Biology Reference
In-Depth Information
Lee 2000). Unfortunately, errors in gene chips have been found that also could
lead unwary biologists to erroneous conclusions (
Knight 2001
). Despite the fact
that microarray production is heavily automated, errors may creep in because
bacterial cultures used to amplify the plasmids with the cDNAs can become con-
taminated. Technicians can make errors such as loading plates into the robots
the wrong way around or taking samples from the wrong well for sequencing.
Estimates suggest between 1 and 5% of the clones in even the best-maintained
microarray sets do not contain the sequence they are supposed to contain. Even
microarrays based on oligos can contain errors if the sequences in the data-
bases are wrong or the wrong strand from the DNA helix is used (the noncod-
ing strand) (
Knight 2001
). Other errors can occur when inadequate experimental
controls are used or replications are not conducted. Erroneous results will con-
tinue to be published until the faulty chips and experimental design methods
are corrected. At the least, the sequences of the spot concerned should be veri-
fied by sequencing and by comparing the result using alternative methods of
monitoring gene expression (
Knight 2001
).
Quality-control methods are being developed for experimental design and
analysis of data (
Taylor et al. 2008, Kauffmann and Huber 2010
). Careful sample
collection, data collection, and experimental design are essential to a successful
experiment with microarrays. Experiments on global gene expression may yield
data for thousands of genes, forcing the experimenter to consider processes,
functions, and mechanisms about which we know very little. More sophisticated
systems are needed to represent the data and incorporate sequence, genetics,
gene expression, homology, regulation, function, and phenotype information
in an organized and usable form (
Lockhart and Winzeler 2000
). At present, dif-
ferent researchers may use different arrays and methods of analyzing the data,
which makes it difficult to compare the results from different laboratories or
from different microarray platforms (
Ionnidis et al. 2009
). Efforts to resolve the
problems include projects such as Empowering the Microarray-based European
Research Area to Take a Lead in Development and Exploitation (EMERALD), a
project that has developed workshops, tutorials, and symposia, as well as qual-
ity-control tools (
Beisvag et al. 2011
). One result has been Minimum Information
About a Microarray Experiment (MIAME) in which scientists are asked to provide
details about the experiment in a uniform format (
Brazma 2009
). In addition,
scientists are expected to deposit their data into public repositories so that oth-
ers can attempt to replicate the experiments (
Edgar et al. 2002, Brazma et al.
2003
). Many publically available software programs were produced to improve
quality control (
Beisvag et al. 2011
). The MAQC consortium also has developed
quality control data (MAQC 2006, 2010).
