Abstract
BackgroundHigh-density oligonucleotide arrays have become a valuable tool for high-throughput gene expression profiling. Increasing the array information density and improving the analysis algorithms are two important computational research topics.ResultsA new algorithm, Match-Only Integral Distribution (MOID), was developed to analyze high-density oligonucleotide arrays. Using known data from both spiking experiments and no-change experiments performed with Affymetrix GeneChip® arrays, MOID and the Affymetrix algorithm implemented in Microarray Suite 4.0 (MAS4) were compared. While MOID gave similar performance to MAS4 in the spiking experiments, better performance was observed in the no-change experiments.MOID also provides a set of alternative statistical analysis tools to MAS4. There are two main features that distinguish MOID from MAS4. First, MOID uses continuous P values for the likelihood of gene presence, while MAS4 resorts to discrete absolute calls. Secondly, MOID uses heuristic confidence intervals for both gene expression levels and fold change values, while MAS4 categorizes the significance of gene expression level changes into discrete fold change calls.ConclusionsThe results show that by using MOID, Affymetrix GeneChip® arrays may need as little as ten probes per gene without compromising analysis accuracy.
Highlights
High-density oligonucleotide arrays have become a valuable tool for highthroughput gene expression profiling
The results show that by using Match-Only Integral Distribution (MOID), Affymetrix GeneChip® arrays may need as little as ten probes per gene without compromising analysis accuracy
Reduced Probe Set Simulation In the studies above, we demonstrated the feasibility of a match-only gene chip design based on the MOID algorithm
Summary
High-density oligonucleotide arrays have become a valuable tool for highthroughput gene expression profiling. Increasing the array information density and improving the analysis algorithms are two important computational research topics. Predictions from the Human Genome Project [2] and Celera Genomics [3] suggest there are about 26,000–40,000 human genes. Other recent studies suggest that these numbers may be an underestimation and that the human genome appears more complicated [4]. Understanding the functions of such a large number of genes has been an unprecedented challenge for functional genomics research. Facing the challenge of annotating such a huge amount of genomic data, increasing array information density and improving analysis algorithms have become (page number not for citation purposes)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
