BETA

BETA Basic and BETA plus requires both binding and expression file, BETA minus only binding file

Required: Binding data, BED format with 3 columns (chrom, chromStart, chromEnd) or 5 columns (chrom, chromStart, chromEnd, name, score)

Example

chrom	start	end	name	score
chr1	1208689	1209509	AR_LNCaP_2	51.58
chr1	1334246	1335348	AR_LNCaP_7	54.55
chr1	2179351	2180790	AR_LNCaP_9	257.72

*** Please do not contain the header in the bed file, and make sure it is tab delimitated.

Required:Differential expression data

  • LIMMA standard output (LIM)

     ID (optional), RefseqID, logFC, AveExpre, T, Pvalue, Adj.P.Value, B

Example

12196	NM_001548_at	-6.945783684	9.632803007	-138.2402671	6.92E-10	2.08E-05	11.83285762
15675	NM_005409_at	-6.11280866	6.322508161	-117.5664651	1.51E-09	2.08E-05	11.57790488
12213	NM_001565_at	-6.352395593	7.838465214	-113.6000902	-113.6000902	2.08E-05	11.51589687

  • Cuffdiff standard output (CUF)

     Test_id, gene_id, gene, locus, sample1, sample2, status, value_1, value_2, log2(foldchange), test_stat, p_value, q_value, significant

Example

NM_000014	NM_000014	-	chr12:9217772-9268558	q1	q2	NOTEST	0.102845	0.0820513	-0.325878	0.498271	0.618293	1	no
NM_000015	NM_000015	-	chr8:18248754-18258723	q1	q2	NOTEST	0.127358	0.30975	1.28221	-1.32328	0.185744	1	no
NM_000016	NM_000016	-	chr1:76190042-76229355	q1	q2	NOTEST	0	0	0	0	1	1	no
NM_000017	NM_000017	-	chr12:121163570-121177811	q1	q2	NOTEST	3.47702	3.62422	0.0598207	-0.195815	0.844755	1	no

   • BETA specific format (BSF)

     GeneID, Regulatory status (value with + or -), statistical value(e.g. FDR or Pvalue, the smaller value, the more significant it is)

Example

NM_000014	-0.325878	0.618293
NM_000015	1.28221	0.185744
NM_000016	0	1
NM_000017	0.0598207	0.844755

   • Other format

    BETA supports other differential expression data format including geneID, regulatory status and statistic values. Set this via parameter --info

*** The differential expression file should contain all the genes in the genome, BETA will use all the info to get the static genes, and isolate the up regulated genes and down regulated genes based on the threshold you input.

*** Make sure your differential expression file do not have the header or add the ‘#’ in the front of your header line.

*** If your gene ID is the official gene symbol, please add the parameter --gname2.

*** Although you can select the type of your differential expression format, in case to make sure BETA get the correct information, you would better set the columns information via --info except you have the same format with the above example.

Option: boundary file (--bf): BED format (6 columns as what showed below)

Example

chrom	start	end	name	score	strand
chr1	521336	521779	3	0.986	+
chr1	839881	840447	19	0.986	+
chr1	968212	968748	48	1	+

*** Please do not contain the header in the bed file, and make sure it is tab delimitated.

Option: Genome annotation (-r): Downloaded from UCSC

BETA provides hg38, hg19, hg18, mm10, and mm9 annotation.The annotation reference file should contain (refseqID chroms strand txstart txend genesymbol) information in order.

Example

refseqID	chrom	strand	start	end	gname2
NM_032291	chr1	+	66999824	67210768	SGIP1
NM_001301823	chr1	+	33546729	33586132	AZIN2
NM_032785	chr1	-	48998526	50489626	AGBL4

*** Please do not contain the header in the bed file, and make sure it is tab delimitated.

Option: Whole genome sequence data: fasta format (--gs)

The format is like:

Example

>chr1: xxxx-yyyyy

ATCGGGACTTGACCC…

>chr2: xxxx-yyyyy

AGCGTGACTAGAGCC…

...

BETA Basic will do the factor function prediction and direct target detecting

Command Line: $ BETA basic –p 3656_peaks.bed –e AR_expr.xls –k LIM –g hg19 --da500 –n basic

-p specifies the name of TF binding data

-e specifies the name of the corresponding differential expression data

-k LIM stand for the LIMMA Format

-g specifies the genome of your data, hg19 for human and mm9 for mouse, others, ignore this one

-n specifies the prefix of the output files, others, BETA will use ‘NA’ instead

--da 500 means get top 500 most significant expression changed genes of up and down

BETA Plus will do TF active and repressive function prediction, direct targets detecting and motif analysis in target regions

Command Line: BETA plus –p 3656_peaks.bed –e AR_expr.xls –k LIM –g hg19 --gs hg19.fa --bl

--gs required for motif scan, specifies a fasta format whole genome sequencing data.

--bl is an optional parameters, it is on when some boundaries considered

For other parameters, see BETA Basic above

BETA Minus detect TF target genes based on regulatory potential score only by binding data

Command Line: $ BETA minus -p 3656_peaks.bed --bl -g hg19

All the parameters in this command line, see BETA Basic above

BETA provide optional parameters for different dataset analysis

-n NAME, --name NAME

This argument is used to name the result file.

-o OUTPUT, --output OUTPUT

The directory to store all the output files.

--gname2

If this switch is on, gene or transcript IDs in files given through -e will be considered as official gene symbols, DEFAULT=FALSE

--info EXPREINFO

Specify the geneID, up/down status and statistical values column of your expression data. DEFAULT:2,5,7 for LIMMA; 2,10,13 for Cuffdiff and 1,2,3 for BETA specific format

--pn PEAKNUMBER

The number of peaks will be contribute to the regulatory potential score. DEFAULT=10000

-d DISTANCE, --distance DISTANCE

Set a number which unit is 'base'. It will get peaks within this distance from gene TSS. DEFAULT=100000 (100kb)

--df DIFF_FDR

Input a number 0~1 as a threshold to pick out the most significant differential expressed genes by FDR or other statistical values, DEFAULT = 1,that is select all the genes

--da DIFF_AMOUNT

Get the most significant differential expressed genes by the percentage(0-1) or number (larger than 1). Genes will be ranked by the statistical values. For example, 2000, BETA will set top 2000 genes of up and down as the differentially expressed genes. DEFAULT = 0.5, that is to get top 50 percent genes of up and down separately

-c CUTOFF, --cutoff CUTOFF

Input a number between 0~1 as a threshold of One tail KS test to determine if two datasets differ significantly, default=1e-3

-r REFERENCE, --reference REFERENCE

the refgene info file downloaded from UCSC genome browser.input this file only if your genome is neither hg19 nor mm9

--bl BOUNDARY LIMIT

Boolean Value. Whether or not use CTCF boundary to get a peak’s associated gene, DEFAULT=FALSE

--bf BOUNDARYFILE

Some BED format boundary file, use this only when You set --bl and the genome is neither hg19 nor mm9

Active or Repressive Function Prediction

Direct Target Prediction Files

Up Regulate Target; Down Regulate Target Format

Chroms	txStart	txEnd	RefseqID	RP	Strands	GeneSymbol
chr19	51376688	51383823	NM_001256080	2.186e-07	+	KLK2
chr19	51376688	51383823	NM_005551	2.186e-07	+	KLK2
chr19	51376688	51383823	NR_045762	2.186e-07	+	KLK2
chr19	51376688	51383823	NR_045763	2.186e-07	+	KLK2
chr19	51376688	51383823	NM_001002231	2.186e-07	+	KLK2
chr1	207191865	207206101	NM_023938	8.822e-07	-	C1orf116
chr1	207191865	207206101	NM_001083924	8.822e-07	-	C1orf116
chr21	42836477	42880085	NM_005656	1.033e-06	-	TMPRSS2
chr21	42836477	42879992	NM_001135099	1.041e-06	-	TMPRSS2

Target Gene Associated Peaks

chrom	start	end	RefseqID	GeneSymbol	Distance	Score
chr19	51354060	51354999	NM_001256080	KLK2	-22159	0.249983590819
chr19	51372841	51373704	NM_001256080	KLK2	-3416	0.529067106385
chr19	51392207	51393248	NM_001256080	KLK2	16039	0.319320493096
chr19	51354060	51354999	NM_005551	KLK2	-22159	0.249983590819
chr19	51372841	51373704	NM_005551	KLK2	-3416	0.529067106385
chr19	51392207	51393248	NM_005551	KLK2	16039	0.319320493096
chr19	51354060	51354999	NR_045762	KLK2	-22159	0.249983590819

Target Gene Associated Motif Analysis

Motif Analysis results were summarized in an html file, click here to see the example

Motif Text Files Also Provided

MotifID   Species   Symbol   DNA BindDom   PSSM   Tscore   Pvalue

ANY QUESTIONS?  Contact Us