SLIMM

SLIMM – Species Level Identification of Microbes from Metagenomes

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

Downloads:

Executables

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

Databases

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.) If you want to use your own custom database follow the instructions provided here.

Source code

You can build SLIMM from its source. Instruction on how to build from source can be found at the SLIMM WIKI

Cite us

If you use SLIMM in your work-flows, don’t forget to cite us.

Dadi TH, Renard BY, Wieler LH, Semmler T, Reinert K. (2017) SLIMM: species level identification of microorganisms from metagenomes. PeerJ 5:e3138 https://doi.org/10.7717/peerj.3138

Mitotool

 

If you are not redirected automatically, go to http://www.mitotool.org/.

Popins

 

If you are not redirected automatically, go to https://github.com/bkehr/popins.

Flexbar

 

If you are not redirected automatically, go to https://github.com/seqan/flexbar.

Mugsy

 

If you are not redirected automatically, go to http://mugsy.sourceforge.net/.

DELLY

 

If you are not redirected automatically, go to http://www.korbel.embl.de/software.html.

Tophat

If you are not redirected automatically, go to http://ccb.jhu.edu/software/tophat/index.shtml.

Bowtie

If you are not redirected automatically, go to http://bowtie-bio.sourceforge.net/index.shtml.

String Similarity Search/Join

String Similarity Search/Join

Abstract

We present in this paper scalable algorithms for optimal string similarity search and join. Our methods are variations of those applied in Masai, our recently published tool for mapping high-throughput DNA sequencing data with unpreceded speed and accuracy. The key features of our approach are filtration with approximate seeds and methods for multiple backtracking. Approximate seeds, compared to exact seeds, increase filtration specificity while preserving sensitivity. Multiple backtracking amortizes the cost of searching a large set of seeds. Combined together, these two methods significantly speed up string similarity search and join operations.

Links

Please Cite

Siragusa, E., Weese D., & Reinert, K. (2013). Scalable String Similarity Search/Join with Approximate Seeds and Multiple Backtracking. EDBT/ICDT ’13, March 18 – 22 2013, Genoa, Italy

Stellar

STELLAR

Abstract

Large-scale comparison of genomic sequences requires reliable tools for the search of local alignments. Practical local aligners are in general fast but heuristic, and hence often miss significant matches. We provide here the local pairwise aligner STELLAR that has full sensitivity, i.e. guarantees to report all matches of a given minimal length and maximal error rate. The aligner is composed of two steps, filtering and verification. For filtering it applies the SWIFT algorithm, for which we have developed a new, exact verification strategy. STELLAR is very practical and fast on very long sequences which makes it a suitable new tool for finding local alignments in the edit or hamming distance model.

Links

Please Cite

Kehr, B., Weese, D., Reinert, K. (2011). STELLAR: fast and exact local alignments. BMC Bioinformatics, 12(Suppl 9):S15, 2011.