A Parsimony Approach to Biological Pathway Reconstruction/Inference for Genomes and Metagenomes

By Yuzhen Ye, T. Doak. PLoS Comput. Biol., 2009

573 citations recorded by the source corpus.

A common biological pathway reconstruction approach—as implemented by many automatic biological pathway services (such as the KAAS and RAST servers) and the functional annotation of metagenomic sequences—starts with the identification of protein functions or families (e.g., KO families for the KEGG database and the FIG families for the SEED database) in the query sequences, followed by a direct mapping of the identified protein families onto pathways. Given a predicted patchwork of individual biochemical steps, some metric must be applied in deciding what pathways actually exist in the genome or metagenome represented by the sequences. Commonly, and straightforwardly, a complete biological pathway can be identified in a dataset if at least one of the steps associated with the pathway is found. We report, however, that this naïve mapping approach leads to an inflated estimate of biological pathways, and thus overestimates the functional diversity of the sample from which the DNA sequences are derived. We developed a parsimony approach, called MinPath (Minimal set of Pathways), for biological pathway reconstructions using protein family predictions, which yields a more conservative, yet more faithful, estimation of the biological pathways for a query dataset. MinPath identified far fewer pathways for the genomes collected in the KEGG database—as compared to the naïve mapping approach—eliminating some obviously spurious pathway annotations. Results from applying MinPath to several metagenomes indicate that the common methods used for metagenome annotation may significantly overestimate the biological pathways encoded by microbial communities.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/2736cb800dcf482265a60a2e9c2c89d0e9ee92e2

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In Molecular Biology

Citations in the corpus

Metabolic Reconstruction for Metagenomic Data and Its Application to the Human Microbiome (2012, methodology): “2 [22].”
Composition of the adult digestive tract bacterial microbiome based on seven mouth surfaces, tonsils, throat and stool samples (2012, methodology): “Pathway information was then recovered by assigning KO gene families to KEGG modules (representing small pathways of approximately 5 to 20 genes) using a combination of MinPath [104], filtering of pathways not consistent with the BLAST-derived taxonomic composition of the community, and gap filling ”
Advances and Challenges in Metatranscriptomic Analysis (2019, methodology): “Once annotations are performed, enzymatic functions may also be mapped to known metabolic pathways, using tools like MinPath (Ye and Doak, 2009) or iPath (Yamada et al., 2011).”
The Road to Metagenomics: From Microbiology to DNA Sequencing Technologies and Bioinformatics (2015, methodology): “Examples of programs available are MinPath (Ye and Doak, 2009) and MetaPath (Liu and Pop, 2010).”
SqueezeMeta, A Highly Portable, Fully Automatic Metagenomic Analysis Pipeline (2018, methodology): “The original functions represented in the bins can be used to infer the presence of metabolic pathways using the MinPath algorithm (Ye and Doak, 2009), that defines each pathway as an unstructured gene set and selects the fewest pathways that can explain the genes observed within each bin.”
Chapter 12: Human Microbiome Analysis (2012, background): “The abundance mass for each enzyme is distributed across its functions in one or more possible pathways; methods for doing this range from the simple assumption that it is equally active in all reference pathways (as currently done by KAAS [94] or MG-RAST [76]) to the elimination of unlikely pathway”
Current challenges and best-practice protocols for microbiome analysis (2019, methodology): “algorithm [166] could be implemented for biological pathway reconstructions based on protein family predictions.”