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

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 6

• Next, 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_files you 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_bed script:

  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