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Before
uploading your abstract, please review these instructions for authors.
When you are done, submit using the online
abstract submission form.
1. The abstract must be prepared as a Microsoft Word document
and submitted electronically
2. Faxed abstracts
will NOT be accepted.
3. Please use Arial, font size 11 and full justification
where possible. Type abstract single spaced. Standard abbreviations may be
used.
4. The entire abstract should not be more than 300 words. It
must include the title, author(s), institution, and email address of the
corresponding author.
5. Type as follows: Title in CAPITAL LETTERS; Presenting
author’s name (underlined), other authors' names, followed by the name of the institution, city,
country and email address of the corresponding author.
6. State the background to the study, the methods used,
summarize the results obtained and state conclusions reached (see sample abstract below).
7. If accepted the abstracts will be printed as received,
editing is the responsibility of the author. You will be notified via email
regarding abstract acceptance by May 01, 2006.
Note: Both poster and oral presentations will be accepted. Because of
restricted program time, only a limited number of oral presentations will be
possible.
8. If you are satisfied that you have checked all the above
points, then you should submit your abstract using the online submission page.
If for any reason you cannot do this, please email either:
Dr. Branwen Hennig: Branwen.Hennig@lshtm.ac.uk
OR
Dr. Melanie Newport: m.j.newport@bsms.ac.uk
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Sample abstract
ENHANCING
THE UTILITY OF SINGLE NUCLEOTIDE POLYMORPHISM LOCATION TYPES IN THE HUMAN
GENOME
R.D. Isokpehi, D.F. Sarpong and F.A. Okojie. Jackson
State University,
Jackson Mississippi
39217, USA.
raphael.isokpehi@jsums.edu
Background
Single Nucleotide Polymorphisms (SNPs) in the human genome can contribute
to population level genetic variations that are linked to disease
susceptibility. High-throughput SNP genotyping techniques are yielding
high-density maps of SNPs that can be used to study complex genetic diseases.
Objectives
The primary objective of this research is to classify and/or prioritize
genes in the Human Genome for epidemiological and biological studies using
binary integration techniques.
Methods
EnsMart (http://www.ensembl.org/)
was used to retrieve Ensembl Gene ID of human gene entries that have the
following SNP attributes(entries): Coding (18,143), Intronic (18,423), 5’ UTR
(6,813), 3’ UTR (13,340), 5’ Upstream (21,932), 3’ Downstream (21,964),
Synonymous SNPs (13,842), Non-Synonymous SNPs (15,155), Stop SNPs (981), and
SNPs with a ka_ks ratio >0.5 (1,828). A binary integration computational
pipeline that encodes the evidence for an attribute as I (present) or 0
(absence) was then used to generate 10-digit binary profiles for each of the
over 22,000 gene entries in the ENSEMBL Human database.
Results
Of the possible 1024 gene clusters, 112 had at least one gene. The gene
cluster abundant for all the samples attributes contained 66 genes including
those not described in the On-line Mendelian Inheritance in Man (OMIM)
database.
Conclusions
The binary integration of genomic data is one of the cost- and
time-effective methods for reducing massive genomic data into gene clusters of
biological or epidemiological relevance. These gene clusters could be useful in
genome annotation, haplotype mapping and suggest future areas of genetic
research.
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