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EMIRGE reconstructs full length ribosomal genes from short read sequencing data.
This project forked from csmiller/emirge
EMIRGE reconstructs full length ribosomal genes from short read sequencing data.
EMIRGE: Expectation-Maximization Iterative Reconstruction of Genes from the Environment Copyright (C) 2010-2012 Christopher S. Miller ([email protected]) 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: 1. emirge.py -- this version was designed for metagenomic data 2. emirge_amplicon.py -- this version was designed for PCR amplicon data, and can handle up to a few million reads where the entire sequencing allocation is devoted to a single gene. In theory it could also be used for RNASeq data where rRNA makes up a large percentage of the reads. CITATIONS ------------------------------ 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. DEPENDENCIES ------------------------------ EMIRGE expects the following programs to be installed and available in your path: -python (tested with version 2.6), with the following packages installed: -BioPython -Cython -pysam -scipy / numpy -usearch (www.drive5.com/usearch/ -- tested with usearch version 6.0.203; versions earlier than this are incompatible). -samtools (http://samtools.sourceforge.net/ -- tested with verison 0.1.18) -bowtie (http://bowtie-bio.sourceforge.net/index.shtml -- tested with version 0.12.7 and 0.12.8) INSTALLATION ------------------------------ 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 HELP ------------------------------ 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 ([email protected]) with any problems, bug reports, or questions At the moment, there is no manual, though running the following is helpful: emirge.py --help EMIRGE OUTPUT ------------------------------ 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 1. The internal EMIRGE ID -- unique for each sequence 2. The accession number of the starting candidate sequence 3. an optional suffix indicating this sequence was split out from another due to evidence in the mapping reads of 2 or more "strains." 4. The Prior, or abundance estimate (used in original publication) 5. The length of the sequence 6. The length-normalized abundance estimate (anecdotally, this is sometimes more accurate if there are lots of different sequence lengths) CANDIDATE SSU DATABASE ------------------------------ You can download a standard candidate SSU database by running the following command: python emirge_download_candidate_db.py This script is included with EMIRGE. The current version of this database was made using Silva release SSURef_111_NR (http://www.arb-silva.de/). Sequences were clustered using uclust at 97% sequence identity, short and long sequences were removed, and non-standard characters were changed to be within {ACTG} (using utils/fix_nonstandard_chars.py). You can use any reference SSU database with emirge, though this one is recommended. No matter your choice, you should run utils/fix_nonstandard_chars.py on your fasta file. 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) OTHER ------------------------------ ** 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.
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