SkimGBS - Revision history

Philippbayer at 03:16, 17 February 2016

2016-02-17T03:16:07Z

Philippbayer at 04:12, 26 August 2015

2015-08-26T04:12:08Z

Philippbayer: /* Dependencies */

2014-09-12T04:07:20Z

‎Dependencies

Philippbayer: /* Download */

2014-09-12T03:51:37Z

‎Download

Philippbayer: /* Filtering GCs and COs */

2014-09-12T03:47:18Z

‎Filtering GCs and COs

Philippbayer: /* Calling recombinations */

2014-09-12T03:43:37Z

‎Calling recombinations

Philippbayer: /* Removing monomorphic SNPs */

2014-09-12T03:42:20Z

‎Removing monomorphic SNPs

Philippbayer: /* Genotyping */

2014-09-12T01:22:21Z

‎Genotyping

Philippbayer at 01:21, 12 September 2014

2014-09-12T01:21:58Z

Philippbayer: /* Filtering GCs and COs */

2014-09-10T23:27:58Z

‎Filtering GCs and COs

@@ Line 23: / Line 23: @@
 As input data, you need BAM alignments for your two parental individuals, and one BAM alignment for each individual of the DH or RIL population.
 === Alignments ===
@@ Line 97: / Line 112: @@
 Usage:
-     perl snp_genotyping_all.pl -b TN10_BC0FH8ACXX_GGCTAC_L008_R1.fastq.par_sorted.bam -gff Contigs_IsobellC1_snp.gff3 -off Contigs_IsobellC1_pseudo_contig.gff3 -f Contigs_IsobellC1.fasta -o IsobellC1_TN10_GBS -c1 T -c2 N -h TN -s 1 > TN10_GBS.out
+     perl snp_genotyping_all.pl -b TN10_BC0FH8ACXX_GGCTAC_L008_R1.fastq.par_sorted.bam -gff Contigs_C1_snp.gff3 -off Contigs_C1_pseudo_contig.gff3 -f Contigs_C1.fasta -o C1_TN10_GBS -c1 T -c2 N -h TN -s 1 > TN10_GBS.out
 Explanation:
@@ Line 105: / Line 120: @@
 -gff Contigs_C1_snp.gff3 (the gff3 file from SGSautoSNP for chromosome C1 containing all SNPs)
--off Contigs_C1_pseudo_contig.gff3 (the offset file to map each chromosome contig to a global chromosome location from)
+-off Contigs_C1_pseudo_contig.gff3 (the offset file to map each chromosome contig to a global chromosome location from fast_multiple_to_single_fasta.py)
--f Contigs_C1.fasta (the chromosome fasta file)
+-f Contigs_C1.fasta (the chromosome fasta file from fast_multiple_to_single_fasta.py)
 -c1 T, -c2 N, -h TN (the letter for the first cultivar, second cultivar, and the combination of both)
@@ Line 125: / Line 140: @@
 This will take all TN samples in the GBS_C8 directory and output both .genotype and .map files for importing to flapjack. The .genotype file is identical to the .dat file Flapjack exports, so you may want to rename it if you like consistency.
-'''Important:''' This script assumes that the first element of your _csv-file is the name of the individual. Example: chrUnrandom_TN99_GBS_TN.genotyping_csv is correct for the individual TN99.
+'''Important:''' This script assumes that the first element of your _csv-file is the name of the individual. Example: chrUnrandom_TN99_GBS_TN.genotyping_csv is correct for the individual TN99. chr_Unrandom_TN99_GBS_TN.genotyping_csv would give you "Unrandom" as the name of all individuals.
 If your chromosome-names contain underscores (i.e., "chrA01_random") this script will generate garbage output where the names of individuals are replaced by "random" or whatever your second element is. Replace the "_" in your chromosome names first before running.
@@ Line 208: / Line 223: @@
 == FAQ ==
-No-one has asked any questions yet - if you have any, send me a mail: philipp.bayer@uqconnect.edu.au
 == Reference ==

← Older revision		Revision as of 04:12, 26 August 2015
Line 1:		Line 1:
−	This page collects the scripts and a manual for our SkimGBS genotyping by sequencing pipeline as published in ~~(add citation)~~	+	This page collects the scripts and a manual for our SkimGBS genotyping by sequencing pipeline as published in Bayer et al. 2015: http://www.ncbi.nlm.nih.gov/pubmed/25754422

	== Dependencies ==		== Dependencies ==

@@ Line 3: / Line 3: @@
 == Dependencies ==
-For the Perl-scripts, just BioPerl. For the Python-scripts, BioPython and at least Python 2.7. we haven't tested the pipeline much with Python 3 but it should work.
+For the Perl-scripts, just BioPerl. For the Python-scripts, BioPython, Pysam and at least Python 2.7. we haven't tested the pipeline much with Python 3 but it should work.
 == Download ==

@@ Line 7: / Line 7: @@
 == Download ==
 * Latest Version 1.0:
-** [http://appliedbioinformatics.com.au/download/Upload_me SkimGBS_pipeline.zip]
+** [http://appliedbioinformatics.com.au/download/Skim_GBS_pipeline_v1.zip Skim_GBS_pipeline_v1.zip]
 == How to install ==

← Older revision		Revision as of 03:47, 12 September 2014
Line 204:		Line 204:

	This will create output files with the 'filtered' suffix.		This will create output files with the 'filtered' suffix.
		+
		+	In the zip archive are also a couple of different plotting and table making scripts I used for the publication.

	== FAQ ==		== FAQ ==

@@ Line 165: / Line 165: @@
      dat_file_crossovers.txt
      dat_file_gene_conversions.txt
 The format of the file is like this, one block per individual:
@@ Line 176: / Line 175: @@
 dat_file_gene_conversions.txt contains all recombinations >20bp and <10kb, crossovers contains everything bigger 10kb.
 === Filtering GCs and COs ===

← Older revision		Revision as of 03:42, 12 September 2014
Line 140:		Line 140:

	grep -v 'TN99' dat_file > cleaned_dat_file		grep -v 'TN99' dat_file > cleaned_dat_file
−
−	~~To impute the cleaned results:~~
−
−	~~python imputeFlapjackSNPs.py some_flapjack_outputfile.dat~~

	=== Sideways imputation ===		=== Sideways imputation ===

@@ Line 101: / Line 101: @@
 Explanation:
-    -b TN10_BC0FH8ACXX_GGCTAC_L008_R1.fastq.par_sorted.bam (the BAM  file for the individual TN10 of the DH population mapped against the whole reference)
+-b TN10_BC0FH8ACXX_GGCTAC_L008_R1.fastq.par_sorted.bam (the BAM  file for the individual TN10 of the DH population mapped against the whole reference)
-    -gff Contigs_C1_snp.gff3 (the gff3 file from SGSautoSNP for chromosome C1 containing all SNPs)
-    -off Contigs_C1_pseudo_contig.gff3 (the offset file to map each chromosome contig to a global chromosome location from)
+-gff Contigs_C1_snp.gff3 (the gff3 file from SGSautoSNP for chromosome C1 containing all SNPs)
-    -f Contigs_C1.fasta (the chromosome fasta file)
-    -c1 T, -c2 N, -h TN (the letter for the first cultivar, second cultivar, and the combination of both)
+-off Contigs_C1_pseudo_contig.gff3 (the offset file to map each chromosome contig to a global chromosome location from)
-    -s 1 (minimum score for each cultivar. There have to be more than, or equal to, 1 read to support an allele )
-    > C1_TN10_GBS (the output file name)
+-f Contigs_C1.fasta (the chromosome fasta file)
 It's best practise to have one output folder per chromosome since this step will generate many files - 3 files per individual per chromosome, so for 20 chromosomes and 92 individuals that means 92 * 3 * 20 = 5520 files.

@@ Line 57: / Line 57: @@
 This will result in one BAM file per chromosome which includes the data of both parental cultivars.
 === SNP-calling ===
@@ Line 98: / Line 101: @@
 Explanation:
-     TN10_BC0FH8ACXX_GGCTAC_L008_R1.fastq.par_sorted.bam - is the BAM  file for the individual TN10 of the DH populatioin mapped against the whole reference.
+     -b TN10_BC0FH8ACXX_GGCTAC_L008_R1.fastq.par_sorted.bam (the BAM  file for the individual TN10 of the DH population mapped against the whole reference)
-     Contigs_C1_snp.gff3 - is the gff3 file from SGSautoSNP for chromosome C1 containing all SNPs
+     -gff Contigs_C1_snp.gff3 (the gff3 file from SGSautoSNP for chromosome C1 containing all SNPs)
-     Contigs_C1_pseudo_contig.gff3 - is the offset file to map each chromosome contig to a global chromosome location from
+     -off Contigs_C1_pseudo_contig.gff3 (the offset file to map each chromosome contig to a global chromosome location from)
-     Contigs_C1.fasta - the chromosome fasta file
+     -f Contigs_C1.fasta (the chromosome fasta file)
-     T - the letter for the first cultivar
+     -c1 T, -c2 N, -h TN (the letter for the first cultivar, second cultivar, and the combination of both)
-     C1_TN10_GBS - the output file name
+    -s 1 (minimum score for each cultivar. There have to be more than, or equal to, 1 read to support an allele )
 It's best practise to have one output folder per chromosome since this step will generate many files - 3 files per individual per chromosome, so for 20 chromosomes and 92 individuals that means 92 * 3 * 20 = 5520 files.

@@ Line 181: / Line 181: @@
 </pre>
-The outer start of that B recombination is the first SNP after the last SNP of the first A block (first |, o), the inner start is the first B, the inner end is the last B, the outer end is the last SNP before the next A.
+Genotypes are A and B, - is missing. The outer start of that B recombination is the first SNP after the last SNP of the first A block (first |, o), the inner start is the first B, the inner end is the last B, the outer end is the last SNP before the next A.
 Here's an example for a filtered GC in two individuals: