CandiSNP classifies, annotates and visualises SNPs on genomes. Provide it with a list of SNP positions and allele frequencies and CandiSNP will return the type of each SNP and an interactive visualisation that you can explore to identify potential causative mutations.
If you use CandiSNP please cite: Etherington, Monaghan et al "Mapping mutations in plant genomes with the user-friendly web application CandiSNP." Plant Methods 2014, 10:306. doi:10.1186/s13007-014-0041-7.
CandiSNP will classify your SNPs from the genome annotations you choose
Set allele frequency range
CandiSNP works on pre-processed, filtered, high-confidence SNPs that you provide, there are lots of ways of preparing SNP data. Here are some hints on how to go about this:
The genome annotations for TAIR10, TAIR9, Rice genome v7, Tomato genome v2.40, Glycine max genome 1.09v8, Grape genome v1 and Maize B73 v5b are currently available in CandiSNP. It is essential that the chromosome names in column 'Chr' match that in the database. Here are the chromosome names for each genome build:
|TAIR10||1, 2, 3, 4, 5, M, C|
|TAIR9||1, 2, 3, 4, 5, M, C|
|Rice genome v7||1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, Un, Sy|
|Tomato genome v2.40||SL2.40ch00, SL2.40ch01, SL2.40ch02, SL2.40ch03, SL2.40ch04, SL2.40ch05, SL2.40ch06, SL2.40ch07, SL2.40ch08, SL2.40ch09, SL2.40ch10, SL2.40ch11, SL2.40ch12|
|Glycine max genome 1.09v8||Gm01, Gm02, Gm03, Gm04, Gm05, Gm06, Gm07, Gm08, Gm09, Gm10, Gm11, Gm12, Gm13, Gm14, Gm15, Gm16, Gm17, Gm18, Gm19, Gm20 and 21 to 2287|
|Grape genome v1||1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 1_random, 10_random, 11_random, 12_random, 13_random, 16_random, 17_random, 18_random, 3_random, 4_random, 5_random, 7_random, 9_random|
|Maize B73 v5b||1, 4, 2, 3, 5, 7, 8, 6, 9, 10, UNKNOWN, Mt, Pt|
Once your file is prepared click or drop your file into the 'Click to select or drop a data file to begin' area. Upload will begin and your data will be analysed. When it is done, an interactive plot will be displayed on the screen.
When the plot appears, a control panel to determine it's data and appearance is also displayed, it has four sections
You will see a plot like this for every contig or chromosome of your chosen assembly.
If you have too many spots to see the distinctions, first set the upper and lower limit of the Allele Frequency slider, this should reduce some. Also note that you can set the transparency of the spots in the plot - use the colour selector and press the 'more' button, the slider that appears lets you set the transparency of the current colour. Lastly, try reducing the number of SNPs in your file - there is a fair chance that you have lots of false positive SNPs if you have too many to plot clearly.