Difference between revisions of "2017 Jayern Lab note"

Latest revision as of 05:11, 19 April 2017

03/02

 perl deconseq.pl -f 11_180bp_1.fastq -dbs chloro -i 90 -c 90
 perl deconseq.pl -f 11_180bp_2.fastq -dbs chloro -i 90 -c 90
 perl deconseq.pl -f 4_180bp_1.fastq -dbs chloro -i 90 -c 90
 perl deconseq.pl -f 4_180bp_2.fastq -dbs chloro -i 90 -c 90

03/03

 perl deconseq.pl -f V_nakashimae_2_1.fastq -dbs chloro -i 90 -c 90
 perl deconseq.pl -f V_nakashimae_2_2.fastq -dbs chloro -i 90 -c 90
 perl deconseq.pl -f V_nepal_1_1.fastq -dbs chloro -i 90 -c 90
 perl deconseq.pl -f V_nepal_1_2.fastq -dbs chloro -i 90 -c 90
 perl deconseq.pl -f V_nipp_1_1.fastq -dbs chloro -i 90 -c 90
 perl deconseq.pl -f V_nipp_1_2.fastq -dbs chloro -i 90 -c 90

03/06

 python PE-pairing.py filtered.V_nakashimae_2_1.fq filtered.V_nakashimae_2_2.fq
 python PE-pairing.py filtered.V_nepal_1_1.fq filtered.V_nepal_1_2.fq
 python PE-pairing.py filtered.V_nipp_1_1.fq filtered.V_nipp_1_2.fq

 bwa index Vreflexo.fasta

03/07

 << SSR marker design >>
 perl gmat.pl -r 5 -m 2 -x 10 -s 0 -i Vradiata.fa
 python 01.ssr.get_seq.py Vradiata.fa.ssr
 python 02.p3in.split_nParts.py Vradiata.fa.ssr.p3in

03/08

 primer3_core < Vradiata.fa.ssr.p3in > Vradiata.fa.ssr.p3out

03/10

 python parsing_primer3_output.py Vradiata.fa.ssr.p3out > Vradiata.fa.ssr.p3out.post
 pyton post.parse_into_gff.py Vradiata.fa.ssr.p3out.post > Vradiata.fa.ssr.p3out.post.gff

03/15

 bowtie-build Vradiata.fa Vradiata
 bowtie-build Vreflexo.fa Vreflexo
 bowtie-build Vangularis.fa Vangularis

03/16

 tophat2 -p 1 -G Vradiata.gff Vradiata 1_1.fastq.gz 1_2.fastq.gz
 tophat2 -p 1 -G Vradiata.gff Vradiata 2_1.fastq.gz 2_2.fastq.gz
 tophat2 -p 1 -G Vradiata.gff Vradiata 4_1.fastq.gz 4_2.fastq.gz
 .  
 .
 .
 tophat2 -p 1 -G Vradiata.gff Vradiata SunhwaN_root_1.fastq.gz SunhwaN_root_2.fastq.gz
 (Total 34 RNA paired end seq)

03/23

 cuffdiff -o cuffdiff_result -b /hayasen/Data/Vigna/Vradiata/Vradiata.fa -p 10
 -L 1,2,4,9,10,11,12,13,17,19,22,23,25,26,27,32,34,36,37,38,K1,K2,K4,
 KyoungwonP_flower_2,KyoungwonP_leaf_2,KyoungwonP_pod_2,KyoungwonP_root,
 S1,S4,S5,SunhwaN_flower,SunhwaN_leaf,SunhwaN_pod,SunhwaN_root 
 -u mergerd_asm/transcript.gtf 1.accepted_hits.bam 2.accepted_hits.bam 
 4.accepted_hits.bam 9.accepted_hits.bam 10.accepted_hits.bam 11.accepted_hits.bam 
 12.accepted_hits.bam 13.accepted_hits.bam 17.accepted_hits.bam 19.accepted_hits.bam 
 22.accepted_hits.bam 23.accepted_hits.bam 25.accepted_hits.bam 26.accepted_hits.bam 
 27.accepted_hits.bam 32.accepted_hits.bam 34.accepted_hits.bam 36.accepted_hits.bam 
 37.accepted_hits.bam 38.accepted_hits.bam K1.accepted_hits.bam K2.accepted_hits.bam 
 K4.accepted_hits.bam KyoungwonP_flower_2.accepted_hits.bam KyoungwonP_leaf_2.accepted_hits.bam
 KyoungwonP_pod_2.accepted_hits.bam KyoungwonP_root.accepted_hits.bam S1.accepted_hits.bam 
 S4.accepted_hits.bam S5.accepted_hits.bam SunhwaN_flower.accepted_hits.bam 
 SunhwaN_leaf.accepted_hits.bam SunhwaN_pod.accepted_hits.bam SunhwaN_root.accepted_hits.bam

03/29

R - heatmap

 library(ggplot2)
 library(reshape2)
 gene <- read.csv("C:/R/test3.tsv", sep="\t")
 row.names(gene) <- gene$Gene_ID
 gene <- gene[,2:5]
 gene_matrix <- data.matrix(gene)
 m <- melt(gene_matrix)
 p <- ggplot(data=m, aes(x=Var2, y=Var1, fill=value)) + geom_tile()
 + theme(text = element_text(size = 20), axis.text.x = element_text(angle = 90, hjust = 1))
 p <- p + scale_fill_gradient2(low="gray44", high="red")
 p

04/18

blat

 (244:/hayasen/Workspace/Jatropha/Transcriptome/Trinity.fasta/)
 blat standard_output.gapfilled.final.fa 1.Trinity.fasta 1.Trinity.fasta.psl
 .
 .
 .

04/19

 perl gmat.pl -r 5 -m 2 -x 10 -s 0 -i ./standard_output.gapfilled.final.fa