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 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 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.
This document provides an introduction and overview of the field of bioinformatics. It discusses how bioinformatics combines computer science and biology to analyze large amounts of biological data. Specifically, it mentions that bioinformatics uses algorithms and techniques from computer science to solve complex biological problems related to areas like molecular biology, genomics, drug discovery, and more. It also outlines some of the key applications of bioinformatics like sequence analysis, protein structure prediction, genome annotation, and comparative genomics. Finally, it provides brief descriptions of important biological databases and resources that bioinformaticians use to store and analyze genomic and protein sequence data.
Closing the Gap in Time: From Raw Data to Real ScienceJustin Johnson
This document discusses science as a service (SaaS) and next-generation sequencing (NGS) data analysis. It summarizes challenges with exponential growth of NGS data, including data management, storage, analysis and sharing. It introduces Edge Bio's approach of distributing computational problems across cloud and HPC resources to avoid bottlenecks. Edge Bio provides full-service NGS analysis pipelines leveraging both commercial and open-source tools.
Data mining involves using machine learning and statistical methods to discover patterns in large datasets and is useful in bioinformatics for analyzing biological data. Bioinformatics analyzes data from sequences, molecules, gene expressions, and pathways. Data mining can help understand these rapidly growing biological datasets. Common data mining tools in bioinformatics include BLAST for sequence comparisons, Entrez for integrated database searching, and ORF Finder for identifying open reading frames. Data mining approaches are well-suited to the enormous volumes of data in bioinformatics databases.
This document provides information about using whole genome sequencing (WGS) for microbial typing and epidemiology. It discusses using WGS for high-resolution strain discrimination and detection of antibiotic resistance and virulence genes. The ideal scenario is a method that can recover all current sequence-based typing information from a single experimental procedure. The document outlines various bioinformatics tools and approaches for WGS analysis including assembly, mapping, annotation, comparison and specialized databases. It emphasizes choosing analysis based on research questions. Gene-by-gene approaches are favored for their ability to classify strains while accounting for recombination. The document lists collaborators and proposes topics for a scientific program on genome-based microbial epidemiology.
Role of bioinformatics in life sciences researchAnshika Bansal
1. The document discusses bioinformatics and summarizes some of its key applications and tools. It describes how bioinformatics merges biology and computer science to solve biological problems by applying computational tools to molecular data.
2. It provides examples of common bioinformatics tasks like retrieving sequences from databases, comparing sequences, analyzing genes and proteins, and viewing 3D structures.
3. The document lists several popular databases for nucleotide sequences, protein sequences, literature, and other biological data. It also introduces common bioinformatics tools for tasks like sequence alignment, translation, and structure analysis.
This document provides an introduction to biological databases and bioinformatics tools. It defines biological sequences and databases, and describes the types of bioinformatics databases including primary, secondary, and composite databases. Examples of specific biological databases like GenBank, EMBL, and SwissProt are outlined. Common bioinformatics tools for sequence analysis, structural analysis, protein function analysis, and homology/similarity searches are listed, including BLAST, FASTA, EMBOSS, ClustalW, and RasMol. Finally, important bioinformatics resources on the web are highlighted.
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 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.
This document provides an introduction and overview of the field of bioinformatics. It discusses how bioinformatics combines computer science and biology to analyze large amounts of biological data. Specifically, it mentions that bioinformatics uses algorithms and techniques from computer science to solve complex biological problems related to areas like molecular biology, genomics, drug discovery, and more. It also outlines some of the key applications of bioinformatics like sequence analysis, protein structure prediction, genome annotation, and comparative genomics. Finally, it provides brief descriptions of important biological databases and resources that bioinformaticians use to store and analyze genomic and protein sequence data.
Closing the Gap in Time: From Raw Data to Real ScienceJustin Johnson
This document discusses science as a service (SaaS) and next-generation sequencing (NGS) data analysis. It summarizes challenges with exponential growth of NGS data, including data management, storage, analysis and sharing. It introduces Edge Bio's approach of distributing computational problems across cloud and HPC resources to avoid bottlenecks. Edge Bio provides full-service NGS analysis pipelines leveraging both commercial and open-source tools.
Data mining involves using machine learning and statistical methods to discover patterns in large datasets and is useful in bioinformatics for analyzing biological data. Bioinformatics analyzes data from sequences, molecules, gene expressions, and pathways. Data mining can help understand these rapidly growing biological datasets. Common data mining tools in bioinformatics include BLAST for sequence comparisons, Entrez for integrated database searching, and ORF Finder for identifying open reading frames. Data mining approaches are well-suited to the enormous volumes of data in bioinformatics databases.
This document provides information about using whole genome sequencing (WGS) for microbial typing and epidemiology. It discusses using WGS for high-resolution strain discrimination and detection of antibiotic resistance and virulence genes. The ideal scenario is a method that can recover all current sequence-based typing information from a single experimental procedure. The document outlines various bioinformatics tools and approaches for WGS analysis including assembly, mapping, annotation, comparison and specialized databases. It emphasizes choosing analysis based on research questions. Gene-by-gene approaches are favored for their ability to classify strains while accounting for recombination. The document lists collaborators and proposes topics for a scientific program on genome-based microbial epidemiology.
Role of bioinformatics in life sciences researchAnshika Bansal
1. The document discusses bioinformatics and summarizes some of its key applications and tools. It describes how bioinformatics merges biology and computer science to solve biological problems by applying computational tools to molecular data.
2. It provides examples of common bioinformatics tasks like retrieving sequences from databases, comparing sequences, analyzing genes and proteins, and viewing 3D structures.
3. The document lists several popular databases for nucleotide sequences, protein sequences, literature, and other biological data. It also introduces common bioinformatics tools for tasks like sequence alignment, translation, and structure analysis.
This document provides an introduction to biological databases and bioinformatics tools. It defines biological sequences and databases, and describes the types of bioinformatics databases including primary, secondary, and composite databases. Examples of specific biological databases like GenBank, EMBL, and SwissProt are outlined. Common bioinformatics tools for sequence analysis, structural analysis, protein function analysis, and homology/similarity searches are listed, including BLAST, FASTA, EMBOSS, ClustalW, and RasMol. Finally, important bioinformatics resources on the web are highlighted.
Cool Informatics Tools and Services for Biomedical ResearchDavid Ruau
This document provides an overview of bioinformatics tools and services for analyzing big data in biomedical research. It discusses traditional bioinformatics tools, analyzing genomic data from microarrays and next-generation sequencing without and with code, interpreting results using protein interaction networks and pathways, tools for data storage, cleaning and visualization, and making research reproducible. Galaxy, R, and programming are presented as useful for automated, reproducible analysis of large genomic datasets.
The document provides information about bioinformatics and BLAST (Basic Local Alignment Search Tool). It defines bioinformatics as the application of information technology to molecular biology. It describes what BLAST is and how it works to compare biological sequences and identify similar sequences in databases. It also lists different BLAST programs and databases that can be used depending on the type of sequence being searched.
Building bioinformatics resources for the global communityExternalEvents
1. The document evaluates different methods for inferring relationships between Salmonella samples based on whole genome sequencing data from large databases. It compares k-mer based methods and site-based methods using 18,997 Salmonella isolates from public databases.
2. Site-based methods like NUCmer and MLST produced more accurate results, but require more computing resources when dealing with large databases. K-mer based methods are faster but more sensitive to assembly and contamination issues.
3. While k-mer methods may be useful for initial filtering, site-based methods are superior for accuracy, though challenges remain in applying them to databases containing tens of thousands of samples. Quality control and computing resources are important considerations.
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.
Bioinformatics combines biology, chemistry, statistics, and computer science to analyze and interpret biological data. It uses algorithms and techniques of computer science to solve complex biological problems. Some key areas of bioinformatics include organizing biological knowledge, performing sequence analysis, predicting protein structure, genome annotation, and comparative genomics. Bioinformatics is essential for applications like pharmaceutical research, gene therapy, forensic analysis, and understanding biological pathways and networks in systems biology.
Next generation sequencing (NGS) allows for the massively parallel sequencing of DNA sequences. NGS technologies can sequence entire genomes in a single run and provide information useful for pathogen identification, outbreak investigation, and molecular diagnostics. NGS workflows involve sample preparation, sequencing using platforms such as Illumina or Ion Torrent, and bioinformatics analysis to assemble and interpret the large amounts of sequencing data produced. NGS has many applications including mutation discovery, microbial genome mapping, and metagenomics.
The document discusses the field of bioinformatics, which involves applying computational techniques and building tools to solve biological problems, such as analyzing genetic sequences and modeling molecular structures. It outlines several applications of bioinformatics, including in medicine for disease research and drug design, as well as in agriculture and animal health. The emergence of bioinformatics is attributed to the convergence of rapid growth in fields like biotechnology and information technology.
Next generation genomics: Petascale data in the life sciencesGuy Coates
Keynote presentation at OGF 28.
The year 2000 saw the release of "The" human genome, the product of a the combined sequencing effort of the whole planet. In 2010, single institutions are sequencing thousands of genomes a year, producing petabytes of data. Furthermore, many of the large scale sequencing projects are based around international collaboration and consortia. The talk will explore how Grid and Cloud technologies are being used to share genomics data around the planet, revolutionizing life science research.
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 discusses next generation sequencing (NGS) data and implications for data stewardship. It notes that NGS allows measuring the full-length transcriptome, including alternatively spliced transcripts specific to samples. This alters gene models and highlights the need to capture gene models and context in data commons for future reuse. The document also recommends that more metadata be captured about samples, experiments, and instruments to provide context and aid in data processing. It emphasizes making data FAIR (findable, accessible, interoperable, and reusable) according to W3C standards to improve data stewardship and enable both human and machine use of data.
Genomic Big Data Management, Integration and Mining - Emanuel WeitschekData Driven Innovation
This document summarizes genomic big data management, integration and mining. It discusses the exponential growth of biological data due to advances in sequencing technologies. Next generation sequencing techniques generate large amounts of short DNA reads. Several public databases contain heterogeneous biological data sources. Effective data management and integration methods are needed to analyze these large and complex datasets. Supervised machine learning can be used to extract knowledge and classify samples. Tools like CAMUR apply rule-based classification to problems like analyzing gene expression from cancer datasets. Future work involves advanced integration systems and new big data approaches for biological data.
Bioinformatics is defined as the application of tools of computation and analysis to the capture and interpretation of biological data. It is an interdisciplinary field, which harnesses computer science, mathematics, physics, and biology
This document outlines a 12-step program for biology to adapt to the era of data-intensive science. It summarizes the author's background and research interests. It then discusses the rapid growth of biological data from techniques like DNA sequencing. It introduces the concept of digital normalization as a way to efficiently process large transcriptome datasets. Finally, it outlines some proposed steps for the field, including investing in computational training, a focus on biological questions, and moving to continuous data updating models.
This document provides an overview and introduction to bioinformatics. It discusses the large amounts of biological sequence data that have been generated and how bioinformatics is needed to analyze this data computationally. The document outlines topics that will be covered, including databases, sequence alignment tools like BLAST, gene finding, and protein analysis. Practical workshops are described that will involve database searching, multiple sequence alignments, and interpreting results to understand molecular biology and solve biomedical problems. Questions are welcomed throughout the workshops.
The document describes an experiment to analyze genomes using comparison tools. The aim is to compare genomes to find conserved and divergent sequences. Two tools are described: Genome VISTA and GenomeBlast, which allow uploading or entering genome sequences to compare regions and find orthologs between species. The protocol retrieves a genome from NCBI, uses Genome VISTA to submit it for comparison, and would show results of similar regions found between the query genome and other species.
The document discusses bioinformatics tools used for analyzing biological data. It begins with an introduction to bioinformatics and then describes several categories of tools: biological databases for storing genomic and protein data; homology tools for sequence alignment and comparison; protein function analysis tools; structural analysis tools; and sequence manipulation and analysis tools. Common tools discussed include BLAST, FASTA, ClustalW, and databases like GenBank. The document concludes by covering applications of bioinformatics in areas like molecular modeling, medicine, and computation.
Enabling Large Scale Sequencing Studies through Science as a ServiceJustin Johnson
Now
“Now” generation sequencing has drastically changed the traditional costs and infrastructure within the sequencing community. There are several technologies, platforms and algorithms that show promise, but it is not always intuitive where to start. This uncertainty is compounded by the fact that commonly used analysis tools are difficult to build, maintain, and run effectively. Sample acquisition and preparation is quickly becoming a bottleneck as projects move from small sample sizes to hundreds or even thousands of samples. We will present case studies highlighting information, methods, challenges and opportunities in leveraging large scale high throughput sequencing and bioinformatics. Specifically we will highlight a recent genome-wide study of methylation patterns in 1575 individuals with Schizophrenia. We will also discuss several cancer transcriptome and exome sequencing projects as well as a human pathogen transcriptome characterization project consisting of multiple organisms and almost a billion reads.
The Future
The Ion Torrent PGM machine is a very promising, rapid throughput, ultra scalable sequencer that could play an integral part in future human health studies. Applications such as microbial whole genome sequencing, metagenomic characterization of environmental and microbiome sample, and targeted resequencing projects stand to benefit from this technology over time. To date we have completed more than 25 runs on a single PGM and will comment on the setup as well as sequence data and analysis.
This document discusses the use of big data analytics in genome analysis. It notes that the human genome contains around 3 gigabytes of data from 3 billion base pairs, and sequencing millions of human genomes would result in hundreds of petabytes of data. Big data analytics is crucial for storing, transforming, and analyzing this large genomic information to uncover medical insights. Genome-wide association studies use various big data and analytics models to explore gene-disease connections. The document then outlines some key applications of analytics in genomics like DNA sequencing libraries, genomic annotation, comparisons, visualization, and synteny, and notes they require systems that can handle large sequence data and complex algorithms. Benefits of big data in genomics include reduced sequencing costs, time
Promoting Wellbeing - Applied Social Psychology - Psychology SuperNotesPsychoTech Services
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
Cool Informatics Tools and Services for Biomedical ResearchDavid Ruau
This document provides an overview of bioinformatics tools and services for analyzing big data in biomedical research. It discusses traditional bioinformatics tools, analyzing genomic data from microarrays and next-generation sequencing without and with code, interpreting results using protein interaction networks and pathways, tools for data storage, cleaning and visualization, and making research reproducible. Galaxy, R, and programming are presented as useful for automated, reproducible analysis of large genomic datasets.
The document provides information about bioinformatics and BLAST (Basic Local Alignment Search Tool). It defines bioinformatics as the application of information technology to molecular biology. It describes what BLAST is and how it works to compare biological sequences and identify similar sequences in databases. It also lists different BLAST programs and databases that can be used depending on the type of sequence being searched.
Building bioinformatics resources for the global communityExternalEvents
1. The document evaluates different methods for inferring relationships between Salmonella samples based on whole genome sequencing data from large databases. It compares k-mer based methods and site-based methods using 18,997 Salmonella isolates from public databases.
2. Site-based methods like NUCmer and MLST produced more accurate results, but require more computing resources when dealing with large databases. K-mer based methods are faster but more sensitive to assembly and contamination issues.
3. While k-mer methods may be useful for initial filtering, site-based methods are superior for accuracy, though challenges remain in applying them to databases containing tens of thousands of samples. Quality control and computing resources are important considerations.
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.
Bioinformatics combines biology, chemistry, statistics, and computer science to analyze and interpret biological data. It uses algorithms and techniques of computer science to solve complex biological problems. Some key areas of bioinformatics include organizing biological knowledge, performing sequence analysis, predicting protein structure, genome annotation, and comparative genomics. Bioinformatics is essential for applications like pharmaceutical research, gene therapy, forensic analysis, and understanding biological pathways and networks in systems biology.
Next generation sequencing (NGS) allows for the massively parallel sequencing of DNA sequences. NGS technologies can sequence entire genomes in a single run and provide information useful for pathogen identification, outbreak investigation, and molecular diagnostics. NGS workflows involve sample preparation, sequencing using platforms such as Illumina or Ion Torrent, and bioinformatics analysis to assemble and interpret the large amounts of sequencing data produced. NGS has many applications including mutation discovery, microbial genome mapping, and metagenomics.
The document discusses the field of bioinformatics, which involves applying computational techniques and building tools to solve biological problems, such as analyzing genetic sequences and modeling molecular structures. It outlines several applications of bioinformatics, including in medicine for disease research and drug design, as well as in agriculture and animal health. The emergence of bioinformatics is attributed to the convergence of rapid growth in fields like biotechnology and information technology.
Next generation genomics: Petascale data in the life sciencesGuy Coates
Keynote presentation at OGF 28.
The year 2000 saw the release of "The" human genome, the product of a the combined sequencing effort of the whole planet. In 2010, single institutions are sequencing thousands of genomes a year, producing petabytes of data. Furthermore, many of the large scale sequencing projects are based around international collaboration and consortia. The talk will explore how Grid and Cloud technologies are being used to share genomics data around the planet, revolutionizing life science research.
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 discusses next generation sequencing (NGS) data and implications for data stewardship. It notes that NGS allows measuring the full-length transcriptome, including alternatively spliced transcripts specific to samples. This alters gene models and highlights the need to capture gene models and context in data commons for future reuse. The document also recommends that more metadata be captured about samples, experiments, and instruments to provide context and aid in data processing. It emphasizes making data FAIR (findable, accessible, interoperable, and reusable) according to W3C standards to improve data stewardship and enable both human and machine use of data.
Genomic Big Data Management, Integration and Mining - Emanuel WeitschekData Driven Innovation
This document summarizes genomic big data management, integration and mining. It discusses the exponential growth of biological data due to advances in sequencing technologies. Next generation sequencing techniques generate large amounts of short DNA reads. Several public databases contain heterogeneous biological data sources. Effective data management and integration methods are needed to analyze these large and complex datasets. Supervised machine learning can be used to extract knowledge and classify samples. Tools like CAMUR apply rule-based classification to problems like analyzing gene expression from cancer datasets. Future work involves advanced integration systems and new big data approaches for biological data.
Bioinformatics is defined as the application of tools of computation and analysis to the capture and interpretation of biological data. It is an interdisciplinary field, which harnesses computer science, mathematics, physics, and biology
This document outlines a 12-step program for biology to adapt to the era of data-intensive science. It summarizes the author's background and research interests. It then discusses the rapid growth of biological data from techniques like DNA sequencing. It introduces the concept of digital normalization as a way to efficiently process large transcriptome datasets. Finally, it outlines some proposed steps for the field, including investing in computational training, a focus on biological questions, and moving to continuous data updating models.
This document provides an overview and introduction to bioinformatics. It discusses the large amounts of biological sequence data that have been generated and how bioinformatics is needed to analyze this data computationally. The document outlines topics that will be covered, including databases, sequence alignment tools like BLAST, gene finding, and protein analysis. Practical workshops are described that will involve database searching, multiple sequence alignments, and interpreting results to understand molecular biology and solve biomedical problems. Questions are welcomed throughout the workshops.
The document describes an experiment to analyze genomes using comparison tools. The aim is to compare genomes to find conserved and divergent sequences. Two tools are described: Genome VISTA and GenomeBlast, which allow uploading or entering genome sequences to compare regions and find orthologs between species. The protocol retrieves a genome from NCBI, uses Genome VISTA to submit it for comparison, and would show results of similar regions found between the query genome and other species.
The document discusses bioinformatics tools used for analyzing biological data. It begins with an introduction to bioinformatics and then describes several categories of tools: biological databases for storing genomic and protein data; homology tools for sequence alignment and comparison; protein function analysis tools; structural analysis tools; and sequence manipulation and analysis tools. Common tools discussed include BLAST, FASTA, ClustalW, and databases like GenBank. The document concludes by covering applications of bioinformatics in areas like molecular modeling, medicine, and computation.
Enabling Large Scale Sequencing Studies through Science as a ServiceJustin Johnson
Now
“Now” generation sequencing has drastically changed the traditional costs and infrastructure within the sequencing community. There are several technologies, platforms and algorithms that show promise, but it is not always intuitive where to start. This uncertainty is compounded by the fact that commonly used analysis tools are difficult to build, maintain, and run effectively. Sample acquisition and preparation is quickly becoming a bottleneck as projects move from small sample sizes to hundreds or even thousands of samples. We will present case studies highlighting information, methods, challenges and opportunities in leveraging large scale high throughput sequencing and bioinformatics. Specifically we will highlight a recent genome-wide study of methylation patterns in 1575 individuals with Schizophrenia. We will also discuss several cancer transcriptome and exome sequencing projects as well as a human pathogen transcriptome characterization project consisting of multiple organisms and almost a billion reads.
The Future
The Ion Torrent PGM machine is a very promising, rapid throughput, ultra scalable sequencer that could play an integral part in future human health studies. Applications such as microbial whole genome sequencing, metagenomic characterization of environmental and microbiome sample, and targeted resequencing projects stand to benefit from this technology over time. To date we have completed more than 25 runs on a single PGM and will comment on the setup as well as sequence data and analysis.
This document discusses the use of big data analytics in genome analysis. It notes that the human genome contains around 3 gigabytes of data from 3 billion base pairs, and sequencing millions of human genomes would result in hundreds of petabytes of data. Big data analytics is crucial for storing, transforming, and analyzing this large genomic information to uncover medical insights. Genome-wide association studies use various big data and analytics models to explore gene-disease connections. The document then outlines some key applications of analytics in genomics like DNA sequencing libraries, genomic annotation, comparisons, visualization, and synteny, and notes they require systems that can handle large sequence data and complex algorithms. Benefits of big data in genomics include reduced sequencing costs, time
Promoting Wellbeing - Applied Social Psychology - Psychology SuperNotesPsychoTech Services
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
- Video recording of this lecture in English language: https://youtu.be/kqbnxVAZs-0
- Video recording of this lecture in Arabic language: https://youtu.be/SINlygW1Mpc
- Link to download the book free: https://nephrotube.blogspot.com/p/nephrotube-nephrology-books.html
- Link to NephroTube website: www.NephroTube.com
- Link to NephroTube social media accounts: https://nephrotube.blogspot.com/p/join-nephrotube-on-social-media.html
One health condition that is becoming more common day by day is diabetes.
According to research conducted by the National Family Health Survey of India, diabetic cases show a projection which might increase to 10.4% by 2030.
Integrating Ayurveda into Parkinson’s Management: A Holistic ApproachAyurveda ForAll
Explore the benefits of combining Ayurveda with conventional Parkinson's treatments. Learn how a holistic approach can manage symptoms, enhance well-being, and balance body energies. Discover the steps to safely integrate Ayurvedic practices into your Parkinson’s care plan, including expert guidance on diet, herbal remedies, and lifestyle modifications.
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Osteoporosis - Definition , Evaluation and Management .pdfJim Jacob Roy
Osteoporosis is an increasing cause of morbidity among the elderly.
In this document , a brief outline of osteoporosis is given , including the risk factors of osteoporosis fractures , the indications for testing bone mineral density and the management of osteoporosis
share - Lions, tigers, AI and health misinformation, oh my!.pptxTina Purnat
• Pitfalls and pivots needed to use AI effectively in public health
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Rasamanikya is a excellent preparation in the field of Rasashastra, it is used in various Kushtha Roga, Shwasa, Vicharchika, Bhagandara, Vatarakta, and Phiranga Roga. In this article Preparation& Comparative analytical profile for both Formulationon i.e Rasamanikya prepared by Kushmanda swarasa & Churnodhaka Shodita Haratala. The study aims to provide insights into the comparative efficacy and analytical aspects of these formulations for enhanced therapeutic outcomes.
2. Introduction: What is bioinformatics?
Can be defined as the body of tools, algorithms needed to handle large
and complex biological information.
Bioinformatics is a new scientific discipline created from the interaction
of biology and computer.
The NCBI defines bioinformatics as:
"Bioinformatics is the field of science in which biology, computer
science, and information technology merge into a single discipline”
3. Genomics era: High-throughput DNA sequencing
The first high-throughput genomics
technology was automated DNA sequencing
in the early 1990.
In September 1999, Celera Genomics
completed the sequencing of the
Drosophila genome.
In 1995, Venter and Hamilton used whole-
genome shotgun sequencing strategy to
sequence the genomes of Mycoplasma and
Haemophilus .
The 3-billion-bp human genome sequence
was generated in a competition between
the publicly funded Human Genome
Project and Celera
4. Top image: confocal detection
by the MegaBACE sequencer
of fluorescently labeled DNA
High-throughput DNA sequencing
That was then. How about
now?
7. Genomics: Completed genomes as of 2010
Currently the genome of the organisms are sequenced:
This generates large amounts of information to be handled by individual
computers.
1598 bacterial/85 archaeal/294 eukaryotic genomes
8. The trend of data growth
0
1
2
3
4
5
6
7
8
1980 1985 1990 1995 2000
Years
Nucleotides(billion)
21st century is a century of biotechnology:
Microarray: Global expression analysis: RNA levels of every
gene in the genome analyzed in parallel. (OUT!)
Replaced by RNA-seq
Proteomics:Global protein analysis generates by large mass
spectra libraries.
Metabolomics:Global metabolite analysis: 25,000 secondary
metabolites characterized
Genomics: New sequence information is being
produced at increasing rates. (The
contents of GenBank double every year)
9. Metagenomics
- “Who is there and what are they doing?”
- Cultivation-independent approaches to study the big impact of microbes
10. How to handle the large amount of information?
Drew Sheneman, New Jersey--The Newark Star Ledger
Answer: bioinformatics and Internet
11. Bioinformatics history
IBM 7090 computer
In1960s: the birth of bioinformatics
Margaret Oakley Dayhoff created:
The first protein database
The first program for sequence assembly
There is a need for computers and algorithms that allow:
Access, processing, storing, sharing, retrieving, visualizing, annotating…
12. Why do we need the Internet?
“omics” projects and the information associated with involve a huge amount
of data that is stored on computers all over the world.
Because it is impossible to maintain up-to-date copies of all relevant
databases within the lab. Access to the data is via the internet.
14. The Commercial Market
Current bioinformatics market is worth 300 million / year
(Half software)
Prediction: $2 billion / year in 5-6 years
~50 Bioinformatics companies:
Genomatrix Software, Genaissance Pharmaceuticals, Lynx, Lexicon Genetics, DeCode
Genetics, CuraGen, AlphaGene, Bionavigation, Pangene, InforMax, TimeLogic,
GeneCodes, LabOnWeb.com, Darwin, Celera, Incyte, BioResearch Online, BioTools,
Oxford Molecular, Genomica, NetGenics, Rosetta, Lion BioScience, DoubleTwist,
eBioinformatics, Prospect Genomics, Neomorphic, Molecular Mining, GeneLogic,
GeneFormatics, Molecular Simulations, Bioinformatics Solutions….
15. Scope of this lab
The lab will touch on the following computational tasks:
Similarity search
Sequence comparison: Alignment, multiple alignment, retrieval
Sequences analysis: Signal peptide, transmembrane domain,…
Protein folding: secondary structure from sequence
Sequence evolution: phylogenetic trees
Make you familiar with bioinformatics resources available on the
web to do these tasks.
16. You have just
cloned a gene
Evolutionary
relationship?
-Phylogenetic
tree
-Accession #?
-Annotation?
Is it already in
databases?
-Sub-localization
-Soluble?
-3D fold
Protein
characteristics?
-% identity?
-Family member?
Is there similar
sequences?
-Alignments?
-Domains?
Is there conserved
regions?
Other
information?
-Expression profile?
-Mutants?
A critical failure of current bioinformatics is the lack of a single software
package that can perform all of these functions.
Applying algorithms to analyze genomics data
17. DNA (nucleotide sequences) databases
They are big databases and searching either one should produce
similar results because they exchange information routinely.
-GenBank (NCBI): http://www.ncbi.nlm.nih.gov
-DDBJ (DNA DataBase of Japan): http://www.ddbj.nig.ac.jp
-TIGR: http://tigr.org/tdb/tgi
-Yeast: http://yeastgenome.org
-Microbes: http://img.jgi.doe.gov/cgi-bin/pub/main.cgi
Specialized databases:Tissues, species…
-ESTs (Expressed Sequence Tags)
~at NCBI http://www.ncbi.nlm.nih.gov/dbEST
~at TIGR http://tigr.org/tdb/tgi
- ...many more!
18. They are big databases too:
-Swiss-Prot (very high level of annotation)
http://au.expasy.org/
-PIR (protein identification resource) the world's most
comprehensive catalog of information on proteins
http://www.pir.uniprot.org/
Translated databases:
-TREMBL (translated EMBL): includes entries that have
not been annotated yet into Swiss-Prot.
http://www.ebi.ac.uk/trembl/access.html
-GenPept (translation of coding regions in GenBank)
-pdb (sequences derived from the 3D structure
Brookhaven PDB) http://www.rcsb.org/pdb/
Protein (amino acid) databases
19. Database homology searching
Use algorithms to efficiently provide mathematical basis of searches
that can be translated to statistical significance.
Assumes that sequence, structure, and function are inter-related.
All similarity searching methods rely on the concepts of alignment
and distance between sequences.
A similarity score is calculated from a distance: the number of DNA
bases or amino acids that are different between two sequences.
20. Calculating alignment scores
Scoring system: Uses scoring matrices that allow biologists to quantify the
quality of sequence alignments.
The raw score S is calculated by summing the scores for each aligned
position and the scores for gaps. Gap creation/extension scores are
inherent to the scoring system in use (BLAST, FASTA…)
The score for an identity or a mismatch is given by the specified substitution
matrix (e.g., BLOSUM62).
21. Devising a scoring system
How the matrices were created:
Very similar sequences were aligned.
From these alignments, the frequency of substitution between
each pair of amino acids was calculated and then PAM1 was built.
After normalizing to log-odds format, the full series of PAM matrices
can be calculated by multiplying the PAM1 matrix by itself.
Some popular scoring matrices are:
PAM (Percent Accepted Mutation): for evolutionary studies.
For example in PAM1, 1 accepted point mutation per 100 amino
acids is required.
BLOSUM (BLOcks amino acid SUbstitution Matrix): for finding
common motifs. For example in BLOSUM62, the alignment is
created using sequences sharing no more than 62% identity.
22. Devising a scoring system
Importance:
Scoring matrices appear in all analysis
involving sequence comparison.
The choice of matrix can strongly influence
the outcome of the analysis.
Understanding theories underlying a given
scoring matrix can aid in making proper
choice:
-Some matrices reflect similarity: good for
database searching
-Some reflect distance: good for phylogenies
Log-odds matrices, a normalisation method for matrix values:
S is the probability that two residues, i and j, are aligned by evolutionary descent
and by chance.
qij are the frequencies that i and j are observed to align in sequences known to
be related. pi and pj are their frequencies of occurrence in the set of sequences.
23. Database search methods: Sequence Alignment
Two broad classes of sequence alignments exist:
Global alignment: not sensitive
Local alignment: faster
QKESGPSSSYC
VQQESGLVRTTC
ESG
ESG
The most widely used local similarity algorithms are:
Smith-Waterman (http://www.ebi.ac.uk/MPsrch/)
Basic Local Alignment Search Tool (BLAST, http://www.ncbi.nih.gov)
Fast Alignment (FASTA, http://fasta.genome.jp; http://www.ebi.ac.uk/fasta33/;
http://www.arabidopsis.org/cgi-bin/fasta/nph-TAIRfasta.pl)
24. Which algorithm to use for database similarity search?
Speed:
BLAST > FASTA > Smith-Waterman (It is VERY SLOW and uses a
LOT OF COMPUTER POWER)
Sensitivity/statistics:
FASTA is more sensitive, misses less homologues
Smith-Waterman is even more sensitive.
BLAST calculates probabilities
FASTA more accurate for DNA-DNA search then BLAST
25. -tuple methods provide optimal alignments
These methods are faster and excellent in comparing sequences.
BLAST and FASTA programs are based on -tuple algorithms:
1-Using query sequence, derive a list of
words of length w (e.g., 3)
2-Keep high-scoring words using a
scoring matrix(e.g. BLOSUM 62)
3-High-scoring words are compared
with database sequences
4-Sequences with many matches to
high-scoring words are used for final
alignments
26. The dilemma: DNA or protein?
Is the comparison of two nucleotide sequences accurate?
By translating into amino acid sequence, are we losing information?
The genetic code is degenerate (Two or more codons can represent
the same amino acid)
Very different DNA sequences may code for similar protein sequences
We certainly do not want to miss those cases!
Search by similarity
Using nucleotide seq. Using amino acid seq.
Tools to search databases
27. Comparing DNA sequences give more random matches:
Reasons for translating
A good alignment with end-gaps A very poor alignment
Almost 50% identity!
Conservation of protein in evolution (DNA similarity decays faster!)
It is almost always better to compare coding sequences in their amino acid form,
especially if they are very divergent.
Very highly similar nucleotide sequences may give better results.
Conclusion:
28. FASTA: Compares a DNA query to DNA database, or a protein query
to protein database
FASTX: Compares a translated DNA query to a protein database
TFASTA: Compares a protein query to a translated DNA database
BLAST and FASTA variants
BLASTN: Compares a DNA query to DNA database.
BLASTP: Compares a protein query to protein database.
BLASTX: Compares the 6-frame translations of DNA query to protein
database.
TBLASTN: Compares a protein query to the 6-frame translations of a DNA
database.
TBLASTX: Compares the 6-frame translations of DNA query to the 6-frame
translations of a DNA database (each sequence is comparable to
BLASTP searches!)
PSI-BLAST: Performs iterative database searches. The results from each round
are incorporated into a 'position specific' score matrix, which is
used for further searching
29. A practical example of sequence alignment
http://www.ncbi.nlm.nih.gov
BLAST results
30. Detailed BLAST results
E value: is the expectation value or probability to find by chance hits similar to
your sequence. The lower the E, the more significant the score.
31. Database searching tips
Use latest database version.
Use BLAST first, then a finer tool (FASTA,…)
Search both strands when using FASTA.
Translate sequences where relevant
Search 6-frame translation of DNA database
E < 0.05 is statistically significant, usually biologically
interesting.
If the query has repeated segments, delete them and
repeat search
32. Most widely used sites for sequence analysis
Sites for alignment of 2 sequences:
T-COFFEE (http://tcoffee.vital-it.ch/cgi-bin/Tcoffee/tcoffee_cgi/index.cgi): more
accurate than ClustalW for sequences with less than 30% identity.
ClustalW (http://www.ch.embnet.org/software/ClustalW.html;
http://align.genome.jp)
bl2sequ (http://www.ncbi.nlm.nih.gov/blast/bl2seq/wblast2.cgi)
LALIGN (http://www.ch.embnet.org/software/LALIGN_form.html)
MultiALIGN (http://prodes.toulouse.inra.fr/multalin/multalin.html)
Sites for DNA to protein translation:
These algorithms can translate DNA sequences in any of the 3 forward or three
reverse sense frames.
Translate (http://au.expasy.org/tools/dna.html)
Translate a DNA sequence: (http://www.vivo.colostate.edu/molkit/translate/index.html)
Transeq (http://www.ebi.ac.uk/emboss/transeq)