An Intelligent System for Automated DNA Base Calling

An investigation into improving the performance of DNA base calling algorithms was conducted. The results have shown that the preprocessing steps performed by ABI sequencer on raw data adversely affects the accuracy of DNA sequencing. This adverse effect has been responsible for relatively high error rates, between 3.5% to 6%, in both ABI and Phred sequencing software. Please note that Phred also uses the processed data generated by ABI sequencer; only their base-calling algorithm is different. To remedy this effect, we have developed and implemented a new filtering technique that preserves the initial information contained in the raw data. This provides qualitatively superior data for the future base calling step. Our proposed filtering step provides mechanical shift compensation, cross-talk filtering, and baseline adjustment. These have been briefly described below. Application of our filtering step on a limited number of DNA data has provided sequences with lower error rate

Accurate DNA Base Caller

The major goal of this project is to develop a new base calling technique that will improve the efficiency of the DNA sequencing process. This will be achieved by increasing the average length of error-free sequencing and enhancing the base identification process at the beginning and end of sequences. This will increase sequencing throughput and reduce the cost of DNA sequencing. Previous work by the PI has demonstrated the ability to extend the error-free read by 30%. This was achieved through work on cross-talk filtering, baseline adjustment, base-spacing prediction and development of a fuzzy base-calling algorithm. Further adaptive capabilities as well as full development and implementation of the methodology is planned. The software will be tested on a large number of DNA sequences and remove specific hardware and operating system requirements, as well as be exploitable over the web. Accurate, inexpensive genomic DNA sequencing will be a cornerstone of 21st century biology

Biodiversity and Ecosystem Informatics - BDEI - Planning Workshop on Biodiversity and Ecosystem Informatics for the Indian River Lagoon, Florida

This proposal solicits funding to organize and conduct a planning workshop that will establish and facilitate research on the informatics needed to address complex issues of biodiversity and ecosystem processes within the Indian River Lagoon. This workshop will provide the opportunity and resources for collaboration and discussion among scientists from diverse fields of biodiversity, ecological sciences, remote sensing, geographic information systems, computer science and intelligent systems. The topics to be discussed will include investigation of novel computational intelligence techniques for modeling, prediction, analysis and database management of the disparate and complex data for the Indian River Lagoon. The explicit products of the proposed workshop will be a white paper and technical report, a formal research agenda that incorporates informatics into existing and planned research, and preparation of a competitive proposal based on the recommendations and preliminary work defined by the workshop

GOALI/IUCP: Prediction of Wood Pulp K-Number with Neural Networks

Lignin holds wood fibers together, and must be removed to produce high strength pulp for kraft paper. The Kappa- or K-number indicates the degree of lignin removal by a pulping process, and is probably the key variable for measuring quality in this process. A difficulty is that it is an off-line measurement. More importantly, there is usually a four hour process delay between when raw materials enter a pulping digester and when the K-number is measured. This makes modeling and control difficult. This Grant Opportunity for Academic Liaison with Industry project uses neural network models to predict K-number as a function of a number of more readily available process parameters. This is a first step in improving the control and responsiveness of this process to changes in chip feed stock. The research team from the University of Maine and S.D. Warren Company will develop characterization and prediction models using data from an operating plant, and compare their long term predicative capability when integrated into digester operations. Throughout, seminar and workshops are part of the technology transfer and model improvement. The impact of this research will be more uniform quality of pulp, even with variable feed stock, and more uniform quality in subsequent bleaching and papermaking processes

Full Length Paper TraceTools: a new DNA basecaller

This paper presents a novel basecalling software, TraceTools, with a unique user-friendly confidence value feature. Because of the differences between ABI data pre-processing and those of this software, TraceTools uses the raw data generated by ABI machines as opposed to the ABI processed data. For developing and testing TraceTools, a comprehensive database of correct DNA sequences corresponding to the ABI raw data was constructed. The ABI raw trace data was obtained from North Carolina State University. This comprehensive database was used for comparing the accuracy of TraceTools with other popular basecalling programs such as Phred and ABI. The results of this comparison on over 3000 data files with around 750 bases per file show that TraceTools performs better than ABI and Phred. The confidence values also provide a reliable measure of success in the bases called. Keywords – ABI, basecalling accuracy, confidence values, DNA basecalling, Phred, TraceTools.