scholarly journals SATé-II: Very Fast and Accurate Simultaneous Estimation of Multiple Sequence Alignments and Phylogenetic Trees

2011 ◽  
Vol 61 (1) ◽  
pp. 90 ◽  
Author(s):  
Kevin Liu ◽  
Tandy J. Warnow ◽  
Mark T. Holder ◽  
Serita M. Nelesen ◽  
Jiaye Yu ◽  
...  
Keyword(s):  
Phylogenetic Trees ◽  
Simultaneous Estimation ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Multiple Sequence Alignments

scholarly journals Accurate inference of tree topologies from multiple sequence alignments using deep learning

2019 ◽  
Author(s):  
Anton Suvorov ◽  
Joshua Hochuli ◽  
Daniel R. Schrider
Keyword(s):  
Deep Learning ◽  
Parameter Space ◽  
Phylogenetic Trees ◽  
Strong Support ◽  
Biological Research ◽  
Learning Approaches ◽  
Sequence Alignments ◽  
Traditional Methods ◽  
Multiple Sequence ◽  
Multiple Sequence Alignments

AbstractReconstructing the phylogenetic relationships between species is one of the most formidable tasks in evolutionary biology. Multiple methods exist to reconstruct phylogenetic trees, each with their own strengths and weaknesses. Both simulation and empirical studies have identified several “zones” of parameter space where accuracy of some methods can plummet, even for four-taxon trees. Further, some methods can have undesirable statistical properties such as statistical inconsistency and/or the tendency to be positively misleading (i.e. assert strong support for the incorrect tree topology). Recently, deep learning techniques have made inroads on a number of both new and longstanding problems in biological research. Here we designed a deep convolutional neural network (CNN) to infer quartet topologies from multiple sequence alignments. This CNN can readily be trained to make inferences using both gapped and ungapped data. We show that our approach is highly accurate, often outperforming traditional methods, and is remarkably robust to bias-inducing regions of parameter space such as the Felsenstein zone and the Farris zone. We also demonstrate that the confidence scores produced by our CNN can more accurately assess support for the chosen topology than bootstrap and posterior probability scores from traditional methods. While numerous practical challenges remain, these findings suggest that deep learning approaches such as ours have the potential to produce more accurate phylogenetic inferences.

scholarly journals Evolutionary Relationships and Sequence-Structure Determinants in Human SARS Coronavirus-2 Spike Proteins for Host Receptor Recognition

2020 ◽  
Author(s):  
Lalitha Guruprasad
Keyword(s):  
Phylogenetic Trees ◽  
Disulfide Bridge ◽  
Sequence Motifs ◽  
Structural Determinants ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Angiotensin Converting Enzyme 2 ◽  
Multiple Sequence Alignments ◽  
Loop 2 ◽  
Spike Proteins

<div>Coronavirus disease 2019 (COVID-19) is a pandemic infectious disease caused by novel Severe Acute Respiratory Syndrome coronavirus-2 (SARS CoV-2). The SARS CoV-2 is transmitted more rapidly and readily than SARS CoV. Both, SARS CoV and SARS CoV-2 via their glycosylated spike proteins recognize the human angiotensin converting enzyme-2 (ACE-2) receptor. We generated multiple sequence alignments and phylogenetic trees for representative spike proteins of CoV and CoV-2 from various host sources in order to analyze the specificity in SARS CoV-2 spike proteins required for causing infection in humans. Our results show that two sequence motifs in the N-terminal domain; "MESEFR" and "SYLTPG" are specific to human SARS CoV-2 and pangolin SARS CoV. In the receptor binding domain (RBD), three sequence loops; VGGNY (loop 1), YQAGSTPC (loop 2), EGFNCY (loop 3) and a tethered disulfide bridge Cys480-Cys488 connecting loops 2 and 3 are structural determinants for the recognition of human ACE-2 receptor. The complete genome analysis of representative SARS CoVs from bat, civet, pangolin, human host sources and human SARS CoV-2 identified the bat genome (GenBank code: MN996532.1) and the pangolin SARS CoV genomes as closest to the recent novel human SARS CoV-2 genomes. The bat CoV genomes (GenBank codes: MG772933 and MG772934) are evolutionary intermediates in the mutagenesis progression towards becoming human SARS CoV-2. </div>

scholarly journals PhyKIT: a broadly applicable UNIX shell toolkit for processing and analyzing phylogenomic data

2021 ◽  
Author(s):  
Jacob L Steenwyk ◽  
Thomas J Buida ◽  
Abigail L Labella ◽  
Yuanning Li ◽  
Xing-Xing Shen ◽  
...  
Keyword(s):  
Information Content ◽  
Phylogenetic Trees ◽  
Supplementary Information ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Sequence Composition ◽  
Multiple Sequence Alignments ◽  
Functional Relationships ◽  
Biology Process ◽  
Rate Evaluation

Abstract Motivation Diverse disciplines in biology process and analyze multiple sequence alignments (MSAs) and phylogenetic trees to evaluate their information content, infer evolutionary events and processes, and predict gene function. However, automated processing of MSAs and trees remains a challenge due to the lack of a unified toolkit. To fill this gap, we introduce PhyKIT, a toolkit for the UNIX shell environment with 30 functions that process MSAs and trees, including but not limited to estimation of mutation rate, evaluation of sequence composition biases, calculation of the degree of violation of a molecular clock, and collapsing bipartitions (internal branches) with low support. Results To demonstrate the utility of PhyKIT, we detail three use cases: (1) summarizing information content in MSAs and phylogenetic trees for diagnosing potential biases in sequence or tree data; (2) evaluating gene-gene covariation of evolutionary rates to identify functional relationships, including novel ones, among genes; and (3) identify lack of resolution events or polytomies in phylogenetic trees, which are suggestive of rapid radiation events or lack of data. We anticipate PhyKIT will be useful for processing, examining, and deriving biological meaning from increasingly large phylogenomic datasets. Availability PhyKIT is freely available on GitHub (https://github.com/JLSteenwyk/PhyKIT), PyPi (https://pypi.org/project/phykit/), and the Anaconda Cloud (https://anaconda.org/JLSteenwyk/phykit) under the MIT license with extensive documentation and user tutorials (https://jlsteenwyk.com/PhyKIT). Supplementary information Supplementary data are available on figshare (doi: 10.6084/m9.figshare.13118600) and are available at Bioinformatics online.

scholarly journals A fast alignment-free bioinformatics procedure to infer accurate distance-based phylogenetic trees from genome assemblies

2019 ◽  
Vol 5 ◽  
Author(s):  
Alexis Criscuolo
Keyword(s):  
Phylogenetic Trees ◽  
Species Trees ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Internal Branch ◽  
Multiple Sequence Alignments ◽  
Fast Running ◽  
Alignment Free ◽  
Multiple Threads ◽  
Genome Assemblies

This paper describes a novel alignment-free distance-based procedure for inferring phylogenetic trees from genome contig sequences using publicly available bioinformatics tools. For each pair of genomes, a dissimilarity measure is first computed and next transformed to obtain an estimation of the number of substitution events that have occurred during their evolution. These pairwise evolutionary distances are then used to infer a phylogenetic tree and assess a confidence support for each internal branch. Analyses of both simulated and real genome datasets show that this bioinformatics procedure allows accurate phylogenetic trees to be reconstructed with fast running times, especially when launched on multiple threads. Implemented in a publicly available script, named JolyTree, this procedure is a useful approach for quickly inferring species trees without the burden and potential biases of multiple sequence alignments.

scholarly journals PhyKIT: a UNIX shell toolkit for processing and analyzing phylogenomic data

2020 ◽  
Author(s):  
Jacob L. Steenwyk ◽  
Thomas J. Buida ◽  
Abigail L. Labella ◽  
Yuanning Li ◽  
Xing-Xing Shen ◽  
...  
Keyword(s):  
Information Content ◽  
Phylogenetic Trees ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Sequence Composition ◽  
Multiple Sequence Alignments ◽  
Functional Relationships ◽  
Link Type ◽  
Biology Process ◽  
Rate Evaluation

AbstractDiverse disciplines in biology process and analyze multiple sequence alignments (MSAs) and phylogenetic trees to evaluate their information content, infer evolutionary events and processes, and predict gene function. However, automated processing of MSAs and trees remains a challenge due to the lack of a unified toolkit. To fill this gap, we introduce PhyKIT, a toolkit for the UNIX shell environment with 30 functions that process MSAs and trees, including but not limited to estimation of mutation rate, evaluation of sequence composition biases, calculation of the degree of violation of a molecular clock, and collapsing bipartitions (internal branches) with low support. To demonstrate the utility of PhyKIT, we detail three use cases: (1) summarizing information content in MSAs and phylogenetic trees for diagnosing potential biases in sequence or tree data; (2) evaluating gene-gene covariation of evolutionary rates to identify functional relationships, including novel ones, among genes; and (3) identify lack of resolution events or polytomies in phylogenetic trees, which are suggestive of rapid radiation events or lack of data. We anticipate PhyKIT will be useful for processing, examining, and deriving biological meaning from increasingly large phylogenomic datasets. PhyKIT is freely available on GitHub (https://github.com/JLSteenwyk/PhyKIT) and documentation including user tutorials are available online (https://jlsteenwyk.com/PhyKIT).

scholarly journals Accurate Inference of Tree Topologies from Multiple Sequence Alignments Using Deep Learning

2019 ◽  
Author(s):  
Anton Suvorov ◽  
Joshua Hochuli ◽  
Daniel R Schrider
Keyword(s):  
Deep Learning ◽  
Parameter Space ◽  
Phylogenetic Trees ◽  
Strong Support ◽  
Biological Research ◽  
Learning Approaches ◽  
Sequence Alignments ◽  
Traditional Methods ◽  
Multiple Sequence ◽  
Multiple Sequence Alignments

Abstract Reconstructing the phylogenetic relationships between species is one of the most formidable tasks in evolutionary biology. Multiple methods exist to reconstruct phylogenetic trees, each with their own strengths and weaknesses. Both simulation and empirical studies have identified several “zones” of parameter space where accuracy of some methods can plummet, even for four-taxon trees. Further, some methods can have undesirable statistical properties such as statistical inconsistency and/or the tendency to be positively misleading (i.e. assert strong support for the incorrect tree topology). Recently, deep learning techniques have made inroads on a number of both new and longstanding problems in biological research. In this study, we designed a deep convolutional neural network (CNN) to infer quartet topologies from multiple sequence alignments. This CNN can readily be trained to make inferences using both gapped and ungapped data. We show that our approach is highly accurate on simulated data, often outperforming traditional methods, and is remarkably robust to bias-inducing regions of parameter space such as the Felsenstein zone and the Farris zone. We also demonstrate that the confidence scores produced by our CNN can more accurately assess support for the chosen topology than bootstrap and posterior probability scores from traditional methods. Although numerous practical challenges remain, these findings suggest that the deep learning approaches such as ours have the potential to produce more accurate phylogenetic inferences.

Sign in / Sign up

Export Citation Format

Share Document