wentaocai / long-non-coding-rna

Long-non-coding-RNA

1.Hisat2 build genome index：

First, using the python scripts included in the HISAT2 package, extract splice-site and exon information from the gene annotation file:

$ extract_splice_sites.py gemome.gtf >genome.ss# Get splicing information $ extract_exons.py genome.gtf >genome.exon#Get exon information

Second, build a HISAT2 index:

$ hisat2-build --ss genome.ss --exon genome.exon genome.fa genome

you can directly use the following command: $ hisat2-build genome.fa genome

2. Mapped to genome：

hisat2 -p 8 --dta -x indexes/genome -1 file1_1.fastq.gz -2 file1_2.fastq.gz -S file1.sam hisat2 -p 8 --dta -x indexes/genome -1 file2_1.fastq.gz -2 file2_2.fastq.gz -S file2.sam

3. Sort sam to bam:

$ samtools sort -@ 8 -o file1.bam file1.sam $ samtools sort -@ 8 -o file2.bam file2.sam

4. Assembly：

$ stringtie -p 8 -G genome.gtf -o file1.gtf –l file1 file1.bam $ stringtie -p 8 -G genome.gtf -o file2.gtf –l file2 file2.bam lncRNA (-f 0.01 -a 10 -j 1 -c 0.01)

5. Merge gtf

$ stringtie --merge -p 8 -G genome.gtf -o stringtie_merged.gtf mergelist.txt

6. get novel transcripts for lncRNA

gffcompare –r genomegtf –G –o merged stringtie_merged.gtf

gffcompare download from http://ccb.jhu.edu/software/stringtie/gff.shtml

7. Qualification：

$ stringtie –e –B -p 8 -G stringtie_merged.gtf -o ballgown/file1/file1.gtf file1.bam $ stringtie –e –B -p 8 -G stringtie_merged.gtf -o ballgown/file2/file2.gtf file2.bam

8. Ballgown DGEs analysis：

library(ballgown) library(RSkittleBrewer) library(genefilter) library(dplyr) library(devtools) pheno_data = read.csv("geuvadis_phenodata.csv")#Read phenotype bg_chrX = ballgown(dataDir = "ballgown", samplePattern = "file", pData=pheno_data)#Read expression bg_chrX_filt = subset(bg_chrX,"rowVars(texpr(bg_chrX)) >1",genomesubset=TRUE)#filtering low expression genes results_transcripts = stattest(bg_chrX_filt,feature="transcript",covariate="sex",adjustvars =c("population"), getFC=TRUE, meas="FPKM")#compare group:sex，factor：population results_genes = stattest(bg_chrX_filt, feature="gene",covariate="sex", adjustvars = c("population"), getFC=TRUE,meas="FPKM") results_transcripts=data.frame(geneNames=ballgown::geneNames(bg_chrX_filt),geneIDs=ballgown::geneIDs(bg_chrX_filt), results_transcripts)#Add gene names and id results_transcripts = arrange(results_transcripts,pval)#按pval sort results_genes = arrange(results_genes,pval) write.csv(results_transcripts, "chrX_transcript_results.csv", row.names=FALSE) write.csv(results_genes, "chrX_gene_results.csv", row.names=FALSE) subset(results_transcripts,results_transcripts$qval<0.05) subset(results_genes,results_genes$qval<0.05)

9. Visualization：

tropical= c('darkorange', 'dodgerblue', 'hotpink', 'limegreen', 'yellow') palette(tropical) fpkm = texpr(bg_chrX,meas="FPKM") fpkm = log2(fpkm+1) boxplot(fpkm,col=as.numeric(pheno_data$sex),las=2,ylab='log2(FPKM+1)') ballgown::transcriptNames(bg_chrX)[12]

12

"NM_012227"

ballgown::geneNames(bg_chrX)[12]

12

"GTPBP6"

plot(fpkm[12,] ~ pheno_data$sex, border=c(1,2), main=paste(ballgown::geneNames(bg_chrX)[12],' : ', ballgown::transcriptNames(bg_chrX)[12]),pch=19, xlab="Sex", ylab='log2(FPKM+1)') points(fpkm[12,] ~ jitter(as.numeric(pheno_data$sex)), col=as.numeric(pheno_data$sex)) plotTranscripts(ballgown::geneIDs(bg_chrX)[1729], bg_chrX, main=c('Gene XIST in sample ERR188234'), sample=c('ERR188234')) plotMeans('MSTRG.56', bg_chrX_filt,groupvar="sex",legend=FALSE)