1 AGENDA 1. Summary 2. Genome comparison 3. Rearrangement - - PowerPoint PPT Presentation

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1 AGENDA 1. Summary 2. Genome comparison 3. Rearrangement - - PowerPoint PPT Presentation

Genome Rearrangements Joao Meidanis Campinas, Brazil 2015 1 AGENDA 1. Summary 2. Genome comparison 3. Rearrangement events 4. Example: mouse vs. human (X-chromosome) 5. Rearrangement distance 6. Known results 7. Current research


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Genome Rearrangements

Joao Meidanis Campinas, Brazil 2015

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AGENDA

1. Summary 2. Genome comparison 3. Rearrangement events 4. Example: mouse vs. human (X-chromosome) 5. Rearrangement distance 6. Known results 7. Current research lines

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  • One of the big challenges of contemporary Biology is to measure evolution
  • Besides point mutations, evolution is known to occur by means of movements
  • f large chunks of DNA (genome rearrangements)
  • The advent of entire genomes brings a whole new facet to this issue
  • As a first estimate of the amount of evolution between two species, one can use

the formula number of events unit of time

  • Our research focus on efficient ways of computing the number of

rearrangement events between two or more genomes

SUMMARY

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AGENDA

1. Summary 2. Genome comparison 3. Rearrangement events 4. Example: mouse vs. human (X-chromosome) 5. Rearrangement distance 6. Known results 7. Current research lines

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GENOME COMPARISON Point mutations

...TATCGATAGACCACTG... ...TATC--TAGACGACTA...

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GENOME COMPARISON Genome rearragements

A B C D E F G H A B C D E F G H Movement of large segments within the genome. Above, segment E – F – G flips over

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AGENDA

1. Summary 2. Genome comparison 3. Rearrangement events 4. Example: mouse vs. human (X-chromosome) 5. Rearrangement distance 6. Known results 7. Current research lines

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REARRANGEMENT EVENTS

  • Insertion / Deletion
  • Reversal
  • Transposition
  • Fission / Fusion
  • Block Interchange
  • Others: duplication, genome doubling
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INSERTION / DELETION Gene gain / loss between genomes

A B C D E F G H I J A B C D E G H I J

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REVERSAL A segment is reversed between genomes

A B C D E F G H I J A B C D E F G H I J

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TRANSPOSITION A segment moves to a new position (or: exchange of two adjacent segments)

A B C D E F G H I J A B E F G C D H I J

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FISSION / FUSION Genome breaks in two / Two genomes join

A B C D E F G H I J A B C D E F G H I J

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BLOCK INTERCHANGE Exchange of two nonadjacent segments

A B C D E F G H I J A B G H E F C D I J

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AGENDA

1. Summary 2. Genome comparison 3. Rearrangement events 4. Example: mouse vs. human (X-chromosome) 5. Rearrangement distance 6. Known results 7. Current research lines

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Figure 2. X-chromosome: from local similarities, to synteny blocks, to breakpoint graph, to rearrangement scenario EXAMPLE: HUMAN AND MOUSE X-CHROMOSOME Pavel Pevzner et al. Genome Res. 2003; 13: 37-45

Thanks to:

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AGENDA

1. Summary 2. Genome comparison 3. Rearrangement events 4. Example: mouse vs. human (X-chromosome) 5. Rearrangement distance 6. Known results 7. Current research lines

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REARRANGEMENT DISTANCE Maximum parsimony approach

Given two genomes, and a set of events, the rearrangement distance between the genomes is the length of the shortest series

  • f events that transforms one genome into

the other. In the previous example: Genome 1: mouse X-chromosome Genome 2: human X-chromosome Set of events: reversals only Distance: 7 events

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AGENDA

1. Summary 2. Genome comparison 3. Rearrangement events 4. Example: mouse vs. human (X-chromosome) 5. Rearrangement distance 6. Known results 7. Current research lines

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KNOWN RESULTS

  • Insertion / Deletion distance : efficient algorithm known
  • Reversal distance : efficient algorithm known
  • Transposition distance : NP-hard; approximative algorithms
  • Fission / Fusion distance : efficient algorithm known
  • Block Interchange distance : efficient algorithm known
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AGENDA

1. Summary 2. Genome comparison 3. Rearrangement events 4. Example: mouse vs. human (X-chromosome) 5. Rearrangement distance 6. Known results 7. Current research lines

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CURRENT RESEARCH LINES

  • Comparions of three or more genomes
  • More realistic models with several operations, or basic operations that can

form several others, e.g., DCJ or SCJ