FunGene: the functional gene pipeline and repository

By Jordan A. Fish, Benli Chai, Qiong Wang, Yanni Sun, C. Titus Brown, James M. Tiedje, James R. Cole, P. Frenzel, D. Berry. Frontiers in Microbiology, 2013

600 citations recorded by the source corpus.

Ribosomal RNA genes have become the standard molecular markers for microbial community analysis for good reasons, including universal occurrence in cellular organisms, availability of large databases, and ease of rRNA gene region amplification and analysis. As markers, however, rRNA genes have some significant limitations. The rRNA genes are often present in multiple copies, unlike most protein-coding genes. The slow rate of change in rRNA genes means that multiple species sometimes share identical 16S rRNA gene sequences, while many more species share identical sequences in the short 16S rRNA regions commonly analyzed. In addition, the genes involved in many important processes are not distributed in a phylogenetically coherent manner, potentially due to gene loss or horizontal gene transfer. While rRNA genes remain the most commonly used markers, key genes in ecologically important pathways, e.g., those involved in carbon and nitrogen cycling, can provide important insights into community composition and function not obtainable through rRNA analysis. However, working with ecofunctional gene data requires some tools beyond those required for rRNA analysis. To address this, our Functional Gene Pipeline and Repository (FunGene; http://fungene.cme.msu.edu/) offers databases of many common ecofunctional genes and proteins, as well as integrated tools that allow researchers to browse these collections and choose subsets for further analysis, build phylogenetic trees, test primers and probes for coverage, and download aligned sequences. Additional FunGene tools are specialized to process coding gene amplicon data. For example, FrameBot produces frameshift-corrected protein and DNA sequences from raw reads while finding the most closely related protein reference sequence. These tools can help provide better insight into microbial communities by directly studying key genes involved in important ecological processes.

Subfields: Ecology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/e9370703b04041771c03793b08dc39fdcbc54c44

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Citations in the corpus

Influence of corn, switchgrass, and prairie cropping systems on soil microbial communities in the upper Midwest of the United States (2016, methodology): “The protein sequences were aligned using the HMMER3 aligner, clustered at 95% identity, and the representative sequences for each cluster classified using the FunGene Pipeline (Fish et al., 2013) to find the nearest match among 675 protein sequences in a curated reference set (Wang et al.”
Ribosomal Database Project: data and tools for high throughput rRNA analysis (2013, methodology): “Several previously published complete linkage algo-rithm implementations take advantage of on disk storage of distances to limit the memory requirements for clustering (31,36,37).”
Using the class 1 integron-integrase gene as a proxy for anthropogenic pollution (2014, background): “spr) (Fish et al., 2013) has over 500 sequences with 499% identity to intI1.”