Sequence embedding for fast construction of guide trees for multiple sequence alignment

Blackshields, Gordon; Sievers, Fabian; Shi, Weifeng; Wilm, Andreas; Higgins, Desmond G.

doi:10.1186/1748-7188-5-21

Cited by 102 publications

(80 citation statements)

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“…We measured the proportion of correctly aligned columns out of all aligned columns in the reference sequences [Total Column (TC) score] of the 12 sequences, embedded in the larger datasets. This type of analysis is widely used and is the basis of the HomFam alignment benchmark system (12).…”

Section: Resultsmentioning

confidence: 99%

“…PartTree (10) groups the sequences quickly into clusters and then clusters the clusters, allowing very large guide trees to be made but at the expense of some accuracy, compared with the default Mafft program on which it is based. Clustal Omega (11) uses the mBed algorithm (12) to cluster the sequences on the basis of a small number of "seed" sequences. For N sequences, S seeds are used where S is typically proportional to logðNÞ.…”

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confidence: 99%

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Simple chained guide trees give high-quality protein multiple sequence alignments

Boyce

Sievers

Higgins

2014

Proc. Natl. Acad. Sci. U.S.A.

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Guide trees are used to decide the order of sequence alignment in the progressive multiple sequence alignment heuristic. These guide trees are often the limiting factor in making large alignments, and considerable effort has been expended over the years in making these quickly or accurately. In this article we show that, at least for protein families with large numbers of sequences that can be benchmarked with known structures, simple chained guide trees give the most accurate alignments. These also happen to be the fastest and simplest guide trees to construct, computationally. Such guide trees have a striking effect on the accuracy of alignments produced by some of the most widely used alignment packages. There is a marked increase in accuracy and a marked decrease in computational time, once the number of sequences goes much above a few hundred. This is true, even if the order of sequences in the guide tree is random.he generation of a multiple sequence alignment (MSA) is standard practice during most comparative analyses of homologous genes or proteins. Since the mid-1980s, most automated MSAs have been made using a heuristic approach that Feng and Doolittle (1) called "progressive alignment." This involves clustering the sequences into a tree or dendrogram-like structure, called a "guide tree" in Higgins et al. (2). This guide tree is then used to align the sequences into progressively larger and larger alignments, following the branching order in the tree. Variations on the method were described by various groups in the 1980s [e.g., Taylor (3) and Barton and Sternberg (4)], but the earliest clear description of the approach is from Hogeweg and Hesper (5). Progressive alignment is a heuristic approach and is not guaranteed to find the best possible alignment for any given scoring scheme. It does, however, allow alignments of many sequences to be made quickly, even on personal computers (6). The quality of the alignments is good enough for the alignments to be used automatically in many analysis pipelines.The computational complexity of the alignment process, once a guide tree is created, is approximately OðNÞ for N sequences of the same length. The creation of the guide tree involves comparing all N sequences to each other to generate a distance matrix, which is clearly going to require OðN 2 Þ time and computer memory. Once the distance matrix is made, it will require a further clustering step that is usually OðN 2 Þ but can be more expensive. For large N, the construction of the guide tree becomes limiting and prevents the routine alignment of more than a few thousand sequences. Over the years, various attempts have been made to get around this problem. One solution is to quickly make a crude guide tree initially and to iterate that from an initial MSA. This approach is adopted in the widely used Muscle (7) and Mafft (8) packages. Barton and Sternberg were the first authors to use iteration, but they used a simple "chained" guide tree topology, effectively aligning the sequences one at a time to a growing...

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Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

Simple chained guide trees give high-quality protein multiple sequence alignments

Boyce

Sievers

Higgins

2014

Proc. Natl. Acad. Sci. U.S.A.

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“…To perform sequence alignments, NDC80 from S. cerevisiae and its orthologs in Saccharomyces bayanus, S. kudriavzevii, S. mikatae (Scannell et al 2011), Lachancea (Kluyveromyces) thermotolerans, Kluyveromyces lactis, and Debaryomyces hansenii (http://genolevures.org) were translated using Transeq (Rice et al 2000) and then aligned using Clustal-O (Blackshields et al 2010). The similarity score was plotted for each position using Plotcon (Rice et al 2000) with a window size of 21 bp.…”

Section: Illumina Sequencingmentioning

confidence: 99%

Coupling Unbiased Mutagenesis to High-throughput DNA Sequencing Uncovers Functional Domains in the Ndc80 Kinetochore Protein of Saccharomyces cerevisiae

et al. 2013

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During mitosis, kinetochores physically link chromosomes to the dynamic ends of spindle microtubules. This linkage depends on the Ndc80 complex, a conserved and essential microtubule-binding component of the kinetochore. As a member of the complex, the Ndc80 protein forms microtubule attachments through a calponin homology domain. Ndc80 is also required for recruiting other components to the kinetochore and responding to mitotic regulatory signals. While the calponin homology domain has been the focus of biochemical and structural characterization, the function of the remainder of Ndc80 is poorly understood. Here, we utilized a new approach that couples high-throughput sequencing to a saturating linker-scanning mutagenesis screen in Saccharomyces cerevisiae. We identified domains in previously uncharacterized regions of Ndc80 that are essential for its function in vivo. We show that a helical hairpin adjacent to the calponin homology domain influences microtubule binding by the complex. Furthermore, a mutation in this hairpin abolishes the ability of the Dam1 complex to strengthen microtubule attachments made by the Ndc80 complex. Finally, we defined a C-terminal segment of Ndc80 required for tetramerization of the Ndc80 complex in vivo. This unbiased mutagenesis approach can be generally applied to genes in S. cerevisiae to identify functional properties and domains.A CCURATE chromosome segregation depends on the attachment between kinetochores and the dynamic ends of spindle microtubules. This attachment requires the Ndc80 complex, an essential and conserved microtubule-binding component of the kinetochore. In vivo, inactivation or depletion of Ndc80 complex components leads to detached kinetochores and severe chromosome segregation defects (reviewed in Kline-Smith et al. 2005). Mirroring kinetochore-microtubule linkages in vivo, recent experiments show that attachments between purified Ndc80 complexes and microtubule tips can persist throughout rounds of microtubule growth and shortening, can withstand tensile forces, can modulate microtubule dynamics, and can respond to regulation by mitotic kinases (Guimaraes et al. 2008;Miller et al. 2008;Tien et al. 2010;DeLuca et al. 2011;Schmidt et al. 2012;Umbreit et al. 2012).The Ndc80 complex ( Figure 1) is a rod-shaped heterotetramer of Ndc80, Nuf2, Spc24, and Spc25 (Osborne et al. 1994;Janke et al. 2001;Wigge and Kilmartin 2001;. The two ends of the complex have globular domains, formed by Ndc80/Nuf2 and Spc24/Spc25, respectively. All four components of the complex are predicted to participate in coiled-coil interactions that link these two globular ends. Structural studies of the complex at atomic resolution have been limited to the two globular domains, revealing a microtubule-binding calponin homology (CH) domain in Ndc80 (Wei et al. 2006(Wei et al. , 2007Ciferri et al. 2008).These structural studies on the globular ends of the Ndc80 complex have provided high-resolution views of key protein-protein interfaces (Alushin et al. 2010;Malvezzi et al. 2013;...

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“…Although at present these trees do not have support measures associated with them, they offer valuable preliminary data analysis and could offer a new way of estimating guide trees for difficult phylogenetic problems. We hope that future improvements to PaHMM-Tree will help to alleviate some of its computational limitations, for example by implementing the mBed algorithm to reduce the number of distance calls, allowing it to process larger data sets (Blackshields et al 2010), or through the implementation of anchor points using suffix trees in the pairHMMs, allowing it to work with longer sequences more quickly (Gusfield 1997 …”

Section: Discussionmentioning

confidence: 99%

Phylogenetic Tree Estimation With and Without Alignment: New Distance Methods and Benchmarking

Bogusz

Whelan

2016

Syst Biol

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Abstract.-Phylogenetic tree inference is a critical component of many systematic and evolutionary studies. The majority of these studies are based on the two-step process of multiple sequence alignment followed by tree inference, despite persistent evidence that the alignment step can lead to biased results. Here we present a two-part study that first presents PaHMMTree, a novel neighbor joining-based method that estimates pairwise distances without assuming a single alignment. We then use simulations to benchmark its performance against a wide-range of other phylogenetic tree inference methods, including the first comparison of alignment-free distance-based methods against more conventional tree estimation methods. Our new method for calculating pairwise distances based on statistical alignment provides distance estimates that are as accurate as those obtained using standard methods based on the true alignment. Pairwise distance estimates based on the two-step process tend to be substantially less accurate. This improved performance carries through to tree inference, where PaHMM-Tree provides more accurate tree estimates than all of the pairwise distance methods assessed. For close to moderately divergent sequence data we find that the two-step methods using statistical inference, where information from all sequences is included in the estimation procedure, tend to perform better than PaHMM-Tree, particularly full statistical alignment, which simultaneously estimates both the tree and the alignment. For deep divergences we find the alignment step becomes so prone to error that our distance-based PaHMM-Tree outperforms all other methods of tree inference. Finally, we find that the accuracy of alignment-free methods tends to decline faster than standard two-step methods in the presence of alignment uncertainty, and identify no conditions where alignment-free methods are equal to or more accurate than standard phylogenetic methods even in the presence of substantial alignment error. [Alignment-free; distance-based phylogenetics; pair Hidden Markov Models; phylogenetic inference; statistical alignment.]

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Sequence embedding for fast construction of guide trees for multiple sequence alignment

Cited by 102 publications

References 29 publications

Simple chained guide trees give high-quality protein multiple sequence alignments

Simple chained guide trees give high-quality protein multiple sequence alignments

Coupling Unbiased Mutagenesis to High-throughput DNA Sequencing Uncovers Functional Domains in the Ndc80 Kinetochore Protein of Saccharomyces cerevisiae

Phylogenetic Tree Estimation With and Without Alignment: New Distance Methods and Benchmarking

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