Facilitating analysis of genomic variation in Olympia oysters

Bioinformatics, FISH546Mackenzie Gavery3/14/13

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*

Methods

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

ResultsCut sites SNP w/in

50bpSNP w/in

100bp

NotI 118 43 64

SbfI 600 177 177

EcoRI 19,996 3,905 12,733

Results

• New data tracks:• Gene ID• GO annotation sub-groups• SNPs• Blastx regions (with frame)

Cut sites SNP w/in 50bp

SNP w/in 100bp

NotI 118 43 64

SbfI 600 177 177

EcoRI 19,996 3,905 12,733

Applications

• Assist with RAD-Seq experimental design• Ask interesting biological questions – are

there differences in the number of SNPs in housekeeping v. inducible genes?

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)

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