Basic Experiment of bioinformatics laboratory.
-Searching basics, AND, OR, NOT, “keywords together”, *
-Searching PMC and PubMed Using Authors name, fields, limits
-Retrieving protein sequences using UniProt and creating multi-fasta files
-Retrieving relevant DNA sequences using nucleotide and creating multi fasta-file. (search by ldh1 NOT hypothetical)
-Performing DNA and protein BLAST and analyzing result
-Pairwise alignment (global, end gap free), calculate identities, dotplot using BioEdit
-Nucleotide composition, complement, reverse complement, DNA to RNA, translate, restriction map, six frame translation using BioEdit
-Multiple sequence analysis using BioEdit
-Tree Generation with MEGA
-Working with single protein sequence: Analyzing protein composition (pepdigest, pepstats), Protein secondary structure by mEmboss: (garnier for protein secondary structure), helixturnhelix for motifs, pepcoil for coiled coil regions
-RNA structure prediction using RNAstructure
The document discusses Cartesian coordinate systems and their use in molecular modeling. It describes how Cartesian coordinates are used to specify unique points in space using distances from perpendicular axes. Molecular graphics software uses Cartesian coordinates to visualize biological molecules by defining an coordinate frame of reference and assigning X, Y, Z coordinates. While Cartesian coordinates are used for modeling, other coordinate systems like cylindrical polar coordinates are sometimes used instead, especially for modeling helical molecules like DNA, and these first need transforming into Cartesian coordinates for visualization.
The yeast two-hybrid system allows for the identification of protein-protein interactions. It involves fusing two interacting proteins to a DNA-binding domain and transcriptional activation domain, so that interaction leads to expression of reporter genes. This in vivo technique uses yeast as a host to screen libraries or characterize proteins of interest. Some applications include identifying novel interactions, protein cascades, and mutations affecting binding.
Docking is a method that predicts the preferred orientation of one molecule binding to another to form a stable complex with minimum energy. It is important for rational drug design, as the results can be used to design inhibitors for target proteins and new drugs. Docking is key to structure-based drug design for lead generation and optimization. Factors like intramolecular and intermolecular forces influence docking. There are rigid and flexible docking methods. Molecular docking has been successfully used in drug discovery for HIV, influenza, and other diseases.
Whole genome sequencing is a technique to sequence the entire genome of an organism. It involves breaking the genome into small fragments, copying the fragments, sequencing the fragments, and reassembling the sequence data into the full genome. Key steps include isolating DNA, fragmenting it, ligating fragments into plasmids, amplifying the plasmids, sequencing the fragments using Sanger sequencing, and assembling the sequence reads into the complete genome. Whole genome sequencing allows researchers to discover coding and non-coding regions, predict disease susceptibility, and perform evolutionary studies by comparing species.
This document discusses various methods for energy minimization in molecular modeling, including non-derivative methods like the simplex method and sequential univariate method, as well as first derivative methods like steepest gradient and conjugate gradient. The goal of energy minimization is to determine the lowest energy conformation of a molecule by systematically adjusting atomic coordinates. Non-derivative methods require only energy evaluations but may need many steps, while derivative methods use gradient and Hessian information to converge more efficiently in fewer steps.
The document discusses protein structure prediction methods such as homology modeling and threading. Homology modeling relies on sequence similarity between the target and template proteins to generate a structural model. It involves aligning the sequences, building the backbone based on the template, and modeling side chains. Threading methods can be used when sequence similarity is low but still detects structural similarity by identifying conserved protein folds from structural databases. Experimental techniques like X-ray crystallography and NMR spectroscopy determine protein structures but have limitations for some proteins.
Primary and secondary databases ppt by puneet kulyanaPuneet Kulyana
This document provides an introduction to databases used for biological data. It defines key terms like data, information, and databases. It describes different types of biological databases including primary databases that contain original experimental data, and secondary databases that contain derived or analyzed data. Examples of primary databases include GenBank, EMBL, and PDB, while secondary databases include PROSITE, PRINTS, and Pfam that contain conserved protein motifs and families. The document also compares primary and secondary databases.
The document discusses Cartesian coordinate systems and their use in molecular modeling. It describes how Cartesian coordinates are used to specify unique points in space using distances from perpendicular axes. Molecular graphics software uses Cartesian coordinates to visualize biological molecules by defining an coordinate frame of reference and assigning X, Y, Z coordinates. While Cartesian coordinates are used for modeling, other coordinate systems like cylindrical polar coordinates are sometimes used instead, especially for modeling helical molecules like DNA, and these first need transforming into Cartesian coordinates for visualization.
The yeast two-hybrid system allows for the identification of protein-protein interactions. It involves fusing two interacting proteins to a DNA-binding domain and transcriptional activation domain, so that interaction leads to expression of reporter genes. This in vivo technique uses yeast as a host to screen libraries or characterize proteins of interest. Some applications include identifying novel interactions, protein cascades, and mutations affecting binding.
Docking is a method that predicts the preferred orientation of one molecule binding to another to form a stable complex with minimum energy. It is important for rational drug design, as the results can be used to design inhibitors for target proteins and new drugs. Docking is key to structure-based drug design for lead generation and optimization. Factors like intramolecular and intermolecular forces influence docking. There are rigid and flexible docking methods. Molecular docking has been successfully used in drug discovery for HIV, influenza, and other diseases.
Whole genome sequencing is a technique to sequence the entire genome of an organism. It involves breaking the genome into small fragments, copying the fragments, sequencing the fragments, and reassembling the sequence data into the full genome. Key steps include isolating DNA, fragmenting it, ligating fragments into plasmids, amplifying the plasmids, sequencing the fragments using Sanger sequencing, and assembling the sequence reads into the complete genome. Whole genome sequencing allows researchers to discover coding and non-coding regions, predict disease susceptibility, and perform evolutionary studies by comparing species.
This document discusses various methods for energy minimization in molecular modeling, including non-derivative methods like the simplex method and sequential univariate method, as well as first derivative methods like steepest gradient and conjugate gradient. The goal of energy minimization is to determine the lowest energy conformation of a molecule by systematically adjusting atomic coordinates. Non-derivative methods require only energy evaluations but may need many steps, while derivative methods use gradient and Hessian information to converge more efficiently in fewer steps.
The document discusses protein structure prediction methods such as homology modeling and threading. Homology modeling relies on sequence similarity between the target and template proteins to generate a structural model. It involves aligning the sequences, building the backbone based on the template, and modeling side chains. Threading methods can be used when sequence similarity is low but still detects structural similarity by identifying conserved protein folds from structural databases. Experimental techniques like X-ray crystallography and NMR spectroscopy determine protein structures but have limitations for some proteins.
Primary and secondary databases ppt by puneet kulyanaPuneet Kulyana
This document provides an introduction to databases used for biological data. It defines key terms like data, information, and databases. It describes different types of biological databases including primary databases that contain original experimental data, and secondary databases that contain derived or analyzed data. Examples of primary databases include GenBank, EMBL, and PDB, while secondary databases include PROSITE, PRINTS, and Pfam that contain conserved protein motifs and families. The document also compares primary and secondary databases.
Force fields are mathematical functions used to describe potential energy in molecular modeling simulations. Common classical force fields include AMBER, CHARMM, GROMACS, GROMOS, and MMFF. AMBER was developed at UCSF and has parameter sets for proteins, nucleic acids, small molecules. GROMACS is a molecular dynamics software that supports different force fields like AMBER and CHARMM. GROMOS is a united atom force field optimized for alkanes. MMFF is derived from quantum calculations and experimental data for drug-like molecules. CHARMM was developed at Harvard and has broad coverage of biomolecules and organic compounds.
Drug and Chemical Databases 2018 - Drug DiscoveryGirinath Pillai
Latest collection of Chemical and Drug Databases for Biological Research as well as Drug Design studies. Databases statistics, links and overview data with CADD introduction.
Proteomics is the study of the structure and function of proteins. It involves identifying and quantifying the proteins expressed by a genome or cell type. Key aspects of proteomics include protein separation techniques like gel electrophoresis, mass spectrometry to identify proteins, and analyzing protein interactions and post-translational modifications. While genomes provide the blueprint, proteomics helps understand the diversity of proteins expressed and how they function together to direct cellular activities. It is a promising tool for disease diagnosis by identifying protein biomarkers.
Bioinformatics is an interdisciplinary field that merges biology, computer science, and information technology. It is applied in areas like genomics, proteomics, and systems biology. While some basic analysis can be done through user-friendly tools, truly customized work requires programming skills and an understanding of underlying algorithms. Bioinformatics is not just a service field but rather involves scientific experimentation throughout the entire analysis process from experimental design to evaluation. It is a dedicated field of research in its own right, not a quick or interchangeable task.
The document discusses the Smith-Waterman algorithm for local sequence alignment. It describes how the algorithm uses dynamic programming to find the optimal local alignment between two sequences without allowing for negative scores. The key steps are initialization of a score matrix, filling the matrix using match/mismatch scores and a gap penalty, and tracing back through the matrix to determine the highest-scoring alignment. An example application of the algorithm aligns the sequences "GATGTAG" and "GAGATGTGC".
Protein structure can be described at several levels of organization. The primary structure is the amino acid sequence, while the secondary structure describes local patterns like alpha helices and beta sheets formed by hydrogen bonds. Tertiary structure refers to the overall 3D shape of a single polypeptide chain. Quaternary structure involves the arrangement of multiple protein subunits. Together these organizational levels allow proteins to carry out their diverse functions in the cell.
This document provides an overview of molecular dynamics (MD) simulation, which calculates the time-dependent behavior of biological molecules. MD simulation can provide detailed information on protein fluctuations and conformational changes. It is used to study protein stability, folding, molecular recognition and other biological processes. The document discusses how MD simulations are set up and run, including using force fields to calculate molecular interactions and numerical integration algorithms to solve equations of motion. It also covers statistical mechanics approaches for relating atomic-level simulation data to macroscopic properties.
The document discusses Ramachandran plots, which are used to visualize allowed regions of dihedral angles phi and psi in protein backbone structures. Ramachandran plots show amino acid residues as dots in a two-dimensional map based on their phi and psi angles. Most residues cluster in favored regions corresponding to alpha helices and beta sheets. The document outlines how Ramachandran plots are constructed and analyzed using various software, and their applications in validating protein structures and understanding relationships between structure and amino acid sequence.
Protein threading is a protein structure prediction method that involves "threading" or placing an amino acid sequence into known protein structure templates to find the best matching fold. The key steps are:
1) A query sequence is threaded into structural positions of templates from a structure library to find sequence-structure alignments
2) Alignments are scored and optimized using an objective function accounting for residue interactions and preferences
3) The highest scoring template is selected as the predicted structure, though loop regions are often not accurately predicted
The experimental methods used by biotechnologists to determine the structures of proteins demand sophisticated equipment and time.
A host of computational methods are developed to predict the location of secondary structure elements in proteins for complementing or creating insights into experimental results.
Chou-Fasman algorithm is an empirical algorithm developed for the prediction of protein secondary structure
This document discusses structure-based and ligand-based drug design approaches. Structure-based design uses the 3D structure of biological targets to dock potential drug molecules. Ligand-based design analyzes similar molecules that bind to the target to derive pharmacophore models or quantitative structure-activity relationships (QSAR) to predict new candidates. Specific structure-based methods covered include docking tools like AutoDock and CDOCKER, and accounting for protein and complex flexibility. Ligand-based methods discussed are QSAR techniques like Comparative Molecular Field Analysis (CoMSIA) and Field Analysis (CoMFA). In conclusion, computational approaches like these are valuable for drug discovery by facilitating the identification and testing of new ligand
Virtual screening uses computer-based methods to identify potential drug candidates by assessing how well compounds interact with biological targets like proteins. It has advantages over laboratory experiments in being lower cost, allowing investigation of compounds that have not been synthesized, and enabling screening of a much larger number of potential compounds. Common virtual screening methods include similarity searching based on molecular fingerprints, pharmacophore searching to identify common chemical features among active molecules, and docking to computationally simulate ligand binding and predict binding affinity.
This document discusses phylogenetic tree construction using distance-based methods. It begins by introducing phylogenetic trees and their use in fields like forensics, disease prediction, and drug discovery. It then outlines the basic steps to construct a phylogenetic tree: sequence alignment, distance calculation, and tree verification. The main distance-based approaches covered are UPGMA, Neighbor-Joining, Fitch-Margoliash, Minimum Evolution. Each method calculates genetic distances differently and has advantages and limitations for reconstructing evolutionary relationships from sequence data.
Scoring system is a set of values for qualifying the set of one residue being substituted by another in an alignment.
It is also known as substitution matrix.
Scoring matrix of nucleotide is relatively simple.
A positive value or a high score is given for a match & negative value or a low score is given for a mismatch.
Scoring matrices for amino acids are more complicated because scoring has to reflect the physicochemical properties of amino acid residues.
(1) There are four levels of protein structure: primary, secondary, tertiary, and quaternary. Experimental methods like X-ray crystallography and NMR spectroscopy can determine protein structures but are expensive and time-consuming. (2) Computational structure prediction methods include homology/comparative modeling, protein threading, and ab initio modeling. Homology modeling is most reliable when the sequence identity is over 30-50% to a template with a known structure. (3) Protein threading is used when there is no clear homolog but the protein may have the same fold as one in PDB. It aligns sequences to structures and evaluates fitness to predict the model.
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.
The document discusses BLAST (Basic Local Alignment Search Tool), an algorithm used to compare a query DNA or protein sequence against a database of sequences. BLAST works by identifying exact or approximate matches between words of 3-11 letters in the query and database sequences. Matches are extended to find local alignments with high scores. Significant alignments are identified based on their score and the expected number of matches by chance (E-value). The document provides examples of how BLAST finds local alignments and calculates E-values. It also describes different BLAST programs and suggestions for using BLAST.
Bioinformatics is an interdisciplinary field that combines biology, computer science, and information technology. It enables the discovery of new biological insights and unifying principles in biology through the merging of these disciplines. There are three main sub-disciplines: developing algorithms and statistics for analyzing large datasets, analyzing various types of biological data like sequences and structures, and developing tools for accessing and managing information.
levels of protein structure , Domains ,motifs & Folds in protein structureAaqib Naseer
Protein structure is hierarchical, with four levels: primary, secondary, tertiary, and quaternary. The primary structure is the amino acid sequence. Secondary structures include alpha helices and beta sheets formed by hydrogen bonding between amino acids in the sequence. Tertiary structure involves folding of the entire chain into a compact 3D structure. Quaternary structure involves the assembly of protein subunits. Other structural features include domains, which are independently folded and functional regions, motifs like loops and barrels formed by secondary structure elements, and folds defined by the arrangement of alpha helices and beta sheets. Understanding protein structure is important for studying protein function and for developing drugs.
This document provides instructions for experiments involving bioinformatics tools and software. It begins with introductory information and a table of contents. The experiments cover topics like downloading sequences from NCBI, performing BLAST searches, converting between protein and nucleotide sequences, downloading and using MEGA and other software for phylogenetic analysis, primer design, sequence cleaning and formatting, and more. Step-by-step instructions are provided for completing each analysis using various online and offline bioinformatics resources.
1
Phylogenetic Analysis Homework assignment
This assignment will be completed on your own and turned in the week of 11/8-11/10.
Introduction
Molecular evolution is the study of how proteins and nucleic acids evolve. Included in this
field are studies of mutations and chromosomal rearrangements, the evolutionary process,
the identification of sequence patterns conferring function in proteins and nucleic acids,
and the reconstruction of the evolutionary history of organisms and the molecules that
they make. All of these studies rely on comparisons of nucleotide or amino acid sequences.
In this tutorial, you will be introduced to some of the fundamental principles of molecular
evolution and the types of bioinformatics tools that are used in evolutionary studies. We
will begin by carrying out a manual sequence comparison, so that the basic concepts can
be introduced, and the remainder of the project will be carried out at The Biology
Workbench, a set of bioinformatics analysis programs managed by The San Diego
Supercomputing Center at the University of California, San Diego.
Objectives
• To introduce the principles of molecular evolution
• To acquaint you with the tools that are available to compare nucleotide and
amino acid sequences
• To learn about the use of protein sequences in reconstructions of evolutionary history
Project
Branching evolution occurs when one ancestral species gives rise to two or more progeny
species. However, speciation events don't involve the vast majority of the genes in a
genome. That is, for most genes, both of the progeny species inherit identical genes from
the ancestor. Following speciation, these genes evolve independently in the separate
lineages. Studies of molecular evolution therefore rely heavily on comparisons of related
sequences from different organisms.
Shown below is an alignment of two homologous sequences that we will use as a starting
place. Homologous sequences are sequences that have descended from a common
ancestral sequence. You can't meaningfully compare sequences unless they are
homologous. This alignment uses the single letter amino acid code, in which G represents
glycine, Q represents glutamine, etc. The aligned proteins have been shown to be involved
in the metabolism of similar, but different, toxic compounds. As you can see, these amino
acid sequences are very similar and it is easy to recognize that they are related by common
descent.
2
dntAc: KMGVDDEVIVSRQNDGSVR
nahAc: KMGIDDEVIVSRQSDGSIR
An expanded version of this alignment is shown below. In this expanded alignment, both
the amino acids and the corresponding DNA nucleotides are shown. For ease of analysis,
the codons have been broken into separate entries in a table.
Alignment of nahAc and dntAc sequences.
K M G V D E V I V
dntAc AAA ATG GGC GTC GAT GAA GTC ATC GTC
nahAc ...
Force fields are mathematical functions used to describe potential energy in molecular modeling simulations. Common classical force fields include AMBER, CHARMM, GROMACS, GROMOS, and MMFF. AMBER was developed at UCSF and has parameter sets for proteins, nucleic acids, small molecules. GROMACS is a molecular dynamics software that supports different force fields like AMBER and CHARMM. GROMOS is a united atom force field optimized for alkanes. MMFF is derived from quantum calculations and experimental data for drug-like molecules. CHARMM was developed at Harvard and has broad coverage of biomolecules and organic compounds.
Drug and Chemical Databases 2018 - Drug DiscoveryGirinath Pillai
Latest collection of Chemical and Drug Databases for Biological Research as well as Drug Design studies. Databases statistics, links and overview data with CADD introduction.
Proteomics is the study of the structure and function of proteins. It involves identifying and quantifying the proteins expressed by a genome or cell type. Key aspects of proteomics include protein separation techniques like gel electrophoresis, mass spectrometry to identify proteins, and analyzing protein interactions and post-translational modifications. While genomes provide the blueprint, proteomics helps understand the diversity of proteins expressed and how they function together to direct cellular activities. It is a promising tool for disease diagnosis by identifying protein biomarkers.
Bioinformatics is an interdisciplinary field that merges biology, computer science, and information technology. It is applied in areas like genomics, proteomics, and systems biology. While some basic analysis can be done through user-friendly tools, truly customized work requires programming skills and an understanding of underlying algorithms. Bioinformatics is not just a service field but rather involves scientific experimentation throughout the entire analysis process from experimental design to evaluation. It is a dedicated field of research in its own right, not a quick or interchangeable task.
The document discusses the Smith-Waterman algorithm for local sequence alignment. It describes how the algorithm uses dynamic programming to find the optimal local alignment between two sequences without allowing for negative scores. The key steps are initialization of a score matrix, filling the matrix using match/mismatch scores and a gap penalty, and tracing back through the matrix to determine the highest-scoring alignment. An example application of the algorithm aligns the sequences "GATGTAG" and "GAGATGTGC".
Protein structure can be described at several levels of organization. The primary structure is the amino acid sequence, while the secondary structure describes local patterns like alpha helices and beta sheets formed by hydrogen bonds. Tertiary structure refers to the overall 3D shape of a single polypeptide chain. Quaternary structure involves the arrangement of multiple protein subunits. Together these organizational levels allow proteins to carry out their diverse functions in the cell.
This document provides an overview of molecular dynamics (MD) simulation, which calculates the time-dependent behavior of biological molecules. MD simulation can provide detailed information on protein fluctuations and conformational changes. It is used to study protein stability, folding, molecular recognition and other biological processes. The document discusses how MD simulations are set up and run, including using force fields to calculate molecular interactions and numerical integration algorithms to solve equations of motion. It also covers statistical mechanics approaches for relating atomic-level simulation data to macroscopic properties.
The document discusses Ramachandran plots, which are used to visualize allowed regions of dihedral angles phi and psi in protein backbone structures. Ramachandran plots show amino acid residues as dots in a two-dimensional map based on their phi and psi angles. Most residues cluster in favored regions corresponding to alpha helices and beta sheets. The document outlines how Ramachandran plots are constructed and analyzed using various software, and their applications in validating protein structures and understanding relationships between structure and amino acid sequence.
Protein threading is a protein structure prediction method that involves "threading" or placing an amino acid sequence into known protein structure templates to find the best matching fold. The key steps are:
1) A query sequence is threaded into structural positions of templates from a structure library to find sequence-structure alignments
2) Alignments are scored and optimized using an objective function accounting for residue interactions and preferences
3) The highest scoring template is selected as the predicted structure, though loop regions are often not accurately predicted
The experimental methods used by biotechnologists to determine the structures of proteins demand sophisticated equipment and time.
A host of computational methods are developed to predict the location of secondary structure elements in proteins for complementing or creating insights into experimental results.
Chou-Fasman algorithm is an empirical algorithm developed for the prediction of protein secondary structure
This document discusses structure-based and ligand-based drug design approaches. Structure-based design uses the 3D structure of biological targets to dock potential drug molecules. Ligand-based design analyzes similar molecules that bind to the target to derive pharmacophore models or quantitative structure-activity relationships (QSAR) to predict new candidates. Specific structure-based methods covered include docking tools like AutoDock and CDOCKER, and accounting for protein and complex flexibility. Ligand-based methods discussed are QSAR techniques like Comparative Molecular Field Analysis (CoMSIA) and Field Analysis (CoMFA). In conclusion, computational approaches like these are valuable for drug discovery by facilitating the identification and testing of new ligand
Virtual screening uses computer-based methods to identify potential drug candidates by assessing how well compounds interact with biological targets like proteins. It has advantages over laboratory experiments in being lower cost, allowing investigation of compounds that have not been synthesized, and enabling screening of a much larger number of potential compounds. Common virtual screening methods include similarity searching based on molecular fingerprints, pharmacophore searching to identify common chemical features among active molecules, and docking to computationally simulate ligand binding and predict binding affinity.
This document discusses phylogenetic tree construction using distance-based methods. It begins by introducing phylogenetic trees and their use in fields like forensics, disease prediction, and drug discovery. It then outlines the basic steps to construct a phylogenetic tree: sequence alignment, distance calculation, and tree verification. The main distance-based approaches covered are UPGMA, Neighbor-Joining, Fitch-Margoliash, Minimum Evolution. Each method calculates genetic distances differently and has advantages and limitations for reconstructing evolutionary relationships from sequence data.
Scoring system is a set of values for qualifying the set of one residue being substituted by another in an alignment.
It is also known as substitution matrix.
Scoring matrix of nucleotide is relatively simple.
A positive value or a high score is given for a match & negative value or a low score is given for a mismatch.
Scoring matrices for amino acids are more complicated because scoring has to reflect the physicochemical properties of amino acid residues.
(1) There are four levels of protein structure: primary, secondary, tertiary, and quaternary. Experimental methods like X-ray crystallography and NMR spectroscopy can determine protein structures but are expensive and time-consuming. (2) Computational structure prediction methods include homology/comparative modeling, protein threading, and ab initio modeling. Homology modeling is most reliable when the sequence identity is over 30-50% to a template with a known structure. (3) Protein threading is used when there is no clear homolog but the protein may have the same fold as one in PDB. It aligns sequences to structures and evaluates fitness to predict the model.
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.
The document discusses BLAST (Basic Local Alignment Search Tool), an algorithm used to compare a query DNA or protein sequence against a database of sequences. BLAST works by identifying exact or approximate matches between words of 3-11 letters in the query and database sequences. Matches are extended to find local alignments with high scores. Significant alignments are identified based on their score and the expected number of matches by chance (E-value). The document provides examples of how BLAST finds local alignments and calculates E-values. It also describes different BLAST programs and suggestions for using BLAST.
Bioinformatics is an interdisciplinary field that combines biology, computer science, and information technology. It enables the discovery of new biological insights and unifying principles in biology through the merging of these disciplines. There are three main sub-disciplines: developing algorithms and statistics for analyzing large datasets, analyzing various types of biological data like sequences and structures, and developing tools for accessing and managing information.
levels of protein structure , Domains ,motifs & Folds in protein structureAaqib Naseer
Protein structure is hierarchical, with four levels: primary, secondary, tertiary, and quaternary. The primary structure is the amino acid sequence. Secondary structures include alpha helices and beta sheets formed by hydrogen bonding between amino acids in the sequence. Tertiary structure involves folding of the entire chain into a compact 3D structure. Quaternary structure involves the assembly of protein subunits. Other structural features include domains, which are independently folded and functional regions, motifs like loops and barrels formed by secondary structure elements, and folds defined by the arrangement of alpha helices and beta sheets. Understanding protein structure is important for studying protein function and for developing drugs.
This document provides instructions for experiments involving bioinformatics tools and software. It begins with introductory information and a table of contents. The experiments cover topics like downloading sequences from NCBI, performing BLAST searches, converting between protein and nucleotide sequences, downloading and using MEGA and other software for phylogenetic analysis, primer design, sequence cleaning and formatting, and more. Step-by-step instructions are provided for completing each analysis using various online and offline bioinformatics resources.
1
Phylogenetic Analysis Homework assignment
This assignment will be completed on your own and turned in the week of 11/8-11/10.
Introduction
Molecular evolution is the study of how proteins and nucleic acids evolve. Included in this
field are studies of mutations and chromosomal rearrangements, the evolutionary process,
the identification of sequence patterns conferring function in proteins and nucleic acids,
and the reconstruction of the evolutionary history of organisms and the molecules that
they make. All of these studies rely on comparisons of nucleotide or amino acid sequences.
In this tutorial, you will be introduced to some of the fundamental principles of molecular
evolution and the types of bioinformatics tools that are used in evolutionary studies. We
will begin by carrying out a manual sequence comparison, so that the basic concepts can
be introduced, and the remainder of the project will be carried out at The Biology
Workbench, a set of bioinformatics analysis programs managed by The San Diego
Supercomputing Center at the University of California, San Diego.
Objectives
• To introduce the principles of molecular evolution
• To acquaint you with the tools that are available to compare nucleotide and
amino acid sequences
• To learn about the use of protein sequences in reconstructions of evolutionary history
Project
Branching evolution occurs when one ancestral species gives rise to two or more progeny
species. However, speciation events don't involve the vast majority of the genes in a
genome. That is, for most genes, both of the progeny species inherit identical genes from
the ancestor. Following speciation, these genes evolve independently in the separate
lineages. Studies of molecular evolution therefore rely heavily on comparisons of related
sequences from different organisms.
Shown below is an alignment of two homologous sequences that we will use as a starting
place. Homologous sequences are sequences that have descended from a common
ancestral sequence. You can't meaningfully compare sequences unless they are
homologous. This alignment uses the single letter amino acid code, in which G represents
glycine, Q represents glutamine, etc. The aligned proteins have been shown to be involved
in the metabolism of similar, but different, toxic compounds. As you can see, these amino
acid sequences are very similar and it is easy to recognize that they are related by common
descent.
2
dntAc: KMGVDDEVIVSRQNDGSVR
nahAc: KMGIDDEVIVSRQSDGSIR
An expanded version of this alignment is shown below. In this expanded alignment, both
the amino acids and the corresponding DNA nucleotides are shown. For ease of analysis,
the codons have been broken into separate entries in a table.
Alignment of nahAc and dntAc sequences.
K M G V D E V I V
dntAc AAA ATG GGC GTC GAT GAA GTC ATC GTC
nahAc ...
Drug designing is a process used in biopharmaceutical industry to discover and develop new drug compounds.
Variety of computational methods are used to identify novel compounds ,design compounds for selectivity and safety.
Structure-based drug design, ligand-based drug design , homology based methods are used depending on how much information is available about drug targets and potential drug compounds.
This document discusses several genes related to stem cell pluripotency, including OCT4, SOX2, NANOG, and LIN28. It provides information on the functions of these genes obtained from searches of PubMed, NCBI Gene, and other bioinformatics databases. Details include OCT4's role in maintaining pluripotency, SOX2's interaction with OCT4 and DNA binding structure, alignments of NANOG mRNA and protein sequences between human and mouse, and conserved domains identified in human and mouse LIN28 proteins through BLAST and CDD searches.
This document provides instructions for advanced searching and saving searches in PubMed. It describes how to use the search history to combine searches, use parentheses to structure complex searches as a single string, and save searches to MyNCBI or by bookmarking the URL. It also explains how to create an RSS feed of a PubMed search to receive automatic updates in an RSS reader like Google Reader.
Lesson 2:Internet Tool in life Sciences ResearchD. ALQahtani
This document provides guidance on searching the biomedical literature database PubMed. It explains that PubMed searches keywords and their synonyms across article fields like title and abstract. Boolean operators like AND and OR can be used to combine search terms. Search filters can refine results by date or journal. Author names should include surname and initials. Citations can be searched by entering them or using the citation matcher. Field tags or the advanced search limit searches to specific fields like author or title. Overall, the document offers tips on designing effective PubMed searches.
InterProscan is a database that combines different protein signature recognition methods to identify distant relationships and infer protein function. It integrates predictive information from partner resources to classify proteins into families and identify their domains and sites. Users can submit novel nucleotide or protein sequences to InterProScan to scan the signatures in the InterPro database. Matches are output in various formats to functionally characterize the submitted sequences. The document then provides steps for using the InterProscan database to analyze protein sequences and view results that identify family membership and conserved sites.
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.
Bringing bioassay protocols to the world of informatics, using semantic annot...Alex Clark
This document discusses bringing bioassay protocols into the world of informatics by using semantic annotations. It describes how measurements from bioassays contain many details that are usually only available as text, and outlines an approach using ontologies, natural language processing, and machine learning to extract this information and make it accessible for searching, comparing datasets, and identifying trends. The goal is to make all bioassay protocol data machine readable by developing common templates and annotation standards that can be applied to existing and new assay data sources.
one complete report from all the 4 labs.pdfstudy help
The document provides instructions for compiling a complete lab report from four biology labs on genomic databases, primer design, PCR, and molecular cloning. It outlines the necessary sections for the report, including an introduction describing the overall question and background, materials and methods, results with data and figures, and a discussion/conclusion section. It also provides additional details on designing a transgene reporter gene based on knowledge gained from the lab exercises, including defining a transgene, necessary gene elements, and ideas for using the transgene.
one complete report from all the 4 labs.pdfstudy help
The document provides instructions for compiling a complete lab report from four biology labs on genomic databases, primer design, PCR, and molecular cloning. It outlines the required sections of the report, including an introduction, materials and methods, results with data, and a discussion/conclusion section. It also provides discussion questions on building a reporter gene or transgene, defining key terms and outlining the necessary gene elements and ideas for using the transgene. The report should integrate results and instructions from all four labs.
The IUPHAR/BPS Guide to PHARMACOLOGY (GtoPdb) is an open, expert-driven database that contains information on over 1,700 pharmacological targets and the substances that act on them. The database provides overviews and detailed information on targets that is manually curated from literature and reviewed by experts. It aims to cover human drug targets and potential future therapeutic targets. New features of the database include search tools to find targets and ligands, information on diseases associated with targets and ligands, organization of ligand families, and comparison of ligand activity across species. The database content is available to download in various formats and its interoperability has been increased through developing an RDF version and submitting data to other sources
The document describes a lab experiment analyzing gene expression data from human fibroblasts in response to serum using microarray analysis. The aims are to analyze the gene expression data using Excel and the ArrayTrack workbench. Key steps include importing microarray data into Excel and pre-treating the data by centering and scaling. ArrayTrack is then used to analyze the data through descriptive statistics, exploring gene expression profiles of gene lists, and using the significance analysis of microarrays (SAM) tool. Additional online databases like Gene Atlas and ArrayExpress are queried to find expression profiles and experimental data for a specific gene, APT13A2, under different conditions.
FindMod is a tool that can predict potential protein post-translational modifications (PTM) and find potential single amino acid substitutions in peptides
A Natural Language Processing Approach to Reviewing Research AbstractsRobert Songer
This document describes a natural language processing approach to reviewing medical research abstracts about cosmetic ingredients and their effects on human skin. The approach involved searching databases for toxicity data and research abstracts on over 28,000 cosmetic compounds. Natural language processing techniques like tokenization, frequency analysis, and collocation detection were used to analyze over 300 unique abstracts. The method identified 16 relevant studies on adverse skin effects, all of which were for toxic compounds rather than non-toxic compounds. The approach reduced the time needed for literature review from hours to seconds compared to manual searching.
In silico 360 Analysis for Drug DevelopmentChris Southan
Introduction:
Consequent to a memorandum of understanding between the Karolinska Institutet and the International Union of Basic and Clinical Pharmacology (IUPHAR) in 2018 a report on academic drug development, including guidelines (ADEV) has been drafted [1]. As part of this exercise, we conceived a triage for comprehensive informatics profiling around the compound, target, disease axis. We have termed this “in slico 360” (INS360) the aim of which was to support ADEV teams since they may lack either internal expertise or external support to do this on their own. Indeed, some past SciLifeLab Drug Discovery and Development Platform projects had been halted because of overlooked competitive impingements or insufficient target validation evidence.
Methods
We assessed the current database landscape, mostly public but including commercial, for potential utility for INS360. We were guided primarily by content coverage, usability, and reputation. We also explored some open property prediction resources for assay interference and toxicological inferences.
Results:
As a first-stop-shop, we selected the IUPHAR/BPS Guide to PHARMACOLOGY with ~900 ligand-target relationships captured via expert curation of journal papers Moving up in scale we evaluated ChEMBL at 1.8 million compounds with 1.1 million assay descriptions and 7,000 targets. With yet another jump we could search the patent corpus with 18 million extracted compounds in SureChEMBL. We explored PubChem that integrates these three with over 500 other sources linked to 96 million compounds, BioAssay results and connectivity into the NCBI Entrez system. The final jump in scale for document-to-chemistry navigation was represented by SciFinder with 155 million structures. On the target side, 360-exploration has the need to encompass literature, structure, genetic variation, splicing, interactions, and disease pathways. From their UniProt links, both GtoPdb and ChEMBL provide these entry points. Navigating genetic association data in support of target validation was enabled by the OpenTargets portal and the GWAS Catalog. We also fount servers that could produce prediction scores from chemical structures for a range of features important for de-risking development.
Conclusion:
This work scoped out initial resource choices for the INS360. We propose that not only ADEV operations but essentially any pharmacology research team has much to gain from this approach and many potential pitfalls can consequently be avoided when approaching key checkpoints, such as preparing a publication. However, support may be needed for both institutions and teams to get the best out of these complex and feature-rich databases.
[1] Southan C, (2019) Towards Academic Drug Development Guidelines, ChemRxiv pre-print no. 8869574
This document provides an overview of several free databases available through the National Institutes of Health for searching chemistry and toxicology information. It describes ChemIDplus, PubChem, TOXNET, and TRI databases and how to search them for chemical and toxicity data, structures, properties and environmental release information.
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
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Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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This presentation was provided by Racquel Jemison, Ph.D., Christina MacLaughlin, Ph.D., and Paulomi Majumder. Ph.D., all of the American Chemical Society, for the second session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session Two: 'Expanding Pathways to Publishing Careers,' was held June 13, 2024.
Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
"Learn about all the ways Walmart supports nonprofit organizations.
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Answers about how you can do more with Walmart!"
This presentation was provided by Rebecca Benner, Ph.D., of the American Society of Anesthesiologists, for the second session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session Two: 'Expanding Pathways to Publishing Careers,' was held June 13, 2024.
The chapter Lifelines of National Economy in Class 10 Geography focuses on the various modes of transportation and communication that play a vital role in the economic development of a country. These lifelines are crucial for the movement of goods, services, and people, thereby connecting different regions and promoting economic activities.
How to Setup Warehouse & Location in Odoo 17 InventoryCeline George
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
How to Setup Warehouse & Location in Odoo 17 Inventory
Bioinfomatics laboratory
1. P a g e | 1
BGE 313
Bioinformatics Laboratory
Submitted To, Submitted By,
Dr. Siraje Arif Mahmud Effat Jahan Tamanna
Associate Professor Roll No: 131647
Dept. of Biotechnology and Genetic Engineering Reg No: 36401
Jahangirnagar University Session: 2012 – 13
2. P a g e | 2
INDEX
Serial
No
Date Name of the Experiment Page Remarks
01 21.04.2016 Searching basics, AND, OR, NOT,
“keywords together”, *
3 – 8
02 21.04.2016 Searching PMC and PubMed Using
Authors name, fields, limits
8 – 10
03 02.05.2016 Retrieving protein sequences using
UniProt and creating multi-fasta files
11
04 02.05.2016 Retrieving relevant DNA sequences
using nucleotide and creating multi
fasta-file. (search by ldh1 NOT
hypothetical)
12
05 02.05.2016 Performing DNA and protein BLAST
and analyzing result
13 – 16
06 03.05.2016 Pairwise alignment (global, end gap
free), calculate identities, dotplot
using BioEdit
17 – 20
07 03.05.2016 Nucleotide composition, complement,
reverse complement, DNA to RNA,
translate, restriction map, six frame
translation using BioEdit
21 – 27
08 03.05.2016 Multiple sequence analysis using
BioEdit
27 – 28
09 03.05.2016 Tree Generation with MEGA 29 – 32
10 09.05.2016 Working with single protein sequence:
Analyzing protein composition
(pepdigest, pepstats), Protein
secondary structure by mEmboss:
(garnier for protein secondary
structure), helixturnhelix for motifs,
pepcoil for coiled coil regions
34 – 36
11 09.05-2016 RNA structure prediction using
RNAstructure
36 – 37
3. P a g e | 3
Experiment No 01
Searching basics, AND, OR, NOT, “keywords together”, *
i. Searching Basics
Methods:
Open PubMed home page
In PubMed search box write alpha amylase and click search. 10639 results will be shown.
To filter the result click on free full text from text availability section. Results will be reduced into
3108 in number.
Then click on 5 years from Publication Dates section. Rest will be reduced into 792 in number.
Click on Review from Article type section. Rest will be reduced into 16 in number.
If we want to clear filter, we have to click clear on the right side of all filter type, or clear all.
Result:
Interpretation:
PubMed is a free search engine accessing primarily the MEDLINE database of references and abstracts
on life sciences and biomedical topics. PubMed is maintained and updated by the National Library of
Medicine on a weekly basis. A search on alpha amylase shows results all articles related to alpha
amylase. If we want to filer results we can click on review which will show reviewed articles. Full free
text will reduce result by filtering free articles and 5 years will reduce results by showing articles which
were published in previous 5 years.
4. P a g e | 4
ii. Boolean Operator Using
a. AND
Methods:
Open PubMed home page
Write gyrase AND topoisomerase and search. 1986 results are found.
Then filtering. Click Free full text, Review and 5 years. Results will be reduced into 1032, 31 and 6.
Result:
Interpretation:
AND requires both terms to be in each item returned. If one is contained in the document and the other is
not, the item is not included in the resulting list (narrows the search). A search on gyrase AND
topoisomerase includes results that all articles will include both keywords.
b. OR
Methods:
Open PubMed home page
Search antibody OR immunoglobulin.
1247231 results are found.
Now filtering. Free full text – 363108, Review - 17602 , 5 years – 7068
5. P a g e | 5
Result:
Interpretation:
Either term (or both) will be in the returned document (Broadens the search). Search on antibody OR
immunoglobulin includes results contains that the articles containing the word antibody (but not
immunoglobulin) and other articles containing the word immunoglobulin (but not antibody) as well as
articles with antibody OR immunoglobulin in either order or number of uses.
c. NOT
Methods:
Open PubMed home page
Search immunoglobulin NOT IgG NOT IgA NOT IgM
693211 results are found.
Now filtering
Free full text - 181743
Review - 10047
5 years – 3618
6. P a g e | 6
Result:
Interpretation:
When the first term is searched, then any records containing the term after the operators are subtracted
from the results. A search on immunoglobulin NOT IgG NOT IgA NOT IgM includes results contains
that the articles about immunoglobulin will exclude IgG, IgA and IgM.
iii. Inverted (“ ”) search
Methods:
Open PubMed home page
Search “alpha amylase”
8015 results are found
Now Filtering
Free full text – 2393
Review – 25
5 years – 12
7. P a g e | 7
Result:
Interpretation:
When any term is searched, then any records containing the term exactly will be shown. A search on
“alpha amylase” includes results contains that the articles will contain the word exactly and the result will
be more specific.
iv. * search
Methods:
Open PubMed home page
Search ldh*
25235 results are found
Now filtering
Free full text – 5691
Review – 96
5 years – 46
8. P a g e | 8
Result:
Interpretation:
When any term is searched with *, then the records containing all the subclasses of the term exactly will
be shown. A search on ldh* includes results contains that the articles will contain all the subclasses of
ldh.
Experiment No 02:
Searching PMC and PubMed Using Authors name, fields, limits
i. Searching PubMed using author name
Methods:
Open PubMed home page
Write Schilling CH (1999) in search box and search.
2 results are found
9. P a g e | 9
Result:
Interpretation:
PubMed is a free search engine accessing primarily the MEDLINE database of references and abstracts
on life sciences and biomedical topics. PubMed is maintained and updated by the National Library of
Medicine on a weekly basis. Search on Schilling CH (1999) shows the result contain the free article by
the author Schilling CH in 1999.
ii. Searching PMC using author name
Methods:
Open PubMed home page
Select PMC.
Write Schilling CH in search box and search.
9 results are found
10. P a g e | 10
Result:
Interpretation:
PubMed Central is a free digital archive of articles, accessible to anyone from anywhere via a basic web
browser. The full text of all PubMed Central articles is free to read, with varying provisions for reuse.
Search on Schilling CH shows the result containing the free article by the author Schilling CH.
11. P a g e | 11
Experiment No 03:
Retrieving protein sequences using UniProt and creating multi-fasta files
Methods:
Open UniProt home page.
Search by writing ldh1.
Filter this by clicking Reviewed (893) from filter by option.
In left side in the box of other organisms write lactobacillus and click go. 16 results will be shown.
Now click on the box of Entry for selecting all.
Now click on download → uncompressed → go
Select all sequences. (Ctrl + A)
Copy all sequences. (Ctrl + C)
Open Notepad.
Paste All Sequences (Ctrl + V)
Now save these sequences. Click file → save as ( Ctrl + S) → select location → write ldh1P.fasta in
file name → click save
Result:
Interpretation:
UniProt is the Universal Protein resource, a central repository of protein data created by combining the
Swiss-Prot, TrEMBL and PIR-PSD databases. We can search any protein sequence from UniProt. We can
create multi fasta files and save them in notepad. We can further use this fasta files when we need them.
12. P a g e | 12
Experiment No: 4
Retrieving relevant DNA sequences using nucleotide and creating multi fasta-
file. (Search by ldh1 NOT hypothetical)
Methods:
Open PubMed Home Page.
Select nucleotide.
Search by writing ldh1 NOT hypothetical. 51 results will be found.
Select Number 4, 5, 6, 9.
Select on the right arrow of Summary. From here select FASTA (text).
Select all sequences (Ctrl + A).
Copy all sequences (Ctrl + C).
Open Notepad.
Paste All Sequences (Ctrl + V)
Now save these sequences. Click file → save as ( Ctrl + S) → select location → write ldh1.fasta
in file name → click save
Result:
Interpretation:
We can search any nucleotide sequence from PubMed. We can create multi fasta files and save them in
notepad. We can further use this fasta files when we need them.
13. P a g e | 13
Experiment No: 05
Performing DNA and protein BLAST and analyzing result
i. Blastn
Methods:
Open blast home page.
Select nucleotide blast
Copy a nucleotide sequence from previously saved ldh1.fasta file.
Paste the sequence into Enter accession number(s), gi(s), or FASTA sequences(s) box.
From Choose Search Set options select nucleotide collection (nr/nt).
From Algorithm parameters select: Max target sequences (50)→ Expect threshold (0.1)
Then click show results in new window and click BLAST.
Methods:
ii. Blastp
Methods:
Open blast home page.
Select nucleotide blast.
Select blastp.
Copy a protein sequence from previously saved ldh1P.fasta file.
Paste the sequence into Enter accession number(s), gi(s), or FASTA sequences(s) box.
From Choose Search Set options select non-redundant protein sequences (nr).
From Algorithm parameters select: Max target sequences (50)→ Expect threshold (0.1)
Then click show results in new window and click BLAST.
14. P a g e | 14
Results:
iii. blastx
Methods:
Open blast home page.
Select nucleotide blast.
Select blastx.
Copy a nucleotide sequence from previously saved ldh1.fasta file.
Paste the sequence into Enter accession number(s), gi(s), or FASTA sequences(s) box.
From Choose Search Set options select non-redundant protein sequences (nr).
From Algorithm parameters select: Max target sequences (50) → Expect threshold (0.1).
Then click show results in new window and click BLAST.
Results:
15. P a g e | 15
iv. tlastn
Methods:
Open blast home page.
Select “nucleotide blast”
Select “tblastn”.
Copy a protein sequence from previously saved “ldh1P.fasta” file.
Paste the sequence into “Enter accession number(s), gi(s), or FASTA sequences(s)” box.
From “Choose Search Set” options select “nucleotide collection (nr/nt)”.
From “Algorithm parameters” select: Max target sequences (50)→ Expect threshold (0.1)
Then click “show results in new window” and click “BLAST”
Results:
16. P a g e | 16
v. tlastx
Methods:
Open blast home page.
Select “nucleotide blast”
Select “tblastx”.
Copy a nucleotide sequence from previously saved “ldh1.fasta” file.
Paste the sequence into “Enter accession number(s), gi(s), or FASTA sequences(s)” box.
From “Choose Search Set” options select “nucleotide collection (nr/nt)”.
From “Algorithm parameters” select: Max target sequences (50)→ Expect threshold (0.1)
Then click “show results in new window” and click “BLAST”
Interpretation:
The Basic Local Alignment Search Tool (BLAST) finds regions of local similarity between sequences. The
program compares nucleotide or protein sequences to sequence databases and calculates the statistical
significance of matches. blastn- Search a nucleotide database using a nucleotide query, blastp- Search
protein database using a protein query, blastx- Search protein database using a translated nucleotide
query, tblast- Search translated nucleotide database using a protein query, tblastx- Search translated
nucleotide database using a translated nucleotide query. During the result of blast, in graphic summary
the red portion indicates the query coverage. From blast result we can download fasta files.
17. P a g e | 17
Experiment No- 06:
Pairwise alignment (global, end gap free), calculate identities, dotplot using BioEdit
i. Pairwise alignment- global
Methods:
Open BioEdit
Click file → open
Select All Files (*.*) from files of type
Open ldh1.fasta file.
Select two sequences.
Click on sequence → pairwise alignment → align two sequences (optimal GLOBAL alignment)
Can click “Shade identities and similarities in alignment window” for color shade or “normal
view” mode for normal view.
Results:
Interpretation:
Global alignments, which attempt to align every residue in every sequence, are most useful when the
sequences in the query set are similar and of roughly equal size. In BioEdit we aligned to sequences
globally. By color shading we noticed identities and similarities in alignment window.
18. P a g e | 18
ii. Pairwise alignment- end gap free
Methods:
Open BioEdit
Click file → open
Select All Files (*.*) from files of type
Open ldh1.fasta file.
Select two sequences.
Click on sequence → pairwise alignment → align two sequence (allow ends to slide)
Can click “Shade identities and similarities in alignment window” for color shade or “normal
view” mode for normal view.
Results:
Interpretation:
In end gap free alignment Gaps that appear before the first or after the last letter of the sequence are for.
Especially preferable whenever one of the sequences is significantly shorter than the other. We used two
sequences to align end gap free. By color shading we observed identities and similarities in alignment
window.
19. P a g e | 19
iii. Calculate identities
Methods:
Open BioEdit
Click file → open
Select All Files (*.*) from files of type
Open ldh1.fasta file.
Select two sequences.
Click on sequence → pairwise alignment → calculate identity/similarity for two sequences.
Interpretation:
Sequence identity is the amount of characters which match exactly between two different sequences.
Hereby, gaps are not counted and the measurement is relational to the shorter of the two sequences. In this
experiment, we found identities between two sequences by BioEdit.
iiv. Dotplot
Methods:
Open BioEdit
Click file → open
Select All Files (*.*) from files of type
Open ldh1.fasta file.
Select two sequences.
Click on sequence → pairwise alignment → Dot plot (Pairwise comparison)
20. P a g e | 20
Result:
Window – 20, mismatch limit - 10
Interpretation:
Dot plot is a graphical method that allows the comparison of two sequences and identify regions of close
similarity between them. The convenience of using dot-plot analysis is that the one graphics shows all
significant pairwise alignments simultaneously. We constructed a dot plot window using BioEdit. We can
see a lot of dots along a diagonal line, which indicates that the two protein sequences contain many
identical amino acids at the same (or very similar) positions along their lengths. This is what we would
expect, because we know that these two proteins are homologues (related proteins).
21. P a g e | 21
Experiment No – 07
Nucleotide composition, complement, reverse complement, DNA to RNA,
translate, restriction map, six frame translation using BioEdit.
i. Nucleotide Composition
Methods:
Open BioBdit
Click file → open → Select All Files (*.*) from files of type
Open ldh1.fasta file
Select one sequence
Click on sequence → nucleic acid → nucleotide composition
Result:
Interpretation:
Nucleotide composition summaries and plots may be obtained by choosing “Nucleotide Composition”
form the “Nucleic Acid” submenu of the “Sequence” menu, respectively. Bar plots show the Molar
percent of each residue in the sequence. For nucleic acids, degenerate nucleotide designations are added
to the plot if and as they are encountered. For example, a sequence that has only A, G, C and T will have
four bars on the graph. We can observe molecular weight, A+T content, G+C content.
22. P a g e | 22
ii. Complement
Methods:
Open BioEdit.
Click file → open.
Select All Files (*.*) from files of type.
Open ldh1.fasta file.
Select one sequence.
Click on sequence → nucleic acid → complement.
For undo select the sequence →Click on sequence → nucleic acid → complement.
Result:
Interpretation:
We can get the complement sequence of the given sequence. If we want to get back the previous
sequence, we have to complement the sequence again.
iii. Reverse Complement
Methods:
Open BioEdit
Click file → open
Select All Files (*.*) from files of type
Open ldh1.fasta file
Select one sequence.
Click on sequence → nucleic acid → reverse complement
For undo select the sequence →Click on sequence → nucleic acid → reverse complement
23. P a g e | 23
Results:
Interpretation:
We can get the reverse complement sequence of the given sequence. If we want to get back the previous
sequence, we have to reverse complement the sequence again.
iv. DNA to RNA
Methods:
Open BioEdit
Click file → open
Select All Files (*.*) from files of type
Open ldh1.fasta file.
Select one sequence.
Click on sequence → nucleic acid → DNA - > RNA
For undo select the sequence →Click on sequence → nucleic acid → RNA -> DNA
Result:
Interpretation:
DNA sequence converts into RNA sequence. In RNA sequence there is no thymine (T), Instead of
thymine there is Uracil (U).
24. P a g e | 24
v. Translate
Methods:
Open BioEdit
Click file → open
Select All Files (*.*) from files of type
Open ldh1.fasta file.
Select one sequence.
Click on sequence → nucleic acid → translate → frame 1, frame 2, frame 3
To get the remaining 3 frames select the sequence →nucleic acid → reverse complement →
again select sequence → nucleic acid → translate → frame 1, frame 2, frame 3
Result:
25. P a g e | 25
Interpretation:
We know that there are six frames. Three are forward frames and three are reverse frames. Selecting a
sequence and clicking by frame 1, frame 2, frame 3 we can get all forward frames. We can also observe
that every three nucleotides code which amino acid. By reverse complement of a sequence we can get
remaining three reverse frame and every three nucleotides code the specific amino acid. In the experiment
we got 3 forward and 3 reverse frames of a selected sequence.
vi. Restriction Map
Methods:
Open BioEdit
Click file → open
Select All Files (*.*) from files of type
Open ldh1.fasta file.
Select one sequence.
Click on sequence → nucleic acid → restriction map → cancel enzyme with degenerate
recognition and large recognition sites → select all enzymes from manufacturer → select circular
DNA (ends joint) → generate map
Results:
26. P a g e | 26
Interpretation:
A restriction map is a map of known restriction sites within a sequence of DNA. Restriction mapping
requires the use of restriction enzymes. Restriction Map accepts a DNA sequence and returns a textual
map showing the positions of restriction endonuclease cut sites. From this map which we found in
experiment show different restriction site which are cut by different restriction enzyme.
vii. Six frame translation
Sorted six frame translation:
Open BioEdit
Click file → open
Select All Files (*.*) from files of type
Open ldh1.fasta file.
Select one sequence.
Click on sequence → nucleic acid → sorted six frame translation → minimum OFR size 40→
start codon ATG → translate
Result:
Unsorted six frame translation:
Open BioEdit
Click file → open
Select All Files (*.*) from files of type
Open ldh1.fasta file.
Select one sequence.
Click on sequence → nucleic acid → unsorted six frame translation → minimum OFR size 40 →
start codon ATG → translate
27. P a g e | 27
Interpretation:
A DNA sequence may be translated in all six reading frames into all possible open reading frames (simple
codon stretches, actually) by highlighting the sequence title in the document window and choosing either
“Sorted Six-Frame Translation” or “Unsorted Six-Frame Translation”
Sorted: ORFs will be reported in order of start position. Negative-frame sequences are sorted according
to their end positions (first position along the positive sequence). The number of sequences which can be
translated and sorted is limited to something above 10,500 sequences. If a sorted translation becomes too
large, resources for storing the sequences to be sorted runs out. If this happens, BioEdit will tell you, then
present the sequences it was able to translate. Multiple sequences may be translated into a single ORF list
suitable for BLAST database creation.
Unsorted: Sequences are reported in the order that their stop codons are encountered in a once through,
6-frame simultaneous pass through the entire sequence. The codon stretches are written into a file as they
are encountered and therefore do not need to be stored in memory. Very long lists can thus be generated.
Currently, only one sequence at a time may be translated this way.
Experiment No 08:
Multiple sequence analysis using BioEdit
Multiple nucleotide sequence analysis:
Open BioEdit
Click file → open
Select All Files (*.*) from files of type
Open ldh1.fasta file
Select all sequences.
Click on accessory application → ClustalW Multiple alignment → Run ClustalW → Shade
identities and similarities in alignment window
28. P a g e | 28
Result:
Multiple nucleotide sequence analysis:
Open BioEdit
Click file → open
Select All Files (*.*) from files of type
Open ldh1P.fasta file.
Select all sequences.
Click on accessory application → ClustalW Multiple alignment → Run ClustalW → Shade
identities and similarities in alignment window
Result:
Interpretation:
Multiple Sequence Alignment (MSA) is generally the alignment of three or more biological sequences
(protein or nucleic acid) of similar length. From the output, homology can be inferred and the
evolutionary relationships between the sequences studied. ClustalW is a multiple sequence alignment tool
for the alignment of DNA or protein sequences. ClustalW calculates the best match for the input
sequences based on the parameters entered and generates an easy to interpret report. In previous
experiment we observed the alignment of more than two sequences for both nucleotide and protein
sequences. By the color shading we can find similarities an identities among the sequences.
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Experiment No 09:
Tree Generation with MEGA.
i. Construct/test maximum likelihood Tree
Methods:
Open BioEdit
Click file → open
Select All Files (*.*) from files of type
Open ldh1P.fasta file.
Select all sequences.
Click on accessory application → ClustalW Multiple alignment → Run ClustalW
Click on file → save as → select location → File name (ldh-P-aln.fasta) → save as type : Fasta
(*.fas, *.fst, *.fsa) → save
Open MEGA 6
Click file → open → ldh-P-aln.fasta → analyze → select protein sequences → Ok
Click phylogeny →construct/test maximum likelihood Tree → Yes
Test of Phylogeny (Bootstrap method) → No. of Bootstrap Replications (50) → substitution type
(amino acid) → Model/ method (dayhoff model) → rates among sites (Gamma distributed with
Invariant sites- G+1) → Compute
Result:
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ii. Construct/ test neighbor joining tree
Methods:
Click phylogeny →construct/ test neighbor joining tree → Yes
Test of Phylogeny (Bootstrap method) → No. of Bootstrap Replications (50) → substitution type
(amino acid) → Model/ method (dayhoff model) → rates among sites (Gamma distributed- G) →
Compute
Result:
iii. Construct/ test minimum- evolution tree
Methods:
Click phylogeny →construct/ test minimum- evolution tree → Yes
Test of Phylogeny (Bootstrap method) → No. of Bootstrap Replications (50) → substitution type
(amino acid) → Model/ method (dayhoff model) → rates among sites (Gamma distributed- G) →
Compute
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Result:
iv. Construct/ test UPGMA tree
Methods:
Click phylogeny →construct/ test UPGMA tree → Yes
Test of Phylogeny (Bootstrap method) → No. of Bootstrap Replications (50) → substitution type
(amino acid) → Model/ method (dayhoff model) → rates among sites (Gamma distributed- G) →
Compute
Result:
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v. Construct/ test maximum parsimony tree
Methods:
Click phylogeny →construct/ test UPGMA tree → Yes
Test of Phylogeny (Bootstrap method) → No. of Bootstrap Replications (50) → substitution type
(amino acid) → Compute
Result:
Interpretation:
A phylogeny, or evolutionary tree, represents the evolutionary relationships among a set of organisms or
groups of organisms, called taxa (singular: taxon). The tips of the tree represent groups of descendent taxa
(often species) and the nodes on the tree represent the common ancestors of those descendants. Two
descendants that split from the same node are called sister groups. With molecular evolutionary genetics
analysis (MEGA) we constructed different types of phylogeny by inputing protein sequences.
Experiment No 10:
Working with single protein sequence: Analyzing protein composition
(pepdigest, pepstats), Protein secondary structure by mEmboss: (garnier for
protein secondary structure, helixturnhelix for motifs, pepcoil for coiled coil
regions.
i. Analyzing protein composition (pepstats)
Methods:
Open mEMBOSS
Click protein → composition → pepstats calculate statistics of protein properties
In input section click on paste → cut and paste protein sequence → Go
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Result:
Interpretation:
pepstats reads one or more protein sequences and writes an output file with various statistics on
the protein properties. This includes: molecular weight, number of residues, average residue
weight, charge, isoelectric point, for each type of amino acid: number, molar percent,
DayhoffStat, for each physico-chemical class of amino acid: number, molar percent; probability
of protein expression in E. coli inclusion bodies, molar extinction coefficient (A280), extinction
coefficient at 1 mg/ml (A280). In previous experiment we input a protein sequence and got these
data.
ii. Analyzing protein composition (pepdigest)- trypsin
Methods:
Open mEMBOSS
Click protein → composition → pepdigest report on protein proteolytic enzyme or reagent
cleavage sites
In input section click on paste → cut and paste protein sequence
In required section select trypsin → Go
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Result:
Analyzing protein composition (pepdigest)- chymotrypsin
Methods:
Open mEMBOSS
Click protein → composition → pepdigest report on protein proteolytic enzyme or reagent
cleavage sites
In input section click on paste → cut and paste protein sequence
In required section select chymotrypsin → Go
Result:
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Interpretation:
This programs allows to input one or more protein sequences and to specify one proteolytic
agent from a list, which might be a proteolytic enzyme or other reagent. It will then write a report
file containing the positions where the agent cuts, together with the peptides produced. The rest
of the file consists of columns holding the following data: start position of the fragment, end
position of the fragment, molecular weight of the fragment, residue before the cut site ('.' if start
of sequence), residue after the second cut site ('.' if end of sequence), sequence of the fragment.
In previous experiment we input a protein sequence and selected the proteolytic enzyme trypsin
and chymotrypsin and finally got these data as result.
iii.Protein secondary structure by mEmboss: ( garnier for protein secondary structure)
Methods:
Open mEMBOSS
Click protein → 2D STRUCTURE → garnier predict protein secondary structure using GOR
method
In input section click on paste → cut and paste protein sequence → Go
Result:
Interpretation:
Garnier is an implementation of the original Garnier Osguthorpe Robson algorithm (GOR I) for
predicting protein secondary structure. It reads an input protein sequence and writes a standard EMBOSS
report file with the predicted secondary structure. The Garnier method is not regarded as the most
accurate prediction, but is simple to calculate on most workstations. In this experiment we input protein
sequence and got secondary structure.
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iv.Protein secondary structure by mEmboss: helixturnhelix for motifs
Methods:
Open mEMBOSS
Click protein → 2D STRUCTURE → helixturnhelix identify nucleic acid-binding motifs in
protein sequences
In input section click on paste → cut and paste any protein sequence → Go
Result:
Interpretation:
helixturnhelix uses the method of Dodd and Egan to identify helix-turn-helix nucleic acid binding motifs
in an input protein sequence. The output is a standard EMBOSS report file describing the location, size
and score of any putative motifs. For the sequence we input we found the output which identify nucleic
acid-binding motifs in protein sequences
Experiment No 11:
RNA structure prediction using RNAstructure
Methods:
Open RNAstructure
Click file→ new sequence
Title- ldh1RNA → sequence (copy and paste 2 lines of sequences from ldh1.fasta) → fold as
RNA → yes → select location → file name - ldh1RNA → save → start → draw structures
Draw → go to structure number/ zoom
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Interpretation:
RNAstructure is a software package for RNA secondary structure prediction and analysis. It predicts
lowest free energy structures and low free energy structures either by using a heuristic or by determining
all possible low free energy structures. From this process we can find RNA secondary structure with
different energy level. The structure with lowest energy is more stable. To perform this process we should
use accurate maximum & minimum energy different, maximum number of structure, window size etc.