Genomeexploration

in A-T G-C spacean introduction to

‘DNA walking’

Jonathan Blakes

Submitted for the degreeMSc Biotechnology and

ComputationJulie NewdollJulie NewdollDawn of the Double Helix Dawn of the Double Helix Oil/Mixed, 2002Oil/Mixed, 2002

Exponential growth of DNA sequencesGenBank release notes December 2007

“from 1982 to the present, the number of bases in GenBank has doubled approximately every 18 months.”

83,874,179,730 base pairs in 80,388,382 entries

EnsEMBLEMBL-EBI

Sanger InstituteWellcome Trust

UCSCUniversity of California

at Santa Cruz

Genome Browsers

Can we understand this Can we understand this informationinformation

a priori,a priori,summarise summarise andand

preserve fine structurepreserve fine structure?

GraphDNA – DNA Walker

Mapping

Mapping4 rotations for each of the 3 previous mappings

2 reflections of each of those

24 possible combinations of cardinal vectors

These are the 3 most parsimonious mappings of those 24

A-T G-C

A-G C-T

A-C G-T

A-T G-C

A-T G-C is consistently smallest

A-T G-C walks• contain more information in less space• are simply easier to print

Genome Exploration

Human chromosome 1250,000,000 bases

S. cerevisiae chromosome 1

EnsEMBL annotation

Duplications

small (~4 base) sequencesoccur several times ineach larger sequence

for each small sequence calculatelineline between occurrences

if the angle and length of 2 or more lines are consistentthen draw lineslines to reveal possible duplications

Can detect duplications by eye

or algorithmically:

Comparison withpublished data

This is a 7 fold contiguous duplication

in the male Y chromosome.

Members of the TSPY (Testis-

specific Y-encoded proteins) family

identified by Skaletsky et al Nature

423 (2003) using a combination of a

whole chromosome dotplot with a 2-

kb window and a custom Perl script

running BLAST alignments of all 5-kb

sequence segments, in 2-kb steps, of

the entire MSY (Male Specific Y).

exons introns

Phylogenetics• Phylogenetics is the reconstruction of evolutionary relatedness from

primary sequence information: DNA or protein• Traditional phylogenetic methods such as ClustalW and TCoffee all

start from a multiple sequence alignment

• Hard to find optimal alignment for many long sequences

• Want to use simple measures such as Manhattan or Euclidean distance derived from DNA walks to produce phylogenies without alignment

• Are these comparable to alignment methods?

Phylogeny algorithmsPublished Distance Matrix from Gilfillan GD, et. al. Microbiology 144 (1998) 829-838of 7 aligned 1798-nucleotide long small rRNA of Candida and Saccharomyces species

neighbour joining UPGMA

UPGMA method

Tree construction

Distance Matrix

Output

Newick format string representation of a tree:

(Bovine:0.69395, (Gibbon:0.36079, (Orang:0.33636, (Gorilla:0.17147, (Chimp:0.19268, Human:0.11927) :0.08386):0.06124):0.15057):0.54939, Mouse:1.21460);

Phylogenies with each mapping

Can summing 3 mappings eliminate bias?

No.

Possible improvements:• A more complex distance measure, perhaps a composite of small

sequence distances• Larger sequences – human / chimpanzee chromosome – where

mapping bias may be informative rather than destructive

Conclusion

DNA walks can• summarise information about nucleotide content in DNA sequences• visualise tandem repeats• uncover more distant relationships such as duplications and retroviral

genomes without expert knowledge or complex algorithms• be overlaid with annotations from Ensembl walks to function as an

alternative to linear genome browsers• be used to construct phylogenetic relationships but are inaccurate• A-T G-C is most useful mapping for viewing 2D walks

Future

3D walks• mappings where one nucleotide opposes another result in information

loss, which can be helpful, but we can’t know what we are missing• use a tetrahedral mapping:• each step starts from the centre and proceeds to a corner• should produce 3D structure like proteins but much bigger• can recover 2D walk by viewing orientation

Acknowledgments

Biosciences

Dr. Gary Robinson (supervisor Biosciences)

Dr. Jürgen Schmidt (course convenor)

Dr. Anthony Baines (Bioinformatics lecturer)

Computing

Dr. Colin Johnson (supervisor Computing)