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| <span style="color: Seagreen;font-weight:bold;font-size:large;">Lab 12. Bioinformatics Exercises: BLAST & Genomes | | <center>[http://bigdata.citytech.cuny.edu/ City Tech/Cornell BioMedical Big Data Week 2020]: '''Pathogen Evolutionary Genomics'''</center> |
| ==Expected Learning Outcomes==
| | <center>Wed, July 22, 2020, 9 am - 12 noon</center> |
| * Be able to perform NCBI BLAST search for homologous sequences in GenBank.
| | <center>'''Instructor:''' Dr Weigang Qiu, Professor, Department of Biological Sciences </center> |
| * Be able to identify homologs in other model organisms.
| | <center>'''Office:''' B402 Belfer Research Building</center> |
| * Be able to identify alternative splice forms a single gene using NCBI web tools
| | <center>'''Email:''' weigang@genectr.hunter.cuny.edu</center> |
| * Be able to analyze locus structure from the information obtained from locus page
| | <center>'''Lab Website:''' http://diverge.hunter.cuny.edu/labwiki/</center> |
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| ==Lab Report III==
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| # The lab report is worth 50 points.
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| # You have to complete the lab report by using the MS Word lab report template provided on Blackboard.
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| # Make sure to name your file with your LAST NAME-Lab12.
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| # you need to EMAIL your file to your T.A. to get credit for your work.
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| ----
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| ==Introduction==
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| Research in molecular genetics requires effective use of online bioinformatic tools to analyze and understand the genetic materials being worked with. The following exercises will expose you to real-world scenarios and introduce you to the methods and tools you can use to solve these problems.
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| In biology, homology is defined as a common or shared evolutionary origin. Therefore, homologous sequences are sequences diverged from a common ancestor. Note that the word "homology" is different from "similarity": homologous structures or sequences may not be similar (e.g., forearms in mammals and birds) and, conversely, similar structures or sequences may not be homologous (e.g., wings in birds and bats).
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| BLAST is a computer algorithm allowing for efficient search of similar sequences in a large database. While BLAST performs a similar function to Google search, you should not use Google to look for similar sequences in a human or other genome. When sequences are similar with a sufficient statistical significance (measured by e-value, see below), we consider these sequences homologous to each other.
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| ----
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| ==Exercise 1. Homology searching using BLAST==
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| # Go to the NCBI-BLAST website at [http://blast.ncbi.nlm.nih.gov/Blast.cgi NCBI/BLAST Home Page]
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| # To know more about BLAST, read the expanded answer by clicking on "Learn more"
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| # Since BLAST finds matches between nucleotide or protein sequences, it needs a "query" sequence as input as well as a "database" to search against. Make sure to know what your "query" sequence is and find the appropriate "database".
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| # Start BLASTing against the mouse genome by clicking "Mouse" under "BLAST Genomes"
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| # Copy and paste the following sequence into the "Enter Query Sequence" box:
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| <div style="font-family:Monospace;line-height:1;width:550px;border-style:solid;border-width:1px;border-color:#AAAAFF;background-color:#EEEEFF;padding-left:5px;padding-right:5px;padding-top:0px;padding-bottom:0px;">
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| CTAGATGCATTTACGAAGGAGACAGAAAACGTCTTTCGGCAATAGCTCTCAAATGCAAAACGACGTCGG
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| CGAGCTGTCCCTTACCTGGAGGCCCGCAGGAGAAGCGCGGTGATCCGAGAGGGTCCCCCAGGGGTGTCCG
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| GTCGGTCTCCCGCTCGCCCAGCAGACGGCTGCGGAAACGGGGCAGCGTTTAAATAACCCCAGCTGGAGAC
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| ATGTCAGGACTTAGCTCCTCCGACAGCCGACGCCGGACGTGTCCCAACTTGACCAGCCCCACAGGAAGAG
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| CTGAGTCAACTCGGCCCAGCCCAGTCCCACCCGTCCCGGAAGCCGCATCCCGGCGAGTCCGGGACCAGGC
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| ACCTGTCACCTCCTGGACCCCAGCAACGAGCCCAGCGCGACCCCGGAGCGGGCCCGAATTCT
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| </div>
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| <ol start="6">
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| <li>Scroll down to the bottom of the page and click "BLAST"
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| <li>Wait for 10-30 seconds for the results to return ('''be patient'''). Once the result page is loaded, locate and copy/write down
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| the following information in your lab report file for the first hit:
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| <ol>
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| <li>Species and strain | |
| <li>Chromosome
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| <li>Length of your query sequence
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| <li>Percent identity, number of matched bases, and number of gaps between the matched sequences
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| </ol> | |
| <li>Click "Genome Data Viewer" at top right will bring you to a genome browser | |
| <li>Mouse-over the green central segment labeled "biological regions, aggregate" (just under the query, in red) and click the link "GenBank View". A standard GenBank file of this gene will load. Locate the 1st "mRNA" feature block and write down the following structural information about this gene in your lab report file:
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| <ol> | |
| <li>Gene ID
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| <li>Total length of the gene
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| <li>Number of introns
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| <li>Which is the non-template (mRNA analog) strand: the above sequence itself or its reverse complement? [Hint: note the word '''complement''' in mRNA and cDNA lines) | |
| </ol> | |
| </ol> | |
| ----
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| ==Exercise 2. Explore the structure of human ''mdm2'' gene==
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| # Search [http://www.ncbi.nlm.nih.gov/sites/entrez?db=Nucleotide GenBank] using the accession AF527840. Read the GenBank file and find out from the feature table how many introns and exons this sequence has according to the "mRNA" and "CDS" features.
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| # Click on "mRNA" and notice that exon sequences are now highlighted
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| # Fill in '''Table 1''' in your lab report file for each EXON you could identify:
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| # Fill in '''Table 2''' in your lab report file for each INTRON you could identify:
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| # Click on "CDS" and notice that coding sequences are now highlighted
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| # Fill in '''Table 3''' in your lab report file for each coding sequence you could identify:
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| # Obtain the intron/exon gene structure and copy into your lab report file. To do this:
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| ##go back to the Genbank page for AF527840 (as instructed above)
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| ##click on the "Graphics" link
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| ##you will see a window with a diagram, showing the genomic sequence in green, the primary transcript in purple, and the coding sequence in red
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| ##copy and paste this diagram into your lab report (or create a desktop picture, crop as needed and paste into your lab report file)
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| # Answer the following questions, in your lab report file:
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| ## What is the total length of exons, introns, and coding sequences of this gene?
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| ## Are all exon sequences code for proteins? Which exons are non-coding in mdm2?
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| ## Align the first 5 bases of all introns. Which bases are conserved near intron start ("donor site")?
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| ## Align the last 5 bases of all introns. Which bases are conserved near intron end ("acceptor site")?
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| ## Using [http://weblogo.berkeley.edu/ WebLogo] and make a sequence logo for the acceptor site and another sequence logo for the donor site. To do so, copy & paste individual sequences at the acceptor site into [http://weblogo.threeplusone.com/create.cgi this text box] and click "Create Logo". Save the resulting image file and paste it into your lab report file. Repeat for the donor-site sequences.
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| <center> | | <center> |
| Table 1. ''mdm2'' Exons
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| {| class="wikitable"
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| |-
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| ! Exon # !! Start Position !! End Position !! Length
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| |-
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| | #1 || 1971 || 2271 || 301
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| |-
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| | #2 || ? || ? || ?
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| |}
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|
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| Table 2. ''mdm2'' Introns
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| {| class="wikitable" | | {| class="wikitable" |
| |- | | |- |
| ! Intro Number !! Start Position !! End Position !! Length !! First 5 bases !! Last 5 bases !! Phase* | | ! Lyme Disease (Borreliella) !! CoV Genome Tracker !! Coronavirus evolutuon |
| |- | | |- |
| | #1 || 2272 || 2987 || 616 || GTACT || TGTAG || ? | | | [[File:Lp54-gain-loss.png|300px|thumbnail| Gains & losses of host-defense genes among Lyme pathogen genomes (Qiu & Martin 2014)]] || |
| |-
| | [[File:Cov-screenshot-1.png|300px|thumbnail| [http://cov.genometracker.org/ Haplotype network] ]] |
| | #2 || ? || ? || ? || ? || ? || ? | | || |
| |}
| | [[File:Cov-screenshot-2.png|300px|thumbnail| Spike protein alignment ]] |
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| * Introns have phases. Phase 0 introns sit between 2 codons, phase 1 intron sit between the 1st codon position and the 2nd codon position, and phase 3 introns sit between the 2nd and 3rd codon position. How would you find out the phase of an intron? [Hint, use Table 3 CDS positions below].
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| Table 3. ''mdm2'' Coding Sequences (CDS)
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| {| class="wikitable"
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| |- | |
| ! CDS # !! Start Position !! End Position !! Length
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| |-
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| | #1 || 2992 || 3072 || 81
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| |-
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| | #2 || ? || ? || ?
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| |} | | |} |
| </center> | | </center> |
| ---- | | ---- |
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| ==Exercise 3. MDM2 homologs in other species== | | ==What is evolutionary genomics?== |
| This exercise will consist in comparing the predicted protein sequences of MDM2 in three species: human (H. sapiens), mouse (M. musculus) and zebrafish (D. rerio).
| | Genomes differ among individuals and species. Evolutionary genomics studies genome variability and genome changes using evolutionary principles. Typical applications in pathogen research include molecular epidemiology (e.g., wildlife origin of SARS-CoV-2 & tracking Covid-19 spread), molecular evolution (e.g., identify key genes and protein sequences contributing to virulence and immune escape), and vaccine design (e.g., influenza vaccine based on latest circulating strains). |
| You will need to download the human MDM2 sequence, find the mouse and fish homologs, and copy each sequence into a MS Word file using the following format:
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| Names (First, Last)
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| [Blank line]
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| >Human MDM2
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| --your amino acid sequence here--
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| >Mouse MDM2
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| --your amino acid sequence here--
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| >Zebrafish MDM2
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| --your amino acid sequence here--
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| First, you will download the human MDM2 protein sequence. You will then use this sequence as a query to identify the mouse and zebrafish sequences. Follow these steps:
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| # Go to this link: https://www.ncbi.nlm.nih.gov/genome/guide/human/
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| # in the box "search for human genes" type in "MDM2"
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| # you will see many hits- the top one corresponds to the human MDM2 locus- click on the link
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| # This is the human MDM2 locus page- there is much information here. Scroll down to "Genomic regions, transcripts, and products"
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| # You see here a map of the known transcripts produced for this locus
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| # now scroll down to "mRNA and Protein(s)"
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| # here, find the entry corresponding to the LONGEST isoform
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| # for each entry, you will see two identifiers : NM_.... and NP_....
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| # NM_... corresponds to the mRNA sequence for this isoform, and NP_.... to its predicted protein sequence
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| # click on the link for the protein sequence for the longest isoform, and find the 'FASTA' format
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| # copy the protein sequence by highlighting all residues from the initial 'M' to the last residue- nothing else
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| # paste the sequence into your word file as instructed above
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| now let's find the mouse homolog using the human sequence as a query:
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| #go to the main NCBI link: https://www.ncbi.nlm.nih.gov
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| #on the right side, under "Popular resources", click on "Blast"
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| #click on 'mouse' to blast the mouse genome- make sure you use the right tool (blastp) and the correct database (refuses protein)
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| #in the window, paste in your human MDM2 sequence- this is your query, and click on "BLAST"
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| #wait a few minutes... you will see your screen refreshing a few times
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| #you get a number of hits- scroll down to the best one (under "alignments") and click on "gene" on the right side, under "related information"
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| #you are now on the mouse MDM2 locus page: find the protein sequence to the LONGEST isoform and paste into your page as above
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| now let us get the Zebrafish homolog:
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| #go to the genome portal : http://zfin.org
| | Genome changes are studied at two distinct levels: (1) within-species/within-population variations (e.g., genomic changes during Covid-19 pandemic), and (2) between-species divergence (e.g., difference between SARS-CoV-1 and SARS-CoV-2). |
| #find the protein sequence and paste into your page as above. Make sure you use the right program and database for a protein BLAST!
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|
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| you will now make an alignment of all three sequences to see potential identities or similarities between them
| | The key for analyzing genome variations within species is "population-thinking", the idea that there is no one individual genome that is standard, normal, or "wildtype". |
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| #this involves two steps: first, the production of an output file by a program called "Clustal W"
| | The key for comparing genomes across species is "tree-thinking", the idea that evolution happens by diversification (like a branching tree), not by climbing a ladder. There is no such thing as "advanced" or "primitive" species. All living species have the exact same evolutionary distances/time of divergence since the origin of life. |
| # and second, the processing of this file to generate an alignment figure by a program called "Boxshade"
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| #go to this link: http://www.genome.jp/tools-bin/clustalw
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| #in the top window, paste in your three sequence by selecting from your first ">" sign to the end of your file (do not take your header, with your names)
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| # click on "multiple alignment"
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| #you will see an 'aln' output file: select the file including the header on top "CLUSTAL 2.1 multiple sequence alignment" down to the bottom of the file (no extra spaces)- copy in the buffer
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| #to do the alignment figure, go to : https://embnet.vital-it.ch/software/BOX_form.html
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| #there, enter your input sequence format as 'ALN' and in the window below that, paste your aln file
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| #click on 'run boxshade'
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| #under results: click on 'boxshade output 1"--- here's your alignment! (this should open with adobe acrobat and might take a bit of time)
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| #the output is a pdf file-- save it and import into your lab report word file (as page 2) by doing an "Insert--- picture from file" in MS Word- you will have two pages: page 1 with your MDM2 sequences, and page 2 with your alignment
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| | ==Case studies from Qiu Lab== |
| | * [http://borreliabase.org Comparative genomics of worldwide Lyme disease pathogens] |
| | * [http://cov.genometracker.org Covid-19 Genome Tracker] |
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|
| | ==Essential bioinformatics skills== |
| | * Linux command-line interface (e.g., BASH shell) |
| | * Familiarity with a programming language (e.g., Python or Perl) |
| | * Data visualization & statistical analysis (e.g., JavaScript; the R statistical computing environment) |
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| | ==Learning Goals== |
| | * Be able to compare evolutionary relationships using phylogenetic trees |
| | * Be able to use command-line tools for batch-processing of genome files |
| | * Be able to perform genome-wide association analysis on the R platform |
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| ==Additonal questions: answer 3 questions from the ones shown below and include in your lab report--== | | ==Schedule== |
| # Explain the following BLAST terms: “Expect” (e-value) [http://blast.ncbi.nlm.nih.gov/Blast.cgi?CMD=Web&PAGE_TYPE=BlastDocs&DOC_TYPE=FAQ#expect Read this FAQ], “Identities”, “Gap”, “Strand”.
| | * 9:00 - 9:25: Introduction; [http://rstudio.org Install R & R Studio]; Download fasta file & save as "ospC-pep.fasta" : [[File:OspC-pep.txt|thumbnail]] |
| # Which is a statistically more significant match by BLAST, a match with an e-value=1e-5 or a match with an e-value of 1?
| | * 9:30 - 10:00: Unix Tutorial ([http://korflab.ucdavis.edu/Unix_and_Perl/current.html#part1 Part I. Unix Basics]) |
| # List and describe individual elements of a typical human gene based on mdm2.
| | * 10:05 - 10:30: Unix Tutorial ([http://korflab.ucdavis.edu/Unix_and_Perl/current.html#part2 Part II Advanced Unix]) |
| #what are two determinants that can lead to the production of isoforms for a specific locus?
| | * 10:35 - 11:00: Tree-thinking Quizzes: Slides [[File:Big-data-phylogeny.pptx|thumbnail]] & Handouts [[File:Pretest.pdf|thumbnail]] |
| # What is the "GT-AG" rule? Explain how to read the sequence logos. Explain the significance of sequence conservation at exon-intron junctions.
| | * 11:05 - 12: Demo: [[Mini-Tutorals#ospC_amplicon_identification|identification of genomic mutations associated with antibiotic resistance]] |
| # Discuss biological significance of alternative splicing, using mdm2 gene as an example. | | ** Slides on background |
| #look at your alignment from part III: what are the black boxes- the grey boxes?
| | ** Demo (on wallace lab server) |
| #do you see many gaps/insertions?do you think there is a pattern?
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| ---- | | ==Exercises & Challenges== |
| | * Finish Tree Thinking Quizzes |
| | * Unix exercises: |
| | ** count the number of sequences using "grep -v" or "wc" |
| | ** display the first 5 lines of a file |
| | ** display the last 5 lines of a file |
| | ** change upper-cases to lower-cases |
| | ** change "|" to "_" |
| | ** replace strings |
