LabAutomation 2006 - SLAS

Where Laboratory Technologies Emerge and Merge
3:30 pm Monday, January 23, 2006 Track 4: Informatics Room: Madera
Wyndham Palm Springs Hotel
Victor Jongeneel
Co-Author(s)
Ludwig Institute for Cancer Research
Marco Pagni, Gregory Theiler
Lausanne, Switzerland
Victor.Jongeneel@licr.org
Swiss Institute of Bioinformatics
Stefan Emler, SmartGene GmbH
An Approach to Automated Bacterial Strain Identification From 16S Ribosomal
DNA Sequences
DNA sequencing is increasingly used in clinical diagnostics in many fields. However, quality management and reliability of results is
often operator-dependent, especially with regard to sequence comparisons to reference data. The sequencing of all or part of the gene
encoding the 16S ribosomal RNA is now a commonly performed test to aid in the identification of bacteria present in clinical, veterinary or
environmental samples. However, relatively little attention has been paid so far to the techniques used to validate the alignement of these
sequences and to deduce biologically meaningful information from them. In particular, sequence variations that are informative regarding
genus or strain are not being distinguished from experimental noise. We have developed a methodology that attempts to integrate all of
the information present in available 16S sequences from reference strains and well-characterized laboratory samples, which can then
be compared to newly acquired sequences, even if they are of lower quality. Specifically, the following information is collected: (i) genusspecific,
reference multiple sequence alignments (MSA); (ii) secondary structure extracted from the European ribosomal RNA database;
(iii) expert knowledge on the regions in the MSA that distinguish one genus from all others; (iv) the discriminant power of each column in
the MSA for identifying the targeted genus. An annotated profile is then generated to encapsulate this information, with an emphasis on
discriminant positions (signatures) in the 16S sequence. Alignment of new sequences obtained in a laboratory setting to profiles obtained
from multiple bacterial reference genera can then be used to predict the species identity of the bacterial isolate with higher accuracy than
using overall similarity percentages or BLAST alignment scores. This technology forms the basis for an accurate, robust, scalable, and fully
automatable system for bacterial strain identification.
4:00 pm Monday, January 23, 2006 Track 4: Informatics Room: Madera
Wyndham Palm Springs Hotel
Bill Harten
Co-Author(s)
UNIconnect LC
Lynn Rasmussen
Woods Cross, Utah
bill.harten@uniconnect.com
Southern Research Institute
Beyond Samples: Track And Control Everything That Affects Quality
A great laboratory tracking system must do more than log samples and outcomes in a database. Product quality depends on the quality of
every element affecting the process. Catching and preventing problems requires tracking intermediate containers, thorough validation, and
capturing required information at each step along the way. People, reagents, and instruments require their own processes, traceability, and
controls to ensure the highest quality and confidence. Authorization and training, instrument calibration and maintenance, and reagent QC
and inventory are a few of the sub-processes that benefit from effective tracking and integrated control.
At our High-Throughput Screening Center at Southern Research Institute, our new UNIFlow system by UNIConnect tracks intermediate
steps and containers plus the sub-processes of the individual factors that determine quality. Carefully defined processes, barcodes, and
capturing the right information at the right time make it practical and efficient. This presentation explains the quality factors we are tracking,
how control is applied, and the way the tools made development fast, simple, and reliable.
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