1998 ABRF DNA Sequence Research Committee Study

1998 ABRF'98 Poster 

• Title
• Abstract
• Table of Contents

Results from the 1998 ABRF DNA Sequence Research Committee Study

George Grills¹, Pamela Scott Adams², Mary Kay Dolejsi³, Susan Hardin⁴, Doug McMinimy⁵, Paul Morrison⁶, John Rush⁷, and Theodore Thannhauser⁸. Association of Biomolecular Resource Facilities (ABRF) DNA Sequence Research Committee.

¹Albert Einstein College of Medicine, Bronx, NY; ²Trudeau Institute, Saranac Lake, NY; ³Fred Hutchinson Cancer Research Center, Seattle, WA; ⁴University of Houston, Houston, TX; ⁵The Jackson Laboratory, Bar Harbor, ME; ⁶Dana-Farber Cancer Institute, Boston, MA; ⁷Howard Hughes Medical Institute, Harvard Medical School Boston, MA; and ⁸Cornell University, Ithaca, NY.

ABSTRACT

The field of automated DNA sequencing is currently undergoing many changes in instrumentation and chemistry. The first goal of this study was to analyze the effect of different sequencing methods on the quality of sequencing results. A wide variety of sequencing groups submitted sequence data for pGEM, a common standard template, using the instruments and chemistries available in their laboratories. Data were collected via an FTP site for sequencing data and via web forms for details of the sequencing conditions. The effects of factors such as different types of instrumentation, enzymes, dye chemistries, reagent dilutions, editing, and base calling programs were examined. The second goal of this study was to create a readily accessible and easily updated resource that could be used as a benchmark for sequencing, enabling researchers to anonymously compare the quality of their sequence data with data generated in other laboratories. The sequencing data collected for the standard template was posted on a web site in a format that can be used for this purpose. The third goal of the study was to take the pulse of the DNA sequencing world. A web based general survey form was used to collect data such as instrumentation, protocols, services, staffing, and sequencing throughput. This data was analyzed to build a current profile of DNA sequencing laboratories.

TABLE OF CONTENTS

Introduction

Methods

pGEM sequence

Results

Figure 1: Summary of Submissions

Fig. 1a: Number of Laboratories per Machine Type

Fig. 1b: Number of Samples per Machine Type

Figure 2: Accuracy of Different Machine Types

Fig. 2a: Accuracy at Different Length of Reads

Fig. 2b: Accuracy at Longest Length of Read

Fig. 2c: Quality at Longest Length of Read

Figure 3: Comparison of Dyes on Different Machine Types

Fig. 3a: Accuracy with Different Dyes and Machine Types

Fig. 3b: Quality with Different Dyes and Machine Types

Figure 4: Enzyme and Dye Comparison

Fig. 4a: Accuracy of Different Dyes and Enzymes

Fig. 4b: Examples of Accuracy with Different Dye Chemistries

Figure 5: Effects of Dilution and Reaction Volume

Fig. 5a: Accuracy with Different Dilution and Reaction Volumes

Fig. 5b: Quality with Different Dilution and Reaction Volumes

Figure 6: Effects of Editing on Sequencing Accuracy

Fig. 6a: Effects of Editing with Different Enzymes and Dyes

Fig. 6b: Overall Effects of Editing on Sequencing Accuracy

Figure 7: Effects of Sample Cleanup on Sequencing Accuracy

Fig. 7a: Effects of Different Cleanup Protocols on Accuracy of all Samples

Fig. 7b: Effects of Cleanup Protocol with a Single Dye and Machine Type

Figure 8: Ranking of Sequences

Fig. 8a: Ranking of All Sequences

Fig. 8b: Sequences from the Top Ten Laboratories

Fig. 8c: Top Three Sequences per Machine Type

Figure 9: The General Survey

Fig. 9a: The General Survey: Details of Services, Finances, Personnel, Instrumentation, Templates, Chemistries, Analysis, and Data Processing

Fig. 9b: The Average Lab Portrait

Conclusions

References

Acknowledgements

DNA Sequencing Committe Picture

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ABRF DNA Sequence Research Committee

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Last modified: October 27, 1998