FunFrame: functional gene ecological analysis pipeline

Weisman, David; Yasuda, Michie; Bowen, Jennifer L.

doi:10.1093/bioinformatics/btt123

Cited by 13 publications

(8 citation statements)

References 18 publications

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“…Our functional gene analysis pipeline had an error rate of 0.0–0.18% when used to analyze pyrosequencing data of known controls (Weisman et al, 2013), furthermore, this pipeline removes more spurious diversity while retaining a greater number of real nirS sequences than approaches based solely on the removal of sequences with unexpected stop codons (Weisman et al, 2013). Finally, the realization that the number of singletons is not evenly distributed over samples from all the plots provides further evidence that this result is not likely a product of random sequencing error.…”

Section: Discussionmentioning

confidence: 99%

“…The remaining sequences were trimmed to 432 bp and were processed using FunFrame (Weisman et al, 2013), a functional gene analysis pipeline we developed for the high throughput analysis of protein coding gene amplicons. Briefly, FunFrame uses HMM-FRAME (Zhang and Sun, 2011) along with a hidden Markov model of the cytochome D1 nirS gene from Pfam (accession PF02239.10) to identify and correct frameshift errors that result from homopolymer misreads.…”

Section: Methodsmentioning

confidence: 99%

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Functional gene pyrosequencing and network analysis: an approach to examine the response of denitrifying bacteria to increased nitrogen supply in salt marsh sediments

et al. 2013

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Functional gene pyrosequencing is emerging as a useful tool to examine the diversity and abundance of microbes that facilitate key biogeochemical processes. One such process, denitrification, is of particular importance because it converts fixed nitrate (NO−3) to N2 gas, which returns to the atmosphere. In nitrogen limited salt marshes, removal of NO−3 prior to entering adjacent waters helps prevent eutrophication. Understanding the dynamics of salt marsh microbial denitrification is thus imperative for the maintenance of healthy coastal ecosystems. We used pyrosequencing of the nirS gene to examine the denitrifying community response to fertilization in experimentally enriched marsh plots. A key challenge in the analysis of sequence data derived from pyrosequencing is understanding whether small differences in gene sequences are ecologically meaningful. We applied a novel approach from information theory to determine that the optimal similarity level for clustering DNA sequences into OTUs, while still capturing the ecological complexity of the system, was 88%. With this clustering, phylogenetic analysis yielded 6 dominant clades of denitrifiers, the largest of which, accounting for more than half of all the sequences collected, had no close cultured representatives. Of the 638 OTUs identified, only 11 were present in all plots and no single OTU was dominant. We did, however, find a large number of specialist OTUs that were present only in a single plot. The high degree of endemic OTUs, while accounting for a large proportion of the nirS diversity in the plots, were found in lower abundance than the generalist taxa. The proportion of specialist taxa increased with increasing supply of nutrients, suggesting that addition of fertilizer may create conditions that expand the niche space for denitrifying organisms and may enhance the genetic capacity for denitrification.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Functional gene pyrosequencing and network analysis: an approach to examine the response of denitrifying bacteria to increased nitrogen supply in salt marsh sediments

et al. 2013

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“…Few tools offer the functional gene reference sets required for analysis. FunFrame (Weisman et al, 2013) is an R-based analysis pipeline for functional gene data, built on analysis tools including HMMFrame (Zhang and Sun, 2011) for frameshift correction and gene translation. However, FunFrame comes customized for only one gene, Cytochrome D1.…”

Section: Introductionmentioning

confidence: 99%

FunGene: the functional gene pipeline and repository

Fish¹,

Chai²,

Wang³

et al. 2013

Front. Microbiol.

558

371

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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.

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“…In this study we use mothur, but other software can be used for this purpose, such as QIIME (Caporaso et al, 2010), RDP (Cole et al, 2009) and Funframe (Weisman et al, 2013); each of these have unique features that might be beneficial for a particular dataset or objective. The basic necessary steps are the sorting of sequences according to barcodes, trimming and quality filtering.…”

Section: Raw Sequence Processingmentioning

confidence: 99%

“…Another problem is that they often cause frameshift errors in protein-coding genes, making it difficult to infer amino acid sequences. There are methods available to specifically correct frameshift errors in functional gene pyrosequencing datasets, such as the FrameBot tool (http://fungene.cme.msu.edu/FunGenePipeline/) and HMM-FRAME (Weisman et al, 2013). Correcting the frameshift errors in pyrosequencing datasets is particularly important for calculating OTUs or phylogenetic distances based on amino acid sequences.…”

Section: Raw Sequence Processingmentioning

confidence: 99%

Classification of pmoA amplicon pyrosequences using BLAST and the lowest common ancestor method in MEGAN

et al. 2014

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The classification of high-throughput sequencing data of protein-encoding genes is not as well established as for 16S rRNA. The objective of this work was to develop a simple and accurate method of classifying large datasets of pmoA sequences, a common marker for methanotrophic bacteria. A taxonomic system for pmoA was developed based on a phylogenetic analysis of available sequences. The taxonomy incorporates the known diversity of pmoA present in public databases, including both sequences from cultivated and uncultivated organisms. Representative sequences from closely related genes, such as those encoding the bacterial ammonia monooxygenase, were also included in the pmoA taxonomy. In total, 53 low-level taxa (genus-level) are included. Using previously published datasets of high-throughput pmoA amplicon sequence data, we tested two approaches for classifying pmoA: a naïve Bayesian classifier and BLAST. Classification of pmoA sequences based on BLAST analyses was performed using the lowest common ancestor (LCA) algorithm in MEGAN, a software program commonly used for the analysis of metagenomic data. Both the naïve Bayesian and BLAST methods were able to classify pmoA sequences and provided similar classifications; however, the naïve Bayesian classifier was prone to misclassifying contaminant sequences present in the datasets. Another advantage of the BLAST/LCA method was that it provided a user-interpretable output and enabled novelty detection at various levels, from highly divergent pmoA sequences to genus-level novelty.

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FunFrame: functional gene ecological analysis pipeline

Abstract: Software, documentation and a complete set of sample data files are available at http://faculty.www.umb.edu/jennifer.bowen/software/FunFrame.zip.

Cited by 13 publications

References 18 publications

Functional gene pyrosequencing and network analysis: an approach to examine the response of denitrifying bacteria to increased nitrogen supply in salt marsh sediments

Functional gene pyrosequencing and network analysis: an approach to examine the response of denitrifying bacteria to increased nitrogen supply in salt marsh sediments

FunGene: the functional gene pipeline and repository

Classification of pmoA amplicon pyrosequences using BLAST and the lowest common ancestor method in MEGAN

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