SLIMM

Abstract

SLIMM is a taxonomic profiling tool that investigates which microorganisms are present in a sequenced sample. SLIMM uses coverage landscape of reference genomes by sequencing reads to remove unlikely genomes from the analysis and subsequently gain more uniquely-mapped reads to assign at lower ranks of a taxonomic tree. This approach enables SLIMM to be sensitive in predicting members of a microbial community while maintaining a low false positive rate. It has been also shown that SLIMM predicts the relative abundances of individual members with lower deviation from the actual relative abundances compared to other methods. SLIMM requires a BAM/SAM alignment file as an input. One can use a read mapper of choice to map raw sequencing reads to obtain the BAM/SAM alignment file required as input for SLIMM.

GETTING STARTED

More details on how to use SLIMM can be found at the SLIMM WIKI

Pre-built executables for Linux and Mac are made available at the RELEASE PAGE.

You may download already INDEXED REFERENCE GENOMES for Bacteria and Archaea groups (Indexed reference genomes of Bacteria and Archaea groups are provided for Yara and Bowtie2 read mappers)

Links

• Download binaries
• View the source code and README on GitHub

Please Cite

• Temesgen Hailemariam Dadi, Bernhard Y. Renard, Lothar H. Wieler, Torsten Semmler, Knut Reinert, “SLIMM: species level identification of microorganisms from metagenomes”, vol. 5, 2017-03-28.
  cite this publication

  @article{fu_mi_publications2119,
   abstract = {Identification and quantification of microorganisms is a significant step in studying
    the alpha and beta diversities within and between microbial communities respectively. Both
    identification and quantification of a given microbial community can be carried out using whole
    genome shotgun sequences with less bias than when using 16S-rDNA sequences. However, shared
    regions of DNA among reference genomes and taxonomic units pose a significant challenge in
    assigning reads correctly to their true origins. The existing microbial community profiling tools
    commonly deal with this problem by either preparing signature-based unique references or assigning
    an ambiguous read to its least common ancestor in a taxonomic tree. The former method is limited
    to making use of the reads which can be mapped to the curated regions, while the latter suffer
    from the lack of uniquely mapped reads at lower (more specific) taxonomic ranks. Moreover, even if
    the tools exhibited good performance in calling the organisms present in a sample, there is still
    room for improvement in determining the correct relative abundance of the organisms. We present a
    new method Species Level Identification of Microorganisms from Metagenomes (SLIMM) which addresses
    the above issues by using coverage information of reference genomes to remove unlikely genomes
    from the analysis and subsequently gain more uniquely mapped reads to assign at lower ranks of a
    taxonomic tree. SLIMM is based on a few, seemingly easy steps which when combined create a tool
    that outperforms state-of-the-art tools in run-time and memory usage while being on par or better
    in computing quantitative and qualitative information at species-level.},
   author = {Temesgen Hailemariam Dadi and Bernhard Y. Renard and Lothar H. Wieler and Torsten Semmler
    and Knut Reinert},
   journal = {PeerJ},
   month = {March},
   pages = {e3138},
   title = {SLIMM: species level identification of microorganisms from metagenomes},
   url = {http://publications.imp.fu-berlin.de/2119/},
   volume = {5},
   year = {2017}
  }

Contact

For questions, comments, or suggestions please contact:

Temesgen Dadi