ppIacerDC

Koning, Elizabeth; Phillips, Malachi; Warnow, Tandy

doi:10.1145/3459930.3469516

Cited by 7 publications

(9 citation statements)

References 17 publications

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“…Part of the RNASim dataset (i.e. RNASim-VS) has been used to study phylogenetic placement accuracy in prior studies ( Balaban et al , 2022 ; Wedell et al , 2022 ), and was specifically used to study APPLES ( Balaban et al , 2020 ), APPLES-2 ( Balaban et al , 2022 ), pplacerDC ( Koning et al , 2021 ) and pplacer-SCAMPP-RAxML. The RNASim Variable-Size (RNASim-VS) datasets provide true phylogenetic trees, true multiple sequence alignments, and estimated maximum likelihood trees computed using FastTree ( Price et al , 2010 ).…”

Section: Methodsmentioning

confidence: 99%

SCAMPP+FastTree: improving scalability for likelihood-based phylogenetic placement

Chu

Warnow

2023

Bioinformatics Advances

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Phylogenetic placement is the problem of placing “query” sequences into an existing tree (called a “backbone tree”). One of the most accurate phylogenetic placement methods to date is the maximum likelihood-based method pplacer, using RAxML to estimate numeric parameters on the backbone tree and then adding the given query sequence to the edge that maximizes the probability that the resulting tree generates the query sequence. Unfortunately, this way of running pplacer fails to return valid outputs on many moderately large backbone trees, and so is limited to backbone trees with at most ∼10,000 leaves. SCAMPP is a technique to enable pplacer to run on larger backbone trees, which operates by finding a small “placement subtree” specific to each query sequence, within which the query sequence are placed using pplacer. That approach matched the scalability and accuracy of APPLES-2, the previous most scalable method. Here, we explore a different aspect of pplacer's strategy: the technique used to estimate numeric parameters on the backbone tree. We confirm anecdotal evidence that using FastTree instead of RAxML to estimate numeric parameters on the backbone tree enables pplacer to scale to much larger backbone trees, almost (but not quite) matching the scalability of APPLES-2 and pplacer-SCAMPP. We then evaluate the combination of these two techniques – SCAMPP and the use of FastTree. We show that this combined approach, pplacer-SCAMPP-FastTree, has the same scalability as APPLES-2, improves on the scalability of pplacer-FastTree, and achieves better accuracy than the comparably scalable methods. Availability https://github.com/gillichu/PLUSplacer-taxtastic.

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Section: Methodsmentioning

confidence: 99%

SCAMPP+FastTree: improving scalability for likelihood-based phylogenetic placement

Chu

Warnow

2023

Bioinformatics Advances

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“…Recently, two divide-and-conquer methods, pplacer-SCAMPP [ 86 ] and pplacer-DC (pplacer-Divide-and-Conquer) [ 87 ], were developed in order to improve accuracy for phylogenetic placement when inserting into trees that are too large for pplacer. Here we describe the pplacer-SCAMPP approach, as a comparison of pplacer-SCAMPP with pplacer-DC on the RNASim VS datasets reported in [ 86 , 87 ] shows that pplacer-SCAMPP is faster, uses less memory, and is more accurate than pplacerDC. In addition, pplacer-SCAMPP is able to scale to trees with 200 000 leaves, whereas pplacer-DC scales only to 100 000 sequences [ 86 , 87 ].…”

Section: Recent Advances In Updating Large Treesmentioning

confidence: 99%

“…Here we describe the pplacer-SCAMPP approach, as a comparison of pplacer-SCAMPP with pplacer-DC on the RNASim VS datasets reported in [ 86 , 87 ] shows that pplacer-SCAMPP is faster, uses less memory, and is more accurate than pplacerDC. In addition, pplacer-SCAMPP is able to scale to trees with 200 000 leaves, whereas pplacer-DC scales only to 100 000 sequences [ 86 , 87 ].…”

Section: Recent Advances In Updating Large Treesmentioning

confidence: 99%

Recent progress on methods for estimating and updating large phylogenies

Zaharias

Warnow

2022

Phil. Trans. R. Soc. B

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With the increased availability of sequence data and even of fully sequenced and assembled genomes, phylogeny estimation of very large trees (even of hundreds of thousands of sequences) is now a goal for some biologists. Yet, the construction of these phylogenies is a complex pipeline presenting analytical and computational challenges, especially when the number of sequences is very large. In the past few years, new methods have been developed that aim to enable highly accurate phylogeny estimations on these large datasets, including divide-and-conquer techniques for multiple sequence alignment and/or tree estimation, methods that can estimate species trees from multi-locus datasets while addressing heterogeneity due to biological processes (e.g. incomplete lineage sorting and gene duplication and loss), and methods to add sequences into large gene trees or species trees. Here we present some of these recent advances and discuss opportunities for future improvements. This article is part of a discussion meeting issue ‘Genomic population structures of microbial pathogens’.

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“…Otherwise, the branch length optimization for S q is initialized with the branch lengths that were already chosen for the most similar previous query sequence. Recently, the updated version pplacerDC has been published, which is optimized for large data sets [330].…”

Section: Approaches To Phylogenetic Placementmentioning

confidence: 99%

“…Several algorithms for PP have been developed in recent years that pursue different strategies to derive suitable placement locations. Although the first approaches were based on maximum likelihood calculations on the basis of multiple sequence alignments [10,12,15,330], more recent methods pursue alignment-free or mixed strategies [13,14,177,332]. Due to the computational demands of ML-based methods, PP has been applied mainly for ampliconbased sequencing studies where reference and query sequences originate solely from designated short genomic regions.…”

Section: Evaluating Phylogenetic Placement Algorithmsmentioning

confidence: 99%

Alignment-free Phylogenetic Placement and its Applications

Blanke¹

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My thanks go to Burkhard Morgenstern for his supervision, support, and encouragement during the last four years. Burkhard is an excellent scientific advisor and it was my honor to work alongside him and receive his thought-out advice and helpful guidance. Furthermore, he always supported me personally and endorsed a healthy balance between work and private life that enabled the satisfying work environment I experienced. I will remember my time as a PhD student happily and ascribe this largely to Burkhard.My thanks go to the members of my thesis advisory committee Johannes Söding and Christoph Bleidorn. With his thoughtful and constructive criticism, Johannes elicited my scientific diligence, expanded my knowledge, and improved my work substantially. Christoph incited my interest in the biological realm, especially the field of biodiversity, which also led to my uptake of new personal hobbies. The obtained perspectives will outlast this work.My thanks go to the members of our working group, specifically Peter Meinicke for prolific discussions, Thomas Dencker for his valued input on being a PhD student and software design, and Rasmus Steinkamp and Britta Leinemann for their swift help regarding all matters. I also thank the reliable and motivated students I had the pleasure working with: Sarah Wendte, Clara Gisela Köhne, Dark Engel, Rebecca Fee Dietlinde Regendantz, and Patricia-Franziska Römer.My thanks go to my friends. Most of all, I thank Leo for his continued support in all phases of life, for challenging my work and working practices, for proofreading this manuscript, for joint vacations, rare visits, frequent calls, and for being there in difficult situations. I also thank Birte, Marlene, Salomea, Stefan, and Tobias for nurturing our long-standing friendships even though I have been unresponsive at times. Moreover, I thank Immo, Justus, Malte, and all the wonderful people at Bouldern in Göttingen who share the enjoyment of our common sport.My thanks go to my family: I thank Tilman for discussions that covered every aspect of life there is to cover, for his many invitations I declined, and for his honest disinterest in my work that puts things into perspective-after all, there is more to life, isn't it? I thank Ulrike for her amiable understanding of personal matters, her kindness and uplifting attitude, and her unwavering support in all stages of my studies and life. I thank Eberhard for his empathetic and congenial participation in my work, our soothing hikes outdoors, and our endless debates on scientific and unscientific matters that continue to widen my horizons. I could not be more fortunate than to have this loving family.Lastly, my thanks go to Adrienne who filled the last years with the life they needed. Thank you for your energy and excitement in mundane routines, for the countless tours in nature, for being there in challenging hours, and for sharing with me this ongoing adventure of life. There is a great comfort in knowing you by my side.

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ppIacerDC

Cited by 7 publications

References 17 publications

SCAMPP+FastTree: improving scalability for likelihood-based phylogenetic placement

SCAMPP+FastTree: improving scalability for likelihood-based phylogenetic placement

Recent progress on methods for estimating and updating large phylogenies

Alignment-free Phylogenetic Placement and its Applications

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