Review
Biochemical Research Methods
Lu Yang, Jun Chen
Summary: Differential abundance analysis (DAA) is a crucial statistical task in microbiome data analysis, and a robust DAA tool is important for identifying reliable microbial candidates. However, different DAA tools for correlated microbiome data (DAA-c) often yield inconsistent results. To address this issue, we conducted a comprehensive evaluation of existing DAA-c tools using real data-based simulations. Our findings indicate that linear model-based methods such as LinDA, MaAsLin2, and LDM are more robust than generalized linear models. Among them, LinDA method performs reasonably well even in the presence of strong compositional effects.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Biochemical Research Methods
Hunyong Cho, Yixiang Qu, Chuwen Liu, Boyang Tang, Ruiqi Lyu, Bridget M. Lin, Jeffrey Roach, M. Andrea Azcarate-Peril, Apoena Aguiar Ribeiro, Michael Love, Kimon Divaris, Di Wu
Summary: Understanding the function of the human microbiome is important, but statistical methods for metatranscriptomics are still in their early stages. A comprehensive evaluation of 10 differential analysis methods was conducted to address this gap. The logistic-beta (LB) test showed the highest sensitivity and controlled type I error well, while the MAST test had inflated type I error. The LB and log-normal (LN) tests successfully identified genes associated with childhood dental disease in a study.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Computer Science, Theory & Methods
Yunbo Tang, Dan Chen, Xiaoli Li
Summary: The study highlights the importance of dimensionality reduction in brain imaging data analysis, categorizing research based on the scale, order, and linearity of the data, while defining criteria for qualitative evaluations. By reducing dimensionality, key insights can be revealed and understanding of brain functional states can be enhanced.
ACM COMPUTING SURVEYS
(2021)
Article
Gastroenterology & Hepatology
Jinlong Ru, Mohammadali Khan Mirzaei, Jinling Xue, Xue Peng, Li Deng
Summary: ViroProfiler is a computer analysis tool for analyzing shotgun viral metagenomic data. It can be run on a local or cloud computing environment. It uses containerization technology to ensure computational reproducibility and facilitate collaborative research.
Article
Biochemical Research Methods
Wei Zhang, Aiyi Liu, Zhiwei Zhang, Guanjie Chen, Qizhai Li
Summary: The study develops a novel approach for differential analysis of microbiome data, which incorporates the unique characteristics of the data, including compositional constraint, excessive zeros, and high dimensionality. Extensive simulations and real data analysis demonstrate the superior performance of the proposed method in various scenarios.
Article
Microbiology
Lu Yang, Jun Chen
Summary: This study provides a comprehensive evaluation of existing differential abundance analysis (DAA) tools, revealing that none of the evaluated methods are simultaneously robust, powerful, and flexible. To overcome the challenge of selecting the best DAA tool, the researchers designed an optimized procedure called ZicoSeq, which combines the strengths of existing methods and can be applied to diverse microbiome datasets for robust biomarker discovery.
Article
Biochemistry & Molecular Biology
Cecile M. Cres, Andrew Tritt, Kristofer E. Bouchard, Ying Zhang
Summary: DL-TODA is a program that uses a deep learning model to classify metagenomic reads, achieving high classification accuracy. It outperforms other state-of-the-art taxonomic classification tools and can be applied to analyze microbiomes from diverse environments.
Article
Biochemistry & Molecular Biology
Yao Lu, Guangyan Zhou, Jessica Ewald, Zhiqiang Pang, Tanisha Shiri, Jianguo Xia
Summary: Microbiome studies have become routine in various scientific fields, requiring user-friendly bioinformatics tools to analyze complex multi-omics datasets. MicrobiomeAnalyst 2.0 is introduced here as a comprehensive tool for statistical analysis, visualization, functional interpretation, and integrative analysis of microbiome data. It features three new modules for data processing, metabolomics profiling, and statistical meta-analysis, along with other improvements such as support for multi-factor analysis and interactive visualizations.
NUCLEIC ACIDS RESEARCH
(2023)
Article
Microbiology
Simon Labarthe, Sandra Plancade, Sebastien Raguideau, Florian Plaza Onate, Emmanuelle Le Chatelier, Marion Leclerc, Beatrice Laroche
Summary: By using nonnegative matrix factorization, this study identified four distinct functional profiles related to fiber degradation in the gut microbiome, which can serve as markers for diet, dysbiosis, inflammation, and disease.
Article
Microbiology
Yue Clare Lou, Jordan Hoff, Matthew R. Olm, Jacob West-Roberts, Spencer Diamond, Brian A. Firek, Michael J. Morowitz, Jillian F. Banfield
Summary: This study applies high-resolution strain-resolved analysis to identify contamination in two large-scale clinical metagenomics datasets. Well-to-well contamination is found in negative controls and biological samples in one dataset, especially among samples that are on the same or adjacent columns or rows of the extraction plate. The strain-resolved workflow also detects externally derived contamination in the other dataset. Overall, contamination is more significant in samples with lower biomass in both datasets.
Article
Biochemistry & Molecular Biology
Mir Henglin, Brian L. Claggett, Joseph Antonelli, Mona Alotaibi, Gino Alberto Magalang, Jeramie D. Watrous, Kim A. Lagerborg, Gavin Ovsak, Gabriel Musso, Olga V. Demler, Ramachandran S. Vasan, Martin G. Larson, Mohit Jain, Susan Cheng
Summary: This study compares the performance of traditional and newer statistical learning methods in different types of metabolomics data and finds that, in the analysis of human metabolomics data, multivariate methods perform better than univariate methods as the number of study subjects increases.
Article
Immunology
Lei Zhang, Ting Chen, Ye Wang, Shengwei Zhang, Qingyu Lv, Decong Kong, Hua Jiang, Yuling Zheng, Yuhao Ren, Wenhua Huang, Peng Liu, Yongqiang Jiang
Summary: Metagenomic next-generation sequencing (mNGS) is a novel strategy increasingly used in clinic for pathogen detection. In this study, three DNA extraction methods were compared, and the enzymatic-based method was found to have excellent performance in long-read mNGS-based pathogen identification and potential diseases diagnosis.
FRONTIERS IN CELLULAR AND INFECTION MICROBIOLOGY
(2022)
Article
Microbiology
Chian Teng Ong, Gry Boe-Hansen, Elizabeth M. Ross, Patrick J. Blackall, Conny Turni, Ben J. Hayes, Ala E. Tabor
Summary: This study compared the effectiveness of different host depletion and DNA extraction methods for bovine vaginal metagenomic samples. The findings indicated that Soft-spin and QIAamp were the most effective methods in reducing host DNA contamination and increasing sequencing depth for microbial reads. Thus, a combination of these methods provided the most robust representation of the vaginal microbial community in cattle.
MICROBIOLOGY SPECTRUM
(2022)
Article
Biochemical Research Methods
Jun Yan, Hongning Zhai, Ling Zhu, Sasha Sa, Xiaojun Ding
Summary: obaDIA is a comprehensive automated tool for analyzing quantitative proteomics data, supporting various abundance matrices and providing rich result visualization and analysis functions.
Review
Biochemistry & Molecular Biology
Qi Wang, Zhaoqian Liu, Anjun Ma, Zihai Li, Bingqiang Liu, Qin Ma
Summary: The human microbiome is closely related to cancer biology and plays a crucial role in cancer treatments, including immunotherapy. This review focuses on the intratumoral microbiome in the context of immuno-oncology-microbiome (IOM) and discusses the available data and computational methods for analyzing microbial profiling. The challenges in data analysis and integration are addressed, and the microorganisms associated with cancer and cancer treatment in IOM are summarized from the literature. Future directions in IOM research are also provided.
TRENDS IN MICROBIOLOGY
(2023)