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bioinformatics algorithms and its basics

The document details a Bioinformatics course under the applied sciences, structured according to UGC Act provisions, focusing on bioinformatics and biostatistics. It outlines prerequisites, course objectives, outcomes, syllabus topics, and assignments that promote understanding of biological databases, computational tools, and data analysis in biology. Additionally, it covers the historical context and development of bioinformatics, delineating its scope, limitations, and the classification of biological databases.

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Established as per the Section 2(f) of the UGC Act, 1956 Approved by AICTE, COA and BCI, New Delhi BIOINFORMATICS School of Applied Sciences
Established as per the Section 2(f) of the UGC Act, 1956 Approved by AICTE, COA and BCI, New Delhi Program: B.Sc BMCs Course Title: Bioinformatics and Biostatistics Course Code: B19MC3030 Course Type: Hardcore Course Presenter: Prashantha C N Course Mentor: Prashantha C N Semester: & Section: 3rd semester Academic Year: 2020-21 Course Pre-requisites: Students should have basic knowledge of biology, statistics and computer sciences. Students should be familiar with the basic concepts of DNA, RNA and Protein. L T P: L=4 T=0 P=4 Pedagogy:
Course objectives 1. The basic objective is to give students an introduction, scope and objectives of bioinformatics 2. Understanding of different types of biological databases and tools used to understand data structures. Course outcome 1. Knowledge and awareness of the basic principles and concepts of biology and biological databases, literature search methods. Pedagogy chart Course code POs/ COs PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PSO1 PSO2 B20MC3030 CO1 3 2 2 1 1 1 3 1 CO2 2 3 3 3 2 1 2 2 3 2 CO3 1 3 3 3 2 1 2 2 3 2 CO4 1 3 2 1 1 1 2 2 3 1
Syllabus Unit Topics Course Outcomes Program Outcomes I Introduction to Bioinformatics, Goal, Scope, Applications, Limitations 1 1 Biological Databases: Types of Databases, 1 2 Literature databases: Open access and open sources, PubMed, PLoS, Biomed Central, 1 2 Information Retrieval from Biological Databases; Sequence Formats, Sequence databases, structural databases, 2 2 Genome Databases: Viral genome database (ICTVdb, VirGen), Bacterial Genomes database (Genomes OnLine Database –GOLD, Microbial Genome Database- MBGD), Organism specific Genome database (OMIM / OMIA, SGD, Worm Base, PlasmoDB, FlyBase, TAIR), and ligand databases. 3 3 Program Outcomes PO-1: Science knowledge: Apply the knowledge of Biology, computer science and mathematics, to develop algorithms and analyze biological programs related to drug discovery, and genomic analysis to solve biological problems. PO-2: Problem analysis: Identify, formulate and analyze problems related to the various domains of Biological sciences such as Molecular Biology, Biotechnology, Genetics, Healthcare, agricultural, animal sciences and Microbial biotechnology. PO-3: Conduct investigations of complex problems: Use research-based knowledge and research methods including design of experiments, analysis and interpretation of data, and synthesis of the information to provide valid conclusions.
Reference Books 1. Andreas D. Baxevanis, B.F. Francis Ouellette. Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins, 2nd Edition, 2009. 2. David W. Mount., Bioinformatics: Sequence and Genome Analysis, Cold Spring Harbor Laboratory Press, New York. 2004. 3. Andrew R. Leach., Molecular Modeling Principles and Applications (2nd Ed.), Prentice Hall, USA. 2001. 4. G. E. Schulz., Principles of Protein Structure, Springer 2009. e-Resources 1. https://youtu.be/w-uk-_TOgR0 2. https://www.youtube.com/watch?v=IrHDOEDtwD4 3. https://www.youtube.com/watch?v=48Xr-H05raA Suggested Readings Unit wise 1. Jin Xiong., Essential Bioinformatics, Cambridge university press (2006).
Presentation Topics Fundamentals of Bioinformatics: Introduction to Bioinformatics, Goal, Scope, Applications, Limitations, Biological Databases: Types of Databases, Biological databases; Literature databases: Open access and open sources, PubMed, PLoS, Biomed Central, Information Retrieval from Biological Databases; Sequence Formats, Sequence databases, structural databases, Genome Databases: Viral genome database (ICTVdb, VirGen), Bacterial Genomes database (Genomes OnLine Database –GOLD, Microbial Genome Database-MBGD), Organism specific Genome database (OMIM / OMIA, SGD, Worm Base, PlasmoDB, FlyBase, TAIR), and ligand databases. Assignments 1. Applications of Bioinformatics 2. Searching literature through PubMed 3. Genome File formats 4. Sequence File formats 5. Understanding of Viral Genome databases 6. Understanding of Bacterial Genome databases 7. Ligand Databases and their applications.
Unit-1: Fundamentals of Bioinformatics Bioinformatics Biology + Mathematical + Statistical + Computational tools = Bioinformatics Biology Natural science that study life and living organism based on physical, chemical, molecular, physiological, development and evolution of organisms. Genetics, Molecular Biology, Biotechnology, Microbiology, Biochemistry, etc Mathematical + Statistical Mathematical formulations to predict the algorithms, study of DNA & Protein interactions, Statistical methods to analyze biological data and predict the interpretations. Study of arithmetic calculations, normalization, standard deviation, probability, correlations & regressions, ANOVA and Variances. Computational Tools Biological Data collection, Storage, Retrieve, Analyze and Interpretation
Bioinformatics is a field of a science in which biology, computer science and information technology merge into a single discipline to analyze biological information using computers and statistical techniques. Biological experiments can generate large amounts of data (macromolecular sequences, structures, expression profiles, pathways etc). Bioinformatics is about acquiring, managing, analyzing and understanding those data.
Bioinformatics is an interdisciplinary field that develops methods and software tools for understanding biological data. As an interdisciplinary field of science, bioinformatics combines biology, computer science, information engineering, mathematics and statistics to analyze and interpret biological data. Bioinformatics now entails the creation and advancement of databases, algorithms, computational and statistical techniques, and theory to solve formal and practical problems arising from the management and analysis of biological data. Definitions of Bioinformatics
History of Bioinformatics The first major bioinformatics project was undertaken by Margaret Dayhoff in 1965, who developed a first protein sequence database called Atlas of Protein Sequence and Structure. She is American Physical Chemist professor at Georgetown University Medical Center Margaret Dayhoff, 1965 She originated one of the first substitution matrices, point accepted mutations (PAM). The one-letter code used for amino acids was developed by her, reflecting an attempt to reduce the size of the data files used to describe amino acid sequences in an era of punch-card computing. Subsequently, in the early 1970s, the Brookhaven National Laboratory established the Protein Data Bank for archiving three-dimensional protein structures. The first sequence alignment algorithm was developed by Needleman and Wunsch in 1970. The first protein structure prediction algorithm was developed by Chou and Fasman in 1974. GenBank was developed in 1980, a fast database search and sequence repository. FASTA was developed by William Pearson BLAST was developed by Stephen Altschul and coworkers.
Bioinformatics differs from a related field known as computational biology. Bioinformatics is limited to sequence, structural, and functional analysis of genes and genomes and their corresponding products and is often considered computational molecular biology. However, computational biology encompasses all biological areas that involve computation. For example, mathematical modeling of ecosystems, population dynamics, application of the game theory in behavioral studies, and phylogenetic construction using fossil records all employ computational tools, but do not necessarily involve biological macromolecules. Bioinformatics Vs Computational Biology
GOAL OF BIOINFORMATICS Bioinformatics has evolved such that the most pressing task now involves the analysis and interpretation of various types of data. Development and implementation of computer programs that enable efficient access to, use and management of, various types of information Development of new algorithms (mathematical formulas) and statistical measures that assess relationships among members of large data sets. For example, there are methods to locate a gene within a sequence, to predict protein structure and/or function, and to cluster protein sequences into families of related sequences. The primary goal of bioinformatics is to increase the understanding of biological processes.  Pattern recognition,  Data mining  Machine learning algorithms and visualization.
Genomics Services • Next Generation sequencing Data Analysis • Microarray Data Analysis Proteomics Services • Proteomic structure prediction • Protein models • Protein-protein interactions • Peptidomics service Drug Discovery Services • Structure based drug discovery • Ligand Based drug discovery • Pharmacophore & Pharmacokinetics • Molecular Docking & Virtual Screening • Phytochemical Extraction & Identification • Chemical Synthesis and Characterization Biostatistics & Bio-IT Services • Clinical Data Analysis • Algorithm Development • Bio-Tools Development • Biological Database Development Bioinformatics • Sequence similarity search • Phylogenetic Analysis • Primer Design • Functional Annotation & Enrichment • Gene Ontology & Pathway prediction • Gene & Protein Prediction Scope of Bioinformatics Bioinfor matics Genomics Proteomi cs Drug Discovery Biostatisti cs & Bio- IT
Applications of Bioinformatics Microbial genome applications Molecular medicine Personalized medicine Preventative medicine Gene therapy Drug development Antibiotic resistance Evolutionary studies Waste cleanup Biotechnology Climate change Studies Alternative energy sources Crop improvement Forensic analysis Bio-weapon creation Insect resistance Improve nutritional quality Development of Drought resistant varieties Veterinary Science Biomedical Informatics Medical Ontologies & standards Clinical data management Machine Learning Computing High performance computing Software optimization & parallelization Cloud Computing Software engineering Machine Learning Data Management
Limitations Computer based programmes have helped in better understanding of various processes of life science. However, there are some limitations of bioinformatics which are listed below: 1) Bioinformatics requires sophisticated laboratory of molecular biology for in-depth study of Biomolecules. Establishment of such laboratories requires lot of funds. 2) Computer based study of life science requires some training about various computer programmes applicable for the study of different processes of life science. Thus special training is required for handling of computer based biological data. 3) There should be uninterrupted electricity (power) supply for computer aided biological investigations. Interruption of power may sometimes lead to loss of huge data from the computer memory. 4) There should be regular checking of computer viruses because viruses may pose several problems such as deletion of data and corruption of the programmes. 5) The maintenance and up keeping of molecular laboratories involves lot of expenditure which sometimes becomes a limiting factor for computer based molecular studies.
Biological Databases Databases are convenient system to properly store, search and retrieve any type of data. A database helps to easily handle and share large amount of data and support large scale analysis by easy access and data updating. Biological databases are libraries of life sciences information, collected from scientific experiments, published literature, high- throughput experiment technology and computational analysis. They contain information from genomics, proteomics, microarray gene expression. Information contained in biological databases includes function, gene structure localization (both cellular and chromosomal), biological sequences and structures. Based on the Biological contents such as DNA, RNA, Protein and functions, databases can be roughly divided into two categories: Primary Databases Secondary Databases Tertiary Databases
Primary Databases Theses are the primary sources of data used to store nucleic acid, protein sequences and structural information of biological macromolecules. Some of primary databases- • EMBL, GenBank, DDBJ (DNA data bank of Japan) • SWISS-PROT(Swiss-Prot ) • PIR (Protein Information Resource) • PDB(Protein Data Bank) (This sequence collection of this database is due to the efforts of basic research from academic industrial and sequencing lab)
Secondary Databases Secondary databases comprise data derived from the results of analyzing primary data. Secondary databases often draw upon information from numerous sources, including other databases (primary and secondary), controlled vocabularies and the scientific literature. They are highly curated, often using a complex combination of computational algorithms and manual analysis and interpretation to derive new knowledge from the public record of science. Example: Uniprot, SwissProt/TrEMBL, PIR, CDD InterPro (protein families, motifs and domains) UniProt Knowledgebase (sequence and functional information on proteins) Ensembl (variation, function, regulation and more layered onto whole genome sequences) UniProt, InterproScan, Prosite, Pfam, PRINTS, BLOCKS
SCOPAND CATH SCOP: a Structural Classification of Proteins Family: 1). All proteins that have residue identities of 30% and greater; 2). proteins with lower sequence identities but whose functions and structures are very similar; for example, globins with sequence identities of 15%. Super family: Families whose proteins have low sequence identities but whose structures and, in many cases, functional features suggest that a common evolutionary origin is probable, are placed together in super families; Common fold: Super families and families are defined as having a common fold if their proteins have the same major secondary structures in the same arrangement and with the same topological connections. Class: The different folds have been grouped into classes. Most of the folds are assigned to one of the five structural classes: 1) all-α, those whose structure is essentially formed by α-helices; 2) all-β, those whose structure is essentially formed by β-sheets; 3) α/β, those with α-helices and β-strands; 4) α+β, those in which α-helices and β-strands are largely segregated; 5) multi-domain, those with domains of different fold and for which no homologues are known at present.
CATH: Class, Architecture, Topology and Homology Class: Mainly based on three major classes are recognized; mainly-alpha, mainly-beta and alpha-beta. Architecture: This describes the overall shape of the domain structure as determined by the orientations of the secondary structures but ignores the connectivity between the secondary structures. Topology: Structures are grouped into fold families at this level depending on both the overall shape and connectivity of the secondary structures. Homology: Sequence identity >= 35%, 60% of larger structure equivalent to smaller SSAP score >= 80.0 and sequence identity >= 20% 60% of larger structure equivalent to smaller SSAP score >= 80.0, 60% of larger structure equivalent to smaller, and domains which have related functions
Tertiary Databases Tertiary databases also called as protein 3D structure database. Protein Data Bank (PDB) Nucleotide Data Bank (NDB) Protein Data Bank (PDB) The Protein Data Bank (PDB) is a database for the three-dimensional structural data of large biological molecules, such as proteins and nucleic acids. The data, typically obtained by X-ray crystallography, NMR spectroscopy, or, increasingly, cryo- electron microscopy, and submitted by biologists and biochemists from around the world, are freely accessible on the Internet via the websites of its member organizations (PDBe, PDBj, RCSB, and BMRB). The PDB is overseen by an organization called the Worldwide Protein Data Bank, wwPDB.
Major Bioinformatics Databases
Literature Databases An extensive search of the information available on a topic which results in a list of references to books, periodicals, and other materials on the topic Mainly - Books ( printed or e-books) • Journals (both) • Research reports (both) • Institutional publications(both) • Govt. publications (both) • Various NGO’s/ INGO’s publications • Internet (Online resources) • Intranet (Offline resources) • Grey Literature
Open Access and Open Source Open source software, like free software, is a kind of software whose source code is available for inspection or modification. Some open source software is available for a fee, but much of it is available at no cost. Open access is a kind of access or availability. This kind of access could apply to any digital content, such as software, music, movies, or news. But we only calls for open access to a certain kind of scientific and scholarly literature.
Sources of information By nature/content of information- 1. Primary e.g. journal , reports 2. Secondary e.g. Books 3. Tertiary e.g. Subject bibliography By format/media/channel a. Hard copy (Print) b. Soft copy(Electronic) A. Printed B. Electronic a. Offline (Intranet) e.g. DVD, CD, cassette b. Online (Internet) Types A and B(a), In Library. HOW CAN WE SEARCH?
Online searching Internet searching for the purpose of academic, business and others. PubMed (Databases) searching exclusively for medical literature. PURPOSE OR NEEDS 1. Review theory 2. Problem statement 3. To improve self knowledge 4. To Know methodology. 5. To assess need of problem 6. To support on tools, methods, findings, data collection 7. To review what was done in past 8. To generate Idea
Searching Literature using PubMed Efficient literature search is essential to the practice of Evidence-Based Medicine. PubMed provides free access to one of the largest searchable biomedical databases. Efficient literature search using PubMed requires a good understanding of the available search strategies and tools. Several PubMed tools including 'Single Citation Matcher', 'Clinical Queries', 'Clipboard', 'Field Tags', and 'Cubby' are highlighted using case based scenarios.
Sequence formats Data is stored in a biological database in the form of sequences or molecular form. Unique file format Representation of data in biological database Categories of file formats Sequence database Molecular database Sequence file formats Gene bank flat-file Format FASTA Format Multi-FASTA Format GCG Format GCG-MSF Format EMBL Format Clustal Format SWIS PROT format
GenBank • Used by NCBI • It is divided into three parts • Header just a direct and very precise or brief introductory part • Features all genes in seq., location of genes in genome, protein product and coding genes etc. • Sequence : ORIGIN atcgatcgatgcgctat //
FASTA  One line header Stats with > followed by name of gene Sequence of gene or protein • Blank spaces • Paragraph marks • Numerals Are all ignored Steric sign * at the end
Sequence Databases Sequence generation 1. First Generation Sequencing 2. Second Generation Sequencing 3. Third Generation Sequencing Types of Sequence Databases Nucleotide Sequence Databases 1. EMBL/DDBJ/GenBank 2. RefSeq 3. Ensembl Protein Sequence Databases 1. TrEMBL 2. GenPept 3. Entrez protein 4. Uniprot Sequence Submission 1. Sequin 2. BankIt 3. Webin
Structural Databases Protein Data Bank – X-Ray Crystallography
Genome Databases Genomic databases are integral parts of human genome informatics, which enjoyed an exponential growth in the post genomic era, as a result of the understanding of the genetic etiology of human disorders and the identification of numerous genomic variants. MethBase A reference methylome database Animal Genome Size Database Aspergillus Genomes Atlas of Genetics and Cytogenetics in Oncology and Haematology SilkBase Bombyx mori genome BGD Bovine Genome Database CGD Candida Genome Database Chicken (Gallus gallus) Genome CYORF Cyanobacteria Gene Annotation Database Cytogenetics Gallery OriDB DNA Replication Origin Database wFleaBase Daphnia Water Flea Genome Database diArk Database for eukaryotic genome and EST sequencing projects DGV Database of Genomic Variants
DGVa Database of Genomic Variants archive GenAge Database of genes related to ageing dbVar Database of genomic structural variation Dog Genome Annotation (Canis familiaris) FlyBase Drosophila Genes and Genome Database Ensembl Tutorial: Browsing genomes Gene Ontology Consortium GMOD Generic Model Organism Database G10K Genome 10K Project: Genomes of 10,000 vertebrate species Genome Sequencing Consortiums and Centers GWIDD Genome Wide Docking Database GOLD Genomes OnLine Database euGenes Genomic Information for Eukaryotic Organisms WormBase Genomics and biology of C. elegans and related nematodes GiardiaDB Giardia lamblia Genomics Resource Google Cloud Life Sciences Honey Bee Genome Project - Apis mellifera HGD Hymenoptera Genome Database GeneLoc Integrated map for each human chromosome IGSR International Genome Sample Resource
IMPC International Mouse Phenotyping Consortium IRDB Inverted Repeats Database in genomic DNA JGI Genome Portal KEGG Organisms: Complete Genomes KEGG Kyoto Encyclopedia of Genes and Genomes MGI Mouse Genome Informatics Mouse Genomes Project Mouse Genomic Imprinting NCBI Human Genome - Guide to Online Information Nematode Genomes PharmGKB Pharmacogenomics Knowledgebase PlasmoDB Plasmodium Genome Resource RGD Rat Genome Database SGD Saccharomyces Genome Database PomBase Schizosaccharomyces pombe Genome Project Telomerase Database TGD Tetrahymena Genome Database UCSC Genome Bioinformatics VEGA Vertebrate Genome Annotation database
Viral genome database
Bacterial Genomes database
Organism specific Genome database
ligand databases
Assignments 1. Applications of Bioinformatics 2. How to Search literature using PubMed 3. Write the importance of Genome Data Viewer 4. Insect Genome Databases and their applications 5. Gene to Disease Databases
Thank you

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bioinformatics algorithms and its basics

  • 1.
    Established as perthe Section 2(f) of the UGC Act, 1956 Approved by AICTE, COA and BCI, New Delhi BIOINFORMATICS School of Applied Sciences
  • 2.
    Established as perthe Section 2(f) of the UGC Act, 1956 Approved by AICTE, COA and BCI, New Delhi Program: B.Sc BMCs Course Title: Bioinformatics and Biostatistics Course Code: B19MC3030 Course Type: Hardcore Course Presenter: Prashantha C N Course Mentor: Prashantha C N Semester: & Section: 3rd semester Academic Year: 2020-21 Course Pre-requisites: Students should have basic knowledge of biology, statistics and computer sciences. Students should be familiar with the basic concepts of DNA, RNA and Protein. L T P: L=4 T=0 P=4 Pedagogy:
  • 3.
    Course objectives 1. Thebasic objective is to give students an introduction, scope and objectives of bioinformatics 2. Understanding of different types of biological databases and tools used to understand data structures. Course outcome 1. Knowledge and awareness of the basic principles and concepts of biology and biological databases, literature search methods. Pedagogy chart Course code POs/ COs PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PSO1 PSO2 B20MC3030 CO1 3 2 2 1 1 1 3 1 CO2 2 3 3 3 2 1 2 2 3 2 CO3 1 3 3 3 2 1 2 2 3 2 CO4 1 3 2 1 1 1 2 2 3 1
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    Syllabus Unit Topics Course Outcomes Program Outcomes IIntroduction to Bioinformatics, Goal, Scope, Applications, Limitations 1 1 Biological Databases: Types of Databases, 1 2 Literature databases: Open access and open sources, PubMed, PLoS, Biomed Central, 1 2 Information Retrieval from Biological Databases; Sequence Formats, Sequence databases, structural databases, 2 2 Genome Databases: Viral genome database (ICTVdb, VirGen), Bacterial Genomes database (Genomes OnLine Database –GOLD, Microbial Genome Database- MBGD), Organism specific Genome database (OMIM / OMIA, SGD, Worm Base, PlasmoDB, FlyBase, TAIR), and ligand databases. 3 3 Program Outcomes PO-1: Science knowledge: Apply the knowledge of Biology, computer science and mathematics, to develop algorithms and analyze biological programs related to drug discovery, and genomic analysis to solve biological problems. PO-2: Problem analysis: Identify, formulate and analyze problems related to the various domains of Biological sciences such as Molecular Biology, Biotechnology, Genetics, Healthcare, agricultural, animal sciences and Microbial biotechnology. PO-3: Conduct investigations of complex problems: Use research-based knowledge and research methods including design of experiments, analysis and interpretation of data, and synthesis of the information to provide valid conclusions.
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    Reference Books 1. AndreasD. Baxevanis, B.F. Francis Ouellette. Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins, 2nd Edition, 2009. 2. David W. Mount., Bioinformatics: Sequence and Genome Analysis, Cold Spring Harbor Laboratory Press, New York. 2004. 3. Andrew R. Leach., Molecular Modeling Principles and Applications (2nd Ed.), Prentice Hall, USA. 2001. 4. G. E. Schulz., Principles of Protein Structure, Springer 2009. e-Resources 1. https://youtu.be/w-uk-_TOgR0 2. https://www.youtube.com/watch?v=IrHDOEDtwD4 3. https://www.youtube.com/watch?v=48Xr-H05raA Suggested Readings Unit wise 1. Jin Xiong., Essential Bioinformatics, Cambridge university press (2006).
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    Presentation Topics Fundamentals ofBioinformatics: Introduction to Bioinformatics, Goal, Scope, Applications, Limitations, Biological Databases: Types of Databases, Biological databases; Literature databases: Open access and open sources, PubMed, PLoS, Biomed Central, Information Retrieval from Biological Databases; Sequence Formats, Sequence databases, structural databases, Genome Databases: Viral genome database (ICTVdb, VirGen), Bacterial Genomes database (Genomes OnLine Database –GOLD, Microbial Genome Database-MBGD), Organism specific Genome database (OMIM / OMIA, SGD, Worm Base, PlasmoDB, FlyBase, TAIR), and ligand databases. Assignments 1. Applications of Bioinformatics 2. Searching literature through PubMed 3. Genome File formats 4. Sequence File formats 5. Understanding of Viral Genome databases 6. Understanding of Bacterial Genome databases 7. Ligand Databases and their applications.
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    Unit-1: Fundamentals of Bioinformatics Bioinformatics Biology+ Mathematical + Statistical + Computational tools = Bioinformatics Biology Natural science that study life and living organism based on physical, chemical, molecular, physiological, development and evolution of organisms. Genetics, Molecular Biology, Biotechnology, Microbiology, Biochemistry, etc Mathematical + Statistical Mathematical formulations to predict the algorithms, study of DNA & Protein interactions, Statistical methods to analyze biological data and predict the interpretations. Study of arithmetic calculations, normalization, standard deviation, probability, correlations & regressions, ANOVA and Variances. Computational Tools Biological Data collection, Storage, Retrieve, Analyze and Interpretation
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    Bioinformatics is afield of a science in which biology, computer science and information technology merge into a single discipline to analyze biological information using computers and statistical techniques. Biological experiments can generate large amounts of data (macromolecular sequences, structures, expression profiles, pathways etc). Bioinformatics is about acquiring, managing, analyzing and understanding those data.
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    Bioinformatics is aninterdisciplinary field that develops methods and software tools for understanding biological data. As an interdisciplinary field of science, bioinformatics combines biology, computer science, information engineering, mathematics and statistics to analyze and interpret biological data. Bioinformatics now entails the creation and advancement of databases, algorithms, computational and statistical techniques, and theory to solve formal and practical problems arising from the management and analysis of biological data. Definitions of Bioinformatics
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    History of Bioinformatics Thefirst major bioinformatics project was undertaken by Margaret Dayhoff in 1965, who developed a first protein sequence database called Atlas of Protein Sequence and Structure. She is American Physical Chemist professor at Georgetown University Medical Center Margaret Dayhoff, 1965 She originated one of the first substitution matrices, point accepted mutations (PAM). The one-letter code used for amino acids was developed by her, reflecting an attempt to reduce the size of the data files used to describe amino acid sequences in an era of punch-card computing. Subsequently, in the early 1970s, the Brookhaven National Laboratory established the Protein Data Bank for archiving three-dimensional protein structures. The first sequence alignment algorithm was developed by Needleman and Wunsch in 1970. The first protein structure prediction algorithm was developed by Chou and Fasman in 1974. GenBank was developed in 1980, a fast database search and sequence repository. FASTA was developed by William Pearson BLAST was developed by Stephen Altschul and coworkers.
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    Bioinformatics differs froma related field known as computational biology. Bioinformatics is limited to sequence, structural, and functional analysis of genes and genomes and their corresponding products and is often considered computational molecular biology. However, computational biology encompasses all biological areas that involve computation. For example, mathematical modeling of ecosystems, population dynamics, application of the game theory in behavioral studies, and phylogenetic construction using fossil records all employ computational tools, but do not necessarily involve biological macromolecules. Bioinformatics Vs Computational Biology
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    GOAL OF BIOINFORMATICS Bioinformaticshas evolved such that the most pressing task now involves the analysis and interpretation of various types of data. Development and implementation of computer programs that enable efficient access to, use and management of, various types of information Development of new algorithms (mathematical formulas) and statistical measures that assess relationships among members of large data sets. For example, there are methods to locate a gene within a sequence, to predict protein structure and/or function, and to cluster protein sequences into families of related sequences. The primary goal of bioinformatics is to increase the understanding of biological processes.  Pattern recognition,  Data mining  Machine learning algorithms and visualization.
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    Genomics Services • NextGeneration sequencing Data Analysis • Microarray Data Analysis Proteomics Services • Proteomic structure prediction • Protein models • Protein-protein interactions • Peptidomics service Drug Discovery Services • Structure based drug discovery • Ligand Based drug discovery • Pharmacophore & Pharmacokinetics • Molecular Docking & Virtual Screening • Phytochemical Extraction & Identification • Chemical Synthesis and Characterization Biostatistics & Bio-IT Services • Clinical Data Analysis • Algorithm Development • Bio-Tools Development • Biological Database Development Bioinformatics • Sequence similarity search • Phylogenetic Analysis • Primer Design • Functional Annotation & Enrichment • Gene Ontology & Pathway prediction • Gene & Protein Prediction Scope of Bioinformatics Bioinfor matics Genomics Proteomi cs Drug Discovery Biostatisti cs & Bio- IT
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    Applications of Bioinformatics Microbialgenome applications Molecular medicine Personalized medicine Preventative medicine Gene therapy Drug development Antibiotic resistance Evolutionary studies Waste cleanup Biotechnology Climate change Studies Alternative energy sources Crop improvement Forensic analysis Bio-weapon creation Insect resistance Improve nutritional quality Development of Drought resistant varieties Veterinary Science Biomedical Informatics Medical Ontologies & standards Clinical data management Machine Learning Computing High performance computing Software optimization & parallelization Cloud Computing Software engineering Machine Learning Data Management
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    Limitations Computer based programmeshave helped in better understanding of various processes of life science. However, there are some limitations of bioinformatics which are listed below: 1) Bioinformatics requires sophisticated laboratory of molecular biology for in-depth study of Biomolecules. Establishment of such laboratories requires lot of funds. 2) Computer based study of life science requires some training about various computer programmes applicable for the study of different processes of life science. Thus special training is required for handling of computer based biological data. 3) There should be uninterrupted electricity (power) supply for computer aided biological investigations. Interruption of power may sometimes lead to loss of huge data from the computer memory. 4) There should be regular checking of computer viruses because viruses may pose several problems such as deletion of data and corruption of the programmes. 5) The maintenance and up keeping of molecular laboratories involves lot of expenditure which sometimes becomes a limiting factor for computer based molecular studies.
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    Biological Databases Databases areconvenient system to properly store, search and retrieve any type of data. A database helps to easily handle and share large amount of data and support large scale analysis by easy access and data updating. Biological databases are libraries of life sciences information, collected from scientific experiments, published literature, high- throughput experiment technology and computational analysis. They contain information from genomics, proteomics, microarray gene expression. Information contained in biological databases includes function, gene structure localization (both cellular and chromosomal), biological sequences and structures. Based on the Biological contents such as DNA, RNA, Protein and functions, databases can be roughly divided into two categories: Primary Databases Secondary Databases Tertiary Databases
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    Primary Databases Theses arethe primary sources of data used to store nucleic acid, protein sequences and structural information of biological macromolecules. Some of primary databases- • EMBL, GenBank, DDBJ (DNA data bank of Japan) • SWISS-PROT(Swiss-Prot ) • PIR (Protein Information Resource) • PDB(Protein Data Bank) (This sequence collection of this database is due to the efforts of basic research from academic industrial and sequencing lab)
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    Secondary Databases Secondary databasescomprise data derived from the results of analyzing primary data. Secondary databases often draw upon information from numerous sources, including other databases (primary and secondary), controlled vocabularies and the scientific literature. They are highly curated, often using a complex combination of computational algorithms and manual analysis and interpretation to derive new knowledge from the public record of science. Example: Uniprot, SwissProt/TrEMBL, PIR, CDD InterPro (protein families, motifs and domains) UniProt Knowledgebase (sequence and functional information on proteins) Ensembl (variation, function, regulation and more layered onto whole genome sequences) UniProt, InterproScan, Prosite, Pfam, PRINTS, BLOCKS
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    SCOPAND CATH SCOP: aStructural Classification of Proteins Family: 1). All proteins that have residue identities of 30% and greater; 2). proteins with lower sequence identities but whose functions and structures are very similar; for example, globins with sequence identities of 15%. Super family: Families whose proteins have low sequence identities but whose structures and, in many cases, functional features suggest that a common evolutionary origin is probable, are placed together in super families; Common fold: Super families and families are defined as having a common fold if their proteins have the same major secondary structures in the same arrangement and with the same topological connections. Class: The different folds have been grouped into classes. Most of the folds are assigned to one of the five structural classes: 1) all-α, those whose structure is essentially formed by α-helices; 2) all-β, those whose structure is essentially formed by β-sheets; 3) α/β, those with α-helices and β-strands; 4) α+β, those in which α-helices and β-strands are largely segregated; 5) multi-domain, those with domains of different fold and for which no homologues are known at present.
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    CATH: Class, Architecture,Topology and Homology Class: Mainly based on three major classes are recognized; mainly-alpha, mainly-beta and alpha-beta. Architecture: This describes the overall shape of the domain structure as determined by the orientations of the secondary structures but ignores the connectivity between the secondary structures. Topology: Structures are grouped into fold families at this level depending on both the overall shape and connectivity of the secondary structures. Homology: Sequence identity >= 35%, 60% of larger structure equivalent to smaller SSAP score >= 80.0 and sequence identity >= 20% 60% of larger structure equivalent to smaller SSAP score >= 80.0, 60% of larger structure equivalent to smaller, and domains which have related functions
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    Tertiary Databases Tertiary databasesalso called as protein 3D structure database. Protein Data Bank (PDB) Nucleotide Data Bank (NDB) Protein Data Bank (PDB) The Protein Data Bank (PDB) is a database for the three-dimensional structural data of large biological molecules, such as proteins and nucleic acids. The data, typically obtained by X-ray crystallography, NMR spectroscopy, or, increasingly, cryo- electron microscopy, and submitted by biologists and biochemists from around the world, are freely accessible on the Internet via the websites of its member organizations (PDBe, PDBj, RCSB, and BMRB). The PDB is overseen by an organization called the Worldwide Protein Data Bank, wwPDB.
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    Literature Databases An extensivesearch of the information available on a topic which results in a list of references to books, periodicals, and other materials on the topic Mainly - Books ( printed or e-books) • Journals (both) • Research reports (both) • Institutional publications(both) • Govt. publications (both) • Various NGO’s/ INGO’s publications • Internet (Online resources) • Intranet (Offline resources) • Grey Literature
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    Open Access andOpen Source Open source software, like free software, is a kind of software whose source code is available for inspection or modification. Some open source software is available for a fee, but much of it is available at no cost. Open access is a kind of access or availability. This kind of access could apply to any digital content, such as software, music, movies, or news. But we only calls for open access to a certain kind of scientific and scholarly literature.
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    Sources of information Bynature/content of information- 1. Primary e.g. journal , reports 2. Secondary e.g. Books 3. Tertiary e.g. Subject bibliography By format/media/channel a. Hard copy (Print) b. Soft copy(Electronic) A. Printed B. Electronic a. Offline (Intranet) e.g. DVD, CD, cassette b. Online (Internet) Types A and B(a), In Library. HOW CAN WE SEARCH?
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    Online searching Internet searchingfor the purpose of academic, business and others. PubMed (Databases) searching exclusively for medical literature. PURPOSE OR NEEDS 1. Review theory 2. Problem statement 3. To improve self knowledge 4. To Know methodology. 5. To assess need of problem 6. To support on tools, methods, findings, data collection 7. To review what was done in past 8. To generate Idea
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    Searching Literature usingPubMed Efficient literature search is essential to the practice of Evidence-Based Medicine. PubMed provides free access to one of the largest searchable biomedical databases. Efficient literature search using PubMed requires a good understanding of the available search strategies and tools. Several PubMed tools including 'Single Citation Matcher', 'Clinical Queries', 'Clipboard', 'Field Tags', and 'Cubby' are highlighted using case based scenarios.
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    Sequence formats Data isstored in a biological database in the form of sequences or molecular form. Unique file format Representation of data in biological database Categories of file formats Sequence database Molecular database Sequence file formats Gene bank flat-file Format FASTA Format Multi-FASTA Format GCG Format GCG-MSF Format EMBL Format Clustal Format SWIS PROT format
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    GenBank • Used byNCBI • It is divided into three parts • Header just a direct and very precise or brief introductory part • Features all genes in seq., location of genes in genome, protein product and coding genes etc. • Sequence : ORIGIN atcgatcgatgcgctat //
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    FASTA  One lineheader Stats with > followed by name of gene Sequence of gene or protein • Blank spaces • Paragraph marks • Numerals Are all ignored Steric sign * at the end
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    Sequence Databases Sequence generation 1.First Generation Sequencing 2. Second Generation Sequencing 3. Third Generation Sequencing Types of Sequence Databases Nucleotide Sequence Databases 1. EMBL/DDBJ/GenBank 2. RefSeq 3. Ensembl Protein Sequence Databases 1. TrEMBL 2. GenPept 3. Entrez protein 4. Uniprot Sequence Submission 1. Sequin 2. BankIt 3. Webin
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    Structural Databases Protein DataBank – X-Ray Crystallography
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    Genome Databases Genomic databasesare integral parts of human genome informatics, which enjoyed an exponential growth in the post genomic era, as a result of the understanding of the genetic etiology of human disorders and the identification of numerous genomic variants. MethBase A reference methylome database Animal Genome Size Database Aspergillus Genomes Atlas of Genetics and Cytogenetics in Oncology and Haematology SilkBase Bombyx mori genome BGD Bovine Genome Database CGD Candida Genome Database Chicken (Gallus gallus) Genome CYORF Cyanobacteria Gene Annotation Database Cytogenetics Gallery OriDB DNA Replication Origin Database wFleaBase Daphnia Water Flea Genome Database diArk Database for eukaryotic genome and EST sequencing projects DGV Database of Genomic Variants
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    DGVa Database ofGenomic Variants archive GenAge Database of genes related to ageing dbVar Database of genomic structural variation Dog Genome Annotation (Canis familiaris) FlyBase Drosophila Genes and Genome Database Ensembl Tutorial: Browsing genomes Gene Ontology Consortium GMOD Generic Model Organism Database G10K Genome 10K Project: Genomes of 10,000 vertebrate species Genome Sequencing Consortiums and Centers GWIDD Genome Wide Docking Database GOLD Genomes OnLine Database euGenes Genomic Information for Eukaryotic Organisms WormBase Genomics and biology of C. elegans and related nematodes GiardiaDB Giardia lamblia Genomics Resource Google Cloud Life Sciences Honey Bee Genome Project - Apis mellifera HGD Hymenoptera Genome Database GeneLoc Integrated map for each human chromosome IGSR International Genome Sample Resource
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    IMPC International MousePhenotyping Consortium IRDB Inverted Repeats Database in genomic DNA JGI Genome Portal KEGG Organisms: Complete Genomes KEGG Kyoto Encyclopedia of Genes and Genomes MGI Mouse Genome Informatics Mouse Genomes Project Mouse Genomic Imprinting NCBI Human Genome - Guide to Online Information Nematode Genomes PharmGKB Pharmacogenomics Knowledgebase PlasmoDB Plasmodium Genome Resource RGD Rat Genome Database SGD Saccharomyces Genome Database PomBase Schizosaccharomyces pombe Genome Project Telomerase Database TGD Tetrahymena Genome Database UCSC Genome Bioinformatics VEGA Vertebrate Genome Annotation database
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    Assignments 1. Applications ofBioinformatics 2. How to Search literature using PubMed 3. Write the importance of Genome Data Viewer 4. Insect Genome Databases and their applications 5. Gene to Disease Databases
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