RNA-seq Bioinformatics

Introduction to bioinformatics for RNA sequence analysis

Alignment Free Expression Estimation (Kallisto)

Kallisto mini lecture

If you would like a refresher on Kallisto, we have made a mini lecture briefly covering the topic. We have also made a mini lecture describing the differences between alignment, assembly, and pseudoalignment.


For more information on Kallisto, refer to the Kallisto project page and Kallisto manual page.


Obtain transcript sequences in fasta format

Note that we already have fasta sequences for the reference genome sequence from earlier in the RNA-seq tutorial. However, Kallisto works directly on target cDNA/transcript sequences. Remember also that we have transcript models for genes on chromosome 22. These transcript models were downloaded from Ensembl in GTF format. This GTF contains a description of the coordinates of exons that make up each transcript but it does not contain the transcript sequences themselves. So currently we do not have transcript sequences needed by Kallisto. There are many places we could obtain these transcript sequences. For example, we could download them directly in Fasta format from the Ensembl FTP site.

To allow us to compare Kallisto results to expression results from StringTie, we will create a custom Fasta file that corresponds to the transcripts we used for the StringTie analysis. How can we obtain these transcript sequences in Fasta format?

We could download the complete fasta transcript database for human and pull out only those for genes on chromosome 22. Or we could use a tool from tophat called gtf_to_fasta to generate a fasta sequence from our GTF file. This approach is convenient because it will also include the sequences for the ERCC spike in controls, allowing us to generate Kallisto abundance estimates for those features as well.

cd $RNA_HOME/refs
gtf_to_fasta $RNA_REF_GTF $RNA_REF_FASTA chr22_ERCC92_transcripts.fa

Use less to view the file chr22_ERCC92_transcripts.fa. Note that this file has messy transcript names. Use the following hairball perl one-liner to tidy up the header line for each fasta sequence

cd $RNA_HOME/refs
cat chr22_ERCC92_transcripts.fa | perl -ne 'if ($_ =~/^\>\d+\s+\w+\s+(ERCC\S+)[\+\-]/){print ">$1\n"}elsif($_ =~ /\d+\s+(ENST\d+)/){print ">$1\n"}else{print $_}' > chr22_ERCC92_transcripts.clean.fa
wc -l chr22_ERCC92_transcripts*.fa

View the resulting ‘clean’ file using less chr22_ERCC92_transcripts.clean.fa. View the end of this file use tail chr22_ERCC92_transcripts.clean.fa. Note that we have one fasta record for each Ensembl transcript on chromosome 22 and we have an additional fasta record for each ERCC spike-in sequence.

Create a list of all transcript IDs for later use:

cd $RNA_HOME/refs
cat chr22_ERCC92_transcripts.clean.fa | grep ">" | perl -ne '$_ =~ s/\>//; print $_' | sort | uniq > transcript_id_list.txt

Build a Kallisto transcriptome index

Remember that Kallisto does not perform alignment or use a reference genome sequence. Instead it performs pseudoalignment to determine the compatibility of reads with targets (transcript sequences in this case). However, similar to alignment algorithms like Tophat or STAR, Kallisto requires an index to assess this compatibility efficiently and quickly.

cd $RNA_HOME/refs
mkdir kallisto
cd kallisto
kallisto index --index=chr22_ERCC92_transcripts_kallisto_index ../chr22_ERCC92_transcripts.clean.fa

Generate abundance estimates for all samples using Kallisto

As we did with StringTie and HT-Seq we will generate transcript abundances for each of our demonstration samples using Kallisto.

echo $RNA_DATA_DIR

cd $RNA_HOME/expression/
mkdir kallisto
cd kallisto

kallisto quant --index=$RNA_HOME/refs/kallisto/chr22_ERCC92_transcripts_kallisto_index --output-dir=UHR_Rep1_ERCC-Mix1 --threads=4 --plaintext $RNA_DATA_DIR/UHR_Rep1_ERCC-Mix1_Build37-ErccTranscripts-chr22.read1.fastq.gz $RNA_DATA_DIR/UHR_Rep1_ERCC-Mix1_Build37-ErccTranscripts-chr22.read2.fastq.gz
kallisto quant --index=$RNA_HOME/refs/kallisto/chr22_ERCC92_transcripts_kallisto_index --output-dir=UHR_Rep2_ERCC-Mix1 --threads=4 --plaintext $RNA_DATA_DIR/UHR_Rep2_ERCC-Mix1_Build37-ErccTranscripts-chr22.read1.fastq.gz $RNA_DATA_DIR/UHR_Rep2_ERCC-Mix1_Build37-ErccTranscripts-chr22.read2.fastq.gz
kallisto quant --index=$RNA_HOME/refs/kallisto/chr22_ERCC92_transcripts_kallisto_index --output-dir=UHR_Rep3_ERCC-Mix1 --threads=4 --plaintext $RNA_DATA_DIR/UHR_Rep3_ERCC-Mix1_Build37-ErccTranscripts-chr22.read1.fastq.gz $RNA_DATA_DIR/UHR_Rep3_ERCC-Mix1_Build37-ErccTranscripts-chr22.read2.fastq.gz

kallisto quant --index=$RNA_HOME/refs/kallisto/chr22_ERCC92_transcripts_kallisto_index --output-dir=HBR_Rep1_ERCC-Mix2 --threads=4 --plaintext $RNA_DATA_DIR/HBR_Rep1_ERCC-Mix2_Build37-ErccTranscripts-chr22.read1.fastq.gz $RNA_DATA_DIR/HBR_Rep1_ERCC-Mix2_Build37-ErccTranscripts-chr22.read2.fastq.gz
kallisto quant --index=$RNA_HOME/refs/kallisto/chr22_ERCC92_transcripts_kallisto_index --output-dir=HBR_Rep2_ERCC-Mix2 --threads=4 --plaintext $RNA_DATA_DIR/HBR_Rep2_ERCC-Mix2_Build37-ErccTranscripts-chr22.read1.fastq.gz $RNA_DATA_DIR/HBR_Rep2_ERCC-Mix2_Build37-ErccTranscripts-chr22.read2.fastq.gz
kallisto quant --index=$RNA_HOME/refs/kallisto/chr22_ERCC92_transcripts_kallisto_index --output-dir=HBR_Rep3_ERCC-Mix2 --threads=4 --plaintext $RNA_DATA_DIR/HBR_Rep3_ERCC-Mix2_Build37-ErccTranscripts-chr22.read1.fastq.gz $RNA_DATA_DIR/HBR_Rep3_ERCC-Mix2_Build37-ErccTranscripts-chr22.read2.fastq.gz

Create a single TSV file that has the TPM abundance estimates for all six samples.

cd $RNA_HOME/expression/kallisto
paste */abundance.tsv | cut -f 1,2,5,10,15,20,25,30 > transcript_tpms_all_samples.tsv
ls -1 */abundance.tsv | perl -ne 'chomp $_; if ($_ =~ /(\S+)\/abundance\.tsv/){print "\t$1"}' | perl -ne 'print "target_id\tlength$_\n"' > header.tsv
cat header.tsv transcript_tpms_all_samples.tsv | grep -v "tpm" > transcript_tpms_all_samples.tsv2
mv transcript_tpms_all_samples.tsv2 transcript_tpms_all_samples.tsv
rm -f header.tsv

Take a look at the final kallisto result file we created:

head transcript_tpms_all_samples.tsv
tail transcript_tpms_all_samples.tsv

Compare transcript and gene abundance estimates from Kallisto to isoform abundance estimates from StringTie and counts from HtSeq-Count

How similar are the results we obtained from each approach?

We can compare the expression value for each Ensembl transcript from chromosome 22 as well as the ERCC spike in controls.

To do this comparison, we need to gather the expression estimates for each of our replicates from each approach. The Kallisto transcript results were neatly organized into a single file above. For Kallisto gene expression estimates, we will simply sum the TPM values for transcripts of the same gene. Though it is ‘apples-to-oranges’, we can also compare Kallisto and StringTie expression estimates to the raw read counts from HtSeq-Count (but only at the gene level in this case). The following R code will pull together the various expression matrix files we created in previous steps and create some visualizations to compare them (for both transcript and gene estimates).

First create the gene version of the Kallisto TPM matrix

cd $RNA_HOME/expression/kallisto
wget https://raw.githubusercontent.com/griffithlab/rnabio.org/master/assets/scripts/kallisto_gene_matrix.pl
chmod +x kallisto_gene_matrix.pl
./kallisto_gene_matrix.pl --gtf_file=$RNA_HOME/refs/chr22_with_ERCC92.gtf  --kallisto_transcript_matrix_in=transcript_tpms_all_samples.tsv --kallisto_transcript_matrix_out=gene_tpms_all_samples.tsv
column -t gene_tpms_all_samples.tsv | less -S

Now load files and summarize results from each approach in R

cd $RNA_HOME/expression
R

R code has been provided below. Run the R commands detailed in this script in your R session.

###R code###
#Load libraries
library(ggplot2)

#Set the base working dir from which to access the input files
working_dir = '/home/ubuntu/workspace/rnaseq/expression'
setwd(working_dir)

#Load in expression matrix files from each expression method
htseq_gene_counts = read.table('htseq_counts/gene_read_counts_table_all_final.tsv', sep="\t", header=TRUE, as.is=1, row.names=1)
stringtie_gene = read.table('stringtie/ref_only/gene_tpm_all_samples.tsv', sep="\t", header=TRUE, as.is=1, row.names=1)
stringtie_tran = read.table('stringtie/ref_only/transcript_tpm_all_samples.tsv', sep="\t", header=TRUE, as.is=1, row.names=1)
stringtie_gene_fpkm = read.table('stringtie/ref_only/gene_fpkm_all_samples.tsv', sep="\t", header=TRUE, as.is=1, row.names=1)
stringtie_tran_fpkm = read.table('stringtie/ref_only/transcript_fpkm_all_samples.tsv', sep="\t", header=TRUE, as.is=1, row.names=1)
kallisto_gene = read.table('kallisto/gene_tpms_all_samples.tsv', sep="\t", header=TRUE, as.is=1, row.names=1)
kallisto_tran = read.table('kallisto/transcript_tpms_all_samples.tsv', sep="\t", header=TRUE, as.is=1, row.names=1)

#Summarize the data.frames created
dim(htseq_gene_counts)
dim(stringtie_gene)
dim(stringtie_gene_fpkm)
dim(kallisto_gene)

dim(stringtie_tran)
dim(stringtie_tran_fpkm)
dim(kallisto_tran)

#Reorganize the data.frames for total consistency
kallisto_names = c("length", "HBR_Rep1", "HBR_Rep2", "HBR_Rep3", "UHR_Rep1", "UHR_Rep2", "UHR_Rep3") 
names(kallisto_gene) = kallisto_names
names(kallisto_tran) = kallisto_names

sample_names = c("HBR_Rep1", "HBR_Rep2", "HBR_Rep3", "UHR_Rep1", "UHR_Rep2", "UHR_Rep3")
gene_names = row.names(kallisto_gene)
tran_names = row.names(kallisto_tran)

htseq_gene_counts = htseq_gene_counts[gene_names, sample_names]
stringtie_gene = stringtie_gene[gene_names, sample_names]
stringtie_gene_fpkm = stringtie_gene_fpkm[gene_names, sample_names]
kallisto_gene = kallisto_gene[gene_names, sample_names]
stringtie_tran = stringtie_tran[tran_names, sample_names]
stringtie_tran_fpkm = stringtie_tran_fpkm[tran_names, sample_names]
kallisto_tran = kallisto_tran[tran_names, sample_names]

#Take a look at the top of each data.frame
head(htseq_gene_counts)
head(stringtie_gene)
head(stringtie_gene_fpkm)
head(kallisto_gene)
head(stringtie_tran)
head(stringtie_tran_fpkm)
head(kallisto_tran)

#1. Plot kallisto gene TPMs vs stringtie gene TPMs - Pick HBR_Rep1 data arbitrarily
stabvar = 0.1
HBR1_gene_data = data.frame(kallisto_gene[,"HBR_Rep1"], stringtie_gene[,"HBR_Rep1"], htseq_gene_counts[,"HBR_Rep1"])
names(HBR1_gene_data) = c("kallisto", "stringtie", "htseq")
p1 = ggplot(HBR1_gene_data, aes(log2(kallisto+stabvar), log2(stringtie+stabvar)))
p1 = p1 + geom_point()
p1 = p1 + geom_point(aes(colour = log2(htseq+stabvar))) + scale_colour_gradient(low = "yellow", high = "red")
p1 = p1 + xlab("Kallisto TPM") + ylab("StringTie TPM") + labs(colour = "HtSeq Counts")
p1 = p1 + labs(title = "HBR1 gene expression values [log2(value + 0.1) scaled]")

#2. Plot kallisto transcript TPMs vs stringtie transcript TPMs - Pick HBR_Rep1 data arbitrarily
# Indicate with the points whether the data are real transcripts vs. spike-in controls
HBR1_tran_data = data.frame(kallisto_tran[,"HBR_Rep1"], stringtie_tran[,"HBR_Rep1"])
names(HBR1_tran_data) = c("kallisto", "stringtie")
spikein_status=grepl("ERCC",tran_names)
p2 = ggplot(HBR1_tran_data, aes(log2(kallisto+stabvar), log2(stringtie+stabvar)))
p2 = p2 + geom_point()
p2 = p2 + geom_point(aes(colour = spikein_status)) 
p2 = p2 + xlab("Kallisto TPM") + ylab("StringTie TPM") + labs(colour = "SpikeIn Status")
p2 = p2 + labs(title = "HBR1 transcript expression values [log2(value + 0.1) scaled]")

#3. Plot stringtie transcript TPMs vs. stringtie transcript FPKMs - Pick HBR_Rep1 data arbitrarily
# Indicate with the points whether the data are real transcripts vs. spike-in controls
HBR1_tran_data2 = data.frame(stringtie_tran[,"HBR_Rep1"], stringtie_tran_fpkm[,"HBR_Rep1"])
names(HBR1_tran_data2) = c("stringtie_TPM", "stringtie_FPKM")
p3 = ggplot(HBR1_tran_data2, aes(log2(stringtie_TPM+stabvar), log2(stringtie_FPKM+stabvar)))
p3 = p3 + geom_point()
p3 = p3 + geom_point(aes(colour = spikein_status))
p3 = p3 + geom_abline(intercept = 0, slope = 1)
p3 = p3 + xlab("StringTie TPM") + ylab("StringTie FPKM") + labs(colour = "SpikeIn Status")
p3 = p3 + labs(title = "HBR1 transcript expression values [log2(value + 0.1) scaled]")

#Print out the plots created above and store in a single PDF file
pdf(file="Tutorial_comparisons.pdf")
print(p1)
print(p2)
print(p3)
dev.off()

#Quit R
quit(save="no")

The output file can be viewed in your browser at the following url. Note, you must replace YOUR_IP_ADDRESS with your own amazon instance IP (e.g., 101.0.1.101)).

http://YOUR_IP_ADDRESS/rnaseq/expression/Tutorial_comparisons.pdf

A file copy of the above R code can be found here.


Create a custom transcriptome database to examine a specific set of genes

Suppose we just want to quickly assess the presence of a particular class of genes only(e.g. ribosomal RNA genes). We can obtain these genes from an Ensembl GTF file. In the example below we will use our chromosome 22 GTF file for demonstration purposes. But in a ‘real world’ experiment you would use a GTF for all chromosomes. Once we have found GTF records for ribosomal RNA genes, we will create a fasta file that contains the sequences for these transcripts, and then index this sequence database for use with Kallisto.

cd $RNA_HOME/refs
grep rRNA $RNA_REF_GTF > genes_chr22_ERCC92_rRNA.gtf
gtf_to_fasta genes_chr22_ERCC92_rRNA.gtf $RNA_REF_FASTA chr22_rRNA_transcripts.fa
cat chr22_rRNA_transcripts.fa | perl -ne 'if ($_ =~/^\>\d+\s+\w+\s+(ERCC\S+)[\+\-]/){print ">$1\n"}elsif($_ =~ /\d+\s+(ENST\d+)/){print ">$1\n"}else{print $_}' > chr22_rRNA_transcripts.clean.fa
cat chr22_rRNA_transcripts.clean.fa

cd $RNA_HOME/refs/kallisto
kallisto index --index=chr22_rRNA_transcripts_kallisto_index ../chr22_rRNA_transcripts.clean.fa

We can now use this index with Kallisto to assess the abundance of rRNA genes in a set of samples.


IN PROGRESS - Perform DE analysis of Kallisto expression estimates using Sleuth

We will now use Sleuth perform a differential expression analysis on the full chr22 data set produced above. Sleuth is a companion tool that starts with the output of Kallisto, performs DE analysis, and helps you visualize the results. It is analagous to Ballgown that we used to perform DE and visualization of the StringTie results in earlier steps.

Regenerate the Kallisto results using the HDF5 format and 100 rounds of bootstrapping (both required for Sleuth to work).

cd $RNA_HOME/de
mkdir -p sleuth/input
mkdir -p sleuth/results
cd sleuth/input

kallisto quant -b 100 --index=$RNA_HOME/refs/kallisto/chr22_ERCC92_transcripts_kallisto_index --output-dir=UHR_Rep1_ERCC-Mix1 --threads=4 $RNA_DATA_DIR/UHR_Rep1_ERCC-Mix1_Build37-ErccTranscripts-chr22.read1.fastq.gz $RNA_DATA_DIR/UHR_Rep1_ERCC-Mix1_Build37-ErccTranscripts-chr22.read2.fastq.gz
kallisto quant -b 100 --index=$RNA_HOME/refs/kallisto/chr22_ERCC92_transcripts_kallisto_index --output-dir=UHR_Rep2_ERCC-Mix1 --threads=4 $RNA_DATA_DIR/UHR_Rep2_ERCC-Mix1_Build37-ErccTranscripts-chr22.read1.fastq.gz $RNA_DATA_DIR/UHR_Rep2_ERCC-Mix1_Build37-ErccTranscripts-chr22.read2.fastq.gz
kallisto quant -b 100 --index=$RNA_HOME/refs/kallisto/chr22_ERCC92_transcripts_kallisto_index --output-dir=UHR_Rep3_ERCC-Mix1 --threads=4 $RNA_DATA_DIR/UHR_Rep3_ERCC-Mix1_Build37-ErccTranscripts-chr22.read1.fastq.gz $RNA_DATA_DIR/UHR_Rep3_ERCC-Mix1_Build37-ErccTranscripts-chr22.read2.fastq.gz

kallisto quant -b 100 --index=$RNA_HOME/refs/kallisto/chr22_ERCC92_transcripts_kallisto_index --output-dir=HBR_Rep1_ERCC-Mix2 --threads=4 $RNA_DATA_DIR/HBR_Rep1_ERCC-Mix2_Build37-ErccTranscripts-chr22.read1.fastq.gz $RNA_DATA_DIR/HBR_Rep1_ERCC-Mix2_Build37-ErccTranscripts-chr22.read2.fastq.gz
kallisto quant -b 100 --index=$RNA_HOME/refs/kallisto/chr22_ERCC92_transcripts_kallisto_index --output-dir=HBR_Rep2_ERCC-Mix2 --threads=4 $RNA_DATA_DIR/HBR_Rep2_ERCC-Mix2_Build37-ErccTranscripts-chr22.read1.fastq.gz $RNA_DATA_DIR/HBR_Rep2_ERCC-Mix2_Build37-ErccTranscripts-chr22.read2.fastq.gz
kallisto quant -b 100 --index=$RNA_HOME/refs/kallisto/chr22_ERCC92_transcripts_kallisto_index --output-dir=HBR_Rep3_ERCC-Mix2 --threads=4 $RNA_DATA_DIR/HBR_Rep3_ERCC-Mix2_Build37-ErccTranscripts-chr22.read1.fastq.gz $RNA_DATA_DIR/HBR_Rep3_ERCC-Mix2_Build37-ErccTranscripts-chr22.read2.fastq.gz

Sleuth is an R package so the following steps will occur in an R session. The following section is an adaptation of the sleuth getting started tutorial.

A separate R tutorial file has been provided in the github repo for this part of the tutorial: Tutorial_KallistoSleuth.R. Run the R commands in this file.

Enter an R session

cd $RNA_HOME/de/sleuth/results
R

Execute the following command in R

#load sleuth library
suppressMessages({
  library("sleuth")
})

#set input and output dirs
datapath = "/home/ubuntu/workspace/rnaseq/de/sleuth/input"
resultdir = '/home/ubuntu/workspace/rnaseq/de/sleuth/results'
setwd(resultdir)

#create a sample to condition metadata description
sample_id = dir(file.path(datapath))
kal_dirs = file.path(datapath, sample_id)
print(kal_dirs)
sample = c("HBR_Rep1_ERCC-Mix2", "HBR_Rep2_ERCC-Mix2", "HBR_Rep3_ERCC-Mix2", "UHR_Rep1_ERCC-Mix1", "UHR_Rep2_ERCC-Mix1", "UHR_Rep3_ERCC-Mix1")
condition = c("HBR", "HBR", "HBR", "UHR", "UHR", "UHR")
s2c = data.frame(sample,condition)
s2c <- dplyr::mutate(s2c, path = kal_dirs)
print(s2c)

#run sleuth on the data
so <- sleuth_prep(s2c, extra_bootstrap_summary = TRUE)
so <- sleuth_fit(so, ~condition, 'full')
so <- sleuth_fit(so, ~1, 'reduced')
so <- sleuth_lrt(so, 'reduced', 'full')
models(so)

#summarize the sleuth results and view 20 most significant DE transcripts
sleuth_table <- sleuth_results(so, 'reduced:full', 'lrt', show_all = FALSE)
sleuth_significant <- dplyr::filter(sleuth_table, qval <= 0.05)
head(sleuth_significant, 20)

#plot an example DE transcript result
p1 = plot_bootstrap(so, "ENST00000328933", units = "est_counts", color_by = "condition")
p2 = plot_pca(so, color_by = 'condition')

#Print out the plots created above and store in a single PDF file
pdf(file="SleuthResults.pdf")
print(p1)
print(p2)
dev.off()

Exercise (OPTIONAL): Do a performance test using a real large dataset

Obtain an entire lane of RNA-seq data for a breast cancer cell line and matched ‘normal’ cell line here:

NOTE: do not attempt this unless you have a lot of free space on your machine (at least 250 GB)

Tumor (download)

Normal (download)

For more information on this data refer to this page: https://github.com/genome/gms/wiki/HCC1395-WGS-Exome-RNA-Seq-Data

Download the data

cd $RNA_HOME/data/
mkdir hcc1395
cd hcc1395
wget https://xfer.genome.wustl.edu/gxfer1/project/gms/testdata/bams/hcc1395/gerald_C1TD1ACXX_8_ACAGTG.bam
wget https://xfer.genome.wustl.edu/gxfer1/project/gms/testdata/bams/hcc1395/gerald_C2DBEACXX_3.bam

Convert BAM to FASTQ

Since the paths above will download BAM files but Kallisto expects FASTQ files for the read data. You will need to convert from BAM back to FASTQ. Try using Picard to do this.

Example BAM to FASTQ conversion commands (note that you need to specify the correct path for your Picard installation), followed by compressing the resulting FastQ files to save space:

java -Xmx2g -jar /home/ubuntu/workspace/rnaseq/student_tools/picard.jar SamToFastq INPUT=gerald_C1TD1ACXX_8_ACAGTG.bam FASTQ=hcc1395_tumor_R1.fastq SECOND_END_FASTQ=hcc1395_tumor_R2.fastq VALIDATION_STRINGENCY=LENIENT
gzip hcc1395_tumor*.fastq
java -Xmx2g -jar /home/ubuntu/workspace/rnaseq/student_tools/picard.jar SamToFastq INPUT=gerald_C2DBEACXX_3.bam FASTQ=hcc1395_normal_R1.fastq SECOND_END_FASTQ=hcc1395_normal_R2.fastq VALIDATION_STRINGENCY=LENIENT
gzip hcc1395_normal*.fastq

Download full transcriptome reference

You will have to get all transcripts instead of just those for a single chromosome. You will also have to create a new index for this new set of transcript sequences.

wget ftp://ftp.ensembl.org/pub/release-89/fasta/homo_sapiens/cdna/Homo_sapiens.GRCh38.cdna.all.fa.gz

Now repeat the concepts above to obtain abundance estimates for all genes.

kallisto index --index=Homo_sapiens.GRCh38.cdna.all_index Homo_sapiens.GRCh38.cdna.all.fa.gz

kallisto quant --index=Homo_sapiens.GRCh38.cdna.all_index --output-dir=normal --threads=4 --plaintext hcc1395/hcc1395_normal_R1.fastq.gz hcc1395/hcc1395_normal_R2.fastq.gz
kallisto quant --index=Homo_sapiens.GRCh38.cdna.all_index --output-dir=tumor --threads=4 --plaintext hcc1395/hcc1395_tumor_R1.fastq.gz hcc1395/hcc1395_tumor_R2.fastq.gz

Note: