Vitis vinifera L.). However, following completion of the sequence of the highly heterozygous genome of Pinot Noir, it has been possible to identify millions of electronic SNPs (eSNPs) thus providing a valuable source for high-throughput genotyping methods.Until recently, only a small number of low- and mid-throughput methods have been used for single nucleotide polymorphism (SNP) discovery and genotyping in grapevine  methods were used for preparation of genomic DNA for the SNPlex assay.Unlike other SNP genotyping methods used to investigate thousands of SNPs in a few genotypes, or a few SNPs in around a thousand genotypes, the SNPlex genotyping system represents a good compromise to investigate several hundred SNPs in a hundred or more samples simultaneously. Therefore, the use of the SNPlex assay, coupled with whole genome amplification (WGA), is a good solution for future applications in well-equipped laboratories. In the last few years, single nucleotide polymorphisms (SNPs) have become the most popular genetic marker system in both animals and plants. Their extraordinary abundance discovered in several genome sequencing projects , combineAt present several SNP identification methods are available such as resequencing of PCR amplicons with or without pre-screening, electronic SNP (eSNP) discovery in expressed sequence tag (EST) and shotgun genomic libraries. In these latter cases, sequences may be computationally screened for polymorphisms to distinguish true polymorphisms from sequencing errors if sufficient redundancy is present .Unlike the first generation molecular markers, such as RFLPs (Restriction Fragment Length Polymorphisms) and RAPDs (Random Amplified Polymorphic DNAs), SNPs can be detected through non-gel-based high-throughput assays, saving both time and money . SeveralVitis vinifera L.) [Until recently, only a few low- and mid-throughput methods have been used for SNP discovery and genotyping in grapevine (fera L.) -12. The fera L.) , clone E1 progeny of Syrah × Pinot Noir (ENTAV 115) cross and in the two parental genotypes. 734 eSNPs passed the quality value using the rule-based method, with a mean of 5 failed SNPs and a 98% call rate per SNPset, while the remaining 79 were discarded from further analyses  were also tested on WGA-DNA of the same 90 individuals. In this set, 15 eSNPs that did not pass the quality value on gDNA analysis were recovered on the WGA-DNA test, whereas three eSNPs that passed the quality value on gDNA failed on WGA-DNA Table . ThirteeTo validate the data obtained with the SNPlex assay, six regions containing SNP4165, SNP4057, SNP4045, SNP0102, SNP0054 and SNP5044 were resequenced in Syrah, Pinot Noir and six progeny. Four of them, SNP4165, SNP4057, SNP4045 and SNP0102, showed a Mendelian segregation and resequencing the corresponding regions confirmed the data obtained with the SNPlex analysis. For SNP5044 and SNP0054, which presented an additional homozygous cluster, an unexpected SNP was found within 10 bp from the target SNP in Syrah and in the progeny belonging to the additional homozygous cluster  and 21% false polymorphisms. This unexpectedly high rate of false polymorphisms might be due to the SNPlex probe design based on a preliminary stage of contig assembly where the coverage of each haplotype in some regions was not sufficient.SNP markers have also been developed based on eSNP discovery in a 6.5× shotgun sequencing coverage of grapevine genome . The effIn the WGA-DNA pilot study the genotyping data obtained from the gDNA and WGA-DNA analyses were thoroughly consistent, confirming previous SNP genotyping studies based on BeadArray ,15 and AResequencing of the SNP0054 and SNP5044 regions, belonging to the small group of SNPs with a distorted segregation, showed an unexpected additional polymorphism within 10 bp adjacent to the target eSNP in the Syrah genotype. Preferential ligation of one allele during probe annealing could explain the occurrence of an artificial homozygous cluster.A large number of SNP genotyping technologies have been developed in the last few years. Different aspects, such as accuracy, reproducibility and level of throughput, should be taken into account when defining the most suitable SNP assay for breeding purposes. Moreover, flexibility, time and cost-effectiveness should be also considered, and in this regard, the turnaround time of the SNPlex analysis using a 3730xl DNA Analyzer (ABI) was about 30 min per sample. Thus 221,184 genotypes can be theoretically generated per day (48 runs/24 hours × 96 capillaries × 48-plex reaction).Unlike other SNP genotyping methods used to investigate either thousands of SNPs in few genotypes (i.e. BeadArray and Affymetrix technologies), or few SNPs in thousands of genotypes (i.e. TaqMan assay), the SNPlex genotyping system represents a good compromise to investigate several hundred SNPs in a hundred or more samples at the same time. Therefore, the use of the SNPlex assay, coupled with a WGA-DNA, is a good solution for medium- to large-scale genotyping studies in well-equipped laboratories.1 Syrah × Pinot Noir progeny and the two parental genotypes was isolated from 50–100 mg of young leaves. After freeze-drying, the leaf material was ground using the MM 300 Mixer Mill  and DNA extraction was performed using the DNeasy 96 Plant Mini Kit  according to the manufacturer's protocol.Genomic DNA of 90 FTen ng of gDNA was amplified by MDA ,19 usingThe 6.5× shotgun sequence of Pinot Noir was the starting point of eSNP discovery. Approximately 6.2 million reads were produced by Sanger sequencing from 43 libraries with inserts of different sizes and assembled into contigs . About 2Allele-specific probes and optimized multiplexed assays using the SNPs of interest were designed by an automated multi-step pipeline . These sSNPlex was carried out on fragmented gDNA at a final concentration ranging from 45 to 225 ng and a final volume of 12.5 μl. Seventeen (fourteen 48-plex and three 47-plex) SNPset were analysed; of these, three SNPset  were also tested on fragmented GenomiPhi amplified gDNA (WGA-DNA) according to the manufacturer's protocol. The protocol was modified for the amount of PCR product used in the hybridisation cycles (3 instead of 1.5 μl).Samples were run on the 3730xl DNA Analyzer (ABI) and data were analyzed using Gene Mapper v. 4.0 software (ABI). Genotype analysis was performed based on the SNPlex_Rules_3730 method following the factory default rules.PCR primers were designed using the Primer3 software  accordin49, I53, I56, I57, I58 and I59) using templates in WGA-DNA. PCR reactions were performed using the following conditions: 1–20 ng of DNA template, 1× PCR buffer , 0.2 mM each dNTP, 0.4 μM of each primer, 1 U HotStarTaq DNA polymerase , and water to a final volume of 12.5 μl. DNA amplifications were performed using a 15 min initial denaturation/activation step, followed by 30 cycles at 94°C for 30 sec, 57°C for 30 sec, and 72°C for 2 min, with a final extension step of 10 min at 72°C. The PCR products were assessed by electrophoresis in 1.5% agarose gels and visualized by ethidium bromide staining. In order to remove unincorporated dNTPs and primers during the amplification reaction, 1 μl of exonuclease-phosphatase  was added to 1 μl of PCR product in a final volume of 6 μl and incubated at 37°C for 45 min followed by 72°C for 15 min.Subsequently, six regions containing SNP4165, SNP4057, SNP4045, SNP0102, SNP0054 and SNP5044 were amplified in Syrah, Pinot Noir, and six progeny (IThe PCR product sequencing was carried out in both directions using the BigDye Terminator Cycle Sequencing Ready Reaction Kit v3.1 (ABI) as follows: 6 μl of PCR purified products, 5× Sequencing buffer, 0.32 μM of primer and 1 μl of BigDye Terminator in a final volume of 10 μl. Sequencing reactions were performed using a 2 min initial denaturation step, followed by 40 cycles at 96°C for 10 sec, 50°C for 5 sec and 60°C for 4 min. Prior to ethanol purification, capillary electrophoresis of PCR products was performed on a 3730xl DNA Analyzer (ABI). The DNA sequence electropherograms were aligned with the Pregap4/Gap4 software package  and useMP carried out the SNPlex assay and data analysis, resequencing and drafted the manuscript. SV contributed to the SNPlex analysis and to discussion of the results. GC carried out the genomic DNA extraction and the MDA sample preparation. DC participated in the designing of the SNPlex assay. AZ carried out the genome assembly and SNP discovery. RV conceptualised the project and contributed to the discussion of the results. MT supervised the SNP-based marker development and genetic mapping and contributed to the discussion of the results. All authors read and approved the final manuscript.Table S1. SNP IASMA ID number, NCBI SNP (ss) IDs, SNP allele, 5' near sequence allele and 3' near sequence allele respectively of the 563 SNP-based markers.Click here for file


SNPs are abundant, codominantly inherited, and sequence-tagged markers. They are highly adaptable to large-scale automated genotyping, and therefore, are most suitable for association studies and applicable to comparative genome analysis. However, discovery of SNPs requires genome sequencing efforts through whole genome sequencing or deep sequencing of reduced representation libraries. Such genome resources are not yet available for many species including catfish. A large resource of ESTs is to become available in catfish allowing identification of large number of SNPs, but reliability of EST-derived SNPs are relatively low because of sequencing errors. This project was designed to answer some of the questions relevant to quality assessment of EST-derived SNPs.wo factors were found to be most significant for validation of EST-derived SNPs: the contig size (number of sequences in the contig) and the minor allele sequence frequency. The larger the contigs were, the greater the validation rate although the validation rate was reasonably high when the contigs contain four or more EST sequences with the minor allele sequence being represented at least twice in the contigs. Sequence quality surrounding the SNP under test is also crucially important. PCR extension appeared to be limited to a very short distance, prohibiting successful genotyping when an intron was present, a surprising finding.Stringent quality assessment measures should be used when working with EST-derived SNPs. In particular, contigs containing four or more ESTs should be used and the minor allele sequence should be represented at least twice. Genotyping primers should be designed from a single exon, completely avoiding introns. Application of such quality assessment measures, along with large resources of ESTs, should provide effective means for SNP identification in species where genome sequence resources are lacking. Most performance traits of agricultural relevance are complex traits that are governed by multiple genes. Due to the large number of genes underlying a single trait and their complex interactions, direct genetic analysis of such traits has been difficult. In the past decade, genetic mapping has demonstrated great promise for the analysis of complex traits. In particular, wide applications of microsatellite markers in animal genome studies have allowed major progress in understanding of genes underlying major performance traits ,2. HowevSNPs are the most abundant type of genetic variation. Theoretically, SNPs can have four alleles, but they have been regarded as bi-allelic as most often, only two alleles have been observed at a given position . SNPs arIn most cases, genome-wide SNP discovery has relied on the availability of a draft genome sequence where SNPs can be detected from sequences of the two chromosomes of a diploid organism in the sequence assembly. This approach was initially feasible only for humans and model species. However, as the cost of genome sequencing decreases, now draft genome sequences have become available for several agriculturally important species including bovine, chickens, horses, and soon the swine and tilapia. However, for most aquaculture species, it may take a while for the generation of entire genome draft sequences. Facing the equal challenge, alternative approaches must be sought. Hayes et al. 2007) was able to identify a large number of SNPs from EST resources in Atlantic salmon, and they recently demonstrated mapping of EST-derived SNPs to genetic linkage map 07 was ab. Their pCatfish is the most important aquaculture species in the United States representing over 60% US aquaculture production. Much progress has been made  in its g-10 and 100 minimum of alignment length for all sequences. Following initial gene identification, related ESTs were further evaluated by manual inspection of the alignments.All catfish EST sequences were downloaded from NCBI dbEST database, including those of blue catfish and channel catfish. CAP3 was used to assemble the contigs with the parameters set at "minmatch 50, overlap similarity 0.95", to have a minimal overlap of 50 bases and a minimal similarity of 95% . For eachtml file for each contig, including the alignment information and SNP information. With the autoSNP program, the parameters for minimum minor allele frequency for SNP detection varied with the contig size (the number of sequences in the contig): 1) a sequence variation is declared as a SNP whenever a mismatch is identified within contigs with four or fewer sequences; 2) a sequence variation is declared as a SNP when the minor allele sequence existed at least twice within contigs with 5–6 sequences; 3) a sequence variation is declared as a SNP when the minor allele sequence existed at least three times within contigs with 7–8 sequences; 4) similarly a sequence variation is declared as a SNP when the minor allele sequence existed at least four times within contigs with 9–12 sequences, and 5) when the minor allele sequence existed at least five times within contigs with 13–16 sequences and so on.The autoSNP program was used to detect putative SNPs from the EST sequences . The proTo evaluate the effect of contig size and minor allele sequence frequency on SNP reliability, the SNPs with different contig sizes and minor allele frequencies were selected for SNP validation. After initial submission of a set of SNPs to Illumina, GoldenGate assay functionality and designability scores were given by Illumina. SNPs with a range of functionality and designability scores were chosen for evaluation in this project. A total of 384 SNPs were selected for this project. Hot spots of SNP occurrence that may have been caused by low sequence quality were selected to test how sequence quality affects SNP genotyping and validation rates. In addition to sequence quality, the effect of intron presence on genotyping and validation rates was tested by including SNPs with four known genomic sequences.A panel of 192 samples were used for genotyping and validation of SNPs including 66 fish from our interspecific mapping resource family F1-2 × Channel catfish-6 (64 backcross progenies plus their two parents), and 21 fish each from three wild channel catfish populations and three domestic channel catfish populations .Genomic DNA (250 ng per sample) was used as template for SNP genotyping using the Illumina's bead array technology according to the manufacturer's protocol for GoldenGate assay . Brieflya2+pb2) where Pa is the allele frequency of the major allele and Pb is the allele frequency of the minor allele [The BeadStudio software was used to analyze the SNPs data. The dye intensities are examined by the software to determine the genotype of each sample for that locus. A locus returning predominantly signal from Cy3 is AA, Cy5 is BB and an equal signal of Cy3 and Cy5 represents a heterozygous individual. Data is returned with the allele call for each locus as well as a Gentrain score, a measure that represents the reliability of that genotyping call. GenTrain scores was used to measure the reliability of SNP detection based on the distribution of genotypic classes, and the calling frequency was used to measure the successful SNP calling rate from all samples . For thir allele .A total of 54,960 catfish ESTs available from GenBank including 44,437 ESTs from channel catfish and 10,523 ESTs from blue catfish were subjected to cluster analysis to identify putative SNPs. The contig assembly resulted in 5,670 contigs with an average size of 5.5 sequences per contig and an average length of 1,001 bp per contig. The assembly included 3,003 contigs with 2 ESTs, 980 contigs with 3 ESTs, and 1,687 contigs with 4 or more ESTs  of the SNPs were identified from contigs with 2–3 sequences, the remaining SNPs were identified from contigs with 4 or more sequences  were polymorphic among these 192 fish [see Additional File Of the total of 384 SNPs tested, SNPs were selected with Quality Scores ranging from 0.5 to 1.0. As shown in Table The percentage of putative SNPs that was validated to be real  was found to be directly correlated with contig sizes (number of sequences in the contig) and the minor sequence allele frequencies Table . In geneFlanking sequence quality greatly affected the SNP success rate. Among the contigs with SNPs, we identified 28 contigs with hot spots of SNP occurrence where a region of sequence was highly variable with many "SNPs" detected. Sequence quality examination suggested low quality scores in the sequencing reactions. We intentionally included these SNPs in this project. Of the 28 SNPs tested, 14 (50%) failed in genotyping, suggesting that high sequence quality is required in the SNP region as they are involved in the genotyping primer binding regions (data not shown).The presence of introns greatly reduced the SNP genotyping success rate. Among the contigs containing SNPs, 4 had genomic DNA information that allowed us to test if the involvement of introns has any effect on SNP genotyping and validation rates. All four SNPs failed to provide genotypes. Clearly, The Bead Array technology depends on very short extension and subsequent ligation for success.in silico comparative analysis. Based on the fact that intron involvement led to the SNP genotyping failures, we conducted comparative sequence analysis of the catfish ESTs with corresponding zebrafish genes as references. The rationale is that if the gene organization is similar in catfish and zebrafish, then sequence similarity comparison would allow the location of SNP sites to be aligned to the zebrafish genome. If the SNP sites are close to the exon-intron junction, then that could have caused the genotyping failures, assuming conservation of gene structure and organization between catfish and zebrafish. As shown in Table Of the 118 failed SNPs, 14 were likely caused by low sequence quality flanking the SNP sites; and 4 were caused by the involvement of introns, as designed in the experiment. The causes for failure of the remaining 99 SNPs were then explored by ESTs were proven to be efficient resources for putative SNP identification ,22,39-41As compared with SNPs identified from genomic sequences, EST-derived SNPs have several advantages. Since ESTs are transcribed sequences, EST-derived SNPs are associated with actual genes allowing use of gene-associated SNPs for mapping and subsequent use in comparative genome studies . This isIn this study, over 33,000 putative SNPs were identified from 55,000 catfish ESTs and 384 of these SNPs were tested using 192 catfish samples. We have found that the contig size (number of sequences in the contig) and minor sequence allele frequency were the two major factors affecting the validation rates of EST-derived SNPs. Small contigs had much lower SNP validation rates. Obviously, in small contigs with 2 or 3 sequences, the alternative base is represented only once, and this could be due to sequencing errors. Similarly, in contigs with 4 sequences when the minor sequence allele is represented only once, it is highly likely that the minor allele is due to sequencing errors. We cannot determine the quality of these SNPs without the sequence trace files. Contigs of 4 or more sequences with the minor sequence allele frequency being present at least twice in the contig provided high levels of SNP validation rates (average 70.9% and up to 89.2%). This makes good sense because it is highly unlikely that sequencing errors of two independently sequenced ESTs to occur at the same base location. When at least two ESTs exhibit an alternative base at the putative SNP sites, it is highly likely that such sequence variations are real. All these findings were not unexpected, but for the first time, we provide experimental data to demonstrate the importance of contig size and minor sequence allele frequency. It is noteworthy that even though the larger contigs provided even greater SNP validation rates, contigs of four sequences with even sequence allele distribution (2:2) provided similarly high validation rates. A minimum of two sequences in the contigs representing the minor allele was required to provide a high SNP validation rate ,22.The presence of minor allele sequence in relation to the contig size is important. For instance, if the minor allele sequence was present only once, then the smaller the contig size, the more likely the SNP could be real. This is because the contig size of ESTs is simply a reflection of expression abundance. If a rarely expressed gene was sequenced twice, with the alternative allele being present once each, one can still expect that the allele frequency could be equal or close to be equal when the transcript is sequenced 10 times. However, if the transcript was already sequenced 10 times with the minor allele sequence being present only once, it is more likely that the minor allele could have been derived from sequencing errors Figure . This reAnother advantage of the SNP identification from EST sequences is its ability to identify the uncommon sequence variants . The monSequence quality flanking the SNP sites was found to be important for successful SNP genotyping using Illumina's Bead Array technology, but not the flanking sequence context as referred to as the Quality Score by Illumina when above 0.5. It is probably true that SNP genotyping primers would have worked properly for the most part even if the sequence context was somewhat simple or A/T-rich, or G/C-rich. However, sequence errors in the SNP region could directly affect the base pairing of the SNP genotyping primers. Low quality sequences could easily generate false SNPs, especially at the beginning or at the end of the sequence. Therefore, sequence quality surrounding the SNP site should be used as one parameter to identify reliable SNPs. However, many EST sequences retrieved from NCBI do not have quality scores or trace files. In such cases, greater caution should be exercised. In particular, hot spot of SNP occurrence should be avoided if possible.in silico comparative sequence and gene structural analysis is important when dealing with EST-derived SNPs.Selection of SNPs to allow both allele-specific and locus-specific primers to be located in a single exon is the key to achieving high success rate of SNP genotyping. We found that all tested SNP sites involving introns failed in genotyping. There seemed to be different reasons for such genotyping failures. The most notable cause is that the genotyping primers are located at exon-intron boundary, leading to non-base pairing of the primers with DNA amplified from genomic DNA Figure . In addiIn this project, we demonstrated that ESTs are powerful resources for SNP identification. In spite of the development of highly efficient methods for SNP identification from genomic sequences, such as using deep sequencing of reduced representation libraries , SNP disThe key to the success of SNP genotyping using EST-derived SNPs is the avoidance of introns. The most important factors for high rate of SNP validation are the contig size (number of sequences in the contig) and the minor sequence allele frequency. Presence of minor sequence allele at least twice in the contig is crucial for EST-derived SNP validation. Use of contigs with at least four sequences, when coupled to the presence of minor sequence allele at least twice in a contig should provide a high level of confidence for the validation of EST-derived SNPs. Quality assessment measures for the EST-derived SNPs presented here should be applicable to EST-derived SNPs from all species. Application of such guidelines, along with the availability of large numbers of ESTs, should make it effective to identify and apply EST-derived SNPs for genome-scale analysis.WS drafted the manuscript, prepared the samples, and performed the putative SNP bioinformatics analysis and genotyping data analysis. ZS assisted the bioinformatics data analysis and samples preparation. TS conducted all the genotyping experiments. HL, PX, SB, and EP assisted in the putative SNP bioinformatics data analysis. HK assisted in drafting and finalizing the manuscript. ZL served as the P.I. for the overall design and execution of the project, and manuscript preparation. All authors read and approved the final manuscript.Polymorphic SNP markers information. The file contains the polymorphic SNP markers information including IDs of SNPs, contigs sequences w/SNP sequence and position, and BLASTX hits.Click here for file


Arrayed primer extension (APEX) is a microarray-based rapid minisequencing methodology that may have utility in 'personalized medicine' applications that involve genetic diagnostics of single nucleotide polymorphisms (SNPs). However, to date there have been few reports that objectively evaluate the assay completion rate, call rate and accuracy of APEX. We have further developed robust assay design, chemistry and analysis methodologies, and have sought to determine how effective APEX is in comparison to leading 'gold-standard' genotyping platforms. Our methods have been tested against industry-leading technologies in two blinded experiments based on Coriell DNA samples and SNP genotype data from the International HapMap Project.In the first experiment, we genotyped 50 SNPs across the entire 270 HapMap Coriell DNA sample set. For each Coriell sample, DNA template was amplified in a total of 7 multiplex PCRs prior to genotyping. We obtained good results for 41 of the SNPs, with 99.8% genotype concordance with HapMap data, at an automated call rate of 94.9% (not including the 9 failed SNPs). In the second experiment, involving modifications to the initial DNA amplification so that a single 50-plex PCR could be achieved, genotyping of the same 50 SNPs across each of 49 randomly chosen Coriell DNA samples allowed extremely robust 50-plex genotyping from as little as 5 ng of DNA, with 100% assay completion rate, 100% call rate and >99.9% accuracy.We have shown our methods to be effective for robust multiplex SNP genotyping using APEX, with 100% call rate and >99.9% accuracy. We believe that such methodology may be useful in future point-of-care clinical diagnostic applications where accuracy and call rate are both paramount. If 'personalized medicine', using genomic knowledge, is to become a reality, then the ability to determine the most appropriate clinical intervention for a patient will require the genotyping of several tens to hundreds of single nucleotide polymorphisms (SNPs) across many genes and their regulatory sequences for that individual patient ,2, rapidGiven the potential utility of APEX for rapid clinical diagnostics, we have developed robust assay design, chemistry and analysis methodologies, and have sought to determine just how effective APEX is in comparison to leading 'gold-standard' genotyping platforms, including Perlegen and Illumina. Our objective was to achieve 100% assay completion rate, call rate and genotyping accuracy rate, for multiple SNPs across multiple samples. Previous studies from our laboratory have reported APEX genotyping accuracies ranging from 98% to 99.8% ,16-18, tWe selected 50 SNPs from the HapMap database that had been previously genotyped and analyzed as part of the third quality control exercise on Illumina and Perlegen platforms, arguably the most accurate and best validated high-throughput methodologies for SNP genotyping to date. The randomly selected SNPs were located across multiple chromosomes and are listed in Additional file Microarray image data were imported into SNP Chart  and anali.e., when the sequence-specific spot intensities are only slightly higher than background signals); 2. non-calls (NNs) generally resulted from sporadic PCR failure for certain amplicons, especially those of a length greater than 650–700 base pairs (bp). Taken together, our results suggested that even if specific SNPs give high NN rates across multiple samples, the genotypes for the remaining samples for these SNPs (for which APEX assay data can be obtained) are still very accurate, despite low signal to noise. We believe that this is due to the redundancy in the genotyping probe design: two classical APEX probes (one probe per DNA strand), plus four allele-specific (ASO) APEX probes (two probes per strand), each replicated five times, for each SNP site. When this redundant data is displayed in a SNP Chart, it is relatively straightforward to interpret the genotype manually  resulted neither in as high an accuracy of genotyping nor in as high a call rate, compared to the dynamic use of multiple probes.Therefore, we have determined that reliance on any single probe type alone , 1.5 mM MgCl2, 200 μM dNTPs without dTTP, 160 μM dTTP, 40 μM dUTP, 0.75 U HotStar Taq DNA polymerase , 1 μL 10 μM primer mixtures (each primer), and 25 ng genomic DNA. Incorporation of the dUTP allowed for the amplified DNA to be enzymatically sheared by uracil N-glycosylase  to produce a DNA size of approximately 50–100 bases, optimal for hybridization to the oligonucleotides on the microarray (see below). Genomic DNA and PCR master mixture were transferred into ABI 384-well reaction plates  using a Biomek FX robot . PCR reactions were performed in a GeneAmp PCR System 9700 ThermoCycler . PCRs were initiated by a 15 min polymerase activation step at 95°C and completed by a final 10 min extension step at 72°C. The PCR cycles were as follows: 35 cycles of 30 s denaturation at 95°C, 30 s annealing at 58°C, and 50 s extension at 72°C.For the first experiment, PCR primers were designed to amplify the regions across the 50 SNPs, based on a melting temperature (Tm of 65°C ± 7°C and performed 50-plex PCR in one single reaction per sample. Each new PCR primer had a common linker sequence designed at its 5' end (5' TACGACTCACTTAGGGAG-3' for each of the left hand PCR primers/5' CGATGTAGGTGACACTAG-3' for each of the right hand PCR primers). The 3' ends of the primers were chosen to have non-complementary bases with respect to each other , in order to reduce the probability of primer interactions and primer-dimer formation. All primers were computationally tested against the human genome and found to amplify single product. The new amplicon sequences were located within the amplicon sequences from the original primer pairs. The multiplex PCR was carried out in a 25 μL reaction containing 20 nM  of each primer plus 20 nM of left and right linker-only primers (left linker: 5' TACGACTCACTTAGGGAG 3'/right linker: 5' CGATGTAGGTGACACTAG 3'), 200 μM dNTPs without dTTP, 160 μM dTTP, 40 μM dUTP, 6 units of HotStar Taq DNA polymerase , 1.5 mM MgCl2 in 1× PCR reaction buffer  with 5 ng of genomic DNA. PCR was performed using a MJR PTC 200 ThermoCycler . PCR was initiated by a 15 min polymerase activation step at 95°C and completed by a final 3 min extension step at 72°C. The reaction procedure consisted of 40 cycles of denaturation at 95°C for 40 s, primer annealing at 55°C for 2 min and one ramping-up step from 55°C to 70°C for 2.5 min (0.1°C/s) , followed by enzyme inactivation for 10 min at 95°C.Aliquots of PCR products were visualized with Gel Red fluorescent nucleic acid dye  staining under ultraviolet (UV) illumination on a 2% agarose gel, following electrophoresis in 0.5× Tris-borate EDTA (TBE) buffer. The 7 subgroup multiplex PCR products were pooled for each individual Coriell sample and precipitated by adding 2.5 volumes of ice-cold 100% ethanol and 0.25 volumes of 10 M ammonium acetate solution. After precipitation at -20°C overnight, the mixture was centrifuged at 20,800 × 2, pH 9.5]. The reaction mixture was applied to the grid of APEX and ASO-APEX probes previously printed on the CodeLink slide that had been washed two times in 95°C pure water and placed on a Thermo Hybaid HyPro20 incubation plate  set at 58°C. The reaction mixture was covered with a small piece of Parafilm™, and the APEX reaction allowed to proceed at 58°C with agitation (setting 1) for 20 min. Following the incubation period, slides were washed with 95°C water to remove the template DNA, enzyme, and excess ddNTPs. Further washing in 0.3% Alconox  and 95°C pure water ensured low background on the array images.The APEX reaction was performed in a total volume of 40 μL by the addition of 17 μL fragmented DNA template, 1 μL of 2 pmol/μL Npg1-positive control template oligonucleotide, 1.25 μM of each fluorescently labeled dideoxynucleotide triphosphate , 5 U Thermo Sequenase™ DNA polymerase  diluted in its dilution buffer, 2× Thermo Sequenase reaction buffer [10×, 260 mM Tris-HCl, 65 mM MgClAs described in the main paper, we directly sequenced three SNP loci in three independent samples: 1. sample 192 (NA18502) at SNP rs3776720; 2. sample 101 (NA18621) at SNP rs12472674; 3. sample 228 (NA19210) at SNP rs4739199. We performed three single-plex PCR reactions using primer pairs from the first experimental design and methods [Additional file arrayWoRxe Auto Biochip Reader , fitted with the following filter sets: 1. A488 – Ex. 480/15× – Em. 530/40 (R110 dye); 2. Cy3 (narrowband) – Ex. 546/11 – Em. HQ570/10 m (Cy3); 3. Texas Red – Ex. 602/13 – Em. 631/23 (Texas Red); 4. Cy5 – Ex. 635/20 – Em. 685/40 (Cy5) . Exposure times for each dye were set up to give approximately 60–70% pixel saturation for selected Npg1 positive control probe spots. Resolution of the imager was set to 10 μm. Four 16-bit TIFF files for each array were obtained (one from each channel) and these were imported into SNP Chart [Slide microarrays were imaged using an NP Chart ,25. SpotGenotyping – Manual CallingManual genotype calling within SNP Chart was carried out as previously described ,16,23.The training set for MACGT   was seleDetailed descriptions of the algorithms used in simple linear discriminant analysis (LDA) with dynamic variable selection have previously been published by our laboratory . A briefIdeally, for variable construction, each genotype call could be based on just one of the four sets of probes: (1) APEX_LEFT; (2) APEX_RIGHT; (3) ASO_1LEFT and ASO_2LEFT; and (4) ASO_1RIGHT and ASO_2RIGHT [Additional file APEX.XL = median  = 1148, andAPEX.YL = median  = 29, and so on, as summarized in Additional file i.e., 519 genotypes across the 41 SNPs [Additional file Our automated genotype calling algorithm is based on the simple linear discriminant analysis (LDA), using dynamic variable selection as special criteria for various classifiers related to multiple probes. LDA is a supervised learning technique which requires a valid training set in order to build the classification (genotyping) model for each SNP. For the complete set of 287 HapMap samples, our dynamic variable LDA-based genotype calling algorithm used the same training set as used by MACGT above  and the other with a minimal number of prototypes (at most 2 prototypes in each class) for each SNP. The two different training sets yielded different performances for the respective test data sets.For automated genotype calling, we started our analysis by fitting the simple LDA-based genotype model using each classifier separately, and then comparing the predicted genotypes with the validated genotypes. Subsequently, we applied our dynamic-variable LDA-based genotyping model on different combinations of the four classifiers. When combining four classifiers together, for each SNP we apply LDA to each pair of variables in Additional file Analogous quantities are computed for other classifierse.g.,Proper weights are obtained by normalizing them, The weights are applied to the posterior probabilities of the respective class to give the final class posterior probabilities. For example, the final posterior probability for XX class isXY, PYY and PNN in a similar manner, the final genotype call is obtained with highest weighted probability. In the last stage, call rate can be adjusted by applying varying thresholds to the 'final weighted probability' (confidence score), and the concordance with the validated genotype set will vary accordingly. The calls were checked for concordance with the validated genotypes from HapMap.After obtaining PAdditional file The author(s) declare that they have no competing interests.M.P. performed the linear discriminant analyses (LDA) using dynamic variable selection. J.R. performed the wet-lab experiments described in this study, and assisted in the design of the initial multiplex PCR. B.W.T. performed the image analysis and MACGT auto-calling and analysis steps, and assisted S.J.T. in the manual genotype calling. Z.E.C. helped design the 50-plex PCR primers, and undertook initial experimental evaluation of these primers. M.P., J.R., B.W.T. and S.J.T. discussed the results and contributed to the preparation of this manuscript. S.J.T. designed and supervised the experiments and analyses, and wrote the paper.The pre-publication history for this paper can be accessed here:List of SNPs, probes and PCR primers. Table that details the rs numbers of the 50 SNPs investigated, as well as the APEX and allele-specific APEX probe sequences, and the PCR primer sequences for the initial experiment.Click here for filePCR multiplex groups. Table that details the 7 groups of multiplex PCRs.Click here for fileGenotyping results from first experiment. Table that lists the complete genotyping results for 287 HapMap samples and 41 SNPs. Includes LDA call and MACGT call , as well as original HapMap call.Click here for fileChart interpretation is given below. Illustrative examples for each genotype case  are shown for the SNP rs1433375. SNP Charts on the left hand side  represent auto-called genotypes, whilst those on the right hand side  represent manually-called genotypes. The y-axes  of each individual genotype class have been set to identical scale values for both auto- and manually-called samples, and clearly show that genotypes can be correctly called  from samples having signal intensities up to an order of magnitude lower than usual. Each chart shows four channel fluorescent intensity data  from thirty rs1433375-specific array spots . Starting at the left hand side of each chart, the first five spots ('LEFT T/C') refer to the left-hand APEX probe that will give either a single T (blue) signal (for homozygous TT genotypes), or a C (green) signal (for homozygous CC genotypes), or a mixture of T and C (heterozygous CT). The next five spots ('RIGHT A/G') refer to the right-hand APEX probe that interrogates the complementary DNA strand nucleotide to that of the left-hand APEX probe, hence gives a single A (yellow) signal (for TT), a single G (red) signal (for CC), or a mixed A and G signal (for TC). The remaining spots represent allele-specific APEX probes in which a base-specific fluorescence signifies the presence of the allele. '_1' probes correspond to the first allele (T in the case of rs1433375), and '_2' probes correspond to the second allele (C). The redundancy and consistency of the data across different probes give high confidence in the assigned genotypes.Click here for fileRe-scaled SNP Charts for rs1433375. This figure is a repeat of Additional file Click here for fileList of PCR primer sequences for 50-plex PCR experiment. PCR primer sequences that were designed for the 50 HapMap SNP loci, with amplicon sizes restricted to between 100 and 200 bp, and with common 5' linkers.Click here for fileGenotyping results from second experiment (50-plex PCR). Table that lists the complete genotyping results for 49 HapMap samples and 50 SNPs. Includes LDA call and MACGT call , as well as original HapMap call.Click here for filePerformance analyses for the different data sets, addressing the redundant probe chemistry. To further determine the effect of probe redundancy in our APEX methodology, we used LDA to reanalyze both data sets  but using non-redundant and partially-redundant probe-specific data. Three tables are shown .Click here for filePerformance analyses for the different data sets, addressing the redundant probe chemistry. To further determine the effect of probe redundancy in our APEX methodology, we used LDA to reanalyze both data sets  but using non-redundant and partially-redundant probe-specific data. Three tables are shown .Click here for filePerformance analyses for the different data sets, addressing the redundant probe chemistry. To further determine the effect of probe redundancy in our APEX methodology, we used LDA to reanalyze both data sets  but using non-redundant and partially-redundant probe-specific data. Three tables are shown .Click here for file1) combines the two ASO-APEX Left probes ; plot (2) combines the two ASO-APEX Right probes ; plot (3) is for the APEX Left probe; plot (4) is for the APEX Right probe. The plots for SNPs rs3776720, rs12472674 and rs4739199 include labeled data-points for the individual Coriell samples that gave rise to discrepancies in genotype calling.Simple scatter plots for all 50 SNPs from 50-plex data set. For each plot the x-axis represents signal values for X allele and the y-axis represents signal values for Y allele. All values are in log scale. Magenta, green, blue and black coloured symbols denote the classes YY, YX, XX and NN (negative control samples), respectively. Plot (Click here for fileData structure for SNP rs12466929 & DNA sample HapMap 101 (Coriell NA18621 – AA). Illustrative table of microarray four-channel intensity data from 30 spots corresponding to one SNP (rs12466929) and one DNA sample.Click here for fileList of explanatory variables listed by appropriate classifiers. Each of these classifiers consists of a pair of explanatory variables, generically denoted by X and Y, corresponding to two candidate alleles in the SNP position. Values are based on the data shown in Additional file Click here for file


Human herpesvirus 3) genome sequences were examined to evaluate descent of strains, structures of lineages and incidence of recombination events. Eighteen complete, published genome sequences were aligned and 494 single nucleotide polymorphisms (SNPs) extracted, each as two alleles. At 281 SNPs, a single sequence differed from all the others. Distributions of the remaining 213 SNPs indicated that the sequences fell into five groups, which coincided with previously recognized phylogenetic groupings, termed E1, E2, J, M1 and M2. The 213-SNP set was divisible into 104 SNPs that were specific to a single group, and 109 cross-group SNPs that defined relationships among groups. This last set was evaluated by criteria of continuities in relationships between groups and breaks in such patterns, to identify crossover points and ascribe them to lineages. For the 99 cross-group SNPs in the genome's long unique region, it was seen that the E2 and M2 groups were almost completely distinct in their SNP alleles, and the E1 group was derived from a recombinant of E2 and M2. A valid phylogenetic tree could thus be constructed for the four E2 and two M2 strains. There was no substantive evidence for recombination within the E2 group or the E1 group (ten strains). The J and M1 groups each contained only one strain, and both were interpreted as having substantial distinct histories plus possible recombinant elements from the E2 and M2 lineages. The view of VZV recombination and phylogeny reached represents a major clarification of deep relationships among VZV lineages.Relationships among varicella-zoster virus (VZV;  Alphaherpesvirinae, genus Varicellovirus, species Human herpesvirus 3) is the causative agent of chickenpox and shingles, and is one of eight herpesviruses that have humans as the natural host. VZV has a double-stranded DNA genome of 125 kbp, which is the smallest among the human herpesviruses (Varicella-zoster virus (VZV)  flanked by a pair of repeat elements in opposing orientations: RL (88 bp) and RS (7.3 kbp). Terminal and internal copies of the repeats are denoted by the prefixes T and I respectively, and the complete DNA molecule then has the arrangement TRL-UL-IRL-IRS-US-TRS  is treated as consisting of two linked components, the long (L) and short (S) regions. Each of these contains a unique sequence  and emboss program suites. Phylogenetic trees were constructed with programs dnapars and pars of the phylip suite , there being very few potentially useful insertion–deletion polymorphisms. The alignment contained 494 SNPs, all of which were present in only two allelic states. The distribution of SNPs across the genome is illustrated in Fig. 1(b)S, and it is entirely credible that higher levels of variation should be associated with this extended repeat element. However, the region of higher SNP incidence clearly also encompasses several thousand residues of the right extremity of UL, and here no ready explanation is apparent.As of July 2008 there were 23 complete genomic sequences for VZV strains available. Three are for vaccine strains and two are for high-passage versions of a separately sequenced strain, and these five were omitted from the analysis described here. An alignment was constructed with the remaining 18 DNA sequences . Singleton SNPs are not of primary relevance for analysing deeper relationships among lineages. Alleles for the 213 non-singleton SNPs are illustrated in Fig. 2(a)L, five in IRS and five in US. IRL contains only two SNPs, both singletons, and so does not appear in the analysis. Within UL, only three cross-group SNP loci are also variable within groups, namely SNPs 17 and 18  and group-specific SNPs. This last subset was taken to include both SNPs that define variation only within a single group, and also SNPs common to all members of a given group and distinct from all strains in other groups. There are 109 cross-group SNPs, which are depicted in Fig. 2(b)It is apparent from Fig. 2(b)L, since the ten cross-group SNPs associated with IRS and US showed high intra-group variation. In order to facilitate recognition of patterns in the array, the SNP set for the five groups was converted to binary form with alleles at each locus coded as 0 and 1 , from 95 for Dumas-relative colouring to 65 for both optimized schemes. The version in Fig. 3(b)Colouring SNPs by reference to any one strain is of course an arbitrary device L among the five groups. The most cogent feature is that the E2 and M2 groups are almost completely composed of distinct alleles, and each of these groups can be regarded as comprising deep lineages that contain little or no admixture from recombination with other lineages. Next, the E1 group's alleles match those of the E2 group for the most part, but there are also substantial sections that match the M2 group (numbers 24–39 and 47–54). The E1 group thus appears to have descended from a recombinant between the E2 and M2 lineages. For the larger M2-like region in E1 (SNPs 24–39) this interpretation is strong with respect to the criteria used to derive the colour scheme: either the E1 lineage is recombinant at this locus and all four other lineages are not, or vice versa. For the smaller M2-like region (SNPs 47–54) the colouring choice is balanced. Smaller regions of heterogeneity of colours in E1, E2 and M2 could represent point mutational or recombinational events, whose SNP signatures are too meagre to be robustly interpretable. Lastly, the single-member J and M1 groups each have large single-coloured sections (for both colours) and also other heterogeneous sections. They thus seem to contain elements that are recombinant between the E2 and M2 lineages, while the patchy, mixed sections may, at least in part, represent deep lineages distinct from those of E2 and M2. Interpretation for J and M1 was limited by the absence of opportunity to refine their SNP sets through identifying variations specific to group or strain.The colouring scheme of Fig. 3(b)L sequences.In no case do the SNPs that define variation within a multi-member group show any substantive indication of recombination, and a valid phylogenetic tree for each group can thus be made showing relationships among members. However, given the clear indication that crossovers were involved in the genesis of at least some of these contemporary groups, a standard phylogenetic tree that includes all groups would not provide an accurate representation of deep relationships among groups. In this section I use the analyses of SNP data described above to reach a representation that takes recombinational events into account, based on the UL cross-group SNPs for the E2 and M2 groups are almost completely distinct, a tree based on only these two groups should be nearly free of any complications due to recombination. Trees were derived using the complete UL alignments for the E2 and M2 strains. Initial work used maximum-parsimony programs, which allowed the contributions of the several subsets of SNPs to be readily distinguished, and this was then supplemented by the computationally more sophisticated BMCMC approach. The two methods gave effectively identical results. Because diversity in the genomic alignment is low, allowance for multiple substitutions made by the BMCMC computation is small; the overall branch length of the BMCMC tree was less than 2 % greater than that of its maximum-parsimony counterpart. The tree for E2 plus M2 from BMCMC is shown in Fig. 4L alignments, and treating M2 as outgroup for E1. This E1 tree is valid for the portion corresponding to the E1-group-specific SNPs; that is, for descent from the earliest group-specific ancestor. However, since it appears that the lineage ancestral to E1 arose by recombination events involving the E2 and M2 lineages, the deeper history of the E1 group can only be indicated by sketched connections to those lineages, as depicted in Fig. 4Since the UThe view of VZV recombination and phylogeny achieved in this paper represents a major clarification in our appreciation of relationships among VZV lineages. While the sequences are highly conserved, there is sufficient diversity in the set of 18 genomes analysed to define major lineages and to give some insight into possible crossover events. Earlier phylogenetic analyses of whole genome sequences had shown that five major groups or clades could be discerned, but that the detail of relationships among these varied along the genome, indicating the presence of recombinant forms  that examination of SNP patterns identified the same major groups as had earlier emerged from tree-building. This indicated that the incidence of recombination has been sufficiently low relative to that of nucleotide substitution to allow development of distinct lineages, at least for the US, appears to be more dynamic than that in UL. The incidence of SNPs in RS is 1.5 times higher than in UL . Such elevated G+C contents are a general feature of herpesviral genomic repeat regions, and are considered likely to result from mutations introduced or fixed during processes of recombination between repeat copies, perhaps involving biased gene conversion (L, of both point mutation and recombination; since the SNP sets for RS and US are small, no further analysis was attempted. It is worth noting that, in crossovers between distinct VZV strains, events involving RS could well be a favoured class, given the recombinational activity between RS copies in single-strain infection.Sequence evolution in the S region, especially Rnversion . I interStudies of VZV geographical variation have been carried out with strategies that sample genome sequences (for instance Barrett The diversity of VZV genomic sequences is recognized as being low compared with that seen in other human herpesviruses . For ins


Tadarida brasiliensis, the Brazilian free-tailed bat. Specifically, we test whether phrase and song composition differ among individuals and between two regions, we determine variability across renditions within individuals, and test whether phrases are randomly ordered and combined. We report three major findings. First, song phrases were highly stereotyped across two regions, so much so that some songs from the two colonies were almost indistinguishable. All males produced songs with the same four types of syllables and the same three types of phrases. Second, we found that although song construction was similar across regions, the number of syllables within phrases, and the number and order of phrases in songs varied greatly within and among individuals. Last, we determined that phrase order, although diverse, deviated from random models. We found broad scale phrase-order rules and certain higher order combinations that were highly preferred. We conclude that free-tailed bat songs are composed of highly stereotyped phrases hierarchically organized by a common set of syntactical rules. However, within global species-specific patterns, songs male free-tailed bats dynamically vary syllable number, phrase order, and phrase repetitions across song renditions.In mammals, complex songs are uncommon and few studies have examined song composition or the order of elements in songs, particularly with respect to regional and individual variation. In this study we examine how syllables and phrases are ordered and combined, ie “syntax”, of the song of  The use of vocal displays by animals to defend territories and attract females is widespread For most birds the production of calls and simple songs is largely innate, but a subgroup of birds known as the vocal learners, which includes the parrots, hummingbirds and oscine songbirds, are endowed with a specialized network of brain nuclei that constitute a “song system” allowing them to produce broad repertoires of songs that are more complex and flexible than the songs of other birds Mus musculus, Singing behavior and examples of vocal syntax are exceedingly rare in mammals. Simple songs resembling the innate songs of non-vocal learning birds have been observed in mice Tadarida brasiliensis). The Brazilian free-tailed bat is an abundant and genetically contiguous subspecies occurring from California to Mexico T. brasiliensis songs are complex vocal signals with multiple types of syllables and phrases  and have average intervals between syllables of 3.7 ms Phrase Order below), each trill was highly distinctive since each phrase was separated from the next by a silent interval of on average 35.6±0.6,  that is much greater than, and did not overlap with the duration of intervals between syllables within each phrase .The second type of phrase is the trill. Trills are composed of short (mean = 3.4 ms), downward FM syllables that can be connected, resulting in sinusoidal patterns . Trill st = 11.6, P<0.0001, df = 22). Due to the greater number of syllables in buzzes compared to trills, buzzes have substantially longer durations . The second feature that distinguishes buzzes from trills is that the spectral structure of successive buzz syllables follows a pattern Phrase Order below), but were highly distinctive; each buzz phrase was separated from the following buzz by a relatively long silent interval  that was much greater than, and does not overlap with the interval between syllables within phrases . We point out that while some songs contained all three types of phrases, other songs had only one or two of the possible phrase types.The third phrase in song is the buzz. Buzzes are also composed of short (mean = 3 ms) downward FM syllables , but theT. brasiliensis. Below we evaluate the composition and temporal patterns of songs in greater depth. In the first section we describe songs recorded in Austin and College Station, identify syllables and phrases that are shared by both colonies and test whether song composition differs between the two locations. In the second section we examine variation of song features in individual bats to determine the degree to which songs vary from one rendition to the next. Finally, in the last section we examine phrase order and elucidate some rules of song construction.In the above section we described the various syllables and phrases of the songs of male Phrase Variables). Chirps were present in the songs of all males from both locations  at the two locations and found that song features were generally similar (Song Variables). However, we found two minor regional differences. First, the proportion of songs with buzzes was greater in College Station (80%) than in Austin  compared with only 2 % in College Station . This resulted in a larger number of phrases per song in College Station compared to Austin  and the number of song types per bat  increased with the number of songs recorded. Neither the number of song variants  nor the number of song types  differed between locations. One caveat of these data is that we had fewer recordings for the majority of bats from College Station compared to Austin  or without the inclusion of consecutive trill and buzz repeats  and 2) three-phrase combinations . For phrase transitions we tested if observed frequencies deviated from those predicted by a random model. For three-phrase combinations, in addition to the random model we tested if observed frequencies deviated from those predicted by a first-order model  phrase transitions or two-phrase combinations (odel see . We thenN = 291 songs). First, we found that transition frequencies deviated significantly from random expectations for the beginning of songs  and for each song phrase . Comparing observed and expected values revealed three major phrase-order rules: 1) songs begin almost exclusively with chirps  trilh chirps . In factN = 232 songs). We tested whether the frequency of three-phrase combinations deviated from two models: a simple random model and a 1st order Markov model, the first-order model . We observed all but one of 22 possible combinations and the most common and least common combinations matched model expectations. Furthermore, twenty of the 22 combinations deviated by only 4% or less from either model. Thus although three-phrase combinations are not randomly generated there are no simple rules for three-phrase combinations; instead song construction is quite variable.Next we examined if rules existed at higher levels, specifically for three-phrase combinations. For these analyses only songs with at least three phrases were used . When we examined observed frequencies we found that over 75% were comprised of only three of the twelve possible combinations: chirp-trill-chirp, chirp-trill-buzz and to a lesser extent trill-chirp-buzz  and a non-migratory population found in eastern Texas . Currently very little is known about the year-to-year roost fidelity of these bats or how large their foraging territories may spread over the course of a single season or throughout their lifetime. Thus, the lack of geographical variation we observed may be attributable to the potentially large areas over which these bats interact with each other.As mate attraction signals, we expected species-specific song features to facilitate the recognition of conspecifics. However within the context of a species-specific song template, regional variation is extremely common. Dramatic differences can occur over small geographic ranges in the form of culturally-transmitted dialects Our results show that the primary source of variability in song construction came from between and within individual variation. Although as much as 30 % of the variation in song features was due to differences among bats, overlap was considerable, and even greater variation occurred across renditions within individuals. A similar pattern emerged when song types were examined. Some song types were shared across many bats but each bat also sang many different song types. The high degree of variability in songs sung by individuals coupled with the overlap in the usage of particular song types between individuals makes it unlikely that a single song feature can be used for distinguishing among individuals.T. brasiliensis. First, T. brasiliensis songs follow three basic syntactical rules: 1) songs always begin with chirps, 2) trills do not follow buzzes and 3) buzzes predominately occur at the end of songs. Second, we discovered that at a broad scale, that is when consecutive repetitions are removed, only a subset of combinations occurs and particular combinations are preferred . Third, although on a broader scale a subset of combinations is disproportionately preferred, within any song, there could be a number of repeated trills or repeated buzzes that contribute to song diversity. Interestingly, similar patterns of syntax, particularly the use of specific orders and the addition or deletion of element repetitions have been documented in various bird species Our analysis of phrase order revealed three important characteristics of song construction in T. brasiliensis song that has been observed in other bats is the use of buzzes. In sac-winged bats, Behr (2006) examined whether song features affected reproductive success. They concluded that the fundamental frequency and duration of long buzzes within songs were the best predictors of reproductive success because they provided females with honest information about the fitness of the singing male. Free-tailed bats use long buzzes in agonistic displays, in defense of territories and during physical confrontations with other males One aspect of T. brasiliensis song in multiple ways, they both have relatively few building blocks  that are used to create many combinations and they both follow simple rules of syntax that are elaborated upon with repetitions of particular notes Our results overwhelmingly indicate that male free-tailed bats dynamically vary syllable number, phrase order, and phrase repetitions. One possible explanation for this diversity is female preference. In sac-winged bats, song complexity, measured by the number of unique syllable types, was positively correlated with the number of females a male had on his territory T. brasiliensis produce complex variable songs that are easily categorized and quantified. We present the first evidence that bats routinely vary songs across renditions via subtle shifts in syllable number, phrase repetition and/or phrase order. Bird song has been the basis for understanding the evolution of vocal complexity as well as the physiology of vocal production. This study provides a quantitative foundation for future research into a complex mammalian vocal signal particularly with respect to vocal plasticity and evolution.In conclusion, T. brasiliensis in Austin, Texas and a second wild colony at Texas A&M, in College Station, Texas. The Austin colony has been maintained by the author (BF) for ten years, and the identity, sex, and history for each individual has been documented. Bats were housed in a wooden structure measuring 4.9 m (length)×3.7 m (width)×3.7 m (height). Two windows allowed filtered sunlight to enter. Humidity was maintained at 60% or above and temperatures varied in the building from approximately 22 to 26 degrees C. Cloth-covered heating pads placed in cages during evening hours provided bats with the option of accessing temperatures reaching 29 degrees C. The bats roosted in fabric pouches positioned along the walls and ceilings of open wooden cages and had access to the entire building. Bats had continual access to water and beetle larvae (Tenebrio molitor), and were also offered a blended mixture of larvae, baby food, and vitamin supplements in the evening This study was conducted at two locations. The first was a captive colony of approximately 60 3) that had regulated light-dark cycles adjusted with a light timer to mimic the natural external photoperiod. The two rooms were connected with a large sliding door that remained open to provide more room for flight. The rooms were temperature and humidity controlled. Within the vivarium bats roosted in artificially constructed bat houses . Bats were trained to feed themselves, and were fed a diet of mealworms supplemented with vitamins and essential fatty acids. All husbandry and experimental procedures were in accordance with NIH guidelines for experiments involving vertebrate animals and were approved by the local IACUC. In some instances individual male bats were recorded singing in the vivarium and in a soundproof recording chamber in the lab. Males and females were housed separately, but were not acoustically isolated.At Texas A&M, groups of free-tailed bats were recorded from year-round natural colony of approximately 100,000 to 250,000 bats located within the university's athletic complex. Within the complex, small groups of bats that were reliably located in easily accessible places were videotaped and recorded once a week in the early afternoon (12:00–2:00 pm) for a 52-week period extending from January 2006 to January 2007, although songs were only detected between March and September. Some groups contained several singing bats, and some groups contained only one singing bat. Bats that produced songs were identified on videotaped recordings because they came to the front-most edge of the roost and performed a territorial display. Some these bats were captured and transferred to a bat vivarium in the Biology department for further behavioral studies. The vivarium consisted of two rooms  and custom-made amplifier. In 2003 and 2004 signals were recorded into a custom-made digital time expander. The time expander recorded a maximum of 1 second that was expanded to 10 seconds at 16 bits and was played onto a computer at a sample rate of 44.1 kHz. In 2005 and 2006, calls were recorded directly onto a computer at a sample rate of 300 kHz using a high-speed data acquisition card  and Avisoft Recorder Software . Both systems allowed recordings up to 150 kHz, well above the frequency content of vocalizations. At Texas A&M, ultrasonic vocalizations were recorded using an externally-polarized condenser microphone  and digitized at 250 kHz sampling rate using the Avisoft UltrasoundGate hardware  for storage on a personal computer running the accompanying Avisoft-Recorder software v. 2.9. For analyses, we used all songs that were of sufficient quality for measurements and syllable identification.We used the following terms to describe vocalizations:Song: vocalizations emitted by males during the mating season that have multiple types of syllables and phrases. Songs were separated by intervals of silence of at least 115 ms (see Defining Songs below)Syllable: the smallest acoustic unit of a vocalization. In this study it is equivalent to one continuous emission surrounded by silence of at least 1 ms. Equivalent to a note.Phrase: a combination of one or more types of syllables that may be repeated in a song. Simple phrases are composed of one type of syllable. Complex phrases are composed of different types of syllables.Song Variant: a unique sequence of phrases. For example the song variant in Song Types: a unique sequence of phrases that does not include consecutive trills and buzzes i.e. one or more buzzes are considered a single buzz and one or more trills are considered a single trill. For example the song type in N = 19,614) using SIGNAL  and calculated the log frequency of intervals per unit time across the range of interval lengths. We then used non-linear regression  to fit a two-process model to the data where a “fast” process represented inter-syllable intervals and a “slow” process represented inter-song intervals Frequently songs were produced in obviously discrete units. However, on other occasions songs were produced in bouts over longer periods of time and the beginnings and endings of multiple songs were difficult to determine. We used bout analysis N = 23). For chirps we calculated the number of type B syllables per phrase and the ratio of type A syllables per type B syllable . We used restricted maximum likelihood  to calculate variance component estimates for region, individual, and rendition . The only exceptions were the proportion of songs with buzzes and the proportion of songs with trills, which were calculated per bat and only analyzed relative to region. To control for unevenness in sampling across bats for each variable we 1) only used bats with at least five songs and 2) for bats with greater than ten songs we randomly selected ten songs from each bat. To further examine variation relative to individuals, for each variable we calculated the coefficient of variation (CV = standard deviation/mean) for 1) the total sample and 2) each individual bat .P>0.05) and so we removed this effect from the model.In addition to phrase and song features we examined the number of song variants and the number of song types per bat for bats in which we had at least four songs. The number of song variants and the number of song types increased linearly with sample effort  indicatirandom model. For three-phrase combinations, in addition to the random model, we used a more complex model, the first-order model. With the random model, predicted frequencies of transitions and three-phrase combinations were based solely on the frequency of the constituent phrases. The random model is the simplest model; the likelihood of any phrase occurring is independent of the identity of any preceding phrases. The first-order model, on the other hand, was calculated from the abundance of transitions from one phrase to the next. It is a first-order Markov model and under this model, the likelihood of a phrase occurring depends on the identity of the previous phrase.The aim of this analysis was to determine if there are rules for constructing songs. To accomplish this we compared phrase order frequencies with those predicted from two models. Specifically, we used chi-squared tests to determine whether the frequency of 1) transitions from one phrase to the next (two-phrase combinations), and 2) three-phrase combinations, deviated from expected. For two-phrase combinations, expected values were calculated using the random model. For the beginning of each song and for each phrase, we determined expected transitions to the following phrase or to the end of song. Thus, for calculations we treated the start and end of songs as “phrases” and so the first phrase of a song was considered the “second phrase” of a start transition. Expected transition frequencies were simply the expected frequencies of the second phrase: the number of times the second phrase (or end) was observed divided by the number of times all possible phrases were observed. By using “possible” phrases we incorporated the fact that some transitions were impossible, such as chirp-chirp and start-end.First we examined transitions using the random model expected three-phrase combination frequencies were calculated as the product of the frequency of each phrase. Again, this model assumes that the current phrase is independent of the previous phrase. For the first-order model expected three-phrase combinations were calculated as the frequency of the first phrase multiplied by the frequency of the first transition multiplied by the frequency of the second transition. Again, this model assumes that the current phrase depends on the probability of a transition from the previous phrase.Next we examined three-phrase combinations. For three-phrase combinations we did not include start and end positions. All three-phrase combinations were calculated for songs with at least three phrases. In essence we used a three-phrase sliding window. For example the song chirp-trill-chirp-buzz has two three phrase combinations: chirp-trill-chirp and trill-chirp-buzz. Using the first-order model described above.Although the primary goal of these analyses was to determine whether phrase order was non-random, they were also used to elucidate phrase-order rules, that is combinations or patterns that were highly non-random and either absent or ubiquitous. To this end we calculated deviations from expected as the difference between observed frequencies and expected frequencies. However, for three-phrase combinations, patterns were not obvious because most of the large number of possible combinations (22 for three-phrase combinations) were observed. Thus, in a final analysis we removed trill repeats and buzz repeats. This is the same as running analyses on song types instead of song variants. This reduced the number of possible three-phrase combinations to twelve, which we then compared to expected frequencies calculated from the All values are presented as means±standard errors unless stated otherwise.Figure S1Time waveforms normalized to a maximum of 1 volt, of the four bats presented in (0.31 MB TIF)Click here for additional data file.Audio S1Song of Austin Bat 1 slowed eight times.(0.72 MB WAV)Click here for additional data file.Audio S2Song of Austin Bat 2 slowed eight times(0.72 MB WAV)Click here for additional data file.Audio S3Song of College Station Bat 1 slowed eight times(0.60 MB WAV)Click here for additional data file.Audio S4Song of College Station Bat 2 slowed eight times(0.34 MB WAV)Click here for additional data file.Movie S1A male bat sings while performing a wing flapping display in front of his territory, a cloth pouch, where some females are roosting.(2.48 MB MOV)Click here for additional data file.Movie S2A slowed version of (1.13 MB MOV)Click here for additional data file.


Population stratification (PS) represents a major challenge in genome-wide association studies. Using the Genetic Analysis Workshop 16 Problem 1 data, which include samples of rheumatoid arthritis patients and healthy controls, we compared two methods that can be used to evaluate population structure and correct PS in genome-wide association studies: the principal-component analysis method and the multidimensional-scaling method. While both methods identified similar population structures in this dataset, principal-component analysis performed slightly better than the multidimensional-scaling method in correcting for PS in genome-wide association analysis of this dataset. In the past few years, the genome-wide association (GWA) approach has become a widely used tool for identifying genetic loci related to disease risk. Population stratification (PS) is a major challenge in GWA studies (GWAS), because of the risk of generating false positives that represent genetic differences from ancestry rather than genes associated with a disease. Among the methods developed for correcting PS in GWAS, the principal-component analysis (PCA) method ,2 and thThe objectives of this study were: 1) to compare the population structures identified by PCA and MDS in the rheumatoid arthritis (RA) dataset of Genetic Analysis Workshop 16 (GAW16); and 2) to evaluate the performance of these two approaches for correcting PS in GWA analyses.GAW16 Problem 1 data, provided by the North American Rheumatoid Arthritis Consortium (NARAC), contained genome-wide data on 868 RA cases and 1,194 controls. Genotype data on 545,080 single-nucleotide polymorphisms (SNPs) were available for analysis.p-value from the Hardy-Weinberg equilibrium test of at least 0.05/545,080 were required.Quality control of genotype data was conducted at both the individual level and the SNP level. At the individual level, a call rate of at least 0.95 was required. Sex discrepancies were examined using the heterozygosity rate of X-chromosome. At the SNP level, a call rate of at least 0.90, a minor allele frequency of at least 0.01, and a r2 < 0.2; iii) each SNP was regressed on the previous two SNPs, and the residual entered into the PCA. SNP loadings on all components deemed significant by the Tracy-Widom statistic [PCA was performed using the computer program EIGENSOFT 2.0 ,2. Theortatistic  were re-r2 < 0.2. Pairwise IBS distance was calculated using all autosomal SNPs that remained after pruning. Five nearest neighbors were identified for each individual based upon the pairwise IBS distance. IBS distance to each of the five nearest neighbors was then transformed into a Z score. Individuals with a minimum Z score among the five nearest neighbors less than -4 were excluded from analysis as population outliers. MDS dimensions were extracted using the "MDS-plot" option.MDS analysis was performed using PLINK1.03 [Three GWA analyses were performed using PLINK 1.03  was calcp-values from Hardy-Weinberg equilibrium test <0.05/545,080, and 10 SNPs on Y-chromosome were excluded from analysis. After genotype data quality control, there were 2,055 subjects and 515,741 SNPs in the analysis dataset.All individuals had call rates >0.95 at the individual level. An examination of sex led to the exclusion of seven individuals due to incorrect or ambiguous sex information when compared with phenotype data. At the SNP level, 5,449 SNPs with call rates <0.90, 23,205 SNPs with minor allele frequencies <0.01, 1,389 SNPs with n = 9) were excluded as outliers with scores on one of the significant principal components more than six standard deviations beyond the sample mean score.In the first round of PCA, 59 components met the criteria for statistical significance using the Tracy-wisdom statistic. Nearly half of the SNPs that deviated from their expected normal quantiles with a distance of at least 1  were in the HLA region , a region that had been reported with higher genetic heterogeneity across different populations. After removing SNPs that deviated from their expected normal quantiles with a distance of at least one and pruning the SNPs based on LD information, 81,636 autosomal SNPs were included in the second round of PCA. This analysis resulted in eight significant components using the Tracy-Widom statistic. A small number of individuals (n = 7) were excluded as outliers with a minimum Z score among five nearest neighbors less than -4. Among the seven outliers, five were also among the nine outliers excluded by PCA. The first eight dimensions were retained for correcting the PS in GWA analysis.In the MDS analysis, 81,652 autosomal SNPs were used to calculate the pairwise IBS distance after SNP pruning. A small number of individuals . Although the analysis adjusted for the leading MDS dimensions was able to reduce the genomic inflation factor to 1.045, the Q-Q plot of this analysis still showed an obvious deviation from the expected line, indicating an uncorrected PS effect.The quantile-quantile (Q-Q) plots of the p-value < 0.05/515,741) was 381 for the analysis using the trend test, 221 for the analysis adjusted for the significant principal components, and 194 for the analysis adjusted for the leading MDS dimensions. The majority of these significant SNPs were from the HLA region. SNP rs2476601 in the PTPN22 gene is a non-HLA SNP that had been associated with risk of RA [p-values of this SNP from the three analyses as well as their corresponding rankings in non-HLA SNPs. After PS correction using either significant principal components or leading MDS dimensions, the p-value of this SNP became less significant and the ranking dropped, suggesting that PS may have also contributed to the previously reported association signals at this SNP.Results from the three GWA analyses were also plotted against their chromosome locations Figure . All thrsk of RA . Table 2Although PCA performed slightly better than MDS in correcting PS in the GWA analysis of this dataset, it would be inappropriate to conclude that PCA is a preferred approach in all GWAS. MDS is a more flexible method in general as compared with PCA. First, PCA requires that underlying data follow a multivariate normal distribution, while MDS imposes no such restriction. Second, PCA requires computation of a covariance matrix first, while MDS can be applied to any kind of distances or similarities. Pairwise IBS distance is only one example of many distance measures to which MDS can be applied. As a special case, MDS can be applied to the covariance matrix used in PCA as well. In this case, the performance of MDS in correcting PS will be equivalent to it of PCA .In this paper, we compared the performance of PCA and MDS in identifying population structure and correcting for PS in GWAS using data provided to GAW16 participants by the NARAC. While the two methods identified similar population structures in this dataset, PCA performed slightly better than MDS in correcting for PS in the GWA analyses of this data set.GAW16: Genetic Analysis Workshop 16; GWA: Genome-wide association; GWAS: GWA studies; IBS: Identity-by-state; LD: Linkage disequilibrium; MDS: Multidimensional scaling; NARAC: North American Rheumatoid Arthritis Consortium; PCA: Principal-component analysis; PS: Population stratification; Q-Q: Quantile-quantile; RA: Rheumatoid arthritis; SNP: Single-nucleotide polymorphism.The authors declare that they have no competing interests.DW participated in discussions of the analytical approach, carried out the majority of the data analyses, and drafted the manuscript. YS participated in discussions of the analytical approach and carried out some of the analyses related to multi-dimensional scaling. PS, JAB, and MAW contributed to acquisition of the data and editorial revision of the manuscript. QL participated in discussions of the analytical approach.


For efficient population identification with the HapMap genotype data, as few informative SNPs as possible are required from the original 4 million SNPs. Recently, Park et al., we obtain a better result, i.e., good classification of the 3 populations using on average 64 SNPs.In this paper, we propose to first rank each feature (SNP) using a ranking measure, i.e., a modified t-test or F-statistics. Then from the ranking list, we form different feature subsets by sequentially choosing different numbers of features  with top ranking values, train and test them by a classifier, e.g., the support vector machine (SVM), thereby finding one subset which has the highest classification accuracy. Compared to the classification method of Park Experimental results show that the both of the modified t-test and F-statistics method are very effective in ranking SNPs about their classification capabilities. Combined with the SVM classifier, a desirable feature subset (with the minimum size and most informativeness) can be quickly found in the greedy manner after ranking all SNPs. Our method is able to identify a very small number of important SNPs that can determine the populations of individuals. A, T, C and G in the genome sequence is replace by one of any other 3 nucleotide, e.g., from AAATCCGG to AAATTCGG, we call this single base variation (C ⇒ T) as a single nucleotide polymorphism (SNP). It has the following three characteristics ,,In) to m measure , which m measure  to ident measure , they ca measure , i.e., CIn this paper, we propose to firstly rank SNPs according to a feature importance ranking measure, i.e., a modified t-test or F-statistics, where the higher the ranking value, the stronger the corresponding classification power. Then, from the ranking list, we sequentially choose different numbers of top ranked SNPs, e.g., 1, 2, 3, ..., 20 and so on, test them through a classifier, e.g., the support vector machine (SVM) ,12 and dThe international HapMap Project provides many kinds of data for researchers in , such asATCG. For example, if one SNP consists of the two allels A and G  is represented by {0, 1, 0}, and "10" (AG) is represented by {1, 0, 0}. As to the F-statistics ranking measure, since it involves the calculation of two single alleles for each bi-allelic SNPs, we will use 1 and 0 to represent the two different alleles, respectively. For example, given the same SNP reference type A/G as the one in the modified t-test ranking measure, we use 1 to represent A and 0 to represent G. Then, in each population we can calculate each allele's frequency and variation for each population, as well as those values for all the populations. Each SNP's F-statistics value is calculated from Equation (7). At the same time, we notice some special conditions. For example, if one locus with reference SNP type A/G only has the value AA for all the individuals, the frequency of the SNP allele A will be 100% and the frequency of allele G will be zero. Referring to Equation (7), either Fst value of that feature as zero. In summary, the greater the numerator and the smaller the denominator in Equation (7), the greater the value Fst and the more important the corresponding feature for classification.Combining all the features together from the 23 chromosomes, i.e., Chromosome 1, 2, ..., 22, X (the phased data of Chromosome Y is not available), we have nearly 4 million SNPs involved in the experiment. For most features (locus), their SNP types  are expressed as bi-allelic SNPs, i.e., consisting of two single alleles from 4 nucleotides see Fig. , all thesee Fig.  for compsee Fig. . For thesee Fig. , accordiWe have 4 simulations to conduct, i.e., 4 different combinations of two rankings (F-statistics and modified t-test) and two classifications . From the 210 samples, we randomly choose 40 samples from YRI and CEU, respectively, and 30 samples from JPT and CHB, respectively, as the training set. The 70 samples left are used as the testing set. Each simulation is repeated for 30 times.We first rank the SNPs of 23 chromosomes, respectively. Then we choose each chromosome's top 100 SNPs, combine the 2300 features together, and rank them again. In this way, features involved in the experiment are greatly reduced and this also will not lead to loss of important information. On the contrary, it will improve the efficiency of the experiment.In each of 30 simulations, we select top 100 SNPs from the ranking list and form 100 different SNP subsets. The first subset consists of only the first top SNP. The second subset is formed by adding the second top SNP into the previous subset, the third top. Subsequently, we evaluate all subsets through the classifier SVM in terms of the classification accuracy. Due to space limitation, we provide classification results of only 11 feature subsets, i.e., the subsets consisting of 1, 10, 20, 30, 40, 50, 60, 70, 80, 90 and 100 SNPs, respectively. Table In the following, we find the subset which leads to the maximal classification accuracy from the 100 SNP subsets  and 1 GHZ RAM. We perform statistics about the running time of the two ranking measures together with the training and testing time. The mean time using the F-statistics to rank all SNPs of 3 populations is 5342.9 seconds, while on average 5728.7 seconds for the modified t-test ranking measures. It may be because that calculating the median value Since features' ranking only indicates the relevance of each feature, those features with the same or close ranking values may have high correlation between each other, i.e., redundancy. Therefore, it is possible for us to further reduce the number of SNPs in our future work.et al.[In this paper, we propose to use two feature importance ranking measures, i.e., the modified t-test and F-statistics, to rank large amount of SNPs, and then use the greedy manner together with a classifier to determine a desirable feature subset, which has the minimum size but leads to the highest classification accuracy. The final results show that both ranking methods are efficient on determining the importance of the SNPs. Although the two ranking measures find nearly the same amount of SNPs, the modified t-test ranking measure tends to be better than the F-statistics measure in terms of the classification accuracy. Compared to the classification method of Park et al., we obtaIn classification on large data sets, feature selection is necessary and shows many advantages such as saving computational time, reducing computational burden and improving efficiency. Feature ranking, as an usual step in many feature selection methods ,17, is aet al.[The original t-test, i.e., the student t-test , can be et al. developetic indicates the t-statistics value for the i-th feature of the c-th class. i-th feature's mean value in the c-th class and i-th feature's mean value for all classes. xij represents the i-th feature of the j-th sample. N is the total number of all the samples for all the C classes and nc is the number of samples for the c-th class. Si is the within-class standard deviation and S0 is set to be the median value of Si for all the features. This t-statistic value of Tibshirani et al. [i is calculated as the greatest t-score for all classes:Here i et al.  measuredi et al. , Wang etAA, AT and TT), Equation (4) can not be used to deal with our problem. We proposed a modified t-test ranking method, in which different nominal values are represented by different vectors to realize the calculation. In the following, we generalized the t-score of each feature in 3 steps:Due to the characteristic of the SNP data , i.e., wF = , and feature i has mi different nominal values represented as 1. Suppose the feature set is mi, i.e., 2. Transform each nominal feature value into a vector with the dimension 3. Replace all the numerical features in Equations (1) and (2) with those vectors (see Equations (5) and (6)).The ranking rule is: the greater the t-scores, the more relevant the features.In our experiment, we will use another ranking measure, i.e., F-statistics, to make a comparison with the modified t-test. The version of F-statistics used in our experiment is based on the definition of , which wC sub-populations and each feature expressed as bi-allelic SNPs , the F-statistics (Fst) is calculated as:Given a SNP genotype data with p and q are corresponding to the two alleles' frequencies, respectively, in one population. Varp represents the variance (See Equation 8) of one allele.where pc designates the frequency of one allele for the c-th population. And the mean frequency is easy to obtain from:Here, Fst value, the more significant the SNP for population classification.The ranking rule is same as the modified t-test, i.e., the larger the γ and the penal parameter ν through the double cross-validation method.Although many classifiers, such as classical neural network, naive Bayes classifier and so on, can be applied in our classification, here we would like to choose the support vector machine (SVM) ,12 in ouLW proposed to use statistic ranking methods to select informative SNPs on the HapMap genotype data for population classification. NZ conducted the algorithm implementations and drafted an early version of the manuscript. LW revised the draft.