O-miner: an integrative platform for automated
analysis and mining of -omics data
Rosalind J. Cutts, Abu Z. Dayem Ullah, Ajanthah Sangaralingam, Emanuela Gadaleta,
Nicholas R. Lemoine and Claude Chelala*
Centre for Molecular Oncology, Barts Cancer Institute, Queen Mary University of London, Charterhouse Square,
London EC1M 6BQ, UK
Received January 30, 2012; Revised April 19, 2012; Accepted April 24, 2012
High-throughput profiling has generated massive
amounts of data across basic, clinical and transla-
tional research fields. However, open source com-
prehensive web tools for analysing data obtained
from different platforms and technologies are still
lacking. To fill this gap and the unmet computational
needs of ongoing research projects, we developed
O-miner, a rapid, comprehensive, efficient web tool
that covers all the steps required for the analysis of
both transcriptomic and genomic data starting from
raw image files through in-depth bioinformatics
analysis and annotation to biological knowledge ex-
traction. O-miner was developed from a biologist
end-user perspective. Hence, it is as simple to use
as possible within the confines of the complexity of
the data being analysed. It provides a strong
analytical suite able to overlay and harness large,
complicated, raw and
profiles with biological/clinical data. Biologists can
use O-miner to analyse and integrate different types
of data and annotations to build knowledge of
relevant altered mechanisms and pathways in
order to identify and prioritize novel targets for
further biological validation. Here we describe the
O-miner and present examples of use. O-miner is
freely available at www.o-miner.org.
High-throughput profiling platforms have produced a
large amount of data with public repositories such as the
Genome Expression Omnibus (1) and ArrayExpress (2,3)
already storing tens of thousands of profiles across differ-
ent experimental conditions. There is a steady growth in
the amount and diversity of profiling results causing chal-
lenges in data analysis and integration as well as a strong
need for novel comprehensive online bioinformatics tools
which are easy to use by biologists and able to process raw
profiles in a single- or global-analysis manner.
Although many methods are now available for low- and
high-level analysis of genomic and transcriptomic experi-
ments (4–7), most require programming knowledge as well
as bioinformatics expertise and results can vary substan-
tially amongst these. Analysis of large data sets may
involve the need for powerful computational resources
as well as time and effort to set up the necessary infra-
structure. For example, the use of aroma.affymetrix (4,8)
for analysis of copy number data involves the creation of
annotation files via a specific directory with a strict direc-
tory structure to organize raw and processed data.
Additionally, there is no analytical tool that can handle
raw and/or partially processed genomics data and
annotate/display results online in a user-friendly manner
that would alleviate the need for bioinformatics expertise
and allow researchers to process their in-house data in
isolation or alongside the accumulated publicly available
data in their area of research.
To overcome these problems, we have developed
O-miner (http://www.o-miner.org), which can analyse the
most popular, and widely used Affymetrix genomics and
transcriptomics array types on the fly starting from raw
obtained from the scanner) or partially processed format
(normalized, segmented and/or binary) with minimal
set-up efforts. The analysis is performed on a dedicated
server removing memory or disk space requirements on
end-user machines. All analytical pipelines are transparent,
robust, welldocumented andbased onwell-established and
recently developed statistical methods. Results can be
viewed online as dynamic HTML reports for easy
navigation through an interactive friendly interface or
downloaded as text, excel or graphics files.
O-miner is comprehensive, robust, memory-efficient
and can easily be extended with new methods and algo-
rithms to cover additional chip types and platforms. In
this article, we provide an overview of O-miner and
discuss both transcriptomics and genomics workflows.
format(CEL image files
*To whom correspondence should be addressed. Tel: +44 207 882 3570; Fax: +44 207 882 3884; Email: firstname.lastname@example.org
Nucleic Acids Research, 2012, Vol. 40, Web Server issuePublished online 17 May 2012
? The Author(s) 2012. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/
by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
We outline some examples of use to show how to perform
low single-level as well as high global-level analysis and to
illustrate how to navigate through obtained results.
Finally, we discuss future updates of the software to
accommodate and link additional data types.
OVERVIEW OF O-MINER
O-miner provides a framework for automated analysis of
different types of -omics data and currently covers the
transcriptomic data. The user must first upload the data
files to be analysed as a zip archive to the O-miner server
using the graphical interface or enter a valid GEO series
number (GSE format). This alleviates the time-consuming
and repetitive task of uploading one data file at a time.
Once data transfer is completed, the ‘File Organizer’
window displays the individual files and can be used for
the assignment of sample names and biological groups
before specifying the analysis options. A unique project
is created for each submitted analysis.
of both genomicand
A general copy number analysis pipeline starts from probe
level raw intensity .CEL data files obtained immediately
normalization and summarization to derive raw copy
number data (normalized log2 ratio sample/reference
format) followed by segmentation and smoothing (seg-
mented format) before thresholding and calling regions
of copy number gain and loss (binary format). To the
best of our knowledge, O-miner is the only freely available
web tool that can accept data submission at any stage of
this pipeline either as .CEL files or partially processed
(normalized, segmented or binary) data files.
Raw CEL intensity files
O-miner enables the two common scenarios of copy
number analysis. The first is a paired analysis option
where each sample is coupled with a specific unique refer-
ence (e.g. a cancer sample with its corresponding matched
normal sample). The second is an unpaired analysis where
each sample uses the same common reference, which is
often the average of a pool of samples. Both options are
possible on a wide variety of Affymetrix platforms
including the widely used GeneChip?Human Mapping
Arrays 10K, 100K Set (50K_Hind240 and 50K_Xba240)
and 500K Set (250K_Nsp and 250K_Sty) as well as
Genome-Wide Human SNP Arrays 5.0 and 6.0. We
have processed and made available precompiled raw
HapMap data (CEL files) from four human populations:
African YRI (from Yoruba in Ibadan, Nigeria), Japanese
JPT (from Tokyo, Japan), Han Chinese CHB (from
Beijing, China) and European CEU (from Utah, USA
with ancestry from northern and western Europe) to use
as a baseline in an unpaired analysis scenario. After ex-
tracting the zip archive, O-miner displays the available
.CEL files list in the ‘File Organizer’ for the user to
create Sample/Reference attributes and define Samples
and References lists. The first file in the Sample Files list
is compared to the first file in the Reference Files list, the
second files in both lists are compared to each other and so
on. In the same manner, files from different enzyme sets
for Human Mapping 100K/500K array sets can be paired
to match and merge array sets originating with the same
sample. The Sample/Reference attributes are not required
for unpaired analysis. Data are categorized by entering a
biological group attribute to define the biological source/
state at the origin of each array (primary, metastasis,
resistant, etc.). O-miner combines the results observed in
the same biological source/state and performs group
O-miner reads CEL intensity files and automatically
builds up the required directory structure and annotation
files to run the methods implemented in the aroma.
affymetrix framework (4,8). Briefly, O-miner performs
initial quality control checks, background correction,
allelic cross-talk calibration, nucleotide-position probe
sequence effects normalization, probe-level summarization
using robust average (for SNP 5.0 and 6.0 arrays) or
log-additive model (for 10, 100 and 500K arrays), PCR
fragment-length effects normalization and calculates raw
copy number estimates (log2 ratios) relative to the chosen
reference. These normalized estimates are used as input for
segmentation methods to identify copy number regions and
further subsequent analysis as explained below.
Partially processed (normalized, segmented or binary)
Normalized data text files as obtained from the raw CEL
analysis described above or from other normalization
methods and algorithms can be used. If uploaded as a
new submission, the ‘File Organizer’ extracts the sample
names from the column headings of the uploaded file and
offers the option to enter a biological group attribute to
define the biological source/state at the origin of each
sample for further subgroup analysis. At this level,
O-miner is ready to apply a segmentation analysis by
offering 10 popular algorithms as implemented in the R
package CGHweb (5). Briefly, these are BioHMM, CBS,
FASeg, cghFLasso, CGHseg, GLAD, LOWESS, Wavelet
smoothing, Quantile Smoothing and Running Average
(9–17). The user selects the method(s) to be used to
derive a consensus profile from multiple probes/samples.
Added to the benefit of assessing segmented profiles from
different algorithms, this also offers the user the possibility
of checking whether a copy number alteration arose as an
artefact of the specified segmentation method. The results
are then ready to be processed to determine the regions of
gains/losses according to user-defined cut-offs based on
the log2 ratio threshold value, consecutive number of
SNPs that form a copy number region (at least 15 SNPs
by default) and frequency of samples where a copy
number event was observed (at least 20% by default).
O-miner offers an option to predict the log2 ratio thresh-
old based on the quantile distribution of segmented raw
copy numbers. Once a threshold is determined the data
could be binary coded (0: no changes, 1: copy number
gain, ?1: copy number loss) for subsequent analysis.
Similarly, users can start their data analysis from this
level by submitting a binary coded data file.
Nucleic AcidsResearch, 2012, Vol.40, WebServer issue W561
Further analysis options
Once regions of gains/losses have been determined,
O-miner can provide physical and cytogenetic mapping in-
formation as well as related gene annotations from UCSC
(18), NCBI RefSeq (19), Ensembl (20) and VEGA (21).
O-miner also investigates regulatory elements, such as
conserved Transcription Factor Binding Sites (22) and
microRNA (23,24). As disease/critical genes are more
likely to be located in copy number regions that are
common/recurrent among samples, O-miner provides the
analysis option of identifying recurrent regions of copy
number alterations within the biological groups being
investigated. These minimum common regions (MCR)
can be calculated by using one of the three robust
methods: CGHregions (25), RJaCGH (26) and MSA (27).
Expression profiling analysis starts from probe level raw
intensity .CEL data files obtained immediately after
scanning, through background correction, normalization
and summarization to derive expression measurements
data (normalized data matrix) followed by filtering to
reduce data dimensionality and differential analysis to
detect de-regulated genes. O-miner accepts data submis-
sion as .CEL files, normalized or filtered data matrix files.
O-miner enables the analysis of paired samples/replicates.
Raw CEL intensity files
A wide variety of Affymetrix platforms including the
widely used GeneChip?Human Genome Arrays U95 Set
(U95A, U95Av2, U95B, U95C, U95D, U95E), U133 Set
(U133A and U133B), U133A 2.0 and U133 Plus 2.0 are
available. After extracting the uploaded archive of array
files, O-miner displays the available .CEL files list in the
‘File Organizer’ window for the user to define the samples
list and biological source/state at the origin of each array. If
performing a paired analysis, the user needs to arrange the
samples in pairs in the related two group lists in the ‘File
Organizer’. If the experiment contains technical replicates,
the user must indicate the replicates in the additional
Organizer’. O-miner combines the results observed in the
same biological source/state and performs differential
analysis between selected groups.
O-miner reads CEL intensity files and runs the quality
control (QC) methods implemented in the R package
ArrayMvout (28) to automatically exclude outliers from
subsequent analysis. An additional manual check could be
followed by normalization using RMA (30), GCRMA
(31) or tRMA (32). These normalized estimates are used
as input for filtering and differential analysis methods to
identify de-regulated expression and run further analyses
as outlined below.
appearin the ‘File
Normalized or filtered data
Normalized data text files as obtained from the raw CEL
analysis described above or from other normalization
methods can be used. If uploaded as a new submission,
the ‘File Organizer’ window displays the sample names as
extracted from the uploaded file and offers the option to
enter a biological group attribute to define the biological
source/state at the origin of each sample for further
subgroup analysis. At this level, O-miner is ready to
apply a filtering step to reduce the dimensionality of the
data by offering three popular methods: interquartile
range (IQR) (soft, intermediate, robust), intensity (25%
or 50% of samples above 100) or standard deviation
(top 10% or 5% most variable probes). Differential
expression analysis is applied to the filtered matrix using
LIMMA (33). O-miner will automatically refresh to
display a ‘LIMMA comparison’ section with the list of
biological groups allowing the user to define the contrast
and design matrices required by LIMMA based on the
user selection of the comparisons between the predefined
biological groups. A number of statistics for differential
expression are provided to refine the de-regulated genes
list according to user-defined cut-offs based on log2 fold
change values (2 by default) and P-values (0.05 by default)
adjusted using Holm (34), Benjamini and Hochberg (BH)
(also known as FDR) (35) or Benjamini and Yekutieli
(BY) (36) multiple testing correction methods.
Further analysis options
GOstats (37) can be used to assess the overrepresentation
of GO terms among the GO annotations for the differen-
tially expressed genes. Additional expression plots can
also be generated from the results page allowing the user
to examine the level/change in expression among the
experimental datasets for a particular gene(s)/probe(s) of
interest in the filtered data. A Venn diagram for up to four
biological groups can be produced to show the common
and specific differentially expressed probes (all, up- or
EXAMPLES OF USE
O-miner provides comprehensive interactive web pages in
a tabbed browsing format that are intended to guide the
user through the key results for their analysis. All data are
also available to download and view locally as text, excel
or image files.
Results are displayed as a tabbed view representing QC,
clustering, MCR (if selected), sample and group informa-
tion. Using the ‘Sample View’ it is possible to browse
through the results obtained for each individual sample
including log2ratio plots and annotated regions of gains
and losses that can also be viewed as a track in the UCSC
The ‘Group View’ summarizes results based on the biolo-
gical groups originally defined by the user including fre-
quency plots and a gene-level view to summarize the gene
content within copy number alterations.
To demonstrate the functionality of O-miner, we
analysed 25 samples from mutated (KIT or PDGFRA)
or wild-type gastrointestinal stromal tumours (GISTs)
profiled using Affymetrix Genome-Wide Human SNP
6.0 platform (GSE20709). We applied an unpaired
analysis using the wild-type patients as baseline. We
W562 Nucleic Acids Research, 2012,Vol.40, Web Server issue
used Picard, Fused Lasso and CBS algorithms for segmen-
tation and applied a minimum physical length of at least
15 consecutive SNPs for putative regions of genetic alter-
ations. The threshold for gains or losses was determined
by O-miner based on the inspection of the quantile distri-
bution of the segmented ratios. O-miner provides straight-
forward access to results for each biological group, and an
easy way to drill down to individual results for a specified
sample. The ‘Sample View’ and ‘Group View’ of obtained
results with related mining options are presented in
Figures 1 and 2, respectively. One can navigate through
putative regions of gains and losses, frequency plots for a
specified sample and automatically view this information
within the UCSC Genome Browser where we could zoom
in to specific regions of interest. This allows the data to be
mined and visualized alongside a large collection of
annotation data tracks. Our results clearly show the hot
spots for copy number loss on chromosomes 1, 14 and 22
as previously reported. The ‘Group View’ can easily be
used to overlay and compare results from the two
mutated sample sets (Figure 3).
To demonstrate further capabilities of O-miner, we
analysed a panel of 12 primary effusion lymphoma (PEL)
cell lines profiled with the Affymetrix GeneChip?Human
Mapping Arrays 500K Set (GSE28684) (38) using an
and thresholding methods were defined as in the previous
PEL-associated genomic amplifications in chromosome
1q, 7, 8 and 12. Furthermore, as the majority of PEL are
co-infected with Epstein–Barr virus (EBV), we segregated
PEL samples into EBV-positive and EBV-negative sub-
groups and investigated the recurrent copy number
alterations in each group using MSA. As shown in
Figure 4A, one could quickly compare and visualize MCR
plotsatthe genome or chromosomelevelforbothbiological
groups. Results are available in HTML, Excel or Bed
formats. Results can also be viewed in the UCSC Genome
Browser (Figure 4B), where we compared a detected MCR
region on chromosome 19p13.3 across the biological sub-
groups and investigated its gene content. In a few seconds,
this visual inspection narrowed down an MCR of genetic
gain specific to the EBV-negative subgroup. By displaying
the RefSeq annotation track, we directly pointed to RFX2,
ACSBG2 and FUT3 genes reported in the original study to
be altered only in the EBV-negative PEL subgroup. We also
identified few other important genes mapping to this MCR
and also relevant to EBV-negative PEL subgroup.
In addition to analysing data from individual studies,
O-miner provides a high-level analysis option. Data from
multiple sources/formats can be merged at different levels
(.CEL files, normalized, segmented or binary) and
submitted to O-miner. This gives the user increased flexibil-
ity for carrying out a global analysis dependent on the data
types available. For example if .CEL files are not available,
it is possible to submit merged partially analysed data
(normalized, segmented or binary coded format). This
also provides a method of submitting larger datasets.
Results are displayed as a tabbed view representing
QC, clustering, differential expression, gene ontology
Figure 1. ‘Sample View’ of the results produced by O-miner for the GIST data (GSE20709). (A) From left to right, each column present the
following information: ‘Samples’ display the identifiers as given by user, ‘Regions’ provides links to display annotated regions of gains and losses in
HTML and Excel formats, ‘UCSC’ displays the results as a track in the UCSC Genome Browser alongside a rich collection of public annotations,
‘Groups’ represent biological groups as defined by user and ‘Plot’ provides links to produce log2ratio plot for each sample with or without filtering
(HTML or image file). For example, clicking on Filtered for sample P17 will produce the log2ratio plot of filtered data in the same page (B).
Nucleic AcidsResearch, 2012, Vol.40, WebServer issueW563
Figure 2. ‘Group View’ of the results produced by O-miner for the GIST data (GSE20709). (A) Users can compare frequency plots between the
different biological groups with or without filtering, for the whole genome or a specific chromosome and obtain a list of the significant genes within
the frequently altered copy number regions. (B) Another tabbed view enables users to browse between the defined biological groups and view
frequency plots for each biological group with or without filtering, at the genome or chromosome level in HTML or pdf formats. For example,
clicking on Filtered for KIT subgroup will produce the log2ratio plot of filtered data for the 14 KIT mutated samples in the same page (C).
Figure 3. ‘Group View’, Compare Frequency Plots option, for the biological groups within the GIST data (GSE20709). O-miner provides a useful
summary of putative regions of copy number gains and losses by providing frequency plots for each defined biological group. The user can easily
compare and contrast results from the two biological groups in this study [14 samples with KIT mutation (A) and 7 samples with PDGFRA
W564 Nucleic Acids Research, 2012,Vol.40, Web Server issue
(if selected) and expression plots. As an example, we
analysed six drug-resistant/parental MIA-PaCa-2 pancre-
atic cell lines profiled using Affymetrix GeneChip?
Human Genome Arrays U133 Plus 2.0 (GSE16648) (39).
After applying QC, normalization using GCRMA and
filtering by standard deviation to select the top 5% of
most variable probes, we performed a differential expres-
sion analysis using LIMMA to compare resistant to
parental cell lines. A typical O-miner tabbed output
includes QC information, differentially expressed genes,
a cluster dendrogram, overrepresented gene ontology
terms and an expression plot generator that could be
used to produce expression plots on the fly to compare
the expression level of a gene(s)/probe(s) of interest
across the array data within the defined biological
groups (Figure 5).
O-miner can also be used to run a rapid global analysis
on transcriptomics data. For example, we analysed .CEL
files from three prostate cell lines (LNCaP, DU145 and
PC3) from three different studies in ArrayExpress/GEO
(E-TABM-948, GSE32474 and E-GEOD-28846). Figure 6
demonstrates additional O-miner output capabilities and
shows a Venn diagram indicating the overlap of differen-
tially expressed probes between the different cell lines and
clustering of expression data across the experimental
CONCLUSIONS AND FUTURE WORK
O-miner is a useful and flexible tool, particularly for biolo-
gists to carry out routine data analysis without the need
for a complex IT infrastructure or in-depth bioinformatics
support. Future plans include the addition of further
analysis pipelines, in particular for methylation, miRNA
and downstream mining of next-generation sequencing
data. In its current version, O-miner allows users to
submit data by giving the GEO series number. For the
moment this is limited to series with samples profiled on
19p13.3 19p13.2 13.11 19p12 11 1119q12q13.213.33
Figure 4. ‘MCR View’ of PEL (EBV-negative and EBV-positive) and non-PEL cell lines (GSE28684). (A) It is possible to identify recurrent regions
of copy number alterations within the biological groups being investigated, compare chromosome plots and explore MCR regions for each biological
group in more detail for the whole genome or a specific chromosome. Results could be exported as Excel or BED files. Results could also be viewed
in the UCSC Genome Browser (B), where one could overlay and compare the detected MCR regions in each biological group, zoom in a specific
MCR region on chromosome 19p13.3 and investigate its gene content using the RefSeq genes track. In a few seconds, a quick visual inspection
narrowed down a smaller region on 19p13.3 where there is an MCR of genetic gain specific to the EBV-negative PEL subgroup. This directly points
to some of the genes reported in the original paper (RFX2, ACSBG2 and FUT3) as well as others important ones mapping to this MCR.
Nucleic AcidsResearch, 2012, Vol.40, WebServer issueW565
Figure 5. Expression analysis of MIA-PaCa-2 resistant/parental samples (GSE16648). O-miner produces a tabbed view of results with quality control
(A), clustering, differential expression, gene ontology and expression plot generator for a particular gene(s)/probe(s) of interest (B).
LNCaP vs DU145 LNCaP vs PC3
DU145 vs PC3
Figure 6. Global-analysis of prostate cell lines from three studies (E-TABM-948, GSE32474 and E-GEOD-28846). (A) Venn diagram showing
overlaps between differentially expressed probes in each comparison. (B) Coloured cluster dendrogram. Each cluster has its colour. The plot
displays the biological groups below in order to quickly compare it with the observed clusters.
W566 Nucleic Acids Research, 2012,Vol.40, Web Server issue
the same platform. We plan to develop this further in
future releases. We are also planning to cover additional
Whole-Transcript arrays and to make O-miner available
as an R package.
Authors thank their colleagues who have tested O-miner.
Breast Cancer Campaign (to R.J.C.); Cancer Research
UK (to A.S and A.Z.D.U). Funding for open access
charge: Cancer Research UK [programme grant reference
Conflict of interest statement. None declared.
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