Our Tools

Lab Tools

Kemet

Palù, M., Basile, A., Zampieri, G., Treu, L., Rossi, A., Morlino, M. S., Campanaro, S.

KEMET – A python tool for KEGG Module evaluation and microbial genome annotation expansion

https://github.com/Matteopaluh/KEMET - actively maintained

Microbial metagenome-assembled genomes (MAGs) are very often incomplete and harbor partial gene sequences that can prevent their identification and annotation. KEgg Module Evaluation Tool (KEMET) allows the expansion of genome annotations by identifying missing orthologs through hidden Markov model profiles.

CoCo

Zampieri, G., Campanaro, S., Angione, C., Treu, L.

Metatranscriptomics-guided genome-scale metabolic modeling of microbial communities

https://github.com/gzampieri/coco_paper - actively maintained

CoCo is an approach for the incorporation of metatranscriptomics data into genome-scale metabolic models (GEMs) of microbial communities. This approach can be applied to complex microbial communities in culture-independent settings, allowing a mechanistic contextualization of multi-omics data on a metagenome scale.

FEEDS

Victor Borin Centurion, Edoardo Bizzotto, Stefano Tonini, Pasquale Filannino, Raffaella Di Cagno, Guido Zampieri, Stefano Campanaro

FEEDS, the Food wastE biopEptiDe claSsifier: From microbial genomes and substrates to biopeptides function

https://github.com/vborincenturion/feeds

FEEDS is a tool that takes a bacteria genome or predicted proteins of yeast genomes to classify the secreted protease profile. It also digests protein substrate sequences to predict peptides and classify the biopeptide sequences with a novel machine-learning method. This tool is useful for identifying potential bioactive compounds in food and discovering novel applications for waste management.

MicroPherret

Bizzotto, E., Fraulini, S., Zampieri, G., Orellana, E., Treu, L., Campanaro, S. 

MICROPHERRET: MICRObial PHEnotypic tRait ClassifieR using machine lEarning Techniques. Currently under revision 

https://github.com/BizzoTL/MICROPHERRET - actively maintained

The recent rapid increase in the reconstruction of microbial genomes led to a pressing need for the development of swift and automated strategies for their functional classification. MICROPHERRET emerges as a solution, employing a combination of supervised machine learning algorithms for the functional classification of prokaryotic genomes from gene annotations. The tool can be applied for the prediction of over 80 diverse phenotypes on both high-quality and low-quality microbial genomes, providing valuable insights into the functional roles of newly sequenced genomes within their micro-ecological contexts.

pan-Draft

De Bernardini, N., Zampieri, G., Campanaro, S., Zimmermann, J., Waschina, S., Treu, L. 

pan-Draft: Automated reconstruction of species-representative metabolic models from multiple genomes.

https://github.com/jotech/gapseq - actively maintained

The accurate reconstruction of genome-scale metabolic models (GEMs) for unculturable species poses challenges due to the incomplete and fragmented genetic information of metagenome-assembled genomes (MAGs). pan-Draft implements a pan-reactome approach exploiting recurrent genetic evidence to determine the solid core structure of species-level GEMs. By comparing MAGs clustered at species-level, pan-Draft addresses challenges of individual genomes, thus providing high-quality draft metabolic models.

Metapresence

Sanguineti, D., Zampieri, G., Campanaro, S., Treu, L. 

Metapresence: a tool for accurate species detection in metagenomics based on the genome-wide distribution of mapping reads. Currently under revision

https://github.com/davidesangui/metapresence - actively maintained

This Python tool performs reliable identification of the species in metagenomic samples based on the distribution of reads mapping on reference genomes. Its functionality is based on two metrics describing the breadth of coverage and the genomic distance between consecutive reads. These two metrics permit to overcome the inherent biases in using relative abundance thresholds, allowing an accurate definition of the microbial communities, even regarding rare species.