1) The document discusses phylogenetics and phylogenetic trees, which are used to visualize evolutionary relationships and reconstruct patterns of evolution among organisms.
2) Ribosomal RNA genes are often used for phylogenetic analysis because they are highly conserved yet show enough variation to distinguish relationships over billions of years of divergence.
3) Examples are given of how phylogenetics is used in fields like epidemiology, conservation biology, and pharmaceutical research.
This document provides an overview of phylogenetics and the key concepts involved. It defines phylogenetics as reconstructing evolutionary relationships through phylogenetic trees. Species concepts and examples of using phylogenetics are discussed. The importance of the tree-of-life in illustrating how all life on Earth is related is explained. Important dates in the evolutionary history of life and commonly used gene sequences like rRNA for constructing phylogenetic trees are also summarized.
This document discusses epigenomics and bioinformatics approaches for profiling the epigenome. It notes that the lab has over 100 people working in areas like hardware engineering, mathematics, and molecular biology. It then summarizes different techniques for profiling the epigenome using next generation sequencing, including MBD-Seq to identify methylated regions and directional RNA-seq to study non-coding RNA and alternative splicing. Integrating these diverse datasets requires normalization methods and reference databases that are still being developed.
The document provides information about a bioinformatics lesson and exam. It states that there will be no lesson on December 4th. It then discusses the structure of the bioinformatics exam, which will randomly select images from a set of 713 images to display on a web page using JavaScript. The remainder of the document discusses various topics in comparative genomics, drug discovery, and other areas of bioinformatics.
The document discusses various topics in bioinformatics and protein structure. It provides an overview of ongoing thesis topics at Biobix including biomarker prediction, methylation, metabolomics, peptidomics, and more. It also discusses the rationale for understanding protein structure and function, levels of protein structure from primary to quaternary, methods for determining structure like X-ray crystallography, and approaches to secondary structure prediction including Chou-Fasman.
This document discusses programming concepts in Perl including variables, flow control, loops, input/output, and subroutines. It provides examples of using different data types like scalars, arrays, and hashes. It also covers reading and writing files in Perl using filehandles, and handling errors when opening files. The document emphasizes best practices for programming in Perl such as developing code in stages, showing activity when running interactively, using comments, and choosing a consistent coding convention.
The document provides an overview of bioinformatics and computational biology. It summarizes that the lab has 10 "genome hackers" who are mostly engineers working in statistics, along with 42 scientists, technicians, geneticists, and clinicians, totaling over 100 people including hardware engineers, mathematicians, and molecular biologists. It then asks what bioinformatics is and lists some of its applications such as sequence analysis, molecular modeling, phylogeny/evolution, ecology/population studies, medical informatics, and image analysis.
This document discusses various data types and data structures in Perl, including scalars, arrays, hashes, references, and object-oriented programming. It provides examples of creating sequences and sequence objects using BioPerl, parsing multi-line sequence data, and accessing GenBank sequence data through the Bio::DB::GenBank module.
This document provides an overview of hidden Markov models (HMMs) and their application to gene prediction. It discusses how HMMs can model insertions and deletions in sequence alignments through their graphical representation using states and transitions. The document also explains how HMMs assign probabilities to sequences based on allowed state emissions and transitions. HMMs allow for more flexible modeling of gapped alignments than profiles or patterns alone.
This document provides an overview of phylogenetics and the key concepts involved. It defines phylogenetics as reconstructing evolutionary relationships through phylogenetic trees. Species concepts and examples of using phylogenetics are discussed. The importance of the tree-of-life in illustrating how all life on Earth is related is explained. Important dates in the evolutionary history of life and commonly used gene sequences like rRNA for constructing phylogenetic trees are also summarized.
This document discusses epigenomics and bioinformatics approaches for profiling the epigenome. It notes that the lab has over 100 people working in areas like hardware engineering, mathematics, and molecular biology. It then summarizes different techniques for profiling the epigenome using next generation sequencing, including MBD-Seq to identify methylated regions and directional RNA-seq to study non-coding RNA and alternative splicing. Integrating these diverse datasets requires normalization methods and reference databases that are still being developed.
The document provides information about a bioinformatics lesson and exam. It states that there will be no lesson on December 4th. It then discusses the structure of the bioinformatics exam, which will randomly select images from a set of 713 images to display on a web page using JavaScript. The remainder of the document discusses various topics in comparative genomics, drug discovery, and other areas of bioinformatics.
The document discusses various topics in bioinformatics and protein structure. It provides an overview of ongoing thesis topics at Biobix including biomarker prediction, methylation, metabolomics, peptidomics, and more. It also discusses the rationale for understanding protein structure and function, levels of protein structure from primary to quaternary, methods for determining structure like X-ray crystallography, and approaches to secondary structure prediction including Chou-Fasman.
This document discusses programming concepts in Perl including variables, flow control, loops, input/output, and subroutines. It provides examples of using different data types like scalars, arrays, and hashes. It also covers reading and writing files in Perl using filehandles, and handling errors when opening files. The document emphasizes best practices for programming in Perl such as developing code in stages, showing activity when running interactively, using comments, and choosing a consistent coding convention.
The document provides an overview of bioinformatics and computational biology. It summarizes that the lab has 10 "genome hackers" who are mostly engineers working in statistics, along with 42 scientists, technicians, geneticists, and clinicians, totaling over 100 people including hardware engineers, mathematicians, and molecular biologists. It then asks what bioinformatics is and lists some of its applications such as sequence analysis, molecular modeling, phylogeny/evolution, ecology/population studies, medical informatics, and image analysis.
This document discusses various data types and data structures in Perl, including scalars, arrays, hashes, references, and object-oriented programming. It provides examples of creating sequences and sequence objects using BioPerl, parsing multi-line sequence data, and accessing GenBank sequence data through the Bio::DB::GenBank module.
This document provides an overview of hidden Markov models (HMMs) and their application to gene prediction. It discusses how HMMs can model insertions and deletions in sequence alignments through their graphical representation using states and transitions. The document also explains how HMMs assign probabilities to sequences based on allowed state emissions and transitions. HMMs allow for more flexible modeling of gapped alignments than profiles or patterns alone.
This document provides an overview of sequence alignment and scoring matrices. It defines key terms like identity, homology, orthologous, and paralogous genes. It discusses different types of scoring matrices including unitary matrices that score matches as 1 and mismatches as 0, and transition/transversion matrices that account for the higher likelihood of transitional mutations in nucleic acids. The document emphasizes that scoring matrices represent underlying evolutionary models and influence sequence analysis outcomes.
This document provides an overview of Python for bioinformatics. It discusses what Python is, why it is useful for bioinformatics, and how to get started with Python. It covers installing Python on the Athena system, using IDEs like Eclipse and PyDev, code sharing with Git and GitHub, basic Python concepts like strings, control structures, and data types like lists and dictionaries. It also provides examples of bioinformatics tasks that can be done in Python like calculating Pi using random numbers.
This document provides an overview of phylogenetic methodologies. It defines key phylogenetic terms like clade, internal node, and outgroups. It discusses different species concepts and how phylogenetic trees illustrate evolutionary relationships. It also covers popular phylogenetic methodologies like distance methods, maximum parsimony, and maximum likelihood. Distance methods calculate pairwise distances and cluster sequences into trees. UPGMA averages these distances while neighbor joining finds the shortest branches. The document highlights the use of phylogenetic analysis across various fields.
The document discusses reading and writing files in Python. It provides examples of opening files for reading, writing, and appending. It demonstrates how to read an entire file, individual lines, and loop through lines. It also shows how to write strings to files and close files once writing is complete. Additional topics covered include a template for reading files line by line and examples of counting lines, words, and characters in a file.
The document discusses database searching algorithms like FASTA and BLAST. It explains the mathematical concepts behind BLAST like using Erdos-Renyi theory to model random sequence alignments and calculate the expected length of the longest random match. It also describes the Karlin-Alschul equation used in BLAST to calculate the statistical significance of matches as the expected number of alignments (E) based on the size of the search space and alignment score. The document provides details on parameters and scoring approaches used in database searching algorithms.
This document provides an overview of GitHub as a hosted Git service and introduces some basic Python concepts including control structures, lists, dictionaries, regular expressions, and BioPython. It demonstrates how to install Biopython and parse sequence data from Swiss-Prot using Biopython modules. It also includes example questions for analyzing sequence data from Swiss-Prot.
This document discusses various topics relating to protein structure and bioinformatics. It begins with an overview of protein structure and why understanding protein structure is important. It then discusses the different levels of protein structure from primary to quaternary structure. Methods for determining protein structure like X-ray crystallography and NMR are mentioned. Databases for storing protein structures like the Protein Data Bank are also summarized. The document touches on topics like protein folding, domains, membrane protein topology, and secondary structure prediction methods.
The document discusses various topics related to drug discovery through bioinformatics and computational approaches. It begins by discussing comparative genomics and using knowledge about model organisms to identify similar biological areas and pathways in other species. It also discusses topics like high-throughput screening of large libraries, the definitions of targets, hits and leads in drug discovery, and approaches like using RNAi and phenotypic screening in model organisms. Finally, it discusses computational methods that can be used throughout the drug discovery process, including for target identification and validation, virtual screening, assessing drug-likeness of compounds, and describing compounds using structural and physicochemical descriptors.
Here are the steps to translate the DNA sequence to its reverse complement using a dictionary and string translation:
1. Define a dictionary that maps DNA nucleotides to their complement (A->T, C->G, etc.)
2. Use the maketrans() string method to generate a translation table
3. Use the translate() string method to translate the sequence
4. Reverse the translated sequence using slicing
Putting it together:
1. complements = {"A":"T", "T":"A", "C":"G", "G":"C"}
2. table = str.maketrans("ACGT", "TGCA")
3. translated = sequence.translate(table)
The document discusses various topics in bioinformatics including:
1) Control structures, lists, dictionaries, and regular expressions in Python.
2) Parsing Swiss-Prot files and extracting amino acid frequencies using Biopython.
3) Functions for working with biological sequences like transcription, translation, and translating between different genetic codes using the Biopython module.
The document discusses the topic of phylogenetics. It begins with definitions of key terms like phylogeny, phylogenetic tree, clade, and orthologous genes. It then provides examples of how phylogenetic methods are used in fields like epidemiology, conservation biology, and pharmaceutical research. The document also discusses choosing appropriate genetic sequences to use in phylogenetic analysis and introduces molecular clock models.
This document provides an overview of phylogenetics and phylogenetic trees. It defines key terms like phylogeny, clade, species concepts, and the tree of life. It discusses using phylogenetic methods to solve crimes, study disease transmission, and aid conservation. Methods covered include distance methods, maximum likelihood, maximum parsimony, and software like PHYLIP. Ribosomal RNA genes are highlighted as useful sequences for constructing deep phylogenetic trees across all life.
This document discusses phylogenetic trees and phylogenetics. It begins with definitions of key terms like clade, species concepts, and branching order in phylogenetic trees. It provides examples of how phylogenetics is used in various fields like forensics, epidemiology, conservation biology, and pharmaceutical research. It also discusses choosing appropriate genetic sequences to use in phylogenetic analysis and highlights the pioneering work of Carl Woese in using rRNA sequences.
Phylogenetic trees depict the evolutionary relationships among various biological species or entities based on similarities and differences in their physical or genetic characteristics. They imply that taxa joined together in the tree have descended from a common ancestor. Phylogenetic trees are useful in fields like bioinformatics, systematics, and phylogenetic comparative methods. However, phylogenetic trees have limitations as they do not necessarily accurately represent evolutionary history and can be confounded by factors like genetic recombination and horizontal gene transfer.
- Evolution is the process of change over generations in inherited traits of organisms due to mutations, genetic recombination, and natural selection.
- Molecular phylogeny uses DNA and protein sequences to reconstruct evolutionary relationships and depict them in phylogenetic trees.
- Phylogenetic trees show how taxa are related through time with internal nodes representing common ancestors and branches representing elapsed time or genetic changes between nodes. Well-constructed phylogenetic trees are important for understanding the evolution and classification of living things.
Dr. Robert Rizza was interviewed about diabetes. He defined diabetes as a disease where blood sugar levels are higher than normal. There are two main types of diabetes: type 1 where the body destroys insulin-producing cells, and type 2 where the pancreas does not produce enough insulin or the body does not respond properly to insulin. Screening for diabetes is recommended starting at age 45 through fasting blood sugar tests or glucose tolerance tests. Treatments include lifestyle changes like diet and exercise as well as medications to increase insulin production, reduce glucose production, or improve insulin function. New treatments still in development include some being studied at Mayo Clinic.
There are many different concepts of what constitutes a species. These concepts include biological, ecological, evolutionary, and phylogenetic species concepts. There is no universal agreement on how to define species. Determining whether species are real, and delineating species boundaries accurately is challenging given the various concepts and lack of consensus on an approach. Higher taxa concepts are also debated in terms of their philosophical reality.
There are many different concepts of what constitutes a species. These concepts include biological, ecological, evolutionary, phylogenetic, and morphological species concepts. There is no universal agreement on how to define species. Applying different concepts can lead to inconsistent estimates of biodiversity. While species are generally considered the basic units of conservation, higher taxa may not be comparable depending on the species concept used.
Phylogenetics is the study of evolutionary relationships between organisms and how they are related through common ancestry. Phylogenetic trees graphically represent hypotheses of ancestor-descendent relationships. They can be constructed using morphological, physiological, or molecular data to identify shared derived character states that cluster organisms into monophyletic groups. Parsimony seeks to minimize evolutionary changes and identify the most parsimonious tree. Phylogenies are useful for studying questions about evolution, biogeography, and coevolution.
A phylogenetic tree is a diagram that represents evolutionary relationships among organisms based on the similarities and differences in their genetic and evolutionary characteristics
The pattern of branching in a phylogenetic tree reflects how species or other groups evolved from a series of common ancestors.
The phylogenetic tree is also called the “Tree of Life” or “Dendrogram”
Here are some key points to focus on for the psychology midterm:
- Memory: Define different types of memory (sensory, short-term, long-term, episodic, semantic, procedural). Understand memory models (Atkinson-Shiffrin, working memory). Know factors that influence memory accuracy and storage.
- Learning: Define classical and operant conditioning. Understand principles of reinforcement, punishment, extinction. Know examples of different conditioning paradigms.
- Cognition: Understand how attention, perception, problem-solving work. Know biases and heuristics. Define language and thinking.
- Development: Know major theories of development (psychoanalytic, cognitive, behavioral). Understand development
Phylogenetic analyses involve constructing evolutionary trees to model the relationships between taxa. There are two main components: 1) tree building to infer branching orders and evolutionary relationships between taxa, and 2) character and rate analysis using trees as frameworks to understand trait evolution. Trees are constructed using clustering algorithms and optimality criteria applied to distance or character data. Parsimony methods infer the most parsimonious tree requiring the fewest evolutionary events, and can be used to analyze molecular and non-molecular data as well as ancestral sequence inference.
This document provides an overview of sequence alignment and scoring matrices. It defines key terms like identity, homology, orthologous, and paralogous genes. It discusses different types of scoring matrices including unitary matrices that score matches as 1 and mismatches as 0, and transition/transversion matrices that account for the higher likelihood of transitional mutations in nucleic acids. The document emphasizes that scoring matrices represent underlying evolutionary models and influence sequence analysis outcomes.
This document provides an overview of Python for bioinformatics. It discusses what Python is, why it is useful for bioinformatics, and how to get started with Python. It covers installing Python on the Athena system, using IDEs like Eclipse and PyDev, code sharing with Git and GitHub, basic Python concepts like strings, control structures, and data types like lists and dictionaries. It also provides examples of bioinformatics tasks that can be done in Python like calculating Pi using random numbers.
This document provides an overview of phylogenetic methodologies. It defines key phylogenetic terms like clade, internal node, and outgroups. It discusses different species concepts and how phylogenetic trees illustrate evolutionary relationships. It also covers popular phylogenetic methodologies like distance methods, maximum parsimony, and maximum likelihood. Distance methods calculate pairwise distances and cluster sequences into trees. UPGMA averages these distances while neighbor joining finds the shortest branches. The document highlights the use of phylogenetic analysis across various fields.
The document discusses reading and writing files in Python. It provides examples of opening files for reading, writing, and appending. It demonstrates how to read an entire file, individual lines, and loop through lines. It also shows how to write strings to files and close files once writing is complete. Additional topics covered include a template for reading files line by line and examples of counting lines, words, and characters in a file.
The document discusses database searching algorithms like FASTA and BLAST. It explains the mathematical concepts behind BLAST like using Erdos-Renyi theory to model random sequence alignments and calculate the expected length of the longest random match. It also describes the Karlin-Alschul equation used in BLAST to calculate the statistical significance of matches as the expected number of alignments (E) based on the size of the search space and alignment score. The document provides details on parameters and scoring approaches used in database searching algorithms.
This document provides an overview of GitHub as a hosted Git service and introduces some basic Python concepts including control structures, lists, dictionaries, regular expressions, and BioPython. It demonstrates how to install Biopython and parse sequence data from Swiss-Prot using Biopython modules. It also includes example questions for analyzing sequence data from Swiss-Prot.
This document discusses various topics relating to protein structure and bioinformatics. It begins with an overview of protein structure and why understanding protein structure is important. It then discusses the different levels of protein structure from primary to quaternary structure. Methods for determining protein structure like X-ray crystallography and NMR are mentioned. Databases for storing protein structures like the Protein Data Bank are also summarized. The document touches on topics like protein folding, domains, membrane protein topology, and secondary structure prediction methods.
The document discusses various topics related to drug discovery through bioinformatics and computational approaches. It begins by discussing comparative genomics and using knowledge about model organisms to identify similar biological areas and pathways in other species. It also discusses topics like high-throughput screening of large libraries, the definitions of targets, hits and leads in drug discovery, and approaches like using RNAi and phenotypic screening in model organisms. Finally, it discusses computational methods that can be used throughout the drug discovery process, including for target identification and validation, virtual screening, assessing drug-likeness of compounds, and describing compounds using structural and physicochemical descriptors.
Here are the steps to translate the DNA sequence to its reverse complement using a dictionary and string translation:
1. Define a dictionary that maps DNA nucleotides to their complement (A->T, C->G, etc.)
2. Use the maketrans() string method to generate a translation table
3. Use the translate() string method to translate the sequence
4. Reverse the translated sequence using slicing
Putting it together:
1. complements = {"A":"T", "T":"A", "C":"G", "G":"C"}
2. table = str.maketrans("ACGT", "TGCA")
3. translated = sequence.translate(table)
The document discusses various topics in bioinformatics including:
1) Control structures, lists, dictionaries, and regular expressions in Python.
2) Parsing Swiss-Prot files and extracting amino acid frequencies using Biopython.
3) Functions for working with biological sequences like transcription, translation, and translating between different genetic codes using the Biopython module.
The document discusses the topic of phylogenetics. It begins with definitions of key terms like phylogeny, phylogenetic tree, clade, and orthologous genes. It then provides examples of how phylogenetic methods are used in fields like epidemiology, conservation biology, and pharmaceutical research. The document also discusses choosing appropriate genetic sequences to use in phylogenetic analysis and introduces molecular clock models.
This document provides an overview of phylogenetics and phylogenetic trees. It defines key terms like phylogeny, clade, species concepts, and the tree of life. It discusses using phylogenetic methods to solve crimes, study disease transmission, and aid conservation. Methods covered include distance methods, maximum likelihood, maximum parsimony, and software like PHYLIP. Ribosomal RNA genes are highlighted as useful sequences for constructing deep phylogenetic trees across all life.
This document discusses phylogenetic trees and phylogenetics. It begins with definitions of key terms like clade, species concepts, and branching order in phylogenetic trees. It provides examples of how phylogenetics is used in various fields like forensics, epidemiology, conservation biology, and pharmaceutical research. It also discusses choosing appropriate genetic sequences to use in phylogenetic analysis and highlights the pioneering work of Carl Woese in using rRNA sequences.
Phylogenetic trees depict the evolutionary relationships among various biological species or entities based on similarities and differences in their physical or genetic characteristics. They imply that taxa joined together in the tree have descended from a common ancestor. Phylogenetic trees are useful in fields like bioinformatics, systematics, and phylogenetic comparative methods. However, phylogenetic trees have limitations as they do not necessarily accurately represent evolutionary history and can be confounded by factors like genetic recombination and horizontal gene transfer.
- Evolution is the process of change over generations in inherited traits of organisms due to mutations, genetic recombination, and natural selection.
- Molecular phylogeny uses DNA and protein sequences to reconstruct evolutionary relationships and depict them in phylogenetic trees.
- Phylogenetic trees show how taxa are related through time with internal nodes representing common ancestors and branches representing elapsed time or genetic changes between nodes. Well-constructed phylogenetic trees are important for understanding the evolution and classification of living things.
Dr. Robert Rizza was interviewed about diabetes. He defined diabetes as a disease where blood sugar levels are higher than normal. There are two main types of diabetes: type 1 where the body destroys insulin-producing cells, and type 2 where the pancreas does not produce enough insulin or the body does not respond properly to insulin. Screening for diabetes is recommended starting at age 45 through fasting blood sugar tests or glucose tolerance tests. Treatments include lifestyle changes like diet and exercise as well as medications to increase insulin production, reduce glucose production, or improve insulin function. New treatments still in development include some being studied at Mayo Clinic.
There are many different concepts of what constitutes a species. These concepts include biological, ecological, evolutionary, and phylogenetic species concepts. There is no universal agreement on how to define species. Determining whether species are real, and delineating species boundaries accurately is challenging given the various concepts and lack of consensus on an approach. Higher taxa concepts are also debated in terms of their philosophical reality.
There are many different concepts of what constitutes a species. These concepts include biological, ecological, evolutionary, phylogenetic, and morphological species concepts. There is no universal agreement on how to define species. Applying different concepts can lead to inconsistent estimates of biodiversity. While species are generally considered the basic units of conservation, higher taxa may not be comparable depending on the species concept used.
Phylogenetics is the study of evolutionary relationships between organisms and how they are related through common ancestry. Phylogenetic trees graphically represent hypotheses of ancestor-descendent relationships. They can be constructed using morphological, physiological, or molecular data to identify shared derived character states that cluster organisms into monophyletic groups. Parsimony seeks to minimize evolutionary changes and identify the most parsimonious tree. Phylogenies are useful for studying questions about evolution, biogeography, and coevolution.
A phylogenetic tree is a diagram that represents evolutionary relationships among organisms based on the similarities and differences in their genetic and evolutionary characteristics
The pattern of branching in a phylogenetic tree reflects how species or other groups evolved from a series of common ancestors.
The phylogenetic tree is also called the “Tree of Life” or “Dendrogram”
Here are some key points to focus on for the psychology midterm:
- Memory: Define different types of memory (sensory, short-term, long-term, episodic, semantic, procedural). Understand memory models (Atkinson-Shiffrin, working memory). Know factors that influence memory accuracy and storage.
- Learning: Define classical and operant conditioning. Understand principles of reinforcement, punishment, extinction. Know examples of different conditioning paradigms.
- Cognition: Understand how attention, perception, problem-solving work. Know biases and heuristics. Define language and thinking.
- Development: Know major theories of development (psychoanalytic, cognitive, behavioral). Understand development
Phylogenetic analyses involve constructing evolutionary trees to model the relationships between taxa. There are two main components: 1) tree building to infer branching orders and evolutionary relationships between taxa, and 2) character and rate analysis using trees as frameworks to understand trait evolution. Trees are constructed using clustering algorithms and optimality criteria applied to distance or character data. Parsimony methods infer the most parsimonious tree requiring the fewest evolutionary events, and can be used to analyze molecular and non-molecular data as well as ancestral sequence inference.
Biodiversity electurespecies and DNA Barcoding handout.pptxRIZWANALI245
This document discusses species concepts and DNA barcoding. It begins by outlining several commonly used species concepts, including the biological, evolutionary, phylogenetic, and ecological species concepts. It then discusses the challenges of developing a single species concept that can apply to all organisms given evolutionary factors. The document goes on to introduce DNA barcoding as a method for identifying organisms, including how it works by targeting the CO1 gene in animals. It discusses examples of how DNA barcoding has helped identify new species and resolve taxonomic uncertainties. However, it also notes there are cases where morphologically distinct species cannot be distinguished by barcodes alone. The document advocates for an integrative taxonomic approach using multiple lines of evidence.
Evolutionary biologists use phylogenetic trees and cladistics to study evolutionary relationships between organisms and construct classifications. Cladistics involves analyzing shared characteristics to hypothesize how groups of organisms evolved from common ancestors over time. A key assumption is that all organisms are related through descent from a shared ancestor. Cladograms graphically represent evolutionary relationships, with shared derived characteristics defining monophyletic clades. Parsimony is used to select the simplest phylogenetic tree that is best supported by evidence.
The document discusses phylogeny and modern taxonomy. It explains that phylogenetic trees show how species are evolutionarily related, similar to how family trees show relationships between people. Phylogeny is the study of evolutionary relatedness among species. A clade represents all organisms that share a single common ancestor on a phylogenetic tree. Modern taxonomy overcomes issues with traditional taxonomy by comparing DNA samples to accurately determine identity and evolutionary relationships between specimens.
The document discusses the evolution of biological classification systems from Linnaeus' original system to modern phylogenetic classification based on evolutionary relationships. It introduces key concepts like binomial nomenclature, phylogeny, clades, homology, molecular clocks, and the three domain system of classifying life into Bacteria, Archaea, and Eukarya. Examples are provided to illustrate phylogenies and how to determine evolutionary relationships between organisms from phylogenetic trees.
Scientists construct phylogenetic trees by analyzing both DNA sequences and protein sequences to identify molecular homologies that indicate evolutionary relationships. They use two main types of methods - distance-based methods that calculate evolutionary distances between sequences to build trees, and character-based methods that directly examine sequence characters to evaluate relationships. Specific genes, as well as whole genomes, can provide data for building phylogenetic trees through these computational analysis methods.
AnMicro-TBRC Seminar on Phylogenetic Analysis (EP.1).
"Evolutionary Tree: the weapon of molecular phylogeneticists"
by Asst. Prof. Pravech Ajawatanawong
CATCH Y TITLE WH AT AM I D O ING IN TH IS PAPE.docxcravennichole326
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S TUD E NT NAME
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S O C 2 0 0 0 -XX
D ATE O F S UBMIS S IO N
1
I. In t r od u ct ion
a . S ociologica l Im a gin a t ion
i. Cor r ela t ion b et ween p er s on a l exp er ien ces of d r u g u s er s a n d
h ea lt h effect ou t com es p a r t ia lly s h a p ed b y s ociet y
ii. Im p or t a n t t o u n d er s t a n d u n d er lyin g r ela t ion s h ip s b et ween
a ll k ey a ct or s
b . Ps ych ot r op ic d r u gs ill effect s a r e a s ocia l p r ob lem
i. S ociet y a t la r ge a ffect ed in clu d in g m a n y d iffer en t a ge gr ou p s ,
b a ck gr ou n d s a n d h is t or ies of p eop le
ii. Q u a lit y of life d efla t ion a n d u n s a t is fa ct or y h ea lt h effect
ou t com es ou t weigh b en efit s fr om t r ea t m en t s t h a t r eq u ir e
p s ych ot r op ic d r u g u s e
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c . Ps ych ot r op ic d r u g u s e effect s on h ea lt h
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1 . G en er a l h ea lt h p r ob lem s (F leis ch h a ck er et a l., 2 0 1 1 ).
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2 0 1 1 ).
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II. Lit er a t u r e Review
a . Poor h ea lt h b eh a vior (F leis ch h a ck er et a l., 2 0 1 1 )
i. WH O Q O L-BRE F r ed u ced q u a lit y of life in p s ych ot r op ic d r u g
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iii. Con t in u ou s con s u m er s h a ve wor s e s ocia l fu n ct ion in g a b ilit y
a n d a r e d ep en d en t on d r u gs
c . S id e E ffect s (Ch eu n g et a l., 2 0 0 3 )
i. H ea lt h y a d oles cen t s con cer n ed wit h m ed ica l r ea s on s of s id e
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fu n ct ion a l im p a ir m en t s of s id e effect s
d . College u s e (G r ek in et a l., 2 0 1 1 )
i. Ps ych ot r op ic d r u g m ixin g wit h a lcoh ol on college ca m p u s es
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This document provides an overview of bioinformatics and biological databases. It discusses how bioinformatics draws from fields like biology, computer science, statistics, and machine learning. Biological databases are important resources for bioinformatics that can be searched and analyzed to answer questions, find similar sequences, locate patterns, and make predictions. The document also outlines common uses of biological databases, such as annotation searches, homology searches, pattern searches, and predictive analyses.
The document discusses the Rh blood group system and its clinical significance. It describes the key observations in 1939 that linked adverse reactions in mothers to stillborn fetuses and blood transfusions from fathers, indicating a relationship. This syndrome is now called hemolytic disease of the fetus and newborn. The Rh system was identified in 1940 through experiments immunizing animals with Rhesus macaque monkey red blood cells. The D antigen is the most important RBC antigen in transfusion practice, as those lacking it do not produce anti-D antibody unless exposed to D antigen through transfusion or pregnancy. Testing for D is routinely performed to ensure D-negative patients receive D-negative blood.
The document discusses views and materialized views in data warehousing and decision support systems. It covers three main points:
1) OLAP queries typically involve aggregate queries, so precomputation is essential for fast response times. Materialized views allow precomputing aggregates across multiple dimensions.
2) Warehouses can be thought of as collections of asynchronously replicated tables and periodically maintained views, renewing interest in efficient view maintenance.
3) Materialized views store the results of views in the database for fast access like a cache, but they require maintenance as underlying tables change. Incremental maintenance algorithms are ideal to efficiently update materialized views.
The document discusses various database concepts including normalization, which is used to design optimal relation schemas by removing redundant data. It also covers transaction processing, which involves executing logical database operations as transactions to maintain data integrity. Database systems use techniques like logging and concurrency control to prevent transaction anomalies and ensure failures can be recovered from.
This document contains a list of names, emails, and study programs of students. It includes their official student code, last name, first name, email, and educational program. There are 20 students listed with their details.
This document discusses the Biological Databases project being conducted by a group of students. The project involves using the video game Minecraft to visualize protein structures retrieved from the Protein Data Bank (PDB). Python scripts are used to import PDB data files and place blocks in Minecraft to represent atoms, with different block colors used to distinguish atom types. SPARQL queries are also employed to search the RDF version of the PDB for protein entries. The goal is to build 3D protein models inside Minecraft for educational and visualization purposes.
The document discusses various bioinformatics tools and algorithms for analyzing protein sequences, including Biopython for working with biological sequence data, the Kyte-Doolittle algorithm for predicting transmembrane regions, and the Chou-Fasman algorithm for predicting secondary structure from amino acid preferences for alpha helices, beta sheets, and random coils. It also provides examples of analyzing Swiss-Prot data to find properties of human proteins and applying these tools and libraries to extract insights from protein sequences.
The document discusses various topics related to analyzing protein sequences using Python and Biopython. It provides examples of using Biopython to parse sequence data from UniProt, calculate lengths and translations of sequences. It also discusses analyzing properties of sequences like molecular weight, isoelectric point, transmembrane regions, and comparing sequences to find conserved motifs. Finally, it introduces hydropathy indices and tools for predicting properties like transmembrane helices from primary sequences.
This document discusses Python functions. It explains that there are built-in functions provided as part of Python and user-defined functions. User-defined functions are created using the def keyword and can take parameters and return values. The body of a function is indented and runs when the function is called. Functions allow code to be reused and organized in a modular way. Examples are provided to demonstrate defining and calling functions with different parameters and return values.
The document provides a recap of Python programming concepts like conditions and statements, while loops, for loops, break and continue statements, and working with strings. It also introduces regular expressions as a way to match patterns in strings using a formal language that can be interpreted by a regular expression processor.
[SUMMARY
This document discusses next generation DNA sequencing technologies. It begins by describing some of the limitations of traditional Sanger sequencing, such as read lengths of 500-1000 bases and throughput of 57,000 bases per run. It then introduces some key next generation sequencing technologies, such as 454 sequencing which uses emulsion PCR and pyrosequencing to achieve read lengths of 20-100 bases but higher throughput of 20-100 Mb per run. Illumina/Solexa sequencing is also discussed, which uses sequencing by synthesis with reversible terminators and laser-based detection. Finally, third generation sequencing technologies are mentioned, such as Pacific Biosciences' single molecule real time sequencing and nanopore sequencing. In summary, the document provides a high-level
The document provides an overview of the history and evolution of various programming languages. It discusses early languages like FORTRAN, LISP, PASCAL, C, and Java. It also covers scripting languages and their uses. The document explains what Python is as a programming language - that it is interpreted, object-oriented, and high-level. It was named after Monty Python and was created by Guido van Rossum. The document then gives examples of using Python to program Minecraft by importing protein data from PDB files and using coordinates to place blocks to visualize proteins in the game.
This document provides an introduction to bio-ontologies and the semantic web. It discusses what ontologies are and how they are used in the bio domain through initiatives like the OBO Foundry. It introduces key semantic web technologies like RDF, URIs, Turtle syntax, and SPARQL query language. It provides examples of ontologies like the Gene Ontology and how ontologies can be represented and queried using these semantic web standards.
This document provides an overview of NoSQL databases, including:
- Key-value stores store data as maps or hashmaps and are efficient for data access but limited in query capabilities.
- Column-oriented stores group attributes into column families and store data efficiently but are operationally challenging.
- Document databases store loosely structured data like JSON and allow retrieving documents by keys or contents.
- Graph databases are suited for interaction networks and path finding but are less suited for tabular data.
The document discusses creating a multicore database project. It recommends taking the following steps:
1. Define what the project is about, what it aims to achieve, and who it is for.
2. Identify information resources and develop a basic data model.
3. Design a user interface mockup without technical constraints, thinking creatively.
This document discusses biological databases and PHP. It begins with an overview of biological databases and examples using BIOSQL to load genetic data from GenBank into a MySQL database. It then provides examples of building a basic 3-tier model with Apache, PHP, and a MySQL backend database. The document also includes a brief introduction to PHP, covering its history, why it is commonly used, and basic syntax like conditional statements.
This document discusses biological databases and SQL. It provides an overview of primary and derived data in biological research, as well as different data levels. It then discusses direct querying of selected bioinformatics databases using SQL and provides examples of 3-tier database models. The document proceeds to discuss rationale for learning SQL to query biological databases and provides definitions and explanations of key SQL concepts like tables, records, queries, data types, keys, integrity rules and constraints.
This document discusses biological databases and bioinformatics. It begins with an overview of bioinformatics as an interdisciplinary field combining biology, computer science, and information technology. It then discusses different types of biological databases, including those focused on sequences, pathways, protein structures, and gene expression. The document outlines some common uses of biological databases, including searching for annotations, identifying similar sequences through homology, searching for patterns, and making predictions. It also briefly discusses comparing data across databases. The summary provides a high-level overview of the key topics and uses of biological databases covered in the document.
The document discusses several topics related to protein structure prediction using Python:
1. It introduces the Chou-Fasman algorithm for predicting protein secondary structure from amino acid sequence. The algorithm calculates preference parameters for each amino acid to be in alpha helices, beta sheets, or other structures.
2. It provides an example of calculating helical propensity.
3. It lists the preference parameters output by the Chou-Fasman algorithm for each amino acid.
4. It outlines the steps of applying the Chou-Fasman algorithm to predict secondary structure elements in a protein sequence.
The document provides information on various Python programming concepts including control structures, lists, dictionaries, regular expressions, exceptions, and biological applications using Biopython. It discusses if/else statements, while and for loops, list operations, dictionary usage, regex patterns, exception handling roles, and gives examples analyzing protein sequences and structures using Biopython.
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LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
How to Manage Your Lost Opportunities in Odoo 17 CRMCeline George
Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
5. Phylogenetics
Introduction
Definitions
Species concept
Examples
The Tree-of-life
Phylogenetics Methodologies
Algorithms
Distance Methods
Maximum Likelihood
Maximum Parsimony
Rooting
Statistical Validation
Conclusions
Orthologous genes
Horizontal Gene Transfer
Phylogenomics
Practical Approach: PHYLIP
Weblems
6. What is phylogenetics ?
Phylogeny (phylo =tribe + genesis)
Phylogenetic trees are about visualising evolutionary
relationships. They reconstruct the pattern of events
that have led to the distribution and diversity of life.
The purpose of a phylogenetic tree is to illustrate how a
group of objects (usually genes or organisms) are
related to one another
Nothing in Biology Makes Sense Except in the Light of
Evolution. Theodosius Dobzhansky (1900-1975)
7. Trees
• Diagram consisting of branches and nodes
• Species tree (how are my species related?)
– contains only one representative from each
species.
– all nodes indicate speciation events
• Gene tree (how are my genes related?)
– normally contains a number of genes from a
single species
– nodes relate either to speciation or gene
duplication events
8. Clade: A set of species which includes all of the species
derived from a single common ancestor
9. S p e c ie s C o n c e p ts from V a rio u s A u th o rs
D .A . B a um a nd K .L . S ha w - E x c lu s iv e g rou p s o f org a n ism s, w h ere a n ex c lu s iv e g rou p is o ne w h ose m e m b ers are a ll m ore c lose ly re la ted to
ea c h oth er th a n to a n y org a n is m s ou ts id e the g rou p .
J . C ra cra ft - A n irred u c ib le c lu ster o f org a n ism s, d iag n osab ly d is tin ct fro m oth er su c h c lu sters, a nd w ith in w h ic h there is a p are n ta l p a ttern o f
a nc estry a nd d esce n t.
C ha rles D a rw in - "F rom these rem arks it w ill b e se e n th at I lo o k a t th e term sp e c ies, as o n e arb itrarily g iv e n for the sa k e o f c o n v e n ie nc e to a set
o f in d iv id u a ls c lose ly rese m b lin g e ac h o ther, a nd th a t it d oes n ot essen tia lly d iffer from the term varie ty, w h ic h is g iv e n to l ess d istin ct a nd
m ore flu c tu a ting form s. T he term varie ty, ag a in, in c o m p aris o n w ith m ere in d iv id u a l d iffere n ces, is a ls o a p p lied arb itrarily, a n d for m ere
c o n ve n ie n ce sa k e " (O rig in o f S p ec ies, 1 st ed., p . 1 0 8 ).
T . D o b zha nsk y - T h e larg est a nd m ost in c lu s ive rep rod u ctiv e c om m u n ity o f sex u a l a nd cross-fertiliz ing in d iv id u a ls w h ic h sh are a c o m m o n g e ne
p o o l. A nd la ter...S ys te m s o f p op u la tio ns, th e g e n e ex c ha ng e b e tw ee n w h ic h is lim ited or p re v e nted b y rep rod u ctiv e is o la ting m e c h a n is m s.
M . G hise lin - T h e m ost ex te ns ive u n its in the n atu ra l e c o n om y, su c h tha t rep r od u ctiv e c om p etitio n oc cu rs am o ng th e ir p arts.
D .M . L a m b ert - G rou p s o f ind iv id u a ls th at d e fin e th em se lv es b y a sp e c ific m a te rec og n itio n s ystem .
J . M a llet - Id e ntifia b le g e n o typ ic c lu sters re c og n iz e d b y a d e fic it o f in term ed iates, b o th a t s ing le lo c i a n d at m u ltip le lo c i.
E . M a y r - G rou p s o f ac tu a lly or p o te n tia lly in terb ree d ing na tu ra l p op u lat io ns w h ic h are rep rod u ctiv e ly is o la ted fro m oth er su c h g rou p s.
C .D . M ich en er - A g rou p o f org a n is m s n o t itse lf d iv is ib le b y p he n etic g ap s resu ltin g from c o nc ord a nt d iffere n ces in c harac ter states (ex c ep t for
m orp hs - su ch as sex , ag e, or caste), b u t sep ara ted b y su ch p h e ne tic g ap s from o ther su c h u n its.
H .E .H . P a tte rso n - T h at m ost in c lu s iv e p op u latio n o f in d iv id u a l b ip are n ta l org a n is m s w h ic h sh are a c o m m o n fertiliz atio n s ystem .
G .G . S im p so n - A lin eag e o f p op u latio ns e v o lv in g w ith tim e, sep arate ly fro m ot h ers, w ith its ow n u n iq u e e v o lu tio n ary ro le a nd te nd e n c ies.
P .H .A . S nea th a nd R .R . S o k a l - T he sm a llest (m ost h o m og e n e ou s) c lu ster th at ca n b e re c og n iz ed u p o n s o m e g iv e n criterio n as b e in g d istin c t
fro m oth er c lu sters.
A .R . T em p leto n - T h e m ost in c lu s ive p op u la tio n o f in d iv id u a ls h a v ing the p o te n ti a l for p h e n otyp ic c o he sio n throu g h in trins ic c o h es io n
m e c ha n is m s (g e ne tic a nd /or d e m og rap h ic - i.e. ec o lo g ic a l -ex c h a ng e ab ility).
E .O . W iley - A s ing le lin e ag e o f a nc estor -d esc e nd a n t p op u latio ns w h ic h m a in ta ins its id e ntity fro m oth er s u ch lin e ag es a nd w h ic h h as its ow n
e v o lu tio n ary te nd e n c ies a nd h istoric a l fate.
S . W rig ht - A sp ec ies in tim e a nd sp ac e is c o m p ose d o f nu m erou s lo ca l p op u latio ns, ea c h o ne in terc o m m u n ica ting a nd in terg rad ing w ith oth ers.
11. Plant Species
Definitions:
> Three different ways to recognize species:
1) Morphological species = the smallest group that is
consistently and persistently distinct (Clusters in
morphospace)
species are recognized initially on the basis of
appearance; the individuals of one species look
different from the individuals of another
12. Species
Definitions:
> Three different ways to recognize species:
2) Biological species = a set of interbreeding or
potentially interbreeding individuals that are
separated from other species by reproductive
barriers
species are unable to interbreed
13. Species
Definitions:
> Three different ways to recognize species:
3) Phylogenetic species = the boundary between
reticulate (among interbreeding individuals) and
divergent relationships (between lineages with no
gene exchange)
14. Phylogenetic species
divergent
boundary
reticulate
recognized by the pattern of ancestor - descendent relationships
15. Species
Definitions:
> Three different ways to recognize species:
4) Phylogenomics species = ability to transmit (and
maintain) a (stable) gene pool
Adresses the Anopheles genome topology
variations
16. Branching Order in a Phylogenetic Tree
• In the tree to the left, A and B share the most recent
common ancestry. Thus, of the species in the
tree, A and B are the most closely related.
• The next most recent common ancestry is C with
the group composed of A and B. Notice that the
relationship of C is with the group containing A
and B. In particular, C is not more closely related to
B than to A. This can be emphasized by the
following two trees, which are equivalent to each
other:
17. More definitions …
Edge, Branch
Leafs
Tips
external node Branch node, internal node
• A common simplifying assumption is that the three is bifurcating,
meaning that each brach node has exactly two descendents.
• The edges, taken together, are sometimes said to define the topology
of the tree
18. Outgroups, rooted versus unrooted
An unrooted reptilian phylogeny with an avian outgroup and
the corresponding rooted phylogeny. The Ri represent modern
reptiles; the Ai, inferred ancestors and the B a bird.
20. Examples
Phylogenetic methods may be used to
solve crimes, test purity of products, and
determine whether endangered species
have been smuggled or mislabeled:
– Vogel, G. 1998. HIV strain analysis debuts in
murder trial. Science 282(5390): 851-853.
– Lau, D. T.-W., et al. 2001. Authentication of
medicinal Dendrobium species by the internal
transcribed spacer of ribosomal DNA. Planta
Med 67:456-460.
21.
22. Examples
– Epidemiologists use phylogenetic methods to
understand the development of
pandemics, patterns of disease transmission, and
development of antimicrobial resistance or
pathogenicity:
• Basler, C.F., et al. 2001. Sequence of the 1918
pandemic influenza virus nonstructural gene (NS)
segment and characterization of recombinant viruses
bearing the 1918 NS genes. PNAS, 98(5):2746-2751.
• Ou, C.-Y., et al. 1992. Molecular epidemiology of HIV
transmission in a dental practice. Science
256(5060):1165-1171.
• Bacillus Antracis:
23.
24. Examples
• Conservation biologists may use these techniques to
determine which populations are in greatest need of
protection, and other questions of population structure:
– Trepanier, T.L., and R.W. Murphy. 2001. The Coachella Valley
fringe-toed lizard (Uma inornata): genetic diversity and
phylogenetic relationships of an endangered species. Mol
Phylogenet Evol 18(3):327-334.
– Alves, M.J., et al. 2001. Mitochondrial DNA variation in the
highly endangered cyprinid fish Anaecypris hispanica:
importance for conservation. Heredity 87(Pt 4):463-473.
• Pharmaceutical researchers may use phylogenetic
methods to determine which species are most closely
related to other medicinal species, thus perhaps sharing
their medicinal qualities:
– Komatsu, K., et al. 2001. Phylogenetic analysis based on 18S
rRNA gene and matK gene sequences of Panax vietnamensis
and five related species. Planta Med 67:461-465.
26. Some Important Dates in History
Origin of the Universe 15 billion yrs
Formation of the Solar System 4.6 "
First Self-replicating System 3.5 "
Prokaryotic-Eukaryotic Divergence 2.0 "
Plant-Animal Divergence 1.0 "
Invertebrate-Vertebrate Divergence 0.5 "
Mammalian Radiation Beginning 0.1 "
31. What Sequence to Use ?
• To infer relationships that span the
diversity of known life, it is
necessary to look at genes
conserved through the billions of
years of evolutionary divergence.
• The gene must display an
appropriate level of sequence
conservation for the divergences of
interest.
.
32. What Sequence to Use ?
• If there is too much change, then
the sequences become
randomized, and there is a limit to
the depth of the divergences that
can be accurately inferred.
• If there is too little change (if the
gene is too conserved), then there
may be little or no change between
the evolutionary branchings of
interest, and it will not be possible to
infer close (genus or species level)
relationships.
33. Ribosomal RNA Genes and Their Sequences
recognized the full potential of rRNA
sequences as a measure of phylogenetic
relatedness. He initially used an RNA
sequencing method that determined about
1/4 of the nucleotides in the 16S rRNA (the
best technology available at the time). This
amount of data greatly exceeded anything
else then available. Using newer methods,
it is now routine to determine the
Carl Woese
sequence of the entire 16S rRNA
molecule. Today, the accumulated 16S
rRNA sequences (about 10,000) constitute
the largest body of data available for
inferring relationships among organisms.
34. What Sequence to Use ?
An example of genes in this category are
those that define the ribosomal RNAs
(rRNAs). Most prokaryotes have three
rRNAs, called the 5S, 16S and 23S
rRNA.
Namea Size (nucleotides) Location
5S 120 Large subunit of ribosome
16S 1500 Small subunit of ribosome
23S 2900 Large subunit of ribosome
a The name is based on the rate that the
molecule sediments (sinks) in water.
Bigger molecules sediment faster than small
ones.
35. Ribosomal RNA Genes and Their Sequences
The extraordinary conservation of rRNA genes can
be seen in these fragments of the small subunit
rRNA gene sequences from organisms spanning
the known diversity of life:
human ...GTGCCAGCAGCCGCGGTAATTCCAGCTCCAATAGCGTATATTAAAGTTGCTGCAGTTAAAAAG...
yeast ...GTGCCAGCAGCCGCGGTAATTCCAGCTCCAATAGCGTATATTAAAGTTGTTGCAGTTAAAAAG...
Corn ...GTGCCAGCAGCCGCGGTAATTCCAGCTCCAATAGCGTATATTTAAGTTGTTGCAGTTAAAAAG...
Escherichia coli ...GTGCCAGCAGCCGCGGTAATACGGAGGGTGCAAGCGTTAATCGGAATTACTGGGCGTAAAGCG...
Anacystis nidulans ...GTGCCAGCAGCCGCGGTAATACGGGAGAGGCAAGCGTTATCCGGAATTATTGGGCGTAAAGCG...
Thermotoga maratima ...GTGCCAGCAGCCGCGGTAATACGTAGGGGGCAAGCGTTACCCGGATTTACTGGGCGTAAAGGG...
Methanococcus vannielii ...GTGCCAGCAGCCGCGGTAATACCGACGGCCCGAGTGGTAGCCACTCTTATTGGGCCTAAAGCG...
Thermococcus celer ...GTGGCAGCCGCCGCGGTAATACCGGCGGCCCGAGTGGTGGCCGCTATTATTGGGCCTAAAGCG...
Sulfolobus sulfotaricus ...GTGTCAGCCGCCGCGGTAATACCAGCTCCGCGAGTGGTCGGGGTGATTACTGGGCCTAAAGCG...
37. Molecular Clock (MC)
• Rate of evolution = rate of mutation
• rate of evolution for any macromolecule is
approximately constant over time (Neutral
Theory of evolution)
• For a given protein the rate of sequence
evolution is approximately constant across
lineages. Zuckerkandl and Pauling (1965)
• This would allow speciation and duplication
events to be dated accurately based on
molecular data
40. • (a) A traditional phylogenetic tree and
• (b) the new phylogenetic tree, each showing the
positions of selected phyla. B, bilateria;
AC, acoelomates; PC, pseudocoelomates;
C, coelomates; P, protostomes; L, lophotrochozoa;
E, ecdysozoa; D, deuterostomes.
41. Molecular Clock (MC)
• Local and approximate molecular
clocks more reasonable
– one amino acid subst. 14.5 My
– 1.3 10-9 substitutions/nucleotide site/year
– Relative rate test (see further)
• ((A,B),C) then measure distance between
(A,C) & (B,C)
42. Proteins evolve at highly different rates
Rate of Change Theoretical Lookback Time
(PAMs / 100 myrs) (myrs)
Pseudogenes 400 45
Fibrinopeptides 90 200
Lactalbumins 27 670
Lysozymes 24 850
Ribonucleases 21 850
Haemoglobins 12 1500
Acid proteases 8 2300
Cytochrome c 4 5000
Glyceraldehyde-P dehydrogenase2 9000
Glutamate dehydrogenase 1 18000
PAM = number of Accepted Point Mutations per 100 amino acids.
43. Phylogenetics
Introduction
Definitions
Species concept
Examples
The Tree-of-life
Phylogenetics Methodologies
Algorithms
Distance Methods
Maximum Likelihood
Maximum Parsimony
Rooting
Statistical Validation
Conclusions
Orthologous genes
Horizontal Gene Transfer
Phylogenomics
Practical Approach: PHYLIP
Weblems
48. Distance matrix methods (upgma, nj, Fitch,...)
• Convert sequence data into a
set of discrete pairwise distance
values (n*(n-1)/2), arranged into
a matrix. Distance methods fit a
tree to this matrix.
• The phylogenetic topology tree
is constructed by using a cluster
analysis method (like upgma or
nj methods).
63. Distance matrix methods (upgma, nj, Fitch,...)
• The phylogeny makes an estimation of
the distance for each pair as the sum
of branch lengths in the path from one
sequence to another through the tree.
easy to perform ;
quick calculation ;
fit for sequences having high similarity scores ;
• drawbacks :
the sequences are not considered as such (loss
of information) ;
all sites are generally equally treated (do not
take into account differences of substitution
rates ) ;
not applicable to distantly divergent sequences.
64.
65.
66. Maximum likelihood
• In this method, the bases
(nucleotides or amino acids) of all
sequences at each site are
considered separately (as
independent), and the log-likelihood
of having these bases are computed
for a given topology by using a
particular probability model.
• This log-likelihood is added for all
sites, and the sum of the log-
likelihood is maximized to estimate
the branch length of the tree.
68. Maximum likelihood
• This procedure is repeated for all
possible topologies, and the topology
that shows the highest likelihood is
chosen as the final tree.
• Notes :
ML estimates the branch lengths of the
final tree ;
ML methods are usually consistent ;
ML is extented to allow differences
between the rate of transition and
transversion.
• Drawbacks
need long computation time to construct a
tree.
71. Maximum Parsimony
Parsimony criterion
• It consists of determining the minimum
number of changes (substitutions) required to
transform a sequence to its nearest neighbor.
Maximum Parsimony
• The maximum parsimony algorithm searches
for the minimum number of genetic events
(nucleotide substitutions or amino-acid
changes) to infer the most parsimonious tree
from a set of sequences.
72. Maximum Parsimony
Occam’s Razor
Entia non sunt multiplicanda praeter necessitatem.
William of Occam (1300-1349)
The best tree is the one which requires the least number of
substitutions
73. Maximum Parsimony
• The best tree is the one which needs the
fewest changes.
– If the evolutionary clock is not constant, the
procedure generates results which can be
misleading ;
– within practical computational limits, this
often leads in the generation of tens or more
"equally most parsimonious trees" which
make it difficult to justify the choice of a
particular tree ;
– long computation time to construct a tree.
84. Maximum Parsimony
• The best tree is the one which
needs the fewest changes.
• Problems :
– If the evolutionary clock is not
constant, the procedure generates
results which can be misleading ;
– within practical computational
limits, this often leads in the
generation of tens or more "equally
most parsimonious trees" which make
it difficult to justify the choice of a
particular tree ;
– long computation time to construct a
tree.
85. Phylogenetics
Introduction
Definitions
Species concept
Examples
The Tree-of-life
Phylogenetics Methodologies
Algorithms
Distance Methods
Maximum Likelihood
Maximum Parsimony
Rooting
Statistical Validation
Conclusions
Orthologous genes
Horizontal Gene Transfer
Phylogenomics
Practical Approach: PHYLIP
Weblems
86. Comparative evaluation of different methods
There is at present no statistical
methods which allow
comparisons of trees obtained
from different phylogenetic
methods, nevertheless many
studies have been made to
compare the relative consistency
of the existing methods.
87. Comparative evaluation of different methods
The consistency depends on many
factors, among these the topology
and branch lengths of the real
tree, the transition/transversion rate
and the variability of the
substitution rates.
One expects that if sequences have
strong phylogenetic
relationship, different methods will
show the same phylogenetic tree
88. Comparison of methods
• Inconsistency
• Neighbour Joining (NJ) is very fast but depends on
accurate estimates of distance. This is more
difficult with very divergent data
• Parsimony suffers from Long Branch Attraction.
This may be a particular problem for very divergent
data
• NJ can suffer from Long Branch Attraction
• Parsimony is also computationally intensive
• Codon usage bias can be a problem for MP and NJ
• Maximum Likelihood is the most reliable but
depends on the choice of model and is very slow
• Methods may be combined
89. Rooting the Tree
• In an unrooted tree the direction of
evolution is unknown
• The root is the hypothesized ancestor
of the sequences in the tree
• The root can either be placed on a
branch or at a node
• You should start by viewing an
unrooted tree
90. Automatic rooting
• Many software packages will root
trees automaticall (e.g. mid-point
rooting in NJPlot)
• Sometimes two trees may look very
different but, in fact, differ only in the
position of the root
• This normally involves assumptions…
BEWARE!
91. Rooting Using an Outgroup
1. The outgroup should be a sequence (or set
of sequences) known to be less closely
related to the rest of the sequences than they
are to each other
2. It should ideally be as closely related as
possible to the rest of the sequences while
still satisfying condition 1
The root must be somewhere between the
outgroup and the rest (either on the node or
in a branch)
92. How confident am I that my tree is correct?
Bootstrap values
Bootstrapping is a statistical
technique that can use random
resampling of data to determine
sampling error for tree topologies
93. Bootstrapping phylogenies
• Characters are resampled with replacement
to create many bootstrap replicate data sets
• Each bootstrap replicate data set is analysed
(e.g. with parsimony, distance, ML etc.)
• Agreement among the resulting trees is
summarized with a majority-rule consensus
tree
• Frequencies of occurrence of
groups, bootstrap proportions (BPs), are a
measure of support for those groups
95. Bootstrap - interpretation
• Bootstrapping is a very valuable and widely used
technique (it is demanded by some journals)
• BPs give an idea of how likely a given branch
would be to be unaffected if additional data, with
the same distribution, became available
• BPs are not the same as confidence intervals.
There is no simple mapping between bootstrap
values and confidence intervals. There is no
agreement about what constitutes a ‘good’
bootstrap value (> 70%, > 80%, > 85% ????)
• Some theoretical work indicates that BPs can be a
conservative estimate of confidence intervals
• If the estimated tree is inconsistent all the
bootstraps in the world won’t help you…..
96. Jack-knifing
• Jack-knifing is very similar to
bootstrapping and differs only in the
character resampling strategy
• Jack-knifing is not as widely
available or widely used as
bootstrapping
• Tends to produce broadly similar
results
97. Statistical evaluation of the obtained phylogenetic trees
At present only sampling techniques allow testing the
topology of a phylogenetic tree
Bootstrapping
» It consists of drawing columns from a sample of
aligned sequences, with replacement, until one gets
a data set of the same size as the original one.
(usually some columns are sampled several times
others left out)
Half-Jacknife
» This technique resamples half of the sequence sites
considered and eliminates the rest. The final sample
has half the number of initial number of sites
without duplication.
98. Weblems
W6.1: The growth hormones in most mammals have very similar ammo acid
sequences. (The growth hormones of the Alpaca, Dog Cat Horse, Rabbit, and
Elephant each differ from that of the Pig at no more than 3 positions out of 191.)
Human growth hormone is very different, differing at 62 positions. The evolution of
growth hormone accelerated sharply in the line leading to humans. By retrieving
and aligning growth hormone sequences from species closely related to humans
and our ancestors, determine where in the evolutionary tree leading to humans the
accelerated evolution of growth hormone took place.
W6.2: Humans are primates, an order that we, apes and monkeys share with lemurs
and tarsiers. On the basis of the Beta-globin gene cluster of human, a
chimpanzee, an old-world monkey, a new-world monkey, a lemur, and a tarsier,
derive a phylogenetic tree of these groups.
W6.3: Primates are mammals, a class we share with marsupials and monotremes;
Extant marsupials live primarily in Australia, except for the opossum, found also in
North and South America. Extant monotremes are limited to two animals from
Australia: the platypus and echidna. Using the complete mitochondnal genome
from human, horse (Equus caballus), wallaroo (Macropus robustus), American
opossum (Didelphis mrgimana), and platypus (Ormthorhynchus anatmus), draw
an evolutionary tree, indicating branch lengths. Are monotremes more closely
related to placental mammals or to marsupials?
W6.4: Mammals are vertebrates, a subphylum that we share with fishes, sharks, birds
and reptiles, amphibia, and primitive jawless fishes (example: lampreys). For the
coelacanth (Latimeria chalumnae), the great white shark (Carcharodon
carcharias), skipjack tuna (Katsuwonus pelamis), sea lamprey (Petromyzon
marinus), frog (Rana Ripens), and Nile crocodile (Crocodylus niloticus), using
sequences of cytochromes c and pancreatic ribonucleases, derive evolutionary
trees of these species.