RStudio CommunityComparing Illumina MiSeq and PacBio SMRT Sequencing of Fecal Samples from Various Animal Sources Potentially Contributing to Microbial Contamination of the Delaware River Watershed
Authors: Tyler Bradley, Jacob R. Price, Christopher M. Sales
Delaware Watershed Research Conferece 2018
Philadelphia, PA - November 2018
Abstract
Next generation sequencing technologies allow for vast amounts of information to be collected about microbial communities in order to better understand their structure and function. There are several sequencing technologies available that allow for different amounts of DNA or RNA to be sequenced. The different approaches and chemistry used by sequencing instrument manufacturers results in large differences in total sequencing yield, read length limitations, and sequence data accuracy. For example, a typical run on one of the most popular sequencing technologies, Illumina MiSeq, may produce 25 million high quality paired end 300 bp reads (2 x 300 bp). Conversely, the new Pacific Biosciences (PacBio) Sequel platform can produce reads of significantly longer lengths (averaging 10-14 kb) at a sacrifice in total yield (~ 365,000 reads) and higher error rates.
Investigators researching microbiomes are faced with the challenge of selecting between technologies and chemistry to maximize the utility of the sequencing data. This can be particularly difficult as significant tradeoffs exist between the additional information that may be contained in longer reads or the negative effects of higher error rates or differences in sequencing depth, as well as their ultimate costs. To investigate these effects, these two technologies were employed to sequence 32 fecal samples targeting the 16S rRNA gene of bacteria. The Illumina MiSeq run targeted the V4-V5 hyper-variable regions (~300-350 bp) while the PacBio Sequel run targeted the full length 16S rRNA gene (~1500 bp). This study examines how closely the taxonomic assignments for these different technologies matched. It was investigated whether restricting sequence length to specific hyper-variable regions resulted in misclassification or overconfidence in taxonomic classification. Overall, both of these technologies resulted in similar taxonomic assignments with 46.4% of matches OTU’s being classified identically to the genus level. Only 1.9% of MiSeq OTUs were matched with a SMRT OTU where both OTUs were assigned differently at the genus level. In addition, these fecal samples, from 9 different animal sources, were used in conjunction with water samples taken from waterways in the Delaware watershed to see what impact on river microbiome these different animals may have. Differential abundances were used to identify genera that are unique to specific animals, in order to determine how these sources may be affecting river water quality. The results from this study will allow us to determine the pros and cons of using short-read vs. long-read sequencing for microbial community analyses and for microbial source tracking.
Developing a New Approach for Proactive Nitrification Monitoring in a Chloraminated Drinking Water Distribution System
Authors: Tyler Bradley, Tim Bartrand, Sheldon Masters
AWWA Water Quality Technology Conference
Toronto, ON, Canada - November 2018
Abstract
This abstract presents a proactive approach to managing nitrification in a chloraminated drinking water distribution system. This deals with an issue that is relevant in most drinking water systems that use chloramines as a primary or secondary disinfectant. The authors will present ways in which Philadelphia are implementing these proactive approaches.
Effective Use of Real-Time Water Quality Data
National Environmental Monitoring Conference
Orange County, CA - August 2016
Abstract
Online sensor data, whether in drinking water production and delivery or elsewhere, require significant care and handling. Required care and handling of the data are dictated by the sensor precision and accuracy, the intended use of the sensor (event detection, process control, research, compliance monitoring or other purposes), and the sensor context (their placement in the water production system, the variability in a monitored parameter at the sensor location, the difference between average conditions at a particular sensor location and a regulatory level or level of concern). This study presents an analysis of real-time water quality monitoring data management and analysis for sensors deployed in Philadelphia Water’s distribution system and used for event detection. First, the data collection, management and analysis tools are described and difficulties encountered and overcome while developing the system are highlighted. Those difficulties include both the complexity of collecting large volumes of data from a dispersed network of sensors and steps required to ensure data conform to requirements of event detection tools. Second, analyses are presented that quantify the required precision and accuracy of sensors deployed in the Philadelphia Water distribution system. Analyses include assessment in the variability of water quality data at several time scales and establishment of sensor performance metrics consistent with their use as part of an early warning system. In general, sensors in current use in Philadelphia Water’s sensor network were shown to meet required precision and accuracy for use as components in an event detection system. As shown in a companion paper to this paper, meeting the requirements was only possible after significant efforts to improve sensor installations and establish a rigorous operations and maintenance (O&M) effort. Finally, analyses for establishing sensor control limits are presented. Control limits are a critical input to event detection algorithms. To establish control limits, long time series of water quality data were analyzed and variability in the observations at the global, seasonal and daily time scales was characterized. Results indicate that control limits that optimize event detection performance (maximize detection of true events and minimize false positive and false negative detections) vary seasonally or possibly at shorter time scales.