VCFtools BEDtools compare, intersect, merge

A Good computational biologist is only as good as the tools he uses (or maybe how good he is at google) rofl kidding ...
Life is always easier when you find the correct tool.
I also adopt the path of least resistance when trying to solve problems that are more common than I imagine.
There's always the good old linux tools for comparing SNPs called from different programs / options

   grep | sed | awk | cut | diff | comm
   see http://ged.msu.edu/angus/tutorials-2011/snp_tutorial.html


and if you are working with NGS data, you most probably already have samtools installed on your system and you might have used bcftools
Did you also know that there's also a (unrelated) set of tools called vcftools?
http://vcftools.sourceforge.net/

The VCFtools package is broadly split into two sections: 

• The vcftools binary program, generally used to analyse VCF files.
• The Vcf.pm perl module, which is a general Perl API containing a core of the utilities vcf-convert, vcf-merge, vcf-compare, vcf-isec, and others.

  Documentation

  • Documentation for the binary executable vcftools
  • The Perl API and scripts
  • Useful shell one-liners

  Examples of usage by topic

  • Installation
  • Annotating
  • Comparing
  • Concatenating
  • Converting
  • Intersections, complements
  • Merging
  • Querying
  • Reordering columns
  • Stats
  • Stripping columns
  • Useful shell one-liners
  • Validating

Then there's  also the highly used BEDTools http://code.google.com/p/bedtools/

which I highly recommend to keep as part of your tools collection. Check out the link below

Do watch out for this 'oversight' in vcftools as pointed out in seqanswers.
Overlap number discrepancy between VCFTools and BEDTools

Usage

Examples of common usage.   Featured

Want to use samtools for polyploid organisms?

Well the short answer is you can't.
Here's what Heng Li has to say
Samtools is designed for diploid genomes. I would recommend to treat a haploid genome as diploid and filtering heterozygotes afterwards. For higher ploidy (>2), samtools does not work well. For pooled resequencing, specialized SNP callers (such as syzygy and a few others) are better.

Other recommendations are

• Freebayes (http://bioinformatics.bc.edu/marthlab/FreeBayes).
• CRISP http://polymorphism.scripps.edu/~vbansal/software/CRISP/

samtools upgrade for exome & target region SNP calling

off the samtools helplist, a new feature that's very helpful for exome / target region NGS studies

BTW, samtools is now able to compute read depth and call SNPs in regions specified by an input BED file. For both "samtools mpileup" (not pileup) and "bcftools view", you may provide the BED via the "-l" option. If the input has two numeric columns, it is parsed as a BED (region list); if has one numeric column, parsed as a position list file, so the "-l" option is backward compatible with old versions. It is also possible to retrieve alignments overlapping a BED file with 

samtools view -L in.bed 

. For mpileup, using "-l" to call SNPs in target/exome regions can be much faster than doing whole-genome calling and then filtering.

Heng

FAQ-SNPs missing when called with more samples

Question
Using mpileup called with 2 different samples, Im getting this
particular SNP which has a gd coverage (DP=55) only in one particular
sample. This is good.

However when mpileup was called with 10 samples, the SNP got lost. Im
just trying to figure out if the SNP got 'drowned' out by the other 9
samples which doesnt have the SNP and hence, wasnt called. Is this how
mpileup works

Excellent answer by Heng Li 

With more samples, you gain power on SNPs shared between samples, but lose power on singleton SNPs. Here is a way of thinking of this. Suppose we have 1% false positive rate (FPR) for one sample. If we call SNPs from 10 samples separately and then combine the calls, the FPR would be around 5% (not 10% because more SNPs are found given 10 samples). To retain a low FPR on singletons we have to be more stringent. Nonetheless, with more samples, we can usually get overall better calls than calling SNPs in each sample separately because information between samples is used more effectively.

Throwing the baby out with the bathwater:Non-Synonymous and Synonymous Coding SNPs Show Similar Likelihood and Effect Size of Human Disease Association

I was literally having a 'oh shoot' moment when i saw this news in GenomeWeb

Synonymous SNPs Shouldn't Be Discounted in Disease, Study Finds

NEW YORK (GenomeWeb News) – Synonymous SNPs that don't change the amino acid sequence encoded by a gene appear just as likely to influence human disease as non-synonymous SNPs that do, according to a paper appearing online recently in PLoS ONE by researchers from Stanford University and the Lucile Packard Children's Hospital.

from the abstract of the paper

The enrichment of disease-associated SNPs around the 80^th base in the first introns might provide an effective way to prioritize intronic SNPs for functional studies. We further found that the likelihood of disease association was positively associated with the effect size across different types of SNPs, and SNPs in the 3′untranslated regions, such as the microRNA binding sites, might be under-investigated. Our results suggest that sSNPs are just as likely to be involved in disease mechanisms, so we recommend that sSNPs discovered from GWAS should also be examined with functional studies.

Hmmmm how is this going to affect your carefully crafted pipeline now? 

Sequence Variant Analyzer SVA

The rapidly evolving high-throughput DNA sequencing technologies have now allowed the fast generation of large amount of sequence data for the purpose of performing such whole-genome sequencing studies, at a reasonable cost. S equence V ariant A nalyzer, or SVA , is a software tool that we have been developing to analyze the genetic variants identified from such studies.

SVA is designed for two specific aims:

(1) To annotate the biological functions of the identified genetic variants, visualize and organize them;
(2) To help find the genetic variants associated with or responsible for the biological traits or medical outcomes of interest.

SVA is:
a program designed to run on a LINUX platform, with a graphical user interface (GUI), meaning that the main functions of this program can be done with clicking buttons.

a program specifically designed for analyzing genetic variants that have already been called (identified) from a whole genome sequencing study. So do not try to find a function here to align short reads and call variants - SVA is not designed for those purposes - many other software tools, for example, BWA and SAMTOOLS , were developed for those purposes.

How it  works