Lymphatic filariasis (LF) is targeted for global elimination. LF elimination programmes in different countries, including Egypt, are supported financially by national and international agencies. The national programme in Egypt is based on mass drug administration (MDA) of an annual dose of a combination of 2 drugs  to all endemic villages. This study aimed primarily to estimate the Total and Government costs of two rounds of MDA conducted in Egypt in 2000 and 2001, the average cost per person treated, and the cost share of the different programme partners.The Total costs reflect the overall annual costs of the MDA programme, and we defined Government costs as those expenditures made by the Egyptian government to develop, implement and sustain the MDA programmes. We used a generic protocol developed in coordination with the Emory Lymphatic Filariasis Support Center. Our study was concerned with all costs to the government, donors and other implementing parties. Cost data were retrospectively gathered from local, regional and national Ministry of Health and Population records. The total estimates for each governorate were based on data from a representative district for the governorate; these were combined with national programme data for a national estimate.The overall Total and Government costs for treating approximately 1,795,553 individuals living in all endemic villages in the year 2000 were US $3,181,000 and US $2,412,000, respectively. In 2001, the number of persons treated increased (29%) and the Total costs were US $3,109,000 while Government costs were US $2,331,000. In 2000, the average Total and Government costs per treated subject were US $1.77 and $1.34, respectively, however, these costs decreased to US $1.34 and $1.00, respectively in 2001. The coverage rate was 86.0% in 2000 and it increased to 88.0% in 2001.The Egyptian government provided 75.8% of all resources, as reflected in the Total cost estimates, and international agencies contributed the rest. Such data highlight both the commitment of the Egyptian government and the significance of the contributions of international bodies toward the LF elimination programme. Wuchereria bancrofti and transmitted by Culex pipiens mosquitoes; it is known to be endemic in rural areas of Egypt. Administratively, Egypt consists of 26 governorates with a population currently estimated at 68 million people, more than 60% residing in the densely populated governorates of the Nile Delta. The disease has a focal distribution, and it is estimated that currently over 2.5 million people are at risk of acquiring W. bancrofti infection. LF is considered one of the most important vector borne diseases in Egypt, posing a major public health problem in 6 governorates in the Nile Delta and the governorates of Giza and Asiut in Upper Egypt.Lymphatic filariasis (LF), also known as elephantiasis is caused by infection with the threadlike nematode In Egypt the Malaria, Filariasis and Leishmaniasis Control Department of the Ministry of Health and Population (MOHP) has worked since the 1970s to map and decrease LF prevalence by focalized treatment initiatives to reduce transmission. Selective treatment of microfilaremic subjects with a 12-day regimen of diethylcarbamazine  was recommended. In 1996, the MOHP changed its anti-LF strategy to selective treatment with single-dose DEC (6 mg/kg) as it was shown to be equally effective. Currently, 178 villages, with >1% LF prevalence, are included in the national elimination programme and covered by multiple MDA rounds. Many of the remaining villages are believed to have infection rates <1%, based on data acquired before 1990. However, mapping activities undertaken in certain governorates (e.g. Dakahlia governorate) in 2000 and 2001 revealed that 12 villages have >1% LF prevalence rates and were therefore added to the MDA programme. In this context, the MOHP is now required to map the remaining previously endemic villages and to initiate a supplemental MDA programme to include any village found to have prevalence rates of 1% or more. This remains an essential step prior to certifying Egypt as country free of LF. Therefore, data from the present study are of great importance to MOHP decision makers for developing a supplemental programme expansion, with a realistic budget estimate and for raising funds essential for its implementation.The present study estimated the overall Total and Government costs of MDA in LF-endemic villages in the Nile Delta and the governorates of Giza and Asiut in Upper Egypt. Approximately 1,795,553 individuals, living in 161 villages in 7 governorates, were treated by MDA in the year 2000 with a coverage rate of 86.0%. The overall Total and Government costs for MDA that year were US $3,181,000 and US $2,412,000, respectively. Average Total and Government costs per treated subject and person at risk in 2000 were US $1.77 and US $1.34, respectively. In 2001, while the number of persons treated increased (29%) and the coverage rate increased to 88.0%, the Total and Government costs decreased to US $3,109,000 and US $2,331,000, respectively. Consequently, the average Total and Government costs per treated subject and per person at risk decreased to US $1.34 and US $1.00, respectively.National agencies of Egypt contributed 75.8% of the Total costs, a fact highlighting the commitment of the Egyptian government toward the LF elimination programme but not minimizing the significance of the contributions of international bodies, such as WHO, EMRO, GSK and the Arab Fund for Economic and Social Development.The results of this study set the stage for the MOHP to initiate a supplemental MDA programme expansion, estimate a realistic budget and raise essential funds for its implementation. The objectives of the supplemental programme are to map villages with a previous history of LF, not included in the current programme, and ensure coverage of eligible villages with needed MDA rounds. This action is necessary prior to certifying Egypt as a country free of LF.The author(s) declare that they have no competing interests.RMRR participated in the study design and its coordination, data collection and analysis, and drafted the manuscript. ASG designed and modified the generic protocol to suit the Egyptian programme and participated in writing the manuscript. HAK shared in the study design, data collection and reviewed the manuscript. All authors read and approved the final manuscript.


Paramyxoviridae, subfamily Pneumovirinae) is a natural respiratory pathogen of rodent species and an important new model for the study of severe viral bronchiolitis and pneumonia. However, despite high virus titers typically detected in infected mouse lung tissue in vivo, cell lines used routinely for virus propagation in vitro are not highly susceptible to PVM infection. We have evaluated several rodent and primate cell lines for susceptibility to PVM infection, and detected highest virus titers from infection of the mouse monocyte-macrophage RAW 264.7 cell line. Additionally, virus replication in RAW 264.7 cells induces the synthesis and secretion of proinflammatory cytokines relevant to respiratory virus disease, including tumor necrosis factor-α (TNF-α), interferon-β (IFN-β), macrophage inflammatory proteins 1α and 1β (MIP-1α and MIP-1β) and the functional homolog of human IL-8, mouse macrophage inflammatory peptide-2 (MIP-2). Identification and characterization of a rodent cell line that supports the replication of PVM and induces the synthesis of disease-related proinflammatory mediators will facilitate studies of molecular mechanisms of viral pathogenesis that will complement and expand on findings from mouse model systems.Pneumonia virus of mice (PVM; family  Amoniewed in . The patiewed in .in vitro propagation of this pathogen is significantly less straightforward. The primate BS-C-1 epithelial cell line supports minimal rates of PVM replication in vitro  for com6 pfu/ml  probed w6 pfu/ml  on RNA p4 pfu per 5 × 106 cells). On day 7, virus titer in the culture supernatants was determined by standard plaque assay [in vivo, we determined that PVM replicated to a limited extent in vitro (< 103 pfu/ml supernatant) in each of the aforementioned cell lines. The mouse monocyte/macrophage RAW 264.7 cell line (established from a tumor induced by Abelson murine leukemia virus) generated the highest virus titers (104 pfu/ml) under culture conditions described. Cells of the RAW 264.7 line also support replication of other unrelated virus pathogens, including murine hepatitis virus and Japanese encephalitis virus [The cell lines evaluated for the ability to support virus replication included rat epithelial L2 and RLE, mouse epithelial LA4, mouse macrophage RAW 267.4 and J774A.1, and primate epithelial A549, BS-C-1, and HEp-2. All were inoculated with PVM on day 0  on day 0 and harvested on days 2 – 5 thereafter. RAW 264.7 is a semi-adherent cell line, and is not well-suited for plaque assays. Here, virus replication was examined qualitatively on western blot of cellular homogenates probed with PVM-specific antisera Figure . Virus w5 copies per microgram total RNA on day 5 of infection. No copies of the virus SH gene were detected in uninfected cells.Virus replication was also examined quantitatively by Q-RT-PCR using the virus SH gene as a target sequence , Figure. PVM repRAW 264.7 cells respond to infection with PVM by producing a variety of proinflammatory mediators. Transcription of interferon-β in response to virus infection was detected by Q-RT-PCR Figure . Cytokinex vivo model of PVM infection will permit further study of biological responses associated with virus infection and the cellular and molecular level.In summary, PVM has recently emerged as a useful novel model for the study respiratory disease in mice ,27-30; tPVM pneumonia virus of miceIFN interferonMIP macrophage inflammatory peptideTNF tumor necrosis factorRSV respiratory syncytial virusARDS acute respiratory distress syndromeQ-RT-PCR quantitative reverse-transcriptase polymerase chain reactionThe author(s) declare that they have no competing interests.KDD, IMMS, BVM and CAB performed experimental work. JBD and HFR conceived of the study, coordinated the research, and wrote and edited the manuscript. All authors read and approved the final manuscript.


Isavirus within the family Orthomyxoviridae. It possesses the major functional characteristics of the virus family including haemagglutinating, receptor destroying enzyme (RDE), and fusion activities associated with the virion surface proteins. It is generally accepted that ISAV agglutinates erythrocytes of several fish species and that the ISAV RDE activity dissolves this haemagglutination reaction except for Atlantic salmon  erythrocytes. We used electron microscopy to examine the physical interaction between ISAV and erythrocytes from Atlantic salmon and rainbow trout (Oncorhynchus mykiss) during haemagglutination. We present evidence that ISAV enters into Atlantic salmon erythrocytes. Atlantic salmon erythrocytes incubated with ISAV for 4 hours showed endocytosis of the virus particles, which is consistent with virus infection. These observations suggest that the lack of dissolution of ISAV-induced haemagglutination of Atlantic salmon erythrocytes favours virus infection of the erythrocytes. Moreover, such a haemagglutination-infection phenotype is fundamentally different from haemagglutination by avian and mammalian orthomyxoviruses, and is indicative of a different pathogenesis for the fish orthomyxovirus.Infectious salmon anaemia (ISA) virus (ISAV) is a fish orthomyxovirus that has recently been assigned to the new genus  Isavirus within the family Orthomyxoviridae. The virus causes a fatal clinical disease in Atlantic salmon with signs of exophthalmia, pale gills, ascites, congestion of gut, enlargement of liver and spleen, petechial hemorrhages in the visceral organs, and severe anemia [Infectious salmon anemia (ISA) virus (ISAV) is a fish orthomyxovirus that has recently been assigned to the new genus e anemia . In othee anemia .Salmo trutta) and the RDE activity allowed the virus to elute from erythrocytes of other fish species except Atlantic salmon, which did not elute after 24 hours [Enveloped viruses like ISAV enter target cells by attachment to receptor molecules on the plasma membrane and this initial attachment determines the host range and tissue tropism of the virus . Hemaggl24 hours . This ha24 hours . In the 9.75 TCID50/ml for NBISA01 or 104.25TCID50/ml for RPC/NB 04-085-1 and 50 μl of 1% erythrocytes of Atlantic salmon or rainbow trout in a microhemagglutination plate, according to the procedure described by Falk et al. [The erythrocytes used for haemagglutination in this study were collected from specific pathogen free Atlantic salmon and rainbow trout weighing 20–30 g. The ISAV strains used were grown in TO cell line as previously described [k et al. . In ordeTo process the samples for electron microscopy, the haemagglutination reactions from each well were pooled and fixed overnight at 4°C in 3% final glutaraldehyde solution in 0.1 M phosphate buffer. The pellets were recovered by centrifugation at 3,000 rpm for 10 minutes and resuspended in 1 ml of 0.1 M phosphate buffer. The samples were centrifuged at 4,000 rpm for 10 minutes twice to completely remove the glutaraldehyde. Secondary fixation was done on the pellet using 1 ml of 1 % osmium tetra-oxide in 0.1 M phosphate buffer for 30 minutes. The fixed samples were centrifuged at 4,000 rpm for 10 minutes and the pellet was embedded with 4% agar in distilled water and cut into pieces before dehydration in ascending concentrations of ethanol . A 5-minute propylene oxide step was done before the infiltration procedure. Infiltration was carried out with a mixture of epon resin and propylene oxide in a ratio of 1:1 and 1:3 each for 30 minutes, and a final overnight infiltration step with 100% epon in vacuum. The pellet pieces were then embedded in epon resin overnight at 60°C. Semi-thin sections (0.5 μm) were cut from two pieces of pellet from each experimental unit and stained with 1% toluidene blue in 1% sodium tetra-borate solution and viewed under light microscope. Ultrathin sections (80 nm) were cut and recovered using copper super grids and double stained with uranyl acetate and Sato's lead stain. The sections were examined using a Hitachi H7500 transmission electron microscope operated at 80 KV. The experimental out put presented in here is a result of replicate observations.2, and 9.4 μl of nuclease-free water. The thermal conditions were one cycle of reverse transcription at 55°C for 30 min, initial denaturation at 95°C for 30s followed by 50 cycles of 95°C for 5s, 59°C for 10s, 72°C for 10s, and data acquisition at 80°C for 2s. Melting curve analysis was performed from 70°C to 95°C in 0.1°C/s increments to assess the specificity of the RT-PCR products. The quantitative  and melting curve data were analyzed using Light Cycler software version 3.5 (Roche Applied Science). The real-time reaction products were also resolved by 1% agarose gel electrophoresis in 0.5 × TBE buffer and stained with ethidium bromide and photographed under 304 nm UV light.Infectious salmon anemia virus real-time RT-PCR was done using the LightCycler 1.2 system with RNA Amplification Kit SYBR Green I (Roche Applied Science) and PCR primers FA-3/RA-3 targeting a 220-bp product on ISAV segment 8 [Transmission electron microscopic analysis of the ultrathin sections showed the NBISA01 virus closely apposed to the cell membrane of Atlantic salmon erythrocytes and the apparent stages of endocytosis where there was an initial close apposition of the virus, the presence of the virus within an invagination of the plasma membrane that has formed a pit, partial closure of the pit, and virus particles in membrane bound vesicles within the cytoplasm of erythrocytes by 4 hours  and moderate mortality in rainbow trout (50%) . In the In conclusion, these results show apparent endocytosis of the high pathogenic NBISA01 strain into erythrocytes of Atlantic salmon to a larger extent and in rainbow trout to a minor extent. The entry of the virus into erythrocytes is favoured by the lack of haemagglutination elution of the pathogenic ISAV strains and is suggestive of virus infection of Atlantic salmon erythrocytes. The low pathogenic RPC/NB 04-085-1 strain was able to closely appose to erythrocytes of both hosts at an earlier sampling point but there was no morphological evidence of endocytosis by erythrocytes of both hosts at the later sampling point.Infectious salmon anaemia virus (ISAV), haemagglutination-esterase (HE), receptor-destroying enzyme (RDE), Tris borate EDTA (TBE).The author(s) declare that they have no competing interests.STW conducted all the experiments and wrote the manuscript. DWW and GMW instructed STW in the use of the electron microscope, interpretation of electron micrographs, and edited the paper. MJTK assisted with running the real-time RT-PCR assays. FSBK conceived the study, coordinated the research efforts and edited the paper. All five co-authors read and approved the final manuscript.


Studies in model organisms suggest that epistasis may play an important role in the etiology of complex diseases and traits in humans. With the era of large-scale genome-wide association studies fast approaching, it is important to quantify whether it will be possible to detect interacting loci using realistic sample sizes in humans and to what extent undetected epistasis will adversely affect power to detect association when single-locus approaches are employed. We therefore investigated the power to detect association for an extensive range of two-locus quantitative trait models that incorporated varying degrees of epistasis. We compared the power to detect association using a single-locus model that ignored interaction effects, a full two-locus model that allowed for interactions, and, most important, two two-stage strategies whereby a subset of loci initially identified using single-locus tests were analyzed using the full two-locus model. Despite the penalty introduced by multiple testing, fitting the full two-locus model performed better than single-locus tests for many of the situations considered, particularly when compared with attempts to detect both individual loci. Using a two-stage strategy reduced the computational burden associated with performing an exhaustive two-locus search across the genome but was not as powerful as the exhaustive search when loci interacted. Two-stage approaches also increased the risk of missing interacting loci that contributed little effect at the margins. Based on our extensive simulations, our results suggest that an exhaustive search involving all pairwise combinations of markers across the genome might provide a useful complement to single-locus scans in identifying interacting loci that contribute to moderate proportions of the phenotypic variance. Although there is growing appreciation that attempting to map genetic interactions in humans may be a fruitful endeavor, there is no consensus as to the best strategy for their detection, particularly in the case of genome-wide association where the number of potential comparisons is enormous. In this article, the authors compare the performance of four different search strategies to detect loci which interact in genome-wide association—a single-locus search, an exhaustive two-locus search, and two, two-stage procedures in which a subset of loci initially identified with single-locus tests are analyzed using a full two-locus model. Their results show that when loci interact, an exhaustive two-locus search across the genome is superior to a two-stage strategy, and in many situations can identify loci which would not have been identified solely using a single-locus search. Their findings suggest that an exhaustive search involving all pairwise combinations of markers across the genome may provide a useful complement to single-locus scans in identifying interacting loci that contribute to moderate proportions of the phenotypic variance. Drosophilla melanogaster and Saccharomyces cerevisiae (yeast) have suggested that epistasis occurs frequently, involves multiple loci, and in some cases produces effects as large as the main effects at the individual loci  in the case of the Either Significant Two-Stage Strategy. Through simulation we found this procedure to provide an accurate test of interaction between two loci [We also determined the power of two simple two-stage strategies. We refer to the first of these strategies as the “Both Significant Two-Stage Strategy” and the second as the “Either Significant Two-Stage Strategy.” In the first stage of both procedures, we identified all loci that were significant in single-locus tests (as above) at one of seven liberal thresholds  variance (the derivation of these components is well known –46 and w2). The mean is the sum of all genotypic means weighted by their frequencies:To illustrate this partitioning formally, consider the 3 × 3 matrix of genotypic means and their frequencies in Dataset S1(4.9 MB XLS)Click here for additional data file.


The Plant Virus Biodiversity and Ecology (PVBE) project has been initiated to survey the biodiversity of viruses affecting vascular plants. The International Committee for the Taxonomy of Viruses recognized a global total of about 2,000 species of viruses as of 2005 . HoweverClosing this gap begins with a broad cataloging and study of viruses affecting wild plants, whether symptomatic or asymptomatic, to gain a more objective view of virus populations in nature. Ostensibly, it ends with a better understanding of the mutualistic interactions between viruses and their hosts. Newly discovered viruses will provide information for those doing basic research on the mechanisms of virus replication, translation, particle assembly, and movement, as well as provide tools for biotechnology . It is possible that some of these new viruses may later be recognized as the origins of future crop scourges. Knowledge now of their existence and ecology will allow us to better prepare and prevent the catastrophic outcomes often associated with such outbreaks. Gathering information on these viruses will also increase our ability to do more extensive sequence comparisons between viruses, thereby allowing better predictions for active site identities within viral proteins, and to identify novel viral proteins and their functions.Not only will these efforts help us understand virus ecology, but there is also great potential for revolutionizing plant ecology. The extended phenotype of a virus may affect a plant's local adaptation to its environment. Endophytes, mycorrhizae, or other symbionts could potentially mediate such interactions. Given that many plant viruses are generalists with respect to host species, it is theoretically possible that such effects may be ecosystem-wide. RNA silencing  has dramKnown virus–vector–host systems exhibit a bewildering complexity. It is increasingly evident that the simple view of viruses as pathogens is outdated. As “little pieces of genetic information” transferred from one organism to another, they have potential for facilitating all sorts of interactions among macroscopic life. Given intimate genetic interactions with their hosts, viruses could potentially be prime drivers of evolutionary change. The degree to which viruses have determined micro- and macro-evolutionary patterns in vascular plants is mostly unexplored, and the relevant questions are not obvious. Comparing virus phylogenies to plant phylogenies will allow us to generate hypotheses about the nature of their coevolution and to push ecology beyond the simple concepts of competition, mutualism, parasitism, and predation.The PVBE effort faces multiple challenges, including creating resources for an effort sustainable beyond the funding period and providing tools for the analysis of geographic and temporal infection patterns that can be applied to other systems. One of our goals is to establish a community resource for information on plant viruses and their relationships with noncrop plant hosts. This will help us to better estimate the global number of plant viruses and to address the following important scientific questions: what is the relationship between virus biodiversity and host biodiversity? how often do coinfections by different viruses occur in natural systems? and what is the incidence of disease-inducing viruses compared with asymptomatic or beneficial viruses in natural systems? We anticipate a wealth of data from The Nature Conservancy's Tallgrass Prairie Preserve area alone, but the addition of other geographical areas will eventually provide a much bigger picture of continental and global trends in plant virus biodiversity and ecology, a new and emerging field of study that will hopefully enable us to answer these important questions.


Most virus detection methods are geared towards the detection of specific single viruses or just a few known targets, and lack the capability to uncover the novel viruses that cause emerging viral infections. To address this issue, we developed a computational method that identifies the conserved viral sequences at the genus level for all viral genomes available in GenBank, and established a virus probe library. The virus probes are used not only to identify known viruses but also for discerning the genera of emerging or uncharacterized ones.Using the microarray approach, the identity of the virus in a test sample is determined by the signals of both genus and species-specific probes. The genera of emerging and uncharacterized viruses are determined based on hybridization of the viral sequences to the conserved probes for the existing viral genera. A detection and classification procedure to determine the identity of a virus directly from detection signals results in the rapid identification of the virus.We have demonstrated the validity and feasibility of the above strategy with a small number of viral samples. The probe design algorithm can be applied to any publicly available viral sequence database. The strategy of using separate genus and species probe sets enables the use of a straightforward virus identity calculation directly based on the hybridization signals. Our virus identification strategy has great potential in the diagnosis of viral infections. The virus genus and specific probe database and the associated summary tables are available at  Traditional viral detection techniques such as R method  can idenOligonucleotide microarrays have been used for monitoring and analyzing viral pathogens -5. TherePreviously reported studies ,3 have eBased on the experiences of identifying novel viruses, such as the SARS coronavirus (SARS-CoV) or the Nipah virus, a virus can exhibit considerable sequence identity with other viruses in the same genus ,7. AlthoIn order to design a virus microarray capable of identifying a larger number of viruses with high accuracy but with fewer probes than previously reported, we have developed a new method for designing probes for the precise and direct identification of viruses at the genus, species, and strain levels. Our method narrows down the computation of conserved sequences to the genus level and eliminates sequence redundancy within a viral genus probe set by considering the mutual sequence similarity among all the viral genomes in the same genus. Compared with the tiling approach, our approach greatly reduces the redundancy and number of conserved probes for a given viral genus. The method involves two separate sets of probes: (i) the conserved sequence probes for genus identification; and (ii) specific probes for viral species or stain identification. The probe set for each genus contains a panel of 70-mer oligonucleotides that not only covers all the sequenced known viruses of the genus but also maximizes the likelihood of detecting emerging or unsequenced viruses. Long 70-mer probes help to alleviate sequence-dependent hybridization variation, improve hybridization efficiency, and enhance the capability of identifying unknown viruses. To compensate for the poor discrimination capability of long probes, additional species- or subtype-specific probes were designed for each fully sequenced virus to enhance the specificity of virus identification. The identity of a virus is determined based on a simple arithmetic algorithm that calculates the average signal intensity of the multiple probes for a virus, in contrast to the intensive computation needed for pattern comparisons. The use of separate genus and species probe sets facilitates a cross-examination strategy to increase the identification accuracy. The identity of a virus is established by the concordant results for both the genus- and cognate-species-specific probes.More than 19,600 full-length viral genomes covering 53 viral families and 214 genera were obtained from ~ 340,000 viral sequences archived in the GenBank viral database (Release 152). The genome segments of the viruses with segmented or multipartite genomes were treated as individual viral sequences. Additional partially sequenced viral genomes were included for computing the conserved genus probes for genera with ≤ 5 fully sequenced members and viruses with segmented or multipartite genomes, which made up a total number of 27,610 viral genome sequences for the computation. Viral genome sequences with more than 99.5% similarity were treated as redundant, and only the longest sequences were retained; the resulting 5,749 nonredundant viral genome sequences were used in the analysis.The probe design strategy employs two separate probe sets: species-specific probes and conserved genus probes. Genera with insufficient viral sequences for identifying conserved probe sequences are marked in the virus probe database and extra species-specific probes are designed for each viral species of each such genus. The species-specific probes were designed using the algorithm described by Chang and Peck , and theG = {vi| i = 1,..., n} is a collection of n viruses, in which each virus vi  is associated with a subset of G, where subset Gi)  more than 75% local sequence similarity in a 50-bp window with any virus vj ∈Gi); or (ii) >15 consecutive bases pairing with vj [where  with vj .vi similar to other viruses in Gi)  = ∑j≠i Iji(x), which is a step contour function from 0 to n-1.3. The sequence segments of virus vi are defined as the regions with the maximum number of similarity hits in Si(x): Ci = {x| Si(x) = Si(x))}.4. The conserved sequences of G is the union of all Ci's: C'(G) = CL (the query sequence) in C'(G) is selected first and aligned against the other sequences in it using the BLASTN program. Any sequences in C'(G)-{CL} having ≥ 80% similarity with CL are grouped together with CL to form C'1(G), the first subgroup of C'(G). CL is then renamed as C(1) to represent the first subgroup. The first subgroup,C'1(G), is then excluded from C'(G). This procedure is repeated for the remaining sequence segments in C'(G) until k such longest sequence segments {C(1),..., Ck) (and k subgroups {C'1(G),..., C'k(G)} are identified, with every sequence in C'(G) assigned to one subgroup. The union of all the longest sequences, G. A simplified illustration delineating the above procedures is provided in 5. The conserved sequence set of P = {pj| j = 1,..., m is selected from each nonredundant sequence in P.6. To prevent significant cross-hybridization to noncognate viral genus sequences, a 70-mer conserved probe set  program  to pick P with the minimum number of probes that can collectively yield similarity hits with all the virus members of G, each pj ∈P is aligned against vi ∈G using the BLASTN program. The BLASTN result between pj and vi can be expressed as an array,Aj × i:7. To search for a subset of P = {pj| j = 1,..., m}, pj ⊆G', and G' = {v'i| i = 1,..., m, where v' i represents similarity hit(s) with vi by pj(s) ∈P. Let Card(pj) be the cardinality of probe pj  in array Aj × i. We first search for a minimum coverage set P such that Card(Card(PS ) for all subsets of P (PS ⊆ P) satisfying the following sequential criteria: (i)  if more than one set of Card(Card(PS), and (iii) randomly pick one of those  if more than one set of P-G.Thus, the 70-mer conserved probe set can be redefined as aaa@3E02@,13, (ii)n) viruses in a viral genus (G), the similarity sequence segments for a given virus vi compared to the other five (n-1) viral genomes (Gi) are designated Iji   and plotted along the vi genome. The highest-ranking (max(Si)) similarity sequences should be the most-conserved sequences (Ci) for virus vi and the other five viral genomes  Fig. . These sG is obtained. Assuming that nine 70-mer candidate conserved probes  are selected by steps 5 and 6 in the probe design algorithm, Table 1  of G. Each "×" in Table 1 , at least two probes , , , , , , , }) that can collectively yield similarity hits with all the virus members of G  is the best selection since the two probes together maximize the similarity hits (max  = 10) with G and therefore increase the likelihood of detecting a virus. Subsequently, probe set  is randomly selected for second-round coverage of G among the three probe sets , , and  because all of them produce the same eight similarity hits with all the virus members of G.The above procedures are repeated until a panel of conserved segments derived from all of the viral genomes in 1 Figure  demonstr1 Figure  indicatevSARS-CoV) was aligned with the other 10 genomes G(SARS-CoV) of the coronavirus genus using the BLASTN program  with the other 10 genomes of the same genus for that particular sequence location . The two viral segments showing the highest number of similarity hits with the other genomes (CSARS-CoV) were extracted for microarray probe design.We subsequently used the coronavirus genus to test the procedure for finding similar viral sequence segments. The SARS-CoV genome ) in step 5 of the above probe design algorithm). These segments can be further sorted into five nonredundant groups by the BLASTN alignment program. The five longest sequences  and specific probes covering 26 species of viruses in the coronavirus (7 species) and flavivirus (19 species) genera see . A blankY-axis) hybridized to the seven test viral samples (X-axis). The possible genus identity (or identities) of a test viral sample can be determined from the magnitude of the genus signal. Figure Y-axis) hybridized to the 7 test viral samples (X-axis). The specific identity of the test virus can be read directly from the extent of the specific signal strength in the chart. Verifying the results for the genus-specific  the number of existing fully sequenced viral genomes in a genus, and (ii) the degree of genome structure diversity for the viral members of the same genus. A larger number of available sequenced viral genomes in a genus makes it easier to identify the conserved sequences of the genus, and greatly enhances the probability of detecting unknown or unsequenced members of the genus. In addition, traditional virus taxonomy is not based on viral genome sequences. Thus, in some cases, the viral genomes are highly divergent among the members of a genus, such that each computed genus probe is only similar to a small fraction of the genus members, or may even be unique. The genus of lentivirus is one example, for which a poor detection rate (55.6%) based on The principle behind the conserved probe design is to search for high similarity sequences that could hybridize to all the virus members of a given genus but not to the viruses of other genera. Therefore, accurate prediction of cross-hybridization is a prerequisite for the feasibility of the probe design method. In general, 70–80% global sequence similarity or a high local sequence similarity between two sequences could cause substantial cross-hybridization ,14-16. SThe accurate detection of specific viruses by species-specific probes depends on avoiding nontarget cross-hybridization. Nevertheless, cross-hybridization to strains of viruses with extremely similar genome sequences or by confounding mechanisms may be inevitable. For example, to investigate the JEV cross-hybridization issue in Figure In addition to the cross-hybridization issue, different probes for the same virus can yield very different signal intensities  ,14-16,21There are, however, limitations to the virus microarray approach described here. An obvious one is that the range of viruses detectable by the method is limited by the content of viral genera archived in the database. Therefore, if the emerging new virus belongs to a heretofore unknown genus, then none of the probes would reveal a detectable hybridization signal.Sum(Fe|G)/Sum(Fe|G)). If more than one genus are present, there will be more than one non-zero Sum(Fe|G) values. One of the genera will be given 100% and the others will be given a percentage smaller than 100%. Whether the signals are true genus signals or cross-hybridization signals can be verified by the presence of hybridization signals from the cognate species-specific probes. Only concordant genus and species identification results are accepted for assigning virus identities, which is one of the advantages of having separate probe sets for genus and species identification.Although our method ensures identification accuracy by verifying the results with the concordant taxonomical association of a genus and species, it is common for an assay sample to contain viruses of more than one genus or more than one species of viruses belonging to the same genus. As described in the Methods section, the viral genus is assigned using a scoring method based on signal strength; that is, Moreover, the situation is more difficult than the above multigenus determination if more than one species belonging to the same genus are present,. Accurate discrimination of true signals from cross-hybridization depends on setting the correct signal thresholds together with discretion based on epidemiology statistics. High threshold values for the signal strengths increase the virus identification accuracy. As shown in Figure In this study we gathered virus information from GenBank which contains viral sequences of varying lengths, qualities and redundancy. It is noted that the viral sequences in GenBank are not well moderated  and taxoMost existing virus detection and identification methods are capable of identifying only a small number of known viruses in a single assay. Parallel screening for a broad spectrum of uncharacterized viruses using microarray technology overcomes this limitation and also provides a means of discovering emerging new viruses. Our probe design concept employs a panel of conserved sequence probes to directly classify emerging or unsequenced viruses at the genus level, and specific probes for known virus identification in a direct-readout manner. Moreover, using the two types of probes to determine the identity of a virus in a complementary way further assures detection accuracy. The ability to detect a large variety of viruses using the virus microarray approach has great potential in facilitating the discovery of emerging viruses and diagnosing diseases of unknown etiology or patients infected with unknown or multiple simultaneous viral pathogens.In summary, we have established a virus probe design method and virus identification process for viral classification and identification based on a microarray approach. Our new probe design strategy and virus detection method allow direct reading of virus identity, and differ significantly from the probe design and virus detection methods described in the literature ,26. The . To our knowledge, this database is the most comprehensive virus probe database currently available to the public. The use of the new probe design algorithm, which does not involve tiling probes, reduces the number of probes on a virus microarray yet retains high identification accuracy. The new probe design method makes it feasible to construct a virus microarray with sufficient probes for identifying all the sequenced viruses that infect animals and plants.Finally, the genera and species-specific probes for more than 5,700 viruses are archived in a database that is available on the Internet F, satisfying the following two criteria is eligible to use in the subsequent viral identification procedure: (i) Fviral sample/Fblank ≥ 3 and (ii) Fviral sample – Fblank > 1500. To take both the number and signal intensity of probes satisfying the above criteria in a given genus probe set into consideration, the following genus signal strength (defined as a percentage) is used to determine the viral genus identity: Sum(Fe|G)/Sum(Fe|G)), where Fe|G represents the hybridization intensity of any eligible genus probe in a genus G, Sum(Fe|G) is the sum of all the Fe|G in G, and Sum(Fe|G)) represents the maximum value of Sum(Fe|G) among all the viral genera represented on a microarray. The viral genus identity in a test sample is assigned to the genus with the highest genus signal strength . The presence of multiple viruses of different genera in a sample will result in a nonzero genus signal strength. We then score and rank the possible virus identities in the specific probe region on the basis of the specific signal strength, defined as the average signal intensity of the multiple probes for a given virus divided by the maximum average signal intensity of the multiple probes for every virus represented on a microarray (expressed as a percentage). As for the genus determination method, the presence of multiple viruses in the sample will result in a nonzero signal strength. Finally, the identity of the virus is established by the concordant results for both the genus- and cognate-species-specific probes.We developed the following identification procedure to compute the identity of a virus directly from the hybridization signals. First, to minimize nonspecific hybridization, any probe with a fluorescence signal intensity, Oligonucleotides of 70 mers were synthesized and modified with 5' amino-linker moieties by standard phosphoramidite-synthesis chemistry on a homemade DNA synthesizer , and desAF014161) and DEN-2 (PL046 strain), DEN-3 (H87 strain), and DEN-4 (H241 strain) used in this study were propagated in C6/36 cells using RPMI 1640 medium containing 5% fetal bovine serum (FBS) . The EV71 (neu strain) stock was propagated in RD cells cultured in DMEM (Dulbecco's modified Eagle's medium) supplemented with 2% FBS, penicillin (100IU/ml), and streptomycin (100 IU/ml). SARS-CoV RNA was kindly provided by the Taiwan Center for Disease Control. The copy number of SARS-CoV genomes in a sample was measured using a commercially available quantitative RT-PCR kit . The amount of viruses used in the study were as follows: (i) 1.75 × 104 pfu of DEN-2, (ii) 3.5 × 103 pfu of DEN-3, (iii) 1.75 × 103 pfu of DEN-4, (iv) 13 μg of total RNA derived from JEV-infected A549 lung adenocarcinoma cells, (v) 1.5 × 104 copy numbers of SARS-CoV, (vi) 2.33 × 103 pfu of EV7, and (vii) a mixture of 2.33 × 103 pfu of EV71 and 100 copy numbers of the SARS-CoV viral genome. The blank control was 11.2 μg of total RNA derived from A549 lung adenocarcinoma cells.The JEV (RP-9 strain, GenBank accession number: The viral genomic sequences were randomly amplified as described previously ,28. The This study was conceived by CCC, KP, and PCY. CCC devised the algorithm, performed the computer programming, and designed the experimental strategy, with guidance from KP and PCY. TTL also worked on the programming and constructed the virus database. CHC was responsible for the mathematical formulation and data analysis. YLL and MSH prepared virus cultures as well as viral genomic materials and drafted the parts of the text related to virology. HYH designed and performed the microarray experiments and data analysis. CCC drafted the manuscript and revisions were made by KP and PCY. All authors read and approved the final manuscript.Example description of step 5 of the probe design algorithm. This file contains a description of step 5 of the probe design algorithm. Example viruses are used to illustrate how this step identifies conserved sequences for a viral genus.Click here for fileVirus probe sequences on the test chip, and hybridization data of the probes to various samples. This file contains information on the probes and experimental hybridization data of the test chips used in an empirical approach to demonstrate the validity of the probe design algorithm.Click here for file


There are few cost-minimisation studies in telemedicine. We have compared the actual costs of providing a telepaediatric service to the potential costs if patients had travelled to see the specialist in person.In November 2000, we established a novel telepaediatric service for selected regional hospitals in Queensland. Instead of transferring patients to Brisbane, the majority of referrals to specialists in Brisbane were dealt with via videoconference. Since the service began, 1499 consultations have been conducted for a broad range of paediatric sub-specialities including burns, cardiology, child development, dermatology, diabetes, endocrinology, gastroenterology, nephrology, neurology, oncology, orthopaedics, paediatric surgery and psychiatry.During a five year period, the total cost of providing 1499 consultations through the telepaediatric service was A$955,996. The estimated potential cost of providing an outpatient service to the same number of patients at the Royal Children's Hospital in Brisbane was A$1,553,264; thus, telepaediatric services resulted in a net saving of approximately A$600,000 to the health service provider.Telepaediatrics was a cheaper method for the delivery of outpatient services when the workload exceeded 774 consultations. A sensitivity analysis showed that the threshold point was most sensitive to changes related to patient travel costs, coordinator salaries and videoconference equipment costs. The study showed substantial savings for the health department, mainly due to reduced costs associated with patient travel. That is, if a patient in a regional or remote area of Queensland needs to travel to see the specialist, the majority of the travel and accommodation costs are reimbursed by the state government. The total cost is approximately A$30 M per year [We have evaluated a novel telepaediatric service in Queensland over a five year period. The service provides a centralised call centre to selected paediatricians in regional sites throughout the state . If a spThe telepaediatric service offers a broad range of paediatric sub-specialties including burns, cardiology, child development, dermatology, diabetes, endocrinology, gastroenterology, nephrology, neurology, oncology, orthopaedics, paediatric surgery and psychiatry -8. The sIn the present study, we examined the costs of providing the telepaediatric service to two regional hospitals in Queensland – at Mackay and Hervey Bay – over a five year period. The hospitals are 1100 km and 350 km north of Brisbane respectively. We compared these costs with the potential costs had patients travelled to Brisbane to see the specialist in person .Telepaediatric service records were reviewed to obtain the number of consultations, types of specialist services offered, videoconference time spent and origin of referrals. We compared the actual costs of providing telepaediatrics to the potential costs if patients travelled to the Royal Children's Hospital (RCH) for an outpatient appointment.The fixed and variable costs for both services were determined. The costs of operating the telepaediatric service included the purchase of videoconference equipment, salaries for the coordinators, salaries for clinical staff, telecommunication charges and miscellaneous project expenses.The main costs associated with the transfer of patients to Brisbane for an outpatient appointment at the RCH included staff salaries, patient travel and accommodation. All costs are described in Australian Dollars ($A) at 2005 prices and include 10% Goods and Services Tax (GST); A$1 = approximately US$0.75. All unit costs were rounded to the nearest dollar value unless otherwise specified.The equipment was three videoconference systems, two computers and one printer. The capital cost of these items was converted into an annual equivalent cost through the process of annuitizing these costs over the expected life of the equipment (5 y) using an annual discount rate of 5% ; this anActual travel costs were provided by the hospital patient travel department for patients who did travel to the RCH. Costs used in this analysis were calculated by determining the average costs of travel, doubling the average cost per fare  and multiplying this cost by the number of consultations that took place from each site. Accommodation costs assumed that 10% of families travelling to Brisbane would require at least one night's accommodation before returning home .The average and variable cost per consultation were determined for both services. The average cost was the total service cost divided by the number of consultations. The variable cost per consultation was the total variable cost divided by the number of consultations.The threshold was calculated at which the cost of providing telepaediatric consultations equalled the cost of providing the same service in the conventional manner, i.e. face to face . This waTotal cost of telepaediatrics = Total cost of RCH outpatientsvc + xVvc = Fftf + xVftfFx = (Fvc - Fftf)/(Vftf - Vvc)x = number of consultations; F = Fixed costs; V = Variable cost; ftf = Face to face vc = Videoconference;A sensitivity analysis was conducted to investigate the effects on the threshold point of the assumptions made in the analysis. Thus the sensitivity could be used to identify those costs that had the greatest effect on the threshold, and those costs which had the least effect. Permission to conduct this research was granted by the RCH Ethics Committee and the RCH Executive Management Committee.In the five year period from November 2000 to October 2005, 1499 consultations were conducted for patients at the two sites. 1139 (76%) of the consultations took place in Mackay, whilst the remainder (360) involved Hervey Bay. A total of 545 h was spent providing clinical consultations via videoconference. Consultations involved medical, nursing and allied health staff for 30 different paediatric sub-specialties including post-acute burns care, cardiology, diabetes and neurology  since there were no fixed costs  and radiology were used as a framework for this analysis -18. The In addition to the economic savings generated by the telepaediatric service, there are other benefits of providing specialist paediatric services by telemedicine. The intangible benefits are often difficult to cost, but should be considered when evaluating the viability of such a service. We know from previous work that the telepaediatric service has made specialist services more easily accessible . As a reOne may argue that the staff requirements to do telepaediatrics are greater because a regional practitioner is in attendance during the consultation as well as the specialist. While this is true, there are other compensating factors to consider. For instance, the telepaediatric service ensures that collaboration takes place between the referring clinicians and specialists in Brisbane. Telepaediatric consultations give the regional staff a convenient method of accessing the opinion of a specialist whilst maintaining responsibility for the primary care of the patient. Consultations conducted via videoconference offer excellent learning opportunities for clinical teaching, collaboration and professional education. Arguably, these factors may also assist with recruitment and retention of medical staff in regional and remote hospitals.In other telemedicine applications, staffing costs have been reduced by using a regional practitioner who is not a doctor. For example, in teleneurology, the medical officer was replaced by a specialist trained nurse . The nurIn its first years of operation, the telepaediatric service in Queensland has depended on research funding provided by the Commonwealth Government. In the longer term, it would seem sensible to use a proportion of the potential savings created by the telepaediatric service to cover operational costs.A limitation of our study is that we estimated the costs of providing telepaediatric services to a group of patients during a defined timeframe and compared this with the potential cost of sending patients to the main tertiary hospital to see the specialist in person. It is unlikely that every consultation coordinated by the telepaediatric service represented a saved journey to Brisbane. However the exact number of patient transfers prevented remains unknown because the study was not a randomised control trial (RCT). Indeed, it is debatable whether a RCT would be possible for a range of ethical and practical reasons.The ratio scores described in this paper demonstrate the relative importance of the elements in a cost analysis. In the present cost analysis, the threshold was most sensitive to changes in the costs of patient travel, followed by the costs of the coordinators' salaries and videoconferencing equipment. The costs that had the least impact on the threshold were the clinical staff salaries, ISDN installation, line rental and call costs, and accommodation. This information is important for planners of telemedicine services.The analysis presented in this paper compares the cost of providing a telepaediatric service to two selected hospitals in Queensland, to the potential costs if patients travel to Brisbane to see the specialist in person. The results show that considerable savings accrued to the health department, mainly due to reduced costs associated with patient travel. The results of the present study are relevant to health service planning in Queensland, and elsewhere in the world.The author(s) declare that they have no competing interests.AS and RW conceived the study and conducted the analysis. AS produced the first draft of the manuscript. PS provided valuable guidance with the interpretation of the economic evaluation. All authors contributed to the review and editing of the manuscript. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:


An algorithm, E-Predict, for microarray-based species identification is presented. E-Predict compares an observed hybridization pattern with a set of theoretical energy profiles. Each profile represents a species that may be identified. DNA microarrays may be used to identify microbial species present in environmental and clinical samples. However, automated tools for reliable species identification based on observed microarray hybridization patterns are lacking. We present an algorithm, E-Predict, for microarray-based species identification. E-Predict compares observed hybridization patterns with theoretical energy profiles representing different species. We demonstrate the application of the algorithm to viral detection in a set of clinical samples and discuss its relevance to other metagenomic applications. Metagenomics, an emerging field of biology, utilizes DNA sequence data to study unculturable microorganisms found in the natural environment. Metagenomic applications include studies of diversity and ecology in microbial communities, detection and identification of representative species in environmental and clinically relevant samples, and discovery of genes or organisms with novel or useful functional properties .Common to all of these applications is the task of identifying (and often quantifying the abundance of) individual genes, species, or even groups of species from the large and often complex sequence space being explored. In the most general approach, shotgun sequencing is used to both identify and quantify individual sequences in a sample of interest -8. In a DNA microarrays are also emerging as an important tool in metagenomics ,16-18. PUse of DNA microarrays in metagenomics introduces a series of analytical challenges. First, the sequence space to explore may be very large, especially in the case of environmental samples. Given the technologic constraints on the total number of probes that can be placed on a microarray, improved algorithms are required for optimal probe selection to maximize coverage. Second, microarray data generated in metagenomic studies can be very complex. In the case of viral diagnostics, nucleic acid extracted from clinical specimens usually contains host and bacterial contaminants in addition to viral RNA and DNA. As a result, hybridization patterns are complicated by substantial amounts of noise introduced by specific and nonspecific cross-hybridization that cannot be anticipated or controlled. Third, multiple and potentially closely related species may be present in a single sample, resulting in complex or even overlapping hybridization patterns. Finally, a species identification strategy based on the use of experimentally derived patterns alone is not feasible, because such empirical controls can be obtained only for a limited number of species available as pure cultures or genomic clones. New analytical tools capable of overcoming these challenges are acutely needed.We have previously reported the development of a DNA microarray-based platform for viral detection and discovery  . This set of profiles included all viruses represented on the microarray and many viruses whose genomes became available after the array design had been completed. All microarray oligonucleotides expected to hybridize to a given viral genome were identified using nucleotide BLAST  alignment . Free enThe general E-Predict algorithm for interpreting observed hybridization patterns is shown in Figure In order to optimize the ability of E-Predict to discriminate between true positive and true negative predictions, we first evaluated the performance of several commonly used normalizations and similarity metrics. For this purpose we constructed a training dataset of 32 microarrays obtained from samples known to be infected by specific viruses. Fifteen microarrays represented independent hybridizations of RNA extracted from HeLa cells - a human cell line that is permanently infected with human papillomavirus (HPV) type 18. The remaining microarrays were obtained from 17 independent clinical specimens from children with respiratory tract infections. Ten specimens contained respiratory syncytial virus (RSV) and seven contained influenza A virus (FluA), as determined by direct fluorescent antibody (DFA) test.Intensity and energy vectors were independently normalized using sum, quadratic, unit-vector, or no normalization Table . SimilarWith the exception of Spearman rank correlation, all considered metrics assigned the highest similarity scores to the match profiles on all 32 microarrays, independent of normalization choice. Not surprisingly, separation between interfamily profiles was greater than that between intrafamily profiles. In addition, changes in normalization and similarity metric had greater impact on intrafamily than on interfamily separation. The best overall separation was determined by calculating the product of the means of the intrafamily and interfamily separations divided by the corresponding standard deviations. Sum normalization of the intensity vectors, quadratic normalization of the energy vectors, and uncentered Pearson correlation as the similarity metric achieved the highest overall separation, producing a mean intrafamily separation of 0.69 (standard deviation 0.17) and a mean interfamily separation of 0.93 (standard deviation 0.08). Therefore, we settled on this combination of normalization and similarity metric parameters as our method of choice.Raw similarity scores, as described above, provide an effective means of ranking viral energy profiles based on similarity to an observed hybridization pattern. However, such ranking provides no explicit information regarding the likelihood that viruses corresponding to the best scoring profiles are actually present in a sample under investigation. For example, two profiles may have identical high scores, but one of the scores may reflect a true positive whereas the other may be the result of over-representation of cross-hybridizing oligonucleotides in a profile.e-transformed similarity scores were approximately normally distributed. Outliers on the right tails of the distributions, assumed to be true positives, were removed , and parameters of the null distributions were estimated as the mean and standard deviation of the remaining observations. These parameters were used to calculate the probability associated with any observed similarity score. Probabilities obtained this way should be interpreted as one-tail P values for the null hypothesis, that the virus represented by the profile is not present in the sample.To facilitate the interpretation of individual raw similarity scores, we sought to develop a test of their statistical significance. For this purpose, we obtained empirical distributions of the scores for every virus profile in the energy matrix. The distributions were based on 1,009 independent microarray experiments collected from a wide range of clinical and nonclinical samples representing different tissues, cell types, and nucleic acid complexities. Given such sample diversity, we assumed that any given virus was present in only a small fraction of all samples. Therefore, the empirical distributions are essentially distributions of true negative scores. The logP values ranged between 8.7 × 10-3 and 7.7 × 10-7 (median 2.1 × 10-5), between 4.0 × 10-4 and 1.4 × 10-8 (median 5.1 × 10-8), and between 1.8 × 10-6 and 1.4 × 10-7 (median 4.7 × 10-7), respectively . Even P values of the most closely related intrafamily virus profiles (purple circles) were separated from those of the match viruses by more than 1.1 (HPV45), 2.1 (human metapneumovirus), and 3.4 (influenza B virus) logs. Although the P values obtained for these profiles are more significant than background, their similarity scores are entirely based on oligonucleotides that also belong to the match virus profiles. P values resulting from such profile overlaps can be easily recognized and masked if desired .As shown in Figure Our laboratory is conducting a series of studies focused on human diseases suspected of having viral etiologies. The E-Predict algorithm was developed to assist in the analysis of samples obtained as part of these investigations. As an illustration of its versatility we present four example applications of E-Predict, as it is used in our laboratory.P = 0.002) and several closely related hepadnaviruses  and RSV (P = 0.008), suggesting a double infection. The sample was independently confirmed to contain FluA and RSV, by DFA and specific PCR, respectively. Complete E-Predict output for this example is available as Additional data file 2. The microarray data have been submitted to the NCBI GEO database [In this example, E-Predict was used to identify the presence of two distinct viral species in the same sample Table . The micdatabase  , despite the fact that the microarray, and therefore the profile, did not contain any oligonucleotides derived from the SARS CoV genome.This example illustrates the ability of E-Predict to identify a virus that was not included in the microarray design. Table P values. However, these predictions were based on oligonucleotides corresponding to a conserved 3'-untranslated region shared by these viruses with the SARS CoV [P > 0.04) in the second iteration, effectively removing these profiles from consideration. Complete E-Predict output for this example is available as Additional data file 3.In addition to the SARS CoV prediction mentioned above, several astrovirus and picornavirus profiles had similarity scores with significant SARS CoV ,33. To iThis example illustrates the use of E-Predict to discriminate between closely related viral species such as human rhinovirus (HRV) serotypes Figure . RhinoviIdentifying individual species present in a complex environmental or clinical sample is an essential component of many current and proposed metagenomic applications. Given a foundation of genomic sequence information, DNA microarrays are a high-throughput and cost-effective methodology for detecting species in an unbiased and highly parallel manner. Metagenomic applications employing DNA microarrays include characterization of microbial communities from environmental samples such as soil and water ,17, pathDespite the increasing use of DNA microarrays for species detection and identification, bioinformatics tools for interpreting hybridization patterns associated with complex clinical and environmental samples are lacking. Existing methods have utilized direct visual inspection of hybridizing oligonucleotides ,37 or inWhen challenged with each of these problems, E-Predict proved to be a useful tool for interpreting hybridization patterns, correctly identifying viruses from diverse viral families present in a variety of clinical samples. In particular, E-Predict does not rely on the use of empirically generated reference hybridization patterns, because species identification is based instead on theoretical hybridization energy profiles. The energy profile matrix currently represents over 1,200 distinct viruses whose complete genomic sequences are known. As new viral genomes are sequenced, profiles are added to the matrix to broaden the range of species detection. For example, addition of the SARS CoV profile enabled accurate identification of the virus, even though no oligonucleotides derived from its genome were present on the microarray. Conversely, even when a perfectly matching profile is not available because of limited sequence coverage, E-Predict will identify the closest related species, as long as such species are represented on the microarray. This feature is particularly useful for detecting novel viruses as well as for discriminating between closely related viruses such as HRV serotypes. Naturally, maximum range and precision of detection is achieved through addition of new profiles and periodic microarray updates to include specific oligonucleotides from newly sequenced species.E-Predict is also useful in overcoming problems related to nucleic acid complexity frequently encountered in clinical samples. For example, E-Predict correctly identified hepatitis B virus in a serum sample, despite the fact that the hybridization pattern was complicated by a low signal-to-noise ratio. In another example, E-Predict deconvoluted a complex hybridization pattern, correctly suggesting the presence of two viruses (FluA and RSV) in a nasopharyngeal aspirate sample. In yet another example, iterative application of E-Predict  to a hybridization pattern involving oligonucleotides derived from seemingly unrelated families (coronaviridae and astroviridae) premitted objective recognition that the pattern represented the presence of only one virus (SARS CoV).Using a training dataset of 32 microarrays derived from samples known to contain specific viral species, we identified a set of normalization and similarity metric parameters, which yielded the best discrimination between true positive and true negative species predictions. The combination of sum normalization of the intensity vectors, quadratic normalization of the energy vectors, and uncentered Pearson correlation as the similarity metric was the optimal choice for our data. However, a different set of parameters may be required for applications that use a different nucleic acid amplification or detection strategy. An independent evaluation of potentially useful normalization and similarity metric parameters is therefore recommended for each specific application of the algorithm.P values can be calculated for each distribution.Using our best combination of normalization and similarity metric parameters, we obtained a set of null distributions representing true negative scores. These distributions were based on over 1,000 independent hybridizations and the assumption that the majority of samples were negative for the presence of any given virus. Although valid for our data, this assumption will not hold for all cases. For example, in applications concerned with bacterial species detection, some species may be present in most or even all samples and others encountered only rarely. In this case, a more complicated model will be required to assess whether a specific distribution represents negative, positive, or both negative and positive scores. For example, in cases in which distributions appear bimodal, one mode may represent true negatives and the other true positives. In some cases, targeted experimental verification of a subset of representative scores may be necessary. If both positive and negative score distributions are available, then Several modifications to the algorithm may potentially result in improved prediction accuracy. First, in the current implementation oligonucleotides exhibiting nonspecific cross-hybridization are filtered and the remaining oligonucleotides are weighted equally. Because oligonucleotides exhibit a continuous range of nonspecific hybridization ,30, a moP value estimation may be needed. In addition, for highly abundant species, care should be taken to avoid saturation of individual oligonucleotides, because E-predict performance drops sharply after 20-25% of oligonucleotides in a given profile are saturated (data not shown).Second, no attempt was made to normalize nucleic acid abundances of individual species, which may vary widely in different samples depending on factors such as target-to-background ratio, number of species present, and efficiency of nucleic acid extraction and amplification. Although individual nucleic acid abundances are difficult or impossible to estimate in most metagenomic applications, particularly before the corresponding species have been identified, in applications in which such estimates can be made, either experimentally or on theoretical grounds, the use of correction factors for calculating similarity scores or stratification of Third, even though viral genomes were used as the basis for calculating energy profiles, the concept can easily be extended to other taxonomy nodes such as genera or families of viruses. This requires every sequence element to be classified at the appropriate node in the taxonomy hierarchy.P values were comparable. However, even if mink astrovirus were the top prediction in the first iteration, SARS CoV would be the top prediction in the second iteration  and therefore would not be missed as a true positive. In our current studies P values in all iterations are estimated using the same set of null probability distributions. In addition, we use two iterations as our default, and essentially never need to run more than three iterations, because detection of more than two or three viruses is rare. However, iterative resolution of hundreds or thousands of species present in a sample may necessitate other normalization methods or adjustments to the null distributions for P value estimation. As an alternative, noniterative algorithms for analyzing overlapping profile signatures are also being explored.Finally, iterative use of E-Predict was intended for identification of multiple species that may be present in a sample. In this setting, it is important to distinguish between true predictions representing unique species present in the sample and misleading predictions arising from partially overlapping profiles. In each iteration it is assumed that the profile attaining the highest score corresponds to the species most likely to be among those present in the sample. When a novel species is present, this assumption may not hold because of limited oligonucleotide coverage. For instance, in the SARS CoV example, although SARS CoV attained a higher similarity score than mink astrovirus, the corresponding In conclusion, E-Predict is a novel computational approach for species identification, which is generally applicable to a wide range of metagenomic applications using DNA microarrays. In particular, as more sequencing efforts are being directed at natural microbial communities, DNA microarrays are bound to become a central tool for various downstream applications such as identification of microbial species or detection of genes and biochemical pathways in such communities. E-Predict addresses an acute need for computational tools that are capable of interpreting the highly complex microarray data obtained through such studies. E-Predict was developed for viral species identification and therefore has immediate implications for medical diagnostics and viral discovery. In addition, the concept of theoretical energy profiles can be extended to represent other microorganisms, particular genes, or biochemical pathways.All patient samples were collected according to protocols approved by the University of California San Francisco Committee on Human Research.HeLa cells were grown to confluence in a T150 tissue culture flask in Dulbeco's modified Eagle medium supplemented with 10% fetal bovine serum and antibiotics. The cells were harvested by adding 10 ml Trizol reagent , and total RNA was isolated according to the manufacturer's protocol. A quantity of HeLa total RNA (50 ng) was used for each amplification and hybridization.With respect to pediatric respiratory samples, frozen nasopharyngeal aspirate samples were thawed and 200 μl aliquots were used to extract RNA using RNeasy Mini Kit  as follows. RLT buffer (750 μl) containing 1% 2-mercaptoethanol was added to each sample and mixed. Then, 1 ml of 100% ethanol was added, and the resulting mixture was applied to the columns in three 650 μl aliquots. The remaining steps were carried out in accordance with the manufacturer's protocol, including on-column DNase digest. RNA was eluted from the columns with 30 μl nuclease-free water, and 9 μl was used for amplification and hybridization. For the hepatitis sample, frozen serum sample was thawed and a 150 μl aliquot was used to extract total nucleic acid using MagNA Pure LC Total Nucleic Acid Isolation Kit , in accordance with the manufacturer's protocol. RNA was eluted in 50 μl nuclease-free water, and 9 μl was used for amplification and hybridization.2 for 3 hours at 37°C. RNA was then extracted using Trizol reagent (Invitrogen), in accordance with the manufacturer's protocol. Linearized polyacrylamide  was used as the carrier during the 2-propanol precipitation. RNA was resuspended in 30 μl nuclease-free water, and 9 μl was used for amplification and hybridization.For HRV serotypes, frozen samples of low passage viral culture supernatants were thawed on ice and pre-filtered with a 0.2 μm syringe filter. Aliquots (200 μl) of the pre-filtered supernatants were treated with 600 U micrococcal nuclease  in the presence of 10 mmol/l CaClMicroarrays used in the study were essentially identical to those previously described . DetaileRNA extracted from the samples was amplified using a modified Round A-B random PCR method , as prevMicroarray data have been submitted to the NCBI GEO database   available as of July 2004 . NucleotControl oligonucleotides and oligonucleotides known to result in nonspecific hybridization were removed from consideration by setting their intensities and energies to zero. The list of these oligonucleotides  was obtained by including 129 oligonucleotides with unnormalized median intensity greater than 500, calculated from 1,009 independent hybridizations described below. The list also included 137 oligonucleotides obtained by clustering of distributions of sum-normalized intensity, based on the same set of 1,009 hybridizations, and visual identification of an outlier cluster with median sum-normalized intensities significantly higher than those observed for most oligonucleotides. Energy vectors were further filtered to exclude terms with energy predictions higher than -30 kcal/mol , because such predictions on our platform do not correspond to detectable array intensities [Null distributions of similarity scores were obtained using a set of 1,009 hybridizations, which included all hybridizations performed on our platform to date. Similarity scores were calculated as described above using uncentered Pearson correlation as the similarity metric, and sum and quadratic normalizations for intensity and energy vectors, respectively. Scores were log-transformed. Right tail outliers corresponding to positive cases were excluded by iterative trimming of the top scores in 1% increments until the best normality fit was obtained, as judged by the Shapiro-Wilk normality test   ,47 by hiThe phylogenetic tree based on nucleotide sequences of VP1 capsid protein was constructed using data from the report by Jonassen and coworkers . SequencNC_003977) and RSV (GenBank: NC_001803).The presence of hepatitis B virus in the hepatitis sample was confirmed using primers Hep_1F (5'-GAC TCG TGG TGG ACT TCT CTC AA-3') and Hep_4R (5'-GAA AGC CCT GCG AAC CAC TGA A-3') with amplified cDNA  (Additional data file  Additio; a text A text file of E-Predict output for the hepatitis exampleClick here for fileA text file of E-Predict output for the FluA/RSV double infection exampleClick here for fileA text file of E-Predict output for the SARS CoV exampleClick here for fileA text file of E-Predict output for the HRV serotypes exampleClick here for fileA tab delimited text file containing the energy profile matrixClick here for fileA text file containing the list of nonspecific oligonucleotides ignored during E-PredictClick here for fileP valuesA tab delimited text file containing the list of profile parameters used to estimate Click here for fileA text file of E-Predict output used to evaluate normalization and similarity metric parametersClick here for file