iDES: Software and Documentation

iDES (Integrated Digital Error Suppression) is a method for the suppression of stereotypical background artifacts in high-throughput sequencing data. This page provides Perl implementations and documentation for input file conversion, background database construction, background polishing, and quality control statistics.

1. Format input files (required)
2. Create background database (required)
3. Perform background polishing
4. Background error report (optional)

perl ides-bam2freq.pl [options] MySample.bam(s) genome.fa targets.bed

Input

1. MySample.bam(s): Single position-sorted BAM file or directory of position-sorted BAM files consisting of paired-end reads (with or without de-duplication). BAM index (BAI) file(s) should be present in the same directory.


2. genome.fa is a reference genome in FASTA format (e.g., hg19.fa).


3. targets.bed is a standard 3-column BED (chr start end) used to restrict FREQ file(s) to genomic regions of interest.

Output

perl ides-makedb.pl [options] dir

Input

1. dir: Directory of source FREQ files for background database. While pre-duplication removal data appear to provide a better substrate for learning background distributions, de-duplicated data can also be modeled.

Output

perl ides-polishbg.pl [options] MySample.freq(s) ides-bgdb.txt

Input

1. MySample.freq(s): Single FREQ file or directory of FREQ files.


2. ides-bgdb.txt: Background database.

Output

perl ides-bgreport.pl [options] input.freq

Input

1. MySample.freq: Single FREQ file (with or without background polishing).

Output

Perform background polishing on pre- and post- barcode-collapsed FREQ files.

1. Download background database and example FREQ files from Newman et al. (2016).

Move tarball (48MB) into working directory and unpack:

tar -zxvf example.tar.gz

Contents:

1. ides-bgdb.txt
2. Sample1.non-deduped.freq.paired.Q30.txt
3. Sample1.barcode-deduped.freq.paired.Q30.txt
4. Sample2.non-deduped.freq.paired.Q30.txt
5. Sample2.barcode-deduped.freq.paired.Q30.txt
2. Create input directory and move FREQ files.

mkdir input
mv *Q30.txt input

3. Create output directory.

mkdir output

4. Run background polishing (make sure all dependencies are installed first).

perl ides-polishbg.pl -o output -t 4 input ides-bgdb.txt

5. Collect error statistics.

for i in input/*Q30.txt; do echo $i && perl ides-bgreport.pl $i; done;

for i in output/*Q30.rmbg.txt; do echo $i && perl ides-bgreport.pl $i; done;

6. Expected output (MD5)

File	Error rate (%) pre-polishing	Error rate (%) post-polishing
Sample1.non-deduped	0.025	0.009
Sample2.non-deduped	0.024	0.0076
Sample1.barcode-deduped	0.011	0.002
Sample2.barcode-deduped	0.0097	0.0015

FAQ

1. Under construction

Reference

Newman AM*, Lovejoy AJ*, Klass DM*, Kurtz DM, Chabon JJ, Scherer FM, Stehr H, Liu CL, Bratman SV, Say C, Zhou L, Carter JN, West RB, Sledge GW, Shrager JB, Loo, Jr BW, Neal JW, Wakelee HA, Diehn M^# and AA Alizadeh^# (2016) Integrated digital error suppression for improved detection of circulating tumor DNA.

Funding

This work was supported by grants from the Department of Defense (A.M.N., M.D., A.A.A.), the National Cancer Institute (A.M.N., 1K99CA187192-01A1; M.D., A.A.A., R01CA188298), the US National Institutes of Health Director’s New Innovator Award Program (M.D., 1-DP2-CA186569), the Ludwig Institute for Cancer Research (M.D., A.A.A.), the CRK Faculty Scholar Fund (M.D.), V-Foundation (A.A.A.), Damon Runyon Cancer Research Foundation (A.A.A.) and a grant from both the Siebel Stem Cell Institute and the Thomas and Stacey Siebel Foundation (A.M.N.).

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