https://github.com/jdblischak/filter_probes

Filter Illumina HT-12 v4 probes for presence of HapMap CEU SNPs
https://github.com/jdblischak/filter_probes

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Filter Illumina HT-12 v4 probes for presence of HapMap CEU SNPs

• Host: GitHub
• URL: https://github.com/jdblischak/filter_probes
• Owner: jdblischak
• Created: 2014-12-08T20:14:55.000Z (about 10 years ago)
• Default Branch: master
• Last Pushed: 2015-03-10T18:33:52.000Z (almost 10 years ago)
• Last Synced: 2024-10-15T23:55:35.197Z (2 months ago)
• Language: Python
• Size: 15.3 MB
• Stars: 3
• Watchers: 3
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

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README

        
## Purpose

Single nucleotide polymorphisms (SNPs) common in a population can

affect the hybridation kinetics of gene expression microarrays. This

is especially troublesome for studies that investigate differences in

gene expression between individuals.

## Usage

Clone the repository:

```

git clone [email protected]:jdblischak/filter_probes.git

cd filter_probes

```

To run sequentially, run:

```

snakemake -s make.py

```

To run in parallel, use the `-c` and `-j` options to submit each job

to your grid computing system. This will vary based on your

setup. Here is an example using grid engine on my work cluster:

```

snakemake -s make.py -j 30 -c "qsub -l h_vmem={params.h_vmem} -N {params.name} -V -j y -cwd -o {log}"

```

This will submit up to 30 jobs at once, each requesting the virtual

memory and name specified for each job in the Snakefile. Since this

can take hours to complete, I recommend submitting the job in the

background immune to hangups, e.g.:

```

nohup snakemake -s make.py -j 30 -c "qsub -l h_vmem={params.h_vmem} -N {params.name} -V -j y -cwd -o {log}" &

```

## Technical description

For our analysis, we used a subset of the 47,321 probes designed to

target genome of the Illumina HT-12v4 bead array that we determined

were not affected by common SNPs in the HapMap CEU population. First,

we mapped the probes to human genome hg19 and kept only those with a

quality score of 37 (40,198 probes; note that we also explicitly

pre-filtered the 5,587 probes which were annotated as spanning

exon-exon junctions to avoid mapping errors). Second, we downloaded

the HapMap CEU SNPs

(http://hapmap.ncbi.nlm.nih.gov/downloads/genotypes/2010-08_phaseII+III/forward/)

and converted their coordinates from hg18 to hg19 using the UCSC

liftOver utility. We kept only those probes which did not overlap any

SNP with a minor allele frequency of at least 5% (34,508

probes). Third, we converted the Illumina probe IDs to Ensembl gene

IDs using the R/Bioconductor package biomaRt ([Durinck et al.,

2009][Durinck2009]) and kept only those probes which are associated

with exactly one Ensembl gene ID (Ensembl 75 - Feb 2014; 22,034

probes). The full pipeline was implemented using the Python package

Snakemake ([Koster & Rahmann, 2012][Koster2012]).

[Durinck2009]: http://www.nature.com/nprot/journal/v4/n8/full/nprot.2009.97.html

[Koster2012]: http://bioinformatics.oxfordjournals.org/content/28/19/2520.long

## Results

* Number of probes designed for human genome: 47231

* Number of probes mapped to hg19: 40198

* Number of probes mapped uniquely to hg19: 37157

* Number of probes mapped with quality score >= 37 to hg19: 35529

* Number of probes mapped without a SNP ( MAF >= 0.05 ): 39133

* Number of probes mapped with quality score >= 37 to hg19 and without a SNP ( MAF >= 0.05 ): 34508

* Number of probes associated with a unique Ensembl gene ID: 22034

## Software

This analysis pipeline requires the following to be installed:

* Python3

* Snakemake

* Samtools

* bedtools

* R package argparse

* R/Bioconductor package biomaRt

The pipeline installs the following:

* bwa

* liftOver