Salvaging Affymetrix probes after probe-level re-annotation.
Salvaging Affymetrix probes after probe-level re-annotation.
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DOI：
10.1186/1756-0500-1-66
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发表时间：
2008-08-19
影响因子：
1.8
通讯作者：
Breit, Timo M
中科院分区：
文献类型：
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作者：
de Leeuw, Wim C;Rauwerda, Han;Jonker, Martijs J;Breit, Timo M
Affymetrix基因芯片可以通过分解原有的探针集，并根据最新的基因组知识（如Entrez Gene）重新组合新的探针集，在探针水平上重新注释。这导致自定义芯片描述文件（CDF）。使用这些定制的CDFs可以提高数据的质量，从而提高相关基因表达研究的结果。然而，基因芯片上44-71%的探针在这个重新注释过程中丢失。虽然这些探针通常是针对不太为人所知的基因，但失去这些探针显然意味着大量损失昂贵的实验数据。因此，生物学家非常不愿意采用这种方法。我们的目标是在这些重新注释过程之后重新引入未受影响的Affymetrix探测集。为此，我们开发了一种算法（CDF-Merger），并将其应用于标准Affymetrix cdf和自定义Brainarray cdf，以获得Hybrid cdf。因此，使用我们的CDF-Merger回收丢失的Affymetrix探针可使探针含量恢复高达94%。由于回收的探针（最多占阵列上探针内容的54%）表示不太可靠的探针集，因此我们使所有探针集定义的起源可追溯，因此生物学家可以在分析中随时选择他们想要使用的探针集子集。最新的混合CDFs加上R环境的可用性使我们的方法易于实现。
Affymetrix GeneChips can be re-annotated at the probe-level by breaking up the original probe-sets and recomposing new probe-sets based on up-to-date genomic knowledge, such as available in Entrez Gene. This results in custom Chip Description Files (CDF). Using these custom CDFs improves the quality of the data and thus the results of related gene expression studies. However, 44–71% of the probes on a GeneChip are lost in this re-annotation process. Although generally aimed at less known genes, losing these probes obviously means a substantial loss of expensive experiment data. Biologists are therefore very reluctant to adopt this approach. We aimed to re-introduce the non-affected Affymetrix probe-sets after these re-annotation procedures. For this, we developed an algorithm (CDF-Merger) and applied it to standard Affymetrix CDFs and custom Brainarray CDFs to obtain Hybrid CDFs. Thus, salvaging lost Affymetrix probes with our CDF-Merger restored probe content up to 94%. Because the salvaged probes (up to 54% of the probe content on the arrays) represent less-reliable probe-sets, we made the origin of all probe-set definitions traceable, so biologists can choose at any time in their analyses, which subset of probe-sets they want to use. The availability of up-to-date Hybrid CDFs plus R environment allows for easy implementation of our approach.