M. A. Basher, A. | bioRxiv Science

bioRxiv2020

Metabolic pathway inference using non-negative matrix factorization with community detection

Machine learning provides a probabilistic framework for metabolic pathway inference from genomic sequence information at different levels of complexity and completion. However, several challenges including pathway features engineering, multiple mapping of enzymatic reactions and emergent or distributed metabolism within populations or communities of cells can limit prediction performance. In this paper, we present triUMPF, triple non-negative matrix factorization (NMF) with community detection for metabolic pathway inference, that combines three stages of NMF to capture myriad relationships between enzymes and pathways within a graph network. This is followed by community detection to extract higher order structure based on the clustering of vertices which share similar statistical properties. We evaluated triUMPF performance using experimental datasets manifesting diverse multi-label properties, including Tier 1 genomes from the BioCyc collection of organismal Pathway/Genome Databases and low complexity microbial communities. Resulting performance metrics equaled or exceeded other prediction methods on organismal genomes with improved precision on multi-organismal datasets. Availability and implementationThe software package, and installation instructions are published on github.com/triUMPF

bioinformatics↗

bioRxiv2020

Leveraging Heterogeneous Network Embedding for Metabolic Pathway Prediction

Metabolic pathway reconstruction from genomic sequence information is a key step in predicting regulatory and functional potential of cells at the individual, population and community levels of organization. Although the most common methods for metabolic pathway reconstruction are gene-centric e.g. mapping annotated proteins onto known pathways using a reference database, pathway-centric methods based on heuristics or machine learning to infer pathway presence provide a powerful engine for hypothesis generation in biological systems. Such methods rely on rule sets or rich feature information that may not be known or readily accessible. Here, we present pathway2vec, a software package consisting of six representational learning based modules used to automatically generate features for pathway inference. Specifically, we build a three layered network composed of compounds, enzymes, and pathways, where nodes within a layer manifest inter-interactions and nodes between layers manifest betweenness interactions. This layered architecture captures relevant relationships used to learn a neural embedding-based low-dimensional space of metabolic features. We benchmark pathway2vec performance based on node-clustering, embedding visualization and pathway prediction using MetaCyc as a trusted source. In the pathway prediction task, results indicate that it is possible to leverage embeddings to improve pathway prediction outcomes. Availability and implementationThe software package, and installation instructions are published on github.com/pathway2vec Contactshallam@mail.ubc.ca

bioinformatics↗