Get ASE counts from BAMs or raw fastq data -- repackage of pipeline by Carlo Artieri
Scripts: MaskReferencefromBED.pl, create_individual_snp_files, CountSNPASE.py, create_phased_bed, GetGeneASE.py
- Required software installed in PATH:
- samtools
- STAR (for mapping, but can use others)
- Required python libraries:
- pysam
- pybedtools
Contents
Simple install procedure:
git clone https://github.com/TheFraserLab/ASEr.git cd ASEr python ./setup.py install
Alternatively, to install without root privileges:
python ./setup.py install --user
First generate a FASTA formatted file containing the genome where each SNP position has been masked by 'N'. An existing genome file can be masked using the MaskReferencefromBED.pl script:
usage: MaskReferencefromBED.pl <SNP BED FILE> <GENOME FASTA FILE> <MASKED OUTPUT FASTA> A list of SNPs in BED format must be supplied as follows: CHR \t 0-POSITION \t 1-POSITION \t REF|ALT e.g. chr02 1242 1243 A|G
The pipeline requires that reads mapped to the masked genome be supplied in SAM or BAM format. Assuming that reads will be mapped with STAR (http://bioinformatics.oxfordjournals.org/content/29/1/15): The masked reference must be used to create a STAR index. STAR's efficiency at mapping spliced transcripts is strongly aided by supplying an annotation file in GTF format. The command to generate a STAR index is:
STAR --runThreadN <NUMBER OF CORES> --runMode genomeGenerate --genomeDir <LOCATION FOR INDEX OUTPUT> --genomeFastaFiles <FIXED MASKED GENOME>.fa --sjdbGTFfile <ANNOTATION>.gtf --sjdbOverhang <READ LENGTH - 1>Now map the FASTQ files to the genome using STAR with the following flags. It is critical that SAM/BAM files contain the MD flag for the pipeline to identify SNPs. Also, because of the increased incidence of errors in the first 6 bp of reads, we trim them off.
STAR --runThreadN <NUMBER OF CORES> --genomeDir <LOCATION OF INDEX> --outFilterMultimapNmax 1 --outFileNamePrefix <PREFIX FOR OUTPUT> --outSAMtype BAM SortedByCoordinate --outSAMattributes MD NH --clip5pNbases 6Next, we must remove duplicate reads from the mapped output. If we use the the samtools or the PICARD tools, we'll create a slight reference allele bias, therefore we should use the rmdup.py program in the WASP package (see: http://biorxiv.org/content/early/2014/11/07/011221)
The duplicate-removed BAM file then needs to be sorted by mate-pair name rather than coordinates:
samtools sort -n [DUPLICATES REMOVED].bam [SORTED PREFIX]
Next we need to generate custom bed files for every individual. These files must contain only exonic SNPs that are heterozygous in that individual. This is necessary because CountSNPASE will randomly choose a SNP to count if more than one SNP is present in a read. As some RNA-seq reads span non-coding sequence, this can lead to SNP counts that cannot contribute to gene-level counts. In addition, no homozygous SNP can possibly contribute to ASE counts, and so randomly picking a homozygous SNP over a heterozygous SNP will lead to lower counts.
These bed files can be generated easily by the
create_individual_snp_filesyou should provide the same SNP bed file that you used in the mapping step script:usage: create_individual_snp_files [-g] [-o outdir] [-j threads] plink snps create_individual_snp_files [-g] [-o outdir] [-j threads] --snpfile snp_bed --exonfile exonfile plink create_individual_snp_files --help Reference Files (positional): plink The plink file, can be a prefix or full path snps A bed file of exonic SNPs. If not available, use the --snpfile and --exonfile flags to generate this list on the fly Generate exonic SNP list: [Use if exon_snp file doesn't already exist] --snpfile snp_bed A bed file of snps (gzipped OK) --exonfile exon_bed A bed file of exons (gzipped OK). Filter Individuals: -f , --filter List of individuals to keep. Either comma-separated or as a file (newline separated --split_name Split the individual name in plink on this character (must be a single character) --split_index Index of split name to compare individuals to Multi(plex) mode arguments: -j , --jobs Divide into # of jobs -w , --walltime Walltime for each job -k , --mem Memory for each job in MB (int) --queue Queue to submit jobs to --cluster Which cluster to use Optional Arguments: -o , --outdir The output directory to write files to -g, --gzip gzip compress the output files -q, --quiet Quiet output -v, --verbose Verbose output -h, --help Show this help and exit.
Now we can count reads overlapping each SNP. The CountSNPASE.py script does this:
usage: CountSNPASE.py -m mode -s <BED> -r <[S/B]AM> [-p] [-b] [-n] [-q] [-v] Count number of reads overlapping each SNP in a sam/bam file. Required arguments: -m mode, --mode mode Operation mode (default: None) -s <BED>, --snps <BED> SNP BED file (default: None) -r <[S/B]AM>, --reads <[S/B]AM> Mapped reads file [sam or bam] (default: None) Universal optional arguments: -p , --prefix Prefix for temp files and output (default: TEST) -b, --bam Mapped read file type is bam (auto-detected if *.bam) (default: False) -n, --noclean Do not delete intermediate files (for debuging) (default: False) -h, --help show this help message and exit Multi(plex) mode arguments: -j , --jobs Divide into # of jobs (default: 100) -w , --walltime Walltime for each job (default: 3:00:00) -k , --mem Memory for each job (default: 5000MB) --queue Queue to submit jobs to (default: batch) --cluster {torque,slurm,normal} Which cluster to use, normal uses threads on this machine (default: torque) --threads Max number of threads to run at a time (normal mode only). (default: 24) Single mode arguments: -f , --suffix Suffix for multiplexing [set automatically] (default: ) Logging options: -q, --quiet Quiet mode, only prints warnings. (default: False) -v, --verbose Verbose mode, prints debug info too. (default: False) --logfile LOGFILE Logfile to write messages too, default is STDERR (default: None) Detailed description of inputs/outputs follows: -s/--snps A tab-delimited BED file with positions of masked SNPs of interest as follows: [CHR] [0 POSITION] [1 POSITION] Additional columns are ignored. -r/--reads A SAM or BAM file containing all of the reads masked to the masked genome. The file shound have all duplicates removed and MUST be sorted by read name (i.e. samtools sort -n ). -m/--mode The script can be run in two modes. In 'single' mode, the entire SNP counting is performed locally. In 'multi' mode, the read file will be split up by the number of specified jobs on the cluster. This is much faster for large SAM/BAM files. OUTPUT: The output of the script is a tab-delimited text file, [PREFIX]_SNP_COUNTS.txt, which contains the following columns: CHR Chromosome where SNP is found POSITION 1-based position of SNP POS_A|C|G|T Count of reads containing A|C|G|T bases at the SNP position on the POSITIVE strand NEG_A|C|G|T Count of reads containing A|C|G|T bases at the SNP position on the NEGATIVE strand SUM_POS_READS Sum of all reads assigned to the SNP on POSITIVE strand SUM_NEG_READS Sum of all reads assigned to the SNP on NEGATIVE strand SUM_READS Sum of all reads assigned to the SNP
Note: this script has a multiplexing mode that can dramatically accelerate its performance by splitting
sam/bam files and running in parallel on all the fragments. This mode will can be enabled with the
-m multi argument. On a simple system it will just use threads up to a maximum of --threads. On
a system with torque or slurm, it will submit its jobs to those systems. The cluster system is
auto-detected, but you will need to provide the queue/partition to run in and other submission variables.
To run GetGeneASE, a bed file of phased SNPs is required. This can be created by running shapeit on the plink data of your individuals. Note: you may wish to run impute2 on your data also ot increase your power to detect SNPs.
To create the bed file from the .haps file output by shapeit, run the
create_phase_bedscript:usage: create_phased_bed -i hap_file [hap_file...] -o bed_file cat hap_file | create_phased_bed > bed_file create_phased_bed --help Create a phased SNP bed file from haplotype data. Files: -i infile [infile ...], --hap_files infile [infile ...] List of haps files, default STDIN -o , --bed_file Output bed file, default STDOUt, gzipped OK Optional Arguments: --chr_format {num,chr} Convert chromsome to number only (num) or to chr# (chr) -h, --help Show this help and exit.
Once we've determined the counts at individual SNPs, we can then obtain the gene/ transcript-level counts with GetGeneASE.py:
usage: GetGeneASE.py -c -p -g -o [-w] [-i] [-t] [-m MIN] [-s] [-h] This script takes the output of CountSNPASE.py and generates gene level ASE counts. Required arguments:: -c , --snpcounts SNP-level ASE counts from CountSNPASE.py (default: None) -p , --phasedsnps BED file of phased SNPs (default: None) -g , --gff GFF/GTF formatted annotation file (default: None) -o , --outfile Gene-level ASE counts output (default: None) Optional arguments:: -w, --writephasedsnps Write a phased SNP-level ASE output file [OUTFILE].snps.txt (default: False) -i , --identifier ID attribute in information column (default: gene_id) -t , --type Annotation feature type (default: exon) -m MIN, --min MIN Min reads to calculate proportion ref/alt biased (default: 10) -s, --stranded Data are stranded? [Default: False] (default: False) -h, --help Show this help message and exit NOTE: SNPs that overlap multiple features on the same strand (or counting from unstranded libraries) will be counted in EVERY feature that they overlap. It is important to filter the annotation to count features of interest! Detailed description of inputs/outputs follows: -p/--phasedsnps A tab-delimited BED file with positions of masked SNPs of interest as follows: [CHR] [0 POSITION] [1 POSITION] [REF|ALT] The fourth column MUST contain the phased SNPs alleles. -g/--gff The script accepts both GTF and GFF annotation files. This should be combined with the -i/--identifier option specifying the identifier in the info column (column 9) that will be used for grouping counts. For example, in a GTF 'gene_id' will group counts by gene with 'transcript_id' with group counts by transcript. In addition, the -t/--type option sets the feature type (column 3) from which to pull features typically you'd want to count from 'exon', but many annotations may use non- standard terms. -m/--min This sets the minimum # of reads required to include a SNP in the calculation of the fraction of SNPs agreeing in allelic direction. -w/--writephasedsnps If this is specified, then the program will output an additional output file named [OUTFILE].snp.txt with phased SNP-level ASE calls. This can be useful for checking SNP consistency across samples. See below for a description of the output. -s/--stranded If the data come from a stranded library prep, then this option will only count reads mapped to the corresponding strand. OUTPUT: The output of the script is a tab-delimited text file set by -o/--outfile, which contains the following columns: FEATURE Name of the counted feature CHROMOSOME Chromosome where feature is found ORIENTATION Orientation of feature (+/-) START-STOP Ultimate 5' and 3' 1-based start and stop positions REFERENCE_COUNTS Total reference allele counts across SNPS (or first allele in the REF|ALT phasing) ALT_COUNTS Total alternate allele counts across SNPs (or second allele in the REF|ALT phasing) TOTAL_SNPS The total number of SNPs overlapped by the feature REF_BIASED Number of REF biased SNPs passing the -m/--min threshold ALT_BIASED Number of ALT biased SNPs passing the -m/--min threshold REF-ALT_RATIO The proportion of SNPs agreeing in direction (0.5 - 1) SNPS A list of all SNPs overlapped by the feature separated by ';' and of the format: [1-based position],[REF_ALLELE]|[ALT_ALLELE],[REF_COUNTS]|[ALT_COUNTS]; If the -w/--writephasedsnps option has been set, it will produce a tab-delimited table with the following columns: CHROMOSOME Chromosome where SNP is found POSITION 1-based position FEATURE Feature in which SNP is found ORIENTATION Orientation of feature (if stranded only reads on this strand are counted) REFERENCE_ALLELE Reference base ALTERNATE_ALLELE Alternate base REF_COUNTS Reference base counts ALT_COUNTS Alternate base counts