Getting the most out of your fungal microarray data: two cost- and time-effective methods

Methods Mol Biol. 2011:722:61-77. doi: 10.1007/978-1-61779-040-9_5.

Abstract

Advances in genome sequencing technologies have facilitated production of a wealth of fungal data; within the last 5 years, experimental costs and labor have diminished, shifting the production bottleneck from genomic data generation to data analysis. Genome sequences and microarrays now exist for many fungi, and transcriptional profiling has been shown to be an efficient way to examine how the entire genome changes in response to many different environments or treatments. Multiple platforms, programs, and protocols exist for analyzing such data, making this task daunting for the bench-based scientist. Furthermore, many existing programs are expensive and require license renewals on a yearly basis for each user in the laboratory. Costs may be prohibitively high for bench-based scientists in academia. Our combined experiences with this kind of analysis have favored two programs, depending upon whether the scientist is working with single- or dual-channel hybridization data. Our protocols are aimed toward helping the bench-based PI get the most possible information from their data, without the need for expensive software or an experienced bioinformaticist.

MeSH terms

  • Ascomycota / genetics
  • Ascomycota / metabolism
  • Bayes Theorem
  • Computational Biology / methods
  • Data Interpretation, Statistical*
  • Fungal Proteins / genetics
  • Fungal Proteins / metabolism*
  • Gene Expression Profiling / methods
  • Gene Expression Profiling / statistics & numerical data*
  • Models, Statistical
  • Oligonucleotide Array Sequence Analysis / methods
  • Oligonucleotide Array Sequence Analysis / statistics & numerical data*
  • Software* / economics
  • Software* / trends
  • Time Factors

Substances

  • Fungal Proteins