We describe a new program for the alignment of multiple biological sequences that is both statistically motivated and fast enough for problem sizes that arise in practice. Our Fast Statistical Alignment program is based on pair hidden Markov models which approximate an insertion/deletion process on a tree and uses a sequence annealing algorithm to combine the posterior probabilities estimated from these models into a multiple alignment. FSA uses its explicit statistical model to produce multiple alignments which are accompanied by estimates of the alignment accuracy and uncertainty for every column and character of the alignment—previously available only with alignment programs which use computationally-expensive Markov Chain Monte Carlo approaches—yet can align thousands of long sequences. Moreover, FSA utilizes an unsupervised query-specific learning procedure for parameter estimation which leads to improved accuracy on benchmark reference alignments in comparison to existing programs. The centroid alignment approach taken by FSA, in combination with its learning procedure, drastically reduces the amount of false-positive alignment on biological data in comparison to that given by other methods. The FSA program and a companion visualization tool for exploring uncertainty in alignments can be used via a web interface at http: //orangutan. math. berkeley. edu/fsa/, and the source code is available at http: //fsa. sourceforge. net/.
The field of biological sequence alignment is very active, with numerous new alignment programs developed every year in response to increasing demand driven by rapidly-dropping sequencing costs. The list of approximately 60 sequence alignment programs on the wikipedia compilation provides a lower bound on the number of available tools and illustrates the confusing choice facing biologists who seek to select the “best” program for their studies. Nevertheless, the ClustalW program [1], [2], published in 1994, remains the most widely-used multiple sequence alignment program. Indeed, in a recent review of multiple sequence alignment [3], the authors remark that “to the best of our knowledge, no significant improvements have been made to the [ClustalW] algorithm since 1994 and several modern methods achieve better performance in accuracy, speed, or both. ” Therefore, it is natural to ask, “Why do alignment programs continue to be developed, and why are new tools not more widely adopted by biologists? ”. A key issue in understanding the popularity of ClustalW is to recognize that it is difficult to benchmark alignment programs. Alignments represent homology relationships among the nucleotides, or amino acids, of the genomes of extant species, and it is impossible to infer the evolutionary history of genomes with absolute certainty. Comparisons of alignment programs therefore rely on databases of structural alignments for proteins and RNA or on gene loci or simulated data for DNA. Each type of benchmark is vulnerable to manipulation and furthermore may not represent the problem setups which are most relevant to biologists. The result is that biologists are confronted with many programs and publications, but it is frequently unclear which approach will give the best results for the everyday problems which they seek to address. Adding to the difficulty of selecting software tools is the blur between programs and ideas. Developers of alignment programs make choices about the objective functions to optimize, the statistical models to use, and the parameters for these models, but the relative impact of individual choices is rarely tested [4]. Discordance among programs is frequently noted [5], but the different architectures of individual programs, and in some cases the lack of open software, makes it difficult to explore novel combinations of existing ideas for improving alignments. In lieu of these issues, biologists have favored the conservative approach of using the tried and trusted ClustalW program, although they frequently use it in conjunction with software which allows for manual editing of alignments [6]. The rationale behind alignment-editing software is that trained experts should be able to correct alignments by visual inspection and that effort is better expended on manually correcting alignments than searching for software that is unlikely to find the “correct” alignment anyway. Although manual editing approaches may be cumbersome, they have been used for large alignments (e. g. , [7]). We therefore approached the alignment problem with the following goals in mind: The “distance-based” approach to sequence alignment, proposed in [13] and implemented in the protein alignment program AMAP [14], offers a useful framework for these goals. Much as distance-based phylogenetic reconstruction methods like Neighbor-Joining build a phylogeny using only pairwise divergence estimates, a distance-based approach to alignment builds a multiple alignment using only pairwise estimations of homology. This is made possible by the sequence annealing technique [14] for constructing multiple alignments from pairwise comparisons. We have implemented our approach in FSA, a new alignment program described below. We give an overview of the structure of FSA and explain the details of its components below. Text S1 contains detailed instructions for using the FSA program and webserver as well as FSA' s companion programs for comparing alignments and working with whole-genome alignments.
Figure 1 shows an overview of the components of the FSA alignment algorithm, described in detail below. The input to FSA is a set of protein, RNA or DNA sequences. These sequences are assumed to be homologous, although FSA is robust to non-homologous sequence. The output of FSA is a global alignment of the input sequences which is a (local) optima of the expected accuracy under FSA' s statistical model. FSA first performs pairwise comparisons of the input sequences to determine the posterior probabilities that individual characters are aligned (note, however, that it does not yet actually align any sequences). While the number of possible pairwise comparisons is quadratic in the number of sequences being aligned, giving unfavorable runtimes for datasets of many sequences, FSA overcomes this problem by reducing the number of pairs of sequences that are compared. This is accomplished using a randomized approach inspired by the Erdös-Rényi theory of random graphs, thereby drastically reducing the computational cost of multiple alignment. After obtaining pairwise estimates of homology at the single-character level, FSA uses the sequence annealing technique [14] to construct a multiple alignment. This approach to alignment seeks to maximize the expected accuracy of the alignment using a steepest-ascent (greedy) algorithm. The architecture of FSA reflects the inherent modularity of the distance-based approach to alignment. FSA' s inference engine uses the flexible HMMoC code-generation tool [15] and has been parallelized with a separate module, alignments of long sequences are anchored with candidate matches found by the MUMmer suffix trie matching tool [16] or the exonerate homology-search program [17], and FSA' s sequence annealing algorithm improves on the original algorithm and implementation in AMAP [14]. The stand-alone visualization tool uses statistical information produced by FSA, but otherwise is completely independent. Each of these components can be improved independently of the others, allowing for rapid future improvements in distance-based alignment. For example, FSA' s entire statistical model could easily be altered to incorporate position-specific features or completely replaced with a discriminative or hybrid generative-discriminative model. The components described here correspond roughly to the simplest mode of operation for FSA, outlined in bold in Figure 1.
Table 1 shows benchmarks of FSA and other alignment programs, including AMAP [14], ClustalW [1], [2], DIALIGN [31], [32], MAFFT [33], MUMMALS [34], MUSCLE [35], Probalign [36], ProbCons [37], T-Coffee [38], and SeqAn: : T-Coffee [39], against the BAliBASE 3 [24] and SABmark 1. 65 databases [25]. FSA in maximum-sensitivity mode had accuracy similar to those of the better-performing programs on BAliBASE 3 and had accuracy superior to that of any other program on SABmark 1. 65 when run in default mode. FSA had higher positive predictive values than any other tested program on all datasets. Remarkably, FSA was the only tested program which achieved a mis-alignment rate <50% on the standard SABmark 1. 65 datasets; all other programs produced more incorrect than correct homology statements. In order to test the robustness of alignment programs to incomplete homology, we modified the BAliBASE 3 database such that every alignment included a single false-positive, an unrelated (random) sequence. This is a realistic setup for biologists who might want to decide whether a sequence is orthologous to a particular protein family. With the exception of FSA, the tested alignment programs suffered a false-positive rate increased by over 25% on this BAliBASE 3+fp dataset, indicating that they aligned the random sequence with the homologous set. In contrast, FSA left the random sequence unaligned and had an essentially-unchanged false-positive rate. Table 2 shows benchmarks of FSA and the other tested alignment programs against the BRAliBase 2. 1 [26] and Consan mix80 [27] databases. FSA outperformed all other programs on both datasets. BRAliBase 2. 1 was assembled from the RFAM [40] RNA database and consists of small RNAs (average length of ∼150 nt). FSA gave improved performance even on this high-identity dataset where most programs did relatively well. The Consan mix80 dataset of alignments of Small and Large Subunit ribosomal RNAs, assembled from the European Ribosomal RNA database [41], was created for training RNA structural alignment programs and provides a test of alignment programs on difficult, large-scale alignments. The four alignments contain from 107 to 254 sequences, each 1–4 kilobases in length, with average percentage identity less than <50%. Two tested alignment programs, ProbConsRNA [42] and SeqAn: : T-Coffee, were unable to align these large datasets. This dataset demonstrates that FSA' s alignment speedup options, including performing inference only on a subset of all possible pairs (–fast) and anchoring alignments instead of using the full dynamic programming matrix (–anchored), are effective heuristics for large datasets. Table 3 shows benchmarks of FSA and other genomic alignment programs, including CHAOS/DIALIGN [20], DIALIGN-TX [31], [32], MAVID [18], MLAGAN [19], Pecan [21] and TBA [28], on simulated alignments of both mammalian and Drosophila DNA sequences. FSA produced higher-accuracy alignments than the other programs on the two Drosophila datasets and only Pecan gave better alignments of the mammalian sequences. The simulated alignments of nonfunctional DNA sequences from nine mammals (human, chimp, baboon, mouse, rat, cat, dog, cow, and pig) were created by Blanchette et al. [28]. The simulated alignments of DNA from the twelve species of Drosophila described in [43] were created with two simulation programs, DAWG [29] and simgenome [30]. As described in [30], the simulated Drosophila genomic alignments were created by parameterizing the DAWG and simgenome programs using whole-genome alignments produced by Pecan for [43]. Although two authors (RKB and IH) of this manuscript contributed to the simgenome program, simgenome was developed prior to FSA and did not influence or contribute to the methodology described here for FSA. FSA' s strong performance on all three datasets of simulated long DNA sequences indicate that it is a useful and accurate tool for genomic alignment. In order to further test the appropriateness of using popular alignment programs to detect homology between sequences, we conducted a large-scale random-sequence experiment. We generated datasets of random sequences to simulate unrelated protein, short DNA, and genomic (long) DNA sequences. The results, shown in Table 4 and Table 5, clearly demonstrate that while for most alignment programs, aligned sequences are not necessarily homologous, FSA leaves random sequences largely unaligned. Biologists commonly align coding regions in both amino acid and nucleotide space, but there have been few studies of the effectiveness of alignment programs across the two regimes. We tested the consistency of alignment programs on coding sequence by aligning all 1,502 genes in Saccharomyces cerevisiae identified as having orthologs in the six related yeast species S. paradoxus, S. mikatae, S. kudriavzevii, S. bayanus, S. castellii, and S. kluyveri ([44]; this dataset was also analyzed in [5]). As shown in Table 6, alignments produced by FSA had higher concordance (0. 943) than those produced by any other program. If a program produces alignments with low concordance between amino acid and nucleotide alignments, then at least one of the alignments must be incorrect (and quite possibly both are). This simple test additionally indicates the effectiveness and robustness of FSA' s query-specific learning. While very different learning procedures are used for amino acid and nucleotide sequence, the concordant alignments inferred by FSA indicate that our results are robust with respect to the details of the learning procedure. We conducted an ablation analysis of FSA' s components to test the effectiveness of each component and ensure that they all contributed to the accuracy of the program. As indicated by the results in Tables 7–10, each optional component of FSA contributes to its accuracy. The importance of each component depends strongly upon the alignment problem. The –fast heuristic for reducing the number of sequence pairs used to construct an alignment results in little loss of accuracy, at least on the benchmarks used in this paper (Tables 7 and 8). As indicated by the small and long RNA benchmarks (Table 8), iterative refinement is important for aligning many sequences and less so for small alignment problems. The anchor annealing procedure appears to be an effective heuristic for aligning long sequences. Anchoring with unique matches (MUMs) causes only a negligible loss of accuracy on the long RNA dataset (Table 8). However, results on simulated long DNA sequences (Table 9) demonstrate that inexact matches, such as those returned by exonerate, must be used during anchor annealing to obtain high sensitivity on very long or distant nonfunctional DNA sequences. Nonfunctional DNA lacks the local constraints which preserve exact matches across distant species in functional (e. g. , coding) sequence. Query-specific learning is important for maintaining FSA' s robustness to non-homologous sequence. While FSA aligned only 4% of random protein sequences in default mode, when run without learning it aligned 13% (Table 10), similar to the 14% aligned by AMAP (Table 4). Biologists commonly perform alignments of hundreds or thousands of 16S ribosomal DNA sequences in order to elucidate evolutionary relationships and build phylogenetic trees. We performed alignments of prokaryotic 16S sequences to compare the speed of commonly-used programs (Table 11). MAFFT was the fastest method by an order of magnitude; MUSCLE and FSA were the next-fastest methods. Many alignment programs were unable to align these large datasets. The results in Table 12 and Table 13 demonstrate the effectiveness of FSA' s parallelization mode. Parallelizing the pairwise comparisons dramatically reduces runtime: When running in –fast mode on a small cluster with 10 processors, FSA can align 500 16S sequences in 20% of the time required without parallelization.
In the Introduction we highlighted four design criteria which we emphasized in the development of FSA. The first goal was to find alignments with high expected accuracy, where an accurate alignment has minimal distance to the truth. This objective function is markedly different from both the maximum-likelihood approaches used by programs such as ClustalW and MUSCLE and the maximum expected sensitivity approaches used by programs such as ProbCons and Pecan. Note that while the objective function used in ProbCons is called “maximum expected accuracy” in the paper [37], it is actually a maximum expected sensitivity objective function, where there is no penalty for over-aligning sequence (c. f. , the results shown in Table 4). Their objective function can be recovered as a special case of our approach by ignoring the gap probabilities in FSA' s objective function (Text S1, “The mathematics of distance-based alignment”). FSA' s explicit search for the most accurate, rather than most likely or most sensitive, alignment is what allows FSA to so dramatically outperform most other programs on tests on unrelated sequence (Table 4). We believe that this accuracy criterion, which gives equal weight to the correctness of all sequence positions, is a natural measure of alignment quality. Downstream analyses, such as phylogenetic reconstruction and evolutionary constraint analysis, are increasingly using indels in addition to homologous characters for more accurate estimation (e. g. , [45], [46]). Thus, it is important that alignments be as “evolutionarily correct” as possible [47], which is the purpose of the accuracy criterion. FSA' s strong performance under the accuracy criterion is due to techniques such as its iterative refinement as well as its explicit attempt to maximize the expected accuracy; programs which explicitly seek to maximize an objective function of the posterior probabilities of character alignment, such as ProbCons or Probalign, could instead seek to maximize the expected accuracy described here and, as a likely result, increase their robustness to non-homologous sequence. However, while we believe that the expected accuracy is a biologically-sensible objective function, it may not be appropriate if the user desires the most sensitive alignment. While FSA can produce the most-sensitive RNA alignments, other programs can produce more sensitive alignments of proteins and genomic sequence, albeit generally at the cost of a tendency to align non-homologous sequence (Table 4). The second goal was to create alignments which are robust to evolutionary distances and different functional constraints on patterns of molecular evolution. FSA' s unsupervised query-specific learning for parameter selection frees the user from unknown systematic biases implicitly introduced by using an alignment program whose parameters were trained on a dataset whose statistics may not reflect those of the sequences to be aligned. For example, it is traditionally challenging to align sequences with unusual base composition, but FSA can easily handle such alignments by automatically learning appropriate parameters. As indicated by our ablation analysis, query-specific learning further increases FSA' s robustness to non-homologous sequences beyond that offered by the minimum-distance objective function alone. We believe that FSA' s unsupervised query-specific learning is the first time a multiple alignment program has been capable of dynamically learning a complete parameterization, wherein parameters can vary for each pair of sequences to be compared, on the fly. This learning method is related to the “pre-training” option in ProbCons, which permits users to learn different models for families of homologous sequences, but does not permit parameterizations to vary between sequence pairs. We also note that the MORPH program for pairwise alignment of sequences with cis-regulatory modules learns model transition parameters from data [48]. While supervised training on curated data can give superior performance on test sets which are statistically-similar to the training data, the practical alignment problems encountered everyday by biologists do not fit into this rigid problem setup. Query-specific learning consistently gives reasonable performance. The third and fourth goals, to develop a single, modular program which can address all typical alignment problems encountered by biologists, are naturally achieved within FSA' s architecture. While almost all alignment programs are designed to either align many short sequences or a few long sequences, we have demonstrated that it is feasible to create a single program which can address both situations. This is made practical by FSA' s modular nature, where the statistical model for pairwise comparisons, the anchoring scheme for finding homology between long sequences, and the sequence annealing procedure are entirely separate and can be individually modified and improved. For example, the parallelization of FSA was designed and developed with minimal changes to the rest of FSA' s code base. Similarly, while FSA' s basic anchoring relies only on exact matches from MUMmer, the anchoring scheme was easily extended to incorporate inexact matches from exonerate [17] and alignment constraints from Mercator [22]. In fact, this flexibility permits FSA to incorporate almost any sources of potential homology information, from predicted transcription factor binding sites to entire gene models. One natural extension of FSA' s approach is to models of RNA alignment which take structure into account. The program Stemloc-AMA [49] uses a model of the pairwise evolution of RNA secondary structure in conjunction with the sequence annealing algorithm to create accurate multiple alignments of structured RNAs. By using Stemloc-AMA' s probabilistic model rather than a Pair HMM and taking advantage of techniques such as query-specific learning, FSA could sum over possible pairwise structural alignments in order to get better estimates of posterior probabilities of character alignment. FSA is a statistical alignment program insofar as it uses an explicit statistical model of alignments and a probabilistic objective function for optimization, but as discussed in “Theoretical justification of distance-based alignment” (Text S1), it also is a distance-based approximation to the “phylogenetic alignment” models of alignments on trees [8]–[11], [50]–[52]. While traditional phylogenetic alignment algorithms are currently too computationally-expensive to use on datasets of more than a few sequences, FSA' s distance-based method allows biologists to use the sophisticated tools of statistical alignment algorithms on practical problems. Furthermore, while we have not addressed the phylogenetic aspects of FSA in detail in this paper, our methods can be adapted to incorporate a complete phylogenetic model (Text S1, “The mathematics of distance-based alignment”). However, we believe that FSA' s current approach, which is agnostic to phylogeny, offers many practical advantages for common genomics analyses. For example, because FSA uses a sum-of-pairs objective function, there is no guide tree to potentially bias downstream phylogenetic reconstructions based on the alignment. Similarly, while most genomic alignment programs require a species tree to perform the alignment, our phylogeny-free approach will be avoid potential biases introduced by using a single species tree to align regions which may have undergone recombination.
Biological sequence alignment is one of the fundamental problems in comparative genomics, yet it remains unsolved. Over sixty sequence alignment programs are listed on Wikipedia, and many new programs are published every year. However, many popular programs suffer from pathologies such as aligning unrelated sequences and producing discordant alignments in protein (amino acid) and codon (nucleotide) space, casting doubt on the accuracy of the inferred alignments. Inaccurate alignments can introduce large and unknown systematic biases into downstream analyses such as phylogenetic tree reconstruction and substitution rate estimation. We describe a new program for multiple sequence alignment which can align protein, RNA and DNA sequence and improves on the accuracy of existing approaches on benchmarks of protein and RNA structural alignments and simulated mammalian and fly genomic alignments. Our approach, which seeks to find the alignment which is closest to the truth under our statistical model, leaves unrelated sequences largely unaligned and produces concordant alignments in protein and codon space. It is fast enough for difficult problems such as aligning orthologous genomic regions or aligning hundreds or thousands of proteins. It furthermore has a companion GUI for visualizing the estimated alignment reliability.
genetics and genomics/bioinformatics
mathematics
computational biology/comparative sequence analysis
computer science
Fast Statistical Alignment