Recent developments in the MAFFT multiple sequence alignment program

The accuracy and scalability of multiple sequence alignment (MSA) of DNAs and proteins have long been and are still important issues in bioinformatics. To rapidly construct a reasonable MSA, we developed the initial version of the MAFFT program in 2002. MSA software is now facing greater challenges in both scalability and accuracy than those of 5 years ago. As increasing amounts of sequence data are being generated by large-scale sequencing projects, scalability is now critical in many situations. The requirement of accuracy has also entered a new stage since the discovery of functional noncoding RNAs (ncRNAs); the secondary structure should be considered for constructing a high-quality alignment of distantly related ncRNAs. To deal with these problems, in 2007, we updated MAFFT to Version 6 with two new techniques: the PartTree algorithm and the Four-way consistency objective function. The former improved the scalability of progressive alignment and the latter improved the accuracy of ncRNA alignment. We review these and other techniques that MAFFT uses and suggest possible future directions of MSA software as a basis of comparative analyses. MAFFT is available at http://align.bmr.kyushu-u.ac.jp/mafft/software/.     

Super pairwise alignment (SPA): an efficient approach to global alignment for homologous sequences.

Sequence analysis is the basis of bioinformatics, while sequence alignment is a fundamental task for sequence analysis. The widely used alignment algorithm, Dynamic Programming, though generating optimal alignment, takes too much time due to its high computation complexity O(N(2)). In order to reduce computation complexity without sacrificing too much accuracy, we have developed a new approach to align two homologous sequences. The new approach presented here, adopting our novel algorithm which combines the methods of probabilistic and combinatorial analysis, reduces the computation complexity to as low as O(N). The computation speed by our program is at least 15 times faster than traditional pairwise alignment algorithms without a loss of much accuracy. We hence named the algorithm Super Pairwise Alignment (SPA). The pairwise alignment execution program based on SPA and the detailed results of the aligned sequences discussed in this article are available upon request.     

PnpProbs: a better multiple sequence alignment tool by better handling of guide trees

Background

This paper describes a new MSA tool called PnpProbs, which constructs better multiple sequence alignments by better handling of guide trees. It classifies sequences into two types: normally related and distantly related. For normally related sequences, it uses an adaptive approach to construct the guide tree needed for progressive alignment; it first estimates the input’s discrepancy by computing the standard deviation of their percent identities, and based on this estimate, it chooses the better method to construct the guide tree. For distantly related sequences, PnpProbs abandons the guide tree and uses instead some non-progressive alignment method to generate the alignment.

Results

To evaluate PnpProbs, we have compared it with thirteen other popular MSA tools, and PnpProbs has the best alignment scores in all but one test. We have also used it for phylogenetic analysis, and found that the phylogenetic trees constructed from PnpProbs’ alignments are closest to the model trees.

Conclusions

By combining the strength of the progressive and non-progressive alignment methods, we have developed an MSA tool called PnpProbs. We have compared PnpProbs with thirteen other popular MSA tools and our results showed that our tool usually constructed the best alignments.

     

Multiple sequence alignment using partial order graphs

MOTIVATION: Progressive Multiple Sequence Alignment (MSA) methods depend on reducing an MSA to a linear profile for each alignment step. However, this leads to loss of information needed for accurate alignment, and gap scoring artifacts. RESULTS: We present a graph representation of an MSA that can itself be aligned directly by pairwise dynamic programming, eliminating the need to reduce the MSA to a profile. This enables our algorithm (Partial Order Alignment (POA)) to guarantee that the optimal alignment of each new sequence versus each sequence in the MSA will be considered. Moreover, this algorithm introduces a new edit operator, homologous recombination, important for multidomain sequences. The algorithm has improved speed (linear time complexity) over existing MSA algorithms, enabling construction of massive and complex alignments (e.g. an alignment of 5000 sequences in 4 h on a Pentium II). We demonstrate the utility of this algorithm on a family of multidomain SH2 proteins, and on EST assemblies containing alternative splicing and polymorphism. AVAILABILITY: The partial order alignment program POA is available at http://www.bioinformatics.ucla.edu/poa.     

MACSE: Multiple Alignment of Coding SEquences accounting for frameshifts and stop codons

Until now the most efficient solution to align nucleotide sequences containing open reading frames was to use indirect procedures that align amino acid translation before reporting the inferred gap positions at the codon level. There are two important pitfalls with this approach. Firstly, any premature stop codon impedes using such a strategy. Secondly, each sequence is translated with the same reading frame from beginning to end, so that the presence of a single additional nucleotide leads to both aberrant translation and alignment.We present an algorithm that has the same space and time complexity as the classical Needleman-Wunsch algorithm while accommodating sequencing errors and other biological deviations from the coding frame. The resulting pairwise coding sequence alignment method was extended to a multiple sequence alignment (MSA) algorithm implemented in a program called MACSE (Multiple Alignment of Coding SEquences accounting for frameshifts and stop codons). MACSE is the first automatic solution to align protein-coding gene datasets containing non-functional sequences (pseudogenes) without disrupting the underlying codon structure. It has also proved useful in detecting undocumented frameshifts in public database sequences and in aligning next-generation sequencing reads/contigs against a reference coding sequence.MACSE is distributed as an open-source java file executable with freely available source code and can be used via a web interface at: http://mbb.univ-montp2.fr/macse.     

PROMALS3D: a tool for multiple protein sequence and structure alignments

Although multiple sequence alignments (MSAs) are essential for a wide range of applications from structure modeling to prediction of functional sites, construction of accurate MSAs for distantly related proteins remains a largely unsolved problem. The rapidly increasing database of spatial structures is a valuable source to improve alignment quality. We explore the use of 3D structural information to guide sequence alignments constructed by our MSA program PROMALS. The resulting tool, PROMALS3D, automatically identifies homologs with known 3D structures for the input sequences, derives structural constraints through structure-based alignments and combines them with sequence constraints to construct consistency-based multiple sequence alignments. The output is a consensus alignment that brings together sequence and structural information about input proteins and their homologs. PROMALS3D can also align sequences of multiple input structures, with the output representing a multiple structure-based alignment refined in combination with sequence constraints. The advantage of PROMALS3D is that it gives researchers an easy way to produce high-quality alignments consistent with both sequences and structures of proteins. PROMALS3D outperforms a number of existing methods for constructing multiple sequence or structural alignments using both reference-dependent and reference-independent evaluation methods.     

A memory-efficient algorithm for multiple sequence alignment with constraints

MOTIVATION: Recently, the concept of the constrained sequence alignment was proposed to incorporate the knowledge of biologists about structures/functionalities/consensuses of their datasets into sequence alignment such that the user-specified residues/nucleotides are aligned together in the computed alignment. The currently developed programs use the so-called progressive approach to efficiently obtain a constrained alignment of several sequences. However, the kernels of these programs, the dynamic programming algorithms for computing an optimal constrained alignment between two sequences, run in (gamman2) memory, where gamma is the number of the constraints and n is the maximum of the lengths of sequences. As a result, such a high memory requirement limits the overall programs to align short sequences only. RESULTS: We adopt the divide-and-conquer approach to design a memory-efficient algorithm for computing an optimal constrained alignment between two sequences, which greatly reduces the memory requirement of the dynamic programming approaches at the expense of a small constant factor in CPU time. This new algorithm consumes only O(alphan) space, where alpha is the sum of the lengths of constraints and usually alpha < n in practical applications. Based on this algorithm, we have developed a memory-efficient tool for multiple sequence alignment with constraints. AVAILABILITY: http://genome.life.nctu.edu.tw/MUSICME.     

SinicView: A visualization environment for comparisons of multiple nucleotide sequence alignment tools

Background  

Deluged by the rate and complexity of completed genomic sequences, the need to align longer sequences becomes more urgent, and many more tools have thus been developed. In the initial stage of genomic sequence analysis, a biologist is usually faced with the questions of how to choose the best tool to align sequences of interest and how to analyze and visualize the alignment results, and then with the question of whether poorly aligned regions produced by the tool are indeed not homologous or are just results due to inappropriate alignment tools or scoring systems used. Although several systematic evaluations of multiple sequence alignment (MSA) programs have been proposed, they may not provide a standard-bearer for most biologists because those poorly aligned regions in these evaluations are never discussed. Thus, a tool that allows cross comparison of the alignment results obtained by different tools simultaneously could help a biologist evaluate their correctness and accuracy.     

Efficient parameterized algorithms for biopolymer structure-sequence alignment

Computational alignment of a biopolymer sequence (e.g., an RNA or a protein) to a structure is an effective approach to predict and search for the structure of new sequences. To identify the structure of remote homologs, the structure-sequence alignment has to consider not only sequence similarity, but also spatially conserved conformations caused by residue interactions and, consequently, is computationally intractable. It is difficult to cope with the inefficiency without compromising alignment accuracy, especially for structure search in genomes or large databases. This paper introduces a novel method and a parameterized algorithm for structure-sequence alignment. Both the structure and the sequence are represented as graphs, where, in general, the graph for a biopolymer structure has a naturally small tree width. The algorithm constructs an optimal alignment by finding in the sequence graph the maximum valued subgraph isomorphic to the structure graph. It has the computational time complexity O(k³N²) for the structure of N residues and its tree decomposition of width t. Parameter k, small in nature, is determined by a statistical cutoff for the correspondence between the structure and the sequence. This paper demonstrates a successful application of the algorithm to RNA structure search used for noncoding RNA identification. An application to protein threading is also discussed     

Predicting the accuracy of multiple sequence alignment algorithms by using computational intelligent techniques

Multiple sequence alignments (MSAs) have become one of the most studied approaches in bioinformatics to perform other outstanding tasks such as structure prediction, biological function analysis or next-generation sequencing. However, current MSA algorithms do not always provide consistent solutions, since alignments become increasingly difficult when dealing with low similarity sequences. As widely known, these algorithms directly depend on specific features of the sequences, causing relevant influence on the alignment accuracy. Many MSA tools have been recently designed but it is not possible to know in advance which one is the most suitable for a particular set of sequences. In this work, we analyze some of the most used algorithms presented in the bibliography and their dependences on several features. A novel intelligent algorithm based on least square support vector machine is then developed to predict how accurate each alignment could be, depending on its analyzed features. This algorithm is performed with a dataset of 2180 MSAs. The proposed system first estimates the accuracy of possible alignments. The most promising methodologies are then selected in order to align each set of sequences. Since only one selected algorithm is run, the computational time is not excessively increased.     

Comparison of heuristic approaches to the generalized tree alignment problem

Two commonly used heuristic approaches to the generalized tree alignment problem are compared in the context of phylogenetic analysis of DNA sequence data. These approaches, multiple sequence alignment + phylogenetic tree reconstruction (MSA+TR) and direct optimization (DO), are alternative heuristic procedures used to approach the nested NP‐Hard optimizations presented by the phylogenetic analysis of unaligned sequences under maximum parsimony. Multiple MSA+TR implementations and DO were compared in terms of optimality score (phylogenetic tree cost) over multiple empirical and simulated datasets with differing levels of heuristic intensity. In all cases examined, DO outperformed MSA+TR with average improvement in parsimony score of 14.78% (5.64–52.59%).     

Tsukuba BB: a branch and bound algorithm for local multiple alignment of DNA and protein sequences.

In this paper we present a branch and bound algorithm for local gapless multiple sequence alignment (motif alignment) and its implementation. The algorithm uses both score-based bounding and a novel bounding technique based on the "consistency" of the alignment. A sequence order independent search tree is used in conjunction with a technique for avoiding redundant calculations inherent in the structure of the tree. This is the first program to exploit the fact that the motif alignment problem is easier for short motifs. Indeed, for a short fixed motif width, the running time of the algorithm is asymptotically linear in the size of the input. We tested the performance of the program on a dataset of 300 E. coli promoter sequences and a dataset of 85 lipocalin protein sequences. For a motif width of 4, the optimal alignment of the entire set of sequences can be found. For the more natural motif width of 6, the program can align 21 sequences of length 100, more than twice the number of sequences which can be aligned by the best previous exact algorithm. The algorithm can relax the constraint of requiring each sequence to be aligned, and align 105 of the 300 promoter sequences with a motif width of 6. For the lipocalin dataset, we introduce a technique for reducing the effective alphabet size with a minimal loss of useful information. With this technique, we show that the program can find meaningful motifs in a reasonable amount of time by optimizing the score over three motif positions.     

Sequence and localization of human NASP: conservation of a Xenopus histone-binding protein.

In this study the sequence and localization of human testicular NASP (nuclear autoantigenic sperm protein) are reported. NASP cDNA contains 2561 nt encoding a protein of 787 amino acids. The open reading frame contains 2446 nt followed by an ochre stop codon (TAA) and 104 nucleotides of untranslated sequence containing a poly(A) addition signal 10 bases upstream of the poly(A) tail. Northern blot analysis of human testis poly(A) mRNA indicates a message of approximately 3.2 kb. Multiple sequence alignment (MSA) analysis of the encoded human NASP amino acid sequence with the sequence for the Xenopus histone-binding protein N1/N2 and the rabbit NASP amino acid sequence demonstrates that the human sequence and the Xenopus sequence have extensive amino acid homology upstream of the rabbit initiation codon. Significantly, there is an 85% identity between the human and the rabbit NASP sequences when the alignment starts at the N-terminal of the rabbit sequence and at amino acid 101 of the human sequence. The nuclear translocation signal found in N1/N2 and rabbit NASP is completely conserved in human NASP. The first histone-binding domain of Xenopus is 70% identical and 90% similar to the human NASP domain. The second histone-binding domain of Xenopus is 48% identical and 71% similar to the human NASP domain. MSA analysis of the three sequences generated an unrooted ancestral tree with two branches, indicating that fewer amino acid changes have occurred between the Xenopus and the human sequences than between the Xenopus and the rabbit sequences. In the human testis, NASP is localized predominantly in primary spermatocytes and round spermatids. Spermatogonia, Sertoli cells, Leydig cells, peritubular cells, and other somatic cells do not stain. Human spermatozoa contain NASP in the acrosomal region. Following the acrosome reaction, some NASP remains in the equatorial and postacrosomal regions. We propose that mammalian testes and sperm contain a histone-binding protein which may play a role in regulating the early events of spermatogenesis.     

Performance comparison between k-tuple distance and four model-based distances in phylogenetic tree reconstruction

Phylogenetic tree reconstruction requires construction of a multiple sequence alignment (MSA) from sequences. Computationally, it is difficult to achieve an optimal MSA for many sequences. Moreover, even if an optimal MSA is obtained, it may not be the true MSA that reflects the evolutionary history of the underlying sequences. Therefore, errors can be introduced during MSA construction which in turn affects the subsequent phylogenetic tree construction. In order to circumvent this issue, we extend the application of the k-tuple distance to phylogenetic tree reconstruction. The k-tuple distance between two sequences is the sum of the differences in frequency, over all possible tuples of length k, between the sequences and can be estimated without MSAs. It has been traditionally used to build a fast ‘guide tree’ to assist the construction of MSAs. Using the 1470 simulated sets of sequences generated under different evolutionary scenarios, the neighbor-joining trees and BioNJ trees, we compared the performance of the k-tuple distance with four commonly used distance estimators including Jukes–Cantor, Kimura, F84 and Tamura–Nei. These four distance estimators fall into the category of model-based distance estimators, as each of them takes account of a specific substitution model in order to compute the distance between a pair of already aligned sequences. Results show that trees constructed from the k-tuple distance are more accurate than those from other distances most time; when the divergence between underlying sequences is high, the tree accuracy could be twice or higher using the k-tuple distance than other estimators. Furthermore, as the k-tuple distance voids the need for constructing an MSA, it can save tremendous amount of time for phylogenetic tree reconstructions when the data include a large number of sequences.     

一个新的多序列比对算法

多序列比对是生物信息学中基础而又重要的序列分析方法.本文提出一种新的多序列比对算法,该算法综合了渐进比对方法和迭代策略,采用加权函数以调整序列的有偏分布,用neighbor-joining方法构建指导树以确定渐进比对的顺序.通过对BAlibASE中142组蛋白质序列比对的测试,验证了本算法的有效性.与Multalin算法比较的结果表明,本算法能有效地提高分歧较大序列的比对准确率.     

Frequency of insertion-deletion,transversion, and transition in the evolution of 5S ribosomal RNA

Summary The problem of choosing an alignment of two or more nucleotide sequences is particularly difficult for nucleic acids, such as 5S ribosomal RNA, which do not code for protein and for which secondary structure is unknown. Given a set of costs for the various types of replacement mutations and for base insertion or deletion, we present a dynamic programming algorithm which finds the optimal (least costly) alignment for a set of N sequences simultaneously, where each sequence is associated with one of the N tips of a given evolutionary tree. Concurrently, protosequences are constructed corresponding to the ancestral nodes of the tree. A version of this algorithm, modified to be computationally feasible, is implemented to align the sequences of 5S RNA from nine organisms. Complete sets of alignments and proto-sequence reconstructions are done for a large number of different con-figurations of mutation costs. Examination of the family of curves of total replacements inferred versus the ratio of transitions/trans-versions inferred, each curve corresponding to a given number of in-sertions-deletions inferred, provides a method for estimating relative costs and relative frequencies for these different types of mutation.     

一个新的核酸序列比对算法及其在序列全局比对中的应用

目前在序列比对中所广泛使用的动态规划算法,虽然能达到最优比对结果,但却由于具有高计算复杂度O(N_2)而极大地降低了计算效率。将多阶段动态规划决策算法用于两两序列比对并用Visual BASIC编程实现,结果发现该新算法在将计算复杂度减小到O(N)的同时,也能够获得较为理想的计算精度,预期将在序列全局比对中起重要作用。     

基于结构信息的RNA多序列比对

多序列比对是生物信息学中重要的基础研究内容,对各种RNA序列分析方法而言,这也是非常重要的一步。不像DNA和蛋白质,许多功能RNA分子的序列保守性要远差于其结构的保守性,因此,对RNA的分析研究要求其多序列比对不仅要考虑序列信息,而且要充分考虑到其结构信息。本文提出了一种考虑了结构信息的同源RNA多序列比对算法,它先利用热力学方法计算出每条序列的配对概率矩阵,得到结构信息,由此构造各条序列的结构信息矢量,结合传统序列比对方法,提出优化目标函数,采用动态规划算法和渐进比对得到最后的多序列比对。试验证实该方法的有效性。     

Using the T-Coffee package to build multiple sequence alignments of protein, RNA, DNA sequences and 3D structures

T-Coffee (Tree-based consistency objective function for alignment evaluation) is a versatile multiple sequence alignment (MSA) method suitable for aligning most types of biological sequences. The main strength of T-Coffee is its ability to combine third party aligners and to integrate structural (or homology) information when building MSAs. The series of protocols presented here show how the package can be used to multiply align proteins, RNA and DNA sequences. The protein section shows how users can select the most suitable T-Coffee mode for their data set. Detailed protocols include T-Coffee, the default mode, M-Coffee, a meta version able to combine several third party aligners into one, PSI (position-specific iterated)-Coffee, the homology extended mode suitable for remote homologs and Expresso, the structure-based multiple aligner. We then also show how the T-RMSD (tree based on root mean square deviation) option can be used to produce a functionally informative structure-based clustering. RNA alignment procedures are described for using R-Coffee, a mode able to use predicted RNA secondary structures when aligning RNA sequences. DNA alignments are illustrated with Pro-Coffee, a multiple aligner specific of promoter regions. We also present some of the many reformatting utilities bundled with T-Coffee. The package is an open-source freeware available from http://www.tcoffee.org/.     

Clearcut: a fast implementation of relaxed neighbor joining

SUMMARY: Clearcut is an open source implementation for the relaxed neighbor joining (RNJ) algorithm. While traditional neighbor joining (NJ) remains a popular method for distance-based phylogenetic tree reconstruction, it suffers from a O(N(3)) time complexity, where N represents the number of taxa in the input. Due to this steep asymptotic time complexity, NJ cannot reasonably handle very large datasets. In contrast, RNJ realizes a typical-case time complexity on the order of N(2)logN without any significant qualitative difference in output. RNJ is particularly useful when inferring a very large tree or a large number of trees. In addition, RNJ retains the desirable property that it will always reconstruct the true tree given a matrix of additive pairwise distances. Clearcut implements RNJ as a C program, which takes either a set of aligned sequences or a pre-computed distance matrix as input and produces a phylogenetic tree. Alternatively, Clearcut can reconstruct phylogenies using an extremely fast standard NJ implementation. AVAILABILITY: Clearcut source code is available for download at: http://bioinformatics.hungry.com/clearcut