This document discusses analyzing genomic variation in Olympia oysters. It aims to generate useful files to annotate SNPs in a non-model species and develop a workflow for distinguishing synonymous and non-synonymous SNPs. The author used existing transcriptome data and over 52,000 SNPs to design RAD-Seq experiments and ask biological questions. Next steps are to determine if SNPs result in protein changes using Blastx and aachanges tools to compare SNPs to gene models.

1. Facilitating analysis of
genomic variation in
Olympia oysters
Bioinformatics, FISH546
Mackenzie Gavery
3/14/13

2. GOALS
• Utilize existing genomic resources
(transcriptome) for RAD-Seq design
• Generate useful generic feature files (gff) to
facilitate functional annotation of SNPs
• Generate a workflow for annotating
synonymous and non-synonymous SNPs in a
non-model species*

3. Methods
• Starting material:
• Transcriptome: 41,000 contigs
• SNP Table (CLCBio) ~52,000
• Annotations: blastx, GO
• Tools:
• Galaxy
• Excel
• Blastx

4. Results
Cut sites SNP w/in SNP w/in
50bp 100bp
NotI 118 43 64
SbfI 600 177 177
EcoRI 19,996 3,905 12,733

5. Results
Cut sites SNP w/in SNP w/in
50bp 100bp
NotI 118 43 64
SbfI 600 177 177
EcoRI 19,996 3,905 12,733
• New data tracks:
• Gene ID
• GO annotation sub-groups
• SNPs
• Blastx regions (with frame)

6. Applications
• Assist with RAD-Seq experimental design
• Ask interesting biological questions – are
there differences in the number of SNPs in
housekeeping v. inducible genes?

7. Next steps
• Do these SNPs result in functional changes
(i.e. do they change the protein)?
1. Blastx custom output
-outfmt “6 sseqid, qseqid, frames…”
2. aachanges (Galaxy)

8. Next steps
• Do these SNPs result in functional changes
(i.e. do they change the protein)?
1. Blastx custom output
-outfmt “6 sseqid, qseqid, frames…”
2. aachanges (Galaxy) SNP bed
gene bed