Rice Imputation Server tutorial

Transcription

1 Rice Imputation Server tutorial Updated: March 30, 2018 Overview The Rice Imputation Server (RIS) takes in rice genomic datasets and imputes data out to >5.2M Single Nucleotide Polymorphisms (SNPs). It utilizes the Global O. sativa Reference Panel of >4500 diverse rice accessions, an interpolated fine-scale recombination map, and the IMPUTE2 software ( RIS expects unphased genotype data in the same input format as for IMPUTE2 (.gen/.sample) and outputs an imputed plink binary dataset (.bed/.bim/.fam). In order to get the most out of your data, you should already be familiar with basic file format conversions using plink1.9 ( plink (.ped/.map) plink (.bed/.bim/.fam) VCF GBS format (.hmp) Download Plink (.bed/.bim/.fam) Use plink to convert to oxford format (.gen/.sample) per chromosome Use TASSEL to convert to plink (.ped/.map) Rice Imputation Server Use plink to convert to other formats and/or to apply SNP filters SNP filter(s) chr1 chr2 chr3 chr4 chr7 chr10 chr5 chr8 chr11 chr6 chr9 chr12 Upload Final imputed, filtered study panel dataset Formatting data for RIS input The RIS pipeline requires genotypic data in OXFORD format (.gen/.sample), one.gen/.sample set per chromosome. All 12 chromosomes are expected. Some commonly used data formats may be converted to OXFORD format using plink1.9, as shown above. Example: Converting plink (.ped/.map) format to OXFORD format by chromosome. for i in {1..12}; do plink --file your_dataset --chr $i --recode oxford --out your_dataset_chr$i; done Note: Please follow naming scheme your_dataset_chrxx as the base name for your.gen and.sample input files where XX = a number indicating chromosome and your_dataset = your fileset name without spaces (use underscore or dash instead). Ensure that there is no space between chr and XX. This naming scheme is important so that the application can correctly take in your fileset.) Also note: Markers must have unique names in order for final assembly of different chromosomes into a single file.

2 Bundling your data RIS accepts files as tar.gz type, so after your data are in OXFORD format by chromosome, place the 12 chromosomes into a single directory / folder (e.g. your_dataset_name). Then compress the directory and its contents into a single tar.gz archive (e.g. your_dataset_name). There are many resources out there that can help you tar your file set. Here are some examples on how to create the archive in different OSs: MacOSX: From the command line (/Applications/Utilities/Terminal), use the following syntax: tar -cvzf your_dataset_name.tar.gz your_dataset_name Linux: From the command line, use the following syntax: tar -cvzf your_dataset_name.tar.gz your_dataset_name Windows: Use 7-zip ( ) to create a tar archive of your directory (e.g. your_dataset_name.tar). Then use 7-zip again to compress the tar archive (e.g. your_dataset_name.tar) with the GZip format. Uploading data From the landing page of RIS, click on the green Impute Now! button. This landing page is also where you can download SNP lists that you can use to filter your imputed data later. Click here to start

3 Fill out project and user information. Upload your compressed genotype data bundle (tar.gz) by either clicking on Choose File and selecting the file or by dragging and dropping the file. You can drag and drop your file here Your project will be added to a queue and our server resources allocated.

4 Checking status The progress of your job can be monitored by the link designated by its project locator ID (green button). Click for project status

5 Retrieving results You will receive an letting you know that your results are ready to be downloaded. These will only be available for 7 days on our servers before they are removed so be sure to download them. 542C5WO Imputed results data bundle Understanding your results data bundle Once you unzip your results data bundle, you will find 120.summary files, one.bed/.bim/.fam binary plink set, and one.log file. The plink binary set is your imputed dataset. The.log file summarizes the steps of the imputation pipeline and records the timing of certain steps. The.summary files (n=10 chunks/chr x 12 chr = 120) are outputs from IMPUTE2 and give concordance statistics on how well each chunk was imputed. For more information on these.summary files, check out the IMPUTE2 site at Filtering your imputed data If you would like, you can filter your imputed data using plink1.9. You may like to do this for a few reasons: 1) to make the large dataset easier to manage or 2) to filter for markers with specific annotations, e.g. genic SNPs.

6 We provide three functional SNP lists (genic SNPs, exonic SNPs, and putative splice sites) that can feed directly into plink1.9 for ease of filtering. These can be downloaded at the landing page (i.e., the first page you arrived at for Rice Imputation Server). Use the --extract flag in your plink command to do the filtering. For more specific information and for other filtering options offered by plink1.9, see its website at Happy rice imputing!