 kDa, was identified within the cosmid MTCY07H7B.25. The gene is designated Rv3597c within the M. tuberculosis H37Rv genome. Sequence alignment revealed 93% identity between the M. leprae and M. tuberculosis antigens at the amino acid sequence level. The finding that some B and T cell epitopes were localized to regions with amino acid substitutions may account for the putative differential responsiveness to this antigen in tuberculosis and leprosy.     

Dynamic use of multiple parameter sets in sequence alignment

The level of conservation between two homologous sequences often varies among sequence regions; functionally important domains are more conserved than the remaining regions. Thus, multiple parameter sets should be used in alignment of homologous sequences with a stringent parameter set for highly conserved regions and a moderate parameter set for weakly conserved regions. We describe an alignment algorithm to allow dynamic use of multiple parameter sets with different levels of stringency in computation of an optimal alignment of two sequences. The algorithm dynamically considers various candidate alignments, partitions each candidate alignment into sections, and determines the most appropriate set of parameter values for each section of the alignment. The algorithm and its local alignment version are implemented in a computer program named GAP4. The local alignment algorithm in GAP4, that in its predecessor GAP3, and an ordinary local alignment program SIM were evaluated on 257716 pairs of homologous sequences from 100 protein families. On 168475 of the 257716 pairs (a rate of 65.4%), alignments from GAP4 were more statistically significant than alignments from GAP3 and SIM.     

A generalized global alignment algorithm

MOTIVATION: Homologous sequences are sometimes similar over some regions but different over other regions. Homologous sequences have a much lower global similarity if the different regions are much longer than the similar regions. RESULTS: We present a generalized global alignment algorithm for comparing sequences with intermittent similarities, an ordered list of similar regions separated by different regions. A generalized global alignment model is defined to handle sequences with intermittent similarities. A dynamic programming algorithm is designed to compute an optimal general alignment in time proportional to the product of sequence lengths and in space proportional to the sum of sequence lengths. The algorithm is implemented as a computer program named GAP3 (Global Alignment Program Version 3). The generalized global alignment model is validated by experimental results produced with GAP3 on both DNA and protein sequences. The GAP3 program extends the ability of standard global alignment programs to recognize homologous sequences of lower similarity. AVAILABILITY: The GAP3 program is freely available for academic use at http://bioinformatics.iastate.edu/aat/align/align.html.     

Calign: aligning sequences with restricted affine gap penalties.

MOTIVATION: Given a genomic DNA sequence, it is still an open problem to determine its coding regions, i.e. the region consisting of exons and introns. The comparison of cDNA and genomic DNA helps the understanding of coding regions. For such an application, it might be adequate to use the restricted affine gap penalties which penalize long gaps with a constant penalty. RESULTS: Several techniques developed for solving the approximate string-matching problem are employed to yield efficient algorithms for computing the optimal alignment with restricted affine gap penalties. In particular, efficient algorithms can be derived based on the suffix automaton with failure transitions and on the diagonalwise monotonicity of the cost tables. We have implemented the above methods in C on Sun workstations running SunOS Unix. Preliminary experiments show that these approaches are very promising for aligning a cDNA sequence with a genomic DNA sequence. AVAILABILITY: Calign is available free of charge by anonymous ftp at: iubio.bio. indiana.edu, directory: molbio/align, files: calign.driver.c calign. c. Another URL reference for the files is http://iubio.bio.indiana.edu/soft/molbio/align/+ ++calign.c.     

Consensus alignment for reliable framework prediction in homology modeling

MOTIVATION: Even the best sequence alignment methods frequently fail to correctly identify the framework regions for which backbones can be copied from the template into the target structure. Since the underprediction and, more significantly, the overprediction of these regions reduces the quality of the final model, it is of prime importance to attain as much as possible of the true structural alignment between target and template. RESULTS: We have developed an algorithm called Consensus that consistently provides a high quality alignment for comparative modeling. The method follows from a benchmark analysis of the 3D models generated by ten alignment techniques for a set of 79 homologous protein structure pairs. For 20-to-40% of the targets, these methods yield models with at least 6 A root mean square deviation (RMSD) from the native structure. We have selected the top five performing methods, and developed a consensus algorithm to generate an improved alignment. By building on the individual strength of each method, a set of criteria was implemented to remove the alignment segments that are likely to correspond to structurally dissimilar regions. The automated algorithm was validated on a different set of 48 protein pairs, resulting in 2.2 A average RMSD for the predicted models, and only four cases in which the RMSD exceeded 3 A. The average length of the alignments was about 75% of that found by standard structural superposition methods. The performance of Consensus was consistent from 2 to 32% target-template sequence identity, and hence it can be used for accurate prediction of framework regions in homology modeling.     

Application of discrete Fourier inter-coefficient difference for assessing genetic sequence similarity

Digital signal processing (DSP) techniques for biological sequence analysis continue to grow in popularity due to the inherent digital nature of these sequences. DSP methods have demonstrated early success for detection of coding regions in a gene. Recently, these methods are being used to establish DNA gene similarity. We present the inter-coefficient difference (ICD) transformation, a novel extension of the discrete Fourier transformation, which can be applied to any DNA sequence. The ICD method is a mathematical, alignment-free DNA comparison method that generates a genetic signature for any DNA sequence that is used to generate relative measures of similarity among DNA sequences. We demonstrate our method on a set of insulin genes obtained from an evolutionarily wide range of species, and on a set of avian influenza viral sequences, which represents a set of highly similar sequences. We compare phylogenetic trees generated using our technique against trees generated using traditional alignment techniques for similarity and demonstrate that the ICD method produces a highly accurate tree without requiring an alignment prior to establishing sequence similarity.     

Optimizing multiple seeds for protein homology search

We present a framework for improving local protein alignment algorithms. Specifically, we discuss how to extend local protein aligners to use a collection of vector seeds or ungapped alignment seeds to reduce noise hits. We model picking a set of seed models as an integer programming problem and give algorithms to choose such a set of seeds. While the problem is NP-hard, and Quasi-NP-hard to approximate to within a logarithmic factor, it can be solved easily in practice. A good set of seeds we have chosen allows four to five times fewer false positive hits, while preserving essentially identical sensitivity as BLASTP.