Code for analyzing RNAseq data from cell lines expressing PcTF, and code for making figures
- Download or obtain data
- FastQC to check the quality of the raw reads
- Trim fastq files for quality and to remove adaptors.
- FastQC to check the quality of the trimmed reads
- Obtain reference genome and gene annotation files
- Generate genome indexes
- STAR: map transcript reads to the reference genome and identify splice junctions
- check quality of raw BAM files
- Sort BAM files, to be in the same order as the reference for downstream analysis
- check quality of sorted BAM files
- Mark duplicates, duplicates will not be removed but will be marked for quality checks
- check quality of mark duplicates BAM files
- Add read groups to BAM files, this done to keep the sample ids organized when creating the merged vcf file
- check quality of add read group BAM files
- Index BAM files
- Identify genes that differentially expressed
fastqc http://www.bioinformatics.babraham.ac.uk/projects/fastqc/
Trimmomatic http://www.usadellab.org/cms/?page=trimmomatic
STAR https://github.com/alexdobin/STAR
bamtools https://github.com/pezmaster31/bamtools
cuffdiff http://cole-trapnell-lab.github.io/cufflinks/cuffdiff/
GEO accession number
$wget ftp://GEO/path
Fastqc reads raw sequence data from high throughput sequencers and runs a set of quality checks to produce a report. Best reports are those whose "per base sequence quality" are included in the green area of the graph & kmer content is good or average.
Example command: $fastqc sampleID.fastq
fastqc Babraham bioinformatics program that that checks for quality of reads
sampleID.fastq path and name of sampleID in fastq format, may also be in fastq.gz format
Reports were saved in fastq_files directory in Project /Project/fastq_files This command will create two outputs: an .html file & an .zip file. Will output sampleID_fastqc.html and sampleID_fastqc.zip files
Trimmomatic performs a variety of useful trimming tasks for illumina paired-end and single ended data.The selection of trimming steps and their associated parameters are supplied on the command line.
The current trimming steps are: ILLUMINACLIP: Cut adapter and other illumina-specific sequences from the read. SLIDINGWINDOW: Perform a sliding window trimming, cutting once the average quality within the window falls below a threshold. LEADING: Cut bases off the start of a read, if below a threshold quality TRAILING: Cut bases off the end of a read, if below a threshold quality CROP: Cut the read to a specified length HEADCROP: Cut the specified number of bases from the start of the read MINLEN: Drop the read if it is below a specified length TOPHRED33: Convert quality scores to Phred-33 TOPHRED64: Convert quality scores to Phred-64 It works with FASTQ (using phred + 33 or phred + 64 quality scores, depending on the Illumina pipeline used), either uncompressed or gzipp'ed FASTQ. Use of gzip format is determined based on the .gz extension.
The parameters selected were slidingwindow:4:30 leading10 trailing25 minlen50 phred33. They were chosen based on a better per sequence quality base and kmer content. A few fastq files were tested to determine the use of phred33 or phred 64. Phred33 was chosen due to the amount of bases ramining after trimming. Other parameters tested but not found to be ideal are sliding4:30 leading30 trailing40, sliding4:40 leading10 trailing25, sliding 4:40 leading30 trailing 40
$java -jar /path/to/trimmomatic-0.36.jar SE -phred33 input.fq.gz output.fq.gz ILLUMINACLIP:/adapters/TruSeq3-SE.fa:2:30:10 LEADING:10 TRAILING:25 SLIDINGWINDOW:4:30 MINLEN:50 Example Command: java -jar /home/sbrotman/GETx_Brain/00_tools/Trimmomatic-0.36/trimmomatic-0.36.jar SE -phred33 /mnt/storage/public/dbgap-8834/femalebrain/SRR598768_1.fastq /home/sbrotman/GETx_Brain/02_raw/trim_female/SRR598768_1_trim_1.fastq ILLUMINACLIP:/home/sbrotman/GETx_Brain/00_tools/Trimmomatic-0.36/adapters/TruSeq3-SE.fa:2:30:10 LEADING:10 TRAILING:25 SLIDINGWINDOW:4:30 MINLEN:50 java indicates that this is a java program and will require java in order to run -jar jar file to follow trimmomatic-0.36.jar tool that will trim the raw fastq files SE SE is for singel end reads. If pair end then PE -phred33 Using phred + 33 or phred + 64 quality scores, depending on the Illumina pipeline used, either uncompressed or gzipp'ed FASTQ input.fq.gz sampeID in fastq format output.fq.gz sampleID output file. Use a descriptive name such as sampleID_minlen50_sliding430_leading30_trailing40.fq ILLUMINACLIP:TruSeq3-SE:2:30:10 Remove Illumina adapters provided in the TruSeq3-PE.fa file (provided). Initially Trimmomatic will look for seed matches (16 bases) allowing maximally 2 mismatches. These seeds will be extended and clipped if in the case of paired end reads a score of 30 is reached (about 50 bases), or in the case of single ended reads a score of 10, (about 17 bases). LEADING:10 Cut bases off the start of a read, if below a threshold quality of 10 TRAILING:25 Cut bases off the end of a read, if below a threshold quality of 25 SLIDINGWINDOW:4:30 Scan the read with a 4-base wide sliding window, cutting when the average quality per base drops below 30 MINLEN:50 Drop the read if it is below a specified length of 50 adapters add pathway to adapters directory
SBATCH script - sbatch NameOfSBATCHscript.sh
- Create and view fastqc reports for the trimmed fastq files
Fastqc reads sequence data from high throughput sequencers and runs a set of quality checks to produce a report. Best reports are those whose "per base sequence quality" are included in the green area of the graph & kmer content is good or average.
Example command: $fastqc sampleID_minlen50_sliding430_leading30_trailing40.fastq fastqc Babraham bioinformatics program that that checks for quality of reads sampleID.fastq path and name of sampleID in fastq format, may also be in fastq.gz format
Reports were saved in fastq_files directory in Project /Project/fastq_files This command will create two outputs: an .html file & an .zip file. Will output sampleID_fastqc.html and sampleID_fastqc.zip files
SBATCH script - sbatch NameOfSBATCHscript.sh
Move fastqc reports to desktop to visualize them as you can't open html in a terminal.
Open new terminal as this will not work if logged into a HPC (high performance computing) cluster
Example command: $scp [email protected]:/Project/fastq_files/sampleID_raw_fastqc.html /Users/Desktop/
scp secure copy (linux command)
/path/to/fastqc.html path to where the files are located
/path/where/you/want/fastqc.html path to where you would like to copy the files to
- Obtain reference genome and gene annotation file
Obtain reference genome and gene annotation file to be used for mapping reads. Use the most relavent current version GRCh38.p7 from gencode. http://www.gencodegenes.org/releases/current.html Genome sequence (GRCh38.p7) ALL GRCh38.p7.genome.fa.gz Nucleotide sequence of the GRCh38.p7 genome assembly version on all regions, including reference chromosomes, scaffolds, assembly patches and haplotypes The sequence region names are the same as in the GTF/GFF3 files Example command $wget ftp://ftp.sanger.ac.uk/pub/gencode/Gencode_human/release_25/GRCh38.p7.genome.fa.gz wget stands for "web get" ftp stands for File transfer program /GRCh38.p7.genome.fa.gz human reference genome in fasta format
Comprehensive gene annotation ALL gencode.v25.chr_patch_hapl_scaff.annotation.gtf.gz It contains the comprehensive gene annotation on the reference chromosomes, scaffolds, assembly patches and alternate loci (haplotypes) This is a superset of the main annotation file Example command $wget ftp://ftp.sanger.ac.uk/pub/gencode/Gencode_human/release_25/gencode.v25.chr_patch_hapl_scaff.annotation.gtf.gz wget stands for "web get" ftp stands for File transfer program gencode.v25.chr_patch_hapl_scaff.annotation.gtf.gz human reference gene annotation file in .gtf format
SBATCH script - sbatch NameOfSBATCHscript.sh
- Generate reference genome index and dictionary
A .dict dictionary of the contig names and sizes and a .fai fasta index file allow efficient random access to the reference bases for downstream analysis and mapping You have to generate these files in order to be able to use a Fasta file as reference. In this step user supplied the reference genome sequences (FASTA files) and annotations(GTF file), from which STAR generate genome indexes that are utilized in the 2nd step. The genome indexes are saved to disk and need only be generated once for each genome/annotation combination. Note - files can not be be zipped (i.e file.gz) use $gunzip file.gz Example command: $gunzip reference.gz gunzip linux command to unzip zipped files reference.gz path and name to zipped file
Index reference genome using STAR Example command: $STAR --runMode genomeGenerate --genomeDir /GRCh38.p7/ --genomeFastaFiles GRCh38.p7.genome.fa.gz --sjdbGTFfile gencode.v25.chr_patch_hapl_scaff.annotation.gtf.gz --runThreadN 8 STAR path to calling STAR read aligner package --runMode genomeGenerate option directs STAR to run genome indices generation job. --genomeDir /path/to/genomeDir --genomeFastaFiles /path/to/genome/fasta --sjdbGTFfile /path/to/annotations.gtf --sjdbOverhang ReadLength-1 --runThreadN NumberOfThreads. Option defines the number of threads to be used for genome generation, it has to be set to the number of available cores on the server node.
Create reference dictionrary using Picard tools CreateSequenceDictionary Example command: $java -Xmx14g -jar picard.jar CreateSequenceDictionary R=GRCh38.p7.genome.fa O=GRCh38.p7.genome.fa.dict java java program and will need java installed in order to run -Xmx14g declares memory -jar tells the program that the following tool is in .jar format picard.jar picard tools from the Broad Institute. A set of command line tools (in Java) for manipulating high-throughput sequencing (HTS) data and formats such as SAM/BAM/CRAM and VCF. CreateSequenceDictionary creates a sequence dictionary for a reference sequence. The output file contains a header but no SAMRecords, and the header contains only sequence records. R=GRCh38.p7.genome.fa R stands for reference. Path and name of reference genome O=GRCh38.p7.genome.fa.dict O stand for output. Path and name of the output reference genome dictionary
SBATCH script - sbatch NameOfSBATCHscript.sh
- STAR: map transcript reads to the reference genome, output as a .bam
STAR read aligner is a 2 pass process. The user supplies the genome files generated in the pervious step (generate genome indexes), as well as the RNA-seq reads (sequences) in the form of FASTA or FASTQ files. STAR maps the reads to the genome, and writes several output files, such as alignments (SAM/BAM), mapping summary statistics, splice junctions, unmapped reads, signal (wiggle) tracks etc. Mapping is controlled by a variety of input parameters (options)
STAR highly recommends using --sjdbGTFfile which specifies the path to the file with annotated transcripts in the standard GTF format. Where STAR will extract splice junctions from this file and use them to greatly improve accuracy of the mapping. While this is optional, and STAR can be run without annotations, using annotations is highly recommended whenever they are available. However this option should not be included for projects that include hybrids, as this might cause a bias towards the reference.
Compatibility with Cufflinks/Cuffdiff. For unstranded RNA-seq data, Cufflinks/Cuffdiff require spliced alignments with XS strand attribute, which STAR will generate with --outSAMstrandField intronMotif option. As required, the XS strand attribute will be generated for all alignments that contain splice junctions. The spliced alignments that have undefined strand (i.e. containing only non-canonical unannotated junctions) will be suppressed. If you have stranded RNA-seq data, you do not need to use any specific STAR options. Instead, you need to run Cufflinks with the library option --library-type options. For example, cufflinks ... --library-type fr-firststrand should be used for the standard dUTP protocol, including Illumina’s stranded Tru-Seq. This option has to be used only for Cufflinks runs and not for STAR runs. In addition, it is recommended to remove the non-canonical junctions for Cufflinks runs using --outFilterIntronMotifs RemoveNoncanonical.
Unstranded vs. stranded RNAseq stranded vs. unstranded RNAseq data is where the strand specificity of origin for each transcript is defined. Without strand information it is difficult and sometimes impossible to accurately quantify gene expression levels for genes with overlapping genomic loci that are transcribed from opposite strands (Zhao et al. 2015) Stranded RNA-seq provides a more accurate estimate of transcript expression compared with non-stranded RNA-seq, and is therefore the recommended RNA-seq approach for future mRNA-seq studies. If your data is unstranded then you will want to include the STAR options --outSAMstrandField intronMotif option. STAR will generate the XS strand attribute for all alignments that contain splice junctions. The spliced alignments that have undefined strand (i.e. containing only non-canonical unannotated junctions) will be suppressed. If you have stranded RNA-seq data, you do not need to use any specific STAR options. Instead, you need to run Cufflinks with the library option --library-type options (Dobin et al. 2012)
First pass: maps fastq files to the reference genome and identifies splice junctions. Example comamnd: $STAR --genomeDir Project_genome --genomeLoad LoadAndKeep --readFilesIn sampleID.fastq --outSAMtype BAM Unsorted --outFileNamePrefix sampleID_pass1. --runThreadN 8 STAR path to calling STAR read aligner package --genomeDir Specifies path to the directory (henceforth called ”genome directory” where the genome indices are stored. This directory has to be created (with mkdir) before STAR run and needs to have writing permissions. Project_genome path and directory to reference genome --genomeLoad mode of shared memory usage for the genome files LoadAndKeep load genome into shared and keep it in memory after run --sjdbGTFfile path to the GTF file with annotations --readFilesIn if pair end reads, include path to both reads with a " " space inbetween _1 _2, /geuvadis_fastq/sampleID_1.fastq /home/kcolney/map_geuvadis/geuvadis_fastq/sampleID_2.fastq sampleID.fastq name and path to sample in fastq format. If paired end samples include both pairs and separate with a space i.e (sample_1.fastq sample_2.fastq) --outSAMtype indicate which output format, BAM unsorted BAM Unsorted output unsorted Aligned.out.bam file. The paired ends of an alignment are always adjacent, and multiple alignments of a read are adjacent as well. This ”unsorted” file can be directly used with downstream software such as HTseq, without the need of name sorting. The order of the reads will match that of the input FASTQ(A) files only if one thread is used --outSAMstrandField * This option depends on the data * For unstranded RNA-seq data, Cufflinks/Cuffdiff require spliced alignments with XS strand attribute, which STAR will generate with --outSAMstrandField intronMotif option intronMotif * This option depends on the data * As required, the XS strand attribute will be generated for all alignments that contain splice junctions. The spliced alignments that have undefined strand (i.e. containing only non-canonical unannotated junctions) will be suppressed. --outFilterIntronMotifs * This option depends on the data * it is recommended to remove the non-canonical junctions for Cufflinks runs using RemoveNoncanonical * This option depends on the data * remove the non-canonical junctions --outFileNamePrefix define the sample id prefix, sampleID_pass1. (bam, will be added by the STAR program) --runThreadN for computing purpose allocate the number of threads, 8
SBATCH script - sbatch NameOfSBATCHscript.sh
For the most sensitive novel junction discovery, it is recommend to run STAR in the 2-pass mode. It does not increase the number of detected novel junctions, but allows to detect more splices reads mapping to novel junctions. The basic idea is to run 1st pass of STAR mapping with the usual parameters, then collect the junctions detected in the first pass, and use them as ”annotated” junctions for the 2nd pass mapping. Second pass: maps fastq files between each other and identifies splice junctions. STAR will utilize annotations formatted as a list of splice junctions coordinates in a text file: --sjdbFileChrStartEnd /path/to/sjdbFile.txt. This file should contains 4 columns separated by tabs: Chr \tab Start \tab End \tab Strand=+/-/. Example command: $STAR --genomeDir Project_genome --readFilesIn sampleID_1.fastq --outSAMtype BAM Unsorted --outFileNamePrefix sampleID_pass2. --sjdbFileChrStartEnd /sampleID1_pass1.SJ.out.tab /sampleID2_pass1.SJ.out.tab /sampleID3_pass1.SJ.out.tab --runThreadN 14 STAR STAR read aligner package --genomeDir define where the genome is location, /star_genome Project_genome path and directory to reference genome --genomeLoad mode of shared memory usage for the genome files LoadAndKeep load genome into shared and keep it in memory after run --sjdbGTFfile path to the GTF file with annotations --readFilesIn if pair end reads, include path to both reads with a " " space inbetween _1 _2, /geuvadis_fastq/sampleID_1.fastq /home/kcolney/map_geuvadis/geuvadis_fastq/sampleID_2.fastq sampleID_1.fastq name and path to sample in fastq format. If paired end samples include both pairs and separate with a space i.e (sample_1.fastq sample_2.fastq) --outSAMtype indicate which output format, BAM unsorted BAM Unsorted output unsorted Aligned.out.bam file. The paired ends of an alignment are always adjacent, and multiple alignments of a read are adjacent as well. This ”unsorted” file can be directly used with downstream software such as HTseq, without the need of name sorting. The order of the reads will match that of the input FASTQ(A) files only if one thread is used --sjdbFileChrStartEnd path to the pass_1.SJ.out.tab files made in the first pass sampleID1_pass1.SJ.out.tab 4 columns separated by tabs: Chr \tab Start \tab End \tab Strand=+/-/. Here Start and End are first and last bases of the introns (1-based chromosome coordinates). This file can be used in addition to the --sjdbGTFfile, in which case STAR will extract junctions from both files. sampleID2_pass1.SJ.out.tab List all the samples from the first pass or all the samples in a group (i.e population, cases and controls, hybrids, males and females) --outFileNamePrefix define the sample id prefix, sampleID_pass1. (bam, will be added by the STAR program) --runThreadN for computing purpose allocate the number of threads, 14
SBATCH script - sbatch NameOfSBATCHscript.sh
- Check initial quality stats on .bam files
Will print basic statistics from input BAM file(s)
Total reads:
Mapped reads:
Forward strand:
Reverse strand:
Failed QC:
Duplicates:
Paired-end reads:
'Proper-pairs':
Both pairs mapped:
Read 1:
Read 2:
Singletons:
Example command: $bamtools stats -in sampleID_pass2.Aligned.out.bam > sampleID_pass2.txt bamtools package stats command to get general alignment statistics -in indicates input file sampleID.bam path and name to bam file
directs output
sampleID_pass2.txt indicated output file name
SBATCH script - sbatch NameOfSBATCHscript.sh
- Sort bam files
An appropriate @HD-SO sort order header tag will be added or an existing one updated if necessary. Example command: $bamtools sort -in sampleID.bam -out sampleID.sorted.bam bamtools package sort command to add header tags -in indicates input file sampleID.bam path and name to bam file -out indicated output file sampleID.sorted.bam the sorted BAM file
SBATCH script - sbatch NameOfSBATCHscript.sh
- Generate stats on sorted bam files
Will print basic statistics from input BAM file(s)
Example command: $bamtools stats -in sampleID_pass2.Aligned.sorted.bam > sampleID_pass2.Aligned.sorted.txt
bamtools package
stats command to get general alignment statistics
-in indicates input file
sampleID.bam path and name to bam file
directs output
sampleID_pass2.txt indicated output file name
SBATCH script - sbatch NameOfSBATCHscript.sh
Compare sorted.bam stats to the original .bam stats, there should be no differences between them We do this to step (bamtools stats) every time we do anything to our bam files as a quality control check
- Mark duplicates & Remove duplicates
Mark duplicates: "Flags" where the duplicate reads are Example command: $java -Xmx8g -jar picard.jar MarkDuplicates INPUT=sampleID.sorted.bam OUTPUT=sampleID.sorted.markdup.bam METRICS_FILE=sampleID.markdup.picardMetrics.txt REMOVE_DUPLICATES=false ASSUME_SORTED=true VALIDATION_STRINGENCY=LENIENT java program called -Xmx8g declares memory picard.jar path to picard jar file MarkDuplicates command to create sequence dictionary INPUT= path to input file, sorted bam file per sample OUTPUT= path and name or output file .markdup to indicate this file will contain duplicates that have been marked METRICS_FILE= file to write duplication metrics to save as sampleID.markdup.picardMetrics.txt REMOVE_DUPLICATES=false If true do not write duplicates to the output file instead of writing them with appropriate flags set. Default value: false. This option can be set to 'null' to clear the default value. Possible values: {true, false} ASSUME_SORTED=true BAM files are sorted because we sorted them in step 6 VALIDATION_STRINGENCY=LENIENT setting stringency to SILENT can improve performance when processing a BAM file in which variable-length data (read, qualities, tags) do not otherwise need to be decoded.
SBATCH script - sbatch NameOfSBATCHscript.sh
- Generate statistics for bam files with duplicates marked
For each sample get the read stats for the mark duplicates BAM files Example command: $bamtools stats -in sampleID_pass2.Aligned.sorted.dupsMark.bam > sampleID_pass2.Aligned.sorted.dupsMark.txt
SBATCH script - sbatch NameOfSBATCHscript.sh
Compare stat results for each sample to the original bam file (sanity check: is there the same number of reads as the original BAM file?) If there is more than 15% of the reads being marked as duplicates may need to consider removing that sample
- Add or replace read groups
For each sample, add a read group to the mark duplicate BAM files (a read group is a "tag" such as a sample ID) Example command: $java -Xmx8g -jar picard.jar AddOrReplaceReadGroups INPUT=sampleID.sorted.rmdup.bam OUTPUT=sampleID.sorted.rmdup.addReadGr.bam RGLB=sampleID RGPL=machineUsed RGPU=laneUsed RGSM=sampleName RGCN=location RGDS=species VALIDATION_STRINGENCY=LENIENT java program called -Xmx8g declares memory picard.jar path to picard jar file AddOrReplaceReadGroups Replaces all read groups in the INPUT file with a single new read group and assigns all reads to this read group in the OUTPUT BAM INPUT= path to input file, sorted bam file per sample OUTPUT= path and name or output file .markdup to indicate this file will contain duplicates that have been marked RGLB= Read Group Library Required (sampleID) RGPL= Read Group platform (e.g. illumina, solid) Required RGPU= Read Group platform unit (eg. run barcode) Required (laneUsed) RGSM= Read Group sample name Required (sampleName or sampleID) RGCN= Read Group sequencing center name Default value: null (i.e. ASU) RGDS= Read Group description Default value: null (speciesName) VALIDATION_STRINGENCY=LENIENT setting stringency to SILENT can improve performance when processing a BAM file in which variable-length data (read, qualities, tags) do not otherwise need to be decoded.
SBATCH script - sbatch NameOfSBATCHscript.sh
- Generate statistics on read group BAM sample files
For each sample get the read stats for the remove duplicates and add read groups BAM files. Statistics on the BAM files should be the same as before the previous step when read groups were modified. Compare stat results for each sample to the markdup.bam file (sanity check: is there the same number of reads as the original BAM file?) Example command: bamtools stats -in sampleID.sorted.markdup.addReadGr.bam
SBATCH script - sbatch NameOfSBATCHscript.sh
- Index BAM files
For each sample index the processed BAM files that are sorted, have marked duplicates, and have read groups added. These will be used to identify callable loci. Indexing is used to "sort" by chromosome and region Output will be sampleID.sorted.markdup.addReadGr.bam.bai Example command: $bamtools index -in sampleID.sorted.markdup.addReadGr.bam bamtools package index Generates index for BAM file -in indicates input file sampleID.sorted.markdup.addReadGrbam path and name to bam file that has been sorted, duplicate reads were removed, and the read groups were added
SBATCH script - sbatch NameOfSBATCHscript.sh
- Differential expression using Cuffdiff
Identify differentially expressed genes and transcripts using cuffdiff. Cuffdiff is used to find significant changes in transcript expression, splicing, and promoter use. Cuffdiff uses a gene annotation file downloaded with a sbatch script from UCSC hg19 Example command: $cuffdiff -use-sample-sheet -o diff_out -b reference.fa -p 8 --library-type fr-firststrand -L set1,set2 -u refence.gtf sampleSet.txt cuffdiff program in cufflinks package that identifies if genes are differentially expressed -use-sample-sheet tells the program to use a text file containing the path to the samples and the group_id -o output diff_out path and name of output directory where all of the output files from the cuffdiff program will be placed -b Providing Cufflinks with the multifasta file your reads were mapped to via this option instructs it to run the bias detection and correction algorithm which can significantly improve accuracy of transcript abundance estimates reference.fa path and name of reference.fasta which the files were mapped to -p Use this many threads to align reads. The default is 1 8 declared 8 threads --library-type * This option depends on the data * unstranded RNA-seq data, Cufflinks/Cuffdiff require spliced alignments with XS strand attribute fr-firststrand * This option depends on the data * should be used for the standard dUTP protocol, including Illumina’s stranded Tru-Seq. -L Specify a label for each sample, which will be included in various output files produced by Cuffdiff. set1,set2 Must have at least 2 labels. (i.e male,female) -u Tells Cufflinks to do an initial estimation procedure to more accurately weight reads mapping to multiple locations in the genome refence.gtf path and name to the gene annotation file. sampleSet.txt text file containing the list of samples
SBATCH script - sbatch NameOfSBATCHscript.sh