Funseq
From GersteinInfo
Contents |
Installation
A. Required Tools
The following tools are REQUIRED for FunSeq:
1) Bedtools
3) Tabix
3) VAT - A good installation guide for VAT can be found here. Only needed for coding analysis. When use '-nc' option in FunSeq, no need to install VAT.
4) TFMpvalue-sc2pv
5) bigWigAverageOverBed
6) R - Only needed for differential gene expression analysis
B. PERL Requirement
1) Please make sure you have Perl 5 and up. Latest PERL can be downloaded here.
2) Install package Parallel::ForkManager (this package is used for parallel running). The PERL library can be found here.
C. FunSeq tool installation
FunSeq is a PERL- and Linux/UNIX-based tool. At the command-line prompt, enter the following:
$ cd FUNSEQ/ $ perl Makefile.PL $ make $ make test $ make install
D. Required Data Files
Please download all the following data files from ' http://funseq.gersteinlab.org/data/ ' and put them in a new folder ' $path/funseq-0.2/data/ ':
1. 1kg.phase1.snp.bed.gz (bed format)
Contents : all 1KG phaseI SNVs in bed format.
Columns : chromosome , SNVs start position (0-based), SNVs end position, MAF (minor allele frequency)
Purpose : to filter out common variants against 1KG SNVs.
1. 1kg.phase1.snp.bed.gz (bed format)
Contents: 1000 Genomes Phase I data with minor allele frequency in bed format. Columns: chromosome, start position (0-based), end position, MAF (minor allele frequency) Purpose: to filter out input SNVs based on user-defined allele-frequency threshold.
2. All_hg19_RS.bw
Contents: binary file containing base-wise gerp score. Downloaded from http://hgdownload.cse.ucsc. edu/gbdb/hg19/bbi/All_hg19_RS.bw * Note: This file is ~7G. If you don’t want to retrieve gerp score for variants, then no need to download this file.
3. HOT_region.bed (bed format)
Contents: highly occupied region from Yip et al., (Yip, et al., 2012) Columns: chromosome, start position, end position, cell line info Purpose: to examine whether variants occur in hot regions.
4. ENCODE.annotation.gz (bed format)
Contents: compiled annotation files from ENCODE, GENCODE v7 and others, including Dnase I hypersensitive sites, transcription factor binding peak, pseudo-genes, non-coding RNAs, enhancer regions (chromhmm, segway and distal regulatory modules (Yip, et al., 2012)). Columns: chromosome, start position, end position, annotation. Purpose: to annotate SNVs in ENCODE regions.
5. ENCODE.tf.bound.union.bed (bed format)
Contents: transcription factor (TF) binding motifs under peak regions. Columns: chromosome, start position, end position, motif name, , strand, TF name Purpose: used for motif breaking analysis
6. gencode.v7.cds.bed (bed format)
Contents: extracted CDS information from GENCODE v7. Columns: chromosome, start position, end position Purpose: locate coding SNVs.
7. gencode.v7.promoter.bed (bed format)
Contents: promoter regions, defined as -2.5kb from transcription start site (TSS) Columns: chromosome, start position, end position, gene. Purpose: to associate promoter SNVs with genes
8. gencode.v7.annotation.GRCh37.cds.gtpc.ttpc.interval
Purpose: used by variant annotation tool (VAT).
9. gencode.v7.annotation.GRCh37.cds.gtpc.ttpc.fa
Purpose: used by variant annotation tool (VAT).
10. drm.gene.bed (bed format)
Contents: distal regulatory module linked to genes. Columns: chromosome, start position, end position, gene, p-value, cell-lines Purpose: to associate enhancer SNVs with genes
11. motif.PFM
Contents: position frequency matrix (PFM) for ENCODE TFs. Purpose: used for motif breaking and gain of motif calculation
12. PPI.hubs.txt
Purpose: defined hub genes in protein-protein interaction network
13. REG.hubs.txt
Purpose: defined hub genes in regulatory network
14. GENE.strong_selection.txt
Purpose: genes under strong negative selection (fraction of rare SNVs among non-synonymous variants).
15. human_ancestor_GRCh37_e59.fa
Contents: contains human ancestral allele in hg19, Ch37. Purpose: for motif breaking calculation in personal or germline genome. * Note: for somatic analysis, this file is not needed.
16. human_g1k_v37.fasta
Contents: human reference genome Purpose: for gain-of-motif analysis
17. sensitive.nc.bed (bed format)
Contents: coordinates of sensitive/ultra-sensitive regions. Purpose: to find SNVs in sensitive/ultra-sensitive regions.
18. ultra.conserved.hg19.bed
Contents: ultra-conserved region in (Bejerano, et al., 2004).
19. motif.score.cut
Contents: pre-calculated PWM scores corresponding to 4e-8. Purpose: to speed up the gain-of-motif analysis
20. regulatory.network
Contents: human regulatory network from (Gerstein, et al., 2012)
21. cancer.genes
Contents: cancer genes from Cancer Gene Census (Futreal, et al., 2004)
22. actionable.gene
Contents: actionable genes from (Wagle, et al., 2012)
Running FunSeq
Usage
Usage : ./funseq -f file -maf maf -m <1/2> -inf <bed/vcf> -outf <bed/vcf>
Options :
-f user input SNVs file
-maf Minor Allele Frequency (MAF) threshold to filter 1KG phaseI SNVs (value 0 ~ 1)
-m 1 - somatic Genome; 2 - germline or personal Genome
-inf input format - BED or VCF
-outf output format - BED or VCF
Default : -maf 0 -m 1 -outf vcf
Input
FunSEQ takes BED or VCF files as input
1. BED format
In addition to the three required BED fields, please prepare your file as follows (5 required fields, tab-delimited):
chrom chromStart chromEnd Reference.allele Alterative.allele ...
* chrom - The name of the chromosome (e.g. chr3, chrY).
* chromStart - The starting position of the feature in the chromosome. The first base in a chromosome is numbered 0. * chromEnd - The ending position of the feature in the chromosome. The chromEnd base is not included in the display of the feature. For example, the first 100 bases of a chromosome are defined as chromStart=0, chromEnd=100, and span the bases numbered 0-99. * Reference.allele - The reference allele of SNVs * Alternative.allele - The alternative allele of SNVs.
2. VCF format (http://www.1000genomes.org/node/101)
The header line names the 8 fixed, mandatory columns. These columns are as follows (tab-delimited):
- CHROM POS ID REF ALT QUAL FILTER INFO
Output
You can download a sample of the output VCF here.
FunSEQ can produce either BED format or VCF format files.
An example of the VCF annotation of a coding variant:
chr1 36205042 . C A . . OTHER=MAF(1kg-phase1)=0;CDS=Yes;VA=1:CLSPN:ENSG00000092853.8:-:prematureStop:4/5:CLSPN-001: \ ENST00000251195.5:3996_3232_1078_E->*:CLSPN-005:ENST00000318121.3:4017_3232_1078_E->*:CLSPN-003:ENST00000373220.3:3825_3040_1014_E->*:CLSPN-004:ENST00000520551.1: \ 3858_3073_1025_E->*;HUB=PPI;GNEG=Yes;GENE=CLSPN;CDSS=4
- OTHER field contains other original information other than the 5 required ones (chrom, chromStart, chromEnd, reference, alternative). When input file is less than 3,000 lines, OTHER also contains the MAF (minor allele frequency) of SNVs in 1KG Phase1 data.
An example of the VCF annotation of a non-coding variant:
chr5 85913480 . T C . . OTHER=MAF(1kg-phase1)=0;CDS=NO;HUB=REG;NCENC=TFP(ETS1),TFP(ELF1),TFP(GATA2),TFP(POU2F2), \ TFP(TBP),TFP(SRF),TFP(ELK4),TPM(TAF1),TFP(STAT3),TFP(GATA3),TFP(SIX5),TFP(YY1),TPM(TBP),TFP(CHD2),TFP(MYC),TFP(IRF1),DHS(MCV-2),TFP(TAF1),TFP(GATA1), \ TFP(ZEB1),TFP(SETDB1),TFP(ZNF143),TFP(NFKB1),TFP(MAX),TFP(GABPA),Enhancer(chromHmm),TFP(STAT1); \ MOTIFBR=85913478#85913493#+#TATA_known1_8mer#TAF1,85913478#85913493#+#TATA_known1_8mer#TBP;GENE=COX7C(promoter);NCDS=4
- NCENC (Non-coding ENCODE annotation) field.
TFP -transcription factor binding peak.
TFM - transcription factor motifs in peak regions.
DHS - DNase1 hypersensitive sites, with number of cell lines (MCV- , total 125 cell lines) information (R.E. Thurman et al., The accessible chromatin landscape of the human genome. Nature 489,75, Sep 2012).
ncRNA - non-coding RNA
Pseudogene
Enhancer - chromHmm (genome segmentation), drm (distal regulatory module)
- MOTIFBR field.
This field is a hash-delimited tag, defined as follows:
motif start # motif end # motif strand # motif name # transcription factor name
An example: " 85913478#85913493#+#TATA_known1_8mer#TAF1 "
- NCRECUR field.
Please be aware of large TF peak and chromHMM regions. Because of the low resolution issues, recurrent information may not indicate functional importance.
