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tutorials:eccb_t2_badasp [2012/09/06 21:31] romainstuder |
tutorials:eccb_t2_badasp [2012/09/08 17:15] (current) romainstuder |
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| + | ==== BADASP ==== | ||
| - | cd /home/bsm4/rstuder/Dropbox/ECCB2012/Tutorial/badasp # Folder of installation | + | BADASP can produce different measures: |
| - | # Run badasp | + | * bad: similar the **Type II** of functional divergence. The threshold to choose depend if we want to be stringeant (i.e. BAD > 4) or more relaxed (BAD > 2). |
| - | <code>python badasp.py seqin=badasp_eg.fas i=1</code> | + | * badn = BADN variant of BAD: similar the **Type I** of functional divergence, between __two__ groups. |
| + | * badx = BADX variant of BAD: similar the **Type II** of functional divergence, between __many__ groups. | ||
| + | * ssc = Livingstone & Barton method (SSC) => doesn't use ancestral reconstruction. Was developed prior to BAD. | ||
| + | * pdad = Property Difference After Duplication (PDAD) method | ||
| + | * eta = Basic Evolutionary Trace Analysis (ETA) => Strictly conserved residues = 1, else = 0. | ||
| + | * etaq = Quantitative variant of ETA | ||
| + | |||
| + | All these methods are described in details in the manual, **chapter 3.1: Functional Specificity Prediction**. | ||
| + | |||
| + | === Installation === | ||
| + | |||
| + | Download the badasp archive and unzip it: | ||
| + | [[http://www.southampton.ac.uk/~re1u06/software/badasp/index.html]] | ||
| + | <code> | ||
| + | wget http://www.southampton.ac.uk/~re1u06/software/downloads/badasp.zip | ||
| + | unzip badasp.zip | ||
| + | </code> | ||
| + | |||
| + | === Analysis of the V-type proton ATPase 116 kDa subunit a gene family === | ||
| + | |||
| + | We want to identify the residues making differences between the **isoforms 1** and **isoforms 4** of the V-type proton ATPase 116 kDa subunit a. | ||
| + | |||
| + | First, visualise briefly the multiple alignment in Jalview. (File "badasp_eg.fas" in the badasp folder. | ||
| + | |||
| + | |||
| + | Execute **badasp** by importing the multiple alignment in FASTA format ("badasp_eg.fas") and activating the interactive mode (i=1): | ||
| + | |||
| + | <code> | ||
| + | cd ./badasp # Folder of installation | ||
| + | python badasp.py seqin=badasp_eg.fas i=1</code> | ||
| + | |||
| + | Badasp will ask for the associated tree, in newick format ("badasp_eg.nsf"): | ||
| <code> | <code> | ||
| - | # Ask for a tree | ||
| Looking for treefile badasp_eg.nsf. | Looking for treefile badasp_eg.nsf. | ||
| Tree: ['seqin=badasp_eg.fas', 'i=1', 'nsfin=badasp_eg.nsf'] <ENTER> to continue | Tree: ['seqin=badasp_eg.fas', 'i=1', 'nsfin=badasp_eg.nsf'] <ENTER> to continue | ||
| Line 12: | Line 43: | ||
| => Press enter | => Press enter | ||
| + | </code> | ||
| Display Tree, with two groups of sequences: | Display Tree, with two groups of sequences: | ||
| V-type proton ATPase 116 kDa subunit a | V-type proton ATPase 116 kDa subunit a | ||
| - | - VPP1 = VPP Isoform 1 (8 genes) | + | * VPP1 = VPP Isoform 1 (8 genes) |
| - | - NVL = VPP Isoform 4 (3 genes) | + | * NVL = VPP Isoform 4 (3 genes) |
| + | <code> | ||
| Rooted Tree (1000 bootstraps). Branch Lengths given. 21 nodes. <ENTER> to continue. | Rooted Tree (1000 bootstraps). Branch Lengths given. 21 nodes. <ENTER> to continue. | ||
| => Press enter | => Press enter | ||
| - | |||
| Tree is rooted at node 21 => perfect | Tree is rooted at node 21 => perfect | ||
| Line 31: | Line 61: | ||
| Choice [default=Q]: q | Choice [default=Q]: q | ||
| Quit Tree Menu? (y/n) [default=Y]: y | Quit Tree Menu? (y/n) [default=Y]: y | ||
| + | </code> | ||
| + | The tree is now loaded and we need to define the two groups to analyse: | ||
| + | |||
| + | <code> | ||
| #*# Grouping Summary #*# | #*# Grouping Summary #*# | ||
| Line 37: | Line 71: | ||
| => Press enter | => Press enter | ||
| - | We need to split the tree on the node 21, so we need to define two groups from the children nodes 20 and 19. | + | # We need to split the tree on the node 21, |
| + | # so we need to define two groups from the children nodes 20 (= VPP1 subfamily) and 19 (= VPP4 subfamily) . | ||
| => Press M, then enter. # Manual grouping | => Press M, then enter. # Manual grouping | ||
| (Tree displayed) | (Tree displayed) | ||
| - | Choice? [default=Q]: c # We collapse node | + | Choice? [default=Q]: c # We collapse nodes |
| Node [default=0]: 20 | Node [default=0]: 20 | ||
| => Type VPP1, then Press enter | => Type VPP1, then Press enter | ||
| - | Choice? [default=Q]: c # We collapse node | + | Choice? [default=Q]: c # We collapse nodes |
| Node [default=0]: 19 | Node [default=0]: 19 | ||
| => Type VPP4, then Press enter | => Type VPP4, then Press enter | ||
| Line 60: | Line 95: | ||
| Use badasp_eg for output filenames? (y/n) [default=Y]: enter | Use badasp_eg for output filenames? (y/n) [default=Y]: enter | ||
| Use these parameters? (y/n) [default=Y]: enter | Use these parameters? (y/n) [default=Y]: enter | ||
| + | </code> | ||
| + | Badasp will now perform some computations. It will reconstruct the ancestral sequences at each node of the tree, using GASP (ref: http:dx.doi.org/10.1186/1471-2105-5-123 ) | ||
| + | <code> | ||
| Making Ancestral Sequences - Variable PAM Weighting | Making Ancestral Sequences - Variable PAM Weighting | ||
| Reading PAM1 matrix from jones.pam | Reading PAM1 matrix from jones.pam | ||
| Line 73: | Line 111: | ||
| ...Done! <ENTER> to continue. | ...Done! <ENTER> to continue. | ||
| ...win(0) <ENTER> to continue. # (many times !) | ...win(0) <ENTER> to continue. # (many times !) | ||
| + | </code> | ||
| + | |||
| + | Now, Badasp will ask you the kind of output you want. | ||
| + | Let's say yes to everything. | ||
| + | |||
| + | <code> | ||
| Output additional, filtered results? (y/n) [default=N]: y | Output additional, filtered results? (y/n) [default=N]: y | ||
| Name for partial results file? [default=badasp_eg.partial.badasp]: enter | Name for partial results file? [default=badasp_eg.partial.badasp]: enter | ||
| Line 121: | Line 165: | ||
| </code> | </code> | ||
| - | <code? | + | |
| - | ### Analysis | + | === Analysis === |
| Open the file in your spreadsheet (or cut&space). | Open the file in your spreadsheet (or cut&space). | ||
| Line 130: | Line 174: | ||
| Color the "BAD", "BADN" and "BAD" columns with a conditional formating, with value > 3. | Color the "BAD", "BADN" and "BAD" columns with a conditional formating, with value > 3. | ||
| - | |||
| - | </code> | ||
| == In Jalview: == | == In Jalview: == | ||
| Line 141: | Line 183: | ||
| Put a vertical line a the root of the tree to split the tree in two. | Put a vertical line a the root of the tree to split the tree in two. | ||
| + | Some sites are interesting, i.e.: | ||
| + | * Positon 3 BAD | ||
| + | * Position 762 BAD | ||
| + | * Position 223 BADX | ||
| - | Positon 3 BAD | + | There are only three genes in the group de VPP4, that explains why the BADX score are very close to the BAD score. |
| - | Position 762 BAD | + | |
| - | Position 223 BADX | + | |
