@article{Dannemann09,
  abstract =	 {{MOTIVATION}: When comparing gene expression levels
                  between species or strains using microarrays,
                  sequence differences between the groups can cause
                  false identification of expression differences. Our
                  simulated dataset shows that a sequence divergence
                  of only 1\% between species can lead to falsely
                  reported expression differences for >50\% of the
                  transcripts-similar levels of effect have been
                  reported previously in comparisons of human and
                  chimpanzee expression. We propose a method for
                  identifying probes that cause such false readings,
                  using only the microarray data, so that problematic
                  probes can be excluded from analysis. We then test
                  the power of the method to detect sequence
                  differences and to correct for falsely reported
                  expression differences. Our method can detect 70\%
                  of the probes with sequence differences using human
                  and chimpanzee data, while removing only 18\% of
                  probes with no sequence differences. Although only
                  70\% of the probes with sequence differences are
                  detected, the effect of removing probes on falsely
                  reported expression differences is more dramatic:
                  the method can remove 98\% of the falsely reported
                  expression differences from a simulated dataset. We
                  argue that the method should be used even when
                  sequence data are available. {CONTACT}:
                  lachmann@eva.mpg.de {SUPPLEMENTARY} {INFORMATION}:
                  Supplementary data are available at Bioinformatics
                  online.},
  author =	 {Dannemann, Michael and Lorenc, Anna and Hellmann,
                  Ines and Khaitovich, Philipp and Lachmann, Michael},
  citeulike-article-id ={6008553},
  citeulike-linkout-0
                  ={http://dx.doi.org/10.1093/bioinformatics/btp492},
  citeulike-linkout-1
                  ={http://bioinformatics.oxfordjournals.org/content/25/21/2772.abstract},
  citeulike-linkout-2
                  ={http://bioinformatics.oxfordjournals.org/content/25/21/2772.full.pdf},
  citeulike-linkout-3 ={http://view.ncbi.nlm.nih.gov/pubmed/19689957},
  citeulike-linkout-4
                  ={http://www.hubmed.org/display.cgi?uids=19689957},
  day =		 {1},
  doi =		 {10.1093/bioinformatics/btp492},
  issn =	 {1367-4811},
  journal =	 {Bioinformatics (Oxford, England)},
  keywords =	 {affy, cross-species},
  month =	 nov,
  number =	 {21},
  pages =	 {2772--2779},
  pmid =	 {19689957},
  posted-at =	 {2009-10-26 08:12:01},
  priority =	 {2},
  title =	 {The effects of probe binding affinity differences on
                  gene expression measurements and how to deal with
                  them.},
  url =		 {http://dx.doi.org/10.1093/bioinformatics/btp492},
  volume =	 {25},
  year =	 {2009}
}

@article{Khaitovich04,
  abstract =	 {We have analyzed gene expression in various brain
                  regions of humans and chimpanzees. Within both human
                  and chimpanzee individuals, the transcriptomes of
                  the cerebral cortex are very similar to each other
                  and differ more between individuals than among
                  regions within an individual. In contrast, the
                  transcriptomes of the cerebral cortex, the caudate
                  nucleus, and the cerebellum differ substantially
                  from each other. Between humans and chimpanzees,
                  10\% of genes differ in their expression in at least
                  one region of the brain. The majority of these
                  expression differences are shared among all brain
                  regions. Whereas genes encoding proteins involved in
                  signal transduction and cell differentiation differ
                  significantly between brain regions within
                  individuals, no such pattern is seen between the
                  species. However, a subset of genes that show
                  expression differences between humans and
                  chimpanzees are distributed nonrandomly across the
                  genome. Furthermore, genes that show an elevated
                  expression level in humans are statistically
                  significantly enriched in regions that are recently
                  duplicated in humans.},
  author =	 {Khaitovich, Philipp and Muetzel, Bjoern and She,
                  Xinwei and Lachmann, Michael and Hellmann, Ines and
                  Dietzsch, Janko and Steigele, Stephan and Do,
                  Hong-Hai H. and Weiss, Gunter and Enard, Wolfgang
                  and Heissig, Florian and Arendt, Thomas and
                  Nieselt-Struwe, Kay and Eichler, Evan E. and
                  P\"{a}\"{a}bo, Svante},
  citeulike-article-id ={1923581},
  citeulike-linkout-0 ={http://dx.doi.org/10.1101/gr.2538704},
  citeulike-linkout-1
                  ={http://www.genome.org/cgi/content/abstract/14/8/1462},
  citeulike-linkout-2 ={http://view.ncbi.nlm.nih.gov/pubmed/15289471},
  citeulike-linkout-3
                  ={http://www.hubmed.org/display.cgi?uids=15289471},
  day =		 {1},
  doi =		 {10.1101/gr.2538704},
  issn =	 {1088-9051},
  journal =	 {Genome Research},
  keywords =	 {misc},
  month =	 aug,
  number =	 {8},
  pages =	 {1462--1473},
  pmid =	 {15289471},
  posted-at =	 {2007-11-15 18:40:51},
  priority =	 {2},
  title =	 {Regional patterns of gene expression in human and
                  chimpanzee brains.},
  url =		 {http://dx.doi.org/10.1101/gr.2538704},
  volume =	 {14},
  year =	 {2004}
}

@article{Dannemann12,
  abstract =	 {{BACKGROUND}:Hybridization differences caused by
                  target sequence differences can be a confounding
                  factor in analyzing gene expression on microarrays,
                  lead to false positives and reduce power to detect
                  real expression differences. We prepared an R
                  Bioconductor compatible package to detect,
                  characterize and remove such probes in Affymetrix
                  {3'IVT} and exon-based arrays on the basis of
                  correlation of signal intensities from probes within
                  probe {sets.RESULTS}:Using completely sequenced
                  mouse genomes we determined type 1 (false negatives)
                  and type 2 (false positives) errors with high
                  accuracy and we show that our method routinely
                  outperforms previous methods. When detecting 76.2\%
                  of known {SNP}/indels in mouse expression data, we
                  obtain at most 5.5\% false positives. At the same
                  level of false positives, {SNEP} detected 72.6\%
                  known {SNP}/indels. We also show that probes with
                  differing binding affinity both hinder differential
                  expression detection and introduce artifacts in
                  cancer-healthy tissue
                  {comparison.CONCLUSIONS}:Detection and removal of
                  binding affinity different probes should be a
                  routine step in Affymetrix data preprocessing. We
                  prepared a user friendly R package, compatible with
                  Bioconductor, that allows the filtering and
                  improving of data from Affymetrix microarrays
                  experiments.},
  author =	 {Dannemann, Michael and Lachmann, Michael and Lorenc,
                  Anna},
  citeulike-article-id ={10573309},
  citeulike-linkout-0 ={http://dx.doi.org/10.1186/1471-2105-13-56},
  day =		 {16},
  doi =		 {10.1186/1471-2105-13-56},
  issn =	 {1471-2105},
  journal =	 {BMC Bioinformatics},
  keywords =	 {geneexpression},
  month =	 apr,
  number =	 {1},
  pages =	 {56+},
  posted-at =	 {2012-04-17 10:07:15},
  priority =	 {2},
  title =	 {'{maskBAD}' - a package to detect and remove
                  Affymetrix probes with binding affinity differences},
  url =		 {http://dx.doi.org/10.1186/1471-2105-13-56},
  volume =	 {13},
  year =	 {2012}
}
