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Soft ComputingA Fusion of Foundations,Methodologies and Applications ISSN 1432-7643Volume 15Number 8 Soft Comput (2011)15:1505-1512DOI 10.1007/s00500-010-0600-4

Automatic summarisation and annotationof microarray data

Pietro H. Guzzi, Maria Teresa DiMartino, Giuseppe Tradigo, PierangeloVeltri, Pierfrancesco Tassone,Pierosandro Tagliaferri, et al.

1 23

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Automatic summarisation and annotation of microarray data

Pietro H. Guzzi • Maria Teresa Di Martino • Giuseppe Tradigo •

Pierangelo Veltri • Pierfrancesco Tassone • Pierosandro Tagliaferri •

Mario Cannataro

Published online: 26 March 2010

� Springer-Verlag 2010

Abstract The study of biological processes within cells is

based on the measurement of the activity of different mol-

ecules, in particular genes and proteins whose activities are

strictly related. The activity of genes is measured through a

systematic investigation carried out by microarrays. Such

technology enables the investigation of all the genes of an

organism in a single experiment, encoding meaningful

biological information. Nevertheless, the preprocessing of

raw microarray data needs automatic tools that standardise

such phase in order to: (a) avoiding errors in analysis pha-

ses, and (b) making comparable the results of different

laboratories. The preprocessing problem is as much relevant

as considering results obtained from analysis platforms of

different vendors. Nevertheless, there is currently a lack of

tools that allow to manage and preprocess multivendor

dataset. This paper presents a software platform (called

GSAT, General-purpose Summarisation and Annotation

Tool) able to manage and preprocess microarray data. The

GSAT allows the summarisation, normalisation and anno-

tation of multivendor microarray data, using web services

technology. First experiments and results on Affymetrix

data samples are also discussed. GSAT is available online at

http://bioingegneria.unicz.it/m-cs as a standalone applica-

tion or as a plugin of the TMEV microarray data analysis

platform.

Keywords Microarray � Genomics � DNA microarray �Summarisation � Normalisation � Annotation

1 Introduction

Biological processes within cells are carried out by genes

and proteins. Genes are related to proteins through the

central dogma of molecular biology that states that genes

encode the formation of proteins. One of the main func-

tions of genes is to regulate the formation of proteins

through the transcription process which produces RNA.

Studying RNA allows to discover meaningful information

about proteins and genes. Using microarray technology, it

is possible to study genes (and consequently their potential

protein transcriptions) in a single experiment (Quacken-

bush 2001).

For processing and studying DNA microarrays, there

exist many different technologies produced by different

vendors as well as algorithms and tools for managing and

analysing obtained data. The scenario is characterised by

heterogeneity of data, formats, and analysis workflows

(Brazma et al. 2001). Results obtained from different lab-

oratories are consequently not directly comparable. Main

P. H. Guzzi (&) � G. Tradigo � P. Veltri � M. Cannataro

Bioinformatics Laboratory, Department of Experimental

Medicine and Clinic, University Magna Graecia,

88100 Catanzaro, Italy

e-mail: hguzzi@unicz.it

G. Tradigo

e-mail: gtradigo@unicz.it

P. Veltri

e-mail: veltri@unicz.it

M. Cannataro

e-mail: cannataro@unicz.it

M. T. Di Martino � P. Tassone � P. Tagliaferri

Medical Oncology Unit, T. Campanella Cancer Center,

University Magna Graecia, 88100 Catanzaro, Italy

e-mail: teresadm@unicz.it

P. Tassone

e-mail: tassone@unicz.it

P. Tagliaferri

e-mail: tagliaferri@unicz.it

123

Soft Comput (2011) 15:1505–1512

DOI 10.1007/s00500-010-0600-4

Author's personal copy

microarray vendors are Affymetrix1 and Illumina2 (Kuhn

et al. 2004).

Let us consider the whole process of analysis through

microarray platforms. The RNA of a sample is extracted

and then is inserted onto a chip that contains a set of probes

to bind the RNA. Probes are constituted of oligonucleotides

or complementary DNA (c-DNA). A light source is used to

bleach the fluorescent markers and the resulting image is

recorded. After the image registration, a preprocessing

phase is needed to remove the noise, recognise the position

of different probes and to identify corresponding genes.

Main differences among vendors platform are: (a) the

fabrication steps, i.e. how probes are attached to the sub-

strate, (b) the number of different probes used for each

gene, e.g. a constant or a variable number for each gene,

and (c) how the probes are designed, e.g. the number and

the choice of nucleotides (Michael et al. 2005).

A typical workflow for analysing microarray data is

structured on three main phases (Hibbs et al. 2005;

Rubinstein et al. 2003): (a) preprocessing, that comprises

summarisation and normalisation, (b) annotation, (c) sta-

tistical-data mining analysis, and (d) biological interpreta-

tion, as depicted in Fig. 1.

Raw data generated from microarray platforms, e.g.

Affymetrix Cel Files or Illumina Tagged Images, need to

be preprocessed. The first step in preprocessing, known as

summarisation, aims to recognise the position of different

genes in raw images, associating different regions of pixels

to the unique gene that generated them as depicted in

Fig. 2. Normalisation aims to correct the variation of gene

expression in the same array due to experimental bias.

Filtering reduces the number of investigated genes on the

basis of biological considerations, e.g. genes of known

functions, or considering statistical criteria (e.g. associated

p-value). Finally, the annotation process associates each

gene to a set of functional information, such as biological

processes that are related to gene, and a set of cross ref-

erence database identifiers.

Statistical and data mining analysis phases aim to

identify biological meaningful genes, e.g. by finding dif-

ferentially expressed genes among two groups of patients

on the basis of their expression values. All extracted

genes are finally related to the biological processes in

which they are involved, e.g. a set of genes that are over-

expressed may be related to the insurgence of a disease.

Although this workflow is universally adopted, the

methods and the software tools used in preprocessing

phase are often different. The large number of fabrication

approaches, chip type, and preprocessing methods, have

to be considered when approaching to the comparison of

results. Differences in preprocessing, i.e. the use of dif-

ferent algorithms, or the use of different parameters, can

cause the selection of different genes and so the induction

of possibly wrong conclusions. To the best of our

knowledge, existing software tools generally allow only

the preprocessing of binary data of a single vendor

microarray, e.g. Affymetrix or Illumina. For instance,

Affymetrix Power Tools (APT) (Welle et al. 2002) is

used for Affymetrix data, while lumi (Du et al. 2008) may

be used for Illumina data. Only the Bioconductor3

framework includes packages for the preprocessing of

both Illumina and Affymetrix arrays such as IlluminaGUI

(Eggle and Schultze 2007) for the former and OneChan-

nelGUI (Sanges et al. 2007) for the latter. Nevertheless, a

Bioconductor single package performing preprocessing

for both vendors does not exist.

From this scenario, in order to enable the sharing and the

cross-comparison of microarray results from different plat-

forms and laboratories, we propose a general purpose soft-

ware platform for preprocessing raw microarray data. We

present General-purpose Summarisation and Annotation

Fig. 1 Workflow of microarray

data analysis

Fig. 2 Summarisation of binary files

1 http://www.affymetrix.com.2 http://www.illumina.com. 3 http://www.bioconductor.org.

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Tool (GSAT) and show that it is able to preprocess multi-

vendor microarray data, simplifiyng the execution of sum-

marisation, normalisation, and annotation tasks. The main

advantage of GSAT is to allow the use of a single platform to

preprocess multivendor datasets.

GSAT is based on web services technology: the core

service of the system is in charge of tracing the last version

of preprocessing libraries released by microarrays vendors,

allowing a transparent access to the right and most updated

versions of preprocessing libraries. It also includes a client

module that implements the preprocessing methods by

wrapping multivendor preprocessing tools. A system pro-

totype and its application to preprocess Affymetrix binary

(CEL) files is presented. The GSAT implementation for

Affymetrix platform comprises three main modules: (a) a

wrapper of the APT, (b) a library manager that is able to

find the needed libraries to realise summarisation and

normalisation, and (c) an annotation manager that is able to

find the needed annotations libraries, i.e. the information

about genes to realise annotation. The system presented

here extends l� CS an early prototype presented in

Cannataro et al. (2008a, b). Major enhancements are the

management of multivendor arrays and the use of the web

services technology.

The rest of the paper is structured as follows. Section 2

discusses related work, Sect. 3 presents the main contri-

bution of this work, Sect. 4 presents a case study discussing

the preprocessing of Affymetrix data using GSAT. Finally,

Sect. 5 concludes the paper and outlines future work.

2 Related work

The preprocessing of microarray data can be structured as a

pipeline of sequential steps, as data feeds along next steps,

it becomes more and more refined. The goals of such

pipeline are: (a) to identify and remove the noise and

artefacts dues to the experimental procedure, (b) to extract

the real value of expression for each gene, (c) to match

each probe with the corresponding nucleotide sequence,

and (d) to enrich such information using functional anno-

tations. Each step can be performed using different algo-

rithms that are designed for each chip of different

platforms. Usually, software tools are designed ad hoc for a

vendor and they do not allow the preprocessing in an

general way. Thus, here we categorise existing algorithms

and tools on the basis of the related chip considering, in

particular Illumina and Affymetrix expression data since

their diffusion.

Illumina BeadArrays uses the so-called beads to bind

probes of nucleotides. Each bead is replicated about 30

times and it is randomly positioned over the array. The

correspondence among pixels and genes is encoded into a

location file. The preprocessing of data obtained from

arrays comprises three main steps (Dunning et al. 2008)

as depicted in Fig. 3: (a) the bead level data, (b) the bead

summary data, and (c) the final analysis. The first level

comprises the processing of obtained images and of

associated location files. This phase comprises the indi-

viduation of estimation of local background for each

region representing a bead. The second level refers to the

translation of images into numerical values and the pro-

duction of single summarised values for each replicated

bead. In this way, different beads that refer to the same

gene are combined together producing a single represen-

tative value.

Different tools for preprocessing Illumina arrays exist

(Dunning et al. 2007). BeadStudio4 is a comprehensive

suite developed by Illumina that implements all the pre-

processing phases. It offers all the preprocessing func-

tionalities such as: managing of proprietary Illumina raw

data, quality control of arrays, summarisation, normalisa-

tion within and among chips, and, finally, annotation of

results. lumi (Du et al. 2008) is a Bioconductor (Reimers

and Carey 2006) package that offers all the preprocessing

functionalities for Illumina arrays. It is able to read binary

Illumina data, to normalise and summarise them, providing

also quality control methods. Finally, it is able to associate

to each identified probe the corresponding nucleotide

sequence. IlluminaGUI (Eggle and Schultze 2007) is a

Bioconductor package that offers preprocessing methods

for quality control, summarisation and normalisation

through a graphical user interface.

Affymetrix arrays use a set of features, used to bind each

gene of interest. Each feature consists of a set of probes of

25 nucleotides specific for each gene. The number and the

kind of features for each gene is different for each chip. For

Fig. 3 Preprocessing of Illumina data

4 http://www.illumina.com.

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instance, the HGU133plus (Affymetrix 2001) array uses 22

features, referred as probeset for each gene, that are

organised as 11 pairs. Each pair is constituted by a perfect

match (PM) and a mis-match (MM) that differ by a single

nucleotide. The first one is used to bind the gene, while the

second one is used to measure the background. The

redundancy of pairs is used to measure the data quality.

The recent HumanGene array (HuGe1.0ST) uses only PM

probes to bind genes. Each gene is bound by approximately

26 probes, referred to as transcript cluster, spreads across

the full length of the gene. Recently developed exon arrays,

e.g. Human Exon for human, are used to bind directly

exons, i.e. the coding regions of genes. They have probe-

sets placed against each exon along the length of the gene.

Similar to HumanGene they have no paired MM spots.

Preprocessing of Affymetrix arrays can be structured as:

(a) background correction and quality control, (b) nor-

malisation, (c) summarisation, and (d) annotation, as

depicted in Fig. 4.

Background correction aims to identify the background

noise and to remove it (Arteaga-Salas et al. 2008; Rocke

and Durbin 2001; Tu et al. 2002).

Normalisation consists of reducing the bias among

chips and within different regions of the same chip (Fujita

et al. 2006; Irizarry et al. 2003a), aiming at removing

non-biological variability within a dataset. Both biological

and technical variations introduce artefacts and variability

into the system. Common causes of such variability are:

variable loading of DNA onto arrays, mixing of DNA

across different areas of the array, variability in the

effectiveness of the labelling reactions among different

arrays. All the algorithms for normalisation share the

same principle: the differences of data points among

sample are a few number, so the majority of points should

have the same values. Algorithms for normalisation of

microarrays can be divided into two major classes: (a)

within-array normalisation algorithms, that seek to

remove variability within a single array, and (b) between-

array normalisation algorithms, that seek to remove var-

iability among a set of arrays.

Summarisation combines multiple preprocessed probe

intensities to a single expression value. All arrays employ

more than one probe for each genes as introduced before.

Summarisation takes into account all of the probes for the

same genes and averages them by enhancing the signal-to-

noise ratio. All of these algorithms are based on several

assumptions on the data distribution and they require a set of

specific libraries in order to correctly access binary data. The

robust multi-array average (RMA) algorithm (Harbron et al.

2007; Irizarry et al. 2003b) is a summarisation method that is

applicable to all the Affymetrix arrays. It is based on a global

mathematical model that considers expression values and

probe affinities. This model is based on the consideration that

the value of intensity for a match is affected by three causes.

A first one, due to the chip, takes into account the amount of

material that binds the chip, i.e. an higher amount of bio-

logical sample produces an higher level of intensity. A sec-

ond one measures the affinity, i.e the ability of the probe to

bind the RNA. Finally, a third one estimates the measure-

ment error. In this way for each probeset, RMA calculates the

summarised values by observing the pattern of values in all

the arrays through a process of model fitting. The Probe

Logarithmic Intensity Error (PLIER) algorithm (http://

www.affymetrix.com/support/technical/technotes/plier_

technote.pdf) is based on a probe affinity parameter, which

represents the strength of a signal produced at a specific

concentration for a given probe. The probe affinities are

calculated using data across arrays. The error model

employed by PLIER assumes that the error is proportional to

the observed intensity, rather than to the background-sub-

tracted intensity. Other summarisation methods take into

account specific properties of chips, such as the summari-

sation proposed in Li and Hung Wong (2001) or in Kapur

et al. (2008).

A process known as annotation associates to each probe

its known annotations such as Gene Symbol or Gene

Ontology (Harris et al. 2004) by matching probes to public

databases or knowledge bases. Often annotation files are

provided by the chip manufacturer and contain different

levels of annotation, e.g. database identifier, description of

molecular function, associated protein domains.

There exist different tools (either commercial and free)

for preprocessing Affymetrix files. The APT (Welle et al.

2002) are a set of tools implementing low level algorithms

for working with Affymetrix GeneChip arrays. They are

able to read a set of CEL files and produce a data matrix. In

order to perform the normalisation and the summarisation,

APT tools need: (a) a set of binary CEL files, (b) a model

algorithm, e.g. PLIER, and (c) the correct libraries that

enable the correct interpretation of the images.

RMA Express5 is a tool that performs the summarisation

but it presents four main drawbacks: (a) it does not provide

the automatic updating of the needed libraries, (b) it

implements only the RMA algorithm, (c) it does not pro-

vide annotation, and (d) it is available only for windows

operating systems. The Automated Microarray Pipeline

(AMP)6, is a web application developed as a part of the

Fig. 4 Preprocessing of Affymetrix data

5 http://rmaexpress.bmbolstad.com/.6 http://compbio.dfci.harvard.edu/amp/.

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TM4 suite. It provides the normalisation of Affymetrix data

but it requires the upload of CEL files to the web server and

it supports only version 3 of Affymetrix CEL files. One-

ChannelGUI (Sanges et al. 2007) is an extension of the

affylmGUI package providing a graphical interface for

Bioconductor libraries. Embedded libraries provide quality

control, noise removal, feature selection and statistical

analysis features for single channel microarrays.

TM4 (Saeed et al. 2003) is a comprehensive software

suite consisting of four main applications. A Microarray

Data Manager (MADAM) stores and retrieves data from a

database. Spotfinder, an image quantification tool able to

read colour array images, remove noise and extract rele-

vant features. Microarray Data Analysis System (MIDAS)

is able to read gene data performing several analyses. In

particular, it implements normalisation, gene filtering, gene

grouping and data mining. A main drawback of this tool is

that it is not able to execute the first preprocessing phase,

i.e. the summarisation, and it needs a preliminary trans-

formation phase. When using Affymetrix array, this phase

can be performed using the APT whose results need to be

loaded into TM4 in a manual way.

The preprocessing tools described so far can be grouped

on the basis of the vendor of the managed array as reported

in Table 1. As discussed, although the importance of the

preprocessing of microarray data and the need for a stan-

dardisation and comparison of methods, there exists a lack

of tools that are able to preprocess different multivendor

microarray data.

3 GSAT

GSAT is a framework to automatically preprocess micro-

array data. Figure 5 depicts the context where GSAT is

located. Laboratories use different instruments to analyse

samples obtaining raw microarray data that have different

syntax and format, e.g. CEL files for Affymetrix and TIFF

(Tagged Image File Format) for Illumina. Those files need

to be preprocessed in order to reduce the noise, extract

information about genes, and normalise the obtained

expression values. Such process requires the use of dif-

ferent tools and algorithms. The use of such tools requires

both biological and bioinformatics expertise.

GSAT sits in the middle between microarray facilities

and statistical and data mining software tools, so its main

functional requirements are:

– interfacing with multi-vendor microarray facilities,

– storing and managing libraries for processing and

annotating raw binary data,

– interfacing with off-the-shelf microarray data analysis

tools, such as TM4.

GSAT provides the following functions: (a) Microarray

Data Acquisition, i.e. reading and managing binary data

produced by different instruments; (b) Microarray Data

Preprocessing, i.e. offering main algorithms for summari-

sation, denoising and normalisation; (c) Microarray Data

Annotation, i.e. annotating the obtained gene expression

values with functional annotation about genes.

3.1 Architecture

GSAT is based on a distributed architecture whose main

modules are a web server and a downloadable client as

depicted in Fig. 6. The GSAT web server contains a web

service that performs the update of two reference dat-

abases: (a) a library references db that stores references

(i.e. URLs) to the libraries needed to preprocess binary

files, e.g. CDF (Chip Definition File) files for Affymetrix;

(b) an annotation library db that stores references (i.e.

URLs) to annotation libraries needed to annotate genes

found in the samples. The web service module periodically

Table 1 Software tools for preprocessing of microarray data

Tool Vendor Functionalities

lumi Illumina Preprocessing

BeadStudio Illumina Preprocessing

IlluminaGui Illumina Preprocessing and analysis

OneChannelGui Affymetrix Preprocessing and analysis

APT Affymetrix Preprocessing

RMAExpress Affymetrix Preprocessing

TM4 Affymetrix Analysis

AMP Affymetrix Preprocessing

Fig. 5 Analysis of microarray data

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verifies the availability of updates to those libraries by

connecting to different repositories maintained by the

microarray vendors. The web server also contains a web

portal that presents information about the GSAT architec-

ture and allows to download it.

The GSAT client offers the preprocessing functionalities

by wrapping existing preprocessing tools. The Prepro-

cessing Wrapper receives the job requests from the GSAT

client, manages them and then invokes the needed pre-

processing tool, e.g. APT tool for Affymetrix binary files.

An instance of the correct preprocessing tool is invoked

whenever summarisation requests are received. After the

job completion, the wrapper reads the output files, orga-

nises them in a table data structure, and stores them on the

file system or transparently sends them to the application

that is using the GSAT client. For instance, we are cur-

rently integrating the GSAT client as a plugin into the TM4

platform.

While the GSAT server stores pointers to the updated

versions of all the preprocessing libraries made available

by the supported microarray vendors, the GSAT client

downloads and stores only the needed libraries. In par-

ticular (see bottom part of Fig. 6), the LibraryDB stores

all the needed libraries to parse binary files while the

AnnotationDB stores the annotation files. It keeps trace

of installed libraries in a local archive encoded in XML.

Currently, we designed the whole architecture of the

GSAT tool and we tested a first prototype able to pre-

process Affymetrix files. The realised tool receives a set

of binary files as input, summarises them, i.e. converts

them into a matrix, and extends the generated data with

annotations. It is based on a wrapper of APT accessible

trough a GUI. An instance of APT is invoked whenever

summarisation requests are received. An ad-hoc module

on the GSAT client periodically verifies the availability

of updates by connecting to the Updater web service

hosted on the GSAT server. It updates the databases

whenever a request for the installation of new libraries is

received or a new version of installed libraries is

available. An ad-hoc registry encoded in XML keeps

trace of new versions for both LibraryDB and Anno-

tationDB databases, as depicted in Fig. 7 for Affymetrix

Chips.

The current version of GSAT is currently under testing

in a joint collaboration between the Bioinformatics Labo-

ratory of University of Catanzaro and the Tommaso

Campanella Cancer Center (Di Martino et al. 2009).

4 Case study: preprocessing Affymetrix data

This section shows the functionalities of GSAT through a

case study on Affymetrix binary files. The preprocessing of

two datasets both available for download on the Affymetrix

web site, a Human Gene 1.0 dataset, Dataset1 hereafter,

and a Human133Plus2 dataset, Dataset2 hereafter, is

presented.

Dataset1 contains various mixture levels of two tissues:

brain and heart from human samples. We selected ten

Fig. 6 The architecture of GSAT

Fig. 7 Fragment of GSAT

archive

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arrays from these to perform our study. Dataset2 is a subset

of the Latin Square dataset developed by Affymetrix.7

The GSAT client initially checks for locally installed

libraries, and asks to the user to select the appropriate ones

for chips. In this case the user selects those for Affymetrix

HumanGene1.0st and HGU133plus array. Figure 8a and b

shows, respectively, the selection of libraries for HGU133

and HumanGene. The client queries the GSAT server for

those libraries and receives the references to the updated

libraries so he/she can download and install them.

Each preprocessing step has different options, among

those the type of algorithm used and its own parameters. At

this step the researcher has to select the preferred algorithm

and its parameters. For instance, considering the sum-

marisation algorithm, he/she can choose to employ the

RMA or the PLIER algorithm and related parameters.

GSAT then reads the binary files and invokes the APT

executable using the user’s specified parameters. Figure 9

shows the GSAT client interface for the selection of

analysis parameters.

When summarisation and annotation are completed,

GSAT writes the results file for subsequent analysis. File is

structured as a table whose attribute columns contain: (a)

the probeset identifier, (b) the identifier of each sample, (c)

the name of the sequence, e.g. the gene name, (d) the strand

of DNA, (e) the position of start and stop coding region, (f)

the total number of probes, (g) the cross reference to pro-

tein and RNA databases, and (h) the Gene Ontology

annotation. Table 2 depicts a fragment of the generated file

for human gene array showing only a subset of columns.

5 Conclusion

The study of gene expression data is nowadays an impor-

tant research strategy in biology and medicine. Microarray

technology enables the investigation of such reality and

uses chips that are able to scan the whole genome. The

correct interpretation of those data relies on a preliminary

preprocessing phase that takes as input binary raw data and

produces as output data for statistical and data mining

analysis. Nevertheless, there exist many preprocessing

methods that can be used, so preprocessed data are not

easily comparable. In this paper we proposed GSAT, a

software platform that simplifies the preprocessing of

multi-vendor binary data. GSAT is based on two main

modules: a web portal that stores references of all the

preprocessing and annotation libraries maintaining them

updated, and a client able to preprocess data. In this way,

through GSAT, users can directly manage binary data

without worrying about locating and invoking the proper

preprocessing tools and chip-specific libraries. Actually,

there exists a first prototype of such system that is able to

preprocess Affymetrix data. Future work will regard the

complete implementation of the system for supporting

Fig. 8 Selection of libraries

Fig. 9 Selection of analysis parameters

Table 2 Example of GSAT generated file

Probesetid Sample 1 […] Sample N Gene Name Swissprot GO

7896736 3.45 […] 7.98 ENST00000359325 …7896817 7.91 […] 9.10 ISG15 GO:0032020

7 http://www.affymetrix.com/support/technical/sample_data/

datasets.affx.

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other microarray formats and the parallel implementation

of preprocessing and annotation modules.

Acknowledgments Authors are grateful to Andrea Greco for his

work on prototype implementation.

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