1. Dr Tan Tin Wee Director Bioinformatics Centre
Basic Overview of Bioinformatics Tools and Biocomputing Applications III
Dr Tan Tin Wee
Director
Bioinformatics Centre

2. More BioComputational Tools
Phylogenetics Analysis
Multiple Sequence Alignment
Profile Searching
Sensitivity and Specificity and Probabilities in the Prediction of Functions

3. Phylogenetic Analysis
Assumption: evolutionary descent
Divergence
Phylogenetic tree
Rooted and unrooted trees
Species X Y A B

4. Rooted and Unrooted Trees
Rooted: ancestral state of the evolved organism or gene is known.
Branches at bifurcation points until terminal branches, or tips/ leaves.
Unrooted trees represent branching order, but does not indicate the root of the last common ancestor

5. Phylogenetic inference for genes
Infancy, inexact science
computational tools based on general mathematical and statistical principles
Phylogenetic reconstructions may conflict with common sense.
Incorrect sequence alignments, inadequate models
All sites within sequences evolve at different rates
unequal rate effects

6. Some algorithms Maximum parsimony maximum likelihood distance methods
UPGMA
paralinear (logdet) distances
Software Packages: PAUP phylogenetic analysis using parsimony PHYLIP phylogenetic inference package MacClade, GAMBIT, MEGA/METREE

7. Limitations Inspection of sequence alignments
Removal of deviant sequences from the phylogenetic inference
Different genes analysed produce different trees
"Bootstrapping" for estimating statistical significance may still have errors in interpretation

8. Uses Molecular TaxonomyB
Uses C
Molecular Taxonomy
16S and 23S rRNA analysis for bacterial classification
18S rRNA analysis of nematodes, drosophila
epidemiological analysis of strain variation eg. In infections pathogens D

9. Multiple Sequence Analysis
Gather a set of sequences of putative similarity or homology
Pairwise comparison for each set of multiple sequences
Build a "tree" of similarity
realignment of all sequences based on "ancestral" sequence padding with gaps etc
Used for generating "profiles"

10. Use Detection of conserved and variable regions Infer gene functions
Variable segments - infer dispensable to function or antigenic variants
Motifs can be used to analyse unknown sequence and infer possible function or relatedness
Motifs as basis for annotation of genome project sequences

11. Software
CLUSTALW
Profile software based on Hidden Markov Models (HMM) statistical models, eg HMMer, HMMPro, META-MEME, PROBE, BLOCKS

12. Example C. elegans genome project
several large gene families of sequence homology - function unknown.
Now classified as putative G-protein coupled receptors (GPCRs).
Have to detect significant similarity between putative Worm GPCRs and experimentally known GPCRs in other species

13. Process
Select a typical unknown sequence BLAST Search against nr database
Inspect hits and E-values
Top scoring hits - mitochondrial L11 ribosomal protein E=0.002 (not low enough to be trusted for annotation)
The rest of top scorers are all nematode-specific unknown sequences
Compare with PSI-BLAST iterative searching at NCBI
Similarity with mammalian GPCRs or the high scoring mt rL11 protein ?

14. Further analysis Gather all nematode specific sequences
WormPep database of non-redundant seqs
Discard seqs of abnormally long or short
Multiple sequence alignment using CLUSTALW
General Profile of multiple alignment using HMMer
Use profile to search database again

15. Results Similarity at significance level detected with Mammalian GPCRs
Find that L11 protein has very significant high score E=5x10
Pitfalls of PSI-Blast - significance of match to the training set during iteration.
Finally, L11 protein may be wrongly annotated and not based on experimental results
-49

16. A.Sensitivity and Specificity of a Fairly Good Test
Total real +ve = 73 Total real - ve = 27
Specificity = (25)/(2+25)=.93 picked up 25 of the 27 negatives, very specific Low false positives
Sensitivity = 70/(70+3)=.96 able to pickup 70 of the total 73 that are known positive- quite sensitive- Low false negatives
Gold standards
Known gold standard + ve ve
+ ve
- ve 70 2 3 25
Exptal test result
N=100

17. B.Increase Sensitivity but Lower Specificity of a Test
Total real +ve = 73 Total real - ve = 27
Specificity = (14)/(13+14)=.52 picked up 14 of the 27 negatives, not very specific high false positives
Sensitivity = 72/(72+1)=.99 able to pickup 72 of the total 73 that are known positive- super sensitive Low false negatives
Known gold standard + ve ve
+ ve
- ve 72 13 1 14
Exptal test result
N=100

18. C.Increase Specificity of a Test but Sensitivity may drop
Total real +ve = 73 Total real - ve = 27
Specificity = (27)/(0+27)=1.0 picked up 27 of the 27 negatives,completely specific increase threshold to zero false positives, true positives will drop
Sensitivity = 50/(50+23)=.68 able to pickup 50 of the total 73 that are known positive- not quite sensitive- Low false negatives
Known gold standard + ve ve
+ ve
- ve 50 23 27
Exptal test result
N=100

19. Trade off involved
If threshold of test set high, so that all the noise disappears, you may also miss out on some true positives, get a lot of false negatives and thus not so sensitive - case C
If threshold of test set low, so that you get as much of the positives as you can get, ie high sensitivity, your non-specific false positive hits start appearing - Case B

20. Computational Predictions of Gene Function
Sensitivity and specificity has similar tradeoffs.
Cutoff threshold values have to be empirically determined or arbitrarily chosen depending on situation