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+##List of initial and common steps to process ddRAD-seq data
+
+Some degree of familiarity with navigating the grid engine environment and linux is assumed. 
+Data is assumed to be obtained from the Illumina 4000 seqencing platform, although the approach has worked for older platforms too.
+We also assume that anaconda and dDocent are installed and in the current path of the user.
+Most steps can be submitted as a job by writing a bash script and requesting multiple cores. 
+
+Some resources by VCU:
+https://goo.gl/hYAqTL
+https://wiki.vcu.edu/display/unix/
+___
+## Procuring sequence files and demultiplexing
+
+Once your libraries are sequenced you will recieve a ftp link from the company. Use `wget` to download the large file, one library at a time or as a series of jobs.
+wget --user=`userID`--password=`userPass` ftp://serverID/folderName/raw_data/*.gz
+
+**_To demultiplex_** your *.gz file, navigate to the directory and utilize gbsx. 
+[GBSX](https://github.com/GenomicsCoreLeuven/GBSX)
+[Herten et al. 2015](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-015-0514-3)
+
+Basic command for Demultiplexing (modify the flags as seen fit for your data)
+/location/of/jdk1.8.0_101/bin/java -jar /location/of/GBSX/GBSX_v1.2.jar --Demultiplexer -f1 /path/to/library/file/XYZ.fq -i /path/to/barcodes.txt -gzip false -rad true -mb 2 -me 1 -ea /path/to/ecori.txt
+
+barcodes.txt is a simple tab seperated file (r*c), where the rows (r) are your individuals in a given library and columns (c=3) are name of the individual, barcode sequence and the last column has EcorI repeated r times.
+In the barcode file the individuals are listed from well A1:A12, then B1:B12 and so on. 
+e.g barcode file: ADD LINK HERE
+ecori.txt is a tab seperated file with the following on the first line:  EcoRI   AATTC
+
+Once done, examine the *.stats file to determine how many reads were retained.
+
+___
+## Trimming 
+NOTE: This step is not neccessary but in some cases has improved the performance of assemblers. We recommend to conduct assembly (implemented in dDocent) both ways for your data to determine which is best.
+      You can also look at the fastq output for your library to determine if end trimming implemented in this step will be required. 
+      
+[fastp](https://github.com/OpenGene/fastp)
+[Chen et al. 2018](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6129281/)
+
+We will be trimming off the ends of our reads to make them all equal length. You can also trim on quality at this stage (not implemented in this documentation).
+EG HERE**
+
+___
+## Mapping and SNP calling (*_Assuming denovo assembly_*)
+
+This step is conducted using dDocent. There are several other options that have been implemented by other folks in lab. 
+Check out: [Chris Friedline](https://github.com/cfriedline)
+            [Brandon Lind)(https://github.com/brandonlind)
+            
+You can either create a seperate environment for dDocent or have all the dependencies and dDocent in your path.
+Please go over the tutorial in detail before starting this step:
+[dDocent web](http://ddocent.com/)
+
+*_Things to keep in mind:_*
+*Create a directory and copy all the fastq files into it.
+*File naming scheme. Pop and Ind should be seperated by "_". 
+*Follow dDocent's naming scheme for fastq files (use `rename` command in bash to change file extensions)
+*We keep the gap penalty, match and mismatch value to default, but you can play around with the similarity threshold. 
+*Trimming can be turned on or off depending upon your settings in the previous step. If you only used fastp to trim on length you can alow dDocent to utilize trimmomatic at this stage.
+  If you have already trimmed on quality in the previous step, turn off this setting. If turned off, make sure to rename your files to match the trim format in dDocent. Also create a symlink here to all the raw fastq files for dDocent to work properly.
+*Both cutoffs in dDocent (Number of unique sequences with X coverage and Number of unique sequences in more than X individuals) can be picked near the asymptote.
+*Once reference.fasta is assembled and SNPs are called, use the file before the final dDocent filtering to conduct all our custom post-filering steps. This file is called TotalRawSNPs.vcf.
+
+ 
+
+
+