EMIRGE: Expectation-Maximization Iterative Reconstruction of Genes from the Environment Copyright (C) 2010-2016 Christopher S. Miller (christopher.s.miller@ucdenver.edu)
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>https://github.com/csmiller/EMIRGE
EMIRGE reconstructs full length ribosomal genes from short read sequencing data. In the process, it also provides estimates of the sequences' abundances.
EMIRGE uses a modification of the EM algorithm to iterate between estimating the expected value of the abundance of all SSU sequences present in a sample and estimating the probabilities for each read that a specific sequence generated that read. At the end of each iteration, those probabilities are used to re-calculate (correct) a consensus sequence for each reference SSU sequence, and the mapping is repeated, followed by the estimations of probabilities. The iterations should usually stop when the reference sequences no longer change from one iteration to the next. Practically, 40-80 iterations is usually sufficient for many samples. Right now EMIRGE uses Bowtie alignments internally, though in theory a different mapper could be used.
EMIRGE was designed for Illumina reads in FASTQ format, from pipeline version >= 1.3
There are two versions of EMIRGE:
If you use EMIRGE in your work, please cite these manuscripts as appropriate.
Miller CS, Baker BJ, Thomas BC, Singer SW, Banfield JF (2011) EMIRGE: reconstruction of full-length ribosomal genes from microbial community short read sequencing data. Genome biology 12: R44. doi:10.1186/gb-2011-12-5-r44.
Miller CS, Handley KM, Wrighton KC, Frischkorn KR, Thomas BC, Banfield JF (2013) Short-Read Assembly of Full-Length 16S Amplicons Reveals Bacterial Diversity in Subsurface Sediments. PloS one 8: e56018. doi:10.1371/journal.pone.0056018.
EMIRGE expects the following programs to be installed and available in your path:
After installing the dependencies listed above, type the following to build EMIRGE:
$ python setup.py build
To install (you may skip straight to this step), type the following as root, or with sudo:
$ python setup.py install
You can also type the following for more options: $ python setup.py --help install
For example, to install to a location in your home directory where you have permission to write, you might type something like:
$ python setup.py install --prefix=$HOME/software
There is a google group (similar to a mailing list) for asking questions about EMIRGE: https://groups.google.com/group/emirge-users
Also, there is some additional information (including a Frequently Asked Questions section) on the github wiki: https://github.com/csmiller/EMIRGE/wiki
Although I encourage use of the google group due to increased volume of support emails, please feel free to contact me directly (christopher.s.miller@ucdenver.edu) with any problems, bug reports, or questions
At the moment, there is no manual, though running the following is helpful:
emirge.py --help
Once an EMIRGE run is completed, run emirge_rename_fasta.py on the final iterations directory, for example:
emirge_rename_fasta.py iter.40 > renamed.fasta
Also see:
emirge_rename_fasta.py --help
Running emirge_rename_fasta.py will provide you with a fasta file with EMIRGE output. Dissecting a single example header:
3326|AF427479.1.1480_m01 Prior=0.000367 Length=1480 NormPrior=0.000414 1 2 3 4 5 6
You can download a standard candidate SSU database by running the following command: python emirge_makedb.py
This script is included with EMIRGE, and by default downloads the current version of the Silva small subunit rRNA database (http://www.arb-silva.de/). Sequences are clustered by default using vsearch at 97% sequence identity, short and long sequences are removed, and non-standard characters are changed to be within {ACTG}. See emirge_makedb.py --help for details.
You can use any reference SSU database with emirge, though SILVA is recommended. If you use another database, you should run utils/fix_nonstandard_chars.py on your fasta file to remove ambiguous characters, which bowtie cannot map against. You will also need to first build a bowtie index, with something like: $ bowtie-build SSU_candidate_db.fasta SSU_candidate_db_btindex
You might also consider changing the offrate (see http://bowtie-bio.sourceforge.net/manual.shtml)
** A note about single-end sequencing:
EMIRGE was designed for and tested on paired-end sequencing reads. However, you can now use EMIRGE on single-end reads as well: simply omit the -2 parameter. Although I have done some basic testing on single-end reads, runs with single reads have NOT been as extensively tested as runs with paired reads. Please let me know how it works for you if you try EMIRGE with single-end reads.