This document discusses multiple sequence alignment techniques. It begins with definitions of key terms like homology, similarity, and conservation. It then describes pairwise alignment and its applications. The rest of the document focuses on multiple sequence alignment methods like progressive alignment, iterative refinement, tree alignment, star alignment, and using genetic algorithms. It provides examples and explanations of popular multiple sequence alignment tools like Clustal W and T-Coffee.
This document discusses sequence alignment methods. It describes global and local alignment, and algorithms used for alignment including dot matrix analysis, dynamic programming, and word/k-tuple methods as implemented in FASTA and BLAST programs. BLAST and FASTA are described as popular tools for sequence database searches that use heuristic methods and word matching to quickly identify regions of local similarity.
Multiple sequence alignment (MSA) aligns three or more biological sequences, like proteins or nucleic acids, to infer homology and evolutionary relationships. There are three main methods - dynamic programming computes an optimal alignment but has high runtime; progressive alignment first does pairwise alignments and adds sequences; iterative alignment successively improves approximations without pairwise alignments. Popular tools for MSA include Clustal W, MAFFT, MUSCLE, and T-Coffee. MSA helps detect similarities, conserved motifs, and structural homologies between sequences.
The document discusses the field of proteomics, which is the large-scale study of proteins, including their functions and structures. It defines proteomics and describes several areas within it, such as functional proteomics, expressional proteomics, and structural proteomics. It outlines typical proteomics experiments and some key methods used, including two-dimensional electrophoresis, mass spectrometry, and protein-protein interaction prediction methods like phylogenetic profiling.
This document provides an overview of phylogenetic analysis, including:
1) Phylogenetic analysis involves inferring evolutionary relationships between taxa by building phylogenetic trees and analyzing character evolution.
2) Phylogenetic trees show the branching patterns and relationships between taxa, with internal nodes representing hypothetical ancestors.
3) Phylogenetic analysis can provide insights into questions like human evolution, disease transmission, and the origins of genetic elements.
This document discusses different types of sequence alignment methods used in bioinformatics to identify similarities between DNA, RNA, and protein sequences. It describes global and local alignment, which aim to identify conserved regions across entire or local subsequences. Pairwise alignment methods like dot matrix, dynamic programming, and word methods are used to compare two sequences. Multiple sequence alignment extends this to three or more sequences, using progressive, iterative, or dynamic programming approaches to infer evolutionary relationships.
Clustal Omega is a fast and scalable program for multiple sequence alignment. It begins by producing pairwise alignments using a word-based heuristic method. It then clusters the sequences using a modified mBed distance method and k-means clustering. Finally, it generates the multiple sequence alignment using the HHAlign package, which aligns profile HMMs built from the sequences. Clustal Omega is widely considered one of the fastest online multiple sequence alignment tools.
Protein structure classification/domain prediction: SCOP and CATH (Bioinforma...SPHStudy
This pdf is about the protein structure classification/domain prediction: SCOP and CATH (Bioinformatics).
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The DNA Data Bank of Japan (DDBJ) is a biological database located in Japan that collects and stores nucleotide sequence data. It began operations in 1986 and exchanges data daily with the European Nucleotide Archive and GenBank to form the International Nucleotide Sequence Database Collaboration (INSDC). DDBJ accepts sequence submissions from researchers worldwide and assigns unique identification numbers to published sequences to recognize intellectual property rights. It also provides search and analysis tools and supercomputing resources to support genomic research.
Sequence alig Sequence Alignment Pairwise alignment:-naveed ul mushtaq
Sequence Alignment Pairwise alignment:- Global Alignment and Local AlignmentTwo types of alignment Progressive Programs for multiple sequence alignment BLOSUM Point accepted mutation (PAM)PAM VS BLOSUM
This document provides an overview of the FASTA software. FASTA is a program used by biologists to study and analyze DNA and protein sequences. It uses a simple text-based format to present sequences and allows for the naming of sequences and inclusion of comments. FASTA is a rapid program that can be used locally or through email servers to find regional similarities between sequences and identify potential matches while ignoring complete sensitivity. It has become a standard tool in biology for sequencing and analyzing proteins and DNA.
1. BLAST is a program that uses computer algorithms to compare a query DNA or protein sequence to sequence databases and identify sequences that resemble the query sequence above a certain threshold.
2. BLAST works by searching for short, exact matches between the query and database sequences, then extends the matches to find similar though not exact alignments.
3. Analyzing the BLAST results can provide information about the evolutionary relationship between the query sequence and matched sequences, such as whether they come from the same gene or protein family.
The document discusses biological databases and retrieval systems. It provides an overview of Entrez, a retrieval system developed by NCBI that allows integrated searches across multiple biological databases. It also describes how Entrez links related data between databases, and some key features of Entrez like limits, preview/index, and history. Additionally, it summarizes specific NCBI databases accessible through Entrez like PubMed and OMIM, as well as another retrieval system called SRS maintained by EBI.
An integrated publicly accessible bioinformatics resource to support genomic/proteomic research and scientific discovery.
Established in 1984, by the National Biomedical Research Foundation (NBRF) Georgetown University Medial Center, Washington D.C., USA.
It is the source of annotated protein databases and analysis tools for the researchers.
Serve as primary resource for the exploration of protein information.
Accessible by text search for entry and list retrieval, and also BLAST search and peptide match.
GenBank is a database that contains annotated nucleotide and protein sequences. It includes genomic DNA, mRNA, and EST sequences. There are three main sections in a GenBank file - the header, features, and sequence. The header provides definition, accession number, organism, and reference information. The features section contains gene and protein annotation. The sequence section displays the actual nucleotide or amino acid sequence. Understanding the GenBank file format helps effectively search and retrieve sequences from this important biological database.
The document provides an overview of the history and scope of bioinformatics. It discusses how bioinformatics emerged from the fields of computer science and biology. The history section outlines major developments from Mendel's work in 1865 to the sequencing of the human genome in 2001. Bioinformatics has various applications in areas like drug development, personalized medicine, and biotechnology. It also has significant scope in India, with growing job opportunities in both the public and private sectors.
The document discusses various types of biological databases. It describes primary databases that contain original data, secondary databases that contain processed data derived from primary databases, and composite databases that collect and filter data from multiple primary databases. Examples of specific biological databases are provided, including nucleic acid databases like GenBank, protein sequence databases like Swiss-Prot, protein structure database PDB, and metabolic pathway database KEGG. Details about the purpose and features of some of these major databases like GenBank, DDBJ, EMBL, Swiss-Prot, and PDB are outlined in the document.
This presentation gives you a detailed information about the swiss prot database that comes under UniProtKB. It also covers TrEMBL: a computer annotated supplement to Swiss-Prot.
Structural databases like PDB, CSD, and CATH contain 3D structural information of proteins, small molecules, and macromolecules determined through techniques like X-ray crystallography and NMR spectroscopy. These databases provide bibliographic data, atomic coordinates, and other details for each entry. PDB contains protein structures, CSD contains organic and metal-organic structures, and CATH classifies protein domains hierarchically. Structural databases have wide applications in structure prediction, analysis, mining, comparison, classification, structure refinement, and database annotation.
Protein databases contain information on protein sequences, structures, and functions. The major protein databases are:
- Protein Data Bank (PDB) which contains 3D protein structures determined via X-ray crystallography or NMR.
- Swiss-Prot which contains manually annotated protein sequences and functions.
- TrEMBL which supplements Swiss-Prot with automatically annotated translations of DNA sequences.
Protein databases are important for comparing proteins, understanding relationships between proteins, and aiding the study of new proteins. Searching databases is often the first step in protein research.
The DNA Data Bank of Japan (DDBJ) is a biological database that collects DNA sequences. It is located at the National Institute of Genetics (NIG) in the Shizuoka prefecture of Japan. It is also a member of the International Nucleotide Sequence Database Collaboration or INSDC.
The European Molecular Biology Laboratory (EMBL) is a molecular biology research institution supported by 22 member states. EMBL was created in 1974 and operates from five sites, performing basic research in molecular biology and molecular medicine. A key function of EMBL is the EMBL Nucleotide Sequence Database, maintained at the European Bioinformatics Institute, which incorporates and distributes nucleotide sequences from public sources as part of an international collaboration.
FASTA is a bioinformatics tool and biological database that is used to compare amino acid sequences of proteins or nucleotide sequences of DNA. It was first described in 1985 by Lipman and Pearson. FASTA performs fast homology searches to find similarities between a query sequence and sequences in a database. While similar to BLAST, FASTA is faster for sequence comparisons. It works by identifying patches of sequence similarity that may contain gaps. Some key FASTA programs include FASTA, TFASTA, FASTS, and FASTX/Y. FASTA is useful for applications like identification of species, establishing phylogeny, DNA mapping, and understanding protein function.
INTRODUCTION OF BIOINFORMATICS
HISTORY
WHAT IS DATABASE
NEED FOR DATABASE
TYPES OF DATABASE
PRIMARY DATABASE
NUCLEIC ACID SEQUENCE DATABASE
GENE BANK
INTRODUCTION
GENE BANK SUBMISSION TOOL
GENE BANK SUBMISSION TYPE
HOW TO RETRIEVE DATA FROM GENEBANK
APPLICATION
CONCLUSION
REFERENCE
This document discusses sequence analysis, which involves subjecting DNA, RNA, or protein sequences to analytical methods to understand their features and functions. It describes DNA and protein sequencing techniques, as well as sequence assembly, alignment, and multiple sequence alignment. It provides steps to demonstrate protein sequencing, including retrieving a human prion protein sequence from NCBI, running BLAST to find similar sequences, performing multiple sequence alignment, and predicting secondary structure. Sequence analysis has applications like finding sequence similarities to infer relationships, identifying intrinsic features, and revealing evolution.
This document discusses biological databases and nucleic acid sequence databases. It describes the three primary nucleotide sequence databases: GenBank, EMBL, and DDBJ. GenBank is hosted by the National Center for Biotechnology Information and contains over 286 million bases and 352,000 sequences. EMBL is hosted by the European Molecular Biology Laboratory and mirrors data daily with GenBank and DDBJ. DDBJ is the DNA Data Bank of Japan and also mirrors data daily with the other two databases. Biological databases are important tools for scientists to understand biology at multiple levels.
In this presentation, I talk about the various tools for the submission of DNA or RNA sequences into various sequence databases. The sequence submission tools talked about in this presentation are BankIt, Sequin and Webin.
Sequence homology search and multiple sequence alignment(1)AnkitTiwari354
Sequence homology is the biological homology between DNA, RNA, or protein sequences, defined in terms of shared ancestry in the evolutionary history of life. Two segments of DNA can have shared ancestry because of three phenomena: either a speciation event (orthologs), or a duplication event (paralogs), or else a horizontal (or lateral) gene transfer event (xenologs).[1]
Homology among DNA, RNA, or proteins is typically inferred from their nucleotide or amino acid sequence similarity. Significant similarity is strong evidence that two sequences are related by evolutionary changes from a common ancestral sequence. Alignments of multiple sequences are used to indicate which regions of each sequence are homologous.
Pairwise Sequence Alignment between HBV and HCC Using Modified Needleman-Wuns...TELKOMNIKA JOURNAL
Ths paper aims to find similarity of Hepatitis B virus (HBV) and Hepatocelluler Carcinoma (HCC) DNA sequences. It is very important in bioformatics task. The similarity of sequence allignments indicates that they have similarity of chemical and physical properties. Mutation of the virus DNA in X region has potential role in HCC. It is observed using pairwise sequence alignment of genotype-A in HBV. The complexity of DNA sequence using dynamic programming, Needleman-Wunsch algorithm, is very high. Therefore, it is purpose to modifiy the method of Needleman Wunsch algorithm for optimum global DNA sequence alignment. The main idea is to optimize filling matrix and backtracking proccess of DNA components.This method can also solve various length of the both sequence alignment.
This research is applied to DNA sequence of 858 hepatitis B virus and 12 carcinoma patient, so that there are 10,296 pairwis of sequences. They are aligned globally using the purposed method and as a result, it is achieved high similarity of 96.547% and validity of 99.854%. Furhthermore, this method has reduced the complexity of original Needleman-Wunsch algorithm The reduction of computational time is as 34.6% and space complexity is as 42.52%.
The DNA Data Bank of Japan (DDBJ) is a biological database located in Japan that collects and stores nucleotide sequence data. It began operations in 1986 and exchanges data daily with the European Nucleotide Archive and GenBank to form the International Nucleotide Sequence Database Collaboration (INSDC). DDBJ accepts sequence submissions from researchers worldwide and assigns unique identification numbers to published sequences to recognize intellectual property rights. It also provides search and analysis tools and supercomputing resources to support genomic research.
Sequence alig Sequence Alignment Pairwise alignment:-naveed ul mushtaq
Sequence Alignment Pairwise alignment:- Global Alignment and Local AlignmentTwo types of alignment Progressive Programs for multiple sequence alignment BLOSUM Point accepted mutation (PAM)PAM VS BLOSUM
This document provides an overview of the FASTA software. FASTA is a program used by biologists to study and analyze DNA and protein sequences. It uses a simple text-based format to present sequences and allows for the naming of sequences and inclusion of comments. FASTA is a rapid program that can be used locally or through email servers to find regional similarities between sequences and identify potential matches while ignoring complete sensitivity. It has become a standard tool in biology for sequencing and analyzing proteins and DNA.
1. BLAST is a program that uses computer algorithms to compare a query DNA or protein sequence to sequence databases and identify sequences that resemble the query sequence above a certain threshold.
2. BLAST works by searching for short, exact matches between the query and database sequences, then extends the matches to find similar though not exact alignments.
3. Analyzing the BLAST results can provide information about the evolutionary relationship between the query sequence and matched sequences, such as whether they come from the same gene or protein family.
The document discusses biological databases and retrieval systems. It provides an overview of Entrez, a retrieval system developed by NCBI that allows integrated searches across multiple biological databases. It also describes how Entrez links related data between databases, and some key features of Entrez like limits, preview/index, and history. Additionally, it summarizes specific NCBI databases accessible through Entrez like PubMed and OMIM, as well as another retrieval system called SRS maintained by EBI.
An integrated publicly accessible bioinformatics resource to support genomic/proteomic research and scientific discovery.
Established in 1984, by the National Biomedical Research Foundation (NBRF) Georgetown University Medial Center, Washington D.C., USA.
It is the source of annotated protein databases and analysis tools for the researchers.
Serve as primary resource for the exploration of protein information.
Accessible by text search for entry and list retrieval, and also BLAST search and peptide match.
GenBank is a database that contains annotated nucleotide and protein sequences. It includes genomic DNA, mRNA, and EST sequences. There are three main sections in a GenBank file - the header, features, and sequence. The header provides definition, accession number, organism, and reference information. The features section contains gene and protein annotation. The sequence section displays the actual nucleotide or amino acid sequence. Understanding the GenBank file format helps effectively search and retrieve sequences from this important biological database.
The document provides an overview of the history and scope of bioinformatics. It discusses how bioinformatics emerged from the fields of computer science and biology. The history section outlines major developments from Mendel's work in 1865 to the sequencing of the human genome in 2001. Bioinformatics has various applications in areas like drug development, personalized medicine, and biotechnology. It also has significant scope in India, with growing job opportunities in both the public and private sectors.
The document discusses various types of biological databases. It describes primary databases that contain original data, secondary databases that contain processed data derived from primary databases, and composite databases that collect and filter data from multiple primary databases. Examples of specific biological databases are provided, including nucleic acid databases like GenBank, protein sequence databases like Swiss-Prot, protein structure database PDB, and metabolic pathway database KEGG. Details about the purpose and features of some of these major databases like GenBank, DDBJ, EMBL, Swiss-Prot, and PDB are outlined in the document.
This presentation gives you a detailed information about the swiss prot database that comes under UniProtKB. It also covers TrEMBL: a computer annotated supplement to Swiss-Prot.
Structural databases like PDB, CSD, and CATH contain 3D structural information of proteins, small molecules, and macromolecules determined through techniques like X-ray crystallography and NMR spectroscopy. These databases provide bibliographic data, atomic coordinates, and other details for each entry. PDB contains protein structures, CSD contains organic and metal-organic structures, and CATH classifies protein domains hierarchically. Structural databases have wide applications in structure prediction, analysis, mining, comparison, classification, structure refinement, and database annotation.
Protein databases contain information on protein sequences, structures, and functions. The major protein databases are:
- Protein Data Bank (PDB) which contains 3D protein structures determined via X-ray crystallography or NMR.
- Swiss-Prot which contains manually annotated protein sequences and functions.
- TrEMBL which supplements Swiss-Prot with automatically annotated translations of DNA sequences.
Protein databases are important for comparing proteins, understanding relationships between proteins, and aiding the study of new proteins. Searching databases is often the first step in protein research.
The DNA Data Bank of Japan (DDBJ) is a biological database that collects DNA sequences. It is located at the National Institute of Genetics (NIG) in the Shizuoka prefecture of Japan. It is also a member of the International Nucleotide Sequence Database Collaboration or INSDC.
The European Molecular Biology Laboratory (EMBL) is a molecular biology research institution supported by 22 member states. EMBL was created in 1974 and operates from five sites, performing basic research in molecular biology and molecular medicine. A key function of EMBL is the EMBL Nucleotide Sequence Database, maintained at the European Bioinformatics Institute, which incorporates and distributes nucleotide sequences from public sources as part of an international collaboration.
FASTA is a bioinformatics tool and biological database that is used to compare amino acid sequences of proteins or nucleotide sequences of DNA. It was first described in 1985 by Lipman and Pearson. FASTA performs fast homology searches to find similarities between a query sequence and sequences in a database. While similar to BLAST, FASTA is faster for sequence comparisons. It works by identifying patches of sequence similarity that may contain gaps. Some key FASTA programs include FASTA, TFASTA, FASTS, and FASTX/Y. FASTA is useful for applications like identification of species, establishing phylogeny, DNA mapping, and understanding protein function.
INTRODUCTION OF BIOINFORMATICS
HISTORY
WHAT IS DATABASE
NEED FOR DATABASE
TYPES OF DATABASE
PRIMARY DATABASE
NUCLEIC ACID SEQUENCE DATABASE
GENE BANK
INTRODUCTION
GENE BANK SUBMISSION TOOL
GENE BANK SUBMISSION TYPE
HOW TO RETRIEVE DATA FROM GENEBANK
APPLICATION
CONCLUSION
REFERENCE
This document discusses sequence analysis, which involves subjecting DNA, RNA, or protein sequences to analytical methods to understand their features and functions. It describes DNA and protein sequencing techniques, as well as sequence assembly, alignment, and multiple sequence alignment. It provides steps to demonstrate protein sequencing, including retrieving a human prion protein sequence from NCBI, running BLAST to find similar sequences, performing multiple sequence alignment, and predicting secondary structure. Sequence analysis has applications like finding sequence similarities to infer relationships, identifying intrinsic features, and revealing evolution.
This document discusses biological databases and nucleic acid sequence databases. It describes the three primary nucleotide sequence databases: GenBank, EMBL, and DDBJ. GenBank is hosted by the National Center for Biotechnology Information and contains over 286 million bases and 352,000 sequences. EMBL is hosted by the European Molecular Biology Laboratory and mirrors data daily with GenBank and DDBJ. DDBJ is the DNA Data Bank of Japan and also mirrors data daily with the other two databases. Biological databases are important tools for scientists to understand biology at multiple levels.
In this presentation, I talk about the various tools for the submission of DNA or RNA sequences into various sequence databases. The sequence submission tools talked about in this presentation are BankIt, Sequin and Webin.
Sequence homology search and multiple sequence alignment(1)AnkitTiwari354
Sequence homology is the biological homology between DNA, RNA, or protein sequences, defined in terms of shared ancestry in the evolutionary history of life. Two segments of DNA can have shared ancestry because of three phenomena: either a speciation event (orthologs), or a duplication event (paralogs), or else a horizontal (or lateral) gene transfer event (xenologs).[1]
Homology among DNA, RNA, or proteins is typically inferred from their nucleotide or amino acid sequence similarity. Significant similarity is strong evidence that two sequences are related by evolutionary changes from a common ancestral sequence. Alignments of multiple sequences are used to indicate which regions of each sequence are homologous.
Pairwise Sequence Alignment between HBV and HCC Using Modified Needleman-Wuns...TELKOMNIKA JOURNAL
Ths paper aims to find similarity of Hepatitis B virus (HBV) and Hepatocelluler Carcinoma (HCC) DNA sequences. It is very important in bioformatics task. The similarity of sequence allignments indicates that they have similarity of chemical and physical properties. Mutation of the virus DNA in X region has potential role in HCC. It is observed using pairwise sequence alignment of genotype-A in HBV. The complexity of DNA sequence using dynamic programming, Needleman-Wunsch algorithm, is very high. Therefore, it is purpose to modifiy the method of Needleman Wunsch algorithm for optimum global DNA sequence alignment. The main idea is to optimize filling matrix and backtracking proccess of DNA components.This method can also solve various length of the both sequence alignment.
This research is applied to DNA sequence of 858 hepatitis B virus and 12 carcinoma patient, so that there are 10,296 pairwis of sequences. They are aligned globally using the purposed method and as a result, it is achieved high similarity of 96.547% and validity of 99.854%. Furhthermore, this method has reduced the complexity of original Needleman-Wunsch algorithm The reduction of computational time is as 34.6% and space complexity is as 42.52%.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
Molecular basis of evolution and softwares used in phylogenetic tree contructionUdayBhanushali111
This document discusses molecular evolution and software used for phylogenetic tree construction. It begins by defining molecular evolution as the process of mutation and selection at the molecular level. It then discusses different types of mutations that can occur in DNA and proteins, such as synonymous, nonsynonymous, nonsense, missense, and frameshift mutations. The document also discusses using molecular data to study evolution and reconstruct phylogenetic relationships. It describes several software programs used for phylogenetic tree construction, including EzEditor, BAli-Phy, Clustal ω, BayesTraits, and fastDNAml, and provides brief summaries of their methods and purposes.
This document summarizes research on constructing a phylogenetic tree for COX genes using multiple sequence alignments with ClustalW. It begins by introducing phylogenetic analysis and the COX gene. It then describes the methodology used, which involved obtaining nucleotide sequences from a COX protein sequence in mice, performing a tBLASTn search to find related genes, aligning the sequences with ClustalW, and constructing rooted and unrooted phylogenetic trees. The results include the input protein sequence, tBLASTn output, ClustalW alignment, and the rooted and unrooted phylogenetic trees produced. It concludes that phylogenetic analysis is important for understanding gene and protein evolution.
This document discusses various bioinformatics tools and their functions. It provides details on multiple sequence alignment tools like CLUSTAL Omega, CLUSTALW, BLAST, and FASTA. It explains that CLUSTAL Omega can align a large number of sequences quickly and accurately using progressive alignment. CLUSTALW performs multiple sequence alignment in three steps - pairwise alignment, guide tree creation, and multiple alignment using the guide tree. BLAST can identify unknown sequences by comparing them to known sequences. FASTA uses short exact matches to find similar regions between sequences. Expasy provides access to databases for proteomics, genomics, and other areas. MASCOT searches peptide mass fingerprinting and shotgun proteomics datasets.
BLAST AND FASTA.pptx12345789999987544321234alizain9604
- BLAST and FASTA are two commonly used bioinformatics tools for analyzing biological sequences like DNA and proteins.
- BLAST was introduced in 1990 and uses a heuristic algorithm to identify regions of local similarity between query and database sequences. It is fast, sensitive, and widely used.
- FASTA, introduced in 1985, also uses a heuristic algorithm to search databases and identify similar matches to a query sequence. It works by breaking sequences into smaller words, identifying regions of similarity, and performing alignments using substitution matrices.
1) The document discusses various bioinformatics databases including nucleotide databases like GenBank that contain nucleic acid sequences, protein databases like PDB that contain 3D protein structures, and specialized databases like dbSNP that contain human single nucleotide variations.
2) It also discusses tools for analyzing sequences like BLAST for similarity searches, multiple sequence alignments, and genome browsers for interactively viewing complete genomes.
3) Feature annotation is described as the process of identifying genes and other biological features in DNA sequences to increase their usefulness to the scientific community.
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.
This document is a research statement by Chien-Wei (Masaki) Lin that summarizes his past and ongoing methodology and collaborative research projects. It discusses his interests in developing statistical methods for analyzing multi-omics data, including power calculation tools, meta-analysis and integrative analysis methods. It also summarizes some of Lin's collaboration projects applying these statistical methods to study topics like brain aging, major depressive disorder, and cardiovascular epidemiology. The document references 18 of Lin's publications and provides an overview of his diverse experience and future research plans developing statistical tools and methods and applying them to biological problems.
This document describes a study that uses machine learning algorithms to efficiently predict DNA-binding proteins. Support vector machines and cascade correlation neural networks are optimized and compared to determine the best performing model. The SVM model achieves 86.7% accuracy at predicting DNA-binding proteins using features like overall charge, patch size, and amino acid composition of proteins. The CCNN model achieves lower accuracy of 75.4%. The study aims to improve on previous work by using the standard jack-knife validation technique to evaluate model performance on unseen data.
[DSC Europe 23][DigiHealth] Vesna Pajic - Machine Learning Techniques for omi...DataScienceConferenc1
The document discusses machine learning techniques for analyzing omics data. It introduces Velsera, a bioinformatics company, and describes how they used machine learning to predict cancer cell line responses to drugs based on gene expression data. Specifically, they cleaned the data, performed feature selection, and tested models like elastic net, GAMs, and XGBoost (which performed best). The final model identified 20 important genes, including one the client was interested in and another potential biomarker the client was unaware of.
This document describes the PRESAGE database, which aims to improve communication among structural genomics researchers. The database contains protein sequence annotations from experimental and computational research. Researchers can submit annotations about protein structures they are studying experimentally or predicting computationally. The annotations are classified as experimental to track experimental progress, or prediction at three levels of detail. The database is publicly available online and allows registered users to receive notifications about annotations of interest.
The document outlines the basic steps in constructing a phylogenetic tree:
1) Assembling and aligning a dataset of DNA or protein sequences of interest.
2) Using computational methods and evolutionary models to build phylogenetic trees from the sequence alignments.
3) Statistically testing and assessing the estimated trees to evaluate which tree topologies best describe the phylogenetic relationships between the sequences.
The process aims to provide a visual representation of how organisms have evolved from a common ancestor over time based on analyses of genetic similarities and differences in their molecular sequences.
The document provides information about various biological sequence databases and bioinformatics tools and resources. It discusses nucleotide sequence databases like GenBank, EMBL, and DDBJ. It also mentions genome-centered databases like NCBI Genomes and Ensembl Genome Browser. Additionally, it covers protein databases like UniProt and PDB. It describes bioinformatics resources at EBI and NCBI like Entrez. Finally, it summarizes tools for sequence retrieval, comparison, and analysis like BLAST, sequence alignment, and genome browsers.
This document summarizes a study that used machine learning algorithms to classify glioblastoma cancer subtypes based on gene expression and copy number variation data. The study found that combining both types of biomarker data provided slightly better classification accuracy than gene expression data alone, and much better than copy number variation data alone. Specifically, random forest algorithms performed best, with an average accuracy of 85.09% across all datasets when using both gene expression and copy number variation data together. The study concludes there is potential to further optimize the combined data for even more accurate cancer subtype classification.
Bioinformatics and phylogenetic analysis uses principles from computer science, statistics, and linguistics to study genomic and proteomic sequences. It involves storing DNA and protein sequences in biological databases and using computational tools like BLAST to analyze sequences. Phylogenetic analysis determines evolutionary relationships between sequences and builds phylogenetic trees representing these relationships. MEGA software is used to construct multiple sequence alignments with ClustalW and build phylogenetic trees through distance-based and character-based methods.
2025 Insilicogen Company Korean BrochureInsilico Gen
Insilicogen is a company, specializes in Bioinformatics. Our company provides a platform to share and communicate various biological data analysis effectively.
Structure formation with primordial black holes: collisional dynamics, binari...Sérgio Sacani
Primordial black holes (PBHs) could compose the dark matter content of the Universe. We present the first simulations of cosmological structure formation with PBH dark matter that consistently include collisional few-body effects, post-Newtonian orbit corrections, orbital decay due to gravitational wave emission, and black-hole mergers. We carefully construct initial conditions by considering the evolution during radiation domination as well as early-forming binary systems. We identify numerous dynamical effects due to the collisional nature of PBH dark matter, including evolution of the internal structures of PBH halos and the formation of a hot component of PBHs. We also study the properties of the emergent population of PBH binary systems, distinguishing those that form at primordial times from those that form during the nonlinear structure formation process. These results will be crucial to sharpen constraints on the PBH scenario derived from observational constraints on the gravitational wave background. Even under conservative assumptions, the gravitational radiation emitted over the course of the simulation appears to exceed current limits from ground-based experiments, but this depends on the evolution of the gravitational wave spectrum and PBH merger rate toward lower redshifts.
Body temperature_chemical thermogenesis_hypothermia_hypothermiaMetabolic acti...muralinath2
Homeothermic animals, poikilothermic animals, metabolic activities, muscular activities, radiation of heat from environment, shivering, brown fat tissue, temperature, cinduction, convection, radiation, evaporation, panting, chemical thermogenesis, hyper pyrexia, hypothermia, second law of thermodynamics, mild hypothrtmia, moderate hypothermia, severe hypothertmia, low-grade fever, moderate=grade fever, high-grade fever, heat loss center, heat gain center
Direct Evidence for r-process Nucleosynthesis in Delayed MeV Emission from th...Sérgio Sacani
The origin of heavy elements synthesized through the rapid neutron capture process (r-process) has been an enduring mystery for over half a century. J. Cehula et al. recently showed that magnetar giant flares, among the brightest transients ever observed, can shock heat and eject neutron star crustal material at high velocity, achieving the requisite conditions for an r-process.A. Patel et al. confirmed an r-process in these ejecta using detailed nucleosynthesis calculations. Radioactive decay of the freshly synthesized nuclei releases a forest of gamma-ray lines, Doppler broadened by the high ejecta velocities v 0.1c into a quasi-continuous spectrum peaking around 1 MeV. Here, we show that the predicted emission properties (light curve, fluence, and spectrum) match a previously unexplained hard gamma-ray signal seen in the aftermath of the famous 2004 December giant flare from the magnetar SGR 1806–20. This MeV emission component, rising to peak around 10 minutes after the initial spike before decaying away over the next few hours, is direct observational evidence for the synthesis of ∼10−6 Me of r-process elements. The discovery of magnetar giant flares as confirmed r-process sites, contributing at least ∼1%–10% of the total Galactic abundances, has implications for the Galactic chemical evolution, especially at the earliest epochs probed by low-metallicity stars. It also implicates magnetars as potentially dominant sources of heavy cosmic rays. Characterization of the r-process emission from giant flares by resolving decay line features offers a compelling science case for NASA’s forthcomingCOSI nuclear spectrometer, as well as next-generation MeV telescope missions.
2. Jens
Martensson
Content
• KEGG GenomeNet -
Introduction
• ClustalW - Introduction
- Algorithm
- Flowchart
• Multiple Alignment Method
- Introduction
• ClustalW – Work process
• Introduction to other Similar
Tools – ClusterΩ / Jalview
• Live demonstration 2
3. Jens
Martensson
KEGG - GenomeNet
• KEGG (Kyoto Encyclopedia of Genes and
Genomes) is a collection of databases
dealing with genomes, biological
pathways, diseases, drugs, and chemical
substances.
• KEGG is utilized
for bioinformatics research and
education, including data analysis
in genomics, metagenomics, metabolom
ics and other omics studies, modeling
and simulation in systems biology,
and translational research in drug
development.
• GenomeNet is one to the Bioinformatics
database with tools
3
4. Jens
Martensson
ClustalW
• ClustalW like the other Clustal tools is
used for aligning multiple nucleotide or
protein sequences in an efficient manner.
• It uses progressive alignment methods-
align the most similar sequences first and
work their way down to the least similar
sequences until a global alignment is
created.
• ClustalW is a matrix-based algorithm-
tools like T Coffee and Dialign are
consistency-based. ClustalW is fairly
efficient algorithm competes - against
other software.
• This program requires three or more
sequences in order to calculate a global
alignment and for pairwise sequence
alignment
4
5. Jens
Martensson
5
Algorithm
• ClustalW uses progressive alignment
methods. sequences with the best
alignment score are aligned first, then
progressively more distant groups of
sequences are aligned.
• This heuristic approach is necessary
due to the time and memory demand
of finding the global optimal solution.
• The first step to the algorithm is
computing a rough distance matrix
between each pair of sequences, also
known as pairwise sequence
alignment.
• The next step is a neighbor-joining
method that usesmidpoint rooting to
create an overall guide tree.
7. Jens
Martensson
7
Multiple Alignment Method
The steps are summarized as follows:
• Compare all sequences pairwise.
• Perform cluster analysis on the pairwise
data to generate a hierarchy for
alignment. This may be in the form of a
binary tree or a simple ordering
• Build the multiple alignment by first
aligning the most similar pair of
sequences, then the next most similar pair
and so on. Once an alignment of two
sequences has been made, then this is
fixed. Thus for a set of sequences A, B, C,
D having aligned A with C and B with D
the alignment of A, B, C, D is obtained by
comparing the alignments of A and C with
that of B and D using averaged scores at
each aligned position.
8. Jens
Martensson
8
ClustalW
For multiple alignment
• ClustaW is a general purpose multiple
alignment program for DNA or proteins.
• ClustalW is produced by Julie D.
Thompson, Toby Gibson of European
Molecular Biology Laboratory, Germany
and Desmond Higgins of European
Bioinformatics Institute, Cambridge, UK.
• ClustalW can create multiple alignments,
manipulate existing alignments, do
profile analysis and create phylogenetic
trees.
• Alignment can be done by 2 methods:
slow/accurate
fast/approximate