An efficient string matching algorithm with K substitutions for nucleotide and amino acid sequences

Given a text of length n, a pattern of length m and an integer k, we present an algorithm for finding all occurrences of the pattern in the text, each with at most k substitutions. The algorithm runs in O(k(m log m + n)) time, and requires O(nk) space. This algorithm has direct implications for nucleotide and amino acid sequence comparisons.     

An efficient string matching algorithm with k differences for nucleotide and amino acid sequences.

There are a few algorithms designed to solve the problem of the optimal alignment of one sequence, the pattern, of length m, with another, longer sequence the text, of length n. These algorithms allow mismatches, deletions and insertions. Algorithms to date run in O(mn) time. Let us define an integer, k, which is the maximal number of differences allowed. We present a simple algorithm showing that sequences can be optimally aligned in O(k2n) time. For long sequences the gain factor over the currently used algorithms is very large.     

An algorithm for approximate tandem repeats.

A perfect single tandem repeat is defined as a nonempty string that can be divided into two identical substrings, e.g., abcabc. An approximate single tandem repeat is one in which the substrings are similar, but not identical, e.g., abcdaacd. In this paper we consider two criterions of similarity: the Hamming distance (k mismatches) and the edit distance (k differences). For a string S of length n and an integer k our algorithm reports all locally optimal approximate repeats, r = umacro ?, for which the Hamming distance of umacro and ? is at most k, in O(nk log (n/k)) time, or all those for which the edit distance of umacro and ? is at most k, in O(nk log k log (n/k)) time. This paper concentrates on a more general type of repeat called multiple tandem repeats. A multiple tandem repeat in a sequence S is a (periodic) substring r of S of the form r = u(a)u', where u is a prefix of r and u' is a prefix of u. An approximate multiple tandem repeat is a multiple repeat with errors; the repeated subsequences are similar but not identical. We precisely define approximate multiple repeats, and present an algorithm that finds all repeats that concur with our definition. The time complexity of the algorithm, when searching for repeats with up to k errors in a string S of length n, is O(nka log (n/k)) where a is the maximum number of periods in any reported repeat. We present some experimental results concerning the performance and sensitivity of our algorithm. The problem of finding repeats within a string is a computational problem with important applications in the field of molecular biology. Both exact and inexact repeats occur frequently in the genome, and certain repeats occurring in the genome are known to be related to diseases in the human.     

K-ary clustering with optimal leaf ordering for gene expression data

MOTIVATION: A major challenge in gene expression analysis is effective data organization and visualization. One of the most popular tools for this task is hierarchical clustering. Hierarchical clustering allows a user to view relationships in scales ranging from single genes to large sets of genes, while at the same time providing a global view of the expression data. However, hierarchical clustering is very sensitive to noise, it usually lacks of a method to actually identify distinct clusters, and produces a large number of possible leaf orderings of the hierarchical clustering tree. In this paper we propose a new hierarchical clustering algorithm which reduces susceptibility to noise, permits up to k siblings to be directly related, and provides a single optimal order for the resulting tree. RESULTS: We present an algorithm that efficiently constructs a k-ary tree, where each node can have up to k children, and then optimally orders the leaves of that tree. By combining k clusters at each step our algorithm becomes more robust against noise and missing values. By optimally ordering the leaves of the resulting tree we maintain the pairwise relationships that appear in the original method, without sacrificing the robustness. Our k-ary construction algorithm runs in O(n(3)) regardless of k and our ordering algorithm runs in O(4(k)n(3)). We present several examples that show that our k-ary clustering algorithm achieves results that are superior to the binary tree results in both global presentation and cluster identification. AVAILABILITY: We have implemented the above algorithms in C++ on the Linux operating system.     

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.     

Improved Approximation Algorithms for Reconstructing the History of Tandem Repeats

Some genetic diseases in human beings are dominated by short sequences repeated consecutively called tandem repeats. Once a region containing tandem repeats is found, it is of great interest to study the history of creating the repeats. The computational problem of reconstructing the duplication history of tandem repeats has been studied extensively in the literature. Almost all previous studies focused on the simplest case where the size of each duplication block is 1. Only recently we succeeded in giving the first polynomial-time approximation algorithm with a guaranteed ratio for a more general case where the size of each duplication block is at most 2; the algorithm achieves a ratio of 6 and runs in O(n^{11}) time. In this paper, we present two new polynomial-time approximation algorithms for this more general case. One of them achieves a ratio of 5 and runs in O(n^9) time, while the other achieves a ratio of 2.5+epsilon for any constant epsilon ≫ 0 but runs slower.     

Improved algorithms for finding gene teams and constructing gene team trees

A gene team is a set of genes that appear in two or more species, possibly in a different order yet with the distance between adjacent genes in the team for each chromosome always no more than a certain threshold δ. A gene team tree is a succinct way to represent all gene teams for every possible value of δ. In this paper, improved algorithms are presented for the problem of finding the gene teams of two chromosomes and the problem of constructing a gene team tree of two chromosomes. For the problem of finding gene teams, Beal et al. had an O(n lg2 n)-time algorithm. Our improved algorithm requires O(n lg t) time, where t ≤ n is the number of gene teams. For the problem of constructing a gene team tree, Zhang and Leong had an O(n lg2 n)-time algorithm. Our improved algorithm requires O(n lg n lglg n) time. Similar to Beal et al.'s gene team algorithm and Zhang and Leong's gene team tree algorithm, our improved algorithms can be extended to k chromosomes with the time complexities increased only by a factor of k.     

Fast exact algorithms for the closest string and substring problems with application to the planted (L, d)-motif model

We present two parameterized algorithms for the closest string problem. The first runs in O(nL + nd · 17.97d) time for DNA strings and in O(nL + nd · 61.86d) time for protein strings, where n is the number of input strings, L is the length of each input string, and d is the given upper bound on the number of mismatches between the center string and each input string. The second runs in O(nL + nd · 13.92d) time for DNA strings and in O(nL + nd · 47.21d) time for protein strings. We then extend the first algorithm to a new parameterized algorithm for the closest substring problem that runs in O((n - 1)m2(L + d · 17.97d · m[log2(d+1)])) time for DNA strings and in O((n - 1)m2(L + d · 61.86d · m[log2(d+1)])) time for protein strings, where n is the number of input strings, L is the length of the center substring, L - 1 + m is the maximum length of a single input string, and d is the given upper bound on the number of mismatches between the center substring and at least one substring of each input string. All the algorithms significantly improve the previous bests. To verify experimentally the theoretical improvements in the time complexity, we implement our algorithm in C and apply the resulting program to the planted (L, d)-motif problem proposed by Pevzner and Sze in 2000. We compare our program with the previously best exact program for the problem, namely PMSPrune (designed by Davila et al. in 2007). Our experimental data show that our program runs faster for practical cases and also for several challenging cases. Our algorithm uses less memory too.     

A new efficient algorithm for the gene-team problem on general sequences

Identifying conserved gene clusters is an important step toward understanding the evolution of genomes and predicting the functions of genes. A famous model to capture the essential biological features of a conserved gene cluster is called the gene-team model. The problem of finding the gene teams of two general sequences is the focus of this paper. For this problem, He and Goldwasser had an efficient algorithm that requires O(mn) time using O(m + n) working space, where m and n are, respectively, the numbers of genes in the two given sequences. In this paper, a new efficient algorithm is presented. Assume m ≤ n. Let C = Σ(α)(∈)(Σ) o(1)(α)o(2)(α), where Σ is the set of distinct genes, and o(1)(α) and o(2)(α) are, respectively, the numbers of copies of α in the two given sequences. Our new algorithm requires O(min{C lg n, mn}) time using O(m + n) working space. As compared with He and Goldwasser's algorithm, our new algorithm is more practical, as C is likely to be much smaller than mn in practice. In addition, our new algorithm is output sensitive. Its running time is O(lg n) times the size of the output. Moreover, our new algorithm can be efficiently extended to find the gene teams of k general sequences in O(k C lg (n(1)n(2). . .n(k)) time, where n(i) is the number of genes in the ith input sequence.     

A suboptimal algorithm for de novo peptide sequencing via tandem mass spectrometry.

Tandem mass spectrometry has emerged to be one of the most powerful high-throughput techniques for protein identification. Tandem mass spectrometry selects and fragments peptides of interest into N-terminal ions and C-terminal ions, and it measures the mass/charge ratios of these ions. The de novo peptide sequencing problem is to derive the peptide sequences from given tandem mass spectral data of k ion peaks without searching against protein databases. By transforming the spectral data into a matrix spectrum graph G = (V, E), where |V| = O(k(2)) and |E| = O(k(3)), we give the first polynomial time suboptimal algorithm that finds all the suboptimal solutions (peptides) in O(p|E|) time, where p is the number of solutions. The algorithm has been implemented and tested on experimental data. The program is available at http://hto-c.usc.edu:8000/msms/menu/denovo.htm.     

An O(n log n)-time algorithm for the restriction scaffold assignment problem.

The restriction scaffold assignment problem takes as input two finite point sets S and T (with S containing more points than T ) and establishes a correspondence between points in S and points in T , such that each point in S maps to exactly one point in T and each point in T maps to at least one point in S. An algorithm is presented that finds a minimum-cost solution for this problem in O(n log n) time, provided that the points in S and T are restricted to lie on a line and the cost function delta is the L(1) metric. This algorithm runs in linear time, if S and T are presorted. This improves the previously best-known O(n (2))-time algorithm for this problem.     

An efficient algorithm for statistical multiple alignment on arbitrary phylogenetic trees.

We present an efficient algorithm for statistical multiple alignment based on the TKF91 model of Thorne, Kishino, and Felsenstein (1991) on an arbitrary k-leaved phylogenetic tree. The existing algorithms use a hidden Markov model approach, which requires at least O( radical 5(k)) states and leads to a time complexity of O(5(k)L(k)), where L is the geometric mean sequence length. Using a combinatorial technique reminiscent of inclusion/exclusion, we are able to sum away the states, thus improving the time complexity to O(2(k)L(k)) and considerably reducing memory requirements. This makes statistical multiple alignment under the TKF91 model a definite practical possibility in the case of a phylogenetic tree with a modest number of leaves.     

The k partition-distance problem

Many applications of data partitioning (clustering) have been well studied in bioinformatics. Consider, for instance, a set N of organisms (elements) based on DNA marker data. A partition divides all elements in N into two or more disjoint clusters that cover all elements, where a cluster contains a non-empty subset of N. Different partitioning algorithms may produce different partitions. To compute the distance and find the consensus partition (also called consensus clustering) between two or more partitions are important and interesting problems that arise frequently in bioinformatics and data mining, in which different distance functions may be considered in different partition algorithms. In this article, we discuss the k partition-distance problem. Given a set of elements N with k partitions of N, the k partition-distance problem is to delete the minimum number of elements from each partition such that all remaining partitions become identical. This problem is NP-complete for general k?>?2 partitions, and no algorithms are known at present. We design the first known heuristic and approximation algorithms with performance ratios 2 to solve the k partition-distance problem in O(k?·?ρ?·?|N|) time, where ρ is the maximum number of clusters of these k partitions and |N| is the number of elements in N. We also present the first known exact algorithm in O(??·?2(?)·k(2)?·?|N|(2)) time, where ? is the partition-distance of the optimal solution for this problem. Performances of our exact and approximation algorithms in testing the random data with actual sets of organisms based on DNA markers are compared and discussed. Experimental results reveal that our algorithms can improve the computational speed of the exact algorithm for the two partition-distance problem in practice if the maximum number of elements per cluster is less than ρ. From both theoretical and computational points of view, our solutions are at most twice the partition-distance of the optimal solution. A website offering the interactive service of solving the k partition-distance problem using our and previous algorithms is available (see http://mail.tmue.edu.tw/~yhchen/KPDP.html).     

An efficient algorithm to compute the landscape of locally optimal RNA secondary structures with respect to the Nussinov-Jacobson energy model.

We make a novel contribution to the theory of biopolymer folding, by developing an efficient algorithm to compute the number of locally optimal secondary structures of an RNA molecule, with respect to the Nussinov-Jacobson energy model. Additionally, we apply our algorithm to analyze the folding landscape of selenocysteine insertion sequence (SECIS) elements from A. Bock (personal communication), hammerhead ribozymes from Rfam (Griffiths-Jones et al., 2003), and tRNAs from Sprinzl's database (Sprinzl et al., 1998). It had previously been reported that tRNA has lower minimum free energy than random RNA of the same compositional frequency (Clote et al., 2003; Rivas and Eddy, 2000), although the situation is less clear for mRNA (Seffens and Digby, 1999; Workman and Krogh, 1999; Cohen and Skienna, 2002),(1) which plays no structural role. Applications of our algorithm extend knowledge of the energy landscape differences between naturally occurring and random RNA. Given an RNA molecule a(1), ... , a(n) and an integer k > or = 0, a k-locally optimal secondary structure S is a secondary structure on a(1), ... , a(n) which has k fewer base pairs than the maximum possible number, yet for which no basepairs can be added without violation of the definition of secondary structure (e.g., introducing a pseudoknot). Despite the fact that the number numStr(k) of k-locally optimal structures for a given RNA molecule in general is exponential in n, we present an algorithm running in time O(n (4)) and space O(n (3)), which computes numStr(k) for each k. Structurally important RNA, such as SECIS elements, hammerhead ribozymes, and tRNA, all have a markedly smaller number of k-locally optimal structures than that of random RNA of the same dinucleotide frequency, for small and moderate values of k. This suggests a potential future role of our algorithm as a tool to detect noncoding RNA genes.     

Dynamics of cardiorespiratory function in Standardbred horses during different intensities of constant-load exercise

Summary Six Standardbred horses were used to evaluate the time course of pulmonary gas exchange, ventilation, heart rate (HR) and acid base balance during different intensities of constant-load treadmill exercise. Horses were exercised at approximately 50%, 75% and 100% maximum oxygen uptake ( max) for 5 min and measurements taken every 30 s throughout exercise. At all work rates, the minute ventilation, respiratory frequency and tidal volume reached steady state values by 60 s of exercise. At 100% max, the oxygen consumption ( ) increased to mean values of approximately 130 ml/kg·min, which represents a 40-fold increase above resting . At the low and moderate work rates, showed no significant change from 30 s to 300 s of exercise. At the high work rate, the mean at 30 s was 80% of the value at 300 s. The HR showed no significant change over time at the moderate work rate but differing responses at the low and high work rates. At the low work rate, the mean HR decreased from 188 beats/min at 30 s to 172 beats/min at 300 s exercise, whereas at the high work rate the mean HR increased from 204 beats/min at 30 s to 221 beats/min at 300 s exercise. No changes in acid base status occurred during exercise at the low work rate. At the moderate work rate, a mild metabolic acidosis occurred which was nonprogressive with time, whereas the high work rate resulted in a progressive metabolic acidosis with a base deficit of 16 mmol/l by 300 s exercise. It is concluded that the kinetics of gas exchange during exercise are more rapid in the horse than in man, despite the relatively greater change in in the horse when going from rest to high intensity exercise.Symbols and abbreviations E minute ventilation - V _T tidal volume - oxygen uptake - carbon dioxide output - oxygen pulse - ventilatory equivalent for oxygen - ventilatory equivalent for carbon dioxide - R respiratory exchange ratio - HR heart rate - SBC standard bicarbonate - STPD standard temperature and pressure dry - BTPS body temperature and pressure saturated - arterial oxygen content - arteriovenous oxygen content difference - Rf respiratory frequency     

Local sequence alignments with monotonic gap penalties.

MOTIVATION: Sequence alignments obtained using affine gap penalties are not always biologically correct, because the insertion of long gaps is over-penalised. There is a need for an efficient algorithm which can find local alignments using non-linear gap penalties. RESULTS: A dynamic programming algorithm is described which computes optimal local sequence alignments for arbitrary, monotonically increasing gap penalties, i.e. where the cost g(k) of inserting a gap of k symbols is such that g(k) >/= g(k-1). The running time of the algorithm is dependent on the scoring scheme; if the expected score of an alignment between random, unrelated sequences of lengths m, n is proportional to log mn, then with one exception, the algorithm has expected running time O(mn). Elsewhere, the running time is no greater than O(mn(m+n)). Optimisations are described which appear to reduce the worst-case run-time to O(mn) in many cases. We show how using a non-affine gap penalty can dramatically increase the probability of detecting a similarity containing a long gap. AVAILABILITY: The source code is available to academic collaborators under licence.     

Effect of cardiogenic gas mixing on arterial O2 and CO2 tensions during breath holding

To examine the effect of cardiogenic gas mixing on gas exchange we measured arterial tension of O2 (PaO2) and arterial tension of CO2 (PaCO2) during 3- to 5-min breath holds (BH) before and after infusing 50 ml of saline into the pericardial space (PCF) of seven anesthetized, paralyzed, mechanically ventilated dogs. During BH the ventilator was disconnected and a bias flow of 50% O2 at 4-5 l/min was delivered through the side ports of a small catheter whose tip was positioned 1 cm cephalad of the carina. Paired runs, alternately with and without PCF, were performed in triplicate in each dog. Initial PaO2 was similar for control runs [81 +/- 3 mmHg (SE)] and PCF runs (78 +/- 3 mmHg; P greater than 0.1). After 3-min BH, PaO2 in PCF runs (33 +/- 3 mmHg) was less than that in control runs (58 +/- 4 mmHg) (P less than 0.001). In contrast, the pattern of PaCO2 during BH did not differ with PCF. After 3-min BH, PaCO2 was 49 +/- 3 mmHg with PCF and 49 +/- 2 mmHg in the control runs (P greater than 0.7). In two dogs, repeated 50-ml reductions in lung volume, produced by rib cage compression, did not alter the time course of PaO2 during BH. Although cardiac output decreased slightly with PCF, hemodynamic changes due to PCF were unlikely to account for the observed fall in PaO2. Our results indicate a substantial effect of cardiogenic gas mixing on O2 uptake when tracheal gas is O2 enriched during breath holding.(ABSTRACT TRUNCATED AT 250 WORDS)     

Computing the partition function and sampling for saturated secondary structures of RNA, with respect to the Turner energy model.

An RNA secondary structure is saturated if no base pairs can be added without violating the definition of secondary structure. Here we describe a new algorithm, RNAsat, which for a given RNA sequence a, an integral temperature 0 相似文献   

Algorithms for sorting unsigned linear genomes by the DCJ operations

MOTIVATION: The double cut and join operation (abbreviated as DCJ) has been extensively used for genomic rearrangement. Although the DCJ distance between signed genomes with both linear and circular (uni- and multi-) chromosomes is well studied, the only known result for the NP-complete unsigned DCJ distance problem is an approximation algorithm for unsigned linear unichromosomal genomes. In this article, we study the problem of computing the DCJ distance on two unsigned linear multichromosomal genomes (abbreviated as UDCJ). RESULTS: We devise a 1.5-approximation algorithm for UDCJ by exploiting the distance formula for signed genomes. In addition, we show that UDCJ admits a weak kernel of size 2k and hence an FPT algorithm running in O(2(2k)n) time.