title: Estimating the similarity of alternative Affymetrix probe sets using transcriptional networks.
abstract: BACKGROUND
  The usefulness of the data from Affymetrix microarray analysis depends largely on the reliability of the files describing the correspondence between probe sets, genes and transcripts. Particularly, when a gene is targeted by several probe sets, these files should give information about the similarity of each alternative probe set pair. Transcriptional networks integrate the multiple correlations that exist between all probe sets and supply much more information than a simple correlation coefficient calculated for two series of signals. In this study, we used the PSAWN (Probe Set Assignment With Networks) programme we developed to investigate whether similarity of alternative probe sets resulted in some specific properties.
  
  
  RESULTS
  PSAWNpy delivered a full textual description of each probe set and information on the number and properties of secondary targets. PSAWNml calculated the similarity of each alternative probe set pair and allowed finding relationships between similarity and localisation of probes in common transcripts or exons. Similar alternative probe sets had very low negative correlation, high positive correlation and similar neighbourhood overlap. Using these properties, we devised a test that allowed grouping similar probe sets in a given network. By considering several networks, additional information concerning the similarity reproducibility was obtained, which allowed defining the actual similarity of alternative probe set pairs. In particular, we calculated the common localisation of probes in exons and in known transcripts and we showed that similarity was correctly correlated with them. The information collected on all pairs of alternative probe sets in the most popular 3' IVT Affymetrix chips is available in tabular form at http://bns.crbm.cnrs.fr/download.html.
  
  
  CONCLUSIONS
  These processed data can be used to obtain a finer interpretation when comparing microarray data between biological conditions. They are particularly well adapted for searching 3' alternative poly-adenylation events and can be also useful for studying the structure of transcriptional networks. The PSAWNpy, (in Python) and PSAWNml (in Matlab) programmes are freely available and can be downloaded at http://code.google.com/p/arraymatic. Tutorials and reference manuals are available at BMC Research Notes online (Additional file 1) or from http://bns.crbm.cnrs.fr/softwares.html.
authors.affiliation: CNRS, CRBM, UMR-5237, 1919 Route de Mende, Montpellier 34293, France. michel.bellis@crbm.cnrs.fr
authors.forename: Michel
authors.initials: M
authors.lastname: Bellis
mesh_terms: D000465:Algorithms; D000818:Animals; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D005091:Exons; D020869:Gene Expression Profiling / Q000379:methods; D053263:Gene Regulatory Networks; D006801:Humans; D051379:Mice; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D015345:Oligonucleotide Probes / Q000235:genetics; D011336:Probability; D011381:Programming Languages; D051381:Rats; D015203:Reproducibility of Results
chemical_list: D015345:Oligonucleotide Probes