- Methodology article
- Open Access
Use of consensus sequences for the design of high density resequencing microarrays: the influenza virus paradigm
- India Leclercq†1, 2,
- Nicolas Berthet†3, 4,
- Christophe Batéjat1,
- Claudine Rousseaux1,
- Philip Dickinson5,
- Iain G Old6,
- Katherine Kong5,
- Giulia C Kennedy5,
- Stewart T Cole7, 8 and
- Jean-Claude Manuguerra1Email author
© Leclercq et al; licensee BioMed Central Ltd. 2010
- Received: 12 July 2010
- Accepted: 20 October 2010
- Published: 20 October 2010
A resequencing microarray called PathogenID v2.0 has been developed and used to explore various strategies of sequence selection for its design. The part dedicated to influenza viruses was based on consensus sequences specific for one gene generated from global alignments of a large number of influenza virus sequences available in databanks.
For each HA (H1, H2, H3, H5, H7 and H9) and NA (N1, N2 and N7) molecular type chosen to be tested, 1 to 3 consensus sequences were computed and tiled on the microarray. A total of 12 influenza virus samples from different host origins (humans, pigs, horses and birds) and isolated over a period of about 50 years were used in this study. Influenza viruses were correctly identified, and in most cases with the accurate information of the time of their emergence.
PathogenID v2.0 microarray demonstrated its ability to type and subtype influenza viruses, often to the level of viral variants, with a minimum number of tiled sequences. This validated the strategy of using consensus sequences, which do not exist in nature, for our microarray design. The versatility, rapidity and high discriminatory power of the PathogenID v2.0 microarray could prove critical to detect and identify viral genome reassortment events resulting in a novel virus with epidemic or pandemic potential and therefore assist health authorities to make efficient decisions about patient treatment and outbreak management.
- Influenza Virus
- H3N2 Virus
- Swine Influenza Virus
- BLASTN Analysis
Rapid detection and identification of infectious pathogens are necessary for proper treatment of infection and outbreak control measures.
Many promising approaches are being developed enabling rapid identification of various infectious pathogens such as bacteria or viruses including multiplex (RT)-PCR amplification strategies or novel technologies such as high-performance electrospray ionization mass spectrometry [1, 2].
DNA microarray technologies provide a mean to screen for thousands of different nucleic acid sequences simultaneously and have been used for the detection of many viruses such as HIV , Hepatitis B and D viruses [4–6], human papillomaviruses , rotaviruses , vaccinia virus , herpesviruses, enteroviruses, flaviviruses , and measles virus genotypes . In addition to these microarrays, which have a spectrum of identification limited to a few viral species, major microarray platforms were developed for the detection of a much broader range of pathogens [12–17].
In humans, influenza A viruses cause recurrent annual epidemics of moderate to severe respiratory diseases. Sixteen hemagglutinin (HA) molecular types (designated H1 to H16) and nine neuraminidase (NA) molecular types (designated N1 to N9) have been identified, although only a few of them occur in viruses causing human diseases. Identification and selection of sequences to be used on a microarray are particularly challenging, especially for rapidly mutating viruses. Antigenic drift is an ongoing process for influenza A viruses resulting in continuous evolution and annual epidemics. Moreover, the segmented nature of the influenza A virus genome can lead to reassortments during coinfection thus contributing to their vast genetic diversity and pandemic potential.
Subtyping and molecular analysis of influenza virus strains were performed by different groups using low density microarrays [18–23]. Most of them were used to identify influenza A viruses H1N1, H3N2, H5N1 and influenza B viruses. Recently, Combimatrix Corporation (Mukilteo, WA) developed a semiconductor-based microarray and an integrated microfluidic array enabling the detection of 15 HA and 9 NA [24, 25]. However, the cost per microarray is too high for routine experimental identification. More recently, a rapid subtyping assay also identifying all the HA and NA subtypes was developed using padlock probes .
Resequencing, using high-density oligonucleotide microarrays, is a technology that enables the rapid identification of genetic variants and the determination of their nucleotide sequence at single base-pair resolution. This allows identification of pathogens with sequences similar but not identical to those represented on the microarray. A resequencing microarray was designed in order to allow identification of genetically diverse human RNA rhinoviruses and enteroviruses, with a minimum number of prototype 5'UTR sequences . One respiratory pathogen resequencing microarray allowed the detection of geographically distant human H1N1, H3N2 and avian H5N1 influenza virus strains covering the period between 1999 and 2005 [27, 28]. More recently, the same team developed a broad-range resequencing microarray for universal detection and identification of all the possible combinations of the 16 hemagglutinin and 9 neuraminidase types of avian influenza viruses in addition to three additional influenza A markers (M, PB2 and NS1) . Their strategy was based on probe sequence selection using in silico modeling .
A resequencing microarray called PathogenID v2.0 has been developed by a consortium of laboratories of Institut Pasteur with Affymetrix Inc. It was used to explore various strategies of sequence selection for its design. The part dedicated to influenza viruses, presented here, was based on consensus sequences specific for one gene. Within the global program, another strategy was explored for the part of the microarray specific for rhabdoviruses. That strategy was based on the use of prototype sequences representative of each of the seven species described in the genus Lyssavirus and chosen within a conserved region of the polymerase gene . Consensus sequences generation was a posteriori used to analyse results. Our strategy presented in this study, uses consensus sequences generated from nucleotide sequence alignments to determine the sequences to be tiled. These sequences characterised by a minimum size of 200 bp reduce the number of probes required for tiling yet still covering the wide diversity of influenza virus strains. To validate our approach, influenza viruses from different host origins, isolated over a period of about 50 years, were chosen. The DNA microarray not only demonstrated its ability to identify the type and subtype of influenza viruses but also had a strong discriminatory power at the level of viral variants.
Description of the influenza A virus sequences tiled on the PathogenID v2.0 microarray
Viral sequence identification
Fragment size (bp)
Influenza genus identification
For each gene, a global alignment including all selected sequences was performed and clusters grouping a maximum number of sequences were established. In each sequence cluster, a common region with the lowest genetic divergence was identified. A consensus sequence was automatically obtained from this common region, characterised by a minimum size of 200 bases and a divergence between the consensus sequence and any given sequence belonging to the cluster of less than 15%. Twelve HA consensus sequences and 9 NA consensus sequences were identified and their positions are detailed in Table 1. Finally, the microarray based on the resequencing approach included 27 influenza viral sequences which corresponded to 7594 bases. For each relevant base of a given consensus or prototype sequence, the array contained eight 25 mer probes (4 sense and 4 antisense). Two of the eight probes represented perfect matches, while the others corresponded to possible mismatches at the central (13th) position of the 25 mers. All together, 60752 probes were then tiled on the microarray, allowing subtyping of influenza viruses from different host species.
Detection of type A influenza viruses
Resequencing microarray output for identification and characterisation of the 12 influenza A viral RNAs
Call rate (%)
1981 - 1987
1972 - 1982
1968 - 1975
1968 - 2005
1988 - 1994
1988 - 1999
1993 - 2008
2000 - 2006
2002 - 2003
2001 - 2008
1975 - 2008
1956 - 1992
1963 - 2007
1974 - 2008
1976 - 1994
1976 - 2006
Viral strain 221
Viral strain 223
2006 - 2007
Viral strain 224
2003 - 2007
For swine and human viruses, a minimum call rate of 35.5% allowed identification of influenza virus with a correct temporality. Indeed, for the majority of strains the BLASTN result was close to that of the sample strain, as outlined by median analysis. The median obtained for the NA gene from the A/Johannesburg/33/94 (H3N2) virus was relatively recent compared to the year of isolation of the strain, probably because the chosen consensus sequence for this gene was highly conserved and thus weakly discriminatory. The time range obtained for the A/Wyoming/3/2003 (H3N2) virus was narrow and limited to the year 2000, probably due to the marked antigenic drift from the year 2000 observed for the H3 gene in human influenza virus strains .
The epidemiology of swine influenza has become increasingly complex over the last decades. Three major influenza A virus subtypes are currently circulating in swine worldwide, but the origins and the antigenic and genetic characteristics of these swine influenza viruses differ with continents or regions of the world. A/swine/Gent/7625/99 (H1N2) virus is the result of a multiple reassortment involving a swine influenza virus with avian-influenza-like internal segments and human H1N1 and H3N2 viruses isolated in 1994 . BLASTN analysis for H1N2 swine virus reflected this multiple reassortment with blasted sequences originating from human, avian and swine hosts (data not shown). Between 1983 and 1985, swine H3N2 viruses appeared in Italy. They contained avian-like internal genes and HA and NA genes from human H3N2 viruses . In BLASTN analysis, the vast majority of the sequences sorted by the algorithm for glycoprotein segments were from human or swine origins only. All equine influenza virus strains were identified and subtyped with the DNA microarray, with a minimum call rate of 48.5%. Medians were often heterogeneous and far from the original strain. This is probably due to (i) the paucity of equine influenza virus sequences available in the databases, (ii) the limited genetic evolution of those viruses leading to equivalent result scores after BLASTN analysis, (iii) the fact that HA and NA sequences tiled on the microarray were shorter than PB2 sequences, due to their extreme genetic diversity in influenza A viruses. For the H3N8 viruses, recent sequences corresponded to avian strains, outlining the fact that H3N8 equine viruses were reassortants between avian viruses and equine H7N7 viruses which have not been isolated in horses for more than 20 years.
All together, the results showed that the PathogenID v2.0 DNA microarray is able not only to provide correct full subtyping of various strains from different host origins but also to identify/detect, after BLASTN, an array of strains genetically related to them. In all cases, the strains output by BLASTN belonged to the same time period thus giving a strong insight into the variant to which it is genetically, and very probably antigenically, related.
Avian sequences reconstructed by the PathogenID v2.0 and BLASTN alignment
Viral strain tested
Sequence size (bp)
BLASTN analysis of the sequence reconstructed by the DNA microarray
A/teal/Italy/3931-38/2005 (H5N2) *
A/mallard/Finland/12072/06 (H3N8) *
A/duck/Denmark/65047/04 (H5N2) *
A/gull/Moscow/3100/2006 (H6N2) *
A/Aquatic Bird/Hong Kong/m603/98 (H11N1)
A/gull/Moscow/3100/2006 (H6N2) *
A/goose/Italy/296426/2003 (H1N1) *
A/Aquatic Bird/Hong Kong/m603/98 (H11N1)
The PathogenID v2.0 microarray demonstrated its ability to type and subtype influenza viruses with a minimum number of tiled sequences and to give additional information close to the individual variant level. Identification of influenza viruses was obtained within the correct period of time in most cases. However, this level of discrimination is limited by the number of sequences available in databases and their level of divergence as illustrated by results obtained with equine viruses. The PathogenID v2.0 microarray should also be able to detect a re-emerging influenza virus, that has already circulated in the population, as happened in 1977 during the Russian influenza outbreak .
BLASTN analysis of the sequences generated by the DNA microarray should allow the determination of the segment origin for PB2, HA and NA genes, even though the consensus sequence design was not based on the origin of the viral host. The avian H3N2 virus isolated from the environment was identified and characterised as having an H3 consensus sequence different from that obtained for human H3N2 viruses. Recently, we have tested the novel human A(H1N1) pandemic virus of swine origin, derived from multiple reassortments, with the DNA microarray and BLASTN analysis determined the segment origin for the PB2, H1 and N1 genes, which were derived from avian, classical swine virus and avian-like swine virus, respectively . Our data validated the strategy of using consensus sequences with all of the serotypes tested, including H1, H3, H7 (this paper) and H5  (H9 and H2 were not tested, because not available in the laboratory) and N1, N2 and N7 (this paper). The simple design strategy used in this study, based on global alignements covering the whole gene, does not need the use of a specific algorithm as the one used for the TessArray RPM-Flu resequencing microarray which was based on probe sequence selection . The consensus sequences which allowed to considerably limiting the number of probes tiled on the non-specialised PathogenID v2.0 microarray would allow the determination of differential diagnosis with other respiratory viruses such as Respiratory Syncytial Virus (RSV), SARS-CoV, and also potential co-infections, which is not possible with microarrays entirely dedicated to influenza viruses. The use of WTA amplification provides considerably better sensitivity and accuracy of detection compared to random RT-PCR  which is compatible with the use of the PathogenID v2.0 resequencing microarray for clinical samples. Furthermore, the use of random hexamers and multiple displacement amplification allowed detection of a new virus which can not be amplified with usual specific primers as shown by the study on the last novel H1N1pdm virus responsible for 2009 influenza pandemic . However, the DNA microarray failed to detect the N8 gene from H3N8 equine viruses. Here, it will be useful to tile other genes on the next generation microarray, including all HA and NA subtypes or the PB1 gene, which has often been involved in reassortment in past pandemics. In this way, one could detect as many reassortants as possible thereby impacting on public health measures.
From many available influenza virus sequences, gene-specific databases containing only the sequences of a specific gene or subtype (e.g., PB2 or H1 subtype) were created and converted to the FASTA format. Then, a multiple-sequence alignment was performed with the data set with BioNumerics software for windows (version 5.1, Applied Maths). Additionally, a neighbor-joining phylogenetic tree was created. Consensus sequences were then designed based upon global alignments of this large number of influenza virus sequences available in databanks.
Influenza virus samples were all viral isolates propagated either in embryonated chicken eggs or in MDCK cell cultures. The virus type was determined by hemagglutination inhibition assay .
Cloacal samples collected from ducks in Baie de Somme, Marquenterre (France) were inoculated via the allantoic route on 11-day-old embryonated chicken eggs. Eggs were incubated at 35°C and allantoic fluids were harvested on day 3 postinfection. Virus titration of the fluids was performed by hemagglutination by using guinea pig erythrocytes. Influenza virus isolation was confirmed by qRT-PCR specific for the M gene.
RNA isolation and amplification
Viral RNA from influenza samples and allantoic fluids were extracted using QIAamp Viral RNA Mini Kit (Qiagen) according to the manufacturer's instructions. Nucleic acid amplification was performed by WTA as described previously .
Hybridisation to microarrays
DNA amounts obtained after amplification were quantified by Quantit (Invitrogen(r)). Five micrograms of DNA was fragmented (GeneChip(r) Resequencing Assay Kit, Affymetrix Inc.) and labelled according to the GeneChip(r) Mapping 100K Assay Manual (Affymetrix Inc.). Microarray hybridisation was conducted at 45°C and array processing was carried out according to the protocol recommended by the manufacturer (Affymetrix Inc.) as previously described .
Analysis of the sequences generated by the The PathogenID v2.0 microarray
After hybridisation to the DNA microarray, the nucleotide sequences of the different genes were determined from the signals as described previously  and compared with sequences in GenBank by BLASTN analysis.
All sequences generated by the microarray were characterised by the call rate, which is the percentage of bases determined by the resequencing algorithm. For determining the time of emergence of the sample strain, median analysis was performed as described: the sequence generated by the microarray showing the highest call rate value was blasted against sequences available in Genbank using the BLASTN 2.2.24+ program . All homologous sequences within the group of sequences showing the highest score (as categorised by the BLASTN program) were selected and their year of circulation used for median calculation and determination of the time range. If the number of these sequences was higher than 100, median analysis was performed on the 100 first sequences only. For avian influenza strains, median analysis was performed as described above and the first sequence(s) showing the highest scores were also detailed in Table 3.
RT-PCR, cloning and sequencing
After identification and subtyping of avian influenza strains by the microarray, oligonucleotides specific for each tiled influenza virus gene were designed and used in RT-PCR assays. Five microliters of extracted RNA was amplified by SuperScript(tm) One-Step RT-PCR system with Platinum(r) Taq DNA polymerase (Invitrogen, Life Technologies, Carlsbad, CA). One-Step RT-PCR was performed with 25 μl reaction volume. The extracted RNA sample (5 μl) was added to RT-PCR mixture (20 μl) consisting of 1X Reaction Mix (a buffer containing 0.4 mM of each dNTP, 2.4 mM MgSO4), 0.5 μl of SuperScript(tm) II RT/Platinum(r) Taq Mix, 0.4 μM primer Forward, 0.4 μM primer Reverse, 0.2 U/μl of RNaseOUT Recombinant Ribonuclease Inhibitor and nuclease-free water. The RT and PCR were carried out with following steps: RT at 45°C for 30 min; 55°C for 15 min, 94°C for 5 min, then PCR cycle 1-30, 94°C for 30 sec, 45°C for 30 sec, 72°C for 30 sec; final extension, 72°C for 5 min.
Amplified products were extracted using QIAquick Gel Extraction kit (Qiagen) according to the manufacturer's instructions and cloned in pCR(r)II.1-TOPO(r) TA cloning vector (Invitrogen). Sequences of influenza virus genes were obtained from 3 individual clones for each gene.
Special thanks to Philippe Caruette from Parc ornithologique du Marquenterre for taking avian samples. We also thank Frederic Fichenick of CIBU for his contribution. This study was supported equally by Grant No. UC1 AI062613 (Kennedy) from the US National Institute of Allergy and Infectious Diseases, National Institutes of Health and by Programme Transversal de Recherche (PTR DEVA n°246) from Institut Pasteur (France). We thank the E.U. funded RIVERS program (Sixth Framework Program SSP-5-B-INFLUENZA 04 405: Resistance of Influenza Viruses in Environmental Reservoirs and Systems) and the TOTAL-INSTITUT PASTEUR fund for additional financial support.
- Ecker DJ, Sampath R, Massire C, Blyn LB, Hall TA, Eshoo MW, Hofstadler SA: Ibis T5000: a universal biosensor approach for microbiology. Nat Rev Microbiol. 2008, 6 (7): 553-558. 10.1038/nrmicro1918.PubMedView ArticleGoogle Scholar
- Gyarmati P, Conze T, Zohari S, LeBlanc N, Nilsson M, Landegren U, Baner J, Belak S: Simultaneous genotyping of all hemagglutinin and neuraminidase subtypes of avian influenza viruses by use of padlock probes. J Clin Microbiol. 2008, 46 (5): 1747-1751. 10.1128/JCM.02292-07.PubMed CentralPubMedView ArticleGoogle Scholar
- Ghedin E, Pumfery A, de la Fuente C, Yao K, Miller N, Lacoste V, Quackenbush J, Jacobson S, Kashanchi F: Use of a multi-virus array for the study of human viral and retroviral pathogens: gene expression studies and ChIP-chip analysis. Retrovirology. 2004, 1: 10-10.1186/1742-4690-1-10.PubMed CentralPubMedView ArticleGoogle Scholar
- Sun ZH, Zheng WL, Mao XM, Zhang B, Lu L, Ma XD, Shi R, Ma WL: Rapid preparation of DNA microarray using PCR for hepatitis B and D virus detection. Di Yi Jun Yi Da Xue Xue Bao. 2003, 23 (7): 677-679.PubMedGoogle Scholar
- Sun ZH, Zheng WL, Zhang B, Lu L, Mao XD, Shi R, Ma WL: Detection of hepatitis D virus by cDNA microarray method. Hepatobiliary Pancreat Dis Int. 2004, 3 (3): 423-427.PubMedGoogle Scholar
- Song Y, Dai E, Wang J, Liu H, Zhai J, Chen C, Du Z, Guo Z, Yang R: Genotyping of hepatitis B virus (HBV) by oligonucleotides microarray. Mol Cell Probes. 2006, 20 (2): 121-127. 10.1016/j.mcp.2005.11.004.PubMedView ArticleGoogle Scholar
- Klaassen CH, Prinsen CF, de Valk HA, Horrevorts AM, Jeunink MA, Thunnissen FB: DNA microarray format for detection and subtyping of human papillomavirus. J Clin Microbiol. 2004, 42 (5): 2152-2160. 10.1128/JCM.42.5.2152-2160.2004.PubMed CentralPubMedView ArticleGoogle Scholar
- Honma S, Chizhikov V, Santos N, Tatsumi M, Timenetsky Mdo C, Linhares AC, Mascarenhas JD, Ushijima H, Armah GE, Gentsch JR: Development and validation of DNA microarray for genotyping group A rotavirus VP4 (P, P, P, P, and P) and VP7 (G1 to G6, G8 to G10, and G12) genes. J Clin Microbiol. 2007, 45 (8): 2641-2648. 10.1128/JCM.00736-07.PubMed CentralPubMedView ArticleGoogle Scholar
- Wang Y, Ma WL, Mao XM, Wu QH, Li L, Wang HM, Xiao WW, Zheng WL: Design and preparation of oligonucleotide microarray for vaccinia virus detection. Di Yi Jun Yi Da Xue Xue Bao. 2004, 24 (2): 180-183.PubMedGoogle Scholar
- Korimbocus J, Scaramozzino N, Lacroix B, Crance JM, Garin D, Vernet G: DNA probe array for the simultaneous identification of herpesviruses, enteroviruses, and flaviviruses. J Clin Microbiol. 2005, 43 (8): 3779-3787. 10.1128/JCM.43.8.3779-3787.2005.PubMed CentralPubMedView ArticleGoogle Scholar
- Neverov AA, Riddell MA, Moss WJ, Volokhov DV, Rota PA, Lowe LE, Chibo D, Smit SB, Griffin DE, Chumakov KM: Genotyping of measles virus in clinical specimens on the basis of oligonucleotide microarray hybridization patterns. J Clin Microbiol. 2006, 44 (10): 3752-3759. 10.1128/JCM.00998-06.PubMed CentralPubMedView ArticleGoogle Scholar
- Lin B, Wang Z, Vora GJ, Thornton JA, Schnur JM, Thach DC, Blaney KM, Ligler AG, Malanoski AP, Santiago J: Broad-spectrum respiratory tract pathogen identification using resequencing DNA microarrays. Genome Res. 2006, 16 (4): 527-535. 10.1101/gr.4337206.PubMed CentralPubMedView ArticleGoogle Scholar
- Leski TA, Lin B, Malanoski AP, Wang Z, Long NC, Meador CE, Barrows B, Ibrahim S, Hardick JP, Aitichou M: Testing and validation of high density resequencing microarray for broad range biothreat agents detection. PLoS One. 2009, 4 (8): e6569-10.1371/journal.pone.0006569.PubMed CentralPubMedView ArticleGoogle Scholar
- Berthet N, Reinhardt AK, Leclercq I, van Ooyen S, Batejat C, Dickinson P, Stamboliyska R, Old IG, Kong KA, Dacheux L: Phi29 polymerase based random amplification of viral RNA as an alternative to random RT-PCR. BMC Mol Biol. 2008, 9: 77-10.1186/1471-2199-9-77.PubMed CentralPubMedView ArticleGoogle Scholar
- Palacios G, Quan PL, Jabado OJ, Conlan S, Hirschberg DL, Liu Y, Zhai J, Renwick N, Hui J, Hegyi H: Panmicrobial oligonucleotide array for diagnosis of infectious diseases. Emerg Infect Dis. 2007, 13 (1): 73-81. 10.3201/eid1301.060837.PubMed CentralPubMedView ArticleGoogle Scholar
- Wang D, Coscoy L, Zylberberg M, Avila PC, Boushey HA, Ganem D, DeRisi JL: Microarray-based detection and genotyping of viral pathogens. Proc Natl Acad Sci USA. 2002, 99 (24): 15687-15692. 10.1073/pnas.242579699.PubMed CentralPubMedView ArticleGoogle Scholar
- Wang D, Urisman A, Liu YT, Springer M, Ksiazek TG, Erdman DD, Mardis ER, Hickenbotham M, Magrini V, Eldred J: Viral discovery and sequence recovery using DNA microarrays. PLoS Biol. 2003, 1 (2): E2-10.1371/journal.pbio.0000002.PubMed CentralPubMedView ArticleGoogle Scholar
- Dawson ED, Moore CL, Dankbar DM, Mehlmann M, Townsend MB, Smagala JA, Smith CB, Cox NJ, Kuchta RD, Rowlen KL: Identification of A/H5N1 influenza viruses using a single gene diagnostic microarray. Anal Chem. 2007, 79 (1): 378-384. 10.1021/ac061920o.PubMedView ArticleGoogle Scholar
- Dankbar DM, Dawson ED, Mehlmann M, Moore CL, Smagala JA, Shaw MW, Cox NJ, Kuchta RD, Rowlen KL: Diagnostic microarray for influenza B viruses. Anal Chem. 2007, 79 (5): 2084-2090. 10.1021/ac061960s.PubMed CentralPubMedView ArticleGoogle Scholar
- Townsend MB, Dawson ED, Mehlmann M, Smagala JA, Dankbar DM, Moore CL, Smith CB, Cox NJ, Kuchta RD, Rowlen KL: Experimental evaluation of the FluChip diagnostic microarray for influenza virus surveillance. J Clin Microbiol. 2006, 44 (8): 2863-2871. 10.1128/JCM.00134-06.PubMed CentralPubMedView ArticleGoogle Scholar
- Dawson ED, Moore CL, Smagala JA, Dankbar DM, Mehlmann M, Townsend MB, Smith CB, Cox NJ, Kuchta RD, Rowlen KL: MChip: a tool for influenza surveillance. Anal Chem. 2006, 78 (22): 7610-7615. 10.1021/ac061739f.PubMedView ArticleGoogle Scholar
- Li J, Chen S, Evans DH: Typing and subtyping influenza virus using DNA microarrays and multiplex reverse transcriptase PCR. J Clin Microbiol. 2001, 39 (2): 696-704. 10.1128/JCM.39.2.696-704.2001.PubMed CentralPubMedView ArticleGoogle Scholar
- Han X, Lin X, Liu B, Hou Y, Huang J, Wu S, Liu J, Mei L, Jia G, Zhu Q: Simultaneously subtyping of all influenza A viruses using DNA microarrays. J Virol Methods. 2008, 152 (1-2): 117-121. 10.1016/j.jviromet.2008.05.027.PubMedView ArticleGoogle Scholar
- Liu RH, Lodes MJ, Nguyen T, Siuda T, Slota M, Fuji HS, McShea A: Validation of a fully integrated microfluidic array device for influenza A subtype identification and sequencing. Anal Chem. 2006, 78 (12): 4184-4193. 10.1021/ac060450v.PubMedView ArticleGoogle Scholar
- Lodes MJ, Suciu D, Elliott M, Stover AG, Ross M, Caraballo M, Dix K, Crye J, Webby RJ, Lyon WJ: Use of semiconductor-based oligonucleotide microarrays for influenza a virus subtype identification and sequencing. J Clin Microbiol. 2006, 44 (4): 1209-1218. 10.1128/JCM.44.4.1209-1218.2006.PubMed CentralPubMedView ArticleGoogle Scholar
- Wang Z, Malanoski AP, Lin B, Kidd C, Long NC, Blaney KM, Thach DC, Tibbetts C, Stenger DA: Resequencing microarray probe design for typing genetically diverse viruses: human rhinoviruses and enteroviruses. BMC Genomics. 2008, 9: 577-10.1186/1471-2164-9-577.PubMed CentralPubMedView ArticleGoogle Scholar
- Wang Z, Daum LT, Vora GJ, Metzgar D, Walter EA, Canas LC, Malanoski AP, Lin B, Stenger DA: Identifying influenza viruses with resequencing microarrays. Emerg Infect Dis. 2006, 12 (4): 638-646.PubMed CentralPubMedView ArticleGoogle Scholar
- Lin B, Blaney KM, Malanoski AP, Ligler AG, Schnur JM, Metzgar D, Russell KL, Stenger DA: Using a resequencing microarray as a multiple respiratory pathogen detection assay. J Clin Microbiol. 2007, 45 (2): 443-452. 10.1128/JCM.01870-06.PubMed CentralPubMedView ArticleGoogle Scholar
- Lin B, Malanoski AP, Wang Z, Blaney KM, Long NC, Meador CE, Metzgar D, Myers CA, Yingst SL, Monteville MR: Universal detection and identification of avian influenza virus by use of resequencing microarrays. J Clin Microbiol. 2009, 47 (4): 988-993. 10.1128/JCM.01346-08.PubMed CentralPubMedView ArticleGoogle Scholar
- Malanoski AP, Lin B, Stenger DA: A model of base-call resolution on broad-spectrum pathogen detection resequencing DNA microarrays. Nucleic Acids Res. 2008, 36 (10): 3194-3201. 10.1093/nar/gkm1156.PubMed CentralPubMedView ArticleGoogle Scholar
- Dacheux L, Berthet N, Dissard G, Holmes EC, Delmas O, Larrous F, Guigon G, Dickinson P, Faye O, Sall AA: Application of broad-spectrum resequencing microarray for genotyping rhabdoviruses. J Virol. 84 (18): 9557-9574. 10.1128/JVI.00771-10.Google Scholar
- Webster RG, Bean WJ, Gorman OT, Chambers TM, Kawaoka Y: Evolution and ecology of influenza A viruses. Microbiol Rev. 1992, 56 (1): 152-179.PubMed CentralPubMedGoogle Scholar
- Cutler DJ, Zwick ME, Carrasquillo MM, Yohn CT, Tobin KP, Kashuk C, Mathews DJ, Shah NA, Eichler EE, Warrington JA: High-throughput variation detection and genotyping using microarrays. Genome Res. 2001, 11 (11): 1913-1925.PubMed CentralPubMedGoogle Scholar
- Russell CA, Jones TC, Barr IG, Cox NJ, Garten RJ, Gregory V, Gust ID, Hampson AW, Hay AJ, Hurt AC: The global circulation of seasonal influenza A (H3N2) viruses. Science. 2008, 320 (5874): 340-346. 10.1126/science.1154137.PubMedView ArticleGoogle Scholar
- Zell R, Motzke S, Krumbholz A, Wutzler P, Herwig V, Durrwald R: Novel reassortant of swine influenza H1N2 virus in Germany. J Gen Virol. 2008, 89 (Pt 1): 271-276. 10.1099/vir.0.83338-0.PubMedView ArticleGoogle Scholar
- Castrucci MR, Donatelli I, Sidoli L, Barigazzi G, Kawaoka Y, Webster RG: Genetic reassortment between avian and human influenza A viruses in Italian pigs. Virology. 1993, 193 (1): 503-506. 10.1006/viro.1993.1155.PubMedView ArticleGoogle Scholar
- Pyhala R, Visakorpi R: Influenza A(H1N1) viruses of the 1977/78 outbreak: isolation and haemagglutination properties. J Hyg (Lond). 1979, 82 (1): 81-88. 10.1017/S0022172400025493.View ArticleGoogle Scholar
- Berthet N, Leclercq I, Dublineau A, Shigematsu S, Burguiere AM, Filippone C, Gessain A, Manuguerra JC: High-density resequencing DNA microarrays in public health emergencies. Nat Biotechnol. 2010, 28 (1): 25-27. 10.1038/nbt0110-25.PubMedView ArticleGoogle Scholar
- Dowdle WAD, Kendal AP, Noble GR: Influenza viruses. Diagnostic Procedures for Viral, Rickettsial and Chlamydial Infections. Edited by: Lennette EH, Schmidt NJ. 1979, American Public Health Association, Washington, DC, 585-609. 5Google Scholar
- Berthet N, Dickinson P, Filliol I, Reinhardt AK, Batejat C, Vallaeys T, Kong KA, Davies C, Lee W, Zhang S: Massively parallel pathogen identification using high-density microarrays. Microbial Biotechnology. 2008, 1 (1): 79-86.PubMed CentralPubMedGoogle Scholar
- Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ: Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 1997, 25 (17): 3389-3402. 10.1093/nar/25.17.3389.PubMed CentralPubMedView ArticleGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.