I would recommend to go for pipeline from this paper

http://www.ncbi.nlm.nih.gov/pubmed/21478889

This is how I do , I also had learned it from asking online forums

Here you go

Index human genome (Picard), we used hg19

Convert reads to Fastq format

Convert reads high quality scores to standard Sanger scores

FOR EACH SAMPLE:

Align samples to genome (BWA), generates SAI files.

Convert SAI to SAM (BWA)

Convert SAM to BAM binary format (SAM Tools)

Sort BAM (SAM Tools)

Index BAM (SAM Tools)

Identify target regions for realignment (Genome Analysis Toolkit)

Realign BAM to get better Indel calling (Genome Analysis Toolkit)

Reindex the realigned BAM (SAM Tools)

Call Indels (Genome Analysis Toolkit)

Call SNPs (Genome Analysis Toolkit)

Realign BAM to get better Indel calling (Genome Analysis Toolkit)

While I agree that you should run this step, the process is designed to give better mapping in regions that contain mismatches due to the presence of indels, not to get better indel calling.

This overall workflow is called the GATK Best Practices workflow which you can see here: https://www.broadinstitute.org/gatk/guide/best-practices.php

It is best to modify some steps based on your particular sequencing types - I.e. the marking duplicate step is not appropriate for amplicon based sequencing.

There are many different ways to call SNPs as well. GATK's HaplotypeCaller is one, but also other programs such as samtools can do this too. It just depends on the sensitivity and specificity you want. If you do a literature search, you'll see that many benchmarking comparisons of variant callers have been performed.

https://www.broadinstitute.org/gatk/guide/best-practices.php

Hi Meriem,

I'm assuming you don't have your own data, but want to know how many SNPs are found in this gene in different populations?

The best two resources are ExAC:

http://exac.broadinstitute.org/gene/ENSG00000136869

You can download the data to an Excel .csv file which gives columns for 6 major populations groups totalling 61,000 individuals (about half of which are European and the other half are Finnish, East Asian, South Asian, Latino and African).

Or the 1000 genomes project:

http://www.ncbi.nlm.nih.gov/variation/tools/1000genomes/

Search "TLR4", and then Downloads - Download data for this region - agregates by population.

This will give you SNP counts for 26 population groups, but only totalling ~2,500 individuals so is only really of use for fairly common variants. The downloaded data will be in VCF format which will be harder to interpret, but you should still be able to view it as a spreadsheet..