Applications Using standard Bioinformatics applications Introduction The overall
Applications Using standard Bioinformatics applications
Introduction
The overall plan for the regeneration of high quality annotation information as contained in the EMBL disk-file ISTN 501 fig. WHAT. eps
Scientific Background To Mer Operon ● Function ● Genetic Structure and Regulation ● Mobility Of The Mer Operon
The principal proteins and their functions fig. PRINCIPLE. eps
Downloading The Raw DNA Sequence
Initial BLAST Sequence Similarity Search
Maxim 18. 1 With BLAST scores, up is down and lower is better
Gene. Mark http: //opal. biology. gatech. edu/Gene. Mark/
The web-based interface to Gene. Mark as running at EBI fig. EBIGENEBANK. eps
Using BLAST to identify specific sequences
Dealing with false negatives and missing proteins
Over predicted genes and false positives
Structural Prediction With SWISS-MODEL http: //www. expasy. org/swissmod/
Maxim 18. 2 The major limitation of ``homology modelling'' is that homology to a known structure is needed
Alternatives to homology modelling
Modelling with SWISS-MODEL
The SWISS-MODEL predicted structure of ORF 2/Mer. P fig. ORF 2 MERP. eps
The SWISS-MODEL predicted structure of ORF 2/Mer. P, second version fig. ORF 2 MERP 2. eps
The SWISS-MODEL predicted structure of ORF 3/Mer. A (A) fig. ORF 3 MERAA. eps
The SWISS-MODEL predicted structure of ORF 3/Mer. AB fig. ORF 3 MERAB. eps
The SWISS-MODEL predicted structure of ORF 6/TNR 5 fig. ORF 6 TNR 5. eps
Deep. View as a Structural Alignment Tool
The ORF 2 and ORF 3_A structures loaded into Deep. View prior to structural alignment fig. DEEPVIEW. eps
Deep. View's Iterative Magic Fit dialogue box fig. DEEPVIEWDIALOG. eps
Structural Alignment created using the Deep. View's Iterative Magic Fit facility fig. DEEPVIEWEXAMPLE. eps
Selecting the current ``layer'' in Deep. View fig. DEEPLAYER. eps
Possible Explanation Behind Mer. A/HMA Duplication Event fig. POSSIBLE. eps
The structural alignment of ORF 3_B and the ``official'' Mercury Reductase X-ray structure fig. CYSTEINES. eps
Maxim 18. 3 Homology modelling can only model protein sequences similar to those which are already known
PROSITE and Sequence Motifs
Maxim 18. 4 Searching large datasets with non-specific, short sequence fragments results in many false positives
Using PROSITE patterns and matrices http: //www. expasy. org/prosite/ http: //www. ebi. ac. uk/interpro/ http: //www. geneontology. org ● http: //www. kegg. org
Phylogenetics
A look at the HMA domain of Mer. A and Mer. P ----------------SWISS-PROT IDs of Mer. P Proteins SWISS-PROT IDs of Mer. A Proteins ----------------MERP_ACICA MERA_ACICA MERP_ALCSP MERA_ALCSP MERP_PSEAE MERA_BACSR MERP_PSEFL MERA_ENTAG MERP_SALTI MERA_PSEAE MERP_SERMA MERA_PSEFL MERP_SHEPU MERA_SERMA MERP_SHIFL MERA_SHEPU MERA_SHIFL MERA_STAEP MERA_STRLI MERA_THIFE -------------------------------
The multiple sequence alignment of the example proteins fig. LISTMERAMERP. eps
The EBI's tree graphical display fig. TREE. eps
Maxim 18. 5 Whenever you make a statement, call for more research (money)!
Maxim 18. 6 Database annotation is hard to do well, so be prepared to update it on a regular basis
Maxim 18. 7 Automation can be very helpful when creating annotation, but to achieve the highest quality, humans are needed to make some value judgments
Maxim 18. 8 Conclusions are based on the available data which, in this case, is the database annotation (which may or may not be current)
Where To From Here?
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