The presentation includes preliminary information about the big data mainly metagenomic data and discussions related to the hurdles in analyzing using conventional approaches. In the later part, brief introduction about machine learning approaches using biological example for each. In the last, work done with special focus on implementation of a machine learning approach Random Forest for the functional annotation and taxonomic classification of metagenomic data.
Building a Community Cyberinfrastructure to Support Marine Microbial Ecology ...Larry Smarr
06.09.15
Invited Talk
2006 Synthetic Biology Symposium
Aliso Creek Inn
Title: Building a Community Cyberinfrastructure to Support Marine Microbial Ecology Metagenomics
Laguna Beach, CA
This document provides an overview of genomics and metagenomics. It begins with an introduction to genomics, describing genome assembly, validation, and metabolic reconstruction. It then covers metagenomics, discussing its history, pitfalls, and potentials. Key points include that genomics analyzes the parts list of a single genome, while metagenomics analyzes the collective genomes of an entire microbial community. Metagenomics has been used to explore novel sequences from various environments, perform comparative analyses between ecosystems, and extract genomes from low-abundance species.
Building an Information Infrastructure to Support Microbial Metagenomic SciencesLarry Smarr
06.01.14
Presentation for the Microbe Project Interagency Team
Title: Building an Information Infrastructure to Support Microbial Metagenomic Sciences
La Jolla, CA
Microbial Metagenomics Drives a New CyberinfrastructureLarry Smarr
06.03.03
Invited Talk
School of Biological Sciences
University of California, Irvine
Title: Microbial Metagenomics Drives a New Cyberinfrastructure
Irvine, CA
The document discusses metagenomics analysis tools and challenges. It summarizes several metagenome analysis portals that provide computational analysis and public sample databases. It also discusses the rapid growth of metagenomic data being produced, challenges around quality control, feature identification, characterization and presentation of metagenomic data, and the need for standardized metadata and data formats. The future directions highlighted include studying strain variation, expanding metadata capture and standards, and developing improved assembly, binning and analysis methods.
Building a Community Cyberinfrastructure to Support Marine Microbial Ecology ...Larry Smarr
06.09.15
Invited Talk
2006 Synthetic Biology Symposium
Aliso Creek Inn
Title: Building a Community Cyberinfrastructure to Support Marine Microbial Ecology Metagenomics
Laguna Beach, CA
This document provides an overview of genomics and metagenomics. It begins with an introduction to genomics, describing genome assembly, validation, and metabolic reconstruction. It then covers metagenomics, discussing its history, pitfalls, and potentials. Key points include that genomics analyzes the parts list of a single genome, while metagenomics analyzes the collective genomes of an entire microbial community. Metagenomics has been used to explore novel sequences from various environments, perform comparative analyses between ecosystems, and extract genomes from low-abundance species.
Building an Information Infrastructure to Support Microbial Metagenomic SciencesLarry Smarr
06.01.14
Presentation for the Microbe Project Interagency Team
Title: Building an Information Infrastructure to Support Microbial Metagenomic Sciences
La Jolla, CA
Microbial Metagenomics Drives a New CyberinfrastructureLarry Smarr
06.03.03
Invited Talk
School of Biological Sciences
University of California, Irvine
Title: Microbial Metagenomics Drives a New Cyberinfrastructure
Irvine, CA
The document discusses metagenomics analysis tools and challenges. It summarizes several metagenome analysis portals that provide computational analysis and public sample databases. It also discusses the rapid growth of metagenomic data being produced, challenges around quality control, feature identification, characterization and presentation of metagenomic data, and the need for standardized metadata and data formats. The future directions highlighted include studying strain variation, expanding metadata capture and standards, and developing improved assembly, binning and analysis methods.
PROKARYOTIC TRANSCRIPTOMICS AND METAGENOMICSLubna MRL
After billions of years of evolution, prokaryotes have developed a huge diversity of regulatory mechanisms, many of which are probably uncharacterized. Now that the powerful tool of whole-transcriptome analysis can be used to study the RNA of bacteria and archaea, a new set of un expected RNA-based regulatory strategies might be revealed.
Metagenomics, together with in vitro evolution and high-throughput screening technologies, provides industry with an unprecedented chance to bring biomolecules into industrial application.
10.02.19
Invited talk
Symposium #1816, Managing the Exaflood: Enhancing the Value of Networked Data for Science and Society
Title: Advancing the Metagenomics Revolution
San Diego, CA
The Emerging Global Collaboratory for Microbial Metagenomics ResearchersLarry Smarr
08.07.30
Invited Talk
Delivered From Calit2@UCSD
Monash University MURPA Lecture
Title: The Emerging Global Collaboratory for Microbial Metagenomics Researchers
Melbourne, Australia
Viral Metagenomics (CABBIO 20150629 Buenos Aires)bedutilh
This is a one-hour lecture about metagenomics, focusing on discovery of viruses and unknown sequence elements. It is part of a one-day workshop about metagenome assembly of crAssphage, a bacteriophage virus found in human gut. The hands-on workflow can be found at http://tbb.bio.uu.nl/dutilh/CABBIO/ and should be doable in one afternoon with supervision. There is also an iPython notebook about this here: https://github.com/linsalrob/CrAPy
Cross-Kingdom Standards in Genomics, Epigenomics and MetagenomicsChristopher Mason
This document outlines plans for multi-site sequencing studies to generate standardized human and bacterial genome sequencing datasets. Samples include a human trio, bacterial isolates, and mixtures, which will be sequenced in triplicate across three sites on various platforms including Illumina HiSeq X Ten, HiSeq 4000, HiSeq 2500, NextSeq 500, Life Tech Ion Proton, Ion S5, Pacific Biosciences, Oxford Nanopore, and others. The goals are to measure intra- and inter-lab variation, sequencing performance at GC extremes, and establish molecular standards for assessing sequencing methods in DNA, RNA, and metagenomics. Data will be analyzed by a team to benchmark tools and published by October 2017.
Metagenomics is the study of metagenomes, genetic material recovered directly from environmental samples. The broad field was referred to as environmental genomics, ecogenomics or community genomics. Recent studies use "shotgun" Sanger sequencing or next generation sequencing (NGS) to get largely unbiased samples of all genes from all the members of the sampled communities.
Meren's pirate presentation at the STAMPS course to talk about the basic concepts most binning algorithms use to bin contigs into genome bins: sequence composition, and differential coverage.
This document provides an overview of metagenomic analysis. It discusses collecting metagenomic data through sampling and sequencing environmental samples. It then covers various bioinformatics approaches used in metagenomic analysis such as assembly, binning, and annotation of sequencing data. Specific tools and algorithms for these approaches are also described, including reference-based and de novo assembly, compositional and similarity-based binning methods like AbundanceBin.
Shotgun metagenomics involves collecting environmental samples, extracting DNA from the samples, sequencing the DNA using shotgun sequencing, and then analyzing the sequence data computationally. Key steps include assembling reads into longer contigs to aid analysis and annotation. While assembly works well for some datasets, challenges include repeats, low coverage of low-abundance species, and strain variation. High coverage, often 10x or more per genome, is critical for robust assembly. The amount of sequencing needed can be substantial, such as terabases of data to deeply sample microbial communities.
Creating a Cyberinfrastructure for Advanced Marine Microbial Ecology Research...Larry Smarr
The document discusses the creation of the Cyberinfrastructure for Advanced Marine Microbial Ecology Research and Analysis (CAMERA) project. CAMERA aims to provide metagenomic sequencing and analysis of marine microbes at high speeds. It will include data from the Sorcerer II expedition and other projects. The document outlines how CAMERA will utilize Calit2's infrastructure including high-performance computing resources and optical networks to enable remote interactive analysis of large-scale genomic and environmental data sets.
Metagenomics is the study of genetic material recovered directly from environmental samples without culturing organisms. It allows researchers to study the 99.9% of microorganisms that cannot be cultured. Metagenomic analyses of ocean samples revealed over a million new genes and unexpected light-energy pathways in bacteria. Metagenomics has two main approaches - sequence-driven which sequences DNA and compares to databases, and function-driven which screens DNA clones for a desired function. Both approaches have limitations but are complementary. Metagenomics has applications in discovering new antibiotics and enzymes and studying human microbiomes and antibiotic resistance.
The document discusses the rise of big data in microbiology due to decreasing costs of DNA sequencing and computational resources. It describes how high-throughput sequencing is generating vast amounts of microbial genomic and metagenomic data. However, analyzing these large, complex datasets presents numerous technical and social challenges for microbiologists, including handling data volume, integrating diverse data types, accessing resources, and incentivizing data sharing. Overcoming these bottlenecks will be key to unlocking the scientific insights contained within the microbial "big data" tidal wave.
Dag Harmsen presented on the evolvement and challenges of cgMLST for the harmonization of bacterial genome sequencing and analysis. Key points include:
- cgMLST (core genome multilocus sequence typing) involves identifying and comparing alleles across a fixed set of core genome genes and has been applied to outbreak investigation and global pathogen nomenclature.
- Tools for cgMLST analysis have been developed and improved to work on read, draft, and complete genome levels and allow scalable, additive analysis of single genes to whole genomes.
- Standardizing a hierarchical cgMLST-based approach and developing common nomenclature poses challenges but is important for microbial genotypic surveillance across laboratories and countries.
Whole genome sequencing of bacteria & analysisdrelamuruganvet
This document discusses the history and advancements of whole genome sequencing of bacteria. It begins with early sequencing methods like Sanger sequencing and describes the development of next generation sequencing technologies like 454 sequencing, Illumina sequencing, and third generation single molecule sequencing. The document then discusses genome assembly, annotation, and various applications of bacterial genome sequencing like identification of genes and SNPs, comparative genomics, and metagenomics. Important databases for bacterial genomic data are also listed.
Metagenomics is the study of microbial communities directly from environmental samples without culturing individual species. It sequences all DNA from a sample simultaneously, bypassing the need for culture. Analysis of metagenomic data involves screening and phylogenetic studies of the large amounts of sequence data. Metagenomics can provide insights into microbial community structure and interactions, and discover novel enzymes and genes with industrial or pharmaceutical applications. Challenges include DNA purification issues, contamination, sequencing errors, and difficulties assembling less abundant genomes from immense metagenomic datasets.
Tom Delmont: From the Terragenome Project to Global Metagenomic Comparisons: ...GigaScience, BGI Hong Kong
This document discusses challenges in comparing metagenomic data from different environments and studies. It argues that when exploring a new environment, multiple methodological approaches should be used to capture natural and methodological variations. When performing global comparisons, methodological variations should be considered for all environments. Defining ecosystems precisely at the microorganism level is important. The author's vision is for projects like the Earth Microbiome Project to use flexible experimental designs informed by different experts to best represent microbial communities.
Metagenomics research is a vast field which studies about the genetic system of the
environmental samples. Binning is a bioinformatics tool. Binning tool helps to analyses the
genomic analysis of the environmental samples.The
This document provides an overview of bioinformatics and discusses key concepts like:
- Bioinformatics combines biology, computer science, and information technology to analyze large amounts of biological data.
- High-throughput DNA sequencing has generated vast genomic data that requires bioinformatics tools and databases accessible via the internet to analyze and share.
- Popular sequence alignment tools like BLAST, FASTA, and ClustalW are used to search databases and compare sequences, helping researchers analyze genes and genomes.
PROKARYOTIC TRANSCRIPTOMICS AND METAGENOMICSLubna MRL
After billions of years of evolution, prokaryotes have developed a huge diversity of regulatory mechanisms, many of which are probably uncharacterized. Now that the powerful tool of whole-transcriptome analysis can be used to study the RNA of bacteria and archaea, a new set of un expected RNA-based regulatory strategies might be revealed.
Metagenomics, together with in vitro evolution and high-throughput screening technologies, provides industry with an unprecedented chance to bring biomolecules into industrial application.
10.02.19
Invited talk
Symposium #1816, Managing the Exaflood: Enhancing the Value of Networked Data for Science and Society
Title: Advancing the Metagenomics Revolution
San Diego, CA
The Emerging Global Collaboratory for Microbial Metagenomics ResearchersLarry Smarr
08.07.30
Invited Talk
Delivered From Calit2@UCSD
Monash University MURPA Lecture
Title: The Emerging Global Collaboratory for Microbial Metagenomics Researchers
Melbourne, Australia
Viral Metagenomics (CABBIO 20150629 Buenos Aires)bedutilh
This is a one-hour lecture about metagenomics, focusing on discovery of viruses and unknown sequence elements. It is part of a one-day workshop about metagenome assembly of crAssphage, a bacteriophage virus found in human gut. The hands-on workflow can be found at http://tbb.bio.uu.nl/dutilh/CABBIO/ and should be doable in one afternoon with supervision. There is also an iPython notebook about this here: https://github.com/linsalrob/CrAPy
Cross-Kingdom Standards in Genomics, Epigenomics and MetagenomicsChristopher Mason
This document outlines plans for multi-site sequencing studies to generate standardized human and bacterial genome sequencing datasets. Samples include a human trio, bacterial isolates, and mixtures, which will be sequenced in triplicate across three sites on various platforms including Illumina HiSeq X Ten, HiSeq 4000, HiSeq 2500, NextSeq 500, Life Tech Ion Proton, Ion S5, Pacific Biosciences, Oxford Nanopore, and others. The goals are to measure intra- and inter-lab variation, sequencing performance at GC extremes, and establish molecular standards for assessing sequencing methods in DNA, RNA, and metagenomics. Data will be analyzed by a team to benchmark tools and published by October 2017.
Metagenomics is the study of metagenomes, genetic material recovered directly from environmental samples. The broad field was referred to as environmental genomics, ecogenomics or community genomics. Recent studies use "shotgun" Sanger sequencing or next generation sequencing (NGS) to get largely unbiased samples of all genes from all the members of the sampled communities.
Meren's pirate presentation at the STAMPS course to talk about the basic concepts most binning algorithms use to bin contigs into genome bins: sequence composition, and differential coverage.
This document provides an overview of metagenomic analysis. It discusses collecting metagenomic data through sampling and sequencing environmental samples. It then covers various bioinformatics approaches used in metagenomic analysis such as assembly, binning, and annotation of sequencing data. Specific tools and algorithms for these approaches are also described, including reference-based and de novo assembly, compositional and similarity-based binning methods like AbundanceBin.
Shotgun metagenomics involves collecting environmental samples, extracting DNA from the samples, sequencing the DNA using shotgun sequencing, and then analyzing the sequence data computationally. Key steps include assembling reads into longer contigs to aid analysis and annotation. While assembly works well for some datasets, challenges include repeats, low coverage of low-abundance species, and strain variation. High coverage, often 10x or more per genome, is critical for robust assembly. The amount of sequencing needed can be substantial, such as terabases of data to deeply sample microbial communities.
Creating a Cyberinfrastructure for Advanced Marine Microbial Ecology Research...Larry Smarr
The document discusses the creation of the Cyberinfrastructure for Advanced Marine Microbial Ecology Research and Analysis (CAMERA) project. CAMERA aims to provide metagenomic sequencing and analysis of marine microbes at high speeds. It will include data from the Sorcerer II expedition and other projects. The document outlines how CAMERA will utilize Calit2's infrastructure including high-performance computing resources and optical networks to enable remote interactive analysis of large-scale genomic and environmental data sets.
Metagenomics is the study of genetic material recovered directly from environmental samples without culturing organisms. It allows researchers to study the 99.9% of microorganisms that cannot be cultured. Metagenomic analyses of ocean samples revealed over a million new genes and unexpected light-energy pathways in bacteria. Metagenomics has two main approaches - sequence-driven which sequences DNA and compares to databases, and function-driven which screens DNA clones for a desired function. Both approaches have limitations but are complementary. Metagenomics has applications in discovering new antibiotics and enzymes and studying human microbiomes and antibiotic resistance.
The document discusses the rise of big data in microbiology due to decreasing costs of DNA sequencing and computational resources. It describes how high-throughput sequencing is generating vast amounts of microbial genomic and metagenomic data. However, analyzing these large, complex datasets presents numerous technical and social challenges for microbiologists, including handling data volume, integrating diverse data types, accessing resources, and incentivizing data sharing. Overcoming these bottlenecks will be key to unlocking the scientific insights contained within the microbial "big data" tidal wave.
Dag Harmsen presented on the evolvement and challenges of cgMLST for the harmonization of bacterial genome sequencing and analysis. Key points include:
- cgMLST (core genome multilocus sequence typing) involves identifying and comparing alleles across a fixed set of core genome genes and has been applied to outbreak investigation and global pathogen nomenclature.
- Tools for cgMLST analysis have been developed and improved to work on read, draft, and complete genome levels and allow scalable, additive analysis of single genes to whole genomes.
- Standardizing a hierarchical cgMLST-based approach and developing common nomenclature poses challenges but is important for microbial genotypic surveillance across laboratories and countries.
Whole genome sequencing of bacteria & analysisdrelamuruganvet
This document discusses the history and advancements of whole genome sequencing of bacteria. It begins with early sequencing methods like Sanger sequencing and describes the development of next generation sequencing technologies like 454 sequencing, Illumina sequencing, and third generation single molecule sequencing. The document then discusses genome assembly, annotation, and various applications of bacterial genome sequencing like identification of genes and SNPs, comparative genomics, and metagenomics. Important databases for bacterial genomic data are also listed.
Metagenomics is the study of microbial communities directly from environmental samples without culturing individual species. It sequences all DNA from a sample simultaneously, bypassing the need for culture. Analysis of metagenomic data involves screening and phylogenetic studies of the large amounts of sequence data. Metagenomics can provide insights into microbial community structure and interactions, and discover novel enzymes and genes with industrial or pharmaceutical applications. Challenges include DNA purification issues, contamination, sequencing errors, and difficulties assembling less abundant genomes from immense metagenomic datasets.
Tom Delmont: From the Terragenome Project to Global Metagenomic Comparisons: ...GigaScience, BGI Hong Kong
This document discusses challenges in comparing metagenomic data from different environments and studies. It argues that when exploring a new environment, multiple methodological approaches should be used to capture natural and methodological variations. When performing global comparisons, methodological variations should be considered for all environments. Defining ecosystems precisely at the microorganism level is important. The author's vision is for projects like the Earth Microbiome Project to use flexible experimental designs informed by different experts to best represent microbial communities.
Metagenomics research is a vast field which studies about the genetic system of the
environmental samples. Binning is a bioinformatics tool. Binning tool helps to analyses the
genomic analysis of the environmental samples.The
This document provides an overview of bioinformatics and discusses key concepts like:
- Bioinformatics combines biology, computer science, and information technology to analyze large amounts of biological data.
- High-throughput DNA sequencing has generated vast genomic data that requires bioinformatics tools and databases accessible via the internet to analyze and share.
- Popular sequence alignment tools like BLAST, FASTA, and ClustalW are used to search databases and compare sequences, helping researchers analyze genes and genomes.
This document discusses challenges and approaches for assembling large metagenomic and genomic datasets using short read sequencing data. Three main challenges are discussed: 1) Assembling the parasitic nematode H. contortus genome due to high polymorphism and repeats. Digital normalization helped enable assembly by reducing redundancy and errors. 2) Assembling the lamprey transcriptome with no reference and too much data. Digital normalization reduced the data volume and enabled assembly. 3) Assembling large soil metagenomic datasets, which are difficult due to their scale and complexity. Data partitioning separates reads into bins to enable "divide and conquer" assembly approaches. While progress has been made, challenges around strain variation, scaffolding, and connecting
WHAT IS BIOINFORMATICS?
Computational Biology/Bioinformatics is the application of computer sciences and allied technologies to answer the questions of Biologists, about the mysteries of life. It has evolved to serve as the bridge between:
Observations (data) in diverse biologically-related disciplines and
The derivations of understanding (information)
APPLICATIONS OF BIOINFORMATICS
Computer Aided Drug Design
Microarray Bioinformatics
Proteomics
Genomics
Biological Databases
Phylogenetics
Systems Biology
Bioinformatics is the application of computational tools and techniques to analyze and interpret biological data. It involves the development of these tools and databases, as well as their application to better understand biological systems and functions at the molecular level through analysis of genetic sequences, protein structures, and more. The goal is to gain a global understanding of cellular functions by analyzing genetic data as dictated by the central dogma of biology, and relating sequence information to protein functions and cellular processes.
Experimental methods and the big data sets improvemed
1. Experiments in systems biology use quantitative data from multiple omics techniques like microarrays, sequencing, proteomics, lipidomics, and metabolomics to study biological systems.
2. Computational models are used to simulate dynamic changes in molecules over time based on precise quantification from experiments.
3. Both hypothesis-generating and hypothesis-driven studies are important in systems biology, with the latter focusing on targeted subsets of molecules or organelles.
Microbiome studies using 16S ribosomal DNA PCR: some cautionary tales.jennomics
Presentation at a workshop conducted by the UC Davis Bioinformatics Core Facility: Using the Linux Command Line for Analysis of High Throughput Sequence Data, September 15-19, 2014
Large scale machine learning challenges for systems biologyMaté Ongenaert
Large scale machine learning challenges for systems biology
by dr. Yvan Saeys - Machine Learning and Data Mining group, Bioinformatics and Systems Biology Division, VIB-UGent Department of Plant Systems Biology
Due to technological advances, the amount of biological data, and the pace at which it is generated has increased dramatically during the past decade. To extract new knowledge from these ever increasing data sets, automated techniques such as data mining and machine learning techniques have become standard practice.
In this talk, I will give an overview of large scale machine learning challenges in bioinformatics and systems biology, highlighting the importance of using scalable and robust techniques such as ensemble learning methods implemented on large computing grids.
I will present some of our state-of-the-art tools to solve problems such as biomarker discovery, large scale network inference, and biomedical text mining at PubMed scale.
This document provides an overview of bioinformatics and discusses key topics in the field. It begins by defining bioinformatics as the application of information technology to the analysis and management of biological data, facilitated by the use of computers. It then lists some common applications of bioinformatics like sequence analysis, molecular modeling, phylogeny analysis, and medical informatics. The document also discusses some of the promises of genomics and bioinformatics for applications in medicine, agriculture, and other fields. It provides a brief history of the emergence of bioinformatics as a field in the 1990s. Finally, it outlines some of the main topics that will be covered in the bioinformatics course, including databases, algorithms, interface design, and computational methods.
Here are some suggestions for open online bioinformatics lectures and courses from famous universities:
- MIT OpenCourseWare has free bioinformatics course materials and videos from MIT courses.
- edX has massive open online courses (MOOCs) in bioinformatics from universities like Harvard, Berkeley, MIT. Some are free to audit.
- Coursera has bioinformatics courses from top universities like Johns Hopkins, University of Toronto, Peking University.
- YouTube has full lecture videos from bioinformatics courses at universities like Stanford, UC San Diego, University of Cambridge.
- Khan Academy has introductory bioinformatics lectures on topics like sequence alignment, gene finding, protein structure.
- EMBL-
This document provides an overview of cloud bioinformatics and the challenges of analyzing large datasets from next-generation sequencing (NGS). It discusses how bioinformatics uses computational methods to study genes, proteins, and genomes. The advent of NGS has led to huge datasets that require high-performance computing. Cloud computing provides access to pooled computing resources in a cost-effective manner and helps address the bioinformatics challenge of assembling and analyzing NGS data. The document also outlines common bioinformatics software and resources available through WestGrid and Galaxy that can be used for sequence assembly, annotation, and other applications.
The document describes a seminar on high-throughput sequencing bioinformatics. It discusses analyzing microbiome samples using 16S rRNA sequencing and tools like Mothur and QIIME. It provides an overview of analyzing 16S sequences, including quality filtering, OTU clustering, classification, and diversity analysis. It also outlines running a Mothur tutorial to analyze a mock microbiome dataset from 21 samples using the Mothur MiSeq standard operating procedure.
Bioinformatics is the application of information technology to the storage, management and analysis of biological information. It involves using computers to analyze biological data, especially DNA and protein sequences. Some key areas of bioinformatics include sequence analysis, molecular modeling, phylogeny/evolution, medical informatics, image analysis, and statistics. Bioinformatics has many applications in medicine like genome analysis for genetic diseases, understanding drug effects, and facilitating drug design. It can also be applied to crop and livestock improvement. Next generation sequencing technologies like Illumina, SOLiD, and 454 have enabled rapid DNA sequencing at a lower cost compared to Sanger sequencing. These new technologies are driving major advances in biological research.
Biotechnophysics: DNA Nanopore SequencingMelanie Swan
Biophysics (not merely bioengineering) is required to understand the fundamental mechanisms of biology in order to make technologies (bench and bioinformatic) for understanding them
This document provides an overview of bioinformatics. It defines bioinformatics as the science of collecting, analyzing and conceptualizing biological data through computational techniques. It discusses that bioinformatics involves managing, organizing and processing biological information from databases, as well as analyzing, visualizing and sharing biological data over the internet. It also outlines some of the goals of bioinformatics like organizing the human and mouse genomes, as well as some applications like genomic and protein sequence analysis, protein structure prediction, and characterizing genomes.
Bioinformatics is an interdisciplinary field that uses computational tools to analyze and manage biological data such as genes, genomes, proteins, and medical information. It involves developing mathematical models to understand relationships in complex biological systems. Key areas include analyzing protein and gene sequences, structures, and functions; understanding evolution and molecular interactions; and developing "virtual cells" through integrated modeling. Major challenges include integrating heterogeneous biological data sources and developing robust computational methods.
Bioinformatics issues and challanges presentation at s p collegeSKUASTKashmir
This document provides an overview of bioinformatics and some key concepts:
- It discusses the exponential growth of biological data from technologies like PCR and microarrays, and how bioinformatics is needed to analyze this data.
- Bioinformatics is defined as integrating biology and computer science to collect, analyze, and interpret large amounts of molecular-level information. It uses databases and tools to study genomes, proteins, and biological processes.
- Major databases like GenBank, EMBL, and SwissProt store DNA, RNA, protein sequences and provide access to researchers. Tools like BLAST are used to search databases and analyze sequences.
- Benefits of bioinformatics include advances in medicine, agriculture, forensics
This document provides an overview of downstream analyses that can be performed after variant identification and filtering in a typical variant calling pipeline. It discusses visualization of variant data in each gene to identify potential causative variants. It also mentions association studies as another type of downstream analysis where variants are tested for association with disease phenotypes. The goal of downstream analyses is to help prioritize variants for further investigation.
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.
[DSC Europe 23][DigiHealth] Vesna Pajic - Machine Learning Techniques for omi...DataScienceConferenc1
The document discusses machine learning techniques for analyzing omics data. It introduces Velsera, a bioinformatics company, and describes how they used machine learning to predict cancer cell line responses to drugs based on gene expression data. Specifically, they cleaned the data, performed feature selection, and tested models like elastic net, GAMs, and XGBoost (which performed best). The final model identified 20 important genes, including one the client was interested in and another potential biomarker the client was unaware of.
Travis Hills of MN is Making Clean Water Accessible to All Through High Flux ...Travis Hills MN
By harnessing the power of High Flux Vacuum Membrane Distillation, Travis Hills from MN envisions a future where clean and safe drinking water is accessible to all, regardless of geographical location or economic status.
Mending Clothing to Support Sustainable Fashion_CIMaR 2024.pdfSelcen Ozturkcan
Ozturkcan, S., Berndt, A., & Angelakis, A. (2024). Mending clothing to support sustainable fashion. Presented at the 31st Annual Conference by the Consortium for International Marketing Research (CIMaR), 10-13 Jun 2024, University of Gävle, Sweden.
ESA/ACT Science Coffee: Diego Blas - Gravitational wave detection with orbita...Advanced-Concepts-Team
Presentation in the Science Coffee of the Advanced Concepts Team of the European Space Agency on the 07.06.2024.
Speaker: Diego Blas (IFAE/ICREA)
Title: Gravitational wave detection with orbital motion of Moon and artificial
Abstract:
In this talk I will describe some recent ideas to find gravitational waves from supermassive black holes or of primordial origin by studying their secular effect on the orbital motion of the Moon or satellites that are laser ranged.
ESR spectroscopy in liquid food and beverages.pptxPRIYANKA PATEL
With increasing population, people need to rely on packaged food stuffs. Packaging of food materials requires the preservation of food. There are various methods for the treatment of food to preserve them and irradiation treatment of food is one of them. It is the most common and the most harmless method for the food preservation as it does not alter the necessary micronutrients of food materials. Although irradiated food doesn’t cause any harm to the human health but still the quality assessment of food is required to provide consumers with necessary information about the food. ESR spectroscopy is the most sophisticated way to investigate the quality of the food and the free radicals induced during the processing of the food. ESR spin trapping technique is useful for the detection of highly unstable radicals in the food. The antioxidant capability of liquid food and beverages in mainly performed by spin trapping technique.
Authoring a personal GPT for your research and practice: How we created the Q...Leonel Morgado
Thematic analysis in qualitative research is a time-consuming and systematic task, typically done using teams. Team members must ground their activities on common understandings of the major concepts underlying the thematic analysis, and define criteria for its development. However, conceptual misunderstandings, equivocations, and lack of adherence to criteria are challenges to the quality and speed of this process. Given the distributed and uncertain nature of this process, we wondered if the tasks in thematic analysis could be supported by readily available artificial intelligence chatbots. Our early efforts point to potential benefits: not just saving time in the coding process but better adherence to criteria and grounding, by increasing triangulation between humans and artificial intelligence. This tutorial will provide a description and demonstration of the process we followed, as two academic researchers, to develop a custom ChatGPT to assist with qualitative coding in the thematic data analysis process of immersive learning accounts in a survey of the academic literature: QUAL-E Immersive Learning Thematic Analysis Helper. In the hands-on time, participants will try out QUAL-E and develop their ideas for their own qualitative coding ChatGPT. Participants that have the paid ChatGPT Plus subscription can create a draft of their assistants. The organizers will provide course materials and slide deck that participants will be able to utilize to continue development of their custom GPT. The paid subscription to ChatGPT Plus is not required to participate in this workshop, just for trying out personal GPTs during it.
PPT on Direct Seeded Rice presented at the three-day 'Training and Validation Workshop on Modules of Climate Smart Agriculture (CSA) Technologies in South Asia' workshop on April 22, 2024.
The debris of the ‘last major merger’ is dynamically youngSérgio Sacani
The Milky Way’s (MW) inner stellar halo contains an [Fe/H]-rich component with highly eccentric orbits, often referred to as the
‘last major merger.’ Hypotheses for the origin of this component include Gaia-Sausage/Enceladus (GSE), where the progenitor
collided with the MW proto-disc 8–11 Gyr ago, and the Virgo Radial Merger (VRM), where the progenitor collided with the
MW disc within the last 3 Gyr. These two scenarios make different predictions about observable structure in local phase space,
because the morphology of debris depends on how long it has had to phase mix. The recently identified phase-space folds in Gaia
DR3 have positive caustic velocities, making them fundamentally different than the phase-mixed chevrons found in simulations
at late times. Roughly 20 per cent of the stars in the prograde local stellar halo are associated with the observed caustics. Based
on a simple phase-mixing model, the observed number of caustics are consistent with a merger that occurred 1–2 Gyr ago.
We also compare the observed phase-space distribution to FIRE-2 Latte simulations of GSE-like mergers, using a quantitative
measurement of phase mixing (2D causticality). The observed local phase-space distribution best matches the simulated data
1–2 Gyr after collision, and certainly not later than 3 Gyr. This is further evidence that the progenitor of the ‘last major merger’
did not collide with the MW proto-disc at early times, as is thought for the GSE, but instead collided with the MW disc within
the last few Gyr, consistent with the body of work surrounding the VRM.
The cost of acquiring information by natural selectionCarl Bergstrom
This is a short talk that I gave at the Banff International Research Station workshop on Modeling and Theory in Population Biology. The idea is to try to understand how the burden of natural selection relates to the amount of information that selection puts into the genome.
It's based on the first part of this research paper:
The cost of information acquisition by natural selection
Ryan Seamus McGee, Olivia Kosterlitz, Artem Kaznatcheev, Benjamin Kerr, Carl T. Bergstrom
bioRxiv 2022.07.02.498577; doi: https://doi.org/10.1101/2022.07.02.498577
1. Computational Analysis of High-throughput
Biological Data Using
Machine Learning Approaches
Ashok K Sharma
1220104
MetaInformatics Laboratory
IISER Bhopal
2. Topics Covered in the Talk
Introduction
Beginning of Genomics
Current Sequencing Scenario
Metagenomic Approaches
The Conventional Approach for Data Analysis- Limitations
Machine Learning Approaches and their Implementations
SVM
HMM
Naive Bayes
Random Forest
Discuss the Work Done So Far
Future Directions
3. Beginning of Genomics
First DNA isolated by Swiss
physician Friedrich
Miescher in 1869
The term genome was used
by German botanist Hans
Winker in 1920
The history of modern genomics began in 1970s
Nucleotide sequence of
bacteriophage lambda DNA
F. Sanger et. al., J Mol Biol, 1982
Whole-genome random
sequencing and assembly of
Haemophilus influenzae Rd
Fleischmann RD et. al., Science, 1995
Sequencing and analysis of the
human genome
ES Lander et. al., Nature, 2001
~48 kb ~1,800 kb
3 billion bp
3 billion USD
10 Years
4. Sequencer Read length ~ Cost /Mb ~ Data/run
Roche 454 400-800bp 20$ 450Mb
Ion Torrent 200bp 2$ 10Mb-1Gb
Illumina 150bp 0.50$ 600Gb
PacBio SMRT ~20kb 1.4$ 350Mb
Ion TorrentRoche 454 sequencer Illumina/Solexa Sequencer
Next Generation Sequencing Technologies
Leading to The Sequencing Era
5. Metagenomics: New Approach to Sequence the Unknown
•The first idea of cloning DNA directly from environmental samples was proposed by Pace in 1985
•The term “metagenome” was coined by Handelsman in 1998
The First Large Scale Metagenomics Project:
Environmental Genome Shotgun Sequencing
of the Sargasso Sea
C. Ventor et. al., Science, 2004
The First Large Scale Organismal Study:
Model Study Comparing the Gut Flora of 124
European individuals
Qin et. al., Nature, 2009
1.6 GB and 1.2 million genes 576.7 GB and 3.3 million genes
98% bacteria cannot be cultured and hence cannot be sequenced
6. Genomics and Metagenomics Have Exponentially
Increased the Sequence databases
Published papers on
“Metagenomics” in
PubMed
$1000
$100M
Cost per
Human Genome
180
140
100
60
20
1984 1988 1992 1996 2000 2004 2008 2012
Sequences(inmillions)
Growth of GenBank
(1984-2013)
• Metagenomic: 538
• Non-Metagenomic: 18787
https://gold.jgi-psf.org/
• Running projects:
10. Genomics vs Metagenomics
…GGATCCATCGTACCGATTC..
…TTACAATTTA…
…CCATGGCCGAAATTTCGTA…
…AGCTAAAATTACCGGGGAT…
Community of
Microbial
Species- Mainly
Unculturable
Fragmentation
of DNA
Sequencing
Analysis
Culture a
Single
Microbe
Fragmentation of
DNA
Sequencing
…AGCTAAAATTACCGGG…
GENOMICS METAGENOMICS
The Metagenomic Challenges
• Assembly
•Taxonomic Assignment
• Metabolic Pathway Construction
• Gene Prediction
• Functional Annotation
• Comparative Analysis
Assembly
DNA Isolation
11. Flow of Presentation
Introduction
Beginning of Genomics
Sequencing era
Metagenomics
The Conventional Approach for Data Analysis- Limitations
Machine learning approaches and their implementations
SVM
HMM
Naive Bayes
Random Forest
Work done
Future directions
12. Conventional Methods Cannot be Used for
Metagenomic Data Analysis
Database : 4.7 million Sequences
Query
Seeds
•Homology Based Approach- BLAST
•Most widely used by researchers
•Dynamic Programming is used
Each sequence is fragmented into seeds and
searched against all sequences of the database
It will take about 17 years on a Xeon 2.6 Ghz PC to carry out the BLAST of
>3 million metagenomic genes from one project
BLAST of 1000 genes against
NR database ~ 1 Day (25.5 Hrs.) ~ 2 Days (47.1 Hrs.)
~10 GB ~13 GB ~17 GB~4 GB< 1 GB
2012
2014
2013
Future
????
NCBI
NR
13. Flow of Presentation
Introduction
Beginning of Genomics
Sequencing era
Metagenomics
The Conventional Approach
Machine learning approaches and their implementations
SVM
HMM
Naive Bayes
Random Forest
Work done
Future directions
14. Key idea: Learnfrom known data and Predicton unknown data
Machine Learning- Valuable Alternatives
Database : 4.7 million SequencesQuery
Searching One against All
Memorize the information
Processing all at Once
• From known examples or dataLearning
• Derives a hypothesis based on training examplesHypothesis
• Based on Hypothesis, predictions on unknown
query
Prediction
16. Properties of Training Examples
• Training Dataset : Well curated and free from
noise.
• Features : Fixed length patterns
MKWMPFVGTMPLVQTKSITDLCAPLC
MMK
KW
WM
MP……………………………….......
M I W . . .
M 0.12 0.34 0.09 . . .
I 0.28 0.19 0.41 . . .
W . . 0.24 . . .
P - 0.17 - - - -
17. Support Vector Machines (SVM)
X2
X1
SVMs finds the maximal margin which separates two classes
Class 1
Class2
18. Support Vector Machines (SVM)
X1
X2
X3
( Ben-Hur, et al., 2008)
Linear
Kernel
Polynomial
Kernel d=2
Gaussian
Kernel,
sigma = 1
X2
X1
Class 1
Class2
19.
20. Carbohydrate Metabolism Amino acid metabolism Nucleotide metabolism
Model 1 Model 2 Model 3
Query Sequence
Prediction
Value 1
Prediction
Value 2
Prediction
Value 3
Feature Extraction
Mapped into high-dimension
feature space
Classification Based on
Maximum Prediction Value
•Dipeptide Frequency
AD : 0.08
RH : 0.02
•Amino Acid Composition
A : 0.14
F : 0.05
21. GPCRpred: an SVM-based method for prediction of families and
subfamilies of G-protein coupled receptors
Bhasin M , and Raghava G P S Nucl. Acids Res. 2004;32:W383-W389
Protein Sequence
SVM
Is GPCR ?
SVM
SVMSVMSVMSVM
GPCR
Recognition
Family
Prediction
Sub-Family
Prediction
99.5%
Accuracy
G-protein coupled
receptors (GPCRs)
important targets for drug
design.
Dipeptide
frequency
used as a
feature
22. Hidden Markov Models (HMM)
• A powerful statistical tool widely used in modeling sequences
• Markov Chains:
AYTGGGTACC
AYT-GGTMCC
AYCGGG-MC-
Making Profiles
What is the
probability of
Rain today?
23. Carbohydrate Metabolism Amino acid metabolism Nucleotide metabolism
HMM Profile Database
QUERY
SEQUENCE
Prediction Based
on Best Profile
Match
24. The Pfam Protein Families Database
• A large collection of protein families, each represented by multiple
sequence alignments and hidden Markov models (HMMs)
• Identification of domains that occur within proteins can provide insights
into their function
Steps used for building of Pfam:
Manually curated collection of protein families (3,071 families)
Each curated family is represented by seed and full alignment
Building HMM profiles using HMMER3.0
Widely used for identification of protein structure and function
Marco Punta et. al., Nucleic Acids Res, 2011
25. Naive Bayes Classifier
1. Simple probabilistic classifier Based on
Bayes’ theorem
2. Goal is to determine the most probable
hypothesis
Prior probability of class
Likelihood of X given that class
X2
X1Class 1 Class 2
Kohenen J. et al. In Silico Biol 2009;9(1-2):23-34.
26. Algorithm: Word sizes between 6 and 9 bases
Word-specific priors: Pi = [n(wi) + 0.5]/(N +1)]
Genus-specific conditional probabilities: P(wi|G) = [m(wi) + Pi]/(M + 1)
Naive Bayesian assignment: P(G|S) = P(S|G) * P(G)/P(S)
Bootstrap confidence estimation: For each query sequence
Naive Bayesian Classifier for Rapid Assignment of
rRNA Sequences into New Bacterial Taxonomy
Qiong Wang et. al., Appl Environ Microbiol, 2007
AUGCGUCAGCUCGAUCGAUCUA
AUGCGUCA
UGCGUCAG
GCGUCAGC
CGUCAGCU
27. Classification and Regression Trees (CART)
X1> C1
1
No Yes
X2> C2
YesNo
2X1> C3
YesNo
2X2> C4
YesNo
1 2
X1
X2
C1 C3
C4
C2
1
2
28. Random Forest
• Collection of unpruned CARTs
• Bagging- avoids overfitting
• Improve prediction accuracy
• Encouraging diversity among the tree
X
Tree 1
Tree 2 Tree 3
Svetnik V. et al., J Chem Inf Comput Sci 2003 Nov-Dec;43(6):1947-58
29. Features of Random Forest
o Cross validation procedure is inbuilt in random forest, as each
tree in the forest has its own training (bootstrap) and test data
(OOB data)
o OOB error rate calculates the overall percentage of
misclassification
o Calculates the important features for the classification
29
30. MODEL
t1
ABC
AB C
A B
t2
ABC
AC
A C
B
t3
ABC
BCA
CB
Query Sequence
Classification Based on the
Majority of votes
Carbohydrate Metabolism Amino acid metabolism Nucleotide metabolism
Feature Extraction
•Dipeptide Frequency
AD : 0.08
RH : 0.02
•Amino Acid Composition
A : 0.14
F : 0.05
31. Prediction of protein-RNA binding site using
Random Forest
Zhi Ping Liu et. al. Bioinformatics,2010
• Protein-RNA interaction plays a key role in number of
biological processes
Dataset:
339 Protein-RNA complexes form RsiteDB
Entangle was used to define the interaction site between
protein chain and RNA
Features:
Interaction propensity, Hydrophobicity, Relative excessive
surface area, Secondary structure, Conservation score and
Side chain environment
32. Machine Learning methods are becoming
popular for Biological Data Analysis
0
100
200
300
400
500
600
700
1976 1993 2003 2013
Numberofpublications
Year
SVM
0
100
200
300
400
500
600
700
1976 1993 2003 2013
Numberofpublications
Year
HMM
0
100
200
300
400
500
600
700
2003 2008 2013
Numberofpublications
Year
Random Forest
http://www.ncbi.nlm.nih.gov/pubmed
33. Flow of Presentation
Introduction
Beginning of Genomics
Sequencing era
Metagenomics
The Conventional Approach
Machine learning approaches and their implementations
SVM
HMM
Naive Bayes
Random Forest
Work done
Future directions
34. Implementation of Machine Learning
for the Analysis of Metagenomic Data
in my Recent Projects
: A fast and accurate functional classifier
of genomic and metagenomic sequences
35. METHODOLOGY: eggNOG database was used
ORF1 ORF2
Sequencing, assembly and ORF predictionMetagenome
Routine task for metagenomic analysis
Class Group Annotation
O Cellular Processes and Signaling Serine-Type endopeptidase
J Information Storage and Processing tRNA synthetase
Functional
Class
Functional
Annotation
2.3 million sequences were
divided in to 22 Functional
Class
Dipeptide as input features
for optimization and
training of Random Forest
Final Random Forest model
was integrated with
RAPsearch2
Manuscript Submitted, 2014
36. Stand alone server
Query Sequence
Genomic Metagenomic
Random Forest
RAPsearch
Functional Class Prediction
Functional Annotation
37. : A Tool for Fast and Accurate
Taxonomic Classification of 16S rRNA
Hypervariable Regions in Metagenomic
Datasets
38. Metagenome
16SrRNA: Marker gene to identify microbial species
Sequencing of either HVR or Complete 16S
Taxonomic Classification
METHODOLOGY: Greengenes database was used
Sequences for hypervariable
regions were extracted and
grouped according to
taxonomic information
4-mer nucleotide
composition were used as
Input feature for training
and optimization of RF
Sequences discarded during
clustering and real
metagenomic 16S sequences
were used for the testing
Routine task for metagenomic analysis
Manuscript Submitted, 2014
39.
40. Flow of Presentation
Introduction
Beginning of Genomics
Sequencing era
Metagenomics
The Conventional Approach
Machine learning approaches and their implementations
SVM
HMM
Naive Bayes
Random Forest
Work done
Future directions
41. Future Directions
• Analysis of metagenomic data generated from
the laboratory projects
• Implementation of machine learning in the
analyses of metagenomic data
• Metabolic pathway analysis and reconstruction
42. Acknowledgement
•Thesis Supervisor : Dr. Vineet Sharma
•Lab Members:
•Dr. Sanjiv Kumar
•Darshan Dhakan
•Ankit Gupta
•Rituja Saxena
•Parul Milttal
•Vishnu Prasoodanan
•Harish K
•Nikhil Chuadhary
•IISER Bhopal for providing the fellowship for doctoral
research
Editor's Notes
History of genomics started when first time dna was isolated by … after the few year later the term genome was given by …. As you all know genome refers to the organisms complete set of the dna which contains the organisms hereditary information.
History of modern genomics began in 1970s when first time sanger time report his methode for determining the order of nucleotides of DNA using chain terminating nucleotide analogues.
In 1982 First bacteriophage genome size of around 48 kb was determined using shotgun digest methode before coming the first automated dna sequencer. From the sequence reading frames for 46 genes were clearly identified.
There was the long wait after bacteriophage genome sequence and than first free living bacteria was sequenced in 1995, complete set was around 1800 kb. It took more than 100 years from the time when first time dna was isolated. The sequencing of h.influenge gave new directions to the genome sequencing and at the same time several large scale genome sequence project for higher eukaryotes were started and cpmpleted.
Only after 6 years, sequencing of human genome was completed in 2001, that was the largest scientific effort in the history of mankind. Sequencing of human genome contains of 2.91 billion of base pairs. Appearance of the genes in the human genome was around 30,000 to 40,000. Human genome was 25 times as large as any previously sequenced genome and eight times as large as the sum of all such genomes. The project cost was around 3 million usd dollar and it took 10 year of the time for completion.
Sanger sequencing was used for sequencing of all genome from lower to higher eukaryotes that was the major bottleneck in the genome sequencing analysis. Because of the time and cost is very high. After the successfull completion of human genome project several new approaches reached in to the market which started the new era of the sequencing.
* bacterial virus, or bacteriophage that infects the bacterial species Escherichia coli (E. coli). *
NGS brings spike in the genomic analysis via fasten the analysis and reduced the cost.
Still majority of the microbes on the earth are still unknown because most of the microbes can not be cultured.
Only a very small fraction of the microbes found in nature have been grown in pure culture, so we lack a comprehensive view of genetic diversity present on the earth surface.
An approach to this problem has emerged called metgenomics or environmental genomics.
This study shed new light on the diversity of life on Earth. A total of more than 1.6 Gb of sequence from Sargasso Sea samples yielded 1.2 million previously unknown gene sequences. Before analysis of the Sargasso Sea data the NCBI non-redundant amino acid (nr) dataset contained some 1.5 million peptides, about 630, 000 of which were classified as bacterial.
Size of the genomic and metagenomic databases increases rapidly. This large amount of the data required efficient tool for fast and accurate analysis.
In case of both genomic and metagenomic analysis the common steps after sequencing is
Assembly
Annotation
And comparision
3. I will not talk much about genomic analysis here. I will maily talk about metagenomic analysis of high throutput data.
Any metagenomic analysis mainly focuses on two points first is who is out there, in this type of analysis the prime focus is to find the organisms present in the specific environment, what is the proporpoin of the individual species present. Who is dominating in that environment and trying to correlate that is dominance of particular type of organisms makes this environment unique and differentiate from others.
Second point is mainly focus on the functional part. Like genes present in that environment, what kind of function they are doing and in which pathway they are present, using the abundance of functions and pathaways of the particular environment here trying to find the number of unique function and unique pathways present in the environment of our choice.
To anwer these two questions, after sequencing analysis followed sequential steps. Will discuss in my next slide.
Together to obtain a new understanding of the numbers and abundance of microbial community how these parameters change in response to external stimuli.
Any metagenomic analysis mainly focuses on two points first is who is out there, in this type of analysis the prime focus is to find the organisms present in the specific environment, what is the proporpoin of the individual species present. Who is dominating in that environment and trying to correlate that is dominance of particular type of organisms makes this environment unique and differentiate from others.
Second point is mainly focus on the functional part. Like genes present in that environment, what kind of function they are doing and in which pathway they are present, using the abundance of functions and pathaways of the particular environment here trying to find the number of unique function and unique pathways present in the environment of our choice.
To anwer these two questions, after sequencing analysis followed sequential steps. Will discuss in my next slide.
Together to obtain a new understanding of the numbers and abundance of microbial community how these parameters change in response to external stimuli.
Any metagenomic analysis mainly focuses on two points first is who is out there, in this type of analysis the prime focus is to find the organisms present in the specific environment, what is the proporpoin of the individual species present. Who is dominating in that environment and trying to correlate that is dominance of particular type of organisms makes this environment unique and differentiate from others.
Second point is mainly focus on the functional part. Like genes present in that environment, what kind of function they are doing and in which pathway they are present, using the abundance of functions and pathaways of the particular environment here trying to find the number of unique function and unique pathways present in the environment of our choice.
To anwer these two questions, after sequencing analysis followed sequential steps. Will discuss in my next slide.
Together to obtain a new understanding of the numbers and abundance of microbial community how these parameters change in response to external stimuli.
In the study of complex communities, it is often necessary to address the question of how much sequence is enough to understand a community and to carry out comparative analyses of related communities. In many cases, this information can be obtained by applying various methods based on 16S rRNA sequence that can reveal a tremendous amount of information about microbial diversity and abundance.
Metagenomics projects differ from traditional microbial-sequencing projects in many respects.
SVMs finds the maximal margin which separates two classes and then outputs the hyperplane separator at the center of the margin.
Data is not linearly separable
Project all the points in to the higher dimensions using mapping
Hoping that severability will improve .. This mapping is called kernel function.
SVMs finds the maximal margin which separates two classes and then outputs the hyperplane separator at the center of the margin.
Data is not linearly separable
Project all the points in to the higher dimensions using mapping
Hoping that severability will improve .. This mapping is called kernel function.
Accurately identifying genes from metagenomic fragments is one of the most fundamental issues. I am discussing here novel gene prediction method MetaGUN for metagenomic fragments based on a machine learning approach of SVM.
π(F) represents vector of initial probabilities
The quality of the seed alignment is the crucial factor in determining the quality of the Pfam resource, influencing not only all data generated within the database but also the outcome of external searches that use our profile HMMs.
With strong independence assumptions between predictors.
Bayesin decision theory came long before, it was studied in the filed of statistical theory and more specifically in the field of pattern recognition.
probable hypothesis: for given data d + initial knowledge about the prior probablities. Prior pbobalities reflects background knowledge.
P(c|x) is the posterior probability of class (target) given predictor (attribute).
P(c) is the prior probability of class.
P(x|c) is the likelihood which is the probability of predictor given class.
P(x) is the prior probability of predictor.
The Ribosomal Database Project (RDP) Classifier, a naive Bayesian classifier, can rapidly and accurately classify bacterial 16S rRNA sequences in to the higher taxonomy.
2. It provides taxonomic assignments from domain to genus, with confidence estimates for each assignment
3. Type sequences with Bergey’s taxonomy (average seq length 1,460 bases and had a range of 1,200 to 1,833 bases)
4. Complete rRNA database sequences with NCBI’s taxonomy near-full-length (1,200 bases) 16S rRNA sequences were obtained and taxonomic information for these databases obtained from genebank.
5. let n(wi) be the number of sequences containing subsequence wi
6. The conditional probability that a member of G contains wi was estimated with the equation..
7. the probability that an unknown query sequence, S, is a member of genus G is....... where P(G) is the prior probability of a sequence being a member of
G .. P(S) the overall probability of observing sequence S from any genus.
8. Overall classification accuracy by query size
In the standard classification situation we have observations from two or more known classes and want to develop a rule for assigning current and new observations in to classes using numerical and/or predictor variables.
Classification trees build these rules by recursive binary partitioning in to the regions that are increasingly homogenous with the respect to the class variable.
These homogenous regions are called nodes. At each step in the fitting of the classification trees a optimization is carried out to select a node, particular variable cut off or group of codes. That results in homogenous subgroup of the data.
Root node: entry point for the collection of the data. Inner node: a que is asked about the data and one child node for per possible answer. Leaf node correspond to the decision to take if reached.
RF uses ensemble of decision trees based on the samples, their class designation and variables. Since results from ensemble models are much more satisfactory when compared to the single model. What happens is that basically the tree is created according to the implemented algorithm and if pruning is enabled, an additional step looks at what nodes/branches can be removed without affecting the performance too much.
2. Two kind of ensembles. Bagging and boosting. in the bagging we dont look back to the earlier tree while in boosting consider the earlier trees and strive to compensate their weakness (leads to overfitting of the data). RF is an example of bagging method.
3. Most popular machine learning now a days bc . 1. its versatile classification algorithm make it suitable for analysis of large data sets. 2. higher prediction accuracy and provide information on variable of importance. 3. they r very effective, fast and easy to use.
Algorithm
1. Boot strapping is used to grow classification trees in the forest. If you have n number of samples than number N cases at random 2/3rd (but with replacement) from the original data used for training and rest 1/3rd prediction called OOB. Error rate is called out of bag error rate.
2. if there is m number of the input variables than a number m << M specified such that at each node variables selected randomly and evaluated for their ability to spilt the data. Variable resulting largest decrease in the impurity is chosen to separate the sample data At the each parent node.. Here impurity measure is Gini impurity.. Decrease in the gini impurity related to the increase in the amount of order in the sample classes, introduce by a split.
3. Random selection of the variables for splitting ensure low correlation bw the trees and prevent over fitting of RFM
Last point: every classification tree in the forest cast for unweighted weight for the sample and finally majority of the votes determine the class of the sample. Single tree in the forest is the weak classifier b/c it trains on the subset of the data.. Thats why contribution of all the trees in the forest is a strong classifier.
4. Training process is completed when forest is fully grown, Trained model can be used to predict the classes of the unknown samples
The expected error rate of the classification of a new sample by a classifier is estimated by cross validation procedure. Such as leave one out or k-fold cross validation . Aggregate OOB error rate from all trees.
In addition to the internal cross validation RF also calculates variable importance
In the forest model values of the predictor variables is randomly shuffled to break the association between the response and the predictor values. As the sum of the gini impurity decreases at every node in the forest for which that variable is used for the splitting.
To calculate the permutation variable importance. Prediction accuracy after permutation is substracted from permutation accuracy before permutation. And averaged overall trees in the forest to give the permutation importance value.
If the predictor never had any meaningful association with the response. Suffeling its value will produce very little or no change in model accuracy. on the other hand if predictor was strongly correlated with response, permutation should create large change or drop in accuracy.
These two measures help to find variable highly related with response and to find small number of variables for good prediction.
An example of predicting RNA binding sites. (a) Actual interface residues with RNA in protein 1R3E:A. (b) Predictions are mapped onto the original structure where different prediction catalogs are represented by different colors. (c) Structure of the protein–RNA complex with an example of prediction in the zoomed part. (d) Mutual interaction propensity between the triplets and nucleotides in the protein. Triplets are listed by sliding residues through the protein sequence. The box part corresponds to the values of residues in the zoomed part of (c). (e) Upper panel shows the interface propensity of each amino acid type in the dataset. It is defined as the proportion of an amino acid in interaction sites divided by the proportion of the residue in the dataset (see more in Supplementary Materials). Lower panel shows the interface propensity of binding with RNA for the residues in the protein. The box part corresponds to the values of the zoomed sites.
PubMed is a free search engine accessing primarily the MEDLINE database of references and abstracts on life sciences and biomedical topics.
590 “naive bayes”