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Simple Rearrangements

Simple Rearrangements. 1. 2. 3. 9. 10. 8. 4. 7. 5. 6. Reversals. Blocks represent conserved genes. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10. Reversals. 1. 2. 3. 9. 10. 8. 4. 7. 5. 6. 1, 2, 3, -8, -7, -6, -5, -4, 9, 10. Blocks represent conserved genes.

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Simple Rearrangements

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  1. Simple Rearrangements

  2. 1 2 3 9 10 8 4 7 5 6 Reversals • Blocks represent conserved genes. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10

  3. Reversals 1 2 3 9 10 8 4 7 5 6 1, 2, 3, -8, -7, -6, -5, -4, 9, 10 • Blocks represent conserved genes. • In the course of evolution or in a clinical context, blocks 1,…,10 could be misread as 1, 2, 3, -8, -7, -6, -5, -4, 9, 10.

  4. Types of Rearrangements Reversal 1 2 3 4 5 6 1 2 -5 -4 -3 6 Translocation 1 2 3 45 6 1 2 6 4 5 3 Fusion 1 2 3 4 5 6 1 2 3 4 5 6 Fission

  5. Sorting by reversals: 5 steps

  6. Sorting by reversals: 4 steps

  7. Sorting by reversals: 4 steps What is the reversal distance for this permutation? Can it be sorted in 3 steps?

  8. From Signed to Unsigned Permutation (Continued) • Construct the breakpoint graph as usual • Notice the alternating cycles in the graph between every other vertex pair • Since these cycles came from the same signed vertex, we will not be performing any reversal on both pairs at the same time; therefore, these cycles can be removed from the graph 0 5 610 915 1612 117 814 1317 183 41 219 2022 21 23

  9. Reversal Distance with Hurdles • Hurdles are obstacles in the genome rearrangement problem • They cause a higher number of required reversals for a permutation to transform into the identity permutation • Let h(π) be the number of hurdles in permutation π • Taking into account of hurdles, the following formula gives a tighter bound on reversal distance: d(π) ≥ n+1 – c(π) + h(π)

  10. Median Problem Goal: find Mso that DAM+DBM+DCMis minimized NP hard for most metric distances

  11. Genome Enumeration for Multichromosome Genomes Genome Enumeration For genomes on gene {1,2,3} 2 2 2 -2 -2 -2

  12. Rearrangement Phylogeny

  13. Compute A Given Tree (Start)

  14. Compute A Given Tree (First Median)

  15. Compute A Given Tree (Second Median)

  16. Compute A Given Tree (Third Median)

  17. Compute A Given Tree (After 1st Iteration)

  18. Binary Encoding

  19. MLBE Sequences

  20. Experimental Results (Equal Content) 80% inversion, 20% transposition

  21. An Example—New Genomes 1 2 3 4 5 6 7 8 9 10 1 -4 5 2 8 10 9 -7 -6 3 … 1 3 5 7 9 1 5 9 -7 3 …

  22. Jackknifing Rate

  23. Support Value Threshold - FP Up to 90% FP can be identified with 85% as the threshold

  24. Jackknife Properties • Jackknifing is necessary and useful for gene order phylogeny, and a large number of errors can be identified • 40% jackknifing rate is reasonable • 85% is a conservative threshold, 75% can also be used • Low support branches should be examined in detail

  25. Protein

  26. In-silico Biochemistry • Online servers exist to determine many properties of your protein sequences • Molecular weight • Extinction coefficients • Half-life • It is also possible to simulate protease digestion • All these analysis programs are available on • www.expasy.ch

  27. Analyzing Local Properties • Many local properties are important for the function of your protein • Hydrophobic regions are potential transmembrane domains • Coiled-coiled regions are potential protein-interaction domains • Hydrophilic stretches are potential loops • You can discover these regions • Using sliding-widow techniques (easy) • Using prediction methods such as hidden Markov Models (more sophisticated)

  28. Sliding-window Techniques • Ideal for identifying strong signals • Very simple methods • Few artifacts • Not very sensitive • Use ProtScale on www.expasy.org • Make the window the same size as the feature you’re looking for

  29. www.expasy.org/cgi-bin/protscale.pl

  30. www.expasy.org/cgi-bin/protscale.pl

  31. www.expasy.org/cgi-bin/protscale.pl

  32. www.expasy.org/cgi-bin/protscale.pl Hphob. / Eisenberg

  33. Transmembrane Domains • Discovering a transmembrane domain tells you a lot about your protein • Many important receptors have 7 transmembrane domains • Transmembrane segments can be found using ProtScale • The most accurate predictions come from using TMHMM

  34. Using TMHMM • TMHMM is the best method for predicting transmembrane domains • TMHMM uses an HMM • Its principle is very different from that of ProtScale • TMHMM output is a prediction

  35. TMHMM vs. ProtScale

  36. >sp|P78588|FREL_CANAX Probable ferric reductase transmembrane component OS=Candida albicans GN=CFL1 PE=3 SV=1 MTESKFHAKYDKIQAEFKTNGTEYAKMTTKSSSGSKTSTSASKSSKSTGSSNASKSSTNA HGSNSSTSSTSSSSSKSGKGNSGTSTTETITTPLLIDYKKFTPYKDAYQMSNNNFNLSIN YGSGLLGYWAGILAIAIFANMIKKMFPSLTNNLSGSISNLFRKHLFLPATFRKKKAQEFS IGVYGFFDGLIPTRLETIIVVIFVVLTGLFSALHIHHVKDNPQYATKNAELGHLIADRTG ILGTFLIPLLILFGGRNNFLQWLTGWDFATFIMYHRWISRVDVLLIIVHAITFSVSDKAT GKYKNRMKRDFMIWGTVSTICGGFILFQAMLFFRRKCYEVFFLIHIVLVVFFVVGGYYHL ESQGYGDFMWAAIAVWAFDRVVRLGRIFFFGARKATVSIKGDDTLKIEVPKPKYWKSVAG GHAFIHFLKPTLFLQSHPFTFTTTESNDKIVLYAKIKNGITSNIAKYLSPLPGNTATIRV LVEGPYGEPSSAGRNCKNVVFVAGGNGIPGIYSECVDLAKKSKNQSIKLIWIIRHWKSLS WFTEELEYLKKTNVQSTIYVTQPQDCSGLECFEHDVSFEKKSDEKDSVESSQYSLISNIK QGLSHVEFIEGRPDISTQVEQEVKQADGAIGFVTCGHPAMVDELRFAVTQNLNVSKHRVE YHEQLQTWA Search with Accession number P78588 http://www.uniprot.org/uniprot/

  37. www.cbs.dtu.dk/services/TMHMM-2.0

  38. www.cbs.dtu.dk/services/TMHMM-2.0

  39. Predicting Post-translational Modifications • Post-translational modifications often occur on similar motifs in different proteins • PROSITE is a database containing a list of known motifs, each associated with a function or a post-translational modification • You can search PROSITE by looking for each motif it contains in your protein (the server does that for you!) • PROSITE entries come with an extensive documentation on each function of the motif

  40. Searching for PROSITE Patterns • Search your protein against PROSITE on ExPAsy • www.expasy.org/tools/scanprosite • PROSITE motifs are written as patterns • Short patterns are not very informative by themselves • They only indicate a possibility • Combine them with other information to draw a conclusion • Remember: Not everything is in PROSITE !

  41. www.expasy.org/tools/scanprosite P12259

  42. www.expasy.org/tools/scanprosite

  43. Interpreting PROSITE Patterns • Check the pattern function: Is it compatible with the protein? • Sometimes patterns suggest nonexistent protein features • For instance : If you find a myristoylation pattern in a prokaryote, ignore it; prokaryotic proteins have no myristoylation ! • Short patterns are more informative if they are conserved across homologous sequences • In that case, you can build a multiple-sequence alignment • This slide shows an example

  44. Patterns and Domains • Patterns are usually the most striking feature of the more general motifs (called domains) • Domains are less conserved than patterns but usually longer • In proteins, domain analysis is gradually replacing pattern analysis

  45. Protein Domains • Proteins are usually made of domains • A domain is an autonomous folding unit • Domains are more than 50 amino acids long • It’s common to find these together: • A regulatory domain • A binding domain • A catalytic domain

  46. Discovering Domains • Researchers discover domains by • Comparing proteins that have similar functions • Aligning those proteins • Identifying conserved segments • A domain is a multiple-sequence alignment formulated as a profile • For each column, a domain indicates which amino acid is more likely to occur

  47. Domain Collections • Scientists have been discovering and characterizing protein domains for more than 20 years • 8 collections of domains have been established • Manual collections are very precise but small • Automatic collections are very extensive but less informative • These collections • Overlap • Have been assembled by different scientists • Have different strengths and weaknesses • We recommend using them all!

  48. The Magnificent 8 • Pfam is the most extensive manual collection • Pfam is often used as a reference

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