Tyler jgi web site fri 1050
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The document provides instructions for using various features of the JGI genome browser and database, including how to view gene models and annotations, search databases, and access related information pages for genes and proteins. Key points covered include how to view DNA and protein sequences, expanded gene tracks, run BLAST searches across multiple databases, and access Gene Ontology and KEGG pathways from protein pages.
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(20 points total)
BIO1001
Fall 2020
To complete the remainder of this project and prepare for your presentation, follow the instructions below. Your presentation should include the answers to all questions indicated below. For your final
presentation, you are expected to
organize your research and
present a rehearsed, 10-12
minute presentation using the rubric at the end of this document as a guideline for preparation.
Learning Objectives
At the conclusion of this phase of the mystery molecule project, students will be able to
1. Effectively navigate sequence databases of the NCBI website
a. BLAST DNA sequence against the human genome
b. Analyze alignment data from genomic
databases
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the scientific literature (primary and secondary resources)
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3. Organize scientific content into a coherent presentation.
4. Communicate research results to a group of peers.
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1. Go to:
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j. Paste a picture of the secondary (worm view) and tertiary (space filling) structures of your protein into your report. Note: structures have not been determined for all proteins, so you may have been given an identifier for a similar protein. Describe things about the structure, for example features of the shape, charge, or hydrophobicity.
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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.
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(20 points total)
BIO1001
Fall 2020
To complete the remainder of this project and prepare for your presentation, follow the instructions below. Your presentation should include the answers to all questions indicated below. For your final
presentation, you are expected to
organize your research and
present a rehearsed, 10-12
minute presentation using the rubric at the end of this document as a guideline for preparation.
Learning Objectives
At the conclusion of this phase of the mystery molecule project, students will be able to
1. Effectively navigate sequence databases of the NCBI website
a. BLAST DNA sequence against the human genome
b. Analyze alignment data from genomic
databases
c. Identify an unknown gene based on DNA sequence
2. Use
the scientific literature (primary and secondary resources)
to analyze gene products, and research the cellular and molecular basis of a disease gene.
3. Organize scientific content into a coherent presentation.
4. Communicate research results to a group of peers.
Step 1: BLAST your DNA sequence
1. Go to:
http://www.ncbi.nlm.nih.gov/
2. Click on BLAST (under “popular resources” at the right side of the screen)
3. Choose the nucleotide blast program (under “Web BLAST”)
4. In the “blastn” tab (on left) enter your DNA sequence into the “enter query sequence” box (obtain your mystery DNA sequences in the Mystery Molecule content area in D2L).
a. In “choose search set” click the “genomic + transcript databases”
b. From the drop down menu below, select “Human genomic + transcript (Human G + T)”
c. Under “program selection” in the next section down, optimize for highly similar sequences (megablast)
5. Submit query (click on “BLAST” at the bottom of the screen) and wait– this may take up to a few minutes.
6. A colorized map of sequence alignment scores will appear. Red indicates very good sequence alignment.
7. Scroll down to descriptions of sequences that produced statistically significant alignments. You should see options for genomic alignments and transcripts. An e-value close to zero, and maximum sequence identity close to 100% are optimal.
8. Identify your mystery gene/transcript from the description, e-value, and % sequence identity.
Step 2: Analysis of gene product
Click on the reference number (left, in the column labeled, “Accession”) for the mRNA transcript or genomic sequence of your mystery gene. Scroll down; you will see many references (titles, authors, years of publication) describing your gene. Clicking on any one of the PubMed reference numbers will link you directly to the original publication. You will need these publications to characterize your gene for your presentation. Bookmark this page—all your references must be from primary or secondary sources (no google, wikipedia, textbooks, blogs, etc.).
You must use a minimum of 7 references that can be found by searching NCBI.
Resources for genomics research
The Rat Genome Database (RGD) provides several genomics tools and resources for researchers, including GBrowse for visualizing and exploring the rat genome, GViewer for viewing genomic regions associated with functions and phenotypes, and RatMine for flexible searching and analysis of integrated rat data. RGD has recently updated GBrowse with new functionality and improved performance, and also provides SNPlotyper for viewing strain-specific SNPs and the Human Genome Browser for comparative purposes. These tools integrate data from multiple sources to facilitate genomics research on the rat model.
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This document provides instructions for students to conduct basic BLAST searches using unknown DNA sequences as queries to identify homologous sequences and determine the identity of the unknown sequences. It describes how to analyze the BLAST results, including the descriptions, alignments and significance values. Students are guided to find homologs of a known sequence on GenBank and explore the BLAST results, alignments and formatting options.
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The document provides an introduction to BLAST (Basic Local Alignment Search Tool), which is an algorithm used to compare gene and protein sequences to those in public databases. It discusses the types of BLAST programs, the BLAST algorithm, input/output, how to perform a BLAST search, and the functions and objectives of BLAST. Specifically, BLAST is faster than previous sequence comparison methods, it outputs alignments and statistical values to evaluate matches, and its main objectives are to identify related sequences and locate domains through local alignments.
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Bioinformatic tools analyze biological sequences to find similarities, domains, and coding regions. BLAST is a widely used tool that compares a query sequence to database sequences to find regions of similarity, helping scientists determine sequence function. Sequence alignment identifies similar character patterns between two or more sequences and can provide information about function, structure, and evolutionary relationships. CpG islands are regions of DNA where cytosine and guanine nucleotides frequently occur next to each other. Methylation of cytosines within CpG islands can regulate gene expression and is an epigenetic mechanism studied in cancer diagnosis.
Apollo annotation guidelines for i5k projects Diaphorina citri
Apollo is a web-based application that supports and enables collaborative genome curation in real time, allowing teams of curators to improve on existing automated gene models through an intuitive interface. Apollo allows researchers to break down large amounts of data into manageable portions to mobilize groups of researchers with shared interests.
2 md2016 annotation
Three groups annotated the genome of Mycoplasma genitalium and found inconsistencies in their annotations. Of the 468 genes, 318 were annotated consistently by all three groups but 45 had conflicting annotations. Errors likely arose from insufficient sequence similarity to determine homology accurately or incorrectly inferring function based on homology alone. Database curation is needed to prevent propagation of erroneous annotations.
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This document describes Genome Annotator light (GAL), a tool for genome analysis and visualization. GAL integrates genome annotation, comparative genomics, and visualization features into a single virtual machine. It uses a MySQL database with a schema based on the Genome Unified Schema. The front end is built with Perl, CGI, GD, PHP, JavaScript, and Ajax. GAL can annotate genomes with varying levels of data, from simple fasta files to fully annotated genomes. It visualizes genomes through features like a genome browser, gene details pages, and synteny viewers. GAL has been implemented on oomycete and cyanobacterial genomes.
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This document summarizes the assembly of the Phytophthora ramorum genome using PacBio long reads. It describes the error correction and assembly process for two P. ramorum strains, Pr102 and ND886. For Pr102, multiple assembly versions (V1-V5) were generated using different error correction and assembly protocols. The V5 assembly resulted in fewer scaffolds, larger size, and fewer gaps compared to previous versions. For ND886, PacBio reads were error corrected and assembled. Both assemblies captured more repetitive elements compared to previous Sanger-based assemblies. Gene predictions were also improved in number and quality.
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The document discusses various topics related to gene prediction including primer designing, restriction mapping, and gene prediction. It provides guidelines for primer designing such as avoiding non-specific binding and dimer formation between primers. It also discusses key concepts in gene prediction such as reading frame consistency between exons, codon and dicodon frequencies that can help distinguish coding from non-coding regions, and position specific scoring matrices to predict translation start sites.
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The document discusses various types of biological databases including sequence databases, structure databases, genome databases, and model organism databases. It provides examples of nucleotide databases like Genbank, DDBJ, EMBL-EBI, and TIGR. Genome browsers like UCSC Genome Browser, Ensembl browser, and Integrated Genome Browser are also mentioned. Other topics covered include the Encyclopedia of Life, India Biodiversity, Barcode of Life, data retrieval schemes, bibliographic databases, and database journals.
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This document outlines the schedule and topics for a class on SNPs and gene expression. The class will have 6 sessions, with groups of 2 students presenting on selected papers in each session. Topics to be covered include an introduction to terminology like forward and reverse genetics; how often SNPs occur in the general population; databases of SNPs like dbSNP and haplotype projects like HapMap; gene expression analysis using microarrays; and experimental design, data analysis, and visualization techniques for microarray data. Papers for each group to present on are also listed. The next class will involve discussing one particular paper on SNPs.
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The document provides information about performing chi-square tests and choosing appropriate statistical tests. It discusses key concepts like the null hypothesis, degrees of freedom, and expected versus observed values. Examples are provided to illustrate chi-square tests for goodness of fit and comparison of proportions. The document also compares parametric and non-parametric tests, providing examples of when each would be used.
26 nov2013seminar
This document summarizes information from several genomics and bioinformatics research groups and projects. It discusses:
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Stat2013
The chi-square test is used to determine if an observed distribution of data differs from the distribution expected if the null hypothesis is true. It requires a contingency table of observed and expected frequencies, a probability value, and degrees of freedom. The chi-square test calculates a test statistic to determine if any difference is statistically significant or likely due to chance. Examples show applying the chi-square test to genetics data on tall and dwarf pea plants and to the distribution of sixes rolled in dice.
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This document discusses the development of a new lightweight version of the Eumicrobedb database called Transcriptomicsdb. The new version reduces the database size and dependencies by decreasing the number of tables and views from 329 to 37 and 127 to 18 respectively in Eumicrobedb-Oracle. It also improves query time from 10 seconds to 1.2 seconds and reduces genome upload time from 12-14 hours to 2 hours. The document describes how the new database will help laboratories with limited hosting facilities to store and analyze sequencing data. It notes that the source code will soon be released to allow others to replicate the database without needing an Oracle license and various bioinformatics packages.
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Pharmacogenetics refers to how genetic differences affect individuals' responses to drugs. It influences different metabolic pathways and is responsible for over 106,000 deaths annually in the US. Certain genetic mutations can determine how effectively drugs are processed in the body. Microarrays are DNA chips that allow researchers to analyze large numbers of genes simultaneously, helping to identify genetic factors influencing drug responses and diseases.
Lecture5,6
Many diseases are caused by genetic mutations. Over 4000 diseases are linked to altered genes, including heart disease, cancer, autoimmune disorders, and diabetes. Specific mutations are associated with certain cancers, such as mutations in the RB1 and BRCA1 genes which can lead to retinoblastoma and breast cancer respectively. Large-scale projects like the Cancer Genome Atlas and ENCODE project aim to catalogue all mutations in cancers and across the human genome. Immunogenetics research examines the genetic links to immune-related disorders. The HapMap and 1000 Genomes projects studied genetic variants and helped map human genetic diversity.
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The document discusses topics to be covered in an IICB course on 8th December 2012, including primer designing, restriction mapping, and gene prediction. It provides information and guidelines on these topics, such as the appropriate length and properties of primers, the four types of restriction enzymes, and methods for gene prediction including patterns, frame consistency, dicodon frequencies, position-specific scoring matrices, and coding potential. Relevant references and websites discussing these techniques are also listed.
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Genome projects, Secondgen and Thirdgen genome sequencing, application of genome sequencing in predicting disease genes
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The document discusses several topics related to biodiversity databases and identification tools:
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This document provides an overview of genome sequencing. It discusses the history of genome sequencing, from early sequencing of small viruses in the 1970s to larger genomes like yeast and the human genome. The document outlines different sequencing technologies over time, from Sanger sequencing to newer single-molecule approaches. It also summarizes key genome projects like ENCODE and 1000 Genomes that have provided insights into non-coding regulatory elements and human genetic variation.
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Tyler jgi web site fri 1050
- 1. P. sojae P. ramorum P. capsici
- 2. Detailed manual in use of JGI site
- 4. Genome Browser Can type or paste text in here Then type return or click “Refresh” Change orientation of sequence Display 3 frame translation (must be zoomed in) Display DNA sequence (must be zoomed in) Sequence similarity to P. ramorum Blue = coding Pink = non-coding EVERYTHING IS CLICKABLE!! Click this to access the DNA sequence Tracks give different versions of gene models as well as various sets of Blast hits Clicking on a track will expand it (see next page) Current “best” gene models
- 5. Expanded track Red models have been manually annotated Clicking on open models will open the protein page
- 6. Clicking on model will open the nucleic acid sequence Clicking on green bar will open the protein sequence introns Clicking names will open gene in its database Clicking gray bar will open sequence alignement red blocks are insertions in target Clicking red dot will set sequence as source Protein page EVERYTHING IS CLICKABLE!! Clickable All clickable Blue indicates EST support for UTR
- 7. Shift click or command-click to choose lists of sequences to get (in fasta format) or do Clustal alignments on Shift click or command-click to choose lists of genomes and/or databases for the list of alignments. Then click “Apply Filter” number of alignments to display Protein page OPTIONS
- 8. Sequence Display FROM PROTEIN PAGE Padding controls how much flanking sequence is displayed Introns (should start with GTxxG) Blue indicates EST support for UTR
- 9. Advanced Search (much easier than Simple Search) Shift click or command-click to search mutilple databses simulataneously It’s also an easy way to switch databases Do NOT type return or enter after your entry - you MUST click “Search” Accepts and recognizes almost any kind of query Output of search
- 10. Links: P -> protein page T -> transcript page G -> genome browser Check to download sequences
- 11. Transcript page Contains more info on annotation Used mostly for manual annotation
- 12. Gene Ontology Click to go to protein page
- 13. KEGG Click to go to protein page