Integrating phylogenetic and functional data in microbiome studies

A Bioinformatics Tool for Identifying Intratumoral Microbes from the ORIEN Dataset

Evidence supports significant interactions among microbes, immune cells, and tumor cells in at least 10%-20% of human cancers, emphasizing the importance of further investigating these complex relationships. However, the implications and significance of tumor-related microbes remain largely unknown. Studies have demonstrated the critical roles of host microbes in cancer prevention and treatment responses. Understanding interactions between host microbes and cancer can drive cancer diagnosis and microbial therapeutics (bugs as drugs). Computational identification of cancer-specific microbes and their associations is still challenging due to the high dimensionality and high sparsity of intratumoral microbiome data, which requires large datasets containing sufficient event observations to identify relationships, and the interactions within microbial communities, the heterogeneity in microbial composition, and other confounding effects that can lead to spurious associations. To solve these issues, we present a bioinformatics tool, microbial graph attention (MEGA), to identify the microbes most strongly associated with 12 cancer types. We demonstrate its utility on a dataset from a consortium of nine cancer centers in the Oncology Research Information Exchange Network. This package has three unique features: species-sample relations are represented in a heterogeneous graph and learned by a graph attention network; it incorporates metabolic and phylogenetic information to reflect intricate relationships within microbial communities; and it provides multiple functionalities for association interpretations and visualizations. We analyzed 2,704 tumor RNA sequencing samples and MEGA interpreted the tissue-resident microbial signatures of each of 12 cancer types. MEGA can effectively identify cancer-associated microbial signatures and refine their interactions with tumors.

SIGNIFICANCE

Studying the tumor microbiome in high-throughput sequencing data is challenging because of the extremely sparse data matrices, heterogeneity, and high likelihood of contamination. We present a new deep learning tool, MEGA, to refine the organisms that interact with tumors.

Efficient computation of contributional diversity metrics from microbiome data with FuncDiv

MOTIVATION

Microbiome datasets with taxa linked to the functions (e.g. genes) they encode are becoming more common as metagenomics sequencing approaches improve. However, these data are challenging to analyze due to their complexity. Summary metrics, such as the alpha and beta diversity of taxa contributing to each function (i.e. contributional diversity), represent one approach to investigate these data, but currently there are no straightforward methods for doing so.

RESULTS

We addressed this gap by developing FuncDiv, which efficiently performs these computations. Contributional diversity metrics can provide novel insights that would be impossible to identify without jointly considering taxa and functions.

AVAILABILITY AND IMPLEMENTATION

FuncDiv is distributed under a GNU Affero General Public License v3.0 and is available at https://github.com/gavinmdouglas/FuncDiv.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.