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mi-faser is a metagenome (metagenome/metatranscriptome) analysis tool developed by Chengsheng Zhu in the BrombergLab @ Rutgers University. The method was significantly sped up and made publicly available by Maximillian Miller (BrombergLabRutgers University and Rostlab @ Technical University of Munich).

mi-faser combines faser (functional annotation of sequencing reads), an algorithm that maps reads to molecular functions encoded by the read-correspondent genes, with a manually curated reference database of protein functions. As our method is optimized for short reads, no pre-assembly is required -- just submit your raw (but quality controlled) raw data AS IS.

As output, mi-faser produces high precision sets of molecular functions identified in the microbiome sequence data. mi-faser's minutes-per-microbiome processing speed is significantly faster than that of other annotation methods (less than 20min/10GB of reads), allowing for large scale comparisons. For instance, microbiome function vectors can be compared between different conditions or time-steps to highlight environment-specific and/or time-dependent changes in functionality.

Note that although mi-faser is specifically targeted to microbiome analysis, it could also potentially be used for the analysis of unassembled bacterial genome data as well.

Repository for source code and stand-alone version (linux/osx/win):

mifaser runs on LINUX, MacOSX and WINDOWS systems.

Open a terminal and checkout the mi-faser repository:

git clone

or download the zipped version:

curl --remote-name

Standalone VS Web Service

The Standalone version of mi-faser partitions the user input into subsets analogue to the Web Service ( However, those partitions are processed sequentially and not in parallel as in the Web Service. Thus the Standalone Version is only recommended for smaller jobs and is mainly thought to provide the *mi-faser* code base.

If you find mifaser useful please cite:

Zhu, Chengsheng, et al. "Functional sequencing read annotation for high precision microbiome analysis." Nucleic acids research 46.4 (2017): e23-e23.

Support online material for mi-faser manuscript:

SOM Data S1. Multi-FASTA file of all the proteins in GS-set.