]>
#Author
Author
#Bayesian_Inference
Bayesian inference in phylogeny generates a posterior distribution for a parameter, composed of a phylogenetic tree and a model of evolution, based on the prior for that parameter and the likelihood of the data, generated by a multiple alignment.
#Bayesian_Inference
MP, TD
#Bayesian_Inference
(http://www.sciencemag.org/content/294/5550/2310.full)
#Bayesian_Inference
Bayesian_Interence
#Boot_Strapping
Example:
CONSENSUS TREE:
The numbers at the forks indicate the number of times the group con-sisting of the species which are to the right of that fork occurred among the trees, out of 1000 trees
+--------------Taxon 4
!
! +---------Taxon 1
+1000.0
! +----Taxon 2
+572.7
· +----Taxon 3
#Boot_Strapping
MP, HY
#Boot_Strapping
The parametric bootstrap: A method of attempting to estimate confidence levels of inferred relationships. Bootstrap proceeds by resampling the original data matrix by replacement of characters. The data sets are obtained by simulation on our best estimate of the tree rather than by resampling columns of the original data matrix.
#Boot_Strapping
Usage:
Programs such as DAMBE, Fast tree, GARLI, PHYLIP, RAXML.
#Boot_Strapping
Boot_Strapping
#Branch-Swampling
Branch_Swampling
#Combinatorial_Extension
MP, HY
#Combinatorial_Extension
Combinatorial extension uses local geometry to align short fragments of the two proteins being analysed and then assembles these fragments into a larger alignment.
#Combinatorial_Extension
Combinatorial_Extension
#Conference_Paper
Conference_Paper
#Cross_Validation
Example: To determine the expression levels of 20 proteins to predict whether a cancer patient will respond to a drug. Cross-validation uses the best fit and will generally include only a subset of the features that are deemed truly informative.
#Cross_Validation
MP, HY
#Cross_Validation
Cross-validation (rotation estimation) is a technique for assessing how the results of a statistical analysis will generalize to an independent data set.
#Cross_Validation
Cross_Validation
#DALI
Example: construction of protein data bank.
#DALI
MP, HY
#DALI
distance matrix alignment is a fragment based method for constructing structural alignment based on similarity patterns between successive hexapeptides in queries.
#DALI
DALI
#DAMBE
MP,HY
#DAMBE
(Data Analysis in Molecular Biology and Evolution)
It is a general-purpose package that reads and converts a number of file formats, and has many features for descriptive statistics in computing.
#DAMBE
Xuhua Xia, Department of Biology
and the Center for Advanced Research in Environmental Genomics
(CAREG), University of Ottawa, Ontario, Canada.
Details: http://dambe.bio.uottawa.ca/dambe.asp.
#DAMBE
Usage: Parsimony, distance, or likelihood methods, including
bootstrapping and jackknifing.
~Analyze: DNA and protein sequence and gene frequencies.
#DAMBE
Advantages: It allows sequences to be fetched over the web while
running DAMBE using simple web browser.
#DAMBE
DAMBE
#DOI
DOI
#Date
Date
#Distance-Wagner
MP,SV
#Distance-Wagner
The distance-Wagner method is intended as an approximation to construction of a most parsimonious tree. Species are added to a tree, each in the best possible place. This is judged by computation of the increase in the length of the tree caused by each possible placement of that species.
#Distance-Wagner
Distance_Wangner
#Dot-matrix_method
Example: identify insertions, deletions or inverted repeats.
#Dot-matrix_method
MP, HY
#Dot-matrix_method
Dot –matrix method is a primary method of producing pairwise alignment.
Is qualitative, simple and time consuming.
#Dot-matrix_method
Problems: noise, lack of clarity, non-intuitiveness, difficulty extracting match summary and match positions on the two sequences
#Dot-matrix_method
Dot_Metrix_Method
#Dynamic_programming
MP, HY
#Dynamic_programming
Dynamic programming is a primary method of producing pairwise alignment.
Can produce global alignments via Needleman-Wunsch algorithm and local alignments via Smith-Waterman algorithm.
#Dynamic_programming
Dynamic_Programming
#Empirical_substitution_models
MP,HY
#Empirical_substitution_models
This model resulted in the development of a set of widely used replacement matrices. In this approach, replacement rates are derived from alignments of protein sequences that are at least 85% identical such that the likelihood of a particular mutation being the result of a set of successive mutations is low.
#Empirical_substitution_models
Ref: Dayhoff et al. 1978.
#Empirical_substitution_models
Empirical_Substitution_Models
#End_Page
End_Page
#Epub_Data
Epub_Date
#Experimental_data
Experimental_Data
#External_data
External_Data
#FastTree
MP
#FastTree
FastTree: Computing Large Minimum Evolution Trees with
Profiles instead of a Distance Matrix, Molecular Biology and Evolution
MP,HY
#FastTree
Written by/released/produced by: Morgan Price of the Adam Arkin's
group, Physical Biosciences Division of Lawrence Berkeley National Laboratory, Berkeley, California
#FastTree
Usage: Bootstrapping, Shimodaira-Hasegawa (SH), maximum
likelihood, GTR.
Analyze: Nucleotide or protein sequences
#FastTree
Advantages: Fast maximum likelihood program
#FastTree
Fast_Tree
#Garli
MD,TD
#Garli
GARLI (Genetic Algorithm for Rapid Likelihood Inference) performs phylogenetic searches on aligned nucleotide, codon and amino acid data sets using the maximum likelihood criterion.
(http://molecularevolution.org/software/phylogenetics/GARLI)
#Garli
(http://molecularevolution.org/resources/activities/garli_activity)
#Garli
Usage:
GARLI main steps of an analysis of a nucleotide data set (both codon and partitioned models) includes setting up and monitoring a run, using features such as constrained searches and bootstrap analyses if needed, and inspecting the output from a run.
#Garli
Garli
#Global_Alignment
MP, HY
#Global_Alignment
Global alignment is a computational approach to sequence alignment.
Mostly used to align every residue in every sequence where the sequences in the query set are of similar and equal size.
#Global_Alignment
Global_Alignmnet
#Glocal_(global-local)_
MP, HY
#Glocal_(global-local)_
Glocal "global-local" is a hybrid method that finds the best possible alignment including the start and end of one or the other sequence where neither global nor local alignment can be useful.
#Glocal_(global-local)_
Global_Global_Local
#Hidden_Markov_Model
MP, HY
#Hidden_Markov_Model
Hidden Markov model is used to produce probability scores for a family of multiple sequence alignment.
#Hidden_Markov_Model
Hidden_Markov_Model
#ISSN
ISSN
#Issue
Issue
#Iterative_Method
MP, HY
#Iterative_Method
Iterative methods: It is an extension of progressive methods attempting to improve the heavy dependence on the accuracy of the initial pairwise alignment.
#Iterative_Method
Iterative_Method
#JC
MP, TD
#JC
The Jukes-Cantor (JC69) model is the simplest DNA substitution model because it assumes equal base frequencies (25%) and equal nucleotide substitution rates for all pairs of the four nucleotides A, T, C, and G. It also does not correct for the higher rate of transitional substitutions in comparison to transversional substitutions.
#JC
(http://www.megasoftware.net/3.1/WebHelp/distancemethods_hc/hc_jukes_cantor_distance.htm)
#JC
Usage: It is special case of F81 model and thus is only used for the original likelihood test.
(http://www.annualreviews.org/doi/pdf/10.1146/annurev.ecolsys.28.1.437)
#JC
JC
#Jack_Knifing
MP, HY
#Jack_Knifing
Jack knifing, is used in statistical interference to estimate the bias and standard error (variance) of a statistic, when a random sample of observations is used to calculate it. This is a method of resampling data in an effort to assess confidence in the hypothesized relationships between taxa.
#Jack_Knifing
Usage: Programs as DAMBE
#Jack_Knifing
Jack_Knifing
#Journal
Journal
#Journal_Article
Journal_Article
#Literature
Literature
#Local_Alignment
Example:
S1= GCGCATGGATTGAGCGA
S2= TGCGCCATTGATGACC
possible alignment:
S’1= ATTGA-G
S’2= ATTGATG
#Local_Alignment
MP, HY
#Local_Alignment
Local alignment is a computational approach to sequence alignment.
Used for aligning dissimilar sequences.
#Local_Alignment
Local_Alignmnet
#Motif_Finding
MP, HY
#Motif_Finding
Motif finding: Constructs global alignment sequences that attempt to align short conserved sequence motifs.
#Motif_Finding
Motif_Finding
#MrBayes
MP,HY
#MrBayes
MrBayes assumes prior distribution of tree topologies and uses MCMC (Markov Chain Monte Carlo) methods to search tree space and infer the posterior distribution of topologies. It reads sequence data in the NEXUS file format, and outputs posterior distribution estimates of trees and parameters.
#MrBayes
Written by/released/produced by: John Huelsenbeck and Fredrik
Ronquist
Details: http://mrbayes.net
#MrBayes
Usage: Bayesian inference
Analyze: Nucleic acid sequences, protein sequences and
morphological characters
#MrBayes
MrBayes
#Multiple_Sequence_Alignment
Example:
S1=AGGTC
S2=GTTCG
S3=TGAAC
Possible alignment AGGGT-C-
-G-T TC G
TG-AAC-
Possible alignment AGGT-C-
GTT—CG
-TGAAAC
#Multiple_Sequence_Alignment
MP, HY
#Multiple_Sequence_Alignment
It is an extension of pairwise alignment to incorporate more than two sequences at a time.
#Multiple_Sequence_Alignment
#Multiple_Sequence_Alignment
Multiple_Sequence_Alignment
#NNI
MP, SV
#NNI
Nearest Neighbor Interchange (NNI): This is a heuristic algorithm for searching through treespace. It proceeds by juxtaposing the positions of neighbors on a phylogenetic tree. If the resulting tree is better, then it is retained. This algorithm is quite a gentle perturbation of the tree and is inferior to either SPR or TBR in terms of completeness of the searsh. On average it will be quicker than SPR or TBR.
#NNI
http://www.dbbm.fiocruz.br/james/GlossaryN.html
#NNI
Programs used:
PAUP, PhyML 3.0
#NNI
NNI
#NONA
Analyze: DNA sequence data or discrete characters
#NONA
MP
#NONA
It searches for most parsimonious trees according to character weights defined by the user a priori.
#NONA
Written by/released/produced by: Pablo Goloboff, Instituto Miguel
Lillo, Tucumán, Argentina
~Details: http://www.cladistics.com/aboutNona.htm.
MP,HY
#NONA
Usage: Parsimony
#NONA
~Advantages: Very fast than other parsimony programs.
#NONA
NONA
#Optimally
MP, TD
#Optimally
An optimality criterion provides a measure of the fit of the data to a given hypothesis. The selection process is determined by the solution that optimizes the criteria used to evaluate the alternative hypotheses. The term has been used to identify the different criteria that are used to infer a phylogenetic tree and include maximum likelihood, Bayesian, maximum parsimony.
#Optimally
Optimaly
#PAUP
Analyze: Molecular sequences, morphological data and other data
types.
#PAUP
MP,HY
#PAUP
(Phylogenetic Analysis Using Parsimony)
PAUP is a comprehensive program and competes with PHYLIP to be responsible for the most trees published.
#PAUP
Written by/ released/produced by: David Swofford and distributed by
Sinauer Associates of Sunderland,Massachusetts.
Details: http://www.sinauer.com/detail.php?id=8060.
Swofford et al. (1998)
#PAUP
Usage: Parsimony, Maximum likelihood and distance matrix methods
#PAUP
PAUP
#PHYLIP
MP, TD
#PHYLIP
PHYLIP (PHYLogeny Inference Package) is a package of 35 programs for inferring phylogenies. It is distributed as source code, documentation files, and a number of different types of executables.
#PHYLIP
(http://evolution.gs.washington.edu/phylip.html)
(http://evolution.gs.washington.edu/phylip/general.html)
#PHYLIP
Usage: Methods available in each program include parsimony, distance matrix, and likelihood methods, including bootstrapping and consensus trees. Data types that can be handled include molecular sequences, gene frequencies, restriction sites and fragments, distance matrices, and discrete characters.
#PHYLIP
PHYLIP
#Permutation_Test
MP, HY
#Permutation_Test
Permutation tests are standard nonparametric methods. It is often called the permutation tail probability test (PTP). Here the columns of the matrix are randomised so that the consensus sequence and the composition for any particular column is maintained, but any signal is lost.
#Permutation_Test
Permutation_Test
#Poisson_Model
MP,HY
#Poisson_Model
This implements a poissson distribution that accurately estimates the number of amino acid replacements when species are closely related.
#Poisson_Model
Ref: Nei et al. (1987)
#Poisson_Model
Poisson_Model
#Progressive_Method
MP, HY
#Progressive_Method
Progressive, hierarchial or tree methods generate multiple sequence alignment by aligning the most similar sequences first and then adding successively less related sequences until the entire query has been incorporated.
#Progressive_Method
Progressive_Method
#Provenance
Provenance
#Resampling
MP, HY
#Resampling
Resampling techniques provide an estimate of dispersion or statistics of uknown or poorly known distribution.
#Resampling
Resampling
#SPR
MP,SV
#SPR
Subtree Pruning Regrafting (SPR) This is a heuristic search algorithm for searching through treespace. It proceeds by breaking off part of the tree and attaching it to another part of the tree. If it finds a better tree, then the new tree is used as a starting tree for another round of SPR. This is a more rigorous algorithm than NNI, but not as robust as TBR. Another name for SPR is cut-and-paste.
#SPR
http://www.dbbm.fiocruz.br/james/GlossaryS.html
#SPR
Programs used:
PAUP, PhyML 3.0
#SPR
SPR
#SSAP
MP, HY
#SSAP
sequential structure alignment program is a program of structural alignment that uses atom-to-atom vectors in structure space.
#SSAP
SSAP
#Sequence_Alignment
MP, HY
#Sequence_Alignment
It is a way of arranging the specific sequences (DNA, RNA, protein) to identify regions of similarity that may be a consequence of functional, structural or evolutionary relationships between the sequences.
#Sequence_Alignment
Sequence_Alignmnet
#Short_Title
Short_Title
#Start_Page
Start_Page
#Structural_Alignment
MP, HY
#Structural_Alignment
Uses information about the secondary and teritiary structure of the specific molecule in aligning sequences.
#Structural_Alignment
Structure_Alignment
#TBR
MP, SV
#TBR
Tree-Bisection-Reconnection (TBR) This is a heuristic algorithm for searching through treespace. It proceeds by breaking a phylogenetic tree into two parts and then reconnecting the two subtrees at all possible branches. If a better tree is found, it is retained and another round of TBR is initiated. This is quite a rigorous method of searching treespace. It is not guaranteed to find the optimal tree, but it is more robust than SPR OR NNI.
#TBR
http://www.dbbm.fiocruz.br/james/GlossaryT.html
#TBR
Programs used:
PAUP
#TBR
TBR
#Title
Title
#Topology-dependent_permutation_
Topology_Dependent_Permutation
#Traversing-Tree-Space
MP
#Traversing-Tree-Space
tree-traversal refers to the process of visiting (examining and/or updating) each node in a tree data structure, exactly once, in a systematic way
#Traversing-Tree-Space
Traversing_Tree_Space
#Type_of_Article
Type_of_Article
#Volume
Volume
#Word_method/K-tuple_method
MP, HY
#Word_method/K-tuple_method
Word method (K-tuple method) is a primary method of producing pairwise alignment. Particularly used in large data base searche.
#Word_method/K-tuple_method
Word_Method/k_tuple_Method
#Year
Year
#data_set_type
Data_Set_Type
#pages
Pages
#pairwise_Alignment
MP, HY
#pairwise_Alignment
Are used to find the local or global alignments of two query sequences
Used for methods that do not require extreme precision.
#pairwise_Alignment
Pairwise_Alignment
#permutation_Tests
Permutation_Tests
ontologyIRI:#Amino_Acid_Model
Amino_Acid_Model
ontologyIRI:#BEAST
BEAST
ontologyIRI:#Beast
MP,SV
ontologyIRI:#Beast
BEAST (Bayesian evolutionary analysis by sampling trees):
It is a powerful and flexible evolutionary analysis package for molecular sequence variation. It is the software architecture for Bayesian analysis of molecular sequences along with an evolutionary tree.
ontologyIRI:#Beast
BEAST: Bayesian evolutionary analysis by sampling trees, BMC Evolutionary Biology 2007, 7:214
ontologyIRI:#Beast
Usage: Bayesian analysis.
Analyze: DNA and protein sequence data.
ontologyIRI:#Beast
Beast
ontologyIRI:#Boot_Strap
Boot_Strap
ontologyIRI:#Branch-Swampling
Branch_Swapping
ontologyIRI:#Character_Based
MP, TD
ontologyIRI:#Character_Based
Whereas the distance based methods compress all sequence information into a single number, the character-based methods attempt to infer the phylogeny based on all the individual characters (nucleotides or amino acids).
ontologyIRI:#Character_Based
Character_Based
ontologyIRI:#Codon_Based_Model
MP,TD
ontologyIRI:#Codon_Based_Model
A codon-based model describes the evolution of protein-coding DNA sequences using substitions between codons. They are used in phylogenetic estimation within the ML framework.
(Goldman and Yang 1994)
- 3 types of codon based models:
(http://e-collection.library.ethz.ch/eserv/eth:3/eth-3-02.pdf)
1/ Parametric model/Muse and Gaut (1994)
The model uses six parameters, α and β for the synonymous and nonsynonymous
substitution rates and the four nucleotide frequencies πx (x =A,G,T,C). The instantaneous rate
between codons i and j is only positive if they differ by exactly one nucleotide.
2/ Empirical model/ Yang and others (1994)
This model has parameter for transversion/transition bias, codon usage bias, physicochemical distances between amino acids (coded by the codons) and ”the variability of the gene or its tendency to undergo nonsynonymous substitution.” Like the first model, it only takes into account codons that are different by one nucleotide.
3/Combined codon model/Whelan and Goldman (2004)
Whelan and Goldman model consider substitutions of two or three consecutive nucleotides as one possible evolutionary event.
ontologyIRI:#Codon_Based_Model
Codon_Based_Model
ontologyIRI:#DNA_Model
DNA_Model
ontologyIRI:#DataFormat
DataFormat
ontologyIRI:#DataType
DataType
ontologyIRI:#Distance_Based
MP,SV
ontologyIRI:#Distance_Based
It requires distance measures between the sequences of the data.
It calculates a measure of the distance between each pair of species and then finds a tree that predicts the observed set of distances as closely as possible or that minimizes discrepancies among pair-wise distances.
Distances are considered as the estimates of the branch length separating that pair of species.
Ref: Inferring Phylogenies by Joseph Felsenstein
ontologyIRI:#Distance_Based
Programs used:
FITCH, KITSCH, NEIGHBOR, DNADIST, RESTDIST
ontologyIRI:#Distance_Based
Ref: http://www.mbio.ncsu.edu/bioedit/appinstall.html
ontologyIRI:#Distance_Based
Distance_Based
ontologyIRI:#F81
Example: We assume that the probability of change from state ‘i’ to state ‘j’ is proportional to the frequency of state ‘j’.
ontologyIRI:#F81
MP, HY
ontologyIRI:#F81
Felsenstein 1981 (F81, nst=1):
Model in which the probability of nucleotide changes were determined by the equilibrium nucleotide frequencies. This is an extension of JC model. There are variable base frequencies, all substitutions equally likely (PAUP, PAML)
ontologyIRI:#F81
Ref:D.H. Bos, D. Posada, Developmental and Comparative Immunology 29 (2005) 211–227.
ontologyIRI:#F81
F81
ontologyIRI:#FASTA
FASTA
ontologyIRI:#GTR
MP,SV
ontologyIRI:#GTR
GTR(General Time-Reversal):
It assumes a symmetric substitution matrix and so the time is reversible. The nucleotides can occur at different frequencies. Distance is made by estimating the base frequencies and the rates and finding ones that exactly predict the observed net transition matrix.
ontologyIRI:#GTR
http://www.life.umd.edu/labs/delwiche/bsci348s/lec/NTSeqEvol.html
ontologyIRI:#GTR
GTR
ontologyIRI:#HKY
MP,TD
ontologyIRI:#HKY
Unlike JC69, “the HKY (Hasegawa, Kishino, Yano, 1985) model assumes a time-reversible process, a non-uniform distribution of nucleotides and different rates for transitions and transversions.”
ontologyIRI:#HKY
(http://lectures.molgen.mpg.de/phylogeny_ws05/exercises/exercises_07.pdf)
ontologyIRI:#HKY
(http://www.annualreviews.org/doi/pdf/10.1146/annurev.ecolsys.28.1.437)
ontologyIRI:#HKY
Usage: HKY85 and F81 are similar and can be used as appropriate models for the alternative hypothesis of likelihood. (The alternative hypothesis answers the question “Does the addition of a substitution parameter provide a signiﬁcant increase in the likelihood”?)
ontologyIRI:#HKY
HKY
ontologyIRI:#Maximum_Likelihood
Example:
The likelihood generally depends upon the phylogeny sites, branch lengths, and other substitution parameters. The method of maximum likelihood estimates the mutational probabilities and finds the values of the parameters which maximize the likelihood function.
ontologyIRI:#Maximum_Likelihood
MP, TD
ontologyIRI:#Maximum_Likelihood
Maximum Likelihood Estimation (MLE) is a method for the inference of phylogeny. Using statistical techniques to assign probabilities to the proposed tree models, the method searches for the tree with the highest probability or likelihood.
ontologyIRI:#Maximum_Likelihood
(http://bioinf.ncl.ac.uk/molsys/data/like.pdf)
(http://www.sciencemag.org/site/feature/data/1050262.pdf)
ontologyIRI:#Maximum_Likelihood
Usage:
The method is used in phylogeny programs such as PAUP*, PHYLIP, and PAML. MLE works well with many nucleotide-based models such as JC, GTR, HKY, F81, K2P.
ontologyIRI:#Maximum_Likelihood
Maximum_Likelihood
ontologyIRI:#Maximum_Parsimony
Example:
Given a set of aligned sequences or taxa, parsimony analysis determines the number of steps of each character on a given tree. The sum over all characters is called tree Length and most parsimonious trees have the minimum tree length needed to explain the
observed distribution of all the characters.
ontologyIRI:#Maximum_Parsimony
MP, TD
ontologyIRI:#Maximum_Parsimony
Maximum parsimony is a character-based method that infers a phylogenetic tree by minimizing the total number of evolutionary steps required to explain a given set of data, or in other words by minimizing the total tree length.
ontologyIRI:#Maximum_Parsimony
(http://bioinf.ncl.ac.uk/molsys/data/characters.pdf)
ontologyIRI:#Maximum_Parsimony
Usage:
MALIGN and POY program use the advantage of maximum parsimony analysis to optimize multiple sequence alignments and cladogram score of the corresponding tree.
ontologyIRI:#Maximum_Parsimony
Maximum_Parsimony
ontologyIRI:#Mega
MP,TD
ontologyIRI:#Mega
MEGA is an integrated tool for conducting automatic and manual sequence alignment, inferring phylogenetic trees, mining web-based databases, estimating rates of molecular evolution, inferring ancestral sequences, and testing evolutionary hypotheses. (http://www.megasoftware.net/)
ontologyIRI:#Mega
(http://www.megasoftware.net/tutorial.php)
ontologyIRI:#Mega
Usage:
MEGA can re-construct a phylogeny using Maximum Likelihood, Minimum Evolution, UPGMA, and Maximum Parsimony methods in addition to Neighbor-Joining. For example, MEGA can re-construct the MP phylogeny using Branch and Bound search method.
ontologyIRI:#Mega
Mega
ontologyIRI:#Method
Method
ontologyIRI:#Model
MP,HY
ontologyIRI:#Model
Model represents the footprint of evolutionary phenomenon that generated the data (such as mutation and selection) and provides framework through which the phylogenetic construction method estimates parameters to find the preferred tree.
The specific model selected for a data set depends on features of the data such as level of variation and frequency.
Benefits:
•Overcome most of the phylogenetic scenarios.
•Provide simplifications, summarizing many evolutionary forces and incorporation of these models leads to improvement in phylogenetic analysis.
Example: Maximum likelihood, neighbor joining and Bayesian methods use models and benefit from them but maximum parsimony does not use models.
ontologyIRI:#Model
Ref:
•Nei M. Phylogenetic analysis in molecular evolutionary genetics. Ann Rev Genet 1996;30:371–403.
•Kuhner MK, Felsenstein J. A simulation comparison of phylogeny algorithms under equal and unequal evolutionary rates. Mol Biol Evol 1994;11:459–68.
•Huelsenbeck JP, Hillis DM. Success of phylogenetic methods in the four-taxon case. Syst Biol 1993;42:247–64.
•Felsenstein J. Cases in which parsimony or compatibility methods will be positively misleading. Syst Zool 1978;27: 401–10.
ontologyIRI:#Model
Model
ontologyIRI:#Model_of_Protein_Evolution
Model_of_Protein_Evolution
ontologyIRI:#Molphy
MP, TD
ontologyIRI:#Molphy
MOLPHY is a free package of programs for molecular phylogenetics based on the Maximum Likelihood (ML) method. The main program of MOLPHY is ProtML which infers evolutionary trees from Protein (amino acid) sequences
ontologyIRI:#Molphy
(http://www.is.titech.ac.jp/~shimo/class/doc/csm96.pdf)
ontologyIRI:#Molphy
using ML criterion.
ontologyIRI:#Molphy
Molphy
ontologyIRI:#NEXUX
NEXUX
ontologyIRI:#Neighbor_Joining
MP,SV
ontologyIRI:#Neighbor_Joining
Neighbor joining method is a bottom-up clustering method for the creation of phonetic trees. This method also applies an algorithm for clustering.
ontologyIRI:#Neighbor_Joining
Programs used: Neighbor
ontologyIRI:#Neighbor_Joining
Neighbor_Joining
ontologyIRI:#Ninja
MP,TD
ontologyIRI:#Ninja
NINJA is a software for large-scale neighbor-joining phylogeny inference.
ontologyIRI:#Ninja
http://nimbletwist.com/software/ninja/docs.html
ontologyIRI:#Ninja
Usage: It expects inputs to be either alignments (in fasta format) or pairwise distance matrices (in phylip format), and can produce both a distance matrix (phylip) and a tree file (newick format).
ontologyIRI:#Ninja
Ninja
ontologyIRI:#Nni
MP, SV
ontologyIRI:#Nni
Nearest Neighbor Interchange is a heuristic algorithm for searching through treespace. It proceeds by juxtaposing the positions of neighbors on a phylogenetic tree. If the resulting tree is better, then it is retained.
ontologyIRI:#Nni
Nni
ontologyIRI:#Nucleotide_Substitution_Model
- Nucleotide Substitution models describe the evolutionary rates at which one nucleotide replaces another. “These models assume that only the instantaneous state of a character is important and the probability of change from state i to state j depends upon the amount of time that has passed and the substitution rate.”
ontologyIRI:#Nucleotide_Substitution_Model
(http://www.life.umd.edu/labs/delwiche/bsci348s/lec/NTSeqEvol.html)
ontologyIRI:#Nucleotide_Substitution_Model
Usage: They are used in molecular phylogenetic analyses and tree likelihood calculation (mostly in Bayesian and maximum likelihood approaches of tree estimation)
ontologyIRI:#Nucleotide_Substitution_Model
Nucleotide_Substitution_Model
ontologyIRI:#Parameter
Parameter
ontologyIRI:#Phylogenetic
Phylogenetic
ontologyIRI:#PhylogeneticTree
PhylogeneticTree
ontologyIRI:#Poy
Analyze: DNA sequence data.
ontologyIRI:#Poy
MP,SV
ontologyIRI:#Poy
POY is a program for phylogenetic analysis of DNA sequence data based on the principle behavior of parsimony. POY uses concepts of homology of DNA sequence data than those of static DNA sequence alignments. This program implements two methods of DNA analysis “optimization alignment” and “fixed-states optimization”
ontologyIRI:#Poy
Exploring the Behavior of POY, a Program for Direct Optimization of Molecular Data, Cladistics 17, S60–S70 (2001)
ontologyIRI:#Poy
Usage: Parsimony
ontologyIRI:#Poy
Poy
ontologyIRI:#Program
These are the tools for interpreting and inferring phylogenetic trees.
ontologyIRI:#Program
Program
ontologyIRI:#Protein_Structure_and_Correlated_Change
Protein_Structure_and_Correlated_Change
ontologyIRI:#Raxml
Analyze: Molecular sequence data.
ontologyIRI:#Raxml
MP,SV
ontologyIRI:#Raxml
It is a fast program for sequential and parallel phylogenetic tree calculations based on the maximum likelihood method. It provides faster heuristic search, use of parallel processing, and a simulated annealing algorithm.
ontologyIRI:#Raxml
RAxML-II: a program for sequential, parallel and distributed inference of large phylogenetic trees, Concurrency Computat.: Pract. Exper. 2005; 17:1705–1723.
ontologyIRI:#Raxml
Usage: Maximum likelihood method, parsimony, bootstrapping, and consensus tree methods.
ontologyIRI:#Raxml
Raxml
ontologyIRI:#Resampling
Resampling
ontologyIRI:#SPR
MP, SV
ontologyIRI:#SPR
Subtree Pruning Regrafting is a heuristic search algorithm for searching through treespace. It proceeds by breaking off part of the tree and attaching it to another part of the tree. If it finds a better tree, then the new tree is used as a starting tree for another round of SPR. This is a more rigorous algorithm than NNI. Another name for SPR is cut-and-paste.
ontologyIRI:#SPR
SPR
ontologyIRI:#SYM
Example: The probability of having a ‘i’ which mutates to a ‘j’ is the same as starting with a ‘j’ which mutates into an ‘i’.
Sites are divided into two classes those that are variable or invariable and incorporated them into the nucleotide models as below:
I: Proportion of invariable sites within the model;
G (Π): Gamma distribution of rates among sites: There is a continuous probability of change in nucleotide sites that determines the shape of gamma distribution. Slow rates have a skewed distribution to right,whereas high rates have a small shape.
ontologyIRI:#SYM
MP,HY
ontologyIRI:#SYM
Symmetric Model: Estimation of evolutionary distances between nucleotide sequences of equal base frequencies, symmetrical substitution matrix (A to T = T to A) (PAUP, PAML)
ontologyIRI:#SYM
•D.H. Bos, D. Posada / Developmental and Comparative Immunology 29 (2005) 211–227
ontologyIRI:#SYM
SYM
ontologyIRI:#Sequence
Sequence
ontologyIRI:#SequenceAlignment
SequenceAlignment
ontologyIRI:#SourceTaxon
SourceTaxon
ontologyIRI:#Splits_Tree
Analyze: molecular sequence data, distance data.
ontologyIRI:#Splits_Tree
MP,SV
ontologyIRI:#Splits_Tree
It is an application for computing unrooted phylogenetic networks from molecular sequence daa. It provides a number of methods for computing split networks from sequences (e.g. median networks), distances (e.g. split decomposition or neighbor-net) and trees (consensus networks and super-networks).
ontologyIRI:#Splits_Tree
Application of Phylogenetic Networks in Evolutionary Studies, Mol. Biol. Evol., 23(2):254-267, 2006.
ontologyIRI:#Splits_Tree
Usage: split decomposition, median network, super networks
ontologyIRI:#Splits_Tree
Splites_Tree
ontologyIRI:#TBR
MP, SV
ontologyIRI:#TBR
Tree-Bisection-Reconnection is a heuristic algorithm for searching through treespace. It proceeds by breaking a phylogenetic tree into two parts and then reconnecting the two subtrees at all possible branches. If a better tree is found, it is retained and another round of TBR is initiated. This is quite a rigorous method of searching treespace. It is not guaranteed to find the optimal tree, but it is more robust than SPR OR NNI.
ontologyIRI:#TBR
TBR
ontologyIRI:#Tree_Puzzle
Analyze: molecular sequence data
ontologyIRI:#Tree_Puzzle
MP,SV
ontologyIRI:#Tree_Puzzle
This is a program for tree estimation and to reconstruct phylogenetic trees. It implements a fast tree search algorithm by the strategy of “quartet puzzling” which allows large data sets. It also computes pair-wise maximum likelihood distances as well as branch lengths for user specified trees. It also conducts a number of statistical tests on the data sets.
ontologyIRI:#Tree_Puzzle
http://www.tree-puzzle.de/
ontologyIRI:#Tree_Puzzle
Usage: Maximum likelihood method
ontologyIRI:#Tree_Puzzle
Tree_Puzzle
ontologyIRI:#UPGMA
Example: If a node leads to two branches, one of which leads on upwards to all mammals and the other on upwards to all birds, the estimate of the total branch length down to the node is half the average of the distances between all( bird, mammal) pairs.
ontologyIRI:#UPGMA
MP,SV
ontologyIRI:#UPGMA
Unweighted Pair Group Method with Arithmetic Mean
This method applies a particular algorithm to a distance matrix to come up with a tree directly.At each step, the nearest two clusters are combined into a higher-level cluster. The distance between any two clusters A and B is taken to be the average of all distances between pairs of objects "x" in A and "y" in B, that is, the mean distance between elements of each cluster.
ontologyIRI:#UPGMA
Programs used:
neighbor
ontologyIRI:#UPGMA
UPGMA
ontologyIRI:#k80
MP,SV
ontologyIRI:#k80
k80 (kimura):
This model uses two rates, ALPHA (Transitions) and BETA (Transversions) as parameters along with time. Transitions are substitutions between nucleotides of the same or similar molecular structure like purines or between pyrimidines. All other substitutions are tranversions and are known to occur less frequently than transitions.
ontologyIRI:#k80
http://www.stats.ox.ac.uk/__data/assets/pdf_file/0003/4296/Basic_Models_of_Nucleotide_Evolution_2.pdf
ontologyIRI:#k80
K80
ontologyIRI:#k80+I
K80+1
ontologyIRI:#k80+P
K80+∏
ontologyIRI:#k80+P+I
K80+∏+1
ontologyIRI:#neXML
NeXML