Category Archives: White papers

We have written a number of white papers, and present them here for your edification. Enjoy, and let us know if you have comments or questions.

Random Community Genomics (metagenomics) – White Paper

 

The random community genomics white paper was written by Rob in the spring of 2006 while traveling around Europe for meetings. You’ll notice that some of the problems have been solved, but many have been ignored!

Random community genomics, sequencing whole DNA without growing the microbes or cloning their DNA is now a reality. Our group, alone, has sequenced in excess of 850 M bp of DNA from environmental samples. Sample preparation and sequencing is very cheap and easy, costing less than $500 per million bp. The major limitation towards the advancement of our understanding of environmental genomics is no longer our ability to see the DNA. It is the lack of access to high-performance computing. Once the technologies and techniques that are being used be selected labs today become commonplace, there will be overwhelming demand for computational power beyond anything that is readily available.

Random community genomics, sequencing whole DNA without growing the microbes or
cloning their DNA is now a reality. Our group, alone, has sequenced in excess of 850 M bp of
DNA from environmental samples. Sample preparation and sequencing is very cheap and easy,
costing less than $500 per million bp.
The major limitation towards the advancement of our understanding of environmental
genomics is no longer our ability to see the DNA. It is the lack of access to high-performance
computing.
Once the technologies and techniques that are being used be selected labs today become
commonplace, there will be overwhelming demand for computational power beyond anything
that is readily available.

You can download the white paper (0.2 MB).

 

Automated analysis of ARISA data using ADAPT system – White Paper

This white paper was written by Robert in 2008 while working on different community profiling projets that combined microbiolgy and computer science.

The white paper describes a computational system consisting of the database ADAPTdb and the program ADAPT for the analysis of Automated Ribosomal Intergenic Spacer Analysis (ARISA) data sets. ARISA is a method for analyzing the composition of microbial communities that is both faster and cheaper than other community profiling techniques. In an application example, we describe the use of the tool for an unpublished data set and compare the results to work previously published using different analysis methods. Although there have been many papers until 2008 that used ARISA to analyze community samples, there were none that described computational approaches that allow the automatic analysis of the raw data sets. We have taken the manual process, automated it, and developed a web-based program for the automatic analysis, including taxonomic classifications, as well as autotrophic/heterotrophic and pathogenic/non-pathogenic comparisons.

This paper was submitted to BMC Bioinformatics and reviewed, but it was never published because of the comments that reviewer #2 made.

If this software is published in BMC Bioinformatics, this software will likely be used by many colleagues who will not look carefully at how ARISA works and doesn’t work. As a result, many analyses of microbial diversity will be highly flawed. Eventually, the community will learn the inaccuracy provided by the program but not before lots of scarce resources are spent and many meaningless papers are published.

That’s a pretty harsh criticism of their fellow microbial ecologists, who are apparently too stupid to understand their work and analyze their data.

Frankly, whoever wrote that review should be ashamed of themselves. Given comments like that there was no incentive for us to carry on making software that would mislead people, and so we never bothered.  The journal was not interested in publishing the paper, and we are not interested in helping people that are idiots.

Here are the complete reviews and the paper so you can decided for yourself. If you use ARISA, please cite this paper as:

Schmieder, R., Haynes, M., Dinsdale, E., Rohwer, F., and Edwards, R.A. Automated analysis of ARISA data using ADAPT system. 2009. http://edwards.sdsu.edu/research/adapt/

The ADAPT paper (1.4 MB).

reviewer #1 comments

Reviewer #2 comments