Hi Andres,

If you are familiar with the R environment, then you could implement the R dgge package http://park.itc.u-tokyo.ac.jp/soil-cosmology/ishii/Programs_files/Rcode_for_DGGE.txt

and when you have made diversity distance matrices using this, apply the diversity indices from vegan or BiodiversityR packages.

Goodluck

Hi André,

First you need to load the same amount of PCR product in each lane (a PCR with the exact same conditions for all the gel, I mean even the same RM).

You're gonna need to analyse the intensity of your bands you can do it with any processor image software. Relativize the intensity of each band to each line and the whole gel.

The number and pixel intensity of bands in a particular sample can be considered respectively proxies of richness and proportional abundance of OTUs. So you can apply Shannon index.

Source:

Dulce Flores-Rentería, Jorge Curiel Yuste, Ana Rincón, Francis Q. Brearley, Juan Carlos García-Gil, Fernando Valladares. 2014. Habitat fragmentation modulates climate change effects on the plant-soil-microbial system in Mediterranean Holm oak forests. In review.

For beta diversity analysis and other statistical tests between your samples you can simply use your presence/absence data to generate a similarity/dissimilarity matrix and go forward and analyse in any softwares like (R-vegan, biodiversityR, primer-E, or XLSTAT). For alpha diversity measures which takes into account the abundance values (since your are not sequencing your bands) for species richness and evenness (chao1, simpson, shannon etc) you need to know the occurance of your bands in each sample. So, Dulce approach will help you get the abundance data or you can use purified DGGE bands for qPCR second step.