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The LO-BaFL method and ALS microarray expression analysis.

Baciu C, Thompson KJ, Mougeot JL, Brooks BR, Weller JW - BMC Bioinformatics (2012)

Bottom Line: Filtering healthy control data from the sALS and CAD studies with LO-BaFL resulted in highly correlated expression levels across many genes.Modifying the BaFL pipeline allowed us to remove noise and systematic errors, improving the power of this study, which had a small sample size.Each bioinformatics approach revealed DE genes not predicted by the other; subsequent PCR assays confirmed seven of twelve candidates, a relatively high success rate.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC 28223, USA.

ABSTRACT

Background: Sporadic Amyotrophic Lateral Sclerosis (sALS) is a devastating, complex disease of unknown etiology. We studied this disease with microarray technology to capture as much biological complexity as possible. The Affymetrix-focused BaFL pipeline takes into account problems with probes that arise from physical and biological properties, so we adapted it to handle the long-oligonucleotide probes on our arrays (hence LO-BaFL). The revised method was tested against a validated array experiment and then used in a meta-analysis of peripheral white blood cells from healthy control samples in two experiments. We predicted differentially expressed (DE) genes in our sALS data, combining the results obtained using the TM4 suite of tools with those from the LO-BaFL method. Those predictions were tested using qRT-PCR assays.

Results: LO-BaFL filtering and DE testing accurately predicted previously validated DE genes in a published experiment on coronary artery disease (CAD). Filtering healthy control data from the sALS and CAD studies with LO-BaFL resulted in highly correlated expression levels across many genes. After bioinformatics analysis, twelve genes from the sALS DE gene list were selected for independent testing using qRT-PCR assays. High-quality RNA from six healthy Control and six sALS samples yielded the predicted differential expression for 7 genes: TARDBP, SKIV2L2, C12orf35, DYNLT1, ACTG1, B2M, and ILKAP. Four of the seven have been previously described in sALS studies, while ACTG1, B2M and ILKAP appear in the context of this disease for the first time. Supplementary material can be accessed at: http://webpages.uncc.edu/~cbaciu/LO-BaFL/supplementary_data.html.

Conclusion: LO-BaFL predicts DE results that are broadly similar to those of other methods. The small healthy control cohort in the sALS study is a reasonable foundation for predicting DE genes. Modifying the BaFL pipeline allowed us to remove noise and systematic errors, improving the power of this study, which had a small sample size. Each bioinformatics approach revealed DE genes not predicted by the other; subsequent PCR assays confirmed seven of twelve candidates, a relatively high success rate.

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Related in: MedlinePlus

The LO-BaFL method flowchart: the steps are given on the left; comments to the right indicate where intermediate datasets were stored in the project database. Note that for each step the output has been made available as flat files.
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Figure 1: The LO-BaFL method flowchart: the steps are given on the left; comments to the right indicate where intermediate datasets were stored in the project database. Note that for each step the output has been made available as flat files.

Mentions: A summary of the entire workflow is shown in Figure1, and the summary of the pipeline effects (shown as percentage of total filtered out per step) is shown in Table1.


The LO-BaFL method and ALS microarray expression analysis.

Baciu C, Thompson KJ, Mougeot JL, Brooks BR, Weller JW - BMC Bioinformatics (2012)

The LO-BaFL method flowchart: the steps are given on the left; comments to the right indicate where intermediate datasets were stored in the project database. Note that for each step the output has been made available as flat files.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3526454&req=5

Figure 1: The LO-BaFL method flowchart: the steps are given on the left; comments to the right indicate where intermediate datasets were stored in the project database. Note that for each step the output has been made available as flat files.
Mentions: A summary of the entire workflow is shown in Figure1, and the summary of the pipeline effects (shown as percentage of total filtered out per step) is shown in Table1.

Bottom Line: Filtering healthy control data from the sALS and CAD studies with LO-BaFL resulted in highly correlated expression levels across many genes.Modifying the BaFL pipeline allowed us to remove noise and systematic errors, improving the power of this study, which had a small sample size.Each bioinformatics approach revealed DE genes not predicted by the other; subsequent PCR assays confirmed seven of twelve candidates, a relatively high success rate.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, NC 28223, USA.

ABSTRACT

Background: Sporadic Amyotrophic Lateral Sclerosis (sALS) is a devastating, complex disease of unknown etiology. We studied this disease with microarray technology to capture as much biological complexity as possible. The Affymetrix-focused BaFL pipeline takes into account problems with probes that arise from physical and biological properties, so we adapted it to handle the long-oligonucleotide probes on our arrays (hence LO-BaFL). The revised method was tested against a validated array experiment and then used in a meta-analysis of peripheral white blood cells from healthy control samples in two experiments. We predicted differentially expressed (DE) genes in our sALS data, combining the results obtained using the TM4 suite of tools with those from the LO-BaFL method. Those predictions were tested using qRT-PCR assays.

Results: LO-BaFL filtering and DE testing accurately predicted previously validated DE genes in a published experiment on coronary artery disease (CAD). Filtering healthy control data from the sALS and CAD studies with LO-BaFL resulted in highly correlated expression levels across many genes. After bioinformatics analysis, twelve genes from the sALS DE gene list were selected for independent testing using qRT-PCR assays. High-quality RNA from six healthy Control and six sALS samples yielded the predicted differential expression for 7 genes: TARDBP, SKIV2L2, C12orf35, DYNLT1, ACTG1, B2M, and ILKAP. Four of the seven have been previously described in sALS studies, while ACTG1, B2M and ILKAP appear in the context of this disease for the first time. Supplementary material can be accessed at: http://webpages.uncc.edu/~cbaciu/LO-BaFL/supplementary_data.html.

Conclusion: LO-BaFL predicts DE results that are broadly similar to those of other methods. The small healthy control cohort in the sALS study is a reasonable foundation for predicting DE genes. Modifying the BaFL pipeline allowed us to remove noise and systematic errors, improving the power of this study, which had a small sample size. Each bioinformatics approach revealed DE genes not predicted by the other; subsequent PCR assays confirmed seven of twelve candidates, a relatively high success rate.

Show MeSH
Related in: MedlinePlus