Pyrad - Revision history

Rf at 17:39, 20 April 2016

2016-04-20T17:39:12Z

2016-04-20T17:36:42Z

2016-04-20T17:22:26Z

2016-04-20T17:20:07Z

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← Older revision		Revision as of 17:39, 20 April 2016
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	* The third stage is about de-replicating (unsure as to meaning) and clustering the short reads. This is done via:		* The third stage is about de-replicating (unsure as to meaning) and clustering the short reads. This is done via:
	pyrad -p params.txt -s 3		pyrad -p params.txt -s 3
		+	* the pattern can be observed, that the "-s" option defines the stage, and the "params.txt" must always be referred to.
		+
		+	The rest of the tutorial describes stages 4 to 7 of the procedure.

@@ Line 19: / Line 19: @@
 * one of "-p", "-d", "-D" and "-n" options are essential.
 * "pyrad -n" generates a "params.txt" file which contains settings for the run. A large part of the analysis can be configured by editing this file.
-* If using a Gridengine job script, be sure to match the "-pe multi" value and number of CPUs in "params.txt" to have the same value.
+* If using a Gridengine job script, there are two important parameters: no. 7 "N processors" and no. 37 "vsearch max threads". Multiply these together and supply the result to the "-pe multi" option in the gridengine jobscript. This is the parallel environment (i.e. total number of threads/cores that pyrad will use).
 * The first stage is de-multiplexing your short read dataset using the barcodes, a processes which will generate new fastq file depending on the barcodes. This is done via:
   pyrad -p params.txt -s 1
 * The second stage editing the raw reads for quality, whereupon they will be converted to fasta. This is done via:
   pyrad -p params.txt -s 2
-* The third stage is about de-replicaiting (unsure as to meaning) and clustering the short reads. This is done via:
+* The third stage is about de-replicating (unsure as to meaning) and clustering the short reads. This is done via:
   pyrad -p params.txt -s 3

← Older revision		Revision as of 17:22, 20 April 2016
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	* The second stage editing the raw reads for quality, whereupon they will be converted to fasta. This is done via:		* The second stage editing the raw reads for quality, whereupon they will be converted to fasta. This is done via:
	pyrad -p params.txt -s 2		pyrad -p params.txt -s 2
		+	* The third stage is about de-replicaiting (unsure as to meaning) and clustering the short reads. This is done via:
		+	pyrad -p params.txt -s 3

@@ Line 22: / Line 22: @@
 * The first stage is de-multiplexing your short read dataset using the barcodes, a processes which will generate new fastq file depending on the barcodes. This is done via:
   pyrad -p params.txt -s 1
-* The second stage editing the raw reads for quality, whereupon they will be converted to fast and is done via:
+* The second stage editing the raw reads for quality, whereupon they will be converted to fasta. This is done via:
   pyrad -p params.txt -s 2

@@ Line 22: / Line 22: @@
 * The first stage is de-multiplexing your short read dataset using the barcodes, a processes which will generate new fastq file depending on the barcodes. This is done via:
   pyrad -p params.txt -s 1
-* The second stage editing the raw reads for quality and is done via:
+* The second stage editing the raw reads for quality, whereupon they will be converted to fast and is done via:
   pyrad -p params.txt -s 2

@@ Line 12: / Line 12: @@
 The go-to tutorial for this is at:
 * http://nbviewer.jupyter.org/gist/dereneaton/1f661bfb205b644086cc/tutorial_RAD_3.0.ipynb
 == highlights ==
@@ Line 19: / Line 20: @@
 * "pyrad -n" generates a "params.txt" file which contains settings for the run. A large part of the analysis can be configured by editing this file.
 * The first stage is to gather statistics about your short read dataset and the barcodes. This is done via:
-  pyRAD -p params.txt -s 1
+  pyrad -p params.txt -s 1
 * The second stage editing the raw reads and is done via:
-  pyRAD -p params.txt -s 2
+  pyrad -p params.txt -s 2