Quartet-based inference of cell differentiation trees from ChIP-Seq histone modification data

Moumi, Nazifa Ahmed; Das, Badhan; Promi, Zarin Tasnim; Bristy, Nishat Anjum; Bayzid, Md. Shamsuzzoha

doi:10.1371/journal.pone.0221270

Cited by 3 publications

(1 citation statement)

References 64 publications

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“… Reaz et al (2014) proposed a new quartet-based method, Quartet Fiduccia–Mattheyses (QFM), and showed that QFM outperforms QMC in terms of tree quality. QFM is being widely used in important phylogenetic studies ( Mason et al 2016 , Moumi et al 2019 , Zhou et al 2022 ), especially along with SVDquartets method ( Chifman and Kubatko 2014 ). Rahman (2018) conducted an experiment where QFM was compared against ASTRAL and showed that Disk-covering Method ( Roshan et al 2004 ) boosted QFM outperforms ASTRAL on the 37-taxa simulated dataset.…”

Section: Introductionmentioning

confidence: 99%

Quartet Fiduccia–Mattheyses revisited for larger phylogenetic studies

Mim,

Zarif-Ul-Alam,

Reaz

et al. 2023

Bioinformatics

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Motivation With the recent breakthroughs in sequencing technology, phylogeny estimation at a larger scale has become a huge opportunity. For accurate estimation of large-scale phylogeny, substantial endeavour is being devoted in introducing new algorithms or upgrading current approaches. In this work, we endeavour to improve the QFM (Quartet Fiduccia and Mattheyses) algorithm to resolve phylogenetic trees of better quality with better running time. QFM was already being appreciated by researchers for its good tree quality, but fell short in larger phylogenomic studies due to its excessively slow running time. Results We have re-designed QFM so that it can amalgamate millions of quartets over thousands of taxa into a species tree with a great level of accuracy within a short amount of time. Named QFM Fast and Improved (QFM-FI), our version is 20,000x faster than the previous version and 400x faster than the widely used variant of QFM implemented in PAUP* on larger datasets. We have also provided a theoretical analysis of the running time and memory requirements of QFM-FI. We have conducted a comparative study of QFM-FI with other state-of-the-art phylogeny reconstruction methods, such as QFM, QMC, wQMC, wQFM and ASTRAL, on simulated as well as real biological datasets. Our results show that QFM-FI improves on the running time and tree quality of QFM and produces trees that are comparable with state-of-the-art methods. Availability and Implementation QFM-FI is open source and available at https://github.com/sharmin-mim/qfm_java. Supplementary information Supplementary Information is available at Bioinformatics online.

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Section: Introductionmentioning

confidence: 99%

Quartet Fiduccia–Mattheyses revisited for larger phylogenetic studies

Mim,

Zarif-Ul-Alam,

Reaz

et al. 2023

Bioinformatics

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wQFM: highly accurate genome-scale species tree estimation from weighted quartets

et al. 2021

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Motivation Species tree estimation from genes sampled from throughout the whole genome is complicated due to the gene tree-species tree discordance. Incomplete lineage sorting (ILS) is one of the most frequent causes for this discordance, where alleles can coexist in populations for periods that may span several speciation events. Quartet-based summary methods for estimating species trees from a collection of gene trees are becoming popular due to their high accuracy and statistical guarantee under ILS. Generating quartets with appropriate weights, where weights correspond to the relative importance of quartets, and subsequently amalgamating the weighted quartets to infer a single coherent species tree can allow for a statistically consistent way of estimating species trees. However, handling weighted quartets is challenging. Results We propose wQFM, a highly accurate method for species tree estimation from multi-locus data, by extending the quartet FM (QFM) algorithm to a weighted setting. wQFM was assessed on a collection of simulated and real biological datasets, including the avian phylogenomic dataset which is one of the largest phylogenomic datasets to date. We compared wQFM with wQMC, which is the best alternate method for weighted quartet amalgamation, and with ASTRAL, which is one of the most accurate and widely used coalescent-based species tree estimation methods. Our results suggest that wQFM matches or improves upon the accuracy of wQMC and ASTRAL. Availability wQFM is available in open source form at https://github.com/Mahim1997/wQFM-2020 Supplementary information Supplementary data are available at Bioinformatics online.

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wQFM: Statistically Consistent Genome-scale Species Tree Estimation from Weighted Quartets

Mahbub

Wahab

Reaz

et al. 2020

Preprint

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Motivation: Species tree estimation from genes sampled from throughout the whole genome is complicated due to the gene tree-species tree discordance. Incomplete lineage sorting (ILS) is one of the most frequent causes for this discordance, where alleles can coexist in populations for periods that may span several speciation events. Quartet-based summary methods for estimating species trees from a collection of gene trees are becoming popular due to their high accuracy and statistical guarantee under ILS. Generating quartets with appropriate weights, where weights correspond to the relative importance of quartets, and subsequently amalgamating the weighted quartets to infer a single coherent species tree allows for a statistically consistent way of estimating species trees. However, handling weighted quartets is challenging. Results: We propose wQFM, a highly accurate method for species tree estimation from multi-locus data, by extending the quartet FM (QFM) algorithm to a weighted setting. wQFM was assessed on a collection of simulated and real biological datasets, including the avian phylogenomic dataset which is one of the largest phylogenomic datasets to date. We compared wQFM with wQMC, which is the best alternate method for weighted quartet amalgamation, and with ASTRAL, which is one of the most accurate and widely used coalescent-based species tree estimation methods. Our results suggest that wQFM matches or improves upon the accuracy of wQMC and ASTRAL.

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Quartet-based inference of cell differentiation trees from ChIP-Seq histone modification data

Cited by 3 publications

References 64 publications

Quartet Fiduccia–Mattheyses revisited for larger phylogenetic studies

Quartet Fiduccia–Mattheyses revisited for larger phylogenetic studies

wQFM: highly accurate genome-scale species tree estimation from weighted quartets

wQFM: Statistically Consistent Genome-scale Species Tree Estimation from Weighted Quartets

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