Segment-based multiple sequence alignment

Towards the accurate alignment of over a million protein sequences: Current state of the art

Large-scale genomics requires highly scalable and accurate multiple sequence alignment methods. Results collected over this last decade suggest accuracy loss when scaling up over a few thousand sequences. This issue has been actively addressed with a number of innovative algorithmic solutions that combine low-level hardware optimization with novel higher-level heuristics. This review provides an extensive critical overview of these recent methods. Using established reference datasets we conclude that albeit significant progress has been achieved, a unified framework able to consistently and efficiently produce high-accuracy large-scale multiple alignments is still lacking.

Multiple genome alignment in the telomere-to-telomere assembly era

With the arrival of telomere-to-telomere (T2T) assemblies of the human genome comes the computational challenge of efficiently and accurately constructing multiple genome alignments at an unprecedented scale. By identifying nucleotides across genomes which share a common ancestor, multiple genome alignments commonly serve as the bedrock for comparative genomics studies. In this review, we provide an overview of the algorithmic template that most multiple genome alignment methods follow. We also discuss prospective areas of improvement of multiple genome alignment for keeping up with continuously arriving high-quality T2T assembled genomes and for unlocking clinically-relevant insights.

Accurate multiple alignment of distantly related genome sequences using filtered spaced word matches as anchor points

Motivation

Most methods for pairwise and multiple genome alignment use fast local homology search tools to identify anchor points, i.e. high-scoring local alignments of the input sequences. Sequence segments between those anchor points are then aligned with slower, more sensitive methods. Finding suitable anchor points is therefore crucial for genome sequence comparison; speed and sensitivity of genome alignment depend on the underlying anchoring methods.

Results

In this article, we use filtered spaced word matches to generate anchor points for genome alignment. For a given binary pattern representing match and don't-care positions, we first search for spaced-word matches, i.e. ungapped local pairwise alignments with matching nucleotides at the match positions of the pattern and possible mismatches at the don't-care positions. Those spaced-word matches that have similarity scores above some threshold value are then extended using a standard X-drop algorithm; the resulting local alignments are used as anchor points. To evaluate this approach, we used the popular multiple-genome-alignment pipeline Mugsy and replaced the exact word matches that Mugsy uses as anchor points with our spaced-word-based anchor points. For closely related genome sequences, the two anchoring procedures lead to multiple alignments of similar quality. For distantly related genomes, however, alignments calculated with our filtered-spaced-word matches are superior to alignments produced with the original Mugsy program where exact word matches are used to find anchor points.

Availability and implementation

http://spacedanchor.gobics.de.

Supplementary information

Supplementary data are available at Bioinformatics online.

MAPT p.V363I mutation

Objective

Patients with corticobasal syndrome (CBS) present with heterogeneous clinical features, including asymmetric parkinsonism, dyspraxia, aphasia, and cognitive impairment; to better understand the genetic etiology of this rare disease, we undertook a genetic analysis of microtubule-associated protein tau (MAPT).

Methods

We performed a genetic evaluation of MAPT mutations in 826 neurologically healthy controls and 173 cases with CBS using the Illumina NeuroChip genotyping array.

Results

We identified 2 patients with CBS heterozygous for a rare mutation in MAPT (p.V363I) that is located in the highly conserved microtubule-binding domain. One patient was pathologically confirmed and demonstrated extensive 4-repeat-tau-positive thread pathology, achromatic neurons, and astrocytic plaques consistent with corticobasal degeneration (CBD).

Conclusions

We report 2 CBS cases carrying the rare p.V363I MAPT mutation, one of which was pathologically confirmed as CBD. Our findings support the notion that this rare coding change is pathogenic.

Whole-Genome Alignment

Whole-genome alignment (WGA) is the prediction of evolutionary relationships at the nucleotide level between two or more genomes. It combines aspects of both colinear sequence alignment and gene orthology prediction and is typically more challenging to address than either of these tasks due to the size and complexity of whole genomes. Despite the difficulty of this problem, numerous methods have been developed for its solution because WGAs are valuable for genome-wide analyses such as phylogenetic inference, genome annotation, and function prediction. In this chapter, we discuss the meaning and significance of WGA and present an overview of the methods that address it. We also examine the problem of evaluating whole-genome aligners and offer a set of methodological challenges that need to be tackled in order to make most effective use of our rapidly growing databases of whole genomes.

The SeqAn C++ template library for efficient sequence analysis: A resource for programmers

BACKGROUND

The use of novel algorithmic techniques is pivotal to many important problems in life science. For example the sequencing of the human genome (Venter et al., 2001) would not have been possible without advanced assembly algorithms and the development of practical BWT based read mappers have been instrumental for NGS analysis. However, owing to the high speed of technological progress and the urgent need for bioinformatics tools, there was a widening gap between state-of-the-art algorithmic techniques and the actual algorithmic components of tools that are in widespread use. We previously addressed this by introducing the SeqAn library of efficient data types and algorithms in 2008 (Döring et al., 2008).

RESULTS

The SeqAn library has matured considerably since its first publication 9 years ago. In this article we review its status as an established resource for programmers in the field of sequence analysis and its contributions to many analysis tools.

CONCLUSIONS

We anticipate that SeqAn will continue to be a valuable resource, especially since it started to actively support various hardware acceleration techniques in a systematic manner.

In Silico Signature Prediction Modeling in Cytolethal Distending Toxin-Producing Escherichia coli Strains

In this study, cytolethal distending toxin (CDT) producer isolates genome were compared with genome of pathogenic and commensal Escherichia coli strains. Conserved genomic signatures among different types of CDT producer E. coli strains were assessed. It was shown that they could be used as biomarkers for research purposes and clinical diagnosis by polymerase chain reaction, or in vaccine development. cdt genes and several other genetic biomarkers were identified as signature sequences in CDT producer strains. The identified signatures include several individual phage proteins (holins, nucleases, and terminases, and transferases) and multiple members of different protein families (the lambda family, phage-integrase family, phage-tail tape protein family, putative membrane proteins, regulatory proteins, restriction-modification system proteins, tail fiber-assembly proteins, base plate-assembly proteins, and other prophage tail-related proteins). In this study, a sporadic phylogenic pattern was demonstrated in the CDT-producing strains. In conclusion, conserved signature proteins in a wide range of pathogenic bacterial strains can potentially be used in modern vaccine-design strategies.

Unicycler: Resolving bacterial genome assemblies from short and long sequencing reads

The Illumina DNA sequencing platform generates accurate but short reads, which can be used to produce accurate but fragmented genome assemblies. Pacific Biosciences and Oxford Nanopore Technologies DNA sequencing platforms generate long reads that can produce complete genome assemblies, but the sequencing is more expensive and error-prone. There is significant interest in combining data from these complementary sequencing technologies to generate more accurate "hybrid" assemblies. However, few tools exist that truly leverage the benefits of both types of data, namely the accuracy of short reads and the structural resolving power of long reads. Here we present Unicycler, a new tool for assembling bacterial genomes from a combination of short and long reads, which produces assemblies that are accurate, complete and cost-effective. Unicycler builds an initial assembly graph from short reads using the de novo assembler SPAdes and then simplifies the graph using information from short and long reads. Unicycler uses a novel semi-global aligner to align long reads to the assembly graph. Tests on both synthetic and real reads show Unicycler can assemble larger contigs with fewer misassemblies than other hybrid assemblers, even when long-read depth and accuracy are low. Unicycler is open source (GPLv3) and available at github.com/rrwick/Unicycler.

Unicycler: Resolving bacterial genome assemblies from short and long sequencing reads

The Illumina DNA sequencing platform generates accurate but short reads, which can be used to produce accurate but fragmented genome assemblies. Pacific Biosciences and Oxford Nanopore Technologies DNA sequencing platforms generate long reads that can produce complete genome assemblies, but the sequencing is more expensive and error-prone. There is significant interest in combining data from these complementary sequencing technologies to generate more accurate "hybrid" assemblies. However, few tools exist that truly leverage the benefits of both types of data, namely the accuracy of short reads and the structural resolving power of long reads. Here we present Unicycler, a new tool for assembling bacterial genomes from a combination of short and long reads, which produces assemblies that are accurate, complete and cost-effective. Unicycler builds an initial assembly graph from short reads using the de novo assembler SPAdes and then simplifies the graph using information from short and long reads. Unicycler uses a novel semi-global aligner to align long reads to the assembly graph. Tests on both synthetic and real reads show Unicycler can assemble larger contigs with fewer misassemblies than other hybrid assemblers, even when long-read depth and accuracy are low. Unicycler is open source (GPLv3) and available at github.com/rrwick/Unicycler.

Multiple sequence alignment modeling: methods and applications

This review provides an overview on the development of Multiple sequence alignment (MSA) methods and their main applications. It is focused on progress made over the past decade. The three first sections review recent algorithmic developments for protein, RNA/DNA and genomic alignments. The fourth section deals with benchmarks and explores the relationship between empirical and simulated data, along with the impact on method developments. The last part of the review gives an overview on available MSA local reliability estimators and their dependence on various algorithmic properties of available methods.

YOC, A new strategy for pairwise alignment of collinear genomes

Background

Comparing and aligning genomes is a key step in analyzing closely related genomes. Despite the development of many genome aligners in the last 15 years, the problem is not yet fully resolved, even when aligning closely related bacterial genomes of the same species. In addition, no procedures are available to assess the quality of genome alignments or to compare genome aligners.

Results

We designed an original method for pairwise genome alignment, named YOC, which employs a highly sensitive similarity detection method together with a recent collinear chaining strategy that allows overlaps. YOC improves the reliability of collinear genome alignments, while preserving or even improving sensitivity. We also propose an original qualitative evaluation criterion for measuring the relevance of genome alignments. We used this criterion to compare and benchmark YOC with five recent genome aligners on large bacterial genome datasets, and showed it is suitable for identifying the specificities and the potential flaws of their underlying strategies.

Conclusions

The YOC prototype is available at https://github.com/ruricaru/YOC. It has several advantages over existing genome aligners: (1) it is based on a simplified two phase alignment strategy, (2) it is easy to parameterize, (3) it produces reliable genome alignments, which are easier to analyze and to use.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0530-3) contains supplementary material, which is available to authorized users.

Open-Phylo: a customizable crowd-computing platform for multiple sequence alignment

Citizen science games such as Galaxy Zoo, Foldit, and Phylo aim to harness the intelligence and processing power generated by crowds of online gamers to solve scientific problems. However, the selection of the data to be analyzed through these games is under the exclusive control of the game designers, and so are the results produced by gamers. Here, we introduce Open-Phylo, a freely accessible crowd-computing platform that enables any scientist to enter our system and use crowds of gamers to assist computer programs in solving one of the most fundamental problems in genomics: the multiple sequence alignment problem.

Genome alignment with graph data structures: a comparison

Background

Recent advances in rapid, low-cost sequencing have opened up the opportunity to study complete genome sequences. The computational approach of multiple genome alignment allows investigation of evolutionarily related genomes in an integrated fashion, providing a basis for downstream analyses such as rearrangement studies and phylogenetic inference.

Graphs have proven to be a powerful tool for coping with the complexity of genome-scale sequence alignments. The potential of graphs to intuitively represent all aspects of genome alignments led to the development of graph-based approaches for genome alignment. These approaches construct a graph from a set of local alignments, and derive a genome alignment through identification and removal of graph substructures that indicate errors in the alignment.

Results

We compare the structures of commonly used graphs in terms of their abilities to represent alignment information. We describe how the graphs can be transformed into each other, and identify and classify graph substructures common to one or more graphs. Based on previous approaches, we compile a list of modifications that remove these substructures.

Conclusion

We show that crucial pieces of alignment information, associated with inversions and duplications, are not visible in the structure of all graphs. If we neglect vertex or edge labels, the graphs differ in their information content. Still, many ideas are shared among all graph-based approaches. Based on these findings, we outline a conceptual framework for graph-based genome alignment that can assist in the development of future genome alignment tools.

A tale of two drug targets: the evolutionary history of BACE1 and BACE2

The beta amyloid (APP) cleaving enzyme (BACE1) has been a drug target for Alzheimer's Disease (AD) since 1999 with lead inhibitors now entering clinical trials. In 2011, the paralog, BACE2, became a new target for type II diabetes (T2DM) having been identified as a TMEM27 secretase regulating pancreatic β cell function. However, the normal roles of both enzymes are unclear. This study outlines their evolutionary history and new opportunities for functional genomics. We identified 30 homologs (UrBACEs) in basal phyla including Placozoans, Cnidarians, Choanoflagellates, Porifera, Echinoderms, Annelids, Mollusks and Ascidians (but not Ecdysozoans). UrBACEs are predominantly single copy, show 35-45% protein sequence identity with mammalian BACE1, are ~100 residues longer than cathepsin paralogs with an aspartyl protease domain flanked by a signal peptide and a C-terminal transmembrane domain. While multiple paralogs in Trichoplax and Monosiga pre-date the nervous system, duplication of the UrBACE in fish gave rise to BACE1 and BACE2 in the vertebrate lineage. The latter evolved more rapidly as the former maintained the emergent neuronal role. In mammals, Ka/Ks for BACE2 is higher than BACE1 but low ratios for both suggest purifying selection. The 5' exons show higher Ka/Ks than the catalytic section. Model organism genomes show the absence of certain BACE human substrates when the UrBACE is present. Experiments could thus reveal undiscovered substrates and roles. The human protease double-target status means that evolutionary trajectories and functional shifts associated with different substrates will have implications for the development of clinical candidates for both AD and T2DM. A rational basis for inhibition specificity ratios and assessing target-related side effects will be facilitated by a more complete picture of BACE1 and BACE2 functions informed by their evolutionary context.

Heuristic alignment methods

Computation of multiple sequence alignment (MSA) is usually formulated as a combinatory optimization problem of an objective function. Solving the problem for virtually all sensible objective functions is known to be NP-complete implying that some heuristics must be adopted. Several general strategies have been proven effective to obtain accurate MSAs in reasonable computational costs. This chapter is devoted to a brief summary of most successful heuristic approaches.

DELLY: structural variant discovery by integrated paired-end and split-read analysis

MOTIVATION

The discovery of genomic structural variants (SVs) at high sensitivity and specificity is an essential requirement for characterizing naturally occurring variation and for understanding pathological somatic rearrangements in personal genome sequencing data. Of particular interest are integrated methods that accurately identify simple and complex rearrangements in heterogeneous sequencing datasets at single-nucleotide resolution, as an optimal basis for investigating the formation mechanisms and functional consequences of SVs.

RESULTS

We have developed an SV discovery method, called DELLY, that integrates short insert paired-ends, long-range mate-pairs and split-read alignments to accurately delineate genomic rearrangements at single-nucleotide resolution. DELLY is suitable for detecting copy-number variable deletion and tandem duplication events as well as balanced rearrangements such as inversions or reciprocal translocations. DELLY, thus, enables to ascertain the full spectrum of genomic rearrangements, including complex events. On simulated data, DELLY compares favorably to other SV prediction methods across a wide range of sequencing parameters. On real data, DELLY reliably uncovers SVs from the 1000 Genomes Project and cancer genomes, and validation experiments of randomly selected deletion loci show a high specificity.

AVAILABILITY

DELLY is available at www.korbel.embl.de/software.html

CONTACT

tobias.rausch@embl.de.

Whole-genome alignment

Whole-genome alignment (WGA) is the prediction of evolutionary relationships at the nucleotide level between two or more genomes. It combines aspects of both colinear sequence alignment and gene orthology prediction, and is typically more challenging to address than either of these tasks due to the size and complexity of whole genomes. Despite the difficulty of this problem, numerous methods have been developed for its solution because WGAs are valuable for genome-wide analyses, such as phylogenetic inference, genome annotation, and function prediction. In this chapter, we discuss the meaning and significance of WGA and present an overview of the methods that address it. We also examine the problem of evaluating whole-genome aligners and offer a set of methodological challenges that need to be tackled in order to make the most effective use of our rapidly growing databases of whole genomes.

Gegenees: fragmented alignment of multiple genomes for determining phylogenomic distances and genetic signatures unique for specified target groups

The rapid development of Next Generation Sequencing technologies leads to the accumulation of huge amounts of sequencing data. The scientific community faces an enormous challenge in how to deal with this explosion. Here we present a software tool, ‘Gegenees’, that uses a fragmented alignment approach to facilitate the comparative analysis of hundreds of microbial genomes. The genomes are fragmented and compared, all against all, by a multithreaded BLAST control engine. Ready-made alignments can be complemented with new genomes without recalculating the existing data points. Gegenees gives a phylogenomic overview of the genomes and the alignment can then be mined for genomic regions with conservation patterns matching a defined target group and absent from a background group. The genomic regions are given biomarker scores forming a uniqueness signature that can be viewed and explored, graphically and in tabular form. A primer/probe alignment tool is also included for specificity verification of currently used or new primers. We exemplify the use of Gegenees on the Bacillus cereus group, on Foot and Mouth Disease Viruses, and on strains from the 2011 Escherichia coli O104:H4 outbreak. Gegenees contributes towards an increased capacity of fast and efficient data mining as more and more genomes become sequenced.

Phylomark, a tool to identify conserved phylogenetic markers from whole-genome alignments

The sequencing and analysis of multiple housekeeping genes has been routinely used to phylogenetically compare closely related bacterial isolates. Recent studies using whole-genome alignment (WGA) and phylogenetics from >100 Escherichia coli genomes has demonstrated that tree topologies from WGA and multilocus sequence typing (MLST) markers differ significantly. A nonrepresentative phylogeny can lead to incorrect conclusions regarding important evolutionary relationships. In this study, the Phylomark algorithm was developed to identify a minimal number of useful phylogenetic markers that recapitulate the WGA phylogeny. To test the algorithm, we used a set of diverse draft and complete E. coli genomes. The algorithm identified more than 100,000 potential markers of different fragment lengths (500 to 900 nucleotides). Three molecular markers were ultimately chosen to determine the phylogeny based on a low Robinson-Foulds (RF) distance compared to the WGA phylogeny. A phylogenetic analysis demonstrated that a more representative phylogeny was inferred for a concatenation of these markers compared to all other MLST schemes for E. coli. As a functional test of the algorithm, the three markers (genomic guided E. coli markers, or GIG-EM) were amplified and sequenced from a set of environmental E. coli strains (ECOR collection) and informatically extracted from a set of 78 diarrheagenic E. coli strains (DECA collection). In the instances of the 40-genome test set and the DECA collection, the GIG-EM system outperformed other E. coli MLST systems in terms of recapitulating the WGA phylogeny. This algorithm can be employed to determine the minimal marker set for any organism that has sufficient genome sequencing.

Comprehensive comparison of graph based multiple protein sequence alignment strategies

Background

Alignment of protein sequences (MPSA) is the starting point for a multitude of applications in molecular biology. Here, we present a novel MPSA program based on the SeqAn sequence alignment library. Our implementation has a strict modular structure, which allows to swap different components of the alignment process and, thus, to investigate their contribution to the alignment quality and computation time. We systematically varied information sources, guiding trees, score transformations and iterative refinement options, and evaluated the resulting alignments on BAliBASE and SABmark.

Results

Our results indicate the optimal alignment strategy based on the choices compared. First, we show that pairwise global and local alignments contain sufficient information to construct a high quality multiple alignment. Second, single linkage clustering is almost invariably the best algorithm to build a guiding tree for progressive alignment. Third, triplet library extension, with introduction of new edges, is the most efficient consistency transformation of those compared. Alternatively, one can apply tree dependent partitioning as a post processing step, which was shown to be comparable with the best consistency transformation in both time and accuracy. Finally, propagating information beyond four transitive links introduces more noise than signal.

Conclusions

This is the first time multiple protein alignment strategies are comprehensively and clearly compared using a single implementation platform. In particular, we showed which of the existing consistency transformations and iterative refinement techniques are the most valid. Our implementation is freely available at http://ekhidna.biocenter.helsinki.fi/MMSA and as a supplementary file attached to this article (see Additional file 1).

Genome sequencing of pediatric medulloblastoma links catastrophic DNA rearrangements with TP53 mutations

Genomic rearrangements are thought to occur progressively during tumor development. Recent findings, however, suggest an alternative mechanism, involving massive chromosome rearrangements in a one-step catastrophic event termed chromothripsis. We report the whole-genome sequencing-based analysis of a Sonic-Hedgehog medulloblastoma (SHH-MB) brain tumor from a patient with a germline TP53 mutation (Li-Fraumeni syndrome), uncovering massive, complex chromosome rearrangements. Integrating TP53 status with microarray and deep sequencing-based DNA rearrangement data in additional patients reveals a striking association between TP53 mutation and chromothripsis in SHH-MBs. Analysis of additional tumor entities substantiates a link between TP53 mutation and chromothripsis, and indicates a context-specific role for p53 in catastrophic DNA rearrangements. Among these, we observed a strong association between somatic TP53 mutations and chromothripsis in acute myeloid leukemia. These findings connect p53 status and chromothripsis in specific tumor types, providing a genetic basis for understanding particularly aggressive subtypes of cancer.

Identification of Salmonella enterica species- and subgroup-specific genomic regions using Panseq 2.0

The pan-genome of a taxonomic group consists of evolutionarily conserved core genes shared by all members and accessory genes that are present only in some members of the group. Group- and subgroup-specific core genes are thought to contribute to shared phenotypes such as virulence and niche specificity. In this study we analyzed 39 Salmonella enterica genomes (16 closed, 23 draft), a species that contains two human-specific serovars that cause typhoid fever, as well as a large number of zoonotic serovars that cause gastroenteritis in humans. Panseq 2.0 was used to define the pan-genome by adjusting the threshold at which group-specific "core" loci are defined. We found the pan-genome to be 9.03 Mbp in size, and that the core genome size decreased, while the number of SNPs/100 bp increased, as the number of strains used to define the core genome increased, suggesting substantial divergence among S. enterica subgroups. Subgroup-specific "core" genes, in contrast, had fewer SNPs/100 bp, likely reflecting their more recent acquisition. Phylogenetic trees were created from the concatenated and aligned pan-genome, the core genome, and multi-locus-sequence typing (MLST) loci. Branch support increased among the trees, and strains of the same serovar grouped closer together as the number of loci used to create the tree increased. Further, high levels of discrimination were achieved even amongst the most closely related strains of S. enterica Typhi, suggesting that the data generated by Panseq may also be of value in short-term epidemiological studies. Panseq provides an easy and fast way of performing pan-genomic analyses, which can include the identification of group-dominant as well as group-specific loci and is available as a web-server and a standalone version at http://lfz.corefacility.ca/panseq/.

Everything at once: comparative analysis of the genomes of bacterial pathogens

The sum of unique genes in all genomes of a bacterial species is referred to as the pan-genome and is comprised of variably absent or present accessory genes and universally present core genes. The accessory genome is an important source of genetic variability in bacterial populations, allowing sub-populations of bacteria to better adapt to specific niches. Such subgroups may themselves have a relatively stable core genome that may influence host preference, virulence, or an association with specific disease syndromes. The core genome provides a useful means of phylogenetic reconstruction as well as contributing to phenotypic heterogeneity. Variation within the pan-genome forms the basis of comparative genotyping techniques, which have evolved alongside technology. Current high-throughput sequencing platforms have created an unprecedented opportunity for comparisons among multiple, closely related genomes. The computer algorithms and software for such comparisons continue to evolve and promise exciting advances in the world of bacterial comparative genomics. We review genotyping techniques based upon phenotypic traits, both core and accessory genomes, and look at some of the software programs currently available to perform whole-genome comparative analyses.

Protein multiple sequence alignment by hybrid bio-inspired algorithms

This article presents an immune inspired algorithm to tackle the Multiple Sequence Alignment (MSA) problem. MSA is one of the most important tasks in biological sequence analysis. Although this paper focuses on protein alignments, most of the discussion and methodology may also be applied to DNA alignments. The problem of finding the multiple alignment was investigated in the study by Bonizzoni and Vedova and Wang and Jiang, and proved to be a NP-hard (non-deterministic polynomial-time hard) problem. The presented algorithm, called Immunological Multiple Sequence Alignment Algorithm (IMSA), incorporates two new strategies to create the initial population and specific ad hoc mutation operators. It is based on the 'weighted sum of pairs' as objective function, to evaluate a given candidate alignment. IMSA was tested using both classical benchmarks of BAliBASE (versions 1.0, 2.0 and 3.0), and experimental results indicate that it is comparable with state-of-the-art multiple alignment algorithms, in terms of quality of alignments, weighted Sums-of-Pairs (SP) and Column Score (CS) values. The main novelty of IMSA is its ability to generate more than a single suboptimal alignment, for every MSA instance; this behaviour is due to the stochastic nature of the algorithm and of the populations evolved during the convergence process. This feature will help the decision maker to assess and select a biologically relevant multiple sequence alignment. Finally, the designed algorithm can be used as a local search procedure to properly explore promising alignments of the search space.

Mugsy: fast multiple alignment of closely related whole genomes

Motivation: The relative ease and low cost of current generation sequencing technologies has led to a dramatic increase in the number of sequenced genomes for species across the tree of life. This increasing volume of data requires tools that can quickly compare multiple whole-genome sequences, millions of base pairs in length, to aid in the study of populations, pan-genomes, and genome evolution.

Results: We present a new multiple alignment tool for whole genomes named Mugsy. Mugsy is computationally efficient and can align 31 Streptococcus pneumoniae genomes in less than 2 hours producing alignments that compare favorably to other tools. Mugsy is also the fastest program evaluated for the multiple alignment of assembled human chromosome sequences from four individuals. Mugsy does not require a reference sequence, can align mixtures of assembled draft and completed genome data, and is robust in identifying a rich complement of genetic variation including duplications, rearrangements, and large-scale gain and loss of sequence.

Availability: Mugsy is free, open-source software available from http://mugsy.sf.net.

Contact:angiuoli@cs.umd.edu

Supplementary information:Supplementary data are available at Bioinformatics online.

PicXAA: greedy probabilistic construction of maximum expected accuracy alignment of multiple sequences

Accurate tools for multiple sequence alignment (MSA) are essential for comparative studies of the function and structure of biological sequences. However, it is very challenging to develop a computationally efficient algorithm that can consistently predict accurate alignments for various types of sequence sets. In this article, we introduce PicXAA (Probabilistic Maximum Accuracy Alignment), a probabilistic non-progressive alignment algorithm that aims to find protein alignments with maximum expected accuracy. PicXAA greedily builds up the multiple alignment from sequence regions with high local similarities, thereby yielding an accurate global alignment that effectively grasps the local similarities among sequences. Evaluations on several widely used benchmark sets show that PicXAA constantly yields accurate alignment results on a wide range of reference sets, with especially remarkable improvements over other leading algorithms on sequence sets with local similarities. PicXAA source code is freely available at: http://www.ece.tamu.edu/~bjyoon/picxaa/.

Computing multiple sequence/structure alignments with the T-coffee package

In this unit, we describe assembly of a multiple sequence alignment using the T-Coffee package. T-Coffee is much more flexible than most related methods (e.g., ClustalW) because it makes it possible to combine many alternative alignments into a single one, based on an estimate of consistency between these alignments. This strategy can be especially useful when one has to decide among the output produced by several alternative methods.

Upcoming challenges for multiple sequence alignment methods in the high-throughput era

This review focuses on recent trends in multiple sequence alignment tools. It describes the latest algorithmic improvements including the extension of consistency-based methods to the problem of template-based multiple sequence alignments. Some results are presented suggesting that template-based methods are significantly more accurate than simpler alternative methods. The validation of existing methods is also discussed at length with the detailed description of recent results and some suggestions for future validation strategies. The last part of the review addresses future challenges for multiple sequence alignment methods in the genomic era, most notably the need to cope with very large sequences, the need to integrate large amounts of experimental data, the need to accurately align non-coding and non-transcribed sequences and finally, the need to integrate many alternative methods and approaches.

Fast statistical alignment

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/.

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.

A consistency-based consensus algorithm for de novo and reference-guided sequence assembly of short reads

MOTIVATION

Novel high-throughput sequencing technologies pose new algorithmic challenges in handling massive amounts of short-read, high-coverage data. A robust and versatile consensus tool is of particular interest for such data since a sound multi-read alignment is a prerequisite for variation analyses, accurate genome assemblies and insert sequencing.

RESULTS

A multi-read alignment algorithm for de novo or reference-guided genome assembly is presented. The program identifies segments shared by multiple reads and then aligns these segments using a consistency-enhanced alignment graph. On real de novo sequencing data obtained from the newly established NCBI Short Read Archive, the program performs similarly in quality to other comparable programs. On more challenging simulated datasets for insert sequencing and variation analyses, our program outperforms the other tools.

AVAILABILITY

The consensus program can be downloaded from http://www.seqan.de/projects/consensus.html. It can be used stand-alone or in conjunction with the Celera Assembler. Both application scenarios as well as the usage of the tool are described in the documentation.

Sequence progressive alignment, a framework for practical large-scale probabilistic consistency alignment

MOTIVATION

Multiple sequence alignment is a cornerstone of comparative genomics. Much work has been done to improve methods for this task, particularly for the alignment of small sequences, and especially for amino acid sequences. However, less work has been done in making promising methods that work on the small-scale practically for the alignment of much larger genomic sequences.

RESULTS

We take the method of probabilistic consistency alignment and make it practical for the alignment of large genomic sequences. In so doing we develop a set of new technical methods, combined in a framework we term 'sequence progressive alignment', because it allows us to iteratively compute an alignment by passing over the input sequences from left to right. The result is that we massively decrease the memory consumption of the program relative to a naive implementation. The general engineering of the challenges faced in scaling such a computationally intensive process offer valuable lessons for planning related large-scale sequence analysis algorithms. We also further show the strong performance of Pecan using an extended analysis of ancient repeat alignments. Pecan is now one of the default alignment programs that has and is being used by a number of whole-genome comparative genomic projects.

AVAILABILITY

The Pecan program is freely available at http://www.ebi.ac.uk/ approximately bjp/pecan/ Pecan whole genome alignments can be found in the Ensembl genome browser.