DIALIGN P: Fast pair-wise and multiple sequence alignment using parallel processors

Schmollinger, Martin; Nieselt, Kay; Kaufmann, Michael; Morgenstern, Burkhard

doi:10.1186/1471-2105-5-128

Cited by 31 publications

(6 citation statements)

References 29 publications

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“…Thus, both approaches fail completely when sequences have no characters in common. New approaches that are a hybrid of alignment methods, an increasingly active research area, are appropriate for data with mixed fragment and genome information (Schmollinger et al, 2004; Subramanian et al, 2005; Liu et al, 2012; Hossain et al, 2013; Ye et al, 2015; Nguyen et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Fully automated sequence alignment methods are comparable to, and much faster than, traditional methods in large data sets: an example with hepatitis B virus

Catanach

Sweet

Nguyen

et al. 2019

PeerJ

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Aligning sequences for phylogenetic analysis (multiple sequence alignment; MSA) is an important, but increasingly computationally expensive step with the recent surge in DNA sequence data. Much of this sequence data is publicly available, but can be extremely fragmentary (i.e., a combination of full genomes and genomic fragments), which can compound the computational issues related to MSA. Traditionally, alignments are produced with automated algorithms and then checked and/or corrected “by eye” prior to phylogenetic inference. However, this manual curation is inefficient at the data scales required of modern phylogenetics and results in alignments that are not reproducible. Recently, methods have been developed for fully automating alignments of large data sets, but it is unclear if these methods produce alignments that result in compatible phylogenies when compared to more traditional alignment approaches that combined automated and manual methods. Here we use approximately 33,000 publicly available sequences from the hepatitis B virus (HBV), a globally distributed and rapidly evolving virus, to compare different alignment approaches. Using one data set comprised exclusively of whole genomes and a second that also included sequence fragments, we compared three MSA methods: (1) a purely automated approach using traditional software, (2) an automated approach including by eye manual editing, and (3) more recent fully automated approaches. To understand how these methods affect phylogenetic results, we compared resulting tree topologies based on these different alignment methods using multiple metrics. We further determined if the monophyly of existing HBV genotypes was supported in phylogenies estimated from each alignment type and under different statistical support thresholds. Traditional and fully automated alignments produced similar HBV phylogenies. Although there was variability between branch support thresholds, allowing lower support thresholds tended to result in more differences among trees. Therefore, differences between the trees could be best explained by phylogenetic uncertainty unrelated to the MSA method used. Nevertheless, automated alignment approaches did not require human intervention and were therefore considerably less time-intensive than traditional approaches. Because of this, we conclude that fully automated algorithms for MSA are fully compatible with older methods even in extremely difficult to align data sets. Additionally, we found that most HBV diagnostic genotypes did not correspond to evolutionarily-sound groups, regardless of alignment type and support threshold. This suggests there may be errors in genotype classification in the database or that HBV genotypes may need a revision.

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Section: Introductionmentioning

confidence: 99%

Fully automated sequence alignment methods are comparable to, and much faster than, traditional methods in large data sets: an example with hepatitis B virus

Catanach

Sweet

Nguyen

et al. 2019

PeerJ

View full text Add to dashboard Cite

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“…DIALIGN was selected because it has many applications in comparative genomics [68]. Also, a recent benchmark study for the alignment of non-coding DNA sequences has concluded that it can produce alignments with high sensitivity as well as specificity to detect constrained sites [69].…”

Section: Methodsmentioning

confidence: 99%

Comparative analysis of acute and chronic corticosteroid pharmacogenomic effects in rat liver: Transcriptional dynamics and regulatory structures

et al. 2010

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BackgroundComprehensively understanding corticosteroid pharmacogenomic effects is an essential step towards an insight into the underlying molecular mechanisms for both beneficial and detrimental clinical effects. Nevertheless, even in a single tissue different methods of corticosteroid administration can induce different patterns of expression and regulatory control structures. Therefore, rich in vivo datasets of pharmacological time-series with two dosing regimens sampled from rat liver are examined for temporal patterns of changes in gene expression and their regulatory commonalities.ResultsThe study addresses two issues, including (1) identifying significant transcriptional modules coupled with dynamic expression patterns and (2) predicting relevant common transcriptional controls to better understand the underlying mechanisms of corticosteroid adverse effects. Following the orientation of meta-analysis, an extended computational approach that explores the concept of agreement matrix from consensus clustering has been proposed with the aims of identifying gene clusters that share common expression patterns across multiple dosing regimens as well as handling challenges in the analysis of microarray data from heterogeneous sources, e.g. different platforms and time-grids in this study. Six significant transcriptional modules coupled with typical patterns of expression have been identified. Functional analysis reveals that virtually all enriched functions (gene ontologies, pathways) in these modules are shown to be related to metabolic processes, implying the importance of these modules in adverse effects under the administration of corticosteroids. Relevant putative transcriptional regulators (e.g. RXRF, FKHD, SP1F) are also predicted to provide another source of information towards better understanding the complexities of expression patterns and the underlying regulatory mechanisms of those modules.ConclusionsWe have proposed a framework to identify significant coexpressed clusters of genes across multiple conditions experimented from different microarray platforms, time-grids, and also tissues if applicable. Analysis on rich in vivo datasets of corticosteroid time-series yielded significant insights into the pharmacogenomic effects of corticosteroids, especially the relevance to metabolic side-effects. This has been illustrated through enriched metabolic functions in those transcriptional modules and the presence of GRE binding motifs in those enriched pathways, providing significant modules for further analysis on pharmacogenomic corticosteroid effects.

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“…When a pair-wise tool is used, the algorithms need to handle permutations, substitutions (with many different matrices depending on the kind of analysis to be performed), deletions and gaps. Moreover, most of the pair-wise algorithms (not the heuristic version) are related to Smith-Waterman and Needleman-Wunsch (two dynamic programming algorithms) and are not efficient on large sequences [38]. Since the presence of sequence patterns seems to be the exception rather than the rule, but assuming that some regularity must govern the lipid-peptide interactions, we considered an alternative paradigm called "alignment-free": this paradigm avoids some problems related to pair-wise analysis [37].…”

Section: Introductionmentioning

confidence: 99%

Transmembrane Peptides as Sensors of the Membrane Physical State

et al. 2018

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Cell membranes are commonly considered fundamental structures having multiple roles such as confinement, storage of lipids, sustain and control of membrane proteins. In spite of their importance, many aspects remain unclear. The number of lipid types is orders of magnitude larger than the number of amino acids, and this compositional complexity is not clearly embedded in any membrane model. A diffused hypothesis is that the large lipid palette permits to recruit and organize specific proteins controlling the formation of specialized lipid domains and the lateral pressure profile of the bilayer. Unfortunately, a satisfactory knowledge of lipid abundance remains utopian because of the technical difficulties in isolating definite membrane regions. More importantly, a theoretical framework where to fit the lipidomic data is still missing. In this work, we wish to utilize the amino acid sequence and frequency of the membrane proteins as bioinformatics sensors of cell bilayers. The use of an alignment-free method to find a correlation between the sequences of transmembrane portion of membrane proteins with the membrane physical state (MPS) suggested a new approach for the discovery of antimicrobial peptides.

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DIALIGN P: Fast pair-wise and multiple sequence alignment using parallel processors

Abstract: Background: Parallel computing is frequently used to speed up computationally expensive tasks in Bioinformatics.

Cited by 31 publications

References 29 publications

Fully automated sequence alignment methods are comparable to, and much faster than, traditional methods in large data sets: an example with hepatitis B virus

Fully automated sequence alignment methods are comparable to, and much faster than, traditional methods in large data sets: an example with hepatitis B virus

Comparative analysis of acute and chronic corticosteroid pharmacogenomic effects in rat liver: Transcriptional dynamics and regulatory structures

Transmembrane Peptides as Sensors of the Membrane Physical State

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