Comparative Analysis of Nucleic Acid and Protein Primary Structures

The review considers the original works on the primary structure of biopolymers carried out from 1983 to 2003. Most works were supported by the Russian program Human Genome and earlier similar Russian programs. Little-known publications of 1983–1993 and recent unpublished results are described in detail. In the field of genome comparisons, these concern the OWEN hierarchic algorithm aligning syntenic regions of two genome sequences. The resulting global alignment is obtained as an ordered chain of local similarities. Alignment of megabase sequences takes several minutes. The concept of local similarity conflicts is generalized to multiple comparisons. New algorithms aligning protein sequences are described and compared with the Smith–Waterman algorithm, which is now most accurate. The ANCHOR hierarchic algorithm generates alignments of much the same accuracy and is twice as rapid as the Smith–Waterman one. The STRSWer algorithm takes into account the secondary structures of proteins under study. With the secondary structures predicted using the PSI-PRED software for pairs of proteins having 10–30% similarity, the average accuracy of alignments generated by STRSWer is 15% higher than that achieved with the Smith–Waterman algorithm.     

Increasing the accuracy of the global alignment of amino acid sequences by constructing a set of alignment candidates

The accuracy of the global Smith-Waterman alignments and Pareto-optimal alignments depending on the degree of sequence similarity (percent of coincidence, % id, and the number of remote fragments NGap) has been examined. An algorithm for constructing a set of three to six alignments has been developed of which the accuracy of the best alignment exceeds on the average the accuracy of the best alignment that can be constructed using the Smith-Waterman algorithm. For weakly homologous sequences (% id 15, NGap 20), the increase in the accuracy is on the average about 8%, with the average accuracy of the global Smith-Waterman alignments being about 38% (the accuracy was estimated on model test sets).     

From analysis of protein structural alignments toward a novel approach to align protein sequences

Alignment of protein sequences is a key step in most computational methods for prediction of protein function and homology-based modeling of three-dimensional (3D)-structure. We investigated correspondence between "gold standard" alignments of 3D protein structures and the sequence alignments produced by the Smith-Waterman algorithm, currently the most sensitive method for pair-wise alignment of sequences. The results of this analysis enabled development of a novel method to align a pair of protein sequences. The comparison of the Smith-Waterman and structure alignments focused on their inner structure and especially on the continuous ungapped alignment segments, "islands" between gaps. Approximately one third of the islands in the gold standard alignments have negative or low positive score, and their recognition is below the sensitivity limit of the Smith-Waterman algorithm. From the alignment accuracy perspective, the time spent by the algorithm while working in these unalignable regions is unnecessary. We considered features of the standard similarity scoring function responsible for this phenomenon and suggested an alternative hierarchical algorithm, which explicitly addresses high scoring regions. This algorithm is considerably faster than the Smith-Waterman algorithm, whereas resulting alignments are in average of the same quality with respect to the gold standard. This finding shows that the decrease of alignment accuracy is not necessarily a price for the computational efficiency.     

Increasing the accuracy of global alignment of amino acid sequences by constructing a set of alignment candidates

The accuracy of global Smith-Waterman alignments and Pareto-optimal alignments depending on the degree of sequence similarity (percent of coincidence, %id, and the number of removed fragments NGap) has been examined. An algorithm for constructing a set of three to six alignments has been developed of which the best alignment on the average exceeds in accuracy the best alignment that can be constructed using the Smith-Waterman algorithm. For weakly homologous sequences (%id 15, NGap 20), the increase in accuracy is on the average about 8%, with the average accuracy of the global Smith-Waterman alignments being about 38% (the accuracy was estimated on model test sets).     

Information about the protein secondary structure improves quality of an alignment of protein sequences

All popular algorithms of pair-wise alignment of protein primary structures (e.g. Smith-Waterman (SW), FASTA, BLAST, et al.) utilize only amino acid sequences. The SW-algorithm is the most accurate among them, i.e. it produces alignments that are most similar to the alignments obtained by superposition of protein 3D-structures. But even the SW-algorithm is unable to restore the 3D-based alignment if similarity of amino acid sequences (%id) is below 30%. We have proposed a novel alignment method that explicitly takes into account the secondary structure of the compared proteins. We have shown that it creates significantly more accurate alignments compared to SW-algorithm. In particular, for sequences with %id < 30% the average accuracy of the new method is 58% compared to 35% for SW-algorithm (the accuracy of an algorithmic sequence alignment is the part of restored position of a "golden standard" alignment obtained by superposition of corresponding 3D-structures). The accuracy of the proposed method is approximately identical both for experimental, and for theoretically predicted secondary structures. Thus the method can be applied for alignment of protein sequences even if protein 3D-structure is unknown. The program is available at ftp://194.149.64.196/STRUSWER/.     

Compressed indexing and local alignment of DNA

MOTIVATION: Recent experimental studies on compressed indexes (BWT, CSA, FM-index) have confirmed their practicality for indexing very long strings such as the human genome in the main memory. For example, a BWT index for the human genome (with about 3 billion characters) occupies just around 1 G bytes. However, these indexes are designed for exact pattern matching, which is too stringent for biological applications. The demand is often on finding local alignments (pairs of similar substrings with gaps allowed). Without indexing, one can use dynamic programming to find all the local alignments between a text T and a pattern P in O(|T||P|) time, but this would be too slow when the text is of genome scale (e.g. aligning a gene with the human genome would take tens to hundreds of hours). In practice, biologists use heuristic-based software such as BLAST, which is very efficient but does not guarantee to find all local alignments. RESULTS: In this article, we show how to build a software called BWT-SW that exploits a BWT index of a text T to speed up the dynamic programming for finding all local alignments. Experiments reveal that BWT-SW is very efficient (e.g. aligning a pattern of length 3 000 with the human genome takes less than a minute). We have also analyzed BWT-SW mathematically for a simpler similarity model (with gaps disallowed), and we show that the expected running time is O(/T/(0.628)/P/) for random strings. As far as we know, BWT-SW is the first practical tool that can find all local alignments. Yet BWT-SW is not meant to be a replacement of BLAST, as BLAST is still several times faster than BWT-SW for long patterns and BLAST is indeed accurate enough in most cases (we have used BWT-SW to check against the accuracy of BLAST and found that only rarely BLAST would miss some significant alignments). AVAILABILITY: www.cs.hku.hk/~ckwong3/bwtsw CONTACT: twlam@cs.hku.hk.     

MATLAB 7.X生物信息工具箱的应用——序列比对(二)

序列比对是基因序列分析中的一项重要工作.本文以人和鼠的基因为对象,介绍MATLAB 7.X生物信息工具箱中的序列比对方法,内容包括从数据库获取序列信息,查找序列的开放阅读框,将核苷酸序列转换为氨基酸序列,绘制比较两氨基酸序列的散点图,用Needleman-Wunsch算法和Smith-Waterman算法进行比对,以及计算两序列的同一性.     

On comparing two structured RNA multiple alignments

We present a method, called BlockMatch, for aligning two blocks, where a block is an RNA multiple sequence alignment with the consensus secondary structure of the alignment in Stockholm format. The method employs a quadratic-time dynamic programming algorithm for aligning columns and column pairs of the multiple alignments in the blocks. Unlike many other tools that can perform pairwise alignment of either single sequences or structures only, BlockMatch takes into account the characteristics of all the sequences in the blocks along with their consensus structures during the alignment process, thus being able to achieve a high-quality alignment result. We apply BlockMatch to phylogeny reconstruction on a set of 5S rRNA sequences taken from fifteen bacteria species. Experimental results showed that the phylogenetic tree generated by our method is more accurate than the tree constructed based on the widely used ClustalW tool. The BlockMatch algorithm is implemented into a web server, accessible at http://bioinformatics.njit.edu/blockmatch. A jar file of the program is also available for download from the web server.     

A new approach to sequence comparison: normalized sequence alignment

The Smith-Waterman algorithm for local sequence alignment is one of the most important techniques in computational molecular biology. This ingenious dynamic programming approach was designed to reveal the highly conserved fragments by discarding poorly conserved initial and terminal segments. However, the existing notion of local similarity has a serious flaw: it does not discard poorly conserved intermediate segments. The Smith-Waterman algorithm finds the local alignment with maximal score but it is unable to find local alignment with maximum degree of similarity (e.g. maximal percent of matches). Moreover, there is still no efficient algorithm that answers the following natural question: do two sequences share a (sufficiently long) fragment with more than 70% of similarity? As a result, the local alignment sometimes produces a mosaic of well-conserved fragments artificially connected by poorly-conserved or even unrelated fragments. This may lead to problems in comparison of long genomic sequences and comparative gene prediction as recently pointed out by Zhang et al. (Bioinformatics, 15, 1012-1019, 1999). In this paper we propose a new sequence comparison algorithm (normalized local alignment ) that reports the regions with maximum degree of similarity. The algorithm is based on fractional programming and its running time is O(n2log n). In practice, normalized local alignment is only 3-5 times slower than the standard Smith-Waterman algorithm.     

A comparison of position-specific score matrices based on sequence and structure alignments

Sequence comparison methods based on position-specific score matrices (PSSMs) have proven a useful tool for recognition of the divergent members of a protein family and for annotation of functional sites. Here we investigate one of the factors that affects overall performance of PSSMs in a PSI-BLAST search, the algorithm used to construct the seed alignment upon which the PSSM is based. We compare PSSMs based on alignments constructed by global sequence similarity (ClustalW and ClustalW-pairwise), local sequence similarity (BLAST), and local structure similarity (VAST). To assess performance with respect to identification of conserved functional or structural sites, we examine the accuracy of the three-dimensional molecular models predicted by PSSM-sequence alignments. Using the known structures of those sequences as the standard of truth, we find that model accuracy varies with the algorithm used for seed alignment construction in the pattern local-structure (VAST) > local-sequence (BLAST) > global-sequence (ClustalW). Using structural similarity of query and database proteins as the standard of truth, we find that PSSM recognition sensitivity depends primarily on the diversity of the sequences included in the alignment, with an optimum around 30-50% average pairwise identity. We discuss these observations, and suggest a strategy for constructing seed alignments that optimize PSSM-sequence alignment accuracy and recognition sensitivity.     

Reconstruction of genuine pair-wise sequence alignment.

In many applications, the algorithmically obtained alignment ideally should restore the "golden standard" (GS) alignment, which superimposes positions originating from the same position of the common ancestor of the compared sequences. The average similarity between the algorithmically obtained and GS alignments ("the quality") is an important characteristic of an alignment algorithm. We proposed to determine the quality of an algorithm, using sequences that were artificially generated in accordance with an appropriate evolution model; the approach was applied to the global version of the Smith-Waterman algorithm (SWA). The quality of SWA is between 97% (for a PAM distance of 60) and 70% (for a PAM distance of 300). The percentage of identical aligned residues is the same for algorithmic and GS alignments. The total length of indels in algorithmic alignments is less than in the GS-mainly due to a substantial decrease in the number of indels in algorithmic alignments.     

Efficient large-scale sequence comparison by locality-sensitive hashing

MOTIVATION: Comparison of multimegabase genomic DNA sequences is a popular technique for finding and annotating conserved genome features. Performing such comparisons entails finding many short local alignments between sequences up to tens of megabases in length. To process such long sequences efficiently, existing algorithms find alignments by expanding around short runs of matching bases with no substitutions or other differences. Unfortunately, exact matches that are short enough to occur often in significant alignments also occur frequently by chance in the background sequence. Thus, these algorithms must trade off between efficiency and sensitivity to features without long exact matches. RESULTS: We introduce a new algorithm, LSH-ALL-PAIRS, to find ungapped local alignments in genomic sequence with up to a specified fraction of substitutions. The length and substitution rate of these alignments can be chosen so that they appear frequently in significant similarities yet still remain rare in the background sequence. The algorithm finds ungapped alignments efficiently using a randomized search technique, locality-sensitive hashing. We have found LSH-ALL-PAIRS to be both efficient and sensitive for finding local similarities with as little as 63% identity in mammalian genomic sequences up to tens of megabases in length     

SCARNA: fast and accurate structural alignment of RNA sequences by matching fixed-length stem fragments

MOTIVATION: The functions of non-coding RNAs are strongly related to their secondary structures, but it is known that a secondary structure prediction of a single sequence is not reliable. Therefore, we have to collect similar RNA sequences with a common secondary structure for the analyses of a new non-coding RNA without knowing the exact secondary structure itself. Therefore, the sequence comparison in searching similar RNAs should consider not only their sequence similarities but also their potential secondary structures. Sankoff's algorithm predicts the common secondary structures of the sequences, but it is computationally too expensive to apply to large-scale analyses. Because we often want to compare a large number of cDNA sequences or to search similar RNAs in the whole genome sequences, much faster algorithms are required. RESULTS: We propose a new method of comparing RNA sequences based on the structural alignments of the fixed-length fragments of the stem candidates. The implemented software, SCARNA (Stem Candidate Aligner for RNAs), is fast enough to apply to the long sequences in the large-scale analyses. The accuracy of the alignments is better or comparable with the much slower existing algorithms. AVAILABILITY: The web server of SCARNA with graphical structural alignment viewer is available at http://www.scarna.org/.     

Searching protein sequence libraries: comparison of the sensitivity and selectivity of the Smith-Waterman and FASTA algorithms.

The sensitivity and selectivity of the FASTA and the Smith-Waterman protein sequence comparison algorithms were evaluated using the superfamily classification provided in the National Biomedical Research Foundation/Protein Identification Resource (PIR) protein sequence database. Sequences from each of the 34 superfamilies in the PIR database with 20 or more members were compared against the protein sequence database. The similarity scores of the related and unrelated sequences were determined using either the FASTA program or the Smith-Waterman local similarity algorithm. These two sets of similarity scores were used to evaluate the ability of the two comparison algorithms to identify distantly related protein sequences. The FASTA program using the ktup = 2 sensitivity setting performed as well as the Smith-Waterman algorithm for 19 of the 34 superfamilies. Increasing the sensitivity by setting ktup = 1 allowed FASTA to perform as well as Smith-Waterman on an additional 7 superfamilies. The rigorous Smith-Waterman method performed better than FASTA with ktup = 1 on 8 superfamilies, including the globins, immunoglobulin variable regions, calmodulins, and plastocyanins. Several strategies for improving the sensitivity of FASTA were examined. The greatest improvement in sensitivity was achieved by optimizing a band around the best initial region found for every library sequence. For every superfamily except the globins and immunoglobulin variable regions, this strategy was as sensitive as a full Smith-Waterman. For some sequences, additional sensitivity was achieved by including conserved but nonidentical residues in the lookup table used to identify the initial region.     

Choosing BLAST options for better detection of orthologs as reciprocal best hits

MOTIVATION: The analyses of the increasing number of genome sequences requires shortcuts for the detection of orthologs, such as Reciprocal Best Hits (RBH), where orthologs are assumed if two genes each in a different genome find each other as the best hit in the other genome. Two BLAST options seem to affect alignment scores the most, and thus the choice of a best hit: the filtering of low information sequence segments and the algorithm used to produce the final alignment. Thus, we decided to test whether such options would help better detect orthologs. RESULTS: Using Escherichia coli K12 as an example, we compared the number and quality of orthologs detected as RBH. We tested four different conditions derived from two options: filtering of low-information segments, hard (default) versus soft; and alignment algorithm, default (based on matching words) versus Smith-Waterman. All options resulted in significant differences in the number of orthologs detected, with the highest numbers obtained with the combination of soft filtering with Smith-Waterman alignments. We compared these results with those of Reciprocal Shortest Distances (RSD), supposed to be superior to RBH because it uses an evolutionary measure of distance, rather than BLAST statistics, to rank homologs and thus detect orthologs. RSD barely increased the number of orthologs detected over those found with RBH. Error estimates, based on analyses of conservation of gene order, found small differences in the quality of orthologs detected using RBH. However, RSD showed the highest error rates. Thus, RSD have no advantages over RBH. AVAILABILITY: Orthologs detected as Reciprocal Best Hits using soft masking and Smith-Waterman alignments can be downloaded from http://popolvuh.wlu.ca/Orthologs.     

MARNA: multiple alignment and consensus structure prediction of RNAs based on sequence structure comparisons

MOTIVATION: Due to the importance of considering secondary structures in aligning functional RNAs, several pairwise sequence-structure alignment methods have been developed. They use extended alignment scores that evaluate secondary structure information in addition to sequence information. However, two problems for the multiple alignment step remain. First, how to combine pairwise sequence-structure alignments into a multiple alignment and second, how to generate secondary structure information for sequences whose explicit structural information is missing. RESULTS: We describe a novel approach for multiple alignment of RNAs (MARNA) taking into consideration both the primary and the secondary structures. It is based on pairwise sequence-structure comparisons of RNAs. From these sequence-structure alignments, libraries of weighted alignment edges are generated. The weights reflect the sequential and structural conservation. For sequences whose secondary structures are missing, the libraries are generated by sampling low energy conformations. The libraries are then processed by the T-Coffee system, which is a consistency based multiple alignment method. Furthermore, we are able to extract a consensus-sequence and -structure from a multiple alignment. We have successfully tested MARNA on several datasets taken from the Rfam database.     

Post-processing long pairwise alignments

MOTIVATION: The local alignment problem for two sequences requires determining similar regions, one from each sequence, and aligning those regions. For alignments computed by dynamic programming, current approaches for selecting similar regions may have potential flaws. For instance, the criterion of Smith and Waterman can lead to inclusion of an arbitrarily poor internal segment. Other approaches can generate an alignment scoring less than some of its internal segments. RESULTS: We develop an algorithm that decomposes a long alignment into sub-alignments that avoid these potential imperfections. Our algorithm runs in time proportional to the original alignment's length. Practical applications to alignments of genomic DNA sequences are described.     

Self-consistently optimized statistical mechanical energy functions for sequence structure alignment.

A quantitative form of the principle of minimal frustration is used to obtain from a database analysis statistical mechanical energy functions and gap parameters for aligning sequences to three-dimensional structures. The analysis that partially takes into account correlations in the energy landscape improves upon the previous approximations of Goldstein et al. (1994, 1995) (Goldstein R, Luthey-Schulten Z, Wolynes P, 1994, Proceedings of the 27th Hawaii International Conference on System Sciences. Los Alamitos, California: IEEE Computer Society Press. pp 306-315; Goldstein R, Luthey-Schulten Z, Wolynes P, 1995, In: Elber R, ed. New developments in theoretical studies of proteins. Singapore: World Scientific). The energy function allows for ordering of alignments based on the compatibility of a sequence to be in a given structure (i.e., lowest energy) and therefore removes the necessity of using percent identity or similarity as scoring parameters. The alignments produced by the energy function on distant homologues with low percent identity (less than 21%) are generally better than those generated with evolutionary information. The lowest energy alignment generated with the energy function for sequences containing prosite signatures but unknown structures is a structure containing the same prosite signature, providing a check on the robustness of the algorithm. Finally, the energy function can make use of known experimental evidence as constraints within the alignment algorithm to aid in finding the correct structural alignment.     

Alignment of Escherichia coli K12 DNA sequences to a genomic restriction map.

We use the extensive published information describing the genome of Escherichia coli and new restriction map alignment software to align DNA sequence, genetic, and physical maps. Restriction map alignment software is used which considers restriction maps as strings analogous to DNA or protein sequences except that two values, enzyme name and DNA base address, are associated with each position on the string. The resulting alignments reveal a nearly linear relationship between the physical and genetic maps of the E. coli chromosome. Physical map comparisons with the 1976, 1980, and 1983 genetic maps demonstrate a better fit with the more recent maps. The results of these alignments are genomic kilobase coordinates, orientation and rank of the alignment that best fits the genetic data. A statistical measure based on extreme value distribution is applied to the alignments. Additional computer analyses allow us to estimate the accuracy of the published E. coli genomic restriction map, simulate rearrangements of the bacterial chromosome, and search for repetitive DNA. The procedures we used are general enough to be applicable to other genome mapping projects.     

Fast, optimal alignment of three sequences using linear gap costs

Alignment algorithms can be used to infer a relationship between sequences when the true relationship is unknown. Simple alignment algorithms use a cost function that gives a fixed cost to each possible point mutation-mismatch, deletion, insertion. These algorithms tend to find optimal alignments that have many small gaps. It is more biologically plausible to have fewer longer gaps rather than many small gaps in an alignment. To address this issue, linear gap cost algorithms are in common use for aligning biological sequence data. More reliable inferences are obtained by aligning more than two sequences at a time. The obvious dynamic programming algorithm for optimally aligning k sequences of length n runs in O(n(k)) time. This is impractical if k>/=3 and n is of any reasonable length. Thus, for this problem there are many heuristics for aligning k sequences, however, they are not guaranteed to find an optimal alignment. In this paper, we present a new algorithm guaranteed to find the optimal alignment for three sequences using linear gap costs. This gives the same results as the dynamic programming algorithm for three sequences, but typically does so much more quickly. It is particularly fast when the (three-way) edit distance is small. Our algorithm uses a speed-up technique based on Ukkonen's greedy algorithm (Ukkonen, 1983) which he presented for two sequences and simple costs.