Concept: Sequence alignment
SUMMARY: Two methods to add unaligned sequences into an existing multiple sequence alignment have been implemented as the “–add” and “–addfragments” options in the MAFFT package. The former option is a basic one and applicable only to full-length sequences, while the latter option is applicable even when the unaligned sequences are short and fragmentary. These methods internally infer the phylogenetic relationship among the sequences in the existing alignment, as well as the phylogenetic positions of unaligned sequences. Benchmarks based on two independent simulations consistently suggest that the “–addfragments” option outperforms recent methods, PaPaRa and PAGAN, in accuracy for difficult problems and that these three methods appropriately handle easy problems. AVAILABILITY: http://mafft.cbrc.jp/alignment/software/ CONTACT: email@example.com SUPPLEMENTARY INFORMATION: Available at Bioinformatics online.
In this article we propose a Fast Optimal Global Sequence Alignment Algorithm, FOGSAA, which aligns a pair of nucleotide/protein sequences faster than any optimal global alignment method including the widely used Needleman-Wunsch (NW) algorithm. FOGSAA is applicable for all types of sequences, with any scoring scheme, and with or without affine gap penalty. Compared to NW, FOGSAA achieves a time gain of (70-90)% for highly similar nucleotide sequences (> 80% similarity), and (54-70)% for sequences having (30-80)% similarity. For other sequences, it terminates with an approximate score. For protein sequences, the average time gain is between (25-40)%. Compared to three heuristic global alignment methods, the quality of alignment is improved by about 23%-53%. FOGSAA is, in general, suitable for aligning any two sequences defined over a finite alphabet set, where the quality of the global alignment is of supreme importance.
Sequence alignment is a long standing problem in bioinformatics. The Basic Local Alignment Search Tool (BLAST) is one of the most popular and fundamental alignment tools. The explosive growth of biological sequences calls for speedup of sequence alignment tools such as BLAST. To this end, we develop high speed BLASTN (HS-BLASTN), a parallel and fast nucleotide database search tool that accelerates MegaBLAST-the default module of NCBI-BLASTN. HS-BLASTN builds a new lookup table using the FMD-index of the database and employs an accurate and effective seeding method to find short stretches of identities (called seeds) between the query and the database. HS-BLASTN produces the same alignment results as MegaBLAST and its computational speed is much faster than MegaBLAST. Specifically, our experiments conducted on a 12-core server show that HS-BLASTN can be 22 times faster than MegaBLAST and exhibits better parallel performance than MegaBLAST. HS-BLASTN is written in C++ and the related source code is available at https://github.com/chenying2016/queries under the GPLv3 license.
MOTIVATION: The expansion of DNA sequencing capacity has enabled the sequencing of whole genomes from a number of related species. These genomes can be combined in a multiple alignment that provides useful information about the evolutionary history at each genomic locus. One area in which evolutionary information can productively be exploited is in aligning a new sequence to a database of existing, aligned genomes. However, existing high-throughput alignment tools are not designed to work effectively with multiple genome alignments. RESULTS: We introduce PhyLAT, the phylogenetic local alignment tool, to compute local alignments of a query sequence against a fixed multiple-genome alignment of closely related species. PhyLAT uses a known phylogenetic tree on the species in the multiple alignment to improve the quality of its computed alignments while also estimating the placement of the query on this tree. It combines a probabilistic approach to alignment with seeding and expansion heuristics to accelerate discovery of significant alignments. We provide evidence, using alignments of human chromosome 22 against a five-species alignment from the UCSC Genome Browser database, that PhyLAT’s alignments are more accurate than those of other commonly used programs, including BLAST, POY, MAFFT, MUSCLE and CLUSTAL. PhyLAT also identifies more alignments in coding DNA than does pairwise alignment alone. Finally, our tool determines the evolutionary relationship of query sequences to the database more accurately than do POY, RAxML, EPA or pplacer.
Profile ALIgNmEnt (PRALINE) is a versatile multiple sequence alignment toolkit. In its main alignment protocol, PRALINE follows the global progressive alignment algorithm. It provides various alignment optimization strategies to address the different situations that call for protein multiple sequence alignment: global profile preprocessing, homology-extended alignment, secondary structure-guided alignment, and transmembrane aware alignment. A number of combinations of these strategies are enabled as well. PRALINE is accessible via the online server http://www.ibi.vu.nl/programs/PRALINEwww/. The server facilitates extensive visualization possibilities aiding the interpretation of alignments generated, which can be written out in pdf format for publication purposes. PRALINE also allows the sequences in the alignment to be represented in a dendrogram to show their mutual relationships according to the alignment. The chapter ends with a discussion of various issues occurring in multiple sequence alignment.
- International journal for numerical methods in biomedical engineering
- Published over 5 years ago
In this paper, a computational modeling for biomechanical analysis of primary blast injuries is presented. The responses of the brain in terms of mechanical parameters under different blast spaces including open, semi-confined, and confined environments are studied. In the study, the effect of direct and indirect blast waves from the neighboring walls in the confined environments will be taken into consideration. A 50th percentile finite element head model is exposed to blast waves of different intensities. In the open space, the head experiences a sudden intracranial pressure (ICP) change, which vanishes in a matter of a few milliseconds. The situation is similar in semi-confined space, but in the confined space, the reflections from the walls will create a number of subsequent peaks in ICP with a longer duration. The analysis procedure is based on a simultaneous interaction simulation of the deformable head and its components with the blast wave propagations. It is concluded that compared with the open and semi-confined space settings, the walls in the confined space scenario enhance the risk of primary blast injuries considerably because of indirect blast waves transferring a larger amount of damaging energy to the head. Copyright © 2013 John Wiley & Sons, Ltd.
- IEEE/ACM transactions on computational biology and bioinformatics / IEEE, ACM
- Published almost 6 years ago
We present ARMiCoRe, a novel approach to a classical bioinformatics problem, viz. multiple sequence alignment (MSA) of gene and protein sequences. Aligning multiple biological sequences is a key step in elucidating evolutionary relationships, annotating newly sequenced segments, and understanding the relationship between biological sequences and functions. Classical MSA algorithms are designed to primarily capture conservations in sequences whereas couplings, or correlated mutations, are well known as an additional important aspect of sequence evolution. (Two sequence positions are coupled when mutations in one are accompanied by compensatory mutations in another). As a result, it is not uncommon for practitioners to hand-tweak a conservation-based alignment to better expose couplings. ARMiCoRe introduces a distinctly pattern mining approach to improving MSAs: using frequent episode mining as a foundational basis, we define the notion of a coupled pattern and demonstrate how the discovery and tiling of coupled patterns using a max-flow approach can yield MSAs that are significantly better than conservation-based alignments. Although we were motivated to improve MSAs for the sake of better exposing couplings, we demonstrate that our MSAs are also improvements in terms of traditional metrics of assessment. We demonstrate the effectiveness of ARMiCoRe on a large collection of datasets.
We describe a Bayesian Markov chain Monte Carlo (MCMC) sampler for protein multiple sequence alignment (MSA) that, as implemented in the program GISMO and applied to large numbers of diverse sequences, is more accurate than the popular MSA programs MUSCLE, MAFFT, Clustal-Ω and Kalign. Features of GISMO central to its performance are: (i) It employs a “top-down” strategy with a favorable asymptotic time complexity that first identifies regions generally shared by all the input sequences, and then realigns closely related subgroups in tandem. (ii) It infers position-specific gap penalties that favor insertions or deletions (indels) within each sequence at alignment positions in which indels are invoked in other sequences. This favors the placement of insertions between conserved blocks, which can be understood as making up the proteins' structural core. (iii) It uses a Bayesian statistical measure of alignment quality based on the minimum description length principle and on Dirichlet mixture priors. Consequently, GISMO aligns sequence regions only when statistically justified. This is unlike methods based on the ad hoc, but widely used, sum-of-the-pairs scoring system, which will align random sequences. (iv) It defines a system for exploring alignment space that provides natural avenues for further experimentation through the development of new sampling strategies for more efficiently escaping from suboptimal traps. GISMO’s superior performance is illustrated using 408 protein sets containing, on average, 235 sequences. These sets correspond to NCBI Conserved Domain Database alignments, which have been manually curated in the light of available crystal structures, and thus provide a means to assess alignment accuracy. GISMO fills a different niche than other MSA programs, namely identifying and aligning a conserved domain present within a large, diverse set of full length sequences. The GISMO program is available at http://gismo.igs.umaryland.edu/.
Since 2004 the European Bioinformatics Institute (EMBL-EBI) has provided access to a wide range of databases and analysis tools via Web Services interfaces. This comprises services to search across the databases available from the EMBL-EBI and to explore the network of cross-references present in the data (e.g. EB-eye), services to retrieve entry data in various data formats and to access the data in specific fields (e.g. dbfetch), and analysis tool services, for example, sequence similarity search (e.g. FASTA and NCBI BLAST), multiple sequence alignment (e.g. Clustal Omega and MUSCLE), pairwise sequence alignment and protein functional analysis (e.g. InterProScan and Phobius). The REST/SOAP Web Services (http://www.ebi.ac.uk/Tools/webservices/) interfaces to these databases and tools allow their integration into other tools, applications, web sites, pipeline processes and analytical workflows. To get users started using the Web Services, sample clients are provided covering a range of programming languages and popular Web Service tool kits, and a brief guide to Web Services technologies, including a set of tutorials, is available for those wishing to learn more and develop their own clients. Users of the Web Services are informed of improvements and updates via a range of methods.
The alignment of sequencing reads to a transcriptome is a common and important step in many RNA-seq analysis tasks. When aligning RNA-seq reads directly to a transcriptome (as is common in the de novo setting or when a trusted reference annotation is available), care must be taken to report the potentially large number of multi-mapping locations per read. This can pose a substantial computational burden for existing aligners, and can considerably slow downstream analysis.