Compare to data from Lianogluo et al LCLse

Quantify coverage at annotated clusters

The full atlas has peaks mapped to genes. (http://www.polyasite.unibas.ch) I will use these and map our data to them. I can then run the QC metrics I had done previously as well as the QTL analysis.

The first step will be processing the data into a format like mine with peakIDs including the gene name. Then I will run feature counts and filter the non used peaks.

Information from site about columns

• The first column stores the chromosome name.

• The second and the third column mark the start and end positions of poly(A) site cluster, respectively.

• The fourth column is the unqiue cluster ID composed of the chromosome name, the strand, the representative poly(A) site of the cluster, and a two letter code for the cluster annotation (TE: terminal exon, DS: 1,000 nt downstream of a terminal exon, EX: exonic, IN: intronic, AU: 1,000 nt upstream in anti-sense direction of a transcription start site, AE: anti-sense to an exon, AI: anti-sense to an intron, IG: intergenic).

• The fifth column stores the number different 3’ end sequencing protocols that support the particular cluster.

• The sixth column stores the strand.

• The seventh column stores information about the poly(A) signal(s) per poly(A) site, including the motif, the location with respect to the cleavage site and the genomic coordinate.

I am going to use clusters.bed

I am only going to keep PAS with an annotated gene. This is in column 8.

The goal is a dataset like /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom_refseqTrans.noties_sm.SAF for feature counts. I need to number the peaks here. To do this I can add a column with a number like I did for my peaks.

First sort the clusters file:

sort -k 1,1 -k2,2n /project2/gilad/briana/threeprimeseq/data/LianoglouLCL/clusters.bed > /project2/gilad/briana/threeprimeseq/data/LianoglouLCL/clusters_sort.bed  

x = wc -l /project2/gilad/briana/threeprimeseq/data/LianoglouLCL/clusters_sort.bed  

seq 1 392912 > cluster.peak.num.txt

paste /project2/gilad/briana/threeprimeseq/data/LianoglouLCL/clusters_sort.bed   cluster.peak.num.txt | column -s $'\t' -t > /project2/gilad/briana/threeprimeseq/data/LianoglouLCL/clusters_sort_named.bed  

Python script to make a SAF.

clusterBed2SAF.py

fout = open("/project2/gilad/briana/threeprimeseq/data/LianoglouLCL/clusters_sort_named.SAF",'w')
fout.write("GeneID\tChr\tStart\tEnd\tStrand\n")
for ln in open("/project2/gilad/briana/threeprimeseq/data/LianoglouLCL/clusters_sort_named.bed"):
    chrom, start, end, uniq, score, strand, extra, gene, name = ln.split()
    if gene==".":
      continue
    else:
      chrom_o=chrom[3:]
      name_i=int(name)
      start_i=int(start)
      end_i=int(end)
      ID = "peak%d:%s:%d:%d:%s:%s"%(name_i, chrom_o, start_i, end_i, strand, gene)
      fout.write("%s\t%s\t%d\t%d\t%s\n"%(ID, chrom_o, start_i, end_i, strand))
fout.close()

I can now use this to run feature counts with my bam files.

AnnotatedClustersFC_TN.sh

#!/bin/bash

#SBATCH --job-name=AnnotatedClustersFC_TN
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=AnnotatedClustersFC_TN.out
#SBATCH --error=AnnotatedClustersFC_TN.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env

featureCounts -O -a /project2/gilad/briana/threeprimeseq/data/LianoglouLCL/clusters_sort_named.SAF -F SAF -o /project2/gilad/briana/threeprimeseq/data/LianoglouLCL_quant/AnnotatedClusters.Total.fc /project2/gilad/briana/threeprimeseq/data/sort/*-T-*-sort.bam -s 2

featureCounts -O -a /project2/gilad/briana/threeprimeseq/data/LianoglouLCL/clusters_sort_named.SAF -F SAF -o /project2/gilad/briana/threeprimeseq/data/LianoglouLCL_quant/AnnotatedClusters.Nuclear.fc /project2/gilad/briana/threeprimeseq/data/sort/*-N-*-sort.bam -s 2

I will next need to fix the headers for the total and nuclear files.

FixHeader_TotalAnnotatedClustersFC.py

infile= open("/project2/gilad/briana/threeprimeseq/data/LianoglouLCL_quant/AnnotatedClusters.Total.fc", "r")
fout = open("/project2/gilad/briana/threeprimeseq/data/LianoglouLCL_quant/AnnotatedClusters.Total.fixed.fc",'w')
for line, i in enumerate(infile):
    if line == 1:
        i_list=i.split()
        libraries=i_list[:6]
        for sample in i_list[6:]:
            full = sample.split("/")[7]
            samp= full.split("-")[2:4]
            lim="_"
            samp_st=lim.join(samp)
            libraries.append(samp_st)
        first_line= "\t".join(libraries)
        fout.write(first_line + '\n')
    else :
        fout.write(i)
fout.close()

FixHeader_NucelarAnnotatedClustersFC.py

infile= open("/project2/gilad/briana/threeprimeseq/data/LianoglouLCL_quant/AnnotatedClusters.Nuclear.fc", "r")
fout = open("/project2/gilad/briana/threeprimeseq/data/LianoglouLCL_quant/AnnotatedClusters.Nuclear.fixed.fc",'w')
for line, i in enumerate(infile):
    if line == 1:
        i_list=i.split()
        libraries=i_list[:6]
        for sample in i_list[6:]:
            full = sample.split("/")[7]
            samp= full.split("-")[2:4]
            lim="_"
            samp_st=lim.join(samp)
            libraries.append(samp_st)
        first_line= "\t".join(libraries)
        fout.write(first_line + '\n')
    else :
        fout.write(i)
fout.close()

This results in 7-11 million reads mapping to the features in the total fraction. This is in comparison to over 10 mill in our peaks.

total_Cov=read.table("../data/LianoglouLCL/AnnotatedClusters.Total.fixed.fc", header=T, stringsAsFactors = F)

peakLength=total_Cov[,6]


total_Cov_m= as.matrix(total_Cov[,7:ncol(total_Cov)])
total_Cov_m=log10(total_Cov_m)

plotDensities(total_Cov_m, legend = "bottomright", main="Pre-filtering")
abline(v = .5, lty = 3)

keep.exprs_T=rowSums(total_Cov_m>.5) >= 26
total_Cov_m_filt= total_Cov_m[keep.exprs_T,]

plotDensities(total_Cov_m_filt, legend = "bottomright", main="Post-filtering")

total_Cov_18486_filt=total_Cov[keep.exprs_T,1:7] %>% separate(Geneid, into=c("peak", "chr", "start", "end", "strand", "gene"), sep=":") %>% select(gene, X18486_T)%>%  group_by(gene) %>% summarize(GeneSum=sum(X18486_T))

tx2gene=read.table("../data/RNAkalisto/ncbiRefSeq.txn2gene.txt" ,header= F, sep="\t", stringsAsFactors = F)

txi.kallisto.tsv <- tximport("../data/RNAkalisto/abundance.tsv", type = "kallisto", tx2gene = tx2gene,countsFromAbundance="lengthScaledTPM" )

Note: importing `abundance.h5` is typically faster than `abundance.tsv`

reading in files with read_tsv

1 
removing duplicated transcript rows from tx2gene
transcripts missing from tx2gene: 99
summarizing abundance
summarizing counts
summarizing length

TXN_abund=as.data.frame(txi.kallisto.tsv$abundance) %>% rownames_to_column(var="gene")
colnames(TXN_abund)=c("gene", "TPM")

Overlap=TXN_abund %>% inner_join(total_Cov_18486_filt,by="gene")

Overlap=Overlap %>% filter(TPM>0) %>% filter(GeneSum>0)
corr_18486Tot=ggplot(Overlap, aes(x=log10(TPM), y= log10(GeneSum))) + geom_point() + labs(title="Total 18486 with Annotated Peaks", x="log10 RNA seq TPM", y="log10 Peak count sum per gene")+ geom_smooth(aes(x=log10(TPM),y=log10(GeneSum)),method = "lm") + annotate("text",x=-5, y=5,label="R2=.47") +geom_density2d(na.rm = TRUE, size = 1, colour = 'red')

#+ geom_text(aes(label=gene),hjust=0, vjust=0)
       
corr_18486Tot       

summary(lm(log10(TPM)~log10(GeneSum),Overlap)) 

Call:
lm(formula = log10(TPM) ~ log10(GeneSum), data = Overlap)

Residuals:
    Min      1Q  Median      3Q     Max 
-7.8184 -0.2344  0.0135  0.2620  2.5192 

Coefficients:
                Estimate Std. Error t value Pr(>|t|)    
(Intercept)    -0.070619   0.015141  -4.664 3.14e-06 ***
log10(GeneSum)  0.594440   0.006294  94.449  < 2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.4692 on 9865 degrees of freedom
Multiple R-squared:  0.4749,    Adjusted R-squared:  0.4748 
F-statistic:  8921 on 1 and 9865 DF,  p-value: < 2.2e-16

cor.test(log10(Overlap$TPM),log10(Overlap$GeneSum))

    Pearson's product-moment correlation

data:  log10(Overlap$TPM) and log10(Overlap$GeneSum)
t = 94.449, df = 9865, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.6785988 0.6993264
sample estimates:
      cor 
0.6891035 

TotCounts_allind=total_Cov[keep.exprs_T,7:45]


SumCounts_Tot=rowSums(TotCounts_allind)

Alllib_Tot_Filt=total_Cov[keep.exprs_T,1:7] %>% separate(Geneid, into=c("peak", "chr", "start", "end", "strand", "gene"), sep=":")


Alllib_Tot_Filt$SumCounts=SumCounts_Tot

Alllib_Tot_Filtbygene=Alllib_Tot_Filt %>% select(gene, SumCounts) %>%  group_by(gene)  %>%  summarize(GeneSum=sum(SumCounts))


TXN_abund_combLibs_tot=TXN_abund %>% inner_join(Alllib_Tot_Filtbygene,by="gene")


TXN_abund_combLibs_tot_n0=TXN_abund_combLibs_tot %>% filter(TPM>0) %>% filter(GeneSum>0)


corr_AllLibTot=ggplot(TXN_abund_combLibs_tot_n0, aes(x=log10(TPM), y= log10(GeneSum))) + geom_point() + labs(title="Total All ind Filtered Annotated Clusters", x="log10 RNA seq TPM", y="log10 Peak count sum per gene All Ind.")+ geom_smooth(aes(x=log10(TPM),y=log10(GeneSum)),method = "lm") + annotate("text",x=-5, y=5,label="R2=.53") +geom_density2d(na.rm = TRUE, size = 1, colour = 'red') + geom_text(aes(label=gene),hjust=0, vjust=0)
       
corr_AllLibTot      

summary(lm(log10(TPM)~log10(GeneSum),TXN_abund_combLibs_tot_n0)) 

Call:
lm(formula = log10(TPM) ~ log10(GeneSum), data = TXN_abund_combLibs_tot_n0)

Residuals:
    Min      1Q  Median      3Q     Max 
-7.8186 -0.2245  0.0180  0.2594  2.4471 

Coefficients:
               Estimate Std. Error t value Pr(>|t|)    
(Intercept)    -1.57143    0.02637  -59.58   <2e-16 ***
log10(GeneSum)  0.70927    0.00654  108.45   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.4606 on 10322 degrees of freedom
Multiple R-squared:  0.5326,    Adjusted R-squared:  0.5326 
F-statistic: 1.176e+04 on 1 and 10322 DF,  p-value: < 2.2e-16

cor.test(log10(TXN_abund_combLibs_tot_n0$TPM),log10(TXN_abund_combLibs_tot_n0$GeneSum),method="spearman")

Warning in cor.test.default(log10(TXN_abund_combLibs_tot_n0$TPM),
log10(TXN_abund_combLibs_tot_n0$GeneSum), : Cannot compute exact p-value
with ties

    Spearman's rank correlation rho

data:  log10(TXN_abund_combLibs_tot_n0$TPM) and log10(TXN_abund_combLibs_tot_n0$GeneSum)
S = 4.5983e+10, p-value < 2.2e-16
alternative hypothesis: true rho is not equal to 0
sample estimates:
      rho 
0.7492704 

TXN_abund_combLibs_tot_n0_noPYRUF= TXN_abund_combLibs_tot_n0 %>% filter(gene !="PYURF")

corr_AllLibTot_noPYRUF=ggplot(TXN_abund_combLibs_tot_n0_noPYRUF, aes(x=log10(TPM), y= log10(GeneSum))) + geom_point() + labs(title="Total All ind Filtered Annotated Clusters", x="log10 RNA seq TPM", y="log10 Peak count sum per gene All Ind.")+ geom_smooth(aes(x=log10(TPM),y=log10(GeneSum)),method = "lm") + annotate("text",x=-5, y=5,label="R2=.54") +geom_density2d(na.rm = TRUE, size = 1, colour = 'red') 

#+ geom_text(aes(label=gene),hjust=0, vjust=0)
       
corr_AllLibTot_noPYRUF      

summary(lm(log10(TPM)~log10(GeneSum),TXN_abund_combLibs_tot_n0_noPYRUF)) 

Call:
lm(formula = log10(TPM) ~ log10(GeneSum), data = TXN_abund_combLibs_tot_n0_noPYRUF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.5931 -0.2252  0.0172  0.2588  2.4468 

Coefficients:
                Estimate Std. Error t value Pr(>|t|)    
(Intercept)    -1.572873   0.026004  -60.48   <2e-16 ***
log10(GeneSum)  0.709829   0.006448  110.08   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.4542 on 10321 degrees of freedom
Multiple R-squared:   0.54, Adjusted R-squared:   0.54 
F-statistic: 1.212e+04 on 1 and 10321 DF,  p-value: < 2.2e-16

cor.test(log10(TXN_abund_combLibs_tot_n0_noPYRUF$TPM),log10(TXN_abund_combLibs_tot_n0_noPYRUF$GeneSum),method="spearman")

Warning in cor.test.default(log10(TXN_abund_combLibs_tot_n0_noPYRUF$TPM), :
Cannot compute exact p-value with ties

    Spearman's rank correlation rho

data:  log10(TXN_abund_combLibs_tot_n0_noPYRUF$TPM) and log10(TXN_abund_combLibs_tot_n0_noPYRUF$GeneSum)
S = 4.5925e+10, p-value < 2.2e-16
alternative hypothesis: true rho is not equal to 0
sample estimates:
      rho 
0.7495161 

Create mapping pheno

Now I can create the File ID file I will use to make the phenotype

create_fileid_AnnotatedCluster_total.py

fout = open("/project2/gilad/briana/threeprimeseq/data/LianoglouLCL_quant/file_id_mapping_total_AnnotatedCluster_head.txt",'w')
infile= open("/project2/gilad/briana/threeprimeseq/data/LianoglouLCL_quant/AnnotatedClusters.Total.fixed.fc", "r")
for line, i in enumerate(infile):
    if line == 0:
        i_list=i.split()
        files= i_list[10:-2]
        for each in files:
            full = each.split("/")[7]
            samp= full.split("-")[2:4]
            lim="_"
            samp_st=lim.join(samp)
            outLine= full[:-1] + "\t" + samp_st
            fout.write(outLine + "\n")
fout.close()

I need to remove the first lines of these files:

awk '{if (NR!=1) {print}}' /project2/gilad/briana/threeprimeseq/data/LianoglouLCL_quant/file_id_mapping_total_AnnotatedCluster_head.txt > /project2/gilad/briana/threeprimeseq/data/LianoglouLCL_quant/file_id_mapping_total_AnnotatedCluster.txt

I want to use the filtered peaks for the QTL analysis so I can write out the FC filtered file.

total_Cov_filt=total_Cov[keep.exprs_T,]


#write.table(total_Cov_filt, file="../data/LianoglouLCL/AnnotatedClusters.Total.fixed.filtered.fc", quote=F, col.names = T, row.names = F)

I need to manually remove the X that was added to the header. I then copy this file to the /project2/gilad/briana/threeprimeseq/data/LianoglouLCL_quant/ directory.

I dont have thier gene file. I can have the phenotpye stay the start and end of the peaks for the QTL analysis. This means I will be calling QTLs 1mb around the start of the peak.

makePheno_AnnotatedClusters_Total.py

#PYTHON 3

dic_IND = {}
dic_BAM = {}

for ln in open("/project2/gilad/briana/threeprimeseq/data/LianoglouLCL_quant/file_id_mapping_total_AnnotatedCluster.txt"):
    bam, IND = ln.split()
    IND = IND.strip()
    dic_IND[bam] = IND
    if IND not in dic_BAM:
        dic_BAM[IND] = []
    dic_BAM[IND].append(bam)


#now I have ind dic with keys as the bam and ind as the values
#I also have a bam dic with ind as the keys and bam as the values  
    
inds=list(dic_BAM.keys()) #list of ind libraries  

#list of genes   

count_file=open("/project2/gilad/briana/threeprimeseq/data/LianoglouLCL_quant/AnnotatedClusters.Total.fixed.filtered.fc", "r")
genes=[]
for line , i in enumerate(count_file):
    if line > 1:
        i_list=i.split()
        id=i_list[0]
        id_list=id.split(":")
        gene=id_list[5]
        if gene not in genes:
            genes.append(gene)
            
#make the ind and gene dic  
dic_dub={}
for g in genes:
    dic_dub[g]={}
    for i in inds:
        dic_dub[g][i]=0


#populate the dictionary  
count_file=open("/project2/gilad/briana/threeprimeseq/data/LianoglouLCL_quant/AnnotatedClusters.Total.fixed.filtered.fc", "r")
for line, i in enumerate(count_file):
    if line > 1:
        i_list=i.split()
        id=i_list[0]
        id_list=id.split(":")
        g= id_list[5]
        values=list(i_list[6:])
        list_list=[]
        for ind,val in zip(inds, values):
            list_list.append([ind, val])
        for num, name in enumerate(list_list):
            dic_dub[g][list_list[num][0]] += int(list_list[num][1])
        

#write the file by acessing the dictionary and putting values in the table ver the value in the dic 
        

fout=open("/project2/gilad/briana/threeprimeseq/data/LianoglouLCL_quant/AnnotatedClusters.Total.fixed.filtered_pheno_Total.txt","w")
peak=["chrom"]
inds_noL=[]
for each in inds:
    indsNA= "NA" + each[:-2]
    inds_noL.append(indsNA)  
fout.write(" ".join(peak + inds_noL) + '\n' )
count_file=open("/project2/gilad/briana/threeprimeseq/data/LianoglouLCL_quant/AnnotatedClusters.Total.fixed.filtered.fc", "r")
for line , i in enumerate(count_file):
    if line > 1:
        i_list=i.split()
        id=i_list[0]
        id_list=id.split(":")
        gene=id_list[5]
        buff=[id]
        start=int(id_list[2])
        end=int(id_list[3])
        buff=[]
        buff.append("chr%s:%d:%d:%s_%s_%s"%(id_list[1], start, end, id_list[5], id_list[4], id_list[0]))
        for x,y in zip(i_list[6:], inds):
            b=int(dic_dub[gene][y])
            t=int(x)
            buff.append("%d/%d"%(t,b))
        fout.write(" ".join(buff)+ '\n')
        
fout.close()

Make a script to run this:

run_makePhen_AnnotatedCluster.sh

#!/bin/bash

#SBATCH --job-name=run_makePhen_AnnotatedCluster
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=run_makePhen_AnnotatedCluster.out
#SBATCH --error=run_makePhen_AnnotatedCluster.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env

python makePheno_AnnotatedClusters_Total.py 

Use leafcutter to prepare this for fastQTL

module load samtools
#zip file 
gzip /project2/gilad/briana/threeprimeseq/data/c/AnnotatedClusters.Total.fixed.filtered_pheno_Total.txt 

module load python
#leafcutter script
python /project2/gilad/briana/threeprimeseq/code/prepare_phenotype_table.py /project2/gilad/briana/threeprimeseq/data/LianoglouLCL_quant/AnnotatedClusters.Total.fixed.filtered_pheno_Total.txt.gz

#source activate three-prime-env
sh /project2/gilad/briana/threeprimeseq/data/LianoglouLCL_quant/AnnotatedClusters.Total.fixed.filtered_pheno_Total.txt.gz_prepare.sh


#keep only 2 PCs
head -n 3 AnnotatedClusters.Total.fixed.filtered_pheno_Total.txt.gz.PCs > /project2/gilad/briana/threeprimeseq/data/LianoglouLCL_quant/AnnotatedClusters.Total.fixed.filtered_pheno_Total.txt.gz.2PCs

Sample list for Fastqtl is /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/SAMPLE.txt

Run FastQTL

APAqtl_nominal_annotatedClusters.sh

#!/bin/bash


#SBATCH --job-name=APAqtl_nominal_transcript
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=APAqtl_nominal_transcript.out
#SBATCH --error=APAqtl_nominal_transcript.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END


for i in 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 
do
/home/brimittleman/software/bin/FastQTL/bin/fastQTL.static --vcf /project2/gilad/briana/YRI_geno_hg19/chr$i.dose.filt.vcf.gz --cov /project2/gilad/briana/threeprimeseq/data/LianoglouLCL_quant/AnnotatedClusters.Total.fixed.filtered_pheno_Total.txt.gz.2PCs --bed /project2/gilad/briana/threeprimeseq/data/LianoglouLCL_quant/AnnotatedClusters.Total.fixed.filtered_pheno_Total.txt.gz.qqnorm_chr$i.gz --out /project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_Annotated/AnnotatedClusters.Total.fixed.filtered_pheno_Total.txt.gz.qqnorm_chr$i.nominal.out --chunk 1 1  --window 5e5 --include-samples /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/SAMPLE.txt
done

APAqtl_permuted_annotatedClusters.sh

#!/bin/bash


#SBATCH --job-name=APAqtl_permuted_annotatedClusters
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=APAqtl_permuted_annotatedClusters.out
#SBATCH --error=APAqtl_permuted_annotatedClusters.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END

for i in 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 
do
/home/brimittleman/software/bin/FastQTL/bin/fastQTL.static --permute 1000 --vcf /project2/gilad/briana/YRI_geno_hg19/chr$i.dose.filt.vcf.gz --cov /project2/gilad/briana/threeprimeseq/data/LianoglouLCL_quant/AnnotatedClusters.Total.fixed.filtered_pheno_Total.txt.gz.2PCs --bed /project2/gilad/briana/threeprimeseq/data/LianoglouLCL_quant/AnnotatedClusters.Total.fixed.filtered_pheno_Total.txt.gz.qqnorm_chr$i.gz --out /project2/gilad/briana/threeprimeseq/data/perm_APAqtl_Annotated/AnnotatedClusters.Total.fixed.filtered_pheno_Total.txt.gz.qqnorm_chr$i.perm.out --chunk 1 1  --window 5e5 --include-samples /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/SAMPLE.txt