Jordan A. Fish

The Ribosomal Database Project: improved alignments and new tools for rRNA analysis

The Ribosomal Database Project (RDP) provides researchers with quality-controlled bacterial and archaeal small subunit rRNA alignments and analysis tools. An improved alignment strategy uses the Infernal secondary structure aware aligner to provide a more consistent higher quality alignment and faster processing of user sequences. Substantial new analysis features include a new Pyrosequencing Pipeline that provides tools to support analysis of ultra high-throughput rRNA sequencing data. This pipeline offers a collection of tools that automate the data processing and simplify the computationally intensive analysis of large sequencing libraries. In addition, a new Taxomatic visualization tool allows rapid visualization of taxonomic inconsistencies and suggests corrections, and a new class Assignment Generator provides instructors with a lesson plan and individualized teaching materials. Details about RDP data and analytical functions can be found at http://rdp.cme.msu.edu/.

Ribosomal Database Project: data and tools for high throughput rRNA analysis

Ribosomal Database Project (RDP; http://rdp.cme.msu.edu/) provides the research community with aligned and annotated rRNA gene sequence data, along with tools to allow researchers to analyze their own rRNA gene sequences in the RDP framework. RDP data and tools are utilized in fields as diverse as human health, microbial ecology, environmental microbiology, nucleic acid chemistry, taxonomy and phylogenetics. In addition to aligned and annotated collections of bacterial and archaeal small subunit rRNA genes, RDP now includes a collection of fungal large subunit rRNA genes. RDP tools, including Classifier and Aligner, have been updated to work with this new fungal collection. The use of high-throughput sequencing to characterize environmental microbial populations has exploded in the past several years, and as sequence technologies have improved, the sizes of environmental datasets have increased. With release 11, RDP is providing an expanded set of tools to facilitate analysis of high-throughput data, including both single-stranded and paired-end reads. In addition, most tools are now available as open source packages for download and local use by researchers with high-volume needs or who would like to develop custom analysis pipelines.

FunGene: the functional gene pipeline and repository

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.

The khmer software package: enabling efficient nucleotide sequence analysis

The khmer package is a freely available software library for working efficiently with fixed length DNA words, or k-mers. khmer provides implementations of a probabilistic k-mer counting data structure, a compressible De Bruijn graph representation, De Bruijn graph partitioning, and digital normalization. khmer is implemented in C++ and Python, and is freely available under the BSD license at https://github.com/dib-lab/khmer/.

Ecological Patterns of nifH Genes in Four Terrestrial Climatic Zones Explored with Targeted Metagenomics Using FrameBot, a New Informatics Tool

ABSTRACT Biological nitrogen fixation is an important component of sustainable soil fertility and a key component of the nitrogen cycle. We used targeted metagenomics to study the nitrogen fixation-capable terrestrial bacterial community by targeting the gene for nitrogenase reductase (nifH). We obtained 1.1 million nifH 454 amplicon sequences from 222 soil samples collected from 4 National Ecological Observatory Network (NEON) sites in Alaska, Hawaii, Utah, and Florida. To accurately detect and correct frameshifts caused by indel sequencing errors, we developed FrameBot, a tool for frameshift correction and nearest-neighbor classification, and compared its accuracy to that of two other rapid frameshift correction tools. We found FrameBot was, in general, more accurate as long as a reference protein sequence with 80% or greater identity to a query was available, as was the case for virtually all nifH reads for the 4 NEON sites. Frameshifts were present in 12.7% of the reads. Those nifH sequences related to the Proteobacteria phylum were most abundant, followed by those for Cyanobacteria in the Alaska and Utah sites. Predominant genera with nifH sequences similar to reads included Azospirillum, Bradyrhizobium, and Rhizobium, the latter two without obvious plant hosts at the sites. Surprisingly, 80% of the sequences had greater than 95% amino acid identity to known nifH gene sequences. These samples were grouped by site and correlated with soil environmental factors, especially drainage, light intensity, mean annual temperature, and mean annual precipitation. FrameBot was tested successfully on three ecofunctional genes but should be applicable to any. IMPORTANCE High-throughput phylogenetic analysis of microbial communities using rRNA-targeted sequencing is now commonplace; however, such data often allow little inference with respect to either the presence or the diversity of genes involved in most important ecological processes. To study the gene pool for these processes, it is more straightforward to assess the genes directly responsible for the ecological function (ecofunctional genes). However, analyzing these genes involves technical challenges beyond those seen for rRNA. In particular, frameshift errors cause garbled downstream protein translations. Our FrameBot tool described here both corrects frameshift errors in query reads and determines their closest matching protein sequences in a set of reference sequences. We validated this new tool with sequences from defined communities and demonstrated the tool’s utility on nifH gene fragments sequenced from soils in well-characterized and major terrestrial ecosystem types. High-throughput phylogenetic analysis of microbial communities using rRNA-targeted sequencing is now commonplace; however, such data often allow little inference with respect to either the presence or the diversity of genes involved in most important ecological processes. To study the gene pool for these processes, it is more straightforward to assess the genes directly responsible for the ecological function (ecofunctional genes). However, analyzing these genes involves technical challenges beyond those seen for rRNA. In particular, frameshift errors cause garbled downstream protein translations. Our FrameBot tool described here both corrects frameshift errors in query reads and determines their closest matching protein sequences in a set of reference sequences. We validated this new tool with sequences from defined communities and demonstrated the tool’s utility on nifH gene fragments sequenced from soils in well-characterized and major terrestrial ecosystem types.

Bacterial community comparisons by taxonomy-supervised analysis independent of sequence alignment and clustering

High-throughput sequencing of 16S rRNA genes has increased our understanding of microbial community structure, but now even higher-throughput methods to the Illumina scale allow the creation of much larger datasets with more samples and orders-of-magnitude more sequences that swamp current analytic methods. We developed a method capable of handling these larger datasets on the basis of assignment of sequences into an existing taxonomy using a supervised learning approach (taxonomy-supervised analysis). We compared this method with a commonly used clustering approach based on sequence similarity (taxonomy-unsupervised analysis). We sampled 211 different bacterial communities from various habitats and obtained ∼1.3 million 16S rRNA sequences spanning the V4 hypervariable region by pyrosequencing. Both methodologies gave similar ecological conclusions in that β-diversity measures calculated by using these two types of matrices were significantly correlated to each other, as were the ordination configurations and hierarchical clustering dendrograms. In addition, our taxonomy-supervised analyses were also highly correlated with phylogenetic methods, such as UniFrac. The taxonomy-supervised analysis has the advantages that it is not limited by the exhaustive computation required for the alignment and clustering necessary for the taxonomy-unsupervised analysis, is more tolerant of sequencing errors, and allows comparisons when sequences are from different regions of the 16S rRNA gene. With the tremendous expansion in 16S rRNA data acquisition underway, the taxonomy-supervised approach offers the potential to provide more rapid and extensive community comparisons across habitats and samples.

The Roles of Standing Genetic Variation and Evolutionary History in Determining the Evolvability of Anti-Predator Strategies

Standing genetic variation and the historical environment in which that variation arises (evolutionary history) are both potentially significant determinants of a populations capacity for evolutionary response to a changing environment. Using the open-ended digital evolution software Avida, we evaluated the relative importance of these two factors in influencing evolutionary trajectories in the face of sudden environmental change. We examined how historical exposure to predation pressures, different levels of genetic variation, and combinations of the two, affected the evolvability of anti-predator strategies and competitive abilities in the presence or absence of threats from new, invasive predator populations. We show that while standing genetic variation plays some role in determining evolutionary responses, evolutionary history has the greater influence on a populations capacity to evolve anti-predator traits, i.e. traits effective against novel predators. This adaptability likely reflects the relative ease of repurposing existing, relevant genes and traits, and the broader potential value of the generation and maintenance of adaptively flexible traits in evolving populations.

Seeing and Experiencing Relativity: A New Tool for Teaching?

“What would you see if you were riding a beam of light?” This thought experiment, which Einstein reports to have “conducted” at the age of 16,1 of course has no sensible answer: as Einstein published a decade later, you could never reach the speed of light.2 But it does make sense to ask what you would see if you were traveling close to the speed of light, and one of the first physicists to embark on this effort was George Gamow in his Mr. Tompkins in Wonderland.3 His protagonist is speeding on a bicycle through a city where the speed of light is lower, thus ingeniously taking advantage of the fact that special relativity scales with v/c: for it to kick in, you either have to move very fast (in rather unfamiliar territory), or light has to be slow (in which case special relativity kicks in at everyday velocities in everyday situations). Gamow provides drawings of what Mr. Tompkins and people at the curb would see in this slow-light city, at least, what they would see if one only took into account two of t...

Sphingomonas wittichii strain rw1 genome-wide gene expression shifts in response to dioxins and clay

Sphingomonas wittichii strain RW1 (RW1) is one of the few strains that can grow on dibenzo-p-dioxin (DD). We conducted a transcriptomic study of RW1 using RNA-Seq to outline transcriptional responses to DD, dibenzofuran (DF), and the smectite clay mineral saponite with succinate as carbon source. The ability to grow on DD is rare compared to growth on the chemically similar DF even though the same initial dioxygenase may be involved in oxidation of both substrates. Therefore, we hypothesized the reason for this lies beyond catabolic pathways and may concern genes involved in processes for cell-substrate interactions such as substrate recognition, transport, and detoxification. Compared to succinate (SUC) as control carbon source, DF caused over 240 protein-coding genes to be differentially expressed, whereas more than 300 were differentially expressed with DD. Stress response genes were up-regulated in response to both DD and DF. This effect was stronger with DD than DF, suggesting a higher toxicity of DD compared to DF. Both DD and DF caused changes in expression of genes involved in active cross-membrane transport such as TonB-dependent receptor proteins, but the patterns of change differed between the two substrates. Multiple transcription factor genes also displayed expression patterns distinct to DD and DF growth. DD and DF induced the catechol ortho- and the salicylate/gentisate pathways, respectively. Both DD and DF induced the shared down-stream aliphatic intermediate compound pathway. Clay caused category-wide down-regulation of genes for cell motility and chemotaxis, particularly those involved in the synthesis, assembly and functioning of flagella. This is an environmentally important finding because clay is a major component of soil microbes’ microenvironment influencing local chemistry and may serve as a geosorbent for toxic pollutants. Similar to clay, DD and DF also affected expression of genes involved in motility and chemotaxis.

Space-efficient read indexing and retrieval based on compressed de Bruijn graphs

We introduce a short-read indexing and retrieval scheme based on a compressed de Bruijn graph data structure.