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Last updated 2015.03.27 by Carlo
This script takes an annotation file in BED format as well as a genome in FASTA format and
outputs a new FASTA file containing the sequences of the annotations (e.g., spliced genes).
USAGE: perl BED2FASTA.pl --genome <genome.fa> --bed <annotation.bed> --out <annotation.fa> --rev
Options and formatting are as follows:
--rev
If this option is specified, reverse transcribe genes on the negative strand so that
their sequence is 5' - 3'.
--help or --h
Print this text.
This script will concatenate any number of tab-delimited tables based on a list of common
identifiers. Tables that do not contain the identifier will have \'NA\'s in place of the
missing cells.
USAGE: perl ConcatenateTables.pl <LIST> <0-based search column> <OUTFILE> <TABLE 1> <TABLE 2> ... <TABLE N>
The <LIST> should contain the identifiers, each on a separate line. The <0-based search
column> tells the script in which column in each table to look for the identifiers (usually
the first, or 0). After specifying the <OUTFILE>, each table should be separated by a
space. They will be concatenated in the order specified.
--help or --h
Print this text.
Counts the number of each nucleotide in a FASTA file and prints the result. By specifying
-e/--each, it prints nucleotide count for each entry in the FASTA file. Note that the
script counts the incidence of all characters, including missing (N) and masked (X)
nucleotides, but also IUPAC codes.
USAGE: CountFASTANucs.py -i [-e] [-h]
Required arguments::
-i , --infile FASTA file
-e, --each Print count for each entry in FASTA
Optional arguments::
-h, --help show this help message and exit
This script takes an annotation file in GTF format as well as a genome in FASTA format and
outputs a new FASTA file containing the sequences of the annotations (e.g., spliced genes).
USAGE: perl GTF2FASTA.pl --genome <genome.fa> --bed <annotation.bed> --out <annotation.fa> --rev
Options and formatting are as follows:
--rev
If this option is specified, reverse transcribe genes on the negative strand so that
their sequence is 5' - 3'.
--help or --h
Print this text.
This script takes a list of genes with ASE values as well as a group of functional
categories and determines whether any of the functional categories shows significant
parental bias in directionality by way of a chi-square test. It then permutes
gene-category assignments to determine how often such a degree of bias would be observed
by chance, producing a category-specific false-discovery rate.
USAGE: perl PerformRankedSignTest.pl --asetable [GENE ASE VALUES] --funcats [FUNCTIONAL CATEGORIES] --output [OUTPUT FILE] --fraction [#] --min [#] --perms [#]
Options and formatting are as follows:
--asetable
A tab-delimited file with two columns, the first of which is the gene name and the
second is the log2(species 1 allele/species 2 allele) ASE value. i.e.:
[GENE] [LOG2 ASE]
--funcats
A tab-delimited list of genes and the functional categories in which they belong. The
genes must be in the first column and the functional categories in the second. Genes
that belong to multiple categories should separate categories by |. i.e.:
[GENE] [FUNCAT 1]|[FUNCAT 2]|[FUNCAT 3]
--output
The tab-delimited file where test values are written with the following columns:
CATEGORY The functional category
SP1_BIASED Number of genes showing species 1 bias (i.e., positive Log2(ASE) values)
SP2_BIASED Number of genes showing species 1 bias (i.e., negative Log2(ASE) values)
SP1_EXPECTED Number of expected genes showing sp1 bias given the proportion of sp1 biased genes among all genes
SP2_EXPECTED Number of expected genes showing sp2 bias given the proportion of sp2 biased genes among all genes
CHI_SQ Chi-square value
FDR How often (1/#perms) is an equal or higher chi-square value observed among permuted data
SP1_GENES Species 1 biased genes, seperated by |
SP2_GENES Species 2 biased genes, seperated by |
--fraction [Default 1]
Analyze only the top X fraction (e.g., 0.25) most biased genes from each species.
Allows you to look for enrichment of direction bias in tails of the ASE distribution.
By default, the script uses all genes.
--min [Default 10]
The minimum number of genes that a functional category must posess to attempt the test.
Due to multiple testing, categories with fewer than ~10 genes typically can't acheive
significance.
--perms [Default 1000]
The number of permutations to run for the purpose of determining the category-specific
FDR.
--help or --h
Print this text.
Scripts: MaskReferencefromBED.pl, CountSNPASE.py, GetGeneASE.py
Required software installed in PATH:
samtools
STAR (for mapping, but can use others)
Pipeline Flow
-------------
1. 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
2. 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>
3. 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
4. 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 XXXX program in the WASP package
(see: http://biorxiv.org/content/early/2014/11/07/011221)
5. 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]
6. 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] [-t] [-n] [-h] [-j] [-w] [-k] [-f]
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)
-t, --single Mapped reads are single-end (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)
Single mode arguments:
-f , --suffix Suffix for multiplexing [set automatically] (default:
)
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
7. 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