Reanalysis of ribosome profiling datasets reveals a novel function of rocaglamide A in perturbing the dynamics of translation elongation via eIF4A

Fajin Li^1,2,3,4, Jianhuo Fang^1,4, Yifan Yu^1,4, Sijia Hao^1,2,3, Qinglin Zeng¹, Qin Zou¹, and Xuerui Yang^1,2,3

¹ MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing 100084, China ² Center for Synthetic & Systems Biology, Tsinghua University, Beijing 100084, China ³ Joint Graduate Program of Peking-Tsinghua-National Institute of Biological Science, Tsinghua University, Beijing 100084, China. ⁴ These authors are main contributors to this work.

Corresponding： [email protected]; [email protected]; [email protected]

Introduction

This file is a description of how the results presented in the manuscript were generated, including the datasets we used, how we downloaded and processed the raw datasets, the codes we used, et al. All scripts used for processing BAM files should be excuted in Linux platform and other scripts can be both used in Linux and windows platform. It should be noted that most of the analyses were based on RiboMiner we developed before.

Datasets

The datasets we used were all downloaded from GEO database. We all collected 26 datasets, containing more than 100 ribosome profiling samples and several iCLIP-seq samples. Please refer to Table S1 for the detailed information of all the collected datasets.

The codes used for downloading all the datasets are:

# use GSE102720 as an example:
$ cat download.sh
workdir=/workdata/LabMember2/lifj/lifj/Project/05.Ribo_seq_human/GSE102720/00.rawdata

for i in SRR59376{40..45};do
    fastq-dump $i
    rm /workdata/LabMember2/lifj/ncbi/public/sra/$i.sra
done

Dependencies

python >= 3.6
RiboMiner = 0.2.3.2
snakemake = 5.31.1
sra-tools = 2.11.0
fastx_toolkit = 0.0.14
cutadapt = 4.0
bowtie >= 1.1.2
STAR = 2.7.7a
samtools = 1.11
R >= 3.6

Preparations

The human reference genome, ncRNA and annotations are downloaded from Ensemble Genome Browser (release 88). Human rRNA sequences are downloaded from UCSC Genome Browser and download method could refer to this protocol.

Download and install some tools and packages

# python >=3.6
conda install -c bioconda ribocode ribominer sra-tools
fastx_toolkit cutadapt bowtie star samtools snakemake

Build index for rRNA alignment:

fastaGenome=hg38_rRNA.fa
bowtie-build -f $fastaGenome human_rRNA

Build index for genome alignment:

fastaGenome=Homo_sapiens.GRCh38.dna.primary_assembly.fa
gtf=Homo_sapiens.GRCh38.88.gtf
srun STAR --runThreadN 8 --runMode genomeGenerate --genomeDir ./STAR_Human_Ensembl_GRCh38_Ensembl --genomeFastaFiles $fastaGenome --sjdbGTFfile $gtf

Get annotations for transcriptome

prepare_transcripts -g <Homo_sapiens.GRCh38.88.gtf> -f <Homo_sapiens.GRCh38.dna.primary_assembly.fa> -o <RiboCode_annot>

OutputTranscriptInfo -c <RiboCode_annot/transcripts_cds.txt> -g <Homo_sapiens.GRCh38.88.gtf> -f <RiboCode_annot/transcripts_sequence.fa> -o <longest.transcripts.info.txt> -O <all.transcripts.info.txt>

Pre-processing of ribosome profiling and Disome-seq data

All ribosome profiling and Disome-seq datasets were processed with a custom snakemake pipeline. Use GSE102720 as an example，run the snakemake file:

snakemake -s Ribo-seq-snakemake.py  --cluster "sbatch -p cmp -N 2 -n 8 -e Ribo.err -o Ribo.out " --jobs 61

Formal analyses

Before doing the following analyses, all datasets we collected should be processed with the Ribo-seq-pipeline.

Calculate polarity scores for each datasets

Set configure files (GSE102720 for example)

bamFiles        readLengths     Offsets bamLegends
../07.STAR/SRR5937640_STAR/SRR5937640.Aligned.toTranscriptome.out.sorted.bam    26,27,28,29,30,31       12,12,12,12,12,13       Naive-DMSO-1
../07.STAR/SRR5937641_STAR/SRR5937641.Aligned.toTranscriptome.out.sorted.bam    26,27,28,29,30,31       12,12,12,12,12,13       Naive-DMSO-2
../07.STAR/SRR5937642_STAR/SRR5937642.Aligned.toTranscriptome.out.sorted.bam    26,27,28,29,30,31       12,12,12,12,12,13       Naive-RocA03
../07.STAR/SRR5937643_STAR/SRR5937643.Aligned.toTranscriptome.out.sorted.bam    26,27,28,29,30,31       12,12,12,12,12,13       Aglaiacized-DMSO-1
../07.STAR/SRR5937644_STAR/SRR5937644.Aligned.toTranscriptome.out.sorted.bam    26,27,28,29,30,31       12,12,12,12,12,13       Aglaiacized-DMSO-2
../07.STAR/SRR5937645_STAR/SRR5937645.Aligned.toTranscriptome.out.sorted.bam    26,27,28,29,30,31       12,12,12,12,12,13       Aglaiacized-RocA03

Calculate polarity scores using RiboMiner

workdir=`pwd`
BamDir=$workdir/../07.STAR
Ref=/workdata/home/lifj/lifj/data/Reference/human
transcript=/workdata/home/lifj/lifj/data/Reference/human/RiboCode_annot_Human/RiboCode_annot/transcripts_sequence.fa
results=$workdir/MA
attribute=$workdir/GSE102720_configure.txt
trans_info=$Ref/longest.transcripts.info.txt
groups='Naive-DMSO,Naive-RocA03,Aglaiacized-DMSO,Aglaiacized-RocA03'
replicates='Naive-DMSO-1,Naive-DMSO-2__Naive-RocA03__Aglaiacized-DMSO-1,Aglaiacized-DMSO-2__Aglaiacized-RocA03'

PolarityCalculation -f $attribute -c $trans_info -o $results/MA_all -n 0
PlotPolarity -i  $results/MA_all_polarity_dataframe.txt -o $results/MA_all -g $groups -r $replicates -y 5 --mode all

Calculate the difference of polarity scores

import pandas as pd

df=pd.read_csv('MA_all_mean_polarity_dataframe.txt',sep="\t")
print(df.shape)
##19636,5
m,n=df.shape
print(df.iloc[0:5,0:(n-1)])
print(df.columns)
df.rename(columns={'Unnamed: 0':'transcript_id'}, inplace = True)
print(df.columns)

df['Naive-RocA03-DMSO(GSE102720)']=df['Naive-RocA03']-df['Naive-DMSO']
df['Aglaiacized-RocA03-DMSO(GSE102720)']=df['Aglaiacized-RocA03']-df['Aglaiacized-DMSO']

df_new=df[['transcript_id','Naive-RocA03-DMSO(GSE102720)','Aglaiacized-RocA03-DMSO(GSE102720)']]
print(df_new.shape)
print(df_new.iloc[0:5,:])

df_new.to_csv('GSE102720_diff_polarity_dataframe.txt',sep="\t",index=False)

Finally, the difference of polarity scores from all datasets were merged. Please refer to total_merged_diff_polarity_202102.txt.

Calculate RPKM for each datasets

use GSE102720 for example.

workdir=`pwd`
BamDir=$workdir/../07.STAR
Ref=/workdata/home/lifj/lifj/data/Reference/human
transcript=/workdata/home/lifj/lifj/data/Reference/human/RiboCode_annot_Human/RiboCode_annot/transcripts_sequence.fa
results=$workdir/MA
attribute=$workdir/GSE102720_configure.txt
trans_info=$Ref/longest.transcripts.info.txt
groups='Naive-DMSO,Naive-RocA03,Aglaiacized-DMSO,Aglaiacized-RocA03'
replicates='Naive-DMSO-1,Naive-DMSO-2__Naive-RocA03__Aglaiacized-DMSO-1,Aglaiacized-DMSO-2__Aglaiacized-RocA03'

mkdir -p $workdir/RPKM
python RPKM.py -f $attribute -c $trans_info -o $workdir/RPKM/MA  --type CDS ## calculate RPKM of each longest transcript
python RPKMmean.py -i $workdir/RPKM/MA_RPKM_dataframe.txt -o $workdir/RPKM/MA -g $groups -r $replicates ## calculate mean RPKM for the replicates

Calculate the log2FC(RPKM) for each transcript

import pandas as pd
import numpy as np
df=pd.read_csv('MA_mean_RPKM_dataframe.txt',sep="\t")
print(df.shape)
##19636,5
m,n=df.shape
print(df.iloc[0:5,0:(n-1)])
print(df.columns)
df.rename(columns={'Unnamed: 0':'transcript_id'}, inplace = True)
print(df.columns)
df['Naive-RocA03-DMSO(GSE102720)']=np.log2(df['Naive-RocA03']/df['Naive-DMSO'])
df['Aglaiacized-RocA03-DMSO(GSE102720)']=np.log2(df['Aglaiacized-RocA03']/df['Aglaiacized-DMSO'])
df_new=df[['transcript_id','Naive-RocA03-DMSO(GSE102720)','Aglaiacized-RocA03-DMSO(GSE102720)']]
print(df_new.shape)
print(df_new.iloc[0:5,:])
df_new.to_csv('GSE102720_diff_RPKM_dataframe.txt',sep="\t",index=False)

Finally, the RPKM of all transcripts from all datasets were merged. Please refer to total_merged_cds_level_RPKM_202102.txt.

Calculate Coverage for each datasets

Calculate coverage using RiboMiner, use GSE102720 for example.

workdir=`pwd`
BamDir=$workdir/../07.STAR
Ref=/workdata/home/lifj/lifj/data/Reference/human
transcript=/workdata/home/lifj/lifj/data/Reference/human/RiboCode_annot_Human/RiboCode_annot/transcripts_sequence.fa
results=$workdir/MA
attribute=$workdir/GSE102720_configure.txt
trans_info=$Ref/longest.transcripts.info.txt
groups='Naive-DMSO,Naive-RocA03,Aglaiacized-DMSO,Aglaiacized-RocA03'
replicates='Naive-DMSO-1,Naive-DMSO-2__Naive-RocA03__Aglaiacized-DMSO-1,Aglaiacized-DMSO-2__Aglaiacized-RocA03'

## coverage
mkdir -p $workdir/coverage

RiboDensityAtEachPosition -c $trans_info -f $attribute -o $workdir/coverage/MA  -U codon

Finally, the coverage of all transcripts from all datasets were merged. Please refer to total_merged_read_coverage_202102.txt.

Re-generation of the results presented in the manuscript

Figure 1A

all used data were deposited in ./data

TranslationRelatedConsitionPairs.txt
total_merged_cds_level_RPKM_202102.txt
total_merged_diff_polarity_202102.txt
total_merged_read_coverage_202102.txt

clustering analyses via RiboPipe.py

#!/usr/bin/env python
# -*- coding:UTF-8 -*-
'''
Author: Li Fajin
Date: 2021-02-15 13:23:59
LastEditors: Li Fajin
LastEditTime: 2022-05-28 12:21:37
Description: file content
'''

import pandas as pd
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import seaborn as sns
import os


'''
###################################
Date: 20220528.15:00
all polarity with Tranlation initiation and elongation

19636 trans x 49 conditionPairs
###################################
'''
ConditionPairs_of_initiation_and_elongation=pd.read_csv("../data/TranslationRelatedConsitionPairs.txt",sep="\t")
polarity_with_initiation_and_elongation=pd.read_csv("../data/total_merged_diff_polarity_202102.txt",sep="\t",index_col=0)
RPKM_with_initiation_and_elongation=pd.read_csv("../data/total_merged_cds_level_RPKM_202102.txt",sep="\t",index_col=0)
RPKM_with_initiation_and_elongation.apply(np.mean, axis=1).describe() ## trans describe
RPKM_with_initiation_and_elongation.apply(np.mean, axis=0).describe() ## sample describe

coverage_with_initiation_and_elongation=pd.read_csv("../data/total_merged_read_coverage_202102.txt",sep="\t",index_col=0)
coverage_with_initiation_and_elongation_sorted=coverage_with_initiation_and_elongation[sorted(coverage_with_initiation_and_elongation.columns)]
coverage_with_initiation_and_elongation_sorted.apply(np.mean, axis=1).describe() ## trans describe
coverage_with_initiation_and_elongation_sorted.apply(np.mean, axis=0).describe() ## sample describe


RPKM_with_initiation_and_elongation_filtered_gt0_in_each_samples=RPKM_with_initiation_and_elongation.loc[RPKM_with_initiation_and_elongation.apply(np.min,axis=1)>=1,:]
coverage_with_initiation_and_elongation_filtered_mean_of_each_trans_gt_pectMean=coverage_with_initiation_and_elongation_sorted.loc[coverage_with_initiation_and_elongation_sorted.apply(np.mean,axis=1)>=coverage_with_initiation_and_elongation_sorted.apply(np.mean,axis=1).describe()['mean'],:]
trans_in_common_associated_with_translation=set(RPKM_with_initiation_and_elongation_filtered_gt0_in_each_samples.index).intersection(set(coverage_with_initiation_and_elongation_filtered_mean_of_each_trans_gt_pectMean.index))
polarity_with_initiation_and_elongation_filtered=polarity_with_initiation_and_elongation.loc[trans_in_common_associated_with_translation,:]


polarity_with_initiation_and_elongation_filtered_vars=np.var(polarity_with_initiation_and_elongation_filtered,axis=1)
polarity_with_initiation_and_elongation_filtered_vars.describe()

trans_filtered_by_polarityVars=polarity_with_initiation_and_elongation_filtered.iloc[np.where(polarity_with_initiation_and_elongation_filtered_vars>polarity_with_initiation_and_elongation_filtered_vars.describe()[1]+polarity_with_initiation_and_elongation_filtered_vars.describe()[2])[0],:]
polarity_with_initiation_and_elongation_filtered_filtered_byPolarityVars=polarity_with_initiation_and_elongation_filtered.drop(trans_filtered_by_polarityVars.index)


plt.rc('font',weight='bold')
sns.set(font_scale=0.5)
fig=plt.figure()
col_c=dict(zip(ConditionPairs_of_initiation_and_elongation['TranslationType'].unique(), ['#2a93d4','#ffb5ba','#79bd9a']))
col_colors=ConditionPairs_of_initiation_and_elongation['TranslationType'].map(col_c)
ax=sns.clustermap(polarity_with_initiation_and_elongation_filtered_filtered_byPolarityVars,method ='ward',metric='euclidean',row_cluster=True,col_cluster=True,
                vmin=-0.5,vmax=0.5,center=0,cmap = 'RdBu_r',col_colors=col_colors.values,yticklabels=False,xticklabels=True)
ax.cax.set_visible(False)
plt.savefig("../results/Figure 1A.pdf")

Figure 1B

set configure file

bamFiles	readLengths	Offsets	bamLegends
../07.STAR/SRR2075925_STAR/SRR2075925.Aligned.toTranscriptome.out.sorted.bam	25,26,27,28	11,11,11,11	DMSO-1
../07.STAR/SRR2075926_STAR/SRR2075926.Aligned.toTranscriptome.out.sorted.bam	25,26,27,28,29	11,11,11,11,12	DMSO-2
../07.STAR/SRR2075927_STAR/SRR2075927.Aligned.toTranscriptome.out.sorted.bam	25,26,27,28,29	11,11,11,11,12	RocA-003
../07.STAR/SRR2075928_STAR/SRR2075928.Aligned.toTranscriptome.out.sorted.bam	24,25,26,27,28,29	11,11,11,11,11,11	RocA-03
../07.STAR/SRR2075929_STAR/SRR2075929.Aligned.toTranscriptome.out.sorted.bam	26,27,28,29	11,11,12,12	RocA-3

All the bam files were generated via the Ribo-seq analyses pipeline.

Metagene via RiboMiner

workdir=`pwd`
BamDir=$workdir/../07.STAR
Ref=/workdata/home/lifj/lifj/data/Reference/human
transcript=/workdata/home/lifj/lifj/data/Reference/human/RiboCode_annot_Human/RiboCode_annot/transcripts_sequence.fa
results=$workdir/MA
attribute=$workdir/GSE70211_configure.txt
trans_info=$Ref/longest.transcripts.info.txt
groups='DMSO,RocA003,RocA03,RocA3'
replicates='DMSO-1,DMSO-2__RocA-003__RocA-03__RocA-3'


MetageneAnalysis -f $attribute -c $trans_info -o $results/MA -U codon -M RPKM -u 100 -d 400 -l 100 -n 10 -m 1  --norm no -y 100 --CI 0.95 --type UTR 
PlotMetageneAnalysis -i $results/MA_unnormed_dataframe.txt -o $results/MA_unnormed -g $groups -r $replicates -u 100 -d 400 --mode mean

Figure 1C

Calculate codon density for each sample

workdir=`pwd`
BamDir=$workdir/../07.STAR
Ref=/workdata/home/lifj/lifj/data/Reference/human
transcript=/workdata/home/lifj/lifj/data/Reference/human/RiboCode_annot_Human/RiboCode_annot/transcripts_sequence.fa
results=$workdir/MA
attribute=$workdir/GSE70211_configure.txt
trans_info=$Ref/longest.transcripts.info.txt
groups='DMSO,RocA003,RocA03,RocA3'
replicates='DMSO-1,DMSO-2__RocA-003__RocA-03__RocA-3'

RiboDensityAtEachPosition -c $trans_info -f $attribute -o $workdir/coverage/MA  -U codon

Statistic read density at the first 75 codons via ProcessCodonDensityAtEachPosition.py script.

## the first 75    
$ python ProcessCodonDensityAtEachPosition.py -i MA_DMSO-1_cds_codon_density.txt -l 1 -r 75 -o DMSO_1_first75.txt
$ python ProcessCodonDensityAtEachPosition.py -i MA_DMSO-2_cds_codon_density.txt -l 1 -r 75 -o DMSO_2_first75.txt
$ python ProcessCodonDensityAtEachPosition.py -i MA_RocA-003_cds_codon_density.txt -l 1 -r 75 -o RocA003_first75.txt
$ python ProcessCodonDensityAtEachPosition.py -i MA_RocA-03_cds_codon_density.txt -l 1 -r 75 -o RocA03_first75.txt
$ python ProcessCodonDensityAtEachPosition.py -i MA_RocA-3_cds_codon_density.txt -l 1 -r 75 -o RocA3_first75.txt

## all CDS
$ python ProcessCodonDensityAtEachPosition.py -i MA_DMSO-1_cds_codon_density.txt  -o DMSO_1_all.txt
$ python ProcessCodonDensityAtEachPosition.py -i MA_DMSO-2_cds_codon_density.txt  -o DMSO_2_all.txt
$ python ProcessCodonDensityAtEachPosition.py -i MA_RocA-003_cds_codon_density.txt -o RocA003_all.txt
$ python ProcessCodonDensityAtEachPosition.py -i MA_RocA-03_cds_codon_density.txt  -o RocA03_all.txt
$ python ProcessCodonDensityAtEachPosition.py -i MA_RocA-3_cds_codon_density.txt -o RocA3_all.txt

Statistics of ratio of the first 75 codons vs the whole CDS region,via RiboDensityAtEachPosition.Rmd.

## Open Rstudio and do it in R platform
rm(list=ls())
setwd('../data')
human_info <- read.table("Homo_sapiens.GRCh38.88.transInfo.txt",sep="\t",stringsAsFactors = F,header = T,check.names = F)

## all counts
DMSO_1_all_counts <- read.table("DMSO_1_all.txt",sep="\t",header = T,stringsAsFactors = F,row.names = 1)
DMSO_2_all_counts <- read.table("DMSO_2_all.txt",sep="\t",header = T,stringsAsFactors = F,row.names = 1)
RocA003_all_counts <- read.table("RocA003_all.txt",sep="\t",header = T,stringsAsFactors = F,row.names = 1)
RocA03_all_counts <- read.table("RocA03_all.txt",sep="\t",header = T,stringsAsFactors = F,row.names = 1)
RocA3_all_counts <- read.table("RocA3_all.txt",sep="\t",header = T,stringsAsFactors = F,row.names = 1)

## first 75 codons
DMSO_1_first75 <- read.table("DMSO_1_first75.txt",sep="\t",header = T,stringsAsFactors = F,row.names = 1)
DMSO_2_first75 <- read.table("DMSO_2_first75.txt",sep="\t",header = T,stringsAsFactors = F,row.names = 1)
RocA003_first75 <- read.table("RocA003_first75.txt",sep="\t",header = T,stringsAsFactors = F,row.names = 1)
RocA03_first75 <- read.table("RocA03_first75.txt",sep="\t",header = T,stringsAsFactors = F,row.names = 1)
RocA3_first75 <- read.table("RocA3_first75.txt",sep="\t",header = T,stringsAsFactors = F,row.names = 1)

## all counts
DMSO_1_factor <- sum(DMSO_1_all_counts$counts)
DMSO_2_factor <- sum(DMSO_2_all_counts$counts)
RocA003_factor <- sum(RocA003_all_counts$counts)
RocA03_factor <- sum(RocA03_all_counts$counts)
RocA3_factor <- sum(RocA3_all_counts$counts)


## normalized
DMSO_1_all_counts['RPM'] <- 10^6*(DMSO_1_all_counts/DMSO_1_factor)
DMSO_2_all_counts['RPM'] <- 10^6*(DMSO_2_all_counts/DMSO_2_factor)
RocA003_all_counts['RPM'] <- 10^6*(RocA003_all_counts/RocA003_factor)
RocA03_all_counts['RPM'] <- 10^6*(RocA03_all_counts/RocA03_factor)
RocA3_all_counts['RPM'] <- 10^6*(RocA3_all_counts/RocA3_factor)

DMSO_1_first75['RPM'] <- 10^6*(DMSO_1_first75/DMSO_1_factor)
DMSO_2_first75['RPM'] <- 10^6*(DMSO_2_first75/DMSO_2_factor)
RocA003_first75['RPM'] <- 10^6*(RocA003_first75/RocA003_factor)
RocA03_first75['RPM'] <- 10^6*(RocA03_first75/RocA03_factor)
RocA3_first75['RPM'] <- 10^6*(RocA3_first75/RocA3_factor)

## mean value of DMSO
DMSO_all_counts <- (DMSO_1_all_counts+DMSO_2_all_counts)/2
DMSO_first75 <- (DMSO_1_first75+DMSO_2_first75)/2

## construct dataframe
all_RPM <- data.frame(DMSO=DMSO_all_counts$RPM,RocA003=RocA003_all_counts$RPM,RocA03=RocA03_all_counts$RPM,RocA3=RocA3_all_counts$RPM)
rownames(all_RPM) <- rownames(RocA003_all_counts)

first75_RPM <- data.frame(DMSO=DMSO_first75$RPM,RocA003=RocA003_first75$RPM,RocA03=RocA03_first75$RPM,RocA3=RocA3_first75$RPM)
rownames(first75_RPM) <- rownames(RocA003_all_counts)

after75_RPM <- all_RPM-first75_RPM


## select common transcript used for analysis, all_CDS_unnormed_transcript_id.txt generated by Figure 1B results.
filtered_trans <- read.table("all_CDS_unnormed_transcript_id.txt",sep="\t",stringsAsFactors = F,header = T)
common_trans <- Reduce(intersect,filtered_trans)

all_RPM_common <- all_RPM[common_trans,]
first75_RPM_common <- first75_RPM[common_trans,]
after75_RPM_common <- after75_RPM[common_trans,]


## visulization
library(ggpubr)
library(reshape2)
library(export)
ratio_reshaped <- melt(ratio)
p<-ggboxplot(ratio_reshaped,x="variable",y="value",fill="variable",palette = "npg",xlab = FALSE,ylab = "Counts ratio of the first 75 codons",size=1,font.label = 16)
pp <- ggpar(p,legend = "right",legend.title = "Samples")
pp
graph2ppt(x=pp,"../results/Figure 1C.ppt")

Figure 1D

DE analysis via DEseq2.R

setwd("../data")
## read data
RNA <- read.table("RNA.txt",sep="\t",stringsAsFactors = F,header = T,check.names = F)
rownames(RNA) <- RNA$gene_id
RNA <- RNA[,-1]

RPF <- read.table("RPF.txt",sep="\t",stringsAsFactors = F,header = T,check.names = F)
rownames(RPF) <- RPF$gene_id
RPF <- RPF[,-1]

human_info <- read.table("Homo_sapiens.GRCh38.88.transInfo.txt",sep="\t",stringsAsFactors = F,check.names = F,header = T)
longest_info <- read.table("longest.transcripts.info.txt",sep="\t",stringsAsFactors = F,check.names = F,header = T)

common_gene_id <- intersect(rownames(RPF),rownames(RNA))

RNA_common <- RNA[common_gene_id,]
RPF_common <- RPF[common_gene_id,]

common_gene_info <- human_info[which(human_info$gene_id%in%common_gene_id),]

## DE
library(DESeq2)
RocA03_RPF <- RPF[,c(1,2,4)]
indice <- Reduce(intersect,lapply(RocA03_RPF,function(x){which(x>=10)}))
RPF_filtered <- RocA03_RPF[indice,]
RPF_filtered_DESeq2 <- RPF_filtered
# construct sample information
colData <- data.frame(sample=colnames(RPF_filtered_DESeq2),condition=rep(c("DMSO","RocA03"),times=c(2,1)))
rownames(colData) <- as.character(colData$sample)

# construct DESeq dataset
dds <- DESeqDataSetFromMatrix(countData = RPF_filtered_DESeq2,colData = colData,design = ~condition)

# normalization
dds <- DESeq(dds)

# get the results
res <- results(dds)
summary(res)
head(res)

# get differential expression genes
table(res$padj<0.05)
res <- res[order(res$padj),]

DE_genes_table <- subset(res,padj < 0.05 & abs(log2FoldChange)>=1)
write.table(DE_genes_table,"DE_genes_table.txt",sep="\t",quote = F,row.names = TRUE,col.names = TRUE)
DE_genes <- rownames(DE_genes_table)

# get expression matrix of DE genes(normalized) and counts
DE_genes_counts_normalized <- counts(dds,normalize=TRUE)[DE_genes,]
DE_genes_counts <- RPF_filtered_DESeq2[DE_genes,]
up_genes_res <- as.data.frame(subset(res,padj<=0.05&log2FoldChange>=1))
down_genes_res <- as.data.frame(subset(res,padj<=0.05&log2FoldChange<=-1))
Unchanged_genes_res <- as.data.frame(res)[setdiff(res@rownames,union(rownames(up_genes_res),rownames(down_genes_res))),]

up_gene_info <- longest_info[match(rownames(up_genes_res),longest_info$gene_id),]
down_gene_info <- longest_info[match(rownames(down_genes_res),longest_info$gene_id),]
unchanged_gene_info <- longest_info[match(rownames(Unchanged_genes_res),longest_info$gene_id),]

write.table(up_gene_info,"374_up_genes_info.txt",sep="\t",quote=F,row.names = F,col.names = T)
write.table(down_gene_info,"643_down_genes_info.txt",sep="\t",quote=F,row.names = F,col.names = T)
write.table(unchanged_gene_info,"3918_unchanged_genes_info.txt",sep="\t",quote=F,row.names = F,col.names = T)

write.table(up_genes_res,"374_up_genes_data.txt",sep="\t",quote=F,row.names = T,col.names = T)
write.table(down_genes_res,"643_down_genes_data.txt",sep="\t",quote=F,row.names = T,col.names = T)

visualization

## volcano plot using ggplot2
log2FC <- res$log2FoldChange
padjust <- res$padj
data_for_plot <- data.frame(log2FC=log2FC,padjust=padjust)
data_for_plot <- na.omit(data_for_plot)
data_for_plot$sig[(data_for_plot$padjust>0.05|data_for_plot$padjust=="NA")|(data_for_plot$log2FC>-1|data_for_plot$log2FC<1)] <- "NO"
data_for_plot$sig[(data_for_plot$padjust<=0.05&data_for_plot$log2FC>=1)] <- "up"
data_for_plot$sig[(data_for_plot$padjust<=0.05 & data_for_plot$log2FC<=-1)] <- "down"
summary(data_for_plot$sig=="up")
summary(data_for_plot$sig=="down")
summary(data_for_plot$sig=="NO")
x_lim <- max(log2FC,-log2FC)
library(ggplot2)
library(RColorBrewer)
theme_set(theme_bw())

p <- ggplot(data_for_plot,aes(log2FC,-1*log10(padjust),color = sig))+geom_point()+xlim(-4,4)+labs(x="log2RPF(RocA03/DMSO)",y="-log10(p.adjust)")

p <- p + scale_color_manual(values =c("#0072B5","grey","#BC3C28"))+
  geom_hline(yintercept=-log10(0.05),linetype=4)+
  geom_vline(xintercept=c(-1,1),linetype=4)
p <- p +theme(panel.grid =element_blank())+
    theme(axis.line = element_line(size=0))+ylim(0,15)
p <- p  +guides(colour = FALSE)
p <- p +theme(axis.text=element_text(size=20),axis.title=element_text(size=20))
p

Figure 1E-F

Metagene analysis via RiboMiner

workdir=`pwd`
BamDir=$workdir/../07.STAR
Ref=/workdata/home/lifj/lifj/data/Reference/human
transcript=/workdata/home/lifj/lifj/data/Reference/human/RiboCode_annot_Human/RiboCode_annot/transcripts_sequence.fa
results=$workdir/MA
attribute=$workdir/GSE70211_configure.txt
trans_info=$Ref/longest.transcripts.info.txt
groups='DMSO,RocA003,RocA03,RocA3'
replicates='DMSO-1,DMSO-2__RocA-003__RocA-03__RocA-3'


MetageneAnalysis -f $attribute -c $trans_info -o $results/MA_RocA03_up_unnormed -U codon -M RPKM -u 100 -d 400 -l 0 -n 0 -m 0 -e 5 --norm no -y 100 --CI 0.95 --type UTR -S 374_up_genes_info.txt
MetageneAnalysis -f $attribute -c $trans_info -o $results/MA_RocA03_down_unnormed -U codon -M RPKM -u 100 -d 400 -l 0 -n 0 -m 0 -e 5 --norm no -y 100 --CI 0.95 --type UTR -S 643_down_genes_info.txt

PlotMetageneAnalysis -i $results/MA_RocA03_up_unnormed_dataframe.txt -o $results/MA_RocA03_up_unnormed -g $groups -r $replicates -u 100 -d 400 --mode mean
PlotMetageneAnalysis -i $results/MA_RocA03_down_unnormed_dataframe.txt -o $results/MA_RocA03_down_unnormed -g $groups -r $replicates -u 100 -d 400 --mode mean

Figure 2A

Disome-seq data analysis was finished by previous snakemake pipeline.

configure file

bamFiles	readLengths	Offsets	bamLegends
../07.STAR/293FT-DMSO-disome-1_STAR/293FT-DMSO-disome-1.Aligned.toTranscriptome.out.sorted.bam	ALL	0	293FT-DMSO-1
../07.STAR/293FT-RocA03-disome-1_STAR/293FT-RocA03-disome-1.Aligned.toTranscriptome.out.sorted.bam	ALL	0	293FT-RocA03-1
../07.STAR/293FT-RocA03-disome-2_STAR/293FT-RocA03-disome-2.Aligned.toTranscriptome.out.sorted.bam	ALL	0	293FT-RocA03-2

Metagene plot via RiboMiner

workdir=`pwd`
BamDir=$workdir/../07.STAR
Ref=/workdata/home/lifj/lifj/data/Reference/human
transcript=/workdata/home/lifj/lifj/data/Reference/human/RiboCode_annot_Human/RiboCode_annot/transcripts_sequence.fa
results=$workdir/MA
attribute=$workdir/Disome_configure.txt
trans_info=$Ref/longest.transcripts.info.txt
groups='293FT-DMSO,293FT-RocA03'
replicates='293FT-DMSO-1__293FT-RocA03-1,293FT-RocA03-2'


MetageneAnalysis -f $attribute -c $trans_info -o $results/MA_RocA03_RUG_unnormed -U codon -M RPKM -u 100 -d 400 -l 0 -n 0 -m 1 -e 30 --norm no -y 100 --CI 0.95 --type UTR -S 374_up_genes_info.txt
MetageneAnalysis -f $attribute -c $trans_info -o $results/MA_RocA03_RDG_unnormed -U codon -M RPKM -u 100 -d 400 -l 0 -n 0 -m 1 -e 30 --norm no -y 100 --CI 0.95 --type UTR -S 643_down_genes_info.txt
PlotMetageneAnalysis -i $results/MA_RocA03_RUG_unnormed_dataframe.txt -o $results/MA_RocA03_RUG_unnormed -g $groups -r $replicates -u 100 -d 400 --mode mean --slide-window y
PlotMetageneAnalysis -i $results/MA_RocA03_RDG_unnormed_dataframe.txt -o $results/MA_RocA03_RDG_unnormed -g $groups -r $replicates -u 100 -d 400 --mode mean --slide-window y

Figure 2B

Calculate disome density of the first 50 codons via ReadsLengthOfSpecificRegions.py

workdir=`pwd`
BamDir=$workdir/../07.STAR
Ref=/workdata/home/lifj/lifj/data/Reference/human
transcript=/workdata/home/lifj/lifj/data/Reference/human/RiboCode_annot_Human/RiboCode_annot/transcripts_sequence.fa
results=$workdir/MA_3D
attribute=$workdir/Disome_configure.txt
trans_info=$Ref/longest.transcripts.info.txt
groups='293FT-DMSO,293FT-RocA03'
replicates='293FT-DMSO-1__293FT-RocA03-1,293FT-RocA03-2'
up_trans=374_up_genes_info.txt
down_trans=643_down_genes_info.txt

mkdir $workdir/Length


python ReadsLengthOfSpecificRegions.py -i $BamDir/293FT-DMSO-disome-1_STAR/293FT-DMSO-disome-1.Aligned.toTranscriptome.out.sorted.bam -o $workdir/Length/293FT_DMSO_1_RUG_100codons -c $trans_info --type CDS -S 374_up_genes_info.txt -l 1 -r 150

python ReadsLengthOfSpecificRegions.py -i $BamDir/293FT-RocA03-disome-1_STAR/293FT-RocA03-disome-1.Aligned.toTranscriptome.out.sorted.bam -o $workdir/Length/293FT_RocA03_1_RUG_100codons -c $trans_info --type CDS -S 374_up_genes_info.txt -l 1 -r 150
python ReadsLengthOfSpecificRegions.py -i $BamDir/293FT-RocA03-disome-2_STAR/293FT-RocA03-disome-2.Aligned.toTranscriptome.out.sorted.bam -o $workdir/Length/293FT_RocA03_2_RUG_100codons -c $trans_info --type CDS -S 374_up_genes_info.txt -l 1 -r 150

## plot in MicroSoft Excell

Figure 2C-D

Calculate read coverage for each transcript via RiboMiner

workdir=`pwd`
BamDir=$workdir/../07.STAR
Ref=/workdata/home/lifj/lifj/data/Reference/human
transcript=/workdata/home/lifj/lifj/data/Reference/human/RiboCode_annot_Human/RiboCode_annot/transcripts_sequence.fa
results=$workdir/MA
attribute=$workdir/Disome_configure.txt
trans_info=$Ref/longest.transcripts.info.txt
groups='293FT-DMSO,293FT-RocA03,HT-29-DMSO,HT-29-RocA03'
replicates='293FT-DMSO__293FT-RocA03__HT-29-DMSO__HT-29-RocA03'

CoverageOfEachTrans -f $attribute -c $trans_info  -o coverage/MA_RUG --mode coverage -S 374_up_genes_info.txt

PlotTransCoverage -i coverage/MA_RUG_293FT-DMSO_RPM_depth.txt -o coverage/293FT_DMSO_NDUFS6 -c $trans_info -t NDUFS6  --mode coverage --id-type gene_name --color lightskyblue --type single-gene --ymax 30
PlotTransCoverage -i coverage/MA_RUG_293FT-RocA03_RPM_depth.txt -o coverage/293FT_RocA03_NDUFS6 -c $trans_info -t NDUFS6  --mode coverage --id-type gene_name --color lightskyblue --type single-gene --ymax 30

Figure 3A

Get gene sets for GO analysis: GO_analysis.txt

$ less -S GO_analysis.txt
RocA-up-regulated       RocA-down-regulated
MT-ND5  RBBP7
MT-ATP6 KPNB1
MT-CYB  HNRNPD
MT-ND4  HNRNPAB
MT-ND1  SLC1A5
HIST1H3B        HUWE1
MT-CO3  XPO1
MT-ND2  HNRNPF
C1QBP   ATP2A2
MT-CO2  CLTC
GPI     ZNF711
HIST1H4B        H2AFY
LDHA    NUCKS1
DYNC1H1 MYC
HIST1H2AH       CTNNB1
NUP205  YWHAE

Codes: GO_analysis.R

setwd("../data")
if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("clusterProfiler")

library(clusterProfiler)
library(org.Hs.eg.db)
GO_analysis <- read.table("GO_analysis.txt",sep="\t",stringsAsFactors = F,header = T,fill = T,check.names = F)
RocA_insensitive <- na.omit(GO_analysis$`RocA-up-regulated`)
RocA_sensitive <- na.omit(GO_analysis$`RocA-down-regulated`)
## construct dataframe
# up
RocA_insensitive_entrezID <- bitr(RocA_insensitive,fromType="SYMBOL", toType="ENTREZID", OrgDb="org.Hs.eg.db")
group_up <- rep("RocA-up-regulated",times=length(RocA_insensitive_entrezID$ENTREZID))
data_up <- data.frame(geneID=RocA_insensitive_entrezID$ENTREZID,group=group_up)

# down
RocA_sensitive_entrezID <- bitr(RocA_sensitive,fromType="SYMBOL", toType="ENTREZID", OrgDb="org.Hs.eg.db")
group_down <- rep("RocA-down-regulated",times=length(RocA_sensitive_entrezID$ENTREZID))
data_down <- data.frame(geneID=RocA_sensitive_entrezID$ENTREZID,group=group_down)

# all
data_all_GO <- rbind(data_up,data_down)

formula <- compareCluster(geneID~group, data=data_all_GO, fun='enrichGO',OrgDb='org.Hs.eg.db',pvalueCutoff=0.05,pAdjustMethod = "BH",qvalueCutoff = 0.05,ont="BP")
summary(formula)
formula <- simplify(formula)
y <- dotplot(formula,showCategory=20,includeAll=TRUE)
y

formula_dataframe <- as.data.frame(formula)
write.table(formula_dataframe,"GO_analysis_dataframe.txt",quote = F,sep="\t",row.names = F,col.names = T)

regenated in Excel:

Figure 3B-E

Fetch cds sequences for RUGs and RDGs via RiboMiner

$ GetProteinCodingSequence -i ./data/RiboCode_annot/transcripts_sequence.fa -c ./data/longest.transcripts.info.txt -o 643_down --mode whole --table 1 -S 643_down_genes_info.txt
19636  transcripts will be used in the follow analysis.

There are 642 transcripts from 643_down_genes_info.txt used for following analysis.
ENST00000625083 filtered--There is a ambiguous nucleotide N in your sequence
ENST00000387347 filtered--There is a ambiguous nucleotide N in your sequence
Warning: sequence is not divisible by 3
Warning: sequence is not divisible by 3
Warning: sequence is not divisible by 3
Notes: There are 3 transcripts whose cds sequence cannot be divided by 3!
Finish the step of extracting sequences!

$ GetProteinCodingSequence -i ./data/RiboCode_annot/transcripts_sequence.fa -c ./data/longest.transcripts.info.txt -o 374_up --mode whole --table 1 -S 374_up_genes_info.txt   19636  transcripts will be used in the follow analysis.

There are 374 transcripts from 374_up_genes_info.txt used for following analysis.
ENST00000625083 filtered--There is a ambiguous nucleotide N in your sequence
ENST00000387347 filtered--There is a ambiguous nucleotide N in your sequence
Warning: sequence is not divisible by 3
Warning: sequence is not divisible by 3
Warning: sequence is not divisible by 3
Warning: sequence is not divisible by 3
Warning: sequence is not divisible by 3
Warning: sequence is not divisible by 3
Notes: There are 6 transcripts whose cds sequence cannot be divided by 3!
Finish the step of extracting sequences!

$ GetProteinCodingSequence -i ./data/RiboCode_annot/transcripts_sequence.fa -c ./data/longest.transcripts.info.txt -o MTGenes --mode whole --table 1 -S MTGene_info.txt

Calculate tAI, CAI, and charges

workdir=.
fasta=./data
Ref=/workdata/LabMember2/lifj/lifj/data/Reference/human

## 1 cAI
cAI -i MT_Genes_cds_sequences.fa,$fasta/374_up_cds_sequences.fa,$fasta/643_down_cds_sequences.fa,$Ref/longest_cds_sequences.fa -o CAI -t MTGenes,RocA-up-regulated,RocA-down-regulated,all -u 0 -d 100 --reference $Ref/longest_cds_sequences.fa

cAIPlot -i CAI_local_cAI_dataframe.txt -o CAI -u 0 -d 100 --mode all --start 5 --window 7 --step 1

## 2 tAI
tAI -i MT_Genes_cds_sequences.fa,$fasta/374_up_cds_sequences.fa,$fasta/643_down_cds_sequences.fa,$Ref/longest_cds_sequences.fa -o tAI -t MTGenes,RocA-up-regulated,RocA-down-regulated,all -u 0 -d 100 --table 1 -N tRNA_GCNs_human.txt
tAIPlot -i tAI_tAI_dataframe.txt -o tAI -u 0 -d 00 --mode all --start 5 --window 7 --step 1

## 3 hydropathy and charge
hydropathyCharge  -i MT_Genes_cds_sequences.fa,$fasta/374_up_cds_sequences.fa,$fasta/643_down_cds_sequences.fa,$Ref/longest_cds_sequences.fa -o Hydro -t MTGenes,RocA-up-regulated,RocA-down-regulated,all --index hydropathy.txt -u 0 -d 100 --table 1
hydropathyCharge  -i MT_Genes_cds_sequences.fa,$fasta/374_up_cds_sequences.fa,$fasta/643_down_cds_sequences.fa,$Ref/longest_cds_sequences.fa  -o Charge -t MTGenes,RocA-up-regulated,RocA-down-regulated,all --index AA_charge.txt -u 0 -d 100 --table 1

PlotHydropathyCharge -i Hydro_values_dataframe.txt -o Hydro -u 0 -d 100 --mode all --ylab "Average Hydrophobicity"
PlotHydropathyCharge -i Charge_values_dataframe.txt -o Charge -u 0 -d 100 --mode all --ylab "Average Charges"

Figure 4A

Motif identification via homer

$ cat run_homer.sh
findMotifs.pl 374_up_CDS.fa human up_CDS -fasta ~/Reference/human/longest_CDS.fa
findMotifs.pl 643_down_CDS.fa human down_CDS -fasta ~/Reference/human/longest_CDS.fa

$ cat homer-RocA03-up-CDS-motif.txt
A	C	G	T
0.686	0.032	0.248	0.034
0.447	0.240	0.138	0.175
0.037	0.026	0.884	0.053
0.709	0.049	0.192	0.050
0.028	0.215	0.550	0.207
0.733	0.160	0.037	0.070
0.040	0.689	0.207	0.064
0.049	0.631	0.192	0.128
0.677	0.157	0.045	0.121
0.149	0.155	0.218	0.477

Select one polypurine motif in CDS of RUGs for plot via Seq2logo.R

setwd("../data")
library(seqLogo)
up_motif3 <- read.table("homer-RocA03-up-CDS-motif.txt",sep="\t",stringsAsFactors = F,header = T)
up_motif3 <- t(up_motif3)
seqLogo(up_motif3,ic.scale = F)

Figure 4B

blast iCLIP-seq reads to CDS of RUGs

bsub -q TEST-A -n 4 -e 374_up.err -o 374_up.out "blastn -subject 374_up_CDS.fa -query ../05.contam/noncontam_SRR3238818.fa -task blastn  -outfmt \"7 qseqid sseqid pident length mismatch gapopen qstart qend sstart send evalue\" -out RocA3_to_RocA03_up_CDS.txt"

bsub -q TEST-A -n 4 -e 374_up.err -o 374_up.out "blastn -subject 374_up_CDS.fa -query ../05.contam/noncontam_SRR3238817.fa -task blastn  -outfmt \"7 qseqid sseqid pident length mismatch gapopen qstart qend sstart send evalue\" -out RocA03_to_RocA03_up_CDS.txt"

bsub -q TEST-A -n 4 -e 374_up.err -o 374_up.out "blastn -subject 374_up_CDS.fa -query ../05.contam/noncontam_SRR3238816.fa -task blastn  -outfmt \"7 qseqid sseqid pident length mismatch gapopen qstart qend sstart send evalue\" -out RocA003_to_RocA03_up_CDS.txt"

bsub -q TEST-A -n 4 -e 374_up.err -o 374_up.out "blastn -subject 374_up_CDS.fa -query ../05.contam/noncontam_SRR3238815.fa -task blastn  -outfmt \"7 qseqid sseqid pident length mismatch gapopen qstart qend sstart send evalue\" -out DMSO2_to_RocA03_up_CDS.txt"

bsub -q TEST-A -n 4 -e 374_up.err -o 374_up.out "blastn -subject 374_up_CDS.fa -query ../05.contam/noncontam_SRR3238814.fa -task blastn  -outfmt \"7 qseqid sseqid pident length mismatch gapopen qstart qend sstart send evalue\" -out DMSO1_to_RocA03_up_CDS.txt"

blast iCLIP-seq reads to 5UTR of RDGs

bsub -q TEST-A -n 4 -e 643_down.err -o 643_down.out "blastn -subject 643_down_5UTR.fa -query ../05.contam/noncontam_SRR3238818.fa -task blastn  -outfmt \"7 qseqid sseqid pident length mismatch gapopen qstart qend sstart send evalue\" -out RocA3_to_RocA03_down_5UTR.txt"

bsub -q TEST-A -n 4 -e 643_down.err -o 643_down.out "blastn -subject 643_down_5UTR.fa -query ../05.contam/noncontam_SRR3238817.fa -task blastn  -outfmt \"7 qseqid sseqid pident length mismatch gapopen qstart qend sstart send evalue\" -out RocA03_to_RocA03_down_5UTR.txt"

bsub -q TEST-A -n 4 -e 643_down.err -o 643_down.out "blastn -subject 643_down_5UTR.fa -query ../05.contam/noncontam_SRR3238816.fa -task blastn  -outfmt \"7 qseqid sseqid pident length mismatch gapopen qstart qend sstart send evalue\" -out RocA003_to_RocA03_down_5UTR.txt"

bsub -q TEST-A -n 4 -e 643_down.err -o 643_down.out "blastn -subject 643_down_5UTR.fa -query ../05.contam/noncontam_SRR3238815.fa -task blastn  -outfmt \"7 qseqid sseqid pident length mismatch gapopen qstart qend sstart send evalue\" -out DMSO2_to_RocA03_down_5UTR.txt"

bsub -q TEST-A -n 4 -e 643_down.err -o 643_down.out "blastn -subject 643_down_5UTR.fa -query ../05.contam/noncontam_SRR3238814.fa -task blastn  -outfmt \"7 qseqid sseqid pident length mismatch gapopen qstart qend sstart send evalue\" -out DMSO1_to_RocA03_down_5UTR.txt"

Searching polyAG motifs via SearchPolypurineMotifs.py

python SearchPolypurineMotifs.py -i DMSO1_to_RocA03_up_CDS_reads.fa -o DMSO1_to_RocA03_up_CDS_reads --kmer 4 --base AG
python SearchPolypurineMotifs.py -i DMSO2_to_RocA03_up_CDS_reads.fa -o DMSO2_to_RocA03_up_CDS_reads --kmer 4 --base AG
python SearchPolypurineMotifs.py -i RocA003_to_RocA03_up_CDS_reads.fa -o RocA003_to_RocA03_up_CDS_reads --kmer 4 --base AG
python SearchPolypurineMotifs.py -i RocA03_to_RocA03_up_CDS_reads.fa -o RocA03_to_RocA03_up_CDS_reads --kmer 4 --base AG
python SearchPolypurineMotifs.py -i RocA3_to_RocA03_up_CDS_reads.fa -o RocA3_to_RocA03_up_CDS_reads --kmer 4 --base AG

python SearchPolypurineMotifs.py -i DMSO1_to_RocA03_down_5UTR_reads.fa -o DMSO1_to_RocA03_down_5UTR_reads --kmer 4 --base AG
python SearchPolypurineMotifs.py -i DMSO2_to_RocA03_down_5UTR_reads.fa -o DMSO2_to_RocA03_down_5UTR_reads --kmer 4 --base AG
python SearchPolypurineMotifs.py -i RocA003_to_RocA03_down_5UTR_reads.fa -o RocA003_to_RocA03_down_5UTR_reads --kmer 4 --base AG
python SearchPolypurineMotifs.py -i RocA03_to_RocA03_down_5UTR_reads.fa -o RocA03_to_RocA03_down_5UTR_reads --kmer 4 --base AG
python SearchPolypurineMotifs.py -i RocA3_to_RocA03_down_5UTR_reads.fa -o RocA3_to_RocA03_down_5UTR_reads --kmer 4 --base AG

python SearchPolypurineMotifs.py -i DMSO1_to_RocA03_up_CDS_reads.fa -o DMSO1_to_RocA03_up_CDS_reads --kmer 6 --base AG
python SearchPolypurineMotifs.py -i DMSO2_to_RocA03_up_CDS_reads.fa -o DMSO2_to_RocA03_up_CDS_reads --kmer 6 --base AG
python SearchPolypurineMotifs.py -i RocA003_to_RocA03_up_CDS_reads.fa -o RocA003_to_RocA03_up_CDS_reads --kmer 6 --base AG
python SearchPolypurineMotifs.py -i RocA03_to_RocA03_up_CDS_reads.fa -o RocA03_to_RocA03_up_CDS_reads --kmer 6 --base AG
python SearchPolypurineMotifs.py -i RocA3_to_RocA03_up_CDS_reads.fa -o RocA3_to_RocA03_up_CDS_reads --kmer 6 --base AG

python SearchPolypurineMotifs.py -i DMSO1_to_RocA03_up_CDS_reads.fa -o DMSO1_to_RocA03_up_CDS_reads --kmer 5 --base AG
python SearchPolypurineMotifs.py -i DMSO2_to_RocA03_up_CDS_reads.fa -o DMSO2_to_RocA03_up_CDS_reads --kmer 5 --base AG
python SearchPolypurineMotifs.py -i RocA003_to_RocA03_up_CDS_reads.fa -o RocA003_to_RocA03_up_CDS_reads --kmer 5 --base AG
python SearchPolypurineMotifs.py -i RocA03_to_RocA03_up_CDS_reads.fa -o RocA03_to_RocA03_up_CDS_reads --kmer 5 --base AG
python SearchPolypurineMotifs.py -i RocA3_to_RocA03_up_CDS_reads.fa -o RocA3_to_RocA03_up_CDS_reads --kmer 5 --base AG

python SearchPolypurineMotifs.py -i DMSO1_to_RocA03_down_5UTR_reads.fa -o DMSO1_to_RocA03_down_5UTR_reads --kmer 6 --base AG
python SearchPolypurineMotifs.py -i DMSO2_to_RocA03_down_5UTR_reads.fa -o DMSO2_to_RocA03_down_5UTR_reads --kmer 6 --base AG
python SearchPolypurineMotifs.py -i RocA003_to_RocA03_down_5UTR_reads.fa -o RocA003_to_RocA03_down_5UTR_reads --kmer 6 --base AG
python SearchPolypurineMotifs.py -i RocA03_to_RocA03_down_5UTR_reads.fa -o RocA03_to_RocA03_down_5UTR_reads --kmer 6 --base AG
python SearchPolypurineMotifs.py -i RocA3_to_RocA03_down_5UTR_reads.fa -o RocA3_to_RocA03_down_5UTR_reads --kmer 6 --base AG

python SearchPolypurineMotifs.py -i DMSO1_to_RocA03_down_5UTR_reads.fa -o DMSO1_to_RocA03_down_5UTR_reads --kmer 5 --base AG
python SearchPolypurineMotifs.py -i DMSO2_to_RocA03_down_5UTR_reads.fa -o DMSO2_to_RocA03_down_5UTR_reads --kmer 5 --base AG
python SearchPolypurineMotifs.py -i RocA003_to_RocA03_down_5UTR_reads.fa -o RocA003_to_RocA03_down_5UTR_reads --kmer 5 --base AG
python SearchPolypurineMotifs.py -i RocA03_to_RocA03_down_5UTR_reads.fa -o RocA03_to_RocA03_down_5UTR_reads --kmer 5 --base AG
python SearchPolypurineMotifs.py -i RocA3_to_RocA03_down_5UTR_reads.fa -o RocA3_to_RocA03_down_5UTR_reads --kmer 5 --base AG

statistic the frequency of poly-purine motifs

## collapse UTR
less -S RocA3_to_RocA03_down_5UTR_reads_polyAG_4_mer.txt|cut -f 3|sort |uniq -c |awk -F " " 'BEGIN{OFS="\t"}{print $2,$1}' |sort > RocA3_to_RocA03_down_5UTR_reads_polyAG_4_mer_collapsed.txt
less -S RocA3_to_RocA03_down_5UTR_reads_polyAG_5_mer.txt|cut -f 3|sort |uniq -c |awk -F " " 'BEGIN{OFS="\t"}{print $2,$1}' |sort > RocA3_to_RocA03_down_5UTR_reads_polyAG_5_mer_collapsed.txt
less -S RocA3_to_RocA03_down_5UTR_reads_polyAG_6_mer.txt|cut -f 3|sort |uniq -c |awk -F " " 'BEGIN{OFS="\t"}{print $2,$1}' |sort > RocA3_to_RocA03_down_5UTR_reads_polyAG_6_mer_collapsed.txt

less -S RocA03_to_RocA03_down_5UTR_reads_polyAG_4_mer.txt|cut -f 3|sort |uniq -c |awk -F " " 'BEGIN{OFS="\t"}{print $2,$1}' |sort > RocA03_to_RocA03_down_5UTR_reads_polyAG_4_mer_collapsed.txt
less -S RocA03_to_RocA03_down_5UTR_reads_polyAG_5_mer.txt|cut -f 3|sort |uniq -c |awk -F " " 'BEGIN{OFS="\t"}{print $2,$1}' |sort > RocA03_to_RocA03_down_5UTR_reads_polyAG_5_mer_collapsed.txt
less -S RocA03_to_RocA03_down_5UTR_reads_polyAG_6_mer.txt|cut -f 3|sort |uniq -c |awk -F " " 'BEGIN{OFS="\t"}{print $2,$1}' |sort > RocA03_to_RocA03_down_5UTR_reads_polyAG_6_mer_collapsed.txt

less -S RocA003_to_RocA03_down_5UTR_reads_polyAG_4_mer.txt|cut -f 3|sort |uniq -c |awk -F " " 'BEGIN{OFS="\t"}{print $2,$1}' |sort > RocA003_to_RocA03_down_5UTR_reads_polyAG_4_mer_collapsed.txt
less -S RocA003_to_RocA03_down_5UTR_reads_polyAG_5_mer.txt|cut -f 3|sort |uniq -c |awk -F " " 'BEGIN{OFS="\t"}{print $2,$1}' |sort > RocA003_to_RocA03_down_5UTR_reads_polyAG_5_mer_collapsed.txt
less -S RocA003_to_RocA03_down_5UTR_reads_polyAG_6_mer.txt|cut -f 3|sort |uniq -c |awk -F " " 'BEGIN{OFS="\t"}{print $2,$1}' |sort > RocA003_to_RocA03_down_5UTR_reads_polyAG_6_mer_collapsed.txt

less -S DMSO1_to_RocA03_down_5UTR_reads_polyAG_4_mer.txt|cut -f 3|sort |uniq -c |awk -F " " 'BEGIN{OFS="\t"}{print $2,$1}' |sort > DMSO1_to_RocA03_down_5UTR_reads_polyAG_4_mer_collapsed.txt
less -S DMSO1_to_RocA03_down_5UTR_reads_polyAG_5_mer.txt|cut -f 3|sort |uniq -c |awk -F " " 'BEGIN{OFS="\t"}{print $2,$1}' |sort > DMSO1_to_RocA03_down_5UTR_reads_polyAG_5_mer_collapsed.txt
less -S DMSO1_to_RocA03_down_5UTR_reads_polyAG_6_mer.txt|cut -f 3|sort |uniq -c |awk -F " " 'BEGIN{OFS="\t"}{print $2,$1}' |sort > DMSO1_to_RocA03_down_5UTR_reads_polyAG_6_mer_collapsed.txt

less -S DMSO2_to_RocA03_down_5UTR_reads_polyAG_4_mer.txt|cut -f 3|sort |uniq -c |awk -F " " 'BEGIN{OFS="\t"}{print $2,$1}' |sort > DMSO2_to_RocA03_down_5UTR_reads_polyAG_4_mer_collapsed.txt
less -S DMSO2_to_RocA03_down_5UTR_reads_polyAG_5_mer.txt|cut -f 3|sort |uniq -c |awk -F " " 'BEGIN{OFS="\t"}{print $2,$1}' |sort > DMSO2_to_RocA03_down_5UTR_reads_polyAG_5_mer_collapsed.txt
less -S DMSO2_to_RocA03_down_5UTR_reads_polyAG_6_mer.txt|cut -f 3|sort |uniq -c |awk -F " " 'BEGIN{OFS="\t"}{print $2,$1}' |sort > DMSO2_to_RocA03_down_5UTR_reads_polyAG_6_mer_collapsed.txt

## collapse CDS
less -S RocA3_to_RocA03_up_CDS_reads_polyAG_4_mer.txt|cut -f 3|sort |uniq -c |awk -F " " 'BEGIN{OFS="\t"}{print $2,$1}' |sort > RocA3_to_RocA03_up_CDS_reads_polyAG_4_mer_collapsed.txt
less -S RocA3_to_RocA03_up_CDS_reads_polyAG_5_mer.txt|cut -f 3|sort |uniq -c |awk -F " " 'BEGIN{OFS="\t"}{print $2,$1}' |sort > RocA3_to_RocA03_up_CDS_reads_polyAG_5_mer_collapsed.txt
less -S RocA3_to_RocA03_up_CDS_reads_polyAG_6_mer.txt|cut -f 3|sort |uniq -c |awk -F " " 'BEGIN{OFS="\t"}{print $2,$1}' |sort > RocA3_to_RocA03_up_CDS_reads_polyAG_6_mer_collapsed.txt

less -S RocA03_to_RocA03_up_CDS_reads_polyAG_4_mer.txt|cut -f 3|sort |uniq -c |awk -F " " 'BEGIN{OFS="\t"}{print $2,$1}' |sort > RocA03_to_RocA03_up_CDS_reads_polyAG_4_mer_collapsed.txt
less -S RocA03_to_RocA03_up_CDS_reads_polyAG_5_mer.txt|cut -f 3|sort |uniq -c |awk -F " " 'BEGIN{OFS="\t"}{print $2,$1}' |sort > RocA03_to_RocA03_up_CDS_reads_polyAG_5_mer_collapsed.txt
less -S RocA03_to_RocA03_up_CDS_reads_polyAG_6_mer.txt|cut -f 3|sort |uniq -c |awk -F " " 'BEGIN{OFS="\t"}{print $2,$1}' |sort > RocA03_to_RocA03_up_CDS_reads_polyAG_6_mer_collapsed.txt

less -S RocA003_to_RocA03_up_CDS_reads_polyAG_4_mer.txt|cut -f 3|sort |uniq -c |awk -F " " 'BEGIN{OFS="\t"}{print $2,$1}' |sort > RocA003_to_RocA03_up_CDS_reads_polyAG_4_mer_collapsed.txt
less -S RocA003_to_RocA03_up_CDS_reads_polyAG_5_mer.txt|cut -f 3|sort |uniq -c |awk -F " " 'BEGIN{OFS="\t"}{print $2,$1}' |sort > RocA003_to_RocA03_up_CDS_reads_polyAG_5_mer_collapsed.txt
less -S RocA003_to_RocA03_up_CDS_reads_polyAG_6_mer.txt|cut -f 3|sort |uniq -c |awk -F " " 'BEGIN{OFS="\t"}{print $2,$1}' |sort > RocA003_to_RocA03_up_CDS_reads_polyAG_6_mer_collapsed.txt

less -S DMSO1_to_RocA03_up_CDS_reads_polyAG_4_mer.txt|cut -f 3|sort |uniq -c |awk -F " " 'BEGIN{OFS="\t"}{print $2,$1}' |sort > DMSO1_to_RocA03_up_CDS_reads_polyAG_4_mer_collapsed.txt
less -S DMSO1_to_RocA03_up_CDS_reads_polyAG_5_mer.txt|cut -f 3|sort |uniq -c |awk -F " " 'BEGIN{OFS="\t"}{print $2,$1}' |sort > DMSO1_to_RocA03_up_CDS_reads_polyAG_5_mer_collapsed.txt
less -S DMSO1_to_RocA03_up_CDS_reads_polyAG_6_mer.txt|cut -f 3|sort |uniq -c |awk -F " " 'BEGIN{OFS="\t"}{print $2,$1}' |sort > DMSO1_to_RocA03_up_CDS_reads_polyAG_6_mer_collapsed.txt

less -S DMSO2_to_RocA03_up_CDS_reads_polyAG_4_mer.txt|cut -f 3|sort |uniq -c |awk -F " " 'BEGIN{OFS="\t"}{print $2,$1}' |sort > DMSO2_to_RocA03_up_CDS_reads_polyAG_4_mer_collapsed.txt
less -S DMSO2_to_RocA03_up_CDS_reads_polyAG_5_mer.txt|cut -f 3|sort |uniq -c |awk -F " " 'BEGIN{OFS="\t"}{print $2,$1}' |sort > DMSO2_to_RocA03_up_CDS_reads_polyAG_5_mer_collapsed.txt
less -S DMSO2_to_RocA03_up_CDS_reads_polyAG_6_mer.txt|cut -f 3|sort |uniq -c |awk -F " " 'BEGIN{OFS="\t"}{print $2,$1}' |sort > DMSO2_to_RocA03_up_CDS_reads_polyAG_6_mer_collapsed.txt

Calculate motif score via PolypurineMotifScore.py

# statistic motif score
python PolypurineMotifScore.py -i ../05.contam/noncontam_SRR3238814.fa -m DMSO1_to_RocA03_up_CDS_reads_polyAG_4_mer_collapsed.txt -o DMSO1_to_RocA03_up_CDS_reads_polyAG_4_mer_collapsed
python PolypurineMotifScore.py -i ../05.contam/noncontam_SRR3238815.fa -m DMSO2_to_RocA03_up_CDS_reads_polyAG_4_mer_collapsed.txt -o DMSO2_to_RocA03_up_CDS_reads_polyAG_4_mer_collapsed
python PolypurineMotifScore.py -i ../05.contam/noncontam_SRR3238816.fa -m RocA003_to_RocA03_up_CDS_reads_polyAG_4_mer_collapsed.txt -o RocA003_to_RocA03_up_CDS_reads_polyAG_4_mer_collapsed
python PolypurineMotifScore.py -i ../05.contam/noncontam_SRR3238817.fa -m RocA03_to_RocA03_up_CDS_reads_polyAG_4_mer_collapsed.txt -o RocA03_to_RocA03_up_CDS_reads_polyAG_4_mer_collapsed
python PolypurineMotifScore.py -i ../05.contam/noncontam_SRR3238818.fa -m RocA3_to_RocA03_up_CDS_reads_polyAG_4_mer_collapsed.txt -o RocA3_to_RocA03_up_CDS_reads_polyAG_4_mer_collapsed


python PolypurineMotifScore.py -i ../05.contam/noncontam_SRR3238814.fa -m DMSO1_to_RocA03_down_5UTR_reads_polyAG_4_mer_collapsed.txt -o DMSO1_to_RocA03_down_5UTR_reads_polyAG_4_mer_collapsed
python PolypurineMotifScore.py -i ../05.contam/noncontam_SRR3238815.fa -m DMSO2_to_RocA03_down_5UTR_reads_polyAG_4_mer_collapsed.txt -o DMSO2_to_RocA03_down_5UTR_reads_polyAG_4_mer_collapsed
python PolypurineMotifScore.py -i ../05.contam/noncontam_SRR3238816.fa -m RocA003_to_RocA03_down_5UTR_reads_polyAG_4_mer_collapsed.txt -o RocA003_to_RocA03_down_5UTR_reads_polyAG_4_mer_collapsed
python PolypurineMotifScore.py -i ../05.contam/noncontam_SRR3238817.fa -m RocA03_to_RocA03_down_5UTR_reads_polyAG_4_mer_collapsed.txt -o RocA03_to_RocA03_down_5UTR_reads_polyAG_4_mer_collapsed
python PolypurineMotifScore.py -i ../05.contam/noncontam_SRR3238818.fa -m RocA3_to_RocA03_down_5UTR_reads_polyAG_4_mer_collapsed.txt -o RocA3_to_RocA03_down_5UTR_reads_polyAG_4_mer_collapsed

python PolypurineMotifScore.py -i ../05.contam/noncontam_SRR3238814.fa -m DMSO1_to_RocA03_up_CDS_reads_polyAG_5_mer_collapsed.txt -o DMSO1_to_RocA03_up_CDS_reads_polyAG_5_mer_collapsed
python PolypurineMotifScore.py -i ../05.contam/noncontam_SRR3238815.fa -m DMSO2_to_RocA03_up_CDS_reads_polyAG_5_mer_collapsed.txt -o DMSO2_to_RocA03_up_CDS_reads_polyAG_5_mer_collapsed
python PolypurineMotifScore.py -i ../05.contam/noncontam_SRR3238816.fa -m RocA003_to_RocA03_up_CDS_reads_polyAG_5_mer_collapsed.txt -o RocA003_to_RocA03_up_CDS_reads_polyAG_5_mer_collapsed
python PolypurineMotifScore.py -i ../05.contam/noncontam_SRR3238817.fa -m RocA03_to_RocA03_up_CDS_reads_polyAG_5_mer_collapsed.txt -o RocA03_to_RocA03_up_CDS_reads_polyAG_5_mer_collapsed
python PolypurineMotifScore.py -i ../05.contam/noncontam_SRR3238818.fa -m RocA3_to_RocA03_up_CDS_reads_polyAG_5_mer_collapsed.txt -o RocA3_to_RocA03_up_CDS_reads_polyAG_5_mer_collapsed


python PolypurineMotifScore.py -i ../05.contam/noncontam_SRR3238814.fa -m DMSO1_to_RocA03_down_5UTR_reads_polyAG_5_mer_collapsed.txt -o DMSO1_to_RocA03_down_5UTR_reads_polyAG_5_mer_collapsed
python PolypurineMotifScore.py -i ../05.contam/noncontam_SRR3238815.fa -m DMSO2_to_RocA03_down_5UTR_reads_polyAG_5_mer_collapsed.txt -o DMSO2_to_RocA03_down_5UTR_reads_polyAG_5_mer_collapsed
python PolypurineMotifScore.py -i ../05.contam/noncontam_SRR3238816.fa -m RocA003_to_RocA03_down_5UTR_reads_polyAG_5_mer_collapsed.txt -o RocA003_to_RocA03_down_5UTR_reads_polyAG_5_mer_collapsed
python PolypurineMotifScore.py -i ../05.contam/noncontam_SRR3238817.fa -m RocA03_to_RocA03_down_5UTR_reads_polyAG_5_mer_collapsed.txt -o RocA03_to_RocA03_down_5UTR_reads_polyAG_5_mer_collapsed
python PolypurineMotifScore.py -i ../05.contam/noncontam_SRR3238818.fa -m RocA3_to_RocA03_down_5UTR_reads_polyAG_5_mer_collapsed.txt -o RocA3_to_RocA03_down_5UTR_reads_polyAG_5_mer_collapsed

python PolypurineMotifScore.py -i ../05.contam/noncontam_SRR3238814.fa -m DMSO1_to_RocA03_up_CDS_reads_polyAG_6_mer_collapsed.txt -o DMSO1_to_RocA03_up_CDS_reads_polyAG_6_mer_collapsed
python PolypurineMotifScore.py -i ../05.contam/noncontam_SRR3238815.fa -m DMSO2_to_RocA03_up_CDS_reads_polyAG_6_mer_collapsed.txt -o DMSO2_to_RocA03_up_CDS_reads_polyAG_6_mer_collapsed
python PolypurineMotifScore.py -i ../05.contam/noncontam_SRR3238816.fa -m RocA003_to_RocA03_up_CDS_reads_polyAG_6_mer_collapsed.txt -o RocA003_to_RocA03_up_CDS_reads_polyAG_6_mer_collapsed
python PolypurineMotifScore.py -i ../05.contam/noncontam_SRR3238817.fa -m RocA03_to_RocA03_up_CDS_reads_polyAG_6_mer_collapsed.txt -o RocA03_to_RocA03_up_CDS_reads_polyAG_6_mer_collapsed
python PolypurineMotifScore.py -i ../05.contam/noncontam_SRR3238818.fa -m RocA3_to_RocA03_up_CDS_reads_polyAG_6_mer_collapsed.txt -o RocA3_to_RocA03_up_CDS_reads_polyAG_6_mer_collapsed

python PolypurineMotifScore.py -i ../05.contam/noncontam_SRR3238814.fa -m DMSO1_to_RocA03_down_5UTR_reads_polyAG_6_mer_collapsed.txt -o DMSO1_to_RocA03_down_5UTR_reads_polyAG_6_mer_collapsed
python PolypurineMotifScore.py -i ../05.contam/noncontam_SRR3238815.fa -m DMSO2_to_RocA03_down_5UTR_reads_polyAG_6_mer_collapsed.txt -o DMSO2_to_RocA03_down_5UTR_reads_polyAG_6_mer_collapsed
python PolypurineMotifScore.py -i ../05.contam/noncontam_SRR3238816.fa -m RocA003_to_RocA03_down_5UTR_reads_polyAG_6_mer_collapsed.txt -o RocA003_to_RocA03_down_5UTR_reads_polyAG_6_mer_collapsed
python PolypurineMotifScore.py -i ../05.contam/noncontam_SRR3238817.fa -m RocA03_to_RocA03_down_5UTR_reads_polyAG_6_mer_collapsed.txt -o RocA03_to_RocA03_down_5UTR_reads_polyAG_6_mer_collapsed
python PolypurineMotifScore.py -i ../05.contam/noncontam_SRR3238818.fa -m RocA3_to_RocA03_down_5UTR_reads_polyAG_6_mer_collapsed.txt -o RocA3_to_RocA03_down_5UTR_reads_polyAG_6_mer_collapsed

Calculate motif enrichment (RocA motif score/DMSO motif score) in Excel

./results/Figure 4B.xlsx

Figure 4C

Calculate density around poly-purine motifs via RiboDensityAroundPolyPurineMotifs.py

workdir=`pwd`
BamDir=/workdata/home/lifj/lifj/Project/05.Ribo_seq_human/GSE70211/07.STAR
Ref=/workdata/home/lifj/lifj/data/Reference/human
transcript=/workdata/home/lifj/lifj/data/Reference/human/RiboCode_annot_Human/RiboCode_annot/transcripts_sequence.fa
results=$workdir/MA_HEK293
attribute=$workdir/GSE70211_configure.txt
trans_info=$Ref/longest.transcripts.info.txt
up_trans=$workdir/374_up_regulated_genes_info.txt
down_trans=$workdir/643_down_regulated_genes_info.txt


## poly-purine
mkdir -p polymotifs/counts_10

## UTR
python RiboDensityAroundPolyPurineMotifs.py -f $attribute -c $trans_info -o polymotifs/counts_10/hek293t_UTR -M RPKM -u 50 -d 50 -l 0
 -n 0 -F $transcript --kmer 4 --type 5UTR -S 643_down_regulated_genes_info.txt  --id-type transcript_id --base AG
python RiboDensityAroundPolyPurineMotifs.py -f $attribute -c $trans_info -o polymotifs/counts_10/hek293t_UTR -M RPKM -u 50 -d 50 -l 0
 -n 0 -F $transcript --kmer 4 --type 5UTR -S 643_down_regulated_genes_info.txt  --id-type transcript_id --base AC
python RiboDensityAroundPolyPurineMotifs.py -f $attribute -c $trans_info -o polymotifs/counts_10/hek293t_UTR -M RPKM -u 50 -d 50 -l 0
 -n 0 -F $transcript --kmer 4 --type 5UTR -S 643_down_regulated_genes_info.txt  --id-type transcript_id --base CT

python RiboDensityAroundPolyPurineMotifs.py -f $attribute -c $trans_info -o polymotifs/counts_10/hek293t_UTR -M RPKM -u 50 -d 50 -l 0
 -n 0 -F $transcript --kmer 5 --type 5UTR -S 643_down_regulated_genes_info.txt  --id-type transcript_id --base AG
python RiboDensityAroundPolyPurineMotifs.py -f $attribute -c $trans_info -o polymotifs/counts_10/hek293t_UTR -M RPKM -u 50 -d 50 -l 0
 -n 0 -F $transcript --kmer 5 --type 5UTR -S 643_down_regulated_genes_info.txt  --id-type transcript_id --base AC
python RiboDensityAroundPolyPurineMotifs.py -f $attribute -c $trans_info -o polymotifs/counts_10/hek293t_UTR -M RPKM -u 50 -d 50 -l 0 -n 0 -F $transcript --kmer 5 --type 5UTR -S 643_down_regulated_genes_info.txt  --id-type transcript_id --base CT

python RiboDensityAroundPolyPurineMotifs.py -f $attribute -c $trans_info -o polymotifs/counts_10/hek293t_UTR -M RPKM -u 50 -d 50 -l 0 -n 0 -F $transcript --kmer 6 --type 5UTR -S 643_down_regulated_genes_info.txt  --id-type transcript_id --base AG
python RiboDensityAroundPolyPurineMotifs.py -f $attribute -c $trans_info -o polymotifs/counts_10/hek293t_UTR -M RPKM -u 50 -d 50 -l 0 -n 0 -F $transcript --kmer 6 --type 5UTR -S 643_down_regulated_genes_info.txt  --id-type transcript_id --base AC
python RiboDensityAroundPolyPurineMotifs.py -f $attribute -c $trans_info -o polymotifs/counts_10/hek293t_UTR -M RPKM -u 50 -d 50 -l 0 -n 0 -F $transcript --kmer 6 --type 5UTR -S 643_down_regulated_genes_info.txt  --id-type transcript_id --base CT

## CDS
python RiboDensityAroundPolyPurineMotifs.py -f $attribute -c $trans_info -o polymotifs/counts_10/hek293t -M RPKM -u 50 -d 50 -l 0 -n 0 -F $transcript --kmer 4 --type CDS -S 374_up_regulated_genes_info.txt  --id-type transcript_id --base AG
python RiboDensityAroundPolyPurineMotifs.py -f $attribute -c $trans_info -o polymotifs/counts_10/hek293t -M RPKM -u 50 -d 50 -l 0 -n 0 -F $transcript --kmer 4 --type CDS -S 374_up_regulated_genes_info.txt  --id-type transcript_id --base AC
python RiboDensityAroundPolyPurineMotifs.py -f $attribute -c $trans_info -o polymotifs/counts_10/hek293t -M RPKM -u 50 -d 50 -l 0 -n 0 -F $transcript --kmer 4 --type CDS -S 374_up_regulated_genes_info.txt  --id-type transcript_id --base CT

python RiboDensityAroundPolyPurineMotifs.py -f $attribute -c $trans_info -o polymotifs/counts_10/hek293t -M RPKM -u 50 -d 50 -l 0 -n 0 -F $transcript --kmer 5 --type CDS -S 374_up_regulated_genes_info.txt  --id-type transcript_id --base AG
python RiboDensityAroundPolyPurineMotifs.py -f $attribute -c $trans_info -o polymotifs/counts_10/hek293t -M RPKM -u 50 -d 50 -l 0 -n 0 -F $transcript --kmer 5 --type CDS -S 374_up_regulated_genes_info.txt  --id-type transcript_id --base AC
python RiboDensityAroundPolyPurineMotifs.py -f $attribute -c $trans_info -o polymotifs/counts_10/hek293t -M RPKM -u 50 -d 50 -l 0 -n 0 -F $transcript --kmer 5 --type CDS -S 374_up_regulated_genes_info.txt  --id-type transcript_id --base CT

python RiboDensityAroundPolyPurineMotifs.py -f $attribute -c $trans_info -o polymotifs/counts_10/hek293t -M RPKM -u 50 -d 50 -l 0 -n 0 -F $transcript --kmer 6 --type CDS -S 374_up_regulated_genes_info.txt  --id-type transcript_id --base AG
python RiboDensityAroundPolyPurineMotifs.py -f $attribute -c $trans_info -o polymotifs/counts_10/hek293t -M RPKM -u 50 -d 50 -l 0 -n 0 -F $transcript --kmer 6 --type CDS -S 374_up_regulated_genes_info.txt  --id-type transcript_id --base AC
python RiboDensityAroundPolyPurineMotifs.py -f $attribute -c $trans_info -o polymotifs/counts_10/hek293t -M RPKM -u 50 -d 50 -l 0 -n 0 -F $transcript --kmer 6 --type CDS -S 374_up_regulated_genes_info.txt  --id-type transcript_id --base CT

polyAG, polyAC and polyCT motifs

$ cat ./data/4mer-5UTR-AG.txt
motif
GGAA
GAAA
AAAA
AGAA
AAGG
AGGA
GAAG
AGAG
AAAG
GAGA
AAGA
AGGG
$ cat ./data/4mer-5UTR-AC.txt
motifs
AAAC
AACA
AACC
ACAA
ACAC
ACCA
ACCC
CAAA
CAAC
CACA
CACC
CCAA
CCAC
CCCA
CCCC

$ cat ./data/4mer-CT.txt
CCCC
CCCT
CCTC
CCTT
CTCC
CTCT
CTTC
CTTT
TCCC
TCCT
TCTC
TCTT
TTCC
TTCT
TTTC
TTTT

Calculate mean density between replicates

## 01. calculate mean
mkdir counts_10_mean_kmer

## CDS
python MeanRiboDensityAroundPolyPurineMotifsFiltered.py -i counts_10/hek293t_DMSO-1_polyAC_4_mer.txt,counts_10/hek293t_DMSO-2_polyAC_4_mer.txt -o counts_10_mean_kmer/hek293t_DMSO_polyAC_4_mer --kmer 4mer-CDS-AC.txt
python MeanRiboDensityAroundPolyPurineMotifsFiltered.py -i counts_10/hek293t_DMSO-1_polyAG_4_mer.txt,counts_10/hek293t_DMSO-2_polyAG_4_mer.txt -o counts_10_mean_kmer/hek293t_DMSO_polyAG_4_mer --kmer 4mer-CDS-AG.txt
python MeanRiboDensityAroundPolyPurineMotifsFiltered.py -i counts_10/hek293t_DMSO-1_polyCT_4_mer.txt,counts_10/hek293t_DMSO-2_polyCT_4_mer.txt -o counts_10_mean_kmer/hek293t_DMSO_polyCT_4_mer --kmer 4mer-CT.txt


python MeanRiboDensityAroundPolyPurineMotifsFiltered.py -i counts_10/hek293t_DMSO-1_polyAC_5_mer.txt,counts_10/hek293t_DMSO-2_polyAC_5_mer.txt -o counts_10_mean_kmer/hek293t_DMSO_polyAC_5_mer --kmer 5mer-CDS-AC.txt
python MeanRiboDensityAroundPolyPurineMotifsFiltered.py -i counts_10/hek293t_DMSO-1_polyAG_5_mer.txt,counts_10/hek293t_DMSO-2_polyAG_5_mer.txt -o counts_10_mean_kmer/hek293t_DMSO_polyAG_5_mer --kmer 5mer-CDS-AG.txt
python MeanRiboDensityAroundPolyPurineMotifsFiltered.py -i counts_10/hek293t_DMSO-1_polyCT_5_mer.txt,counts_10/hek293t_DMSO-2_polyCT_5_mer.txt -o counts_10_mean_kmer/hek293t_DMSO_polyCT_5_mer --kmer 5mer-CT.txt


python MeanRiboDensityAroundPolyPurineMotifsFiltered.py -i counts_10/hek293t_DMSO-1_polyAC_6_mer.txt,counts_10/hek293t_DMSO-2_polyAC_6_mer.txt -o counts_10_mean_kmer/hek293t_DMSO_polyAC_6_mer --kmer 6mer-CDS-AC.txt
python MeanRiboDensityAroundPolyPurineMotifsFiltered.py -i counts_10/hek293t_DMSO-1_polyAG_6_mer.txt,counts_10/hek293t_DMSO-2_polyAG_6_mer.txt -o counts_10_mean_kmer/hek293t_DMSO_polyAG_6_mer --kmer 6mer-CDS-AG.txt
python MeanRiboDensityAroundPolyPurineMotifsFiltered.py -i counts_10/hek293t_DMSO-1_polyCT_6_mer.txt,counts_10/hek293t_DMSO-2_polyCT_6_mer.txt -o counts_10_mean_kmer/hek293t_DMSO_polyCT_6_mer --kmer 6mer-CT.txt

cp counts_10/hek293t_*.txt counts_10_mean_kmer

## UTR
python MeanRiboDensityAroundPolyPurineMotifsFiltered.py -i counts_10/hek293t_UTR_DMSO-1_polyAC_4_mer.txt,counts_10/hek293t_UTR_DMSO-2_polyAC_4_mer.txt -o counts_10_mean_kmer/hek293t_UTR_DMSO_polyAC_4_mer --kmer 4mer-5UTR-AC.txt
python MeanRiboDensityAroundPolyPurineMotifsFiltered.py -i counts_10/hek293t_UTR_DMSO-1_polyAG_4_mer.txt,counts_10/hek293t_UTR_DMSO-2_polyAG_4_mer.txt -o counts_10_mean_kmer/hek293t_UTR_DMSO_polyAG_4_mer --kmer 4mer-5UTR-AG.txt
python MeanRiboDensityAroundPolyPurineMotifsFiltered.py -i counts_10/hek293t_UTR_DMSO-1_polyCT_4_mer.txt,counts_10/hek293t_UTR_DMSO-2_polyCT_4_mer.txt -o counts_10_mean_kmer/hek293t_UTR_DMSO_polyCT_4_mer --kmer 4mer-CT.txt


python MeanRiboDensityAroundPolyPurineMotifsFiltered.py -i counts_10/hek293t_UTR_DMSO-1_polyAC_5_mer.txt,counts_10/hek293t_UTR_DMSO-2_polyAC_5_mer.txt -o counts_10_mean_kmer/hek293t_UTR_DMSO_polyAC_5_mer --kmer 5mer-5UTR-AC.txt
python MeanRiboDensityAroundPolyPurineMotifsFiltered.py -i counts_10/hek293t_UTR_DMSO-1_polyAG_5_mer.txt,counts_10/hek293t_UTR_DMSO-2_polyAG_5_mer.txt -o counts_10_mean_kmer/hek293t_UTR_DMSO_polyAG_5_mer --kmer 5mer-5UTR-AG.txt
python MeanRiboDensityAroundPolyPurineMotifsFiltered.py -i counts_10/hek293t_UTR_DMSO-1_polyCT_5_mer.txt,counts_10/hek293t_UTR_DMSO-2_polyCT_5_mer.txt -o counts_10_mean_kmer/hek293t_UTR_DMSO_polyCT_5_mer --kmer 5mer-CT.txt

python MeanRiboDensityAroundPolyPurineMotifsFiltered.py -i counts_10/hek293t_UTR_DMSO-1_polyAC_6_mer.txt,counts_10/hek293t_UTR_DMSO-2_polyAC_6_mer.txt -o counts_10_mean_kmer/hek293t_UTR_DMSO_polyAC_6_mer --kmer 6mer-5UTR-AC.txt
python MeanRiboDensityAroundPolyPurineMotifsFiltered.py -i counts_10/hek293t_UTR_DMSO-1_polyAG_6_mer.txt,counts_10/hek293t_UTR_DMSO-2_polyAG_6_mer.txt -o counts_10_mean_kmer/hek293t_UTR_DMSO_polyAG_6_mer --kmer 6mer-5UTR-AG.txt
python MeanRiboDensityAroundPolyPurineMotifsFiltered.py -i counts_10/hek293t_UTR_DMSO-1_polyCT_6_mer.txt,counts_10/hek293t_UTR_DMSO-2_polyCT_6_mer.txt -o counts_10_mean_kmer/hek293t_UTR_DMSO_polyCT_6_mer --kmer 6mer-CT.txt

Calculate ratio between RocA and DMSO

## 02. calculate ratio
python CalRiboDensityRatioFilter.py -c counts_10_mean_kmer/hek293t_DMSO_polyAC_4_mer_mean.txt -t counts_10_mean_kmer/hek293t_RocA-003
_polyAC_4_mer.txt -o counts_10_mean_kmer/hek293t_RocA-003_polyAC_4_mer.ratio --kmer 4mer-CDS-AC.txt
python CalRiboDensityRatioFilter.py -c counts_10_mean_kmer/hek293t_DMSO_polyAC_4_mer_mean.txt -t counts_10_mean_kmer/hek293t_RocA-03_polyAC_4_mer.txt -o counts_10_mean_kmer/hek293t_RocA-03_polyAC_4_mer.ratio --kmer 4mer-CDS-AC.txt
python CalRiboDensityRatioFilter.py -c counts_10_mean_kmer/hek293t_DMSO_polyAC_4_mer_mean.txt -t counts_10_mean_kmer/hek293t_RocA-3_polyAC_4_mer.txt -o counts_10_mean_kmer/hek293t_RocA-3_polyAC_4_mer.ratio --kmer 4mer-CDS-AC.txt

python CalRiboDensityRatioFilter.py -c counts_10_mean_kmer/hek293t_DMSO_polyAG_4_mer_mean.txt -t counts_10_mean_kmer/hek293t_RocA-003_polyAG_4_mer.txt -o counts_10_mean_kmer/hek293t_RocA-003_polyAG_4_mer.ratio --kmer 4mer-CDS-AG.txt
python CalRiboDensityRatioFilter.py -c counts_10_mean_kmer/hek293t_DMSO_polyAG_4_mer_mean.txt -t counts_10_mean_kmer/hek293t_RocA-03_polyAG_4_mer.txt -o counts_10_mean_kmer/hek293t_RocA-03_polyAG_4_mer.ratio --kmer 4mer-CDS-AG.txt
python CalRiboDensityRatioFilter.py -c counts_10_mean_kmer/hek293t_DMSO_polyAG_4_mer_mean.txt -t counts_10_mean_kmer/hek293t_RocA-3_polyAG_4_mer.txt -o counts_10_mean_kmer/hek293t_RocA-3_polyAG_4_mer.ratio --kmer 4mer-CDS-AG.txt

python CalRiboDensityRatioFilter.py -c counts_10_mean_kmer/hek293t_DMSO_polyCT_4_mer_mean.txt -t counts_10_mean_kmer/hek293t_RocA-003_polyCT_4_mer.txt -o counts_10_mean_kmer/hek293t_RocA-003_polyCT_4_mer.ratio --kmer 4mer-CT.txt
python CalRiboDensityRatioFilter.py -c counts_10_mean_kmer/hek293t_DMSO_polyCT_4_mer_mean.txt -t counts_10_mean_kmer/hek293t_RocA-03_polyCT_4_mer.txt -o counts_10_mean_kmer/hek293t_RocA-03_polyCT_4_mer.ratio --kmer 4mer-CT.txt
python CalRiboDensityRatioFilter.py -c counts_10_mean_kmer/hek293t_DMSO_polyCT_4_mer_mean.txt -t counts_10_mean_kmer/hek293t_RocA-3_polyCT_4_mer.txt -o counts_10_mean_kmer/hek293t_RocA-3_polyCT_4_mer.ratio --kmer 4mer-CT.txt

python CalRiboDensityRatioFilter.py -c counts_10_mean_kmer/hek293t_DMSO_polyAC_5_mer_mean.txt -t counts_10_mean_kmer/hek293t_RocA-003_polyAC_5_mer.txt -o counts_10_mean_kmer/hek293t_RocA-003_polyAC_5_mer.ratio --kmer 5mer-CDS-AC.txt
#python CalRiboDensityRatioFilter.py -c counts_10_mean_kmer/hek293t_DMSO_polyAC_5_mer_mean.txt -t counts_10_mean_kmer/hek293t_RocA-03_polyAC_5_mer.txt -o counts_10_mean_kmer/hek293t_RocA-03_polyAC_5_mer.ratio --kmer 5mer-CDS-AC.txt
python CalRiboDensityRatioFilter.py -c counts_10_mean_kmer/hek293t_DMSO_polyAC_5_mer_mean.txt -t counts_10_mean_kmer/hek293t_RocA-3_polyAC_5_mer.txt -o counts_10_mean_kmer/hek293t_RocA-3_polyAC_5_mer.ratio --kmer 5mer-CDS-AC.txt

python CalRiboDensityRatioFilter.py -c counts_10_mean_kmer/hek293t_DMSO_polyAG_5_mer_mean.txt -t counts_10_mean_kmer/hek293t_RocA-003_polyAG_5_mer.txt -o counts_10_mean_kmer/hek293t_RocA-003_polyAG_5_mer.ratio --kmer 5mer-CDS-AG.txt
python CalRiboDensityRatioFilter.py -c counts_10_mean_kmer/hek293t_DMSO_polyAG_5_mer_mean.txt -t counts_10_mean_kmer/hek293t_RocA-03_polyAG_5_mer.txt -o counts_10_mean_kmer/hek293t_RocA-03_polyAG_5_mer.ratio --kmer 5mer-CDS-AG.txt
python CalRiboDensityRatioFilter.py -c counts_10_mean_kmer/hek293t_DMSO_polyAG_5_mer_mean.txt -t counts_10_mean_kmer/hek293t_RocA-3_polyAG_5_mer.txt -o counts_10_mean_kmer/hek293t_RocA-3_polyAG_5_mer.ratio --kmer 5mer-CDS-AG.txt

python CalRiboDensityRatioFilter.py -c counts_10_mean_kmer/hek293t_DMSO_polyCT_5_mer_mean.txt -t counts_10_mean_kmer/hek293t_RocA-003_polyCT_5_mer.txt -o counts_10_mean_kmer/hek293t_RocA-003_polyCT_5_mer.ratio --kmer 5mer-CT.txt
python CalRiboDensityRatioFilter.py -c counts_10_mean_kmer/hek293t_DMSO_polyCT_5_mer_mean.txt -t counts_10_mean_kmer/hek293t_RocA-03_polyCT_5_mer.txt -o counts_10_mean_kmer/hek293t_RocA-03_polyCT_5_mer.ratio --kmer 5mer-CT.txt
python CalRiboDensityRatioFilter.py -c counts_10_mean_kmer/hek293t_DMSO_polyCT_5_mer_mean.txt -t counts_10_mean_kmer/hek293t_RocA-3_polyCT_5_mer.txt -o counts_10_mean_kmer/hek293t_RocA-3_polyCT_5_mer.ratio --kmer 5mer-CT.txt

plot the ratio values

# 03. plot ratio
python PlotRatio.py -i  counts_10_mean_kmer/hek293t_RocA-003_polyAG_4_mer.ratio,counts_10_mean_kmer/hek293t_RocA-003_polyAC_4_mer.ratio -o counts_10_mean_kmer/RocA003_4_mer -t RocA003-AG,RocA003-AC -u 50 -d 50  --slide-window
python PlotRatio.py -i  counts_10_mean_kmer/hek293t_RocA-03_polyAG_4_mer.ratio,counts_10_mean_kmer/hek293t_RocA-03_polyAC_4_mer.ratio -o counts_10_mean_kmer/RocA03_4_mer -t RocA03-AG,RocA03-AC -u 50 -d 50  --slide-window
python PlotRatio.py -i  counts_10_mean_kmer/hek293t_RocA-3_polyAG_4_mer.ratio,counts_10_mean_kmer/hek293t_RocA-3_polyAC_4_mer.ratio -o counts_10_mean_kmer/RocA3_4_mer -t RocA3-AG,RocA3-AC -u 50 -d 50  --slide-window


python PlotRatio.py -i  counts_10_mean_kmer/hek293t_RocA-003_polyAG_4_mer.ratio,counts_10_mean_kmer/hek293t_RocA-003_polyCT_4_mer.ratio -o counts_10_mean_kmer/RocA003_4_mer_AG_CT -t RocA003-AG,RocA003-CT -u 50 -d 50  --slide-window
python PlotRatio.py -i  counts_10_mean_kmer/hek293t_RocA-03_polyAG_4_mer.ratio,counts_10_mean_kmer/hek293t_RocA-03_polyCT_4_mer.ratio -o counts_10_mean_kmer/RocA03_4_mer_AG_CT -t RocA03-AG,RocA03-CT -u 50 -d 50  --slide-window
python PlotRatio.py -i  counts_10_mean_kmer/hek293t_RocA-3_polyAG_4_mer.ratio,counts_10_mean_kmer/hek293t_RocA-3_polyCT_4_mer.ratio -o counts_10_mean_kmer/RocA3_4_mer_AG_CT -t RocA3-AG,RocA3-CT -u 50 -d 50  --slide-window

python PlotRatio.py -i  counts_10_mean_kmer/hek293t_RocA-003_polyAG_5_mer.ratio,counts_10_mean_kmer/hek293t_RocA-003_polyAC_5_mer.ratio -o counts_10_mean_kmer/RocA003_5_mer -t RocA003-AG,RocA003-AC -u 50 -d 50  --slide-window
python PlotRatio.py -i  counts_10_mean_kmer/hek293t_RocA-03_polyAG_5_mer.ratio,counts_10_mean_kmer/hek293t_RocA-03_polyAC_5_mer.ratio -o counts_10_mean_kmer/RocA03_5_mer -t RocA03-AG,RocA03-AC -u 50 -d 50  --slide-window
python PlotRatio.py -i  counts_10_mean_kmer/hek293t_RocA-3_polyAG_5_mer.ratio,counts_10_mean_kmer/hek293t_RocA-3_polyAC_5_mer.ratio -o counts_10_mean_kmer/RocA3_5_mer -t RocA3-AG,RocA3-AC -u 50 -d 50  --slide-window


python PlotRatio.py -i  counts_10_mean_kmer/hek293t_RocA-003_polyAG_5_mer.ratio,counts_10_mean_kmer/hek293t_RocA-003_polyCT_5_mer.ratio -o counts_10_mean_kmer/RocA003_5_mer_AG_CT -t RocA003-AG,RocA003-CT -u 50 -d 50  --slide-window
python PlotRatio.py -i  counts_10_mean_kmer/hek293t_RocA-03_polyAG_5_mer.ratio,counts_10_mean_kmer/hek293t_RocA-03_polyCT_5_mer.ratio -o counts_10_mean_kmer/RocA03_5_mer_AG_CT -t RocA03-AG,RocA03-CT -u 50 -d 50  --slide-window
python PlotRatio.py -i  counts_10_mean_kmer/hek293t_RocA-3_polyAG_5_mer.ratio,counts_10_mean_kmer/hek293t_RocA-3_polyCT_5_mer.ratio -o counts_10_mean_kmer/RocA3_5_mer_AG_CT -t RocA3-AG,RocA3-CT -u 50 -d 50  --slide-window


python PlotRatio.py -i  counts_10_mean_kmer/hek293t_RocA-003_polyAG_6_mer.ratio,counts_10_mean_kmer/hek293t_RocA-003_polyAC_6_mer.ratio -o counts_10_mean_kmer/RocA003_6_mer -t RocA003-AG,RocA003-AC -u 50 -d 50  --slide-window
python PlotRatio.py -i  counts_10_mean_kmer/hek293t_RocA-03_polyAG_6_mer.ratio,counts_10_mean_kmer/hek293t_RocA-03_polyAC_6_mer.ratio -o counts_10_mean_kmer/RocA03_6_mer -t RocA03-AG,RocA03-AC -u 50 -d 50  --slide-window
python PlotRatio.py -i  counts_10_mean_kmer/hek293t_RocA-3_polyAG_6_mer.ratio,counts_10_mean_kmer/hek293t_RocA-3_polyAC_6_mer.ratio -o counts_10_mean_kmer/RocA3_6_mer -t RocA3-AG,RocA3-AC -u 50 -d 50  --slide-window

python PlotRatio.py -i  counts_10_mean_kmer/hek293t_RocA-003_polyAG_6_mer.ratio,counts_10_mean_kmer/hek293t_RocA-003_polyCT_6_mer.ratio -o counts_10_mean_kmer/RocA003_6_mer_AG_CT -t RocA003-AG,RocA003-CT -u 50 -d 50  --slide-window
python PlotRatio.py -i  counts_10_mean_kmer/hek293t_RocA-03_polyAG_6_mer.ratio,counts_10_mean_kmer/hek293t_RocA-03_polyCT_6_mer.ratio -o counts_10_mean_kmer/RocA03_6_mer_AG_CT -t RocA03-AG,RocA03-CT -u 50 -d 50  --slide-window
python PlotRatio.py -i  counts_10_mean_kmer/hek293t_RocA-3_polyAG_6_mer.ratio,counts_10_mean_kmer/hek293t_RocA-3_polyCT_6_mer.ratio -o counts_10_mean_kmer/RocA3_6_mer_AG_CT -t RocA3-AG,RocA3-CT -u 50 -d 50  --slide-window

## UTR
python PlotRatio.py -i  counts_10_mean_kmer/hek293t_UTR_RocA-003_polyAG_4_mer.ratio,counts_10_mean_kmer/hek293t_UTR_RocA-003_polyAC_4_mer.ratio -o counts_10_mean_kmer/RocA003_UTR_4_mer -t RocA003-AG,RocA003-AC -u 50 -d 50  --slide-window
python PlotRatio.py -i  counts_10_mean_kmer/hek293t_UTR_RocA-03_polyAG_4_mer.ratio,counts_10_mean_kmer/hek293t_UTR_RocA-03_polyAC_4_mer.ratio -o counts_10_mean_kmer/RocA03_UTR_4_mer -t RocA03-AG,RocA03-AC -u 50 -d 50  --slide-window
python PlotRatio.py -i  counts_10_mean_kmer/hek293t_UTR_RocA-3_polyAG_4_mer.ratio,counts_10_mean_kmer/hek293t_UTR_RocA-3_polyAC_4_mer.ratio -o counts_10_mean_kmer/RocA3_UTR_4_mer -t RocA3-AG,RocA3-AC -u 50 -d 50  --slide-window

python PlotRatio.py -i  counts_10_mean_kmer/hek293t_UTR_RocA-003_polyAG_4_mer.ratio,counts_10_mean_kmer/hek293t_UTR_RocA-003_polyCT_4_mer.ratio -o counts_10_mean_kmer/RocA003_UTR_4_mer_AG_CT -t RocA003-AG,RocA003-CT -u 50 -d 50  --slide-window
python PlotRatio.py -i  counts_10_mean_kmer/hek293t_UTR_RocA-03_polyAG_4_mer.ratio,counts_10_mean_kmer/hek293t_UTR_RocA-03_polyCT_4_mer.ratio -o counts_10_mean_kmer/RocA03_UTR_4_mer_AG_CT -t RocA03-AG,RocA03-CT -u 50 -d 50  --slide-window
python PlotRatio.py -i  counts_10_mean_kmer/hek293t_UTR_RocA-3_polyAG_4_mer.ratio,counts_10_mean_kmer/hek293t_UTR_RocA-3_polyCT_4_mer.ratio -o counts_10_mean_kmer/RocA3_UTR_4_mer_AG_CT -t RocA3-AG,RocA3-CT -u 50 -d 50  --slide-window


python PlotRatio.py -i  counts_10_mean_kmer/hek293t_UTR_RocA-003_polyAG_5_mer.ratio,counts_10_mean_kmer/hek293t_UTR_RocA-003_polyAC_5_mer.ratio -o counts_10_mean_kmer/RocA003_UTR_5_mer -t RocA003-AG,RocA003-AC -u 50 -d 50  --slide-window
python PlotRatio.py -i  counts_10_mean_kmer/hek293t_UTR_RocA-03_polyAG_5_mer.ratio,counts_10_mean_kmer/hek293t_UTR_RocA-03_polyAC_5_mer.ratio -o counts_10_mean_kmer/RocA03_UTR_5_mer -t RocA03-AG,RocA03-AC -u 50 -d 50  --slide-window
python PlotRatio.py -i  counts_10_mean_kmer/hek293t_UTR_RocA-3_polyAG_5_mer.ratio,counts_10_mean_kmer/hek293t_UTR_RocA-3_polyAC_5_mer.ratio -o counts_10_mean_kmer/RocA3_UTR_5_mer -t RocA3-AG,RocA3-AC -u 50 -d 50  --slide-window

python PlotRatio.py -i  counts_10_mean_kmer/hek293t_UTR_RocA-003_polyAG_5_mer.ratio,counts_10_mean_kmer/hek293t_UTR_RocA-003_polyCT_5_mer.ratio -o counts_10_mean_kmer/RocA003_UTR_5_mer_AG_CT -t RocA003-AG,RocA003-CT -u 50 -d 50  --slide-window
python PlotRatio.py -i  counts_10_mean_kmer/hek293t_UTR_RocA-03_polyAG_5_mer.ratio,counts_10_mean_kmer/hek293t_UTR_RocA-03_polyCT_5_mer.ratio -o counts_10_mean_kmer/RocA03_UTR_5_mer_AG_CT -t RocA03-AG,RocA03-CT -u 50 -d 50  --slide-window
python PlotRatio.py -i  counts_10_mean_kmer/hek293t_UTR_RocA-3_polyAG_5_mer.ratio,counts_10_mean_kmer/hek293t_UTR_RocA-3_polyCT_5_mer.ratio -o counts_10_mean_kmer/RocA3_UTR_5_mer_AG_CT -t RocA3-AG,RocA3-CT -u 50 -d 50  --slide-window

python PlotRatio.py -i  counts_10_mean_kmer/hek293t_UTR_RocA-003_polyAG_6_mer.ratio,counts_10_mean_kmer/hek293t_UTR_RocA-003_polyAC_6_mer.ratio -o counts_10_mean_kmer/RocA003_UTR_6_mer -t RocA003-AG,RocA003-AC -u 50 -d 50  --slide-window
python PlotRatio.py -i  counts_10_mean_kmer/hek293t_UTR_RocA-03_polyAG_6_mer.ratio,counts_10_mean_kmer/hek293t_UTR_RocA-03_polyAC_6_mer.ratio -o counts_10_mean_kmer/RocA03_UTR_6_mer -t RocA03-AG,RocA03-AC -u 50 -d 50  --slide-window
python PlotRatio.py -i  counts_10_mean_kmer/hek293t_UTR_RocA-3_polyAG_6_mer.ratio,counts_10_mean_kmer/hek293t_UTR_RocA-3_polyAC_6_mer.ratio -o counts_10_mean_kmer/RocA3_UTR_6_mer -t RocA3-AG,RocA3-AC -u 50 -d 50  --slide-window

python PlotRatio.py -i  counts_10_mean_kmer/hek293t_UTR_RocA-003_polyAG_6_mer.ratio,counts_10_mean_kmer/hek293t_UTR_RocA-003_polyCT_6_mer.ratio -o counts_10_mean_kmer/RocA003_UTR_6_mer_AG_CT -t RocA003-AG,RocA003-CT -u 50 -d 50  --slide-window
python PlotRatio.py -i  counts_10_mean_kmer/hek293t_UTR_RocA-03_polyAG_6_mer.ratio,counts_10_mean_kmer/hek293t_UTR_RocA-03_polyCT_6_mer.ratio -o counts_10_mean_kmer/RocA03_UTR_6_mer_AG_CT -t RocA03-AG,RocA03-CT -u 50 -d 50  --slide-window
python PlotRatio.py -i  counts_10_mean_kmer/hek293t_UTR_RocA-3_polyAG_6_mer.ratio,counts_10_mean_kmer/hek293t_UTR_RocA-3_polyCT_6_mer.ratio -o counts_10_mean_kmer/RocA3_UTR_6_mer_AG_CT -t RocA3-AG,RocA3-CT -u 50 -d 50  --slide-window

Figure 4D

Calculate disome density around polypurine moitfs

workdir=`pwd`
BamDir=$workdir/../07.STAR
Ref=~/Reference/human/hg38/ensemble
transcript=~/Reference/human/hg38/ensemble/RiboCode_annot_Human/RiboCode_annot/transcripts_sequence.fa
results=$workdir/test
attribute=$workdir/Disome_configure.txt
trans_info=$Ref/longest.transcripts.info.txt
up_trans=$workdir/374_up_genes_info.txt
down_trans=$workdir/643_down_genes_info.txt


# poly-purine
mkdir -p polymotifs/counts_10

## UTR
python RiboDensityAroundPolyPurineMotifs.py -f $attribute -c $trans_info -o polymotifs/counts_10/test_5UTR -M RPKM -u 100 -d 100 -l 0
-n 0 -F $transcript --kmer 4 --type 5UTR -S $down_trans  --id-type transcript_id --base AG
python RiboDensityAroundPolyPurineMotifs.py -f $attribute -c $trans_info -o polymotifs/counts_10/test_5UTR -M RPKM -u 100 -d 100 -l 0
-n 0 -F $transcript --kmer 4 --type 5UTR -S $down_trans  --id-type transcript_id --base AC
python RiboDensityAroundPolyPurineMotifs.py -f $attribute -c $trans_info -o polymotifs/counts_10/test_5UTR -M RPKM -u 100 -d 100 -l 0
-n 0 -F $transcript --kmer 4 --type 5UTR -S $down_trans --id-type transcript_id --base CT


python RiboDensityAroundPolyPurineMotifs.py -f $attribute -c $trans_info -o polymotifs/counts_10/test_5UTR -M RPKM -u 100 -d 100 -l 0
-n 0 -F $transcript --kmer 5 --type 5UTR -S $down_trans  --id-type transcript_id --base AG
python RiboDensityAroundPolyPurineMotifs.py -f $attribute -c $trans_info -o polymotifs/counts_10/test_5UTR -M RPKM -u 100 -d 100 -l 0
-n 0 -F $transcript --kmer 5 --type 5UTR -S $down_trans  --id-type transcript_id --base AC
python RiboDensityAroundPolyPurineMotifs.py -f $attribute -c $trans_info -o polymotifs/counts_10/test_5UTR -M RPKM -u 100 -d 100 -l 0
-n 0 -F $transcript --kmer 5 --type 5UTR -S $down_trans --id-type transcript_id --base CT


python RiboDensityAroundPolyPurineMotifs.py -f $attribute -c $trans_info -o polymotifs/counts_10/test_5UTR -M RPKM -u 100 -d 100 -l 0 -n 0 -F $transcript --kmer 6 --type 5UTR -S $down_trans  --id-type transcript_id --base AG
python RiboDensityAroundPolyPurineMotifs.py -f $attribute -c $trans_info -o polymotifs/counts_10/test_5UTR -M RPKM -u 100 -d 100 -l 0 -n 0 -F $transcript --kmer 6 --type 5UTR -S $down_trans  --id-type transcript_id --base AC
python RiboDensityAroundPolyPurineMotifs.py -f $attribute -c $trans_info -o polymotifs/counts_10/test_5UTR -M RPKM -u 100 -d 100 -l 0 -n 0 -F $transcript --kmer 6 --type 5UTR -S $down_trans --id-type transcript_id --base CT

## CDS
python RiboDensityAroundPolyPurineMotifs.py -f $attribute -c $trans_info -o polymotifs/counts_10/test_CDS -M RPKM -u 100 -d 100 -l 0 -n 0 -F $transcript --kmer 4 --type CDS -S $up_trans --id-type transcript_id --base AG
python RiboDensityAroundPolyPurineMotifs.py -f $attribute -c $trans_info -o polymotifs/counts_10/test_CDS -M RPKM -u 100 -d 100 -l 0 -n 0 -F $transcript --kmer 4 --type CDS -S $up_trans --id-type transcript_id --base AC
python RiboDensityAroundPolyPurineMotifs.py -f $attribute -c $trans_info -o polymotifs/counts_10/test_CDS -M RPKM -u 100 -d 100 -l 0 -n 0 -F $transcript --kmer 4 --type CDS -S $up_trans --id-type transcript_id --base CT

python RiboDensityAroundPolyPurineMotifs.py -f $attribute -c $trans_info -o polymotifs/counts_10/test_CDS -M RPKM -u 100 -d 100 -l 0 -n 0 -F $transcript --kmer 5 --type CDS -S $up_trans --id-type transcript_id --base AG
python RiboDensityAroundPolyPurineMotifs.py -f $attribute -c $trans_info -o polymotifs/counts_10/test_CDS -M RPKM -u 100 -d 100 -l 0 -n 0 -F $transcript --kmer 5 --type CDS -S $up_trans --id-type transcript_id --base AC
python RiboDensityAroundPolyPurineMotifs.py -f $attribute -c $trans_info -o polymotifs/counts_10/test_CDS -M RPKM -u 100 -d 100 -l 0 -n 0 -F $transcript --kmer 5 --type CDS -S $up_trans --id-type transcript_id --base CT


python RiboDensityAroundPolyPurineMotifs.py -f $attribute -c $trans_info -o polymotifs/counts_10/test_CDS -M RPKM -u 100 -d 100 -l 0 -n 0 -F $transcript --kmer 6 --type CDS -S $up_trans --id-type transcript_id --base AG
python RiboDensityAroundPolyPurineMotifs.py -f $attribute -c $trans_info -o polymotifs/counts_10/test_CDS -M RPKM -u 100 -d 100 -l 0 -n 0 -F $transcript --kmer 6 --type CDS -S $up_trans --id-type transcript_id --base AC
python RiboDensityAroundPolyPurineMotifs.py -f $attribute -c $trans_info -o polymotifs/counts_10/test_CDS -M RPKM -u 100 -d 100 -l 0 -n 0 -F $transcript --kmer 6 --type CDS -S $up_trans --id-type transcript_id --base CT

Calculate ratio

python CalRiboDensityRatio.py counts_10/test_CDS_293FT-DMSO_polyAG_4_mer.txt counts_10/test_CDS_293FT-RocA03_polyAG_4_mer.txt counts_10/test_CDS_293FT_RocA03_polyAG_4_mer.ratio
python CalRiboDensityRatio.py counts_10/test_CDS_293FT-DMSO_polyAG_5_mer.txt counts_10/test_CDS_293FT-RocA03_polyAG_5_mer.txt counts_10/test_CDS_293FT_RocA03_polyAG_5_mer.ratio
python CalRiboDensityRatio.py counts_10/test_CDS_293FT-DMSO_polyAG_6_mer.txt counts_10/test_CDS_293FT-RocA03_polyAG_6_mer.txt counts_10/test_CDS_293FT_RocA03_polyAG_6_mer.ratio

python CalRiboDensityRatio.py counts_10/test_CDS_293FT-DMSO_polyAC_4_mer.txt counts_10/test_CDS_293FT-RocA03_polyAC_4_mer.txt counts_10/test_CDS_293FT_RocA03_polyAC_4_mer.ratio
python CalRiboDensityRatio.py counts_10/test_CDS_293FT-DMSO_polyAC_5_mer.txt counts_10/test_CDS_293FT-RocA03_polyAC_5_mer.txt counts_10/test_CDS_293FT_RocA03_polyAC_5_mer.ratio
python CalRiboDensityRatio.py counts_10/test_CDS_293FT-DMSO_polyAC_6_mer.txt counts_10/test_CDS_293FT-RocA03_polyAC_6_mer.txt counts_10/test_CDS_293FT_RocA03_polyAC_6_mer.ratio

python CalRiboDensityRatio.py counts_10/test_CDS_293FT-DMSO_polyCT_4_mer.txt counts_10/test_CDS_293FT-RocA03_polyCT_4_mer.txt counts_10/test_CDS_293FT_RocA03_polyCT_4_mer.ratio
python CalRiboDensityRatio.py counts_10/test_CDS_293FT-DMSO_polyCT_5_mer.txt counts_10/test_CDS_293FT-RocA03_polyCT_5_mer.txt counts_10/test_CDS_293FT_RocA03_polyCT_5_mer.ratio
python CalRiboDensityRatio.py counts_10/test_CDS_293FT-DMSO_polyCT_6_mer.txt counts_10/test_CDS_293FT-RocA03_polyCT_6_mer.txt counts_10/test_CDS_293FT_RocA03_polyCT_6_mer.ratio

Plot ratio

# 03. plot ratio
python PlotRatio.py -i  counts_10/test_CDS_293FT_RocA03_polyAG_4_mer.ratio,counts_10/test_CDS_293FT_RocA03_polyAC_4_mer.ratio -o counts_10/test_CDS_293FT_4mer_AG_AC -t RocA03-AG,RocA03-AC -u 100 -d 100  --slide-window
python PlotRatio.py -i  counts_10/test_CDS_293FT_RocA03_polyAG_4_mer.ratio,counts_10/test_CDS_293FT_RocA03_polyCT_4_mer.ratio -o counts_10/test_CDS_293FT_4mer_AG_CT -t RocA03-AG,RocA03-CT -u 100 -d 100  --slide-window

python PlotRatio.py -i  counts_10/test_CDS_293FT_RocA03_polyAG_5_mer.ratio,counts_10/test_CDS_293FT_RocA03_polyAC_5_mer.ratio -o counts_10/test_CDS_293FT_5mer_AG_AC -t RocA03-AG,RocA03-AC -u 100 -d 100  --slide-window
python PlotRatio.py -i  counts_10/test_CDS_293FT_RocA03_polyAG_5_mer.ratio,counts_10/test_CDS_293FT_RocA03_polyCT_5_mer.ratio -o counts_10/test_CDS_293FT_5mer_AG_CT -t RocA03-AG,RocA03-CT -u 100 -d 100  --slide-window

python PlotRatio.py -i  counts_10/test_CDS_293FT_RocA03_polyAG_6_mer.ratio,counts_10/test_CDS_293FT_RocA03_polyAC_6_mer.ratio -o counts_10/test_CDS_293FT_6mer_AG_AC -t RocA03-AG,RocA03-AC -u 100 -d 100  --slide-window
python PlotRatio.py -i  counts_10/test_CDS_293FT_RocA03_polyAG_6_mer.ratio,counts_10/test_CDS_293FT_RocA03_polyCT_6_mer.ratio -o counts_10/test_CDS_293FT_6mer_AG_CT -t RocA03-AG,RocA03-CT -u 100 -d 100  --slide-window

Figure 4E

Metagene analysis via RiboMiner

workdir=`pwd`
BamDir=$workdir/../07.STAR
Ref=/workdata/home/lifj/lifj/data/Reference/human
transcript=/workdata/home/lifj/lifj/data/Reference/human/RiboCode_annot_Human/RiboCode_annot/transcripts_sequence.fa
results=$workdir/MA
attribute=$workdir/GSE102720_configure.txt
trans_info=$Ref/longest.transcripts.info.txt
groups='Naive-DMSO,Naive-RocA03,Aglaiacized-DMSO,Aglaiacized-RocA03'
replicates='Naive-DMSO-1,Naive-DMSO-2__Naive-RocA03__Aglaiacized-DMSO-1,Aglaiacized-DMSO-2__Aglaiacized-RocA03'

mkdir -p $results

MetageneAnalysis -f $attribute -c $trans_info -o $results/MA_RocA03_up_unnormed -U codon -M RPKM -u 100 -d 400 -l 0 -n 0 -m 0 -e 5 --norm no -y 100 --CI 0.95 --type UTR -S 374_up_genes_info.txt
MetageneAnalysis -f $attribute -c $trans_info -o $results/MA_RocA03_down_unnormed -U codon -M RPKM -u 100 -d 400 -l 0 -n 0 -m 0 -e 5 --norm no -y 100 --CI 0.95 --type UTR -S 643_down_genes_info.txt

PlotMetageneAnalysis -i $results/MA_RocA03_up_unnormed_dataframe.txt -o $results/MA_RocA03_up_unnormed -g $groups -r $replicates -u 100 -d 400 --mode mean
PlotMetageneAnalysis -i $results/MA_RocA03_down_unnormed_dataframe.txt -o $results/MA_RocA03_down_unnormed -g $groups -r $replicates -u 100 -d 400 --mode mean

Figure 4F

This is similar to Figure 4C and Figure 4D

Figure 6A

configure files for 4 tumor cell samples

$ cat ./data/attributes_375.txt
bamFiles	readLengths	Offsets	bamLegends
../07.STAR/SRR11542147_STAR/SRR11542147.Aligned.toTranscriptome.out.sorted.bam	26,27,28,29,30,31	12,12,12,12,12,13	DMSO-1-375
../07.STAR/SRR11542148_STAR/SRR11542148.Aligned.toTranscriptome.out.sorted.bam	26,27,28,29,30,31	12,12,12,12,12,13	DMSO-2-375
../07.STAR/SRR11542155_STAR/SRR11542155.Aligned.toTranscriptome.out.sorted.bam	25,26,27,28,29,30,31	12,12,12,12,12,12,13	RocA03-375
../07.STAR/SRR11542156_STAR/SRR11542156.Aligned.toTranscriptome.out.sorted.bam	25,26,27,28,29,30,31	12,12,12,12,12,12,13	RocA3-375

$ cat ./data/attributes_520.txt
bamFiles	readLengths	Offsets	bamLegends
../07.STAR/SRR11542143_STAR/SRR11542143.Aligned.toTranscriptome.out.sorted.bam	25,26,27,28,29,30,31	12,12,12,12,12,12,13	DMSO-1-520
../07.STAR/SRR11542144_STAR/SRR11542144.Aligned.toTranscriptome.out.sorted.bam	25,26,27,28,29,30,31	12,12,12,12,12,12,13	DMSO-2-520
../07.STAR/SRR11542151_STAR/SRR11542151.Aligned.toTranscriptome.out.sorted.bam	25,26,27,28,29,30,31	12,12,12,12,12,12,13	RocA03-520
../07.STAR/SRR11542152_STAR/SRR11542152.Aligned.toTranscriptome.out.sorted.bam	25,26,27,28,29,30,31	12,12,12,12,12,12,13	RocA3-520

$ cat ./data/attributes_936.txt
bamFiles	readLengths	Offsets	bamLegends
../07.STAR/SRR11542145_STAR/SRR11542145.Aligned.toTranscriptome.out.sorted.bam	26,27,28,29,30,31	12,12,12,12,12,13	DMSO-1-936
../07.STAR/SRR11542146_STAR/SRR11542146.Aligned.toTranscriptome.out.sorted.bam	26,27,28,29,30,31	12,12,12,12,12,13	DMSO-2-936
../07.STAR/SRR11542153_STAR/SRR11542153.Aligned.toTranscriptome.out.sorted.bam	26,27,28,29,30,31	12,12,12,12,12,13	RocA03-936
../07.STAR/SRR11542154_STAR/SRR11542154.Aligned.toTranscriptome.out.sorted.bam	27,28,29,30,31	12,12,12,12,13	RocA3-936

$ cat ./data/attributes_1650.txt
bamFiles	readLengths	Offsets	bamLegends
../07.STAR/SRR11542141_STAR/SRR11542141.Aligned.toTranscriptome.out.sorted.bam	30,31,32,33,34	12,13,13,13,13	DMSO-1-1650
../07.STAR/SRR11542142_STAR/SRR11542142.Aligned.toTranscriptome.out.sorted.bam	30,31,32,33,34	12,13,13,13,13	DMSO-2-1650
../07.STAR/SRR11542149_STAR/SRR11542149.Aligned.toTranscriptome.out.sorted.bam	30,31,32,33,34	12,13,13,13,13	RocA03-1650
../07.STAR/SRR11542150_STAR/SRR11542150.Aligned.toTranscriptome.out.sorted.bam	30,31,32,33,34	12,13,13,13,13	RocA3-1650

Metagene analysis via RiboMiner

workdir=`pwd`
BamDir=$workdir/../07.STAR
Ref=/workdata/home/lifj/lifj/data/Reference/human
transcript=/workdata/home/lifj/lifj/data/Reference/human/RiboCode_annot_Human/RiboCode_annot/transcripts_sequence.fa
results=$workdir/MA_375
attribute=$workdir/attributes_375.txt
trans_info=$Ref/longest.transcripts.info.txt
groups='DMSO-375,RocA03-375,RocA3-375'
replicates='DMSO-1-375,DMSO-2-375__RocA03-375__RocA3-375'

mkdir -p $results

MetageneAnalysis -f $attribute -c $trans_info -o $results/MA_375_RocA03-down_unnormed -U codon -M RPKM -u 100 -d 400 -l 0 -n 0 -m 0 -e 5 --norm no -y 100 --CI 0.95 --type UTR -S 643_down_genes_info.txt
MetageneAnalysis -f $attribute -c $trans_info -o $results/MA_375_RocA03-up_unnormed -U codon -M RPKM -u 100 -d 400 -l 0 -n 0 -m 0 -e 5 --norm no -y 100 --CI 0.95 --type UTR -S 374_up_genes_info.txt

PlotMetageneAnalysis -i $results/MA_375_RocA03-down_unnormed_dataframe.txt -o $results/MA_375_unnormed_RocA03-down -g $groups -r $replicates -u 100 -d 400 --mode mean
PlotMetageneAnalysis -i $results/MA_375_RocA03-up_unnormed_dataframe.txt -o $results/MA_375_unnormed_RocA03-up -g $groups -r $replicates -u 100 -d 400 --mode mean

workdir=`pwd`
BamDir=$workdir/../07.STAR
Ref=/workdata/home/lifj/lifj/data/Reference/human
transcript=/workdata/home/lifj/lifj/data/Reference/human/RiboCode_annot_Human/RiboCode_annot/transcripts_sequence.fa
results=$workdir/MA_520
attribute=$workdir/attributes_520.txt
trans_info=$Ref/longest.transcripts.info.txt
groups='DMSO-520,RocA03-520,RocA3-520'
replicates='DMSO-1-520,DMSO-2-520__RocA03-520__RocA3-520'

mkdir -p $results

MetageneAnalysis -f $attribute -c $trans_info -o $results/MA_520_RocA03-down_unnormed -U codon -M RPKM -u 100 -d 400 -l 0 -n 0 -m 0 -e 5 --norm no -y 100 --CI 0.95 --type UTR -S 643_down_genes_info.txt
MetageneAnalysis -f $attribute -c $trans_info -o $results/MA_520_RocA03-up_unnormed -U codon -M RPKM -u 100 -d 400 -l 0 -n 0 -m 0 -e 5 --norm no -y 100 --CI 0.95 --type UTR -S 374_up_genes_info.txt

PlotMetageneAnalysis -i $results/MA_520_RocA03-down_unnormed_dataframe.txt -o $results/MA_520_unnormed_RocA03-down -g $groups -r $replicates -u 100 -d 400 --mode mean
PlotMetageneAnalysis -i $results/MA_520_RocA03-up_unnormed_dataframe.txt -o $results/MA_520_unnormed_RocA03-up -g $groups -r $replicates -u 100 -d 400 --mode mean


workdir=`pwd`
BamDir=$workdir/../07.STAR
Ref=/workdata/home/lifj/lifj/data/Reference/human
transcript=/workdata/home/lifj/lifj/data/Reference/human/RiboCode_annot_Human/RiboCode_annot/transcripts_sequence.fa
results=$workdir/MA_936
attribute=$workdir/attributes_936.txt
trans_info=$Ref/longest.transcripts.info.txt
groups='DMSO-936,RocA03-936,RocA3-936'
replicates='DMSO-1-936,DMSO-2-936__RocA03-936__RocA3-936'

mkdir -p $results

MetageneAnalysis -f $attribute -c $trans_info -o $results/MA_936_RocA03-down_unnormed -U codon -M RPKM -u 100 -d 400 -l 0 -n 0 -m 0 -e 5 --norm no -y 100 --CI 0.95 --type UTR -S 643_down_genes_info.txt
MetageneAnalysis -f $attribute -c $trans_info -o $results/MA_936_RocA03-up_unnormed -U codon -M RPKM -u 100 -d 400 -l 0 -n 0 -m 0 -e 5 --norm no -y 100 --CI 0.95 --type UTR -S 374_up_genes_info.txt
PlotMetageneAnalysis -i $results/MA_936_RocA03-down_unnormed_dataframe.txt -o $results/MA_936_unnormed_RocA03-down -g $groups -r $replicates -u 100 -d 400 --mode mean
PlotMetageneAnalysis -i $results/MA_936_RocA03-up_unnormed_dataframe.txt -o $results/MA_936_unnormed_RocA03-up -g $groups -r $replicates -u 100 -d 400 --mode mean

workdir=`pwd`
BamDir=$workdir/../07.STAR
Ref=/workdata/home/lifj/lifj/data/Reference/human
transcript=/workdata/home/lifj/lifj/data/Reference/human/RiboCode_annot_Human/RiboCode_annot/transcripts_sequence.fa
results=$workdir/MA_1650
attribute=$workdir/attributes_1650.txt
trans_info=$Ref/longest.transcripts.info.txt
groups='DMSO-1650,RocA03-1650,RocA3-1650'
replicates='DMSO-1-1650,DMSO-2-1650__RocA03-1650__RocA3-1650'

mkdir -p $results

MetageneAnalysis -f $attribute -c $trans_info -o $results/MA_1650_RocA03-down_unnormed -U codon -M RPKM -u 100 -d 400 -l 0 -n 0 -m 0 -e 5 --norm no -y 100 --CI 0.95 --type UTR -S 643_down_genes_info.txt
MetageneAnalysis -f $attribute -c $trans_info -o $results/MA_1650_RocA03-up_unnormed -U codon -M RPKM -u 100 -d 400 -l 0 -n 0 -m 0 -e 5 --norm no -y 100 --CI 0.95 --type UTR -S 374_up_genes_info.txt
PlotMetageneAnalysis -i $results/MA_1650_RocA03-down_unnormed_dataframe.txt -o $results/MA_1650_unnormed_RocA03-down -g $groups -r $replicates -u 100 -d 400 --mode mean
PlotMetageneAnalysis -i $results/MA_1650_RocA03-up_unnormed_dataframe.txt -o $results/MA_1650_unnormed_RocA03-up -g $groups -r $replicates -u 100 -d 400 --mode mean

Figure 6B

The methods are the same as that for Figure 4C and Figure 4D

Figure 7: the model of translation regulation of RocA

RDGs=IRGs RUGs=ERGs

Name		Name	Last commit message	Last commit date
Latest commit History 3 Commits
data		data
metadata		metadata
results		results
scripts		scripts
LICENSE		LICENSE
README.html		README.html
README.md		README.md

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

Repository files navigation

Reanalysis of ribosome profiling datasets reveals a novel function of rocaglamide A in perturbing the dynamics of translation elongation via eIF4A

Introduction

Datasets

Dependencies

Preparations

Pre-processing of ribosome profiling and Disome-seq data

Formal analyses

Calculate polarity scores for each datasets

Calculate RPKM for each datasets

Calculate Coverage for each datasets

Re-generation of the results presented in the manuscript

Figure 1A

Figure 1B

Figure 1C

Figure 1D

Figure 1E-F

Figure 2A

Figure 2B

Figure 2C-D

Figure 3A

Figure 3B-E

Figure 4A

Figure 4B

Figure 4C

Figure 4D

Figure 4E

Figure 4F

Figure 6A

Figure 6B

Figure 7: the model of translation regulation of RocA

About

Releases

Packages

Languages

License

xryanglab/RocA

Folders and files

Latest commit

History

Repository files navigation

Reanalysis of ribosome profiling datasets reveals a novel function of rocaglamide A in perturbing the dynamics of translation elongation via eIF4A

Introduction

Datasets

Dependencies

Preparations

Pre-processing of ribosome profiling and Disome-seq data

Formal analyses

Calculate polarity scores for each datasets

Calculate RPKM for each datasets

Calculate Coverage for each datasets

Re-generation of the results presented in the manuscript

Figure 1A

Figure 1B

Figure 1C

Figure 1D

Figure 1E-F

Figure 2A

Figure 2B

Figure 2C-D

Figure 3A

Figure 3B-E

Figure 4A

Figure 4B

Figure 4C

Figure 4D

Figure 4E

Figure 4F

Figure 6A

Figure 6B

Figure 7: the model of translation regulation of RocA

About

Topics

Resources

License

Stars

Watchers

Forks

Releases

Packages 0

Languages

Packages