Low-bandwidth and non-compute intensive remote identification of microbes from raw sequencing reads. (LBANCIRIOMFRSR in short)

Supplementary data for our manuscript: http://dx.doi.org/10.1371/journal.pone.0083784

Client

The source code for the client in on bitbucket. Instructions to install it or run it as a container are available there: https://bitbucket.org/lgautier/dnasnout-client

The version of the client described in our manuscript is 0.0.5, but we improved the interface and created a Docker image to make its installation and use easier.

When working with human samples, first map your reads against the human genome and start from the remaining unaligned reads.

Benchmark

Our benchmark can be reproduced with the following script:
bench.py
with this list of reference genomes:
SVA_Bacteria.csv
(note: this is creating load on what is a test server - consider running it only if really needed rather than out of curiosity).

Server

Our server is at:
http://tapir.cbs.dtu.dk/

The code and data for running a similar server:
https://drive.google.com/folderview?id=0B9JY8op4DgEeb2d3UGp4bnZtSGs&usp=sharing

The code and history for dnasnout and Tapir is avaible in full on bitbucket.:
https://bitbucket.org/lgautier/dnasnout
However, be warned that this is not really well packaged or meant to be of general use.

All entries present in the database when running the benchmark are also available for reference:
list_reference_June6_2013.csv.bz2

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