EURASIP Journal on Advances in Signal Processing
Volume 2008 (2008), Article ID 520641, 11 pages
doi:10.1155/2008/520641

Copyright © 2008 G. Kornaros and S. Blionas. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper presents a reconfigurable architecture of a lab-on-chip (LoC) microarray device capable to process data either in genotyping or in gene expression applications in a fraction of the time that is required by the usual software methods running on a standard computer. The entire LoC consists of a microfluidics part for the sample preparation and hybridization, a microsystem part including the application specific array of sensors for the electronic detection, and finally a reconfigurable processing part for the data analysis. The proposed data processing and analysis electronic module are an embedded multicore reconfigurable system-on-chip designed to analyze data from the forthcoming high-density oligonucleotide microarrays. The proposed architecture employs reconfigurable technology and has the capacity to process data from microarrays of various sizes from small size ones used in genotyping up to large-scale gene expression arrays. Additionally, the embedded processing cores feature reconfigurable circuitry for implementing the intense part of the processing, supplementing the various computational needs of the diverse applications for microarray real-time data processing and for a scalable reconfigurable architecture to handle also the future high-density microarrays.

1. Introduction

Microarrays are a significant part of the lab-on-chip (LoC) research area and are dedicated either for the parallel assessment of gene expression for hundreds or thousands of genes in a single experiment, or for genotyping molecular diagnostics applications and particularly for pharmacogenomics. Despite the wide employment of microarrays in molecular biology and genetics, technical problems still exist, for example, identifying and recognizing reliable data using image processing techniques. Currently, the microarray data analysis is done with offline photographic methods, and further quality assessment of the data, after segmentation spot/background, grid matching, and noise suppression [1, 2], follows. These further data processing steps require a larger number of data to be stored, particularly when the number of spots on the microarray is of several thousands (gene expression applications), and then to process them for the quality assessment. Those issues so far did not allow the full integration and operation of the LoC microarrays either as standalone devices for possible consumer applications (e.g., self-tests), or as intelligent systems creating much less data for further processing [3]. Electronic hybridization detection allows high integration level of LoCs but the reading of the array of the sensors and the further data processing in case of large microarrays (the number of the sensors on the microarray with embedded electronic detection capability may reach nowadays several thousands), and also the on-chip processing of microarrays, still remain an open research issue [4–7].

This paper presents the architecture of the electronic part of a fully integrated robust biomedical, biodiagnostics electronic microsystem. This architecture processes the measurements of the electronic hybridization detection sensors and hosted at a disposable device-cartridge which first extracts the DNA from a blood drop, then, it amplifies the fragmented tiny DNA samples (using PCR) and finally runs biological protocols which evaluate the analyzed substance. It can be encapsulated in a single, portable, self-contained device-unit, significantly reducing the risks of cross-contamination inherent in conventional analysis methods. An array of embedded sensors monitors the hybridization of the sample with the biological material put on the microarray spots. The LoC is controlled by the proposed architecture that monitors and adjusts the process of the data produced by the electronic hybridization detection. Subsequently, it executes an automated methodology to flexibly execute normalization, transformation, and removal of unreliable spot raw data. The proposed architecture due to its modularity is capable to further process data ranging from small microarrays (few hundreds of spots) up to large multithousands of spots microarrays producing vast amount of data and evaluate the final results for molecular diagnostics examinations.

Targeted application areas are mutation detection for gene expression, genotyping, and pharmacogenomics. Additionally, the proposed architecture of the LoC device could be used also for prediction, prevention, and even early diagnosis or predisposition of specific diseases. Molecular diagnosis of infectious diseases, virus molecular detection, and so forth are also possible applications. Forthcoming genetic tests not only will be dedicated for diagnostic of diseases but also for personalized medicine treatment (pharmacogenetics). They will also provide information to optimize drug therapy increasing the efficiency and minimizing the adverse effects of the developed drugs.

Section 2 of the paper describes the state of the art of the microfluidics sample preparation module of the LoC, and of the electronic hybridization detection presenting two different approaches, the photonic sensors alternative and the capacitive sensors one. Section 3 presents the data processing algorithms that are required to analyze the electronic hybridization detection sensor data and decide about the existence of the examined mutations. Section 4 is describing two alternative architectures for the data analysis of LoC data. Finally, Section 5 is presenting an emulation of these architectures as well as performance evaluation results, and Section 6 shows the processing of the data of a microarray with 8500 spots.

2. State of the Art for the Lab-on-Chip

The lab-on-chip consists of subsystems for the sample preparation for the electronic hybridization detection and the data analysis. The sample preparation subsystems in case of DNA analysis concerns DNA extraction, PCR, and hybridization (microfluidics subsystem). Then, electronic hybridization detection subsystem concerns the measurement of hybridization “degree” using dedicated biosensors and their associated reading circuitry. Finally, data analysis is the targeted subsystem by this paper and concerns the flow of data from the reading circuitry to the data analysis subsystem and the processing of data by this. Below, there are presented indicatively state of the art implementation approaches for the first two subsystems that are not the target areas of this paper, namely, the sample preparation and electronic hybridization detection subsystems.

DNA extraction and amplification are usually prerequisite steps that are needed so as to provide a sufficient number of copies of the target gene sequences to enable visualization using specific detection modules, and thus identification or characterization of gene sequences. Conventional genetic analysis in clinical laboratories typically requires bench-top equipment and either manual or robotic transfer of liquids (e.g., 10–500 uL) between tubes (or microwells in the case of microliter plates) for separate steps of the process. Using conventional approaches, DNA extraction is most commonly implemented by initially rupturing the cells (cell lysis) in a buffer solution (e.g., a solution including SDS), then capture of the released DNA with either silica particles in a filter-type format, or silica-coated paramagnetic beads which can then be immobilized with a magnet. This allows all other cellular debris to be washed away, after which this “template” DNA can be eluted from the beads and resuspended in a liquid buffer ready for amplification using the polymerase chain reaction (PCR). PCR involves cycling the DNA through a series of temperatures using a programmable thermal cycler. Initially, the two strands of the template DNA duplex are separated by denaturation at , then short synthetic DNA “primer” sequences are annealed to the ends of the target section of template sequence (i.e., at a temperature usually between 50–), from which the Taq polymerase enzyme “zips” together the nucleotides present in the reaction mixture to build a new DNA sequence complimentary to template. By cycling the reaction through this process, usually between 25 and 40 times, the number of available copies of DNA increases exponentially, so as to yield a sufficient of DNA enabling detection and analysis. DNA analysis will continue either to identify specific sequences in specific parts of the DNA or to compare the expression of genes of various samples and extract results for the role of genes in specific diseases.

Microfluidics parts of LoCs usually implement these sample preparation steps fully automatically. The early years usually were made by silicon and glass. Microfluidics technology has made great strides in recent years [8–10]. Nowadays a trend toward polymers as substrate material has been observed ([11], for review see Zhang et al. [12]). Plastic substrates are less expensive and easier to manipulate in mass production than silica-based substrates. Advances in polymer engineering have led recently to the development of a biochip device consisting of a plastic microfluidic chip, a printed circuit board (PCB), and a Motorola eSensor microarray chip. The plastic chip includes a mixing unit for rare cell capture using immunomagnetic separation, a cell preconcentration/purification/lysis/PCR unit, and a DNA microarray chamber.

The developed LoC uses composite materials based mainly on plastic foils (especially PDMS) and different types of fibers (especially silicon carbide fibers). A modular technology for the micrfluidics part of the biochip is under development in the Tyndall National Institute. A similar technology, without the use of metallized fibers, is also reported in the literature [11].

Concerning the hybridization chamber that hosts the electronic detection part of the LoC, it was designed considering various constraints and functions. The main chamber that accommodates the hybridization of the sample DNA with the biological material of the spots is as small as possible; it allows the measurement of all biological spots with pitch 300 μm centre to centre and 170 μm diameter spots. A heater controls and stabilizes the temperature. The chamber is isolated from external light and has the smallest auto fluorescence as possible. The packaging of the chamber onto the sensors is predicted to be easy for the final assembly of the device. Probes grafting is performed before the chamber assembly. If optical detection is employed then alignment between biological spots and sensors is mandatory, while in the case of the capacitive sensors this is not required. The whole system is compact and is designed for optimized volumes (capillaries, hybridization chamber, tanks). Finally, it is of low consumption, robust, while at the same time ensures waterproofness.

Several protocols for microarray-based SNP and mutation analysis have been developed (as reviewed in [13]). There is the tiled arrays approach [14] that allows a variety of electronic detection techniques. Tiled arrays involve the generation of an array of oligos that vary in specific positions in order to create perfect matches to the fragmented DNA molecules which will bind strongly or mismatches that will result in weaker binding.

In the photodetection context, tiled oligonucleotide arrays are suitable for single color detection [14]. The fragmented DNA molecules are labeled with a fluorophore probe, and the more or less binding pairs result in relative intensities of the oligo spots that have to be compared. This requires the same amount of functional oligo to be deposited (by spotting) or synthesized [15] at each spot. The aim is to minimize variations in the amount of arrayed oligo, which will impede the analysis of single color intensities. The optical setup for the detection includes an excitation light source, typically LEDs or a laser, optical filters to separate the excitation light wavelength for the fluorescence wavelength, and a detector. There is a range of microarray scanners available for scanning and detection of DNA microarray-based platforms. The lowest cost and least sensitive is a CCD- (or CMOS-) based imaging system, where the whole microarray is illuminated with the excitation light source, and image processing is used to determine the results. Alternatively, a laser scanning-based microarray scanner can be used. In this configuration, a laser beam is raster scanned across the microarray device. The fluorescence is collected via appropriate optics and filters into a photomultiplier tube (PMT). Also a 2D array of photon counting sensors on a single chip could enable detection of images of fluorescent hybridized DNA samples. It utilizes the high speed operation and low light level detection capability of the 2nd generation silicon detectors, the Geiger Mode-APD [16]. These devices produced using CMOS compatible processing are low power as appropriate for POC and portable applications and will have a low-cost base.

MEMS sensors are based on mechanical movements and deformations of their micromachined components, such as single-clamped suspended beams (cantilevers), double-clamped suspended beams (bridges), or suspended diaphragms.

In capacitive detection, the displacement is measured as a change in the capacitance of a plane capacitor. An approach for the detector array based on the stress induced on a thin silicon membrane due to reactions between the receptor DNA deposited on the membrane surface and the sample under investigation will be explored. This kind of detectors has been successfully applied in biological applications employing silicon cantilevers and optical or piezoresistive detection. Capacitive detection could challenge the sensitivity and flexibility achieved by both of these techniques.

Capacitive DNA sensors arrays based on the exploitation of surface stress changes and subsequent bending of an ultra silicon thin membrane are to be fabricated. The membrane will seal the capacitor plates from the electrolyte solution thus enabling capacitive detection.

In this array, each element of the array will be a capacitor comprised of an ultra thin silicon membrane suspended over a cavity and a counter electrode on the substrate. Operation of the device will rely on the induced stress due to the reaction between the receptor DNA, a number of ultra thin silicon membranes covering a shallow cavity formed into a silicon dioxide layer etched on a silicon substrate containing the counter electrode of the capacitor detector. In Figure 1, the basic idea is illustrated. The hybridization process (b) results in membrane deflection due to the change of the surface free energy that eliminates the need for attaching labels to detect specific binding. Special provision will be taken so that the device accommodates for the microfluidics to be incorporated on the system.

3. Microarray Data Processing Algorithm

Statistical analysis of microarray data can essentially process massive amounts of data and can also adjust for various sources of variability in order to identify the important genes or existing mutations amongst a large number which are interrogated. This section summarizes some of the issues involved and provides a brief review of the processing algorithm mostly used by the researchers and will be accommodated by the proposed architecture.

All microarray LoC experiments involve a number of distinct steps. The design of an experiment involves the following: (i)the number and the type of the genes’ mutations to be interrogated,(ii)for each of the above mutations of a gene, the exact sequence of bases named oligos should be printed on the LoC,(iii)the design of appropriate sources of RNA to be hybridized, and(iv)the number of replicates for each of the oligos on the LoC for increased statistical confidence. After hybridization that completes the data acquisition from the LoC sensors takes place, next several data filtering steps must follow. The data must be processed to acquire mutant and wild values; these are represented as red and green in traditional microarrays. In addition, the background intensities should be estimated so as to correct the mutant and wild values. The aim is to adjust for sensor-bias and for any systematic variation other than that due to the differences between the RNA samples being examined. Then, the corrected values are further analyzed to decide about the existence of a mutation in a sample or to select differentially expressed (DE) genes or to find groups of genes whose expression profiles can reliably classify the different RNA sources into meaningful groups. The discussion in this section corresponds roughly to these data analysis steps.

The following notation will be used throughout this section. The mutant and wild sensor measurements are denoted as and for each spot. The background intensity will be . Having estimated the background intensity, it is almost universal practice to correct the mean values of the measured and intensities by subtracting the mean value of the background, and . These adjusted intensities form the primary data for all subsequent analyses.

The motivation for background adjustment is that a spot’s measured intensity includes a contribution not specifically due to the hybridization of the target to the probe. For example, nonspecific hybridization may occur and/or fluorescence may be emitted from other chemicals on the detection part of the LoC (in the case of photosensor-based hybridization detection). If such a contribution is present, we would like to measure and remove it so as to obtain a more accurate quantification of the actual hybridization. Research has begun as discussed in [17] on more sophisticated methods of background adjustment which will produce positive adjusted intensities even when the background estimate happens to be larger than the foreground. Empirical experience suggests that local background estimates often overestimate the true background while the morphological method may underestimate it, and these differences have a marked impact on the -values for less intense spots. There is a need for further research on adaptive background correction methodologies which can produce intensities with consistent behavior regardless of background estimator method used.

The data produced by the developed LoC after hybridization are processed to infer if specific mutations are present in the examined sample and consequently to decide on what is the appropriate medicine cluster that a patient should get. Assuming that replicas have been chosen, the microarray is partitioned to subarrays that correspond to groups of sensors. Each subarray is spotted with the wild-type probes and with the mutant probes. There are also spots of an oligo that will never hybridize in order to be used as control and background reference; these are the nonbinding control probes. The calculations to be carried out on the data for a mutation for both the wild and the mutant spots are summarized by the steps of the following algorithm.

Step 1. calculation of the mean values for the wild, the mutant and the nonbinding control spots: (1) where is the number of replicas for each probe.

Step 2. calculation of standard deviation (SD) for the wild, the mutant and the nonbinding control spots: (2)

Step 3. the coefficient of variation is then calculated and expressed as a percentage: (3) If the calculated CV% is below 60 (as studied in [11]) then jump to Step 5.

If the calculated CV% is over 60 then continue to Step 4.

Step 4 (Calculation of New Mean Values Excluding the Outliers). Assuming that we would like to keep our measurements within the 95% confidence interval, then this defines a distance of , where we will keep our measurements. All measurements outside this region () will be considered as outliers and they will be excluded. Calculation of new mean values is excluding the outliers. This final mean value of the reference group is memorized to be used in the next step for all the other groups of the LoC.

Step 5 (Background Correction). Sources of variation in the microarray such as unequal quantities of starting RNA or differences in hybridization conditions across the array usually affect expression intensities. It is therefore required the task of correction of microarray data so that to determine more meaningful and accurate biological data. This is referred to as background correction. The final values for wild and mutant probes are calculated by subtracting the background mean value from the calculated mean value after the outliers step (for both wild and mutant), so as to result with the final hybridization detection value of a probe.

Step 6 (Decision about the Existence of an SNP). The calculations produce the ratio of the mutant and the wild mean values (). According to the research results in [11], if this ratio is greater than 2 then the specific mutation is considered as existing.

An alternative approach is to use the log-differential expression ratio. This is expressed as for each spot. It is convenient to use base-2 logarithms for the ratio so that is units of 2-fold change. On this scale, represents equal expression, represents a 2-fold change between the RNA samples, represents a 4-fold change, and so on. Hence, in case of using log values then the threshold is the value 1.

Other statistical approaches commonly used to improve significance estimates are a penalized -test and a -test using intensity-dependent variance estimates; these are assuming that photographic methods are used to extract the hybridization results, but also apply to our capacitive-based microarray. However, as shown in [17], the major shortcoming of the -statistic is that the replicate ratios can occasionally be extremely similar due to randomness, producing thus an artificially low standard deviationand high values. False positives stemming from this effect prevent the standard -statistic from serving as a reliable or useful test of which genes are truly regulated.

The above steps are repeated for all the interrogated mutations in the LoC, and according to the predefined rules the cluster where a patient belongs is defined and an indication on the disposable LoC informs the consumer (patient), about this decision.

4. System-on-Chip Architecture for Microarray Data Analysis

We describe and evaluate in detail the two alternative architectures of the single-core and multicore approach. Also, the details for the data analysis of the microarray of a custom Lab-on-Chip are described.

4.1. Microarray Data Analysis on a Single Core CPU with Accelerator

The reading process of the sensors’ values is the first step before the data analysis part; this reading requires a conversion of the indication of the analog sensor to a digital value. Depending on the sensor type, two options exist for the analog to digital conversion. In case of the photosensors traditional A/D, converters are used and their parallel output is forwarded to the data bus to be transferred to the appropriate processing core for further data analysis. In case of the capacitive sensors, capacitance measurement is carried out by measuring frequency; an interface reader (IFRD) is a simple circuitry converting the capacitance changes to digital pulses and subsequently to an arithmetic value if the microarray is capacitive-based. The conversion requires about 1 microsecond for each measurement as discussed in [18], and it allows for a frequency of reading up to 1 MHz. Using one reading circuitry, it will need a total of 1 second for spots. These data are forwarded to the processing core in the case of single core architecture.

As a first approach, the entire microarray could be read and monitored by a single core microprocessor, yet simple and energy efficient in order to comply with the requirements of a cheap, portable, and flexible microsystem for pharmacogenomic applications. This microprogrammed-based system offers itself for easy update of the algorithms in firmware; these algorithms perform the data processing while at the same time do not cause excessive processing delay. However, high-throughput microarrays with thousands of spots, for achieving real-time performance (less than 1 second waiting for getting the result) will obviously require more processing power as it will be shown at the next section. In the genomics area, the biologists need to compare the expression level of thousands of genes in the same time using at the same time many of these high-end microarrays in parallel. In the next part of the section, we present a reconfigurable system-on-chip with the capacity to handle such applications in real time.

The organization of the LoC microsystem board is depicted in Figure 2. The device is controlled by the firmware loaded in single core microprocessor (MicroBlaze operating at 100 MHz). This same core will be responsible to provide a user interface and postprocessing the analysis results via the μBlaze CPU core. The embedded microprocessor executes the feature extraction algorithm to decide in which category the patient under analysis belongs to.

In order to evaluate and design a scalable architecture to elaborate large volume of DNA microarray data, we used field-programmable gate array (FPGA) technology. The first target of evaluation is the use of a hardware acceleration unit to perform the computation intensive processing parts. We implemented a single core MicroBlaze-based system on FPGA which executes the processing algorithm depicted in Section 3, while on the other hand we developed a pure hardware accelerator to perform the core algorithmic functions. Regarding the resources in FPGA, the MicroBlaze cost is 730 slices, while a hardware block to calculate the SD result is 155 slices of a Virtex-4 XC4VFX12-FF668-10C device.

Table 1 shows that it is required 3400 milliseconds until the calculations of the standard deviations (i.e., a square root operation) are completed for all mutations on the microarray. Obviously, it is very beneficial to adopt a hardware accelerator unit since the performance is considerably improved.

Nevertheless, manipulating data from larger-scale microarrays will ask for more increased processing power.

Hence, in addition to hardware acceleration there are needed more efficient solutions mostly based on multiple processing cores to achieve real-time operation.

In the next Section 4.2, the architecture of a multicore system is described to meet the processing requirements of data analysis for real-time operation for the current microarray defining also a scalable architecture for real-time operation for future higher-end microarrays.

4.2. A Multicore Reconfigurable Architecture for Microarray Data Processing

Multithousand sensor microarrays for gene expression analysis produce large volume of data that necessitate the employment of a scalable processing microarchitecture and adjustment of the quality control algorithm of Section 3 for parallel processing. The critical components of preprocessing are identified, evaluated, and accelerated in order to minimize the processing time and assure real-time operation. Figure 3 shows the organization of the proposed reconfigurable architecture.

Considering the processing core frequency that reaches 100 MHZ and using 10 reading circuitries and pipelining of the measurements allow for a reading frequency of 10 MHz. These data are distributed by the IFRD Arbiter synchroniser to the appropriate core for the data processing.

Each interface reader (IFRD) block is assigned to a set of lines of the microarray. If the sensors are CCD-based or photosensors, then multiple analog-to-digital converters can be used instead to the left part of the IFRD with negligible changes to the right part, which is interfacing to the data processing farm. Assuming IFRD blocks and processors, a simple interconnection bus-based scheme is employed in order to build a low complexity system; this allows each IFRD block to send the retrieved values to the appropriate processor. The IFRD Arbiter is responsible to initiate and synchronize the transfers. The protocol supported by the Arbiter is crucial for the efficient management of data transfers and triggering of the processing phases; it defines the following system parameters. (i)The FIFOs in effect in each IFRD block that are needed to maintain temporary read raw values.(ii)The timing of transfer-events: the IFRD Arbiter triggers the reading process in a wave like fashion in order to avoid conflicts over the shared bus. An additional reason is that the order of completion of the processing is known in advance and thus the results are expected to arrive in the shared RAM in order. However, in order to accelerate the processing, a more relaxed approach is adopted: the retrieval of data from the IFRD blocks is not enforced on a strict time window basis. This is also facilitated by the principle of operation of the LoC. Different IFRD blocks may have to send the retrieved values to the same processor, since these belong to the same “gene” (replicas of it for the statistical processing). This methodology is used in each large-scale microarray in order to obtain more accurate results by placing the same biological material on different locations so as to avoid microarray area variability side effects. In addition, the computations for the mean values calculations for each of the genes may start just as the first two replicas’ values for each gene arrive to the local BRAM of each MicroBlaze core.

The IFRD Arbiter is also in this case responsible to synchronize the transfers. The Arbiter acts as a Master and triggers the transfer of each ready value to the correct MicroBlaze. This is also the reason why eventually a small FIFO maybe will be required at each RFID block to store the intermediate read values. Each value read from a sensor at coordinates is considered as the body of a packet send to a specific processor with a “coreID” identifier—which is the destination. This core handles all the values of the replicas of this gene with the same “geneID” identifier—which stands for the source field of the packet. Each processor handles a number of genes and does not need to wait until all the values arrive. It is obvious that the processing of the mean value starts as soon as there are data in the local BRAM.

The processing in this first phase consists of the sensor data processing algorithm. The simple operations, additions, and subtractions are performed in software while the more complex ones by the hardware accelerator that resides on the second port of the local BRAM. This accelerator block shown in Figure 3 is able to calculate a square root function. When a value is placed at address sq_addr_source, then the accelerator is triggered and 11 clock cycles later (with a clock cycle time of 10 nanoseconds) the result is placed at address sq_addr_result. At the same time the MicroBlaze has already erased the content of sq_addr_source and then waits for the outcome to appear.

After the SD result of a gene is computed a third-processing level starts, which aims to identify the outliers that discard them and recalculate normalized mean values. In a fully constrained relaxed system (without the IFRD Arbiter to cause artificial delay), this phase causes the OPB bus to operate at full throughput. However, this final phase does not last long compared to the rest of the processing.

5. Benchmark Measurements of the Single and Multicore Reconfigurable Architectures

The implementation of the system-on-chip (SoC) of both alternative architectures (single and multicore) using, respectively, one and four MicroBlaze CPUs is done in a XC4VFX20-FF668 FPGA using the ML405 prototype board from Xilinx (see Figure 4). In the case of the multicore alternative, each MicroBlaze CPU is responsible to handle the processing of 106 (425/4) mutations retrieved from 10 subarrays (we implemented 10 replicas) of spots. An on-chip timer is triggered when we initiate the calculations until each step completes; thus real-time measurements at clock cycle granularity were achieved. The on-board SRAM is used as a shared memory among all MicroBlazes to exchange messages and to store the final normalized data.

In order to evaluate the performance benefits against the additional cost in resources, we used a single core, so as to examine the existence of only one single mutation. The entire algorithm of Section 2 is executed using floating point representation of values (without compromising on the accuracy of the results), with and without hardware acceleration.

Using floating point values in the algorithm increases the accuracy but at the cost of increased execution time. Thus, enabling the option of using the hardware acceleration units (floating point, barrel shifter, and integer divider unit) of the MicroBlaze CPU is a challenging alternative. Figure 5 shows that using the embedded hardware floating-point units of the MicroBlaze core gives a boost of almost 3-fold speedup; if the rest hardware units of MicroBlaze are also enabled then, as Figure 5 depicts, does not payoff in the scope of this application.

Moreover, employing hardware accelerator for the square root and the division operations improves significantly the performance. Figure 6 compares the cost in clock cycles of executing the entire algorithm of Section 2 using a single core, so as to examine the existence of one single mutation using floating point representation (without compromising on the accuracy of the results), with and without hardware acceleration. The plot shows the breakdown of the execution time for each group of steps according to Section 3. Step 2 performs the standard deviation calculation that costs 660 K clock cycles. It is obvious that the lack of use of the hardware acceleration part has the counter effect of increased runtime. Hence, it is advantageous to use the hardware core to calculate the square root as discussed also in the previous section; adopting this core gives a total time of 19 K clock cycles.

The Virtex4-FX20 device allowed us to implement an SoC with four MicroBlaze cores. Given that we have the list of retrieved values arriving in the local memory of each CPU, the next step is to run the algorithm for each of the interrogated mutations. It must be noted that many mutations are manipulated by the same CPU. Figure 7 depicts the execution time from the single MicroBlaze system and the fully parallel multicore system using four cores. The overhead due to communication is negligible leading to significant improvement of the total running time.

Table 2 summarizes the cost of the implemented system and of the individual components that are critical for performance and the on-chip resources. The system designer can determine the right option to enable during the design and development according to the requirements, balancing cost of silicon area versus processing time. Currently, the system-on-FPGA has the capacity to run the described algorithm for 425 mutations with 10 replicas each in less than 400 milliseconds.

After the execution of the algorithm is completed for every interrogated mutation then the extracted results must be further analyzed to be shown to the user, either for genotyping analysis, or for gene expression. The architectural option made was to utilize the on-board SRAM with a PLB interface controller; one MicroBlaze acting as a Master to be responsible to transfer the results of the processed data to an external host for further use and visualization.

6. Processing Results from a Custom Microarray by the System-on-FPGA

The proposed multicore architecture was prototyped on an FPGA platform (Virtex-4-FX20) and was used to process data from a glass slide microarray. The microarray featured 8500 spots for 425 mutations variations and their associated wild type with 10 replicas for each of them. This microarray was designed by the Genomics Lab of the Welcome Trust of Oxford University. The probes on the microarray were designed to investigate the following factors to determine their effect on the accuracy of oligonucleotide arrays: (i)isotherm versus nonisothermal probe design,(ii)oligonucleotide probe length,(iii)position of mismatch,(iv)influence of different types of DNA variation (size of deletion or insertion and nature of substitution-mismatched base pairs do not have equal stability),(v)analysis of both strands,(vi)length of linker,(vii)use of control probes.

In order to investigate all these parameters and select the most efficient design of probes for each of 20 selected mutations, a large number of probes were required; therefore the microarray format selected for use was a custom array.

The total number of individual mutations examined was 20 and a total of 425 variations. The specific names of the mutations and of the disease that they are related with are not disclosed here due to ongoing patenting process. Thus, we will use here numbers 1–20 as name and to keep a track of them. MUT will stand for mutation and WT for their associated wild type. For each mutation are printed two kind of probes on the microarray, the probe sequence that binds to the antisense strand and the probe sequence that binds to the sense strand (MUTA and MUTS, resp., with their associated WTA and WTS).

A dedicated software program of Oxford University was used allowing varying parameters so that isothermal probes can be designed, with different lengths, and with the position of the mismatch varying around the centre position by a desired distance.

For each sequence, the probes were designed following the isothermal approach (I stands for isothermal), using a 15-mer linker (oligo comprised of 15 bases), within a 5-degree window (70– in the dedicated software program) and also following a lower isothermal (63–) so to test the effect of this (I2 stands for the lower isothermal). Pm probes stand for 25-mer Affymetrix style probes. Thus, the following variants of each mutation are interrogated in the designed microarray shown in Table 3.

The custom array is being fabricated by Oxford Gene Technologies (OGT). The arrays are fabricated using in situ oligonucleotide synthesis by an ink-jet printing method. The 8.5 K array from OGT uses a hybridization chamber with 8500 oligos in it.

The microarray was clustered in subarrays of spots. Each such subarray gives 425 total variations of 20 mutations with their associated wild types. A total of 425 probes were hosted in this array along with their associated wild type, including the positive and nonbinding control sequences. These are used to identify faulty hybridization cases and to define the background correction value. Each of these probes has 10-spot replicas. The algorithm processes 10 measurements for the mutant probe and another 10 for the associated wild type.

Figure 8 shows the microarray used for the evaluation of the performance of the proposed architecture. Even if hybridization took place only with Cy3 labelled target, the array was scanned with both channels (red and green), and then only the green channel was analyzed. The picture was taken by an Agilent scanner.

Oxford has carried out hybridizations using a normal DNA control (without the mutation) and DNAs heterozygous or homozygous for the mutation. The produced scanner data was processed by the proposed emulated architecture on the FPGA executing the algorithm of Section 3, and the results were used to select the appropriate probes to interrogate the studied mutations.

The hybridization performance of a particular probe was compared between the wild type hybridization and the mutant hybridization. The ratio of the intensity change for that probe was then compared to all the other probes for the same allele present on the array.

The Mutation-3 case (antisense strand) data analysis results for the normal DNA control hybridization is shown in Figure 9.

The Mutation-3 case (sense strand) data analysis results are shown in Figure 10.

From the calculations of the algorithm, antisense wild type variation 11 and mutant type 8 (3-WTA-I-11, 3-MUTA-I-9) were selected as the most appropriate probes to detect mutation 3.

The data analysis (of normalized values) was carried out using the proposed architecture and allowed the selection of a number of probe pairs suitable for the detection of 18 of the 20 mutations. The total time required to produce these results was 0.4 seconds.

These hybridizations have provided Oxford with a significant amount of data and hopefully it will allow them to substantially decrease the number of probes to be tested in the future.

7. Conclusions

When scaling to multithousand sensor microarrays, the data volume increases significantly along with the processing time. The data analysis of the results retrieved from microarrays requires processing power and is a time-consuming, cumbersome, and often error-prone task. A data processing algorithm that was presented is capable to analyze the electronic hybridization detection sensor data of a LoC and to decide about the existence of the interrogated mutations. Two alternative architectures for the data analysis were emulatied and their performance was evaluated. Data taken from an implemented microarray of 8.500 spots was processed by the emulated architecture, and the results are presented.

The presented architecture is a robust data analysis circuitry for a Lab-on-Chip, which provides increased reliability by automating spot detection and data processing by on-chip dedicated highly integrated hardware. In particular, the proposed data processing and analysis electronic module are an embedded multicore reconfigurable scalable system-on-chip architecture which is capable to process in a fraction of nowadays processing time data of the current microarrays but also of the future multithousand sensor microarrays. Hence, the integrated microsystem is ideal for a range of applications, from small compact devices optimized for genotyping and pharmacogenomic applications up to gene expression analysis.

Acknowledgments

The authors would like to thank Dr. Lorne Lonie, Marta Paolucci, and Dr. Jiannis Ragoussis from the Genomics Lab of the Wellcome Trust Centre for Human Genetics of the Oxford University for their data to evaluate the performance of the proposed architecture. Also they would like to acknowledge the state of the art section that describes the development of Paul Galvin, Marin Georghe, Mihai Dinca of TYNDALL, Cork, Ireland, (sample preparation), Jean Hue from LETI, Grenoble Cedex, France, (hybridization), and Stephen Bellis from SensL, Cork, Ireland, and Stavros Chantzandroulis from NCSR Demokritos, Agia Paraskevi Attika, Greece, (photosensor and capacitive hybridization electronic detection, resp.).

References

1. F. Model, T. König, C. Piepenbrock, and P. Adorján, “Statistical process control for large scale microarray experiments,” Bioinformatics, vol. 18, no. 90001, pp. S155–S163, 2002.
2. N. Brändle, H. Bischof, and H. Lapp, “Robust DNA microarray image analysis,” Machine Vision and Applications, vol. 15, no. 1, pp. 11–28, 2003.
3. S. Bellis, S. Blionas, J. Carrera, et al., “Competitive technology approaches for electronic hybridisation detection in a microsystem with microfluidics for diagnosis genetic tests,” in Proceedings of the 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS '06), pp. 4103–4106, New York, NY, USA, August-September 2006.
4. M. Bicego, M. Del Rosario Martinez, and V. Murino, “A supervised data-driven approach for microarray spot quality classification,” Pattern Analysis & Applications, vol. 8, no. 1-2, pp. 181–187, 2005.
5. V. Rodellar, F. Díaz, B. Belean, et al., “Genomic microarray processing on a FPGA for portable remote applications,” in Proceedings of the 3rd Southern Conference on Programmable Logic (SPL '07), pp. 13–18, Mar del Plata, Argentina, February 2007.
6. F. Valafar, “Pattern recognition techniques in microarray data analysis: a survey,” Annals of the New York Academy of Sciences, vol. 980, no. 1, pp. 41–64, 2002.
7. S. Mukherjee, “Classifying microarray data using support vector machines,” in A Practical Approach to Microarray Data Analysis, D. Berrar, W. Dubitzky, and M. Granzow, Eds., pp. 166–185, Kluwer Academic Publishers, Dordrecht, The Netherlands, 2003.
8. R. B. Fair, A. Khlystov, V. Srinivasan, V. K. Pamula, and K. N. Weaver, “Integrated chemical/biochemical sample collection, pre-concentration, and analysis on a digital microfluidic lab-on-a-chip platform,” in Lab-on-a-Chip: Platforms, Devices, and Applications, L. A. Smith and D. Sobek, Eds., vol. 5591 of Proceedings of SPIE, pp. 113–124, Philadelphia, Pa, USA, October 2004.
9. T. Xu and K. Chakrabarty, “A cross-referencing-based droplet manipulation method for high-throughput and pin-constrained digital microfluidic arrays,” in Proceedings of the Design, Automation & Test in Europe Conference & Exhibition (DATE '07), pp. 1–6, Nice, France, April 2007.
10. W. L. Hwang, F. Su, and K. Chakrabarty, “Automated design of pin-constrained digital microfluidic arrays for lab-on-a-chip applications,” in Proceedings of the 43rd ACM/IEEE Design Automation Conference (DAC '06), pp. 925–930, San Francisco, Calif, USA, July 2006.
11. I. Ragoussis, M. Paolucci, and L. Lornie, “D5.1Development of a robust mutation, detection protocol,” Micro2DNA ICT-027333-STP EC project, July 2007.
12. C. Zhang, J. Xu, W. Ma, and W. Zheng, “PCR microfluidic devices for DNA amplification,” Biotechnology Advances, vol. 24, no. 3, pp. 243–284, 2006.
13. A.-C. Syvänen, “Accessing genetic variation: genotyping single nucleotide polymorphisms,” Nature Reviews Genetics, vol. 2, no. 12, pp. 930–942, 2001.
14. D. J. Cutler, M. E. Zwick, M. M. Carrasquillo, et al., “High-throughput variation detection and genotyping using microarrays,” Genome Research, vol. 11, no. 11, pp. 1913–1925, 2001.
15. T. R. Hughes, M. Mao, A. R. Jones, et al., “Expression profiling using microarrays fabricated by an ink-jet oligonucleotide synthesizer,” Nature Biotechnology, vol. 19, no. 4, pp. 342–347, 2001.
16. J. C. Jackson, A. P. Morrison, D. Phelan, and A. Mathewson, “A novel silicon Geiger-mode avalanche photodiode,” in Proceedings of the IEEE International Electron Devices Meeting (IEDM '02), pp. 797–800, San Francisco, Calif, USA, December 2002.
17. Y. H. Yang, M. J. Buckley, S. Dudoit, and T. P. Speed, “Comparison of methods for image analysis on cDNA microarray data,” Journal of Computational and Graphical Statistics, vol. 11, no. 1, pp. 108–136, 2002.
18. G. Kornaros, et al., “Architecture of a consumer lab-on-chip for pharmacogenomics,” in Proceedings of the IEEE International Conference on Consumer Electroncs (ICCE '08), Las Vegas, USA, January 2008.