This document provides an overview of molecular phylogenetics and computational methods for reconstructing evolutionary relationships between genetic sequences. It discusses key topics like molecular evolution, calculating genetic distances, clustering algorithms like UPGMA and neighbor joining, and cladistic methods like parsimony. The document also explains important concepts in phylogenetics including orthologs and paralogs, phenetic versus cladistic approaches, and maximum likelihood methods.
This presentation entitled 'Molecular phylogenetics and its application' deals with all the developmental ideas and basics in the field of bioinformatics.
A physical map of a chromosome or a genome that shows the physical locations of genes and other DNA sequences of interest. Physical maps are used to help scientists identify and isolate genes by positional cloning.
According to the ICSM (Intergovernmental Committee on Surveying and Mapping), there are five different types of maps: General Reference, Topographical, Thematic, Navigation Charts and Cadastral Maps and Plans.
Evolutionary tree or physlogenetic tree and it's types like rooted and unrooted labeled or unlabelled. How to construct physlogenetic tree and limitations of physlogenetic tree.
lecture for doctorate students while I was working as researcher assisstance about phylogenetic science, definition,
Understand the most basic concepts of phylogeny
Understand the difference between orthology, paralogy and xenology.
Be able to compute simple phylogenetic trees
Understand what bootstrapping means in phylogeny
This presentation entitled 'Molecular phylogenetics and its application' deals with all the developmental ideas and basics in the field of bioinformatics.
A physical map of a chromosome or a genome that shows the physical locations of genes and other DNA sequences of interest. Physical maps are used to help scientists identify and isolate genes by positional cloning.
According to the ICSM (Intergovernmental Committee on Surveying and Mapping), there are five different types of maps: General Reference, Topographical, Thematic, Navigation Charts and Cadastral Maps and Plans.
Evolutionary tree or physlogenetic tree and it's types like rooted and unrooted labeled or unlabelled. How to construct physlogenetic tree and limitations of physlogenetic tree.
lecture for doctorate students while I was working as researcher assisstance about phylogenetic science, definition,
Understand the most basic concepts of phylogeny
Understand the difference between orthology, paralogy and xenology.
Be able to compute simple phylogenetic trees
Understand what bootstrapping means in phylogeny
Taxonomy is the branch of science concerned with the classification of organisms. A taxonomic designation is more than just a name. Ideally, it reflects evolutionary history and the relationship between organisms. Traditionally, taxonomic classification has relied upon morphological features and physiological characteristics. However, for bacterial taxonomy, phenotypic approaches have proven insufficient. Unrelated bacteria can exhibit identical traits, closely related bacteria can have divergent features, and methods for accurate identification may be too cumbersome for routine use. In contrast, molecular taxonomy approaches use data derived from hereditary material and provide a robust view of genetic relatedness. Advances in technology have been accompanied by improvements in the cost, speed, and availability of molecular methods. Here, we provide a brief history of approaches to prokaryotic classification and describe how molecular taxonomy is redefining our understanding of bacterial evolution and the tree of life.
Introduction
History
Genetic mapping
DNA Markers
Physical mapping
Importance
Drawback
Conclusion
References
uses genetic techniques to construct maps showing the positions of genes and other sequence features on a genome.
Genetic techniques include cross-breeding experiments or, in the case of humans, the examination of family histories (pedigrees).
The first genome to be sequenced was that of Haemophilus influenzae in 1995.
The E. coli genome was completely sequenced in 1997.
Yeast (Saccharomyces cerevisiae) (12.8 x 106 bp) and worm (Caenorhabditis elegans) genomes were the first eukaryotic genomes to be sequenced in 1999.
Genomes of Drosophila melanogaster and Arabidopsis thaliana were sequenced in 2000.
In some organisms, there are special tissues in which chromosomes undergo structural specializations.
Such specialized chromosomes are generally termed as SPECIAL TYPES OF CHROMOSOMES.
It includes the information related to a bioinformatics tool BLAST (Basic Local Alignment Search Tool), BLAST is in-silico hybridisation to find regions of similarity between biological sequences. The program compares nucleotide or protein sequences to sequence databases and calculates the statistical significance. This presentation too contains the input - output format, Blast process and its types .
Introduction
Transcriptome analysis
Goal of functional genomics
Why we need functional genomics
Technique
1. At DNA level
2.At RNA level
3. At protein level
4. loss of function
5. functional genomic and bioinformatics
Application
Latest research and reviews
Websites of functional genomics
Conclusions
Reference
Taxonomy is the branch of science concerned with the classification of organisms. A taxonomic designation is more than just a name. Ideally, it reflects evolutionary history and the relationship between organisms. Traditionally, taxonomic classification has relied upon morphological features and physiological characteristics. However, for bacterial taxonomy, phenotypic approaches have proven insufficient. Unrelated bacteria can exhibit identical traits, closely related bacteria can have divergent features, and methods for accurate identification may be too cumbersome for routine use. In contrast, molecular taxonomy approaches use data derived from hereditary material and provide a robust view of genetic relatedness. Advances in technology have been accompanied by improvements in the cost, speed, and availability of molecular methods. Here, we provide a brief history of approaches to prokaryotic classification and describe how molecular taxonomy is redefining our understanding of bacterial evolution and the tree of life.
Introduction
History
Genetic mapping
DNA Markers
Physical mapping
Importance
Drawback
Conclusion
References
uses genetic techniques to construct maps showing the positions of genes and other sequence features on a genome.
Genetic techniques include cross-breeding experiments or, in the case of humans, the examination of family histories (pedigrees).
The first genome to be sequenced was that of Haemophilus influenzae in 1995.
The E. coli genome was completely sequenced in 1997.
Yeast (Saccharomyces cerevisiae) (12.8 x 106 bp) and worm (Caenorhabditis elegans) genomes were the first eukaryotic genomes to be sequenced in 1999.
Genomes of Drosophila melanogaster and Arabidopsis thaliana were sequenced in 2000.
In some organisms, there are special tissues in which chromosomes undergo structural specializations.
Such specialized chromosomes are generally termed as SPECIAL TYPES OF CHROMOSOMES.
It includes the information related to a bioinformatics tool BLAST (Basic Local Alignment Search Tool), BLAST is in-silico hybridisation to find regions of similarity between biological sequences. The program compares nucleotide or protein sequences to sequence databases and calculates the statistical significance. This presentation too contains the input - output format, Blast process and its types .
Introduction
Transcriptome analysis
Goal of functional genomics
Why we need functional genomics
Technique
1. At DNA level
2.At RNA level
3. At protein level
4. loss of function
5. functional genomic and bioinformatics
Application
Latest research and reviews
Websites of functional genomics
Conclusions
Reference
General bacteriology / /certified fixed orthodontic courses by Indian dental...Indian dental academy
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Automated sequencing of genomes require automated gene assignment
Includes detection of open reading frames (ORFs)
Identification of the introns and exons
Gene prediction a very difficult problem in pattern recognition
Coding regions generally do not have conserved sequences
Much progress made with prokaryotic gene prediction
Eukaryotic genes more difficult to predict correctly
Introduction to Modern Biosystemaics for Fungal ClassificationMrinal Vashisth
This is a more specific version of the slide-set "Major Characteristics Used in Microbial Classification". A presentation I could not deliver for some reasons yet turned out to be pretty nice. I hope to deliver it some day, but for the time being I am making it public. I hope it would be of some use. :)
Guest lecture on comparative genomics for University of Dundee BS32010, delivered 21/3/2016
Workshop/other materials available at DOI:10.5281/zenodo.49447
Unit 2: Phylogeny
LECTURE LEARNING GOALS
1. Define phylogeny, and describe what a phylogenetic tree can reveal about the species it models.
2. Describe how to construct a phylogenetic tree, and the complexities that create mistakes.
3. Explain how to root a tree, and contrast how to root the tree of life.
Comparative genomics: Genomic features are compared, evolutionary relationship
The major principle of comparative genomics is that common features of two organisms will often be encoded within the DNA that is evolutionarily conserved between them. orthologous sequences,
Started as soon as the whole genomes of two organisms became available (that is, the genomes of the bacteria Haemophilus influenzae and Mycoplasma genitalium) in 1995, comparative genomics is now a standard component of the analysis of every new genome sequence. comparative genomics studies of small model organisms (for example the model Caenorhabditis elegans and closely related Caenorhabditis briggsae) are of great importance to advance our understanding of general mechanisms of evolution
Computational tools for analyzing sequences and complete genomes. Application of comparative genomics in agriculture and medicine.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
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We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
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Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
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Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
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The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
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Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
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2. Topics
• i. Molecular Evolution
• ii. Calculating Distances
• iii. Clustering Algorithms
• iv. Cladistic Methods
• v. Computer Software
3. Evolution
• The theory of evolution is the
foundation upon which all of
modern biology is built.
• From anatomy to behavior to genomics, the
scientific method requires an appreciation of
changes in organisms over time.
• It is impossible to evaluate relationships among
gene sequences without taking into consideration
the way these sequences have been modified over
time
4. Relationships
Similarity searches and multiple alignments of
sequences naturally lead to the question:
“How are these sequences related?”
and more generally:
“How are the organisms from which
these sequences come related?”
5. Taxonomy
• The study of the relationships between groups of
organisms is called taxonomy, an ancient and
venerable branch of classical biology.
• Taxonomy is the art of classifying things into
groups — a quintessential human behavior —
established as a mainstream scientific field by
Carolus Linnaeus (1707-1778).
6.
7. Phylogenetics
• Evolutionary theory states that groups of similar
organisms are descended from a common ancestor.
• Phylogenetic systematics (cladistics) is a method
of taxonomic classification based on their
evolutionary history.
• It was developed by Willi Hennig,
a German entomologist, in 1950.
8. Cladistic Methods
• Evolutionary relationships are documented by
creating a branching structure, termed a phylogeny
or tree, that illustrates the relationships between the
sequences.
• Cladistic methods construct a tree (cladogram) by
considering the various possible pathways of
evolution and choose from among these the best
possible tree.
• A phylogram is a tree with branches that are
proportional to evolutionary distances.
9.
10. Molecular Evolution
• Phylogenetics often makes use of numerical data,
(numerical taxonomy) which can be scores for
various “character states” such as the size of a
visible structure or it can be DNA sequences.
• Similarities and differences between organisms can
be coded as a set of characters, each with two or
more alternative character states.
• In an alignment of DNA sequences, each position
is a separate character, with four possible character
states, the four nucleotides.
11. DNA is a good tool for taxonomy
DNA sequences have many advantages
over classical types of taxonomic
characters:
– Character states can be scored unambiguously
– Large numbers of characters can be scored for
each individual
– Information on both the extent and the nature of
divergence between sequences is available
(nucleotide substitutions, insertion/deletions, or
genome rearrangements)
12. A aat tcg ctt cta gga atc tgc cta
atc ctg
B ... ..a ..g ..a .t. ... ... t..
... ..a
C ... ..a ..c ..c ... ..t ... ...
... t.a
D ... ..a ..a ..g ..g ..t ... t.t
Each nucleotide difference is a character
..t t..
13. Sequences Reflect Relationships
• After working with sequences for a while, one develops an
intuitive understanding that “for a given gene, closely related
organisms have similar sequences and more distantly related
organisms have more dissimilar sequences. These
differences can be quantified”.
• Given a set of gene sequences, it should be possible to
reconstruct the evolutionary relationships among genes
and among organisms.
14.
15. What Sequences to Study?
• Different sequences accumulate changes at
different rates - chose level of variation that is
appropriate to the group of organisms being
studied.
– Proteins (or protein coding DNAs) are constrained by
natural selection - better for very distant relationships
– Some sequences are highly variable (rRNA spacer
regions, immunoglobulin genes), while others are
highly conserved (actin, rRNA coding regions)
– Different regions within a single gene can evolve at
different rates (conserved vs. variable domains)
16. (globin) Ancestral gene
A
Duplication
(hemoglobin) A B (myoglobin)
Speciation
A1 B1 A2 B2
(mouse) (human)
17. Orthologs vs. Paralogs
• When comparing gene sequences, it is important
to distinguish between identical vs. merely similar
genes in different organisms.
• Orthologs are homologous genes in different
species with analogous functions.
• Paralogs are similar genes that are the result of a
gene duplication.
– A phylogeny that includes both orthologs and paralogs
is likely to be incorrect.
– Sometimes phylogenetic analysis is the best way to
determine if a new gene is an ortholog or paralog to
other known genes.
18. Terminologies of phylogeny
• Phylogenetic (binary) tree: A tree is a graph composed of
nodes and branches, in which any two nodes are connected
by a unique path.
• Nodes: Nodes in phylogenetic trees are called taxonomic
units (TUs) Usually, taxonomic units are represented by
sequences (DNA or RNA nucleotides or amino acids).
• Branches: Branches in phylogenetic trees indicate
descent/ancestry relationships among the TUs.
• Terminal (external) nodes: The terminal nodes are also
called the external nodes, leaves, or tips of the tree and are
also called extant taxonomic units or operational taxonomic
units (OTUs)
19. Terminologies of phylogeny
• Internal nodes: The internal nodes are nodes, which are
not terminal. They are also called ancestral TUs.
• Root: The root is a node from which a unique path leads to
any other node, in the direction of evolutionary time. The
root is the common ancestor of all TU’s under study.
• Topology: The topology is the branching pattern of a tree.
• Branch length: The lengths of the branches determine the
metrics of a tree. In phylogenetic trees, lengths of branches
are measured in units of evolutionary time.
21. Genes vs. Species
• Relationships calculated from sequence data represent
the relationships between genes, this is not necessarily
the same as relationships between species.
• Your sequence data may not have the same
phylogenetic history as the species from which they
were isolated.
• Different genes evolve at different speeds, and there is
always the possibility of horizontal gene transfer
(hybridization, vector mediated DNA movement, or
direct uptake of DNA).
22. Cladistic vs. Phenetic
Within the field of taxonomy there are two
different methods and philosophies of building
phylogenetic trees: cladistic and phenetic
– Phenetic methods construct trees (phenograms) by
considering the current states of characters without
regard to the evolutionary history that brought the
species to their current phenotypes.
– Cladistic methods rely on assumptions about
ancestral relationships as well as on current data.
23. Phenetic Methods
• Computer algorithms based on the phenetic model rely on
Distance Methods to build of trees from sequence data.
• Phenetic methods count each base of sequence difference
equally, so a single event that creates a large change in
sequence (insertion/deletion or recombination) will move two
sequences far apart on the final tree.
• Phenetic approaches generally lead to faster algorithms and
they often have nicer statistical properties for molecular data.
• The phenetic approach is popular with molecular
evolutionists because it relies heavily on objective character
data (such as sequences) and it requires relatively few
assumptions.
24. Cladistic Methods
• For character data about the physical traits of
organisms (such as morphology of organs etc.)
and for deeper levels of taxonomy, the cladistic
approach is almost certainly superior.
• Cladistic methods are often difficult to
implement with molecular data because all of
the assumptions are generally not satisfied.
25. Distances Measurements
• It is often useful to measure the genetic distance between
two species, between two populations, or even between
two individuals.
• The entire concept of numerical taxonomy is based on
computing phylogenies from a table of distances.
• In the case of sequence data, pairwise distances must be
calculated between all sequences that will be used to build
the tree - thus creating a distance matrix.
• Distance methods give a single measurement of the
amount of evolutionary change between two sequences
since divergence from a common ancestor.
26. DNA Distances
• Distances between pairs of DNA sequences are relatively
simple to compute as the sum of all base pair differences
between the two sequences.
– this type of algorithm can only work for pairs of sequences that are
similar enough to be aligned
• Generally all base changes are considered equal
• Insertion/deletions are generally given a larger weight than
replacements (gap penalties).
• It is also possible to correct for multiple substitutions at a
single site, which is common in distant relationships and
for rapidly evolving sites.
27.
28. Amino Acid Distances
• Distances between amino acid sequences are a bit more
complicated to calculate.
• Some amino acids can replace one another with relatively little
effect on the structure and function of the final protein while
other replacements can be functionally devastating.
• From the standpoint of the genetic code, some amino acid
changes can be made by a single DNA mutation while others
require two or even three changes in the DNA sequence.
• In practice, what has been done is to calculate tables of
frequencies of all amino acid replacements within families of
related protein sequences in the databanks: i.e. PAM and
BLOSSUM
29. The PAM 250 scoring matrix
A R N D C Q E G H I L K M F P S T W Y V
A 2
R -2 6
N 0 0 2
D 0 -1 2 4
C -2 -4 4 -5 4
Q 0 1 1 2 -5 4
E 0 -1 1 3 -5 2 4
G 1 -3 0 1 -3 -1 0 5
H -1 2 2 1 -3 3 1 -2 6
I -1 -2 -2 -2 -2 -2 -2 -3 -2 5
L -2 -3 -3 -4 -6 -2 -3 -4 -2 2 6
K -1 3 1 0 -5 1 0 -2 0 -2 -3 5
M -1 0 -2 -3 -5 -1 -2 -3 -2 2 4 0 6
F -4 -4 -4 -6 -4 -5 -5 -5 -2 1 2 -5 0 9
P 1 0 -1 -1 -3 0 -1 -1 0 -2 -3 -1 -2 -5 6
S 1 0 1 0 0 -1 0 1 -1 -1 -3 0 -2 -3 1 3
T 1 -1 0 0 -2 -1 0 0 -1 0 -2 0 -1 -2 0 1 3
W -6 2 -4 -7 -8 -5 -7 -7 -3 -5 -2 -3 -4 0 -6 -2 -5 17
Y -3 -4 -2 -4 0 -4 -4 -5 0 -1 -1 -4 -2 7 -5 -3 -3 0 10
V 0 -2 -2 -2 -2 -2 -2 -1 -2 4 2 -2 2 -1 -1 -1 0 -6 -2 4
Dayhoff, M, Schwartz, RM, Orcutt, BC (1978) A model of evolutionary change in proteins. in Atlas of Protein
Sequence and Structure, vol 5, sup. 3, pp 345-352. M. Dayhoff ed., National Biomedical Research Foundation,
Silver Spring, MD.
30. Clustering Algorithms
Clustering algorithms use distances to calculate
phylogenetic trees. These trees are based solely on
the relative numbers of similarities and differences
between a set of sequences.
– Start with a matrix of pairwise distances
– Cluster methods construct a tree by linking the least
distant pairs of taxa, followed by successively more
distant taxa.
31. UPGMA
• The simplest of the distance methods is the UPGMA
(Unweighted Pair Group Method using Arithmetic averages)
• The PHYLIP programs DNADIST and PROTDIST
calculate absolute pairwise distances between a group of
sequences. Then the GCG program GROWTREE uses
UPGMA to build a tree.
• Many multiple alignment programs such as PILEUP use a
variant of UPGMA to create a dendrogram of DNA
sequences which is then used to guide the multiple alignment
algorithm.
32. Neighbor Joining
• The Neighbor Joining method is the most popular
way to build trees from distance measurements
(Saitou and Nei 1987, Mol. Biol. Evol. 4:406)
– Neighbor Joining corrects the UPGMA method for its (frequently
invalid) assumption that the same rate of evolution applies to each
branch of a tree.
– The distance matrix is adjusted for differences in the rate of
evolution of each taxon (branch).
– Neighbor Joining has given the best results in simulation studies
and it is the most computationally efficient of the distance
algorithms (N. Saitou and T. Imanishi, Mol. Biol. Evol. 6:514 (1989)
33. Cladistic methods
• Cladistic methods are based on the assumption that a
set of sequences evolved from a common ancestor by
a process of mutation and selection without mixing
(hybridization or other horizontal gene transfers).
• These methods work best if a specific tree, or at least
an ancestral sequence, is already known so that
comparisons can be made between a finite number of
alternate trees rather than calculating all possible trees
for a given set of sequences.
34. Parsimony
• Parsimony is the most popular method for
reconstructing ancestral relationships.
• Parsimony allows the use of all known evolutionary
information in building a tree
– In contrast, distance methods compress all of the
differences between pairs of sequences into a single
number
35. Building Trees with Parsimony
• Parsimony involves evaluating all possible trees
and giving each a score based on the number of
evolutionary changes that are needed to explain
the observed data.
• The best tree is the one that requires the fewest
base changes for all sequences to derive from a
common ancestor.
36. Parsimony Example
• Consider four sequences: ATCG, TTCG,
ATCC, and TCCG
• Imagine a tree that branches at the first
position, grouping ATCG and ATCC on
one branch, TTCG and TCCG on the other
branch.
• Then each branch splits, for a total of 3
nodes on the tree (Tree #1)
37. Compare Tree #1 with one that first divides ATCC on its own
branch, then splits off ATCG, and finally divides TTCG from
TCCG (Tree #2).
Trees #1 and #2 both have three nodes, but when all of the
distances back to the root (# of nodes crossed) are summed,
the total is equal to 8 for Tree #1 and 9 for Tree #2.
Tree Tree
#1 #2
38. Maximum Likelihood
• The method of Maximum Likelihood attempts to
reconstruct a phylogeny using an explicit model of
evolution.
• This method works best when it is used to test (or
improve) an existing tree.
• Even with simple models of evolutionary change,
the computational task is enormous, making this
the slowest of all phylogenetic methods.
39. Assumptions for Maximum Likelihood
• The frequencies of DNA transitions (C<->T,A<->G) and
transversions (C or T<->A or G).
• The assumptions for protein sequence changes are taken
from the PAM matrix - and are quite likely to be violated in
“real” data.
• Since each nucleotide site evolves independently, the tree is
calculated separately for each site. The product of the
likelihood's for each site provides the overall likelihood of
the observed data.
40. Computer Software for Phylogenetics
Due to the lack of consensus among evolutionary biologists
about basic principles for phylogenetic analysis, it is not
surprising that there is a wide array of computer software
available for this purpose.
– PHYLIP is a free package that includes 30 programs that compute
various phylogenetic algorithms on different kinds of data.
– The GCG package (available at most research institutions) contains
a full set of programs for phylogenetic analysis including simple
distance-based clustering and the complex cladistic analysis
program PAUP (Phylogenetic Analysis Using Parsimony)
– CLUSTALX is a multiple alignment program that includes the
ability to create trees based on Neighbor Joining.
– DNAStar
– MacClade is a well designed cladistics program that allows the user
to explore possible trees for a data set.
41. Phylogenetics on the Web
• There are several phylogenetics servers available
on the Web
– some of these will change or disappear in the near future
– these programs can be very slow so keep your sample sets small
• The Institut Pasteur, Paris has a PHYLIP server at:
http://bioweb.pasteur.fr/seqanal/phylogeny/phylip-uk.html
• Louxin Zhang at the Natl. University of Singapore has a WebPhylip server:
http://sdmc.krdl.org.sg:8080/~lxzhang/phylip/
• The Belozersky Institute at Moscow State University has their own
"GeneBee" phylogenetics server:
http://www.genebee.msu.su/services/phtree_reduced.html
• The Phylodendron website is a tree drawing program with a nice user
interface and a lot of options, however, the output is limited to gifs at
72 dpi - not publication quality.
http://iubio.bio.indiana.edu/treeapp/treeprint-form.html
42. Other Web Resources
• Joseph Felsenstein (author of PHYLIP) maintains a
comprehensive list of Phylogeny programs at:
http://evolution.genetics.washington.edu/phylip
/software.html
• Introduction to Phylogenetic Systematics,
Peter H. Weston & Michael D. Crisp, Society of Australian Systematic
Biologists
http://www.science.uts.edu.au/sasb/WestonCrisp.html
• University of California, Berkeley Museum of
Paleontology (UCMP)
http://www.ucmp.berkeley.edu/clad/clad4.html
43. Software Hazards
• There are a variety of programs for Macs and PCs,
but you can easily tie up your machine for many
hours with even moderately sized data sets (i.e.
fifty 300 bp sequences)
• Moving sequences into different programs can be
a major hassle due to incompatible file formats.
• Just because a program can perform a given
computation on a set of data does not mean that
that is the appropriate algorithm for that type of
data.
44. Conclusions
Given the huge variety of methods for computing
phylogenies, how can the biologist determine what
is the best method for analyzing a given data set?
– Published papers that address phylogenetic issues generally
make use of several different algorithms and data sets in order
to support their conclusions.
– In some cases different methods of analysis can work
synergistically
• Neighbor Joining methods generally produce just one tree, which can
help to validate a tree built with the parsimony or maximum likelihood
method
– Using several alternate methods can give an indication of the
robustness of a given conclusion.