CSA: An efficient algorithm to improve circular DNA multiple alignment

Background  

The comparison of homologous sequences from different species is an essential approach to reconstruct the evolutionary history of species and of the genes they harbour in their genomes. Several complete mitochondrial and nuclear genomes are now available, increasing the importance of using multiple sequence alignment algorithms in comparative genomics. MtDNA has long been used in phylogenetic analysis and errors in the alignments can lead to errors in the interpretation of evolutionary information. Although a large number of multiple sequence alignment algorithms have been proposed to date, they all deal with linear DNA and cannot handle directly circular DNA. Researchers interested in aligning circular DNA sequences must first rotate them to the "right" place using an essentially manual process, before they can use multiple sequence alignment tools.     

Murasaki: a fast, parallelizable algorithm to find anchors from multiple genomes

Background

With the number of available genome sequences increasing rapidly, the magnitude of sequence data required for multiple-genome analyses is a challenging problem. When large-scale rearrangements break the collinearity of gene orders among genomes, genome comparison algorithms must first identify sets of short well-conserved sequences present in each genome, termed anchors. Previously, anchor identification among multiple genomes has been achieved using pairwise alignment tools like BLASTZ through progressive alignment tools like TBA, but the computational requirements for sequence comparisons of multiple genomes quickly becomes a limiting factor as the number and scale of genomes grows.

Methodology/Principal Findings

Our algorithm, named Murasaki, makes it possible to identify anchors within multiple large sequences on the scale of several hundred megabases in few minutes using a single CPU. Two advanced features of Murasaki are (1) adaptive hash function generation, which enables efficient use of arbitrary mismatch patterns (spaced seeds) and therefore the comparison of multiple mammalian genomes in a practical amount of computation time, and (2) parallelizable execution that decreases the required wall-clock and CPU times. Murasaki can perform a sensitive anchoring of eight mammalian genomes (human, chimp, rhesus, orangutan, mouse, rat, dog, and cow) in 21 hours CPU time (42 minutes wall time). This is the first single-pass in-core anchoring of multiple mammalian genomes. We evaluated Murasaki by comparing it with the genome alignment programs BLASTZ and TBA. We show that Murasaki can anchor multiple genomes in near linear time, compared to the quadratic time requirements of BLASTZ and TBA, while improving overall accuracy.

Conclusions/Significance

Murasaki provides an open source platform to take advantage of long patterns, cluster computing, and novel hash algorithms to produce accurate anchors across multiple genomes with computational efficiency significantly greater than existing methods. Murasaki is available under GPL at http://murasaki.sourceforge.net.     

Cgaln: fast and space-efficient whole-genome alignment

Background  

Whole-genome sequence alignment is an essential process for extracting valuable information about the functions, evolution, and peculiarities of genomes under investigation. As available genomic sequence data accumulate rapidly, there is great demand for tools that can compare whole-genome sequences within practical amounts of time and space. However, most existing genomic alignment tools can treat sequences that are only a few Mb long at once, and no state-of-the-art alignment program can align large sequences such as mammalian genomes directly on a conventional standalone computer.     

Using multiple alignments to improve seeded local alignment algorithms

Multiple alignments among genomes are becoming increasingly prevalent. This trend motivates the development of tools for efficient homology search between a query sequence and a database of multiple alignments. In this paper, we present an algorithm that uses the information implicit in a multiple alignment to dynamically build an index that is weighted most heavily towards the promising regions of the multiple alignment. We have implemented Typhon, a local alignment tool that incorporates our indexing algorithm, which our test results show to be more sensitive than algorithms that index only a sequence. This suggests that when applied on a whole-genome scale, Typhon should provide improved homology searches in time comparable to existing algorithms.     

Comparison methods for branching and axillary flowering sequences

Comparing branching and axillary flowering patterns accurately is a major issue both in botany and in various agronomic contexts. Data take the form of sequences which naturally represent the underlying structural information of branching and axillary flowering patterns. Various comparison methods are proposed based either on sequence alignment or on the computation of dissimilarity measures between (hidden) Markovian models built from sets of sequences. Sequence alignment is a natural complement to the exploratory tools and statistical models proposed in Guédon et al. (J. Theor. Biol. 212 (2001) 481) with the distinctive feature of applying to individual sequences. Comparison methods may also be used to reveal some grouping within a set of sequences or to evaluate the strength of a predefined grouping of sequences. The proposed approach is illustrated by examples corresponding to different plant species and different biological or agronomic objectives.     

EnteriX 2003: Visualization tools for genome alignments of Enterobacteriaceae

We describe EnteriX, a suite of three web-based visualization tools for graphically portraying alignment information from comparisons among several fixed and user-supplied sequences from related enterobacterial species, anchored on a reference genome (http://bio.cse.psu.edu/). The first visualization, Enteric, displays stacked pairwise alignments between a reference genome and each of the related bacteria, represented schematically as PIPs (Percent Identity Plots). Encoded in the views are large-scale genomic rearrangement events and functional landmarks. The second visualization, Menteric, computes and displays 1 Kb views of nucleotide-level multiple alignments of the sequences, together with annotations of genes, regulatory sites and conserved regions. The third, a Java-based tool named Maj, displays alignment information in two formats, corresponding roughly to the Enteric and Menteric views, and adds zoom-in capabilities. The uses of such tools are diverse, from examining the multiple sequence alignment to infer conserved sites with potential regulatory roles, to scrutinizing the commonalities and differences between the genomes for pathogenicity or phylogenetic studies. The EnteriX suite currently includes >15 enterobacterial genomes, generates views centered on four different anchor genomes and provides support for including user sequences in the alignments.     

A novel feature-based method for whole genome phylogenetic analysis without alignment: application to HEV genotyping and subtyping

Traditional phylogenetic analysis is based on multiple sequence alignment. With the development of worldwide genome sequencing project, more and more completely sequenced genomes become available. However, traditional sequence alignment tools are impossible to deal with large-scale genome sequence. So, the development of new algorithms to infer phylogenetic relationship without alignment from whole genome information represents a new direction of phylogenetic study in the post-genome era. In the present study, a novel algorithm based on BBC (base-base correlation) is proposed to analyze the phylogenetic relationships of HEV (Hepatitis E virus). When 48 HEV genome sequences are analyzed, the phylogenetic tree that is constructed based on BBC algorithm is well consistent with that of previous study. When compared with methods of sequence alignment, the merit of BBC algorithm appears to be more rapid in calculating evolutionary distances of whole genome sequence and not requires any human intervention, such as gene identification, parameter selection. BBC algorithm can serve as an alternative to rapidly construct phylogenetic trees and infer evolutionary relationships.     

Comparison of sequence profiles. Strategies for structural predictions using sequence information

Distant homologies between proteins are often discovered only after three-dimensional structures of both proteins are solved. The sequence divergence for such proteins can be so large that simple comparison of their sequences fails to identify any similarity. New generation of sensitive alignment tools use averaged sequences of entire homologous families (profiles) to detect such homologies. Several algorithms, including the newest generation of BLAST algorithms and BASIC, an algorithm used in our group to assign fold predictions for proteins from several genomes, are compared to each other on the large set of structurally similar proteins with little sequence similarity. Proteins in the benchmark are classified according to the level of their similarity, which allows us to demonstrate that most of the improvement of the new algorithms is achieved for proteins with strong functional similarities, with almost no progress in recognizing distant fold similarities. It is also shown that details of profile calculation strongly influence its sensitivity in recognizing distant homologies. The most important choice is how to include information from diverging members of the family, avoiding generating false predictions, while accounting for entire sequence divergence within a family. PSI-BLAST takes a conservative approach, deriving a profile from core members of the family, providing a solid improvement without almost any false predictions. BASIC strives for better sensitivity by increasing the weight of divergent family members and paying the price in lower reliability. A new FFAS algorithm introduced here uses a new procedure for profile generation that takes into account all the relations within the family and matches BASIC sensitivity with PSI-BLAST like reliability.     

Fast algorithms for large-scale genome alignment and comparison

We describe a suffix-tree algorithm that can align the entire genome sequences of eukaryotic and prokaryotic organisms with minimal use of computer time and memory. The new system, MUMmer 2, runs three times faster while using one-third as much memory as the original MUMmer system. It has been used successfully to align the entire human and mouse genomes to each other, and to align numerous smaller eukaryotic and prokaryotic genomes. A new module permits the alignment of multiple DNA sequence fragments, which has proven valuable in the comparison of incomplete genome sequences. We also describe a method to align more distantly related genomes by detecting protein sequence homology. This extension to MUMmer aligns two genomes after translating the sequence in all six reading frames, extracts all matching protein sequences and then clusters together matches. This method has been applied to both incomplete and complete genome sequences in order to detect regions of conserved synteny, in which multiple proteins from one organism are found in the same order and orientation in another. The system code is being made freely available by the authors.     

Multi-species sequence comparison: the next frontier in genome annotation

Multi-species comparisons of DNA sequences are more powerful for discovering functional sequences than pairwise DNA sequence comparisons. Most current computational tools have been designed for pairwise comparisons, and efficient extension of these tools to multiple species will require knowledge of the ideal evolutionary distance to choose and the development of new algorithms for alignment, analysis of conservation, and visualization of results.     

A plant biologists’ guide to phylogenetic analysis of biological macromolecule sequences

Phylogenetic analysis has become a common step in characterization of gene and protein sequences. However, despite the availability of numerous affordable and more-or-less intuitive software tools, construction of biologically relevant, informative phylogenetic trees remains a process involving several critical steps that are inherently non-algorithmic, i.e., dependent on decisions made by the user. These steps involve, but are not limited to, setting the aims of the phylogenetic study, choosing sequences to be analyzed, and selecting methods employed in sequence alignment construction, as well as algorithms and parameters used to construct the actual phylogenetic tree. This review aims towards providing guidance for these decisions, as well as illustrating common pitfalls and problems occurring during phylogenetic analysis of plant gene sequences.     

Statistical Alignment with a Sequence Evolution Model Allowing Rate Heterogeneity along the Sequence

We present a stochastic sequence evolution model to obtain alignments and estimate mutation rates between two homologous sequences. The model allows two possible evolutionary behaviors along a DNA sequence in order to determine conserved regions and take its heterogeneity into account. In our model, the sequence is divided into slow and fast evolution regions. The boundaries between these sections are not known. It is our aim to detect them. The evolution model is based on a fragment insertion and deletion process working on fast regions only and on a substitution process working on fast and slow regions with different rates. This model induces a pair hidden Markov structure at the level of alignments, thus making efficient statistical alignment algorithms possible. We propose two complementary estimation methods, namely, a Gibbs sampler for Bayesian estimation and a stochastic version of the EM algorithm for maximum likelihood estimation. Both algorithms involve the sampling of alignments. We propose a partial alignment sampler, which is computationally less expensive than the typical whole alignment sampler. We show the convergence of the two estimation algorithms when used with this partial sampler. Our algorithms provide consistent estimates for the mutation rates and plausible alignments and sequence segmentations on both simulated and real data.     

The UniMarker (UM) method for synteny mapping of large genomes

MOTIVATION: Synteny mapping, or detecting regions that are orthologous between two genomes, is a key step in studies of comparative genomics. For completely sequenced genomes, this is increasingly accomplished by whole-genome sequence alignment. However, such methods are computationally expensive, especially for large genomes, and require rather complicated post-processing procedures to filter out non-orthologous sequence matches. RESULTS: We have developed a novel method that does not require sequence alignment for synteny mapping of two large genomes, such as the human and mouse. In this method, the occurrence spectra of genome-wide unique 16mer sequences present in both the human and mouse genome are used to directly detect orthologous genomic segments. Being sequence alignment-free, the method is very fast and able to map the two mammalian genomes in one day of computing time on a single Pentium IV personal computer. The resulting human-mouse synteny map was shown to be in excellent agreement with those produced by the Mouse Genome Sequencing Consortium (MGSC) and by the Ensembl team; furthermore, the syntenic relationship of segments found only by our method was supported by BLASTZ sequence alignment.     

GWFASTA: server for FASTA search in eukaryotic and microbial genomes

Similarity searches are a powerful method for solving important biological problems such as database scanning, evolutionary studies, gene prediction, and protein structure prediction. FASTA is a widely used sequence comparison tool for rapid database scanning. Here we describe the GWFASTA server that was developed to assist the FASTA user in similarity searches against partially and/or completely sequenced genomes. GWFASTA consists of more than 60 microbial genomes, eight eukaryote genomes, and proteomes of annotatedgenomes. Infact, it provides the maximum number of databases for similarity searching from a single platform. GWFASTA allows the submission of more than one sequence as a single query for a FASTA search. It also provides integrated post-processing of FASTA output, including compositional analysis of proteins, multiple sequences alignment, and phylogenetic analysis. Furthermore, it summarizes the search results organism-wise for prokaryotes and chromosome-wise for eukaryotes. Thus, the integration of different tools for sequence analyses makes GWFASTA a powerful toolfor biologists.     

Sequence alignment by cross-correlation.

Many recent advances in biology and medicine have resulted from DNA sequence alignment algorithms and technology. Traditional approaches for the matching of DNA sequences are based either on global alignment schemes or heuristic schemes that seek to approximate global alignment algorithms while providing higher computational efficiency. This report describes an approach using the mathematical operation of cross-correlation to compare sequences. It can be implemented using the fast fourier transform for computational efficiency. The algorithm is summarized and sample applications are given. These include gene sequence alignment in long stretches of genomic DNA, finding sequence similarity in distantly related organisms, demonstrating sequence similarity in the presence of massive (approximately 90%) random point mutations, comparing sequences related by internal rearrangements (tandem repeats) within a gene, and investigating fusion proteins. Application to RNA and protein sequence alignment is also discussed. The method is efficient, sensitive, and robust, being able to find sequence similarities where other alignment algorithms may perform poorly.     

Getting more from less: algorithms for rapid protein identification with multiple short peptide sequences

We describe two novel sequence similarity search algorithms, FASTS and FASTF, that use multiple short peptide sequences to identify homologous sequences in protein or DNA databases. FASTS searches with peptide sequences of unknown order, as obtained by mass spectrometry-based sequencing, evaluating all possible arrangements of the peptides. FASTF searches with mixed peptide sequences, as generated by Edman sequencing of unseparated mixtures of peptides. FASTF deconvolutes the mixture, using a greedy heuristic that allows rapid identification of high scoring alignments while reducing the total number of explored alternatives. Both algorithms use the heuristic FASTA comparison strategy to accelerate the search but use alignment probability, rather than similarity score, as the criterion for alignment optimality. Statistical estimates are calculated using an empirical correction to a theoretical probability. These calculated estimates were accurate within a factor of 10 for FASTS and 1000 for FASTF on our test dataset. FASTS requires only 15-20 total residues in three or four peptides to robustly identify homologues sharing 50% or greater protein sequence identity. FASTF requires about 25% more sequence data than FASTS for equivalent sensitivity, but additional sequence data are usually available from mixed Edman experiments. Thus, both algorithms can identify homologues that diverged 100 to 500 million years ago, allowing proteomic identification from organisms whose genomes have not been sequenced.