This document provides an overview of phylogenetics, which is the study of evolutionary relationships among biological entities using molecular data. It discusses why molecular data is important for phylogenetics, some key applications of phylogenetics like classification and forensics, and some important concepts like what a phylogeny is and that evolutionary dates and times are confounded. It also briefly outlines some alternative representations of phylogenies and major stages in phylogenetic analyses.
Multiple Sequence Alignment-just glims of viewes on bioinformatics.Arghadip Samanta
Multiple sequence alignment is used to infer evolutionary relationships by comparing homologous sequences. It involves aligning three or more biological sequences, such as protein, DNA, or RNA that are assumed to share a common ancestor. The document discusses methods for multiple sequence alignment including progressive alignment, which builds alignments sequentially according to a guide tree, and divide-and-conquer algorithms, which divide the problem into subproblems. It also describes using the resulting multiple sequence alignment for phylogenetic analysis to construct evolutionary trees and assess shared ancestry among sequences.
The MEGA software is one of the most widely used software tools in molecular taxonomy and bioinformatics. This module describes how MEGA can be employed in a classroom setting to teach the fundamentals of molecular taxonomy.
Phylogeny is the evolutionary history of a taxonomic group as represented by a phylogenetic tree. The goals of phylogeny are to show relationships between species based on evolutionary time. There are several tools and methods used to build phylogenetic trees, including distance-based methods like UPGMA and neighbor joining which use sequence similarities, and character-based methods like maximum parsimony. Software like MEGA, Dendroscope, and Phylotree.js can be used to construct and visualize phylogenetic trees.
Presentation of extant gene tree-species tree methods for phylogenetic reconstruction, with a focus on methods implemented or soon to be implemented in RevBayes. Finishes with pointers to a RevBayes tutorial demonstrating the current capabilities of the program.
This presentation entitled 'Molecular phylogenetics and its application' deals with all the developmental ideas and basics in the field of bioinformatics.
Molecular Evolution and Phylogenetics (2009)Hernán Dopazo
This document provides an introduction to molecular evolution and phylogenetics. It discusses the objectives of constructing phylogenetic trees, including understanding the ancestral-descendant relationships between taxa. Several key developments in the field are outlined, such as the introduction of molecular data in the 1960s, and early methods like distance matrix approaches. The document also gives examples of how phylogenetic trees are applied across biology, for instance in fields like evolutionary genetics, population genetics, and molecular clock analysis. Finally, it discusses uses of phylogenetics in bioinformatics, including phylogenomics and predicting gene function.
This document provides an overview of phylogenetics, which is the study of evolutionary relationships among biological entities using molecular data. It discusses why molecular data is important for phylogenetics, some key applications of phylogenetics like classification and forensics, and some important concepts like what a phylogeny is and that evolutionary dates and times are confounded. It also briefly outlines some alternative representations of phylogenies and major stages in phylogenetic analyses.
Multiple Sequence Alignment-just glims of viewes on bioinformatics.Arghadip Samanta
Multiple sequence alignment is used to infer evolutionary relationships by comparing homologous sequences. It involves aligning three or more biological sequences, such as protein, DNA, or RNA that are assumed to share a common ancestor. The document discusses methods for multiple sequence alignment including progressive alignment, which builds alignments sequentially according to a guide tree, and divide-and-conquer algorithms, which divide the problem into subproblems. It also describes using the resulting multiple sequence alignment for phylogenetic analysis to construct evolutionary trees and assess shared ancestry among sequences.
The MEGA software is one of the most widely used software tools in molecular taxonomy and bioinformatics. This module describes how MEGA can be employed in a classroom setting to teach the fundamentals of molecular taxonomy.
Phylogeny is the evolutionary history of a taxonomic group as represented by a phylogenetic tree. The goals of phylogeny are to show relationships between species based on evolutionary time. There are several tools and methods used to build phylogenetic trees, including distance-based methods like UPGMA and neighbor joining which use sequence similarities, and character-based methods like maximum parsimony. Software like MEGA, Dendroscope, and Phylotree.js can be used to construct and visualize phylogenetic trees.
Presentation of extant gene tree-species tree methods for phylogenetic reconstruction, with a focus on methods implemented or soon to be implemented in RevBayes. Finishes with pointers to a RevBayes tutorial demonstrating the current capabilities of the program.
This presentation entitled 'Molecular phylogenetics and its application' deals with all the developmental ideas and basics in the field of bioinformatics.
Molecular Evolution and Phylogenetics (2009)Hernán Dopazo
This document provides an introduction to molecular evolution and phylogenetics. It discusses the objectives of constructing phylogenetic trees, including understanding the ancestral-descendant relationships between taxa. Several key developments in the field are outlined, such as the introduction of molecular data in the 1960s, and early methods like distance matrix approaches. The document also gives examples of how phylogenetic trees are applied across biology, for instance in fields like evolutionary genetics, population genetics, and molecular clock analysis. Finally, it discusses uses of phylogenetics in bioinformatics, including phylogenomics and predicting gene function.
A phylogenetic tree is a model about the evolutionary relationship between operational taxonomic units(OTUs) based on homologous character.
Dandrogram: general term for a branching diagram
Cladogram: branching diagram without branch length estimates
Phylogram or phylogenetic tree: branching diagram with branch length estimates
A tree is composed of nodes and branches & one bracnch connects any two adjacent nodes. Nodes represent the taxonomic units.
E.G. Two very similar sequence will be neighbours on the outer branches and will be connected by a common internal branch.
This document outlines the process of constructing phylogenetic trees to delineate relationships among Coronaviridae species using protein sequences. It describes:
1) Choosing nucleocapsid and membrane proteins as molecular markers and collecting sequences from NCBI.
2) Performing multiple sequence alignment on the proteins using MUSCLE in MEGA, which is more accurate than ClustalW.
3) Selecting maximum likelihood as the tree-building method because it uses all sequence information without reducing it to distances and makes fewer assumptions than other methods.
1. Molecular phylogenetics is the study of evolutionary relationships among biological entities using molecular data like DNA, RNA, and protein sequences.
2. The first phylogenetic tree based on molecular data was constructed in 1967 by Fitch and Margoliash. This helped establish the significance of molecular evidence in taxonomy.
3. Phylogenetic studies use molecular techniques to assess historical evolutionary relationships, while phylogeographic studies examine geographic distributions of species. Molecular data revolutionized our understanding of evolutionary relationships.
Survey of softwares for phylogenetic analysisArindam Ghosh
The document discusses the process of phylogenetic analysis using cytochrome c oxidase subunit 1 (COX1) gene sequences from several organisms: human, bovine, zebrafish, pig, and sheep. It provides the COX1 protein sequences for each organism downloaded from UniProt. The sequences will be aligned using Clustal Omega and a phylogenetic tree will be constructed using Clustal W2 to analyze the evolutionary relationships between the organisms.
This document discusses phylogenetic studies and the construction of phylogenetic trees. It notes that fossil records are unreliable, so phylogenetic trees are primarily based on molecular sequencing data and morphological data. There are several assumptions made in phylogenetic analysis, including that sequences are homologous, phylogenetic divergence is bifurcating, and each position in a sequence evolved independently. The document outlines different types of phylogenetic trees, steps in phylogenetic analysis like choosing molecular markers and tree building methods, and criteria for assessing the reliability of phylogenetic trees.
Construction of phylogenetic tree from multiple gene trees using principal co...IAEME Publication
This document describes a method for constructing a phylogenetic tree from multiple gene trees using principal component analysis. Multiple gene trees are generated from different protein sequences from various organisms. Distance matrices are calculated for each gene tree and combined into a single data matrix. Principal component analysis is performed on the data matrix to extract the first principal component, which represents the consensus distance vector combining information from all gene trees. A phylogenetic tree is then generated from the consensus distance vector using UPGMA, providing a species tree that integrates information from multiple genes. The method is demonstrated on protein sequence data from primates and placental mammals.
This document discusses phylogenetic analysis and taxonomy. It introduces key terms and concepts related to phylogeny and classification, including that all species can be viewed as branches on the tree of life. It explains that phylogenetic analysis studies evolutionary relationships between species, and is informed by morphological and molecular data. The document also discusses how phylogenetic trees are hypotheses about evolutionary descent that can be tested against independent sources of data.
The document discusses FASTA, a sequence alignment software tool. It describes the history and development of FASTA, which was originally designed for protein sequence similarity searching and later expanded to support DNA and translated DNA searches. FASTA uses local sequence alignment and heuristic methods to quickly search databases and find similar sequences. It supports various types of searches for protein, nucleotide, and translated sequences.
PMC Poster - phylogenetic algorithm for morphological dataYiteng Dang
This project developed a new algorithm to infer phylogenetic trees from morphological data involving inapplicable characters. The algorithm consists of a downpass and uppass to assign values to internal nodes and optimize the tree locally at each node. It finds the optimal tree according to parsimony principles. While tested successfully on sample trees, further work is needed to prove optimality for all trees and extend the approach to multi-character data.
This document provides instructions for constructing a phylogenetic tree using maximum likelihood methods in PhyML. It describes collecting homologous sequences, aligning them with tools like ClustalW, manually editing the alignment, selecting an appropriate substitution model with programs like jModelTest, running PhyML with the alignment and model to generate an initial tree, and then iteratively improving the tree by removing rogue taxa and refining the process until a satisfactory tree is produced.
This document discusses post-tree analysis techniques for phylogenetic workflows, including merging datasets, visualizing phylogenies, performing phylogenetically independent contrasts to remove the effect of shared ancestry, reconstructing ancestral states of discrete traits along a phylogeny, and estimating rates of evolution for traits. Examples are provided for visualizing trees and reconstructing ancestral states of Hawaiian lobelioids.
The document discusses several projects being undertaken by the iPlant collaborative including building phylogenetic trees of up to 500,000 plant species, developing software for visualizing and exploring large phylogenetic trees, creating a social networking site for the plant science community, supporting data analysis for the Thousand Plant Transcriptome Project, resolving conflicting taxonomic names, reconciling gene and species trees, and analyzing trait evolution on phylogenetic trees.
The document discusses challenges in identifying causal variants for complex diseases from sequencing data. It notes that while ideal situations may involve finding a variant common in all affected individuals and absent in unaffected, reality involves sifting through around 3.5 million SNPs. Methods like genome-wide association studies and focusing on exonic variants can help prioritize, but functional variants may also reside outside of protein coding regions. Considering combinations of variants through statistical genetics approaches may be needed to explain disease heritability. Quality control, annotation, and filtering are important but finding causal variants remains difficult.
Finding Needles in Genomic Haystacks with “Wide” Random Forest: Spark Summit ...Spark Summit
Recent advances in genome sequencing technologies and bioinformatics have enabled whole-genomes to be studied at population-level rather then for small number of individuals. This provides new power to whole genome association studies (WGAS
), which now seek to identify the multi-gene causes of common complex diseases like diabetes or cancer.
As WGAS involve studying thousands of genomes, they pose both technological and methodological challenges. The volume of data is significant, for example the dataset from 1000 Genomes project with genomes of 2504 individuals includes nearly 85M genomic variants with raw data size of 0.8 TB. The number of features is enormous and greatly exceeds the number of samples, which makes it challenging to apply traditional statistical approaches.
Random forest is one of the methods that was found to be useful in this context, both because of its potential for parallelization and its robustness. Although there is a number of big data implementations available (including Spark ML) they are tuned for typical dataset with large number of samples and relatively small number of variables, and either fail or are inefficient in the GWAS context especially, that a costly data preprocessing is usually required.
To address these problems, we have developed the RandomForestHD – a Spark based implementation optimized for highly dimensional data sets. We have successfully RandomForestHD applied it to datasets beyond the reach of other tools and for smaller datasets found its performance superior. We are currently applying RandomForestHD, released as part of the VariantSpark toolkit, to a number of WGAS studies.
In the presentation we will introduce the domain of WGAS and related challenges, present RandomForestHD with its design principles and implementation details with regards to Spark, compare its performance with other tools, and finally showcase the results of a few WGAS applications.
The document describes a study that integrated genetic, epigenetic, and transcriptomic data from a large cohort to identify quantitative trait loci (QTLs) and causal relationships between molecular phenotypes. The study identified eQTLs, meQTLs, and aceQTLs and found 240 loci associated with all three data types. Bayesian networks were constructed for each multi-QTL to model potential causal relationships, though most networks showed independence between phenotypes. Future work involves expanding the analysis and better modeling causal effects.
The iPlant Tree of Life Project and ToolkitNaim Matasci
The iPlant Tree of Life Project and Toolkit: Building a Cyberinfrastructure for Plant Science Research
Given at Evolution 2011
An overview of the iPlant and iPToL project
The document discusses the tree of life and efforts to construct a complete digital tree of life. It notes that an ideal tree of life would be complete, continuously updated, and digitally available. Several challenges are outlined, including how to handle incongruences between trees and how to synthesize data across the entire tree. Efforts are underway to synthesize published phylogenetic data and make tree of life resources and data openly available online. Remaining challenges include improving taxonomic coverage, incorporating time information, and enabling community feedback and annotation. The goal is to move beyond a single tree and provide tools to enable custom synthesis and integration with other resources and data.
Three groups annotated the genome of Mycoplasma genitalium and found inconsistencies in their annotations. Of the 468 genes, 318 were annotated consistently by all three groups but 45 had conflicting annotations. Errors likely arose from insufficient sequence similarity to determine homology accurately or incorrectly inferring function based on homology alone. Database curation is needed to prevent propagation of erroneous annotations.
A phylogenetic tree is a model about the evolutionary relationship between operational taxonomic units(OTUs) based on homologous character.
Dandrogram: general term for a branching diagram
Cladogram: branching diagram without branch length estimates
Phylogram or phylogenetic tree: branching diagram with branch length estimates
A tree is composed of nodes and branches & one bracnch connects any two adjacent nodes. Nodes represent the taxonomic units.
E.G. Two very similar sequence will be neighbours on the outer branches and will be connected by a common internal branch.
This document outlines the process of constructing phylogenetic trees to delineate relationships among Coronaviridae species using protein sequences. It describes:
1) Choosing nucleocapsid and membrane proteins as molecular markers and collecting sequences from NCBI.
2) Performing multiple sequence alignment on the proteins using MUSCLE in MEGA, which is more accurate than ClustalW.
3) Selecting maximum likelihood as the tree-building method because it uses all sequence information without reducing it to distances and makes fewer assumptions than other methods.
1. Molecular phylogenetics is the study of evolutionary relationships among biological entities using molecular data like DNA, RNA, and protein sequences.
2. The first phylogenetic tree based on molecular data was constructed in 1967 by Fitch and Margoliash. This helped establish the significance of molecular evidence in taxonomy.
3. Phylogenetic studies use molecular techniques to assess historical evolutionary relationships, while phylogeographic studies examine geographic distributions of species. Molecular data revolutionized our understanding of evolutionary relationships.
Survey of softwares for phylogenetic analysisArindam Ghosh
The document discusses the process of phylogenetic analysis using cytochrome c oxidase subunit 1 (COX1) gene sequences from several organisms: human, bovine, zebrafish, pig, and sheep. It provides the COX1 protein sequences for each organism downloaded from UniProt. The sequences will be aligned using Clustal Omega and a phylogenetic tree will be constructed using Clustal W2 to analyze the evolutionary relationships between the organisms.
This document discusses phylogenetic studies and the construction of phylogenetic trees. It notes that fossil records are unreliable, so phylogenetic trees are primarily based on molecular sequencing data and morphological data. There are several assumptions made in phylogenetic analysis, including that sequences are homologous, phylogenetic divergence is bifurcating, and each position in a sequence evolved independently. The document outlines different types of phylogenetic trees, steps in phylogenetic analysis like choosing molecular markers and tree building methods, and criteria for assessing the reliability of phylogenetic trees.
Construction of phylogenetic tree from multiple gene trees using principal co...IAEME Publication
This document describes a method for constructing a phylogenetic tree from multiple gene trees using principal component analysis. Multiple gene trees are generated from different protein sequences from various organisms. Distance matrices are calculated for each gene tree and combined into a single data matrix. Principal component analysis is performed on the data matrix to extract the first principal component, which represents the consensus distance vector combining information from all gene trees. A phylogenetic tree is then generated from the consensus distance vector using UPGMA, providing a species tree that integrates information from multiple genes. The method is demonstrated on protein sequence data from primates and placental mammals.
This document discusses phylogenetic analysis and taxonomy. It introduces key terms and concepts related to phylogeny and classification, including that all species can be viewed as branches on the tree of life. It explains that phylogenetic analysis studies evolutionary relationships between species, and is informed by morphological and molecular data. The document also discusses how phylogenetic trees are hypotheses about evolutionary descent that can be tested against independent sources of data.
The document discusses FASTA, a sequence alignment software tool. It describes the history and development of FASTA, which was originally designed for protein sequence similarity searching and later expanded to support DNA and translated DNA searches. FASTA uses local sequence alignment and heuristic methods to quickly search databases and find similar sequences. It supports various types of searches for protein, nucleotide, and translated sequences.
PMC Poster - phylogenetic algorithm for morphological dataYiteng Dang
This project developed a new algorithm to infer phylogenetic trees from morphological data involving inapplicable characters. The algorithm consists of a downpass and uppass to assign values to internal nodes and optimize the tree locally at each node. It finds the optimal tree according to parsimony principles. While tested successfully on sample trees, further work is needed to prove optimality for all trees and extend the approach to multi-character data.
This document provides instructions for constructing a phylogenetic tree using maximum likelihood methods in PhyML. It describes collecting homologous sequences, aligning them with tools like ClustalW, manually editing the alignment, selecting an appropriate substitution model with programs like jModelTest, running PhyML with the alignment and model to generate an initial tree, and then iteratively improving the tree by removing rogue taxa and refining the process until a satisfactory tree is produced.
This document discusses post-tree analysis techniques for phylogenetic workflows, including merging datasets, visualizing phylogenies, performing phylogenetically independent contrasts to remove the effect of shared ancestry, reconstructing ancestral states of discrete traits along a phylogeny, and estimating rates of evolution for traits. Examples are provided for visualizing trees and reconstructing ancestral states of Hawaiian lobelioids.
The document discusses several projects being undertaken by the iPlant collaborative including building phylogenetic trees of up to 500,000 plant species, developing software for visualizing and exploring large phylogenetic trees, creating a social networking site for the plant science community, supporting data analysis for the Thousand Plant Transcriptome Project, resolving conflicting taxonomic names, reconciling gene and species trees, and analyzing trait evolution on phylogenetic trees.
The document discusses challenges in identifying causal variants for complex diseases from sequencing data. It notes that while ideal situations may involve finding a variant common in all affected individuals and absent in unaffected, reality involves sifting through around 3.5 million SNPs. Methods like genome-wide association studies and focusing on exonic variants can help prioritize, but functional variants may also reside outside of protein coding regions. Considering combinations of variants through statistical genetics approaches may be needed to explain disease heritability. Quality control, annotation, and filtering are important but finding causal variants remains difficult.
Finding Needles in Genomic Haystacks with “Wide” Random Forest: Spark Summit ...Spark Summit
Recent advances in genome sequencing technologies and bioinformatics have enabled whole-genomes to be studied at population-level rather then for small number of individuals. This provides new power to whole genome association studies (WGAS
), which now seek to identify the multi-gene causes of common complex diseases like diabetes or cancer.
As WGAS involve studying thousands of genomes, they pose both technological and methodological challenges. The volume of data is significant, for example the dataset from 1000 Genomes project with genomes of 2504 individuals includes nearly 85M genomic variants with raw data size of 0.8 TB. The number of features is enormous and greatly exceeds the number of samples, which makes it challenging to apply traditional statistical approaches.
Random forest is one of the methods that was found to be useful in this context, both because of its potential for parallelization and its robustness. Although there is a number of big data implementations available (including Spark ML) they are tuned for typical dataset with large number of samples and relatively small number of variables, and either fail or are inefficient in the GWAS context especially, that a costly data preprocessing is usually required.
To address these problems, we have developed the RandomForestHD – a Spark based implementation optimized for highly dimensional data sets. We have successfully RandomForestHD applied it to datasets beyond the reach of other tools and for smaller datasets found its performance superior. We are currently applying RandomForestHD, released as part of the VariantSpark toolkit, to a number of WGAS studies.
In the presentation we will introduce the domain of WGAS and related challenges, present RandomForestHD with its design principles and implementation details with regards to Spark, compare its performance with other tools, and finally showcase the results of a few WGAS applications.
The document describes a study that integrated genetic, epigenetic, and transcriptomic data from a large cohort to identify quantitative trait loci (QTLs) and causal relationships between molecular phenotypes. The study identified eQTLs, meQTLs, and aceQTLs and found 240 loci associated with all three data types. Bayesian networks were constructed for each multi-QTL to model potential causal relationships, though most networks showed independence between phenotypes. Future work involves expanding the analysis and better modeling causal effects.
The iPlant Tree of Life Project and ToolkitNaim Matasci
The iPlant Tree of Life Project and Toolkit: Building a Cyberinfrastructure for Plant Science Research
Given at Evolution 2011
An overview of the iPlant and iPToL project
The document discusses the tree of life and efforts to construct a complete digital tree of life. It notes that an ideal tree of life would be complete, continuously updated, and digitally available. Several challenges are outlined, including how to handle incongruences between trees and how to synthesize data across the entire tree. Efforts are underway to synthesize published phylogenetic data and make tree of life resources and data openly available online. Remaining challenges include improving taxonomic coverage, incorporating time information, and enabling community feedback and annotation. The goal is to move beyond a single tree and provide tools to enable custom synthesis and integration with other resources and data.
Three groups annotated the genome of Mycoplasma genitalium and found inconsistencies in their annotations. Of the 468 genes, 318 were annotated consistently by all three groups but 45 had conflicting annotations. Errors likely arose from insufficient sequence similarity to determine homology accurately or incorrectly inferring function based on homology alone. Database curation is needed to prevent propagation of erroneous annotations.
Darwin’s Magic: Evolutionary Computation in Nanoscience, Bioinformatics and S...Natalio Krasnogor
In this talk I will overview ten years of research in the application of evolutionary computation ideas in the natural sciences. The talk will take us on a tour that will cover problems in nanoscience, e.g. controlling self-‐organizing systems, optimizing scanning probe microscopy, etc., problems arising in bioinformatics, such as predicting protein structures and their features, to challenges emerging in systems and synthetic biology. Although the algorithmic solutions involved in these problems are different from each other, at their core, they retain Darwin’s wonderful insights. I will conclude the talk by giving a personal view on why EC has been so successful and where, in my mind, the future lies.
Visual Exploration of Clinical and Genomic Data for Patient StratificationNils Gehlenborg
Talk presented at the Simons Foundation Biotech Symposium "Complex Data Visualization: Approach and Application" (12 September 2014)
http://www.simonsfoundation.org/event/complex-data-visualization-approach-and-application/
In this talk I describe how we integrated a sophisticated computational framework directly into the StratomeX visualization technique to enable rapid exploration of tens of thousands of stratifications in cancer genomics data, creating a unique and powerful tool for the identification and characterization of tumor subtypes. The tool can handle a wide range of genomic and clinical data types for cohorts with hundreds of patients. StratomeX also provides direct access to comprehensive data sets generated by The Cancer Genome Atlas Firehose analysis pipeline.
http://stratomex.caleydo.org
The document outlines the basic steps in constructing a phylogenetic tree:
1) Assembling and aligning a dataset of DNA or protein sequences of interest.
2) Using computational methods and evolutionary models to build phylogenetic trees from the sequence alignments.
3) Statistically testing and assessing the estimated trees to evaluate which tree topologies best describe the phylogenetic relationships between the sequences.
The process aims to provide a visual representation of how organisms have evolved from a common ancestor over time based on analyses of genetic similarities and differences in their molecular sequences.
Inference and informatics in a 'sequenced' worldJoe Parker
Short lecture relating my recent work on real-time phylogenomics, implications for bioinformatics research and future directions of genomic/phylogenetic modelling to explicitly account for phylogeny, synteny and identity through coloured graphs.
University of Reading, 2nd August 2017
Abstract: The focus in this session will be put on the differences between standard DNA mapping and RNAseq-specific transcript mapping: identifying splice variants and isoforms. The issue of transcript quantification and genomic variants that can be identified from RNAseq data will be discussed.
Variant (SNPs/Indels) calling in DNA sequences, Part 1 Denis C. Bauer
This document discusses various topics related to mapping short sequencing reads to a reference genome, including:
- File formats like FASTQ that store sequencing reads and BAM/SAM formats for aligned reads.
- Alignment algorithms like hash table-based (MAQ, BWA) and suffix tree-based (BWA, Bowtie) mappers.
- Visualizing alignments using the Integrative Genomics Viewer (IGV).
- Performing quality control on BAM files by checking the percentage of mapped reads and coverage uniformity.
- The next session will focus on identifying genomic variants from mapped reads through SNP/indel calling and filtering.
Utility of transcriptome sequencing for phylogeneticEdizonJambormias2
This document discusses the utility of transcriptome sequencing (RNA-Seq) for phylogenetic inference and character evolution in systematics. It provides examples of recent studies that have used transcriptome data to generate nuclear marker sets and resolve phylogenetic relationships for diverse lineages, including plants, animals, and fungi. The review highlights how comparative transcriptomics has also provided insights into topics like polyploidy, horizontal gene transfer, and character evolution. While transcriptomics offers a rich source of nuclear markers for phylogenetics, it also faces challenges from tissue quality requirements and only sequencing expressed genes at a particular developmental stage.
This document provides information about a QIIME workshop. It includes instructions on how to get started with QIIME, an overview of the typical QIIME analysis pipeline from raw sequencing data to results, and details on specific QIIME tools and files like the mapping file, OTU table, and parameters file. The document also discusses moving image analysis of the human microbiome using QIIME.
Introduction to 16S rRNA gene multivariate analysisJosh Neufeld
Short introductory talk on multivariate statistics for 16S rRNA gene analysis given at the 2nd Soil Metagenomics conference in Braunschweig Germany, December 2013. A previous talk had discussed quality filtering, chimera detection, and clustering algorithms.
Network Biology: A paradigm for modeling biological complex systemsGanesh Bagler
These slides are part of the two lectures delivered at the as part of the 'National Workshop on Network Modelling and Graph Theory' (Dec 14-16, 2017) at Department of Mathematics, Dibrugarh University, Assam, India.
(1) Network Biology: A paradigm for integrative modeling of biological complex systems -- 14 Dec 2017, 3:30pm
(2) Applications of network modeling in biomedicine -- 15 Dec 2017, 9:00pm
Sponsored by UGC under SAP DRS (II)
(1) Workshop link: https://www.dibru.ac.in/upcoming-events/2981-national-workshop-on-network-modelling-and-graph-theory
(2) The Workshop Flyer: https://www.dibru.ac.in/images/uploaded_files/2017/Nov/National_Workshop_on_Network_Modelling_and_Graph_Theory.pdf
iPlant Taxonomic Name Resolution Service v. 3Naim Matasci
This document describes the Taxonomic Name Resolution Service, which standardizes plant names by correcting spelling errors and alternative spellings to match a standard list of accepted names. It addresses issues like synonymy, misidentifications, and outdated names. The service aims to resolve taxonomic uncertainty in plant names. It is a computer-assisted tool that integrates with other biodiversity data systems. The document outlines challenges in resolving plant names and lists collaborators involved in developing and funding the service.
The iPlant Collaborative: A Cyberinfrastructure for the Life SciencesNaim Matasci
The iPlant Collaborative provides cyberinfrastructure resources for life science research through its data storage system, analysis tools in the Discovery Environment, cloud computing resources on Atmosphere, and other services accessible through portals or APIs to help address challenges of data volume, fragmented tools and lack of reproducibility in biological research. It offers access to petabytes of data and thousands of computational cores through tools for data management, analysis, visualization and sharing of results.
This document summarizes a taxonomic name resolution service that standardizes plant names by correcting spelling errors and alternative spellings to a standard list of names, and converts out-of-date names to currently accepted names. It discusses issues with taxonomic uncertainty such as non-existent names, synonyms, and misidentifications. The document also provides an example of resolving multiple scientific names for a single species to a currently accepted name. Finally, it notes some current issues with the service including only handling plant taxa, lexical reconciliation of names, lack of an API, and performance challenges with caching.
The document discusses gene tree reconciliation, which involves projecting gene trees onto a species tree to account for evolutionary events like gene duplications, losses, and horizontal transfer. It outlines existing cyberinfrastructure for generating and visualizing reconciliations, and proposes ways to extend this, such as allowing users to submit their own gene trees and alignments for reconciliation, integrating visualization tools, and storing multiple reconciliations per gene tree. A goal is to "make tree reconciliation phylotastic" by building components to allow users more flexibility in generating reconciliations from their own data.
Phylogenetic Workflows: Tree Building and Post-tree Analyses
Given at the Dept for Ecology and Evolutionary Biology, University of Arizona in 2011
A phylogenetic workflow example showcasing iPlant Cyberinfrastructure
The iPlant Tree of Life Project and ToolkitNaim Matasci
The iPlant Tree of Life Project and Toolkit: Building aCyberinfrastructure for Plant Science Research
Given at the National Museum of National History in 2011
An overview of iPlant and iPToL
The TNRS: a Taxonomic Name Resolution Service for PlantsNaim Matasci
The document discusses the Taxonomic Name Resolution Service (TNRS) which is a tool that standardizes plant names. It resolves issues like misspellings, outdated names, and synonyms to map names to currently accepted taxonomic names. The TNRS is open source software available on GitHub and via web services and APIs documented on tnrs.iplantcollaborative.org to help unify plant names across data sources.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing.
The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
leewayhertz.com-AI in predictive maintenance Use cases technologies benefits ...alexjohnson7307
Predictive maintenance is a proactive approach that anticipates equipment failures before they happen. At the forefront of this innovative strategy is Artificial Intelligence (AI), which brings unprecedented precision and efficiency. AI in predictive maintenance is transforming industries by reducing downtime, minimizing costs, and enhancing productivity.
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
A Comprehensive Guide to DeFi Development Services in 2024Intelisync
DeFi represents a paradigm shift in the financial industry. Instead of relying on traditional, centralized institutions like banks, DeFi leverages blockchain technology to create a decentralized network of financial services. This means that financial transactions can occur directly between parties, without intermediaries, using smart contracts on platforms like Ethereum.
In 2024, we are witnessing an explosion of new DeFi projects and protocols, each pushing the boundaries of what’s possible in finance.
In summary, DeFi in 2024 is not just a trend; it’s a revolution that democratizes finance, enhances security and transparency, and fosters continuous innovation. As we proceed through this presentation, we'll explore the various components and services of DeFi in detail, shedding light on how they are transforming the financial landscape.
At Intelisync, we specialize in providing comprehensive DeFi development services tailored to meet the unique needs of our clients. From smart contract development to dApp creation and security audits, we ensure that your DeFi project is built with innovation, security, and scalability in mind. Trust Intelisync to guide you through the intricate landscape of decentralized finance and unlock the full potential of blockchain technology.
Ready to take your DeFi project to the next level? Partner with Intelisync for expert DeFi development services today!
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/temporal-event-neural-networks-a-more-efficient-alternative-to-the-transformer-a-presentation-from-brainchip/
Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional streaming data, facilitating advancements in object detection, action recognition, speech enhancement and language model/sequence generation. Through the utilization of polynomial-based continuous convolutions, TENNs streamline models, expedite training processes and significantly diminish memory requirements, achieving notable reductions of up to 50x in parameters and 5,000x in energy consumption compared to prevailing methodologies like transformers.
Integration with BrainChip’s Akida neuromorphic hardware IP further enhances TENNs’ capabilities, enabling the realization of highly capable, portable and passively cooled edge devices. This presentation delves into the technical innovations underlying TENNs, presents real-world benchmarks, and elucidates how this cutting-edge approach is positioned to revolutionize edge AI across diverse applications.
Azure API Management to expose backend services securely
Phylogenetic Workflows
1. Phylogenetic Workflows: Tree Building and Post-tree Analyses Naim Matasci The iPlant Collaborative Plant Biology 2011 August 6-10, 2011
2. Why is the tree of life important? “ Knowledge of evolutionary relationships is fundamental to biology, yielding new insights across the plant sciences, from comparative genomics and molecular evolution, to plant development, to the study of adaptation, speciation, community assembly, and ecosystem functioning.”
3. Nothing in biology makes sense except in the light of evolution. T. G. Dobzahnsky
Our understanding of the phylogeny of the half million known species of green plants has expanded dramatically over the past two decades, The task of assembling a comprehensive "tree of life" for them presents a Grand Challenge. Also part of the grand challenge is developing the necessary infrastructre to view and use the tree of life, to put it into the hands of plant biologists
Left tree: Maple tree phylogeny from D. Ackerly Left picture: Joe Felsenstein, ca. 1980 Right picture: Ranger cluster at TACC