Biomedical subjects

Joshua A Steele

Publications and source records attributed to Joshua A Steele.

3 recordsLinked to original sources

PubMed2006

Annually reoccurring bacterial communities are predictable from ocean conditions.

Factors influencing patterns in the distribution and abundance of plant and animal taxa modulate ecosystem function and ecosystem response to environmental change, which is often taken to infer low functional redundancy among such species, but such relationships are poorly known for microbial communities. Using high-resolution molecular fingerprinting, we demonstrate the existence of extraordinarily repeatable temporal patterns in the community composition of 171 operational taxonomic units of marine bacterioplankton over 4.5 years at our Microbial Observatory site, 20 km off the southern California coast. These patterns in distribution and abundance of microbial taxa were highly predictable and significantly influenced by a broad range of both abiotic and biotic factors. These findings provide statistically robust demonstration of temporal patterning in marine bacterial distribution and abundance, which suggests that the distribution and abundance of bacterial taxa may modulate ecosystem function and response and that a significant subset of the bacteria exhibit low levels of functional redundancy as documented for many plant and animal communities.

Bacteria↗

PubMed2006

Local similarity analysis reveals unique associations among marine bacterioplankton species and environmental factors.

MOTIVATION: Characterizing the diversity of microbial communities and understanding the environmental factors that influence community diversity are central tenets of microbial ecology. The development and application of cultivation independent molecular tools has allowed for rapid surveying of microbial community composition at unprecedented resolutions and frequencies. There is a growing need to discern robust patterns and relationships within these datasets which provide insight into microbial ecology. Pearson correlation coefficient (PCC) analysis is commonly used for identifying the linear relationship between two species, or species and environmental factors. However, this approach may not be able to capture more complex interactions which occur in situ; thus, alternative analyses were explored. RESULTS: In this paper we introduced local similarity analysis (LSA), which is a technique that can identify more complex dependence associations among species as well as associations between species and environmental factors without requiring significant data reduction. To illustrate its capability of identifying relationships that may not otherwise be identified by PCC, we first applied LSA to simulated data. We then applied LSA to a marine microbial observatory dataset and identified unique, significant associations that were not detected by PCC analysis. LSA results, combined with results from PCC analysis were used to construct a theoretical ecological network which allows for easy visualization of the most significant associations. Biological implications of the significant associations detected by LSA were discussed. We also identified additional applications where LSA would be beneficial. AVAILABILITY: The algorithms are implemented in Splus/R and they are available upon request from the corresponding author.

Bacteria↗

PubMed2006

A dynamic programming algorithm for binning microbial community profiles.

MOTIVATION: A number of community profiling approaches have been widely used to study the microbial community composition and its variations in environmental ecology. Automated Ribosomal Intergenic Spacer Analysis (ARISA) is one such technique. ARISA has been used to study microbial communities using 16S-23S rRNA intergenic spacer length heterogeneity at different times and places. Owing to errors in sampling, random mutations in PCR amplification, and probably mostly variations in readings from the equipment used to analyze fragment sizes, the data read directly from the fragment analyzer should not be used for down stream statistical analysis. No optimal data preprocessing methods are available. A commonly used approach is to bin the reading lengths of the 16S-23S intergenic spacer. We have developed a dynamic programming algorithm based binning method for ARISA data analysis which minimizes the overall differences between replicates from the same sampling location and time. RESULTS: In a test example from an ocean time series sampling program, data preprocessing identified several outliers which upon re-examination were found to be because of systematic errors. Clustering analysis of the ARISA from different times based on the dynamic programming algorithm binned data revealed important features of the biodiversity of the microbial communities.

Algorithms↗