Ultra-large alignments using phylogeny-aware profiles

Nguyen, Nam; Mirarab, Siavash; Kumar, Karthik; Warnow, Tandy

doi:10.1186/s13059-015-0688-z

Cited by 130 publications

(181 citation statements)

References 33 publications

(79 reference statements)

Supporting

Mentioning

179

Contrasting

Order By: Relevance

“…Numerous MSA methods have been developed, but only a few of these can analyze large datasets, and even fewer have been demonstrated to have good accuracy beyond a few hundred sequences [4]. The impact of multiple sequence alignment on downstream analyses is known to be substantial, with errors in multiple sequence alignment producing increased error rates in phylogeny estimation, false detection of positive selection, difficulties in detecting active sites in proteins, etc.…”

Section: Introductionmentioning

confidence: 99%

“…Our group has developed several techniques [4, 12, 17, 18] to improve the scalability of multiple sequence alignment methods to large datasets, of which PASTA [18] and UPP [4] provide the largest improvements. PASTA is an iterative divide-and-conquer method for co-estimating trees and alignments, in which each iteration begins with a maximum likelihood tree computed in the previous iteration, and then uses the tree to partition the sequences into small subsets that are local within the tree.…”

Section: Introductionmentioning

confidence: 99%

“…As shown in [18], using PASTA with MAFFT [19] on the subsets made it possible to align ultra-large datasets, including one with 1,000,000 sequences, and to do so with high accuracy. UPP uses a different approach: it selects a random subset of the sequences, computes an alignment and tree (called the backbone alignment and backbone tree) on the subset using PASTA, and then represents this PASTA alignment using an ensemble of Hidden Markov Models (HMMs), each computed on a small subset of the sequences (see [4] and “Methods” section for a description of how this ensemble is built). Each remaining sequence is then aligned to the backbone alignment using the best-scoring HMM in the ensemble.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Scaling statistical multiple sequence alignment to large datasets

Nute

Warnow

2016

BMC Genomics

Self Cite

View full text Add to dashboard Cite

BackgroundMultiple sequence alignment is an important task in bioinformatics, and alignments of large datasets containing hundreds or thousands of sequences are increasingly of interest. While many alignment methods exist, the most accurate alignments are likely to be based on stochastic models where sequences evolve down a tree with substitutions, insertions, and deletions. While some methods have been developed to estimate alignments under these stochastic models, only the Bayesian method BAli-Phy has been able to run on even moderately large datasets, containing 100 or so sequences. A technique to extend BAli-Phy to enable alignments of thousands of sequences could potentially improve alignment and phylogenetic tree accuracy on large-scale data beyond the best-known methods today.ResultsWe use simulated data with up to 10,000 sequences representing a variety of model conditions, including some that are significantly divergent from the statistical models used in BAli-Phy and elsewhere. We give a method for incorporating BAli-Phy into PASTA and UPP, two strategies for enabling alignment methods to scale to large datasets, and give alignment and tree accuracy results measured against the ground truth from simulations. Comparable results are also given for other methods capable of aligning this many sequences.ConclusionsExtensions of BAli-Phy using PASTA and UPP produce significantly more accurate alignments and phylogenetic trees than the current leading methods.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-3101-8) contains supplementary material, which is available to authorized users.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Scaling statistical multiple sequence alignment to large datasets

Nute

Warnow

2016

BMC Genomics

Self Cite

View full text Add to dashboard Cite

show abstract

“…This is also similar to UPP (Nguyen et al , 2015) in applying an accurate method to randomly-selected sequences. UPP uses PASTA (Mirarab et al , 2015), which is a combination of L-INS-i option of MAFFT, OPAL (Wheeler and Kececioglu, 2007) and FastTree (Price et al , 2010), to align core sequences, and then uses HMMalign (Finn et al , 2011) to add the remaining sequences to the core alignment.…”

Section: Resultsmentioning

confidence: 90%

“…Using a newer version (7.294), we reexamined the difference between normal guide trees and random chains, using two different benchmark criteria. For reference, other methods designed for large data, Clustal Omega version 1.2.1 (Sievers et al , 2011) and UPP version 2.0 (Nguyen et al , 2015) with several different options were included into the comparison.…”

Section: Methodsmentioning

confidence: 99%

Application of the MAFFT sequence alignment program to large data—reexamination of the usefulness of chained guide trees

2016

View full text Add to dashboard Cite

Motivation: Large multiple sequence alignments (MSAs), consisting of thousands of sequences, are becoming more and more common, due to advances in sequencing technologies. The MAFFT MSA program has several options for building large MSAs, but their performances have not been sufficiently assessed yet, because realistic benchmarking of large MSAs has been difficult. Recently, such assessments have been made possible through the HomFam and ContTest benchmark protein datasets. Along with the development of these datasets, an interesting theory was proposed: chained guide trees increase the accuracy of MSAs of structurally conserved regions. This theory challenges the basis of progressive alignment methods and needs to be examined by being compared with other known methods including computationally intensive ones.Results: We used HomFam, ContTest and OXFam (an extended version of OXBench) to evaluate several methods enabled in MAFFT: (1) a progressive method with approximate guide trees, (2) a progressive method with chained guide trees, (3) a combination of an iterative refinement method and a progressive method and (4) a less approximate progressive method that uses a rigorous guide tree and consistency score. Other programs, Clustal Omega and UPP, available for large MSAs, were also included into the comparison. The effect of method 2 (chained guide trees) was positive in ContTest but negative in HomFam and OXFam. Methods 3 and 4 increased the benchmark scores more consistently than method 2 for the three datasets, suggesting that they are safer to use.Availability and Implementation: http://mafft.cbrc.jp/alignment/software/Contact: katoh@ifrec.osaka-u.ac.jpSupplementary information: Supplementary data are available at Bioinformatics online.

show abstract

Revisiting Evaluation of Multiple Sequence Alignment Methods

Warnow

2020

Methods in Molecular Biology

View full text Add to dashboard Cite

Ultra-large alignments using phylogeny-aware profiles

Cited by 130 publications

References 33 publications

Scaling statistical multiple sequence alignment to large datasets

Scaling statistical multiple sequence alignment to large datasets

Application of the MAFFT sequence alignment program to large data—reexamination of the usefulness of chained guide trees

Revisiting Evaluation of Multiple Sequence Alignment Methods

Contact Info

Product

Resources

About