Single nucleotide polymorphisms (SNPs) are important tools in studying complex genetic traits and genome evolution. Computational strategies for SNP discovery make use of the large number of sequences present in public databases (in most cases as expressed sequence tags (ESTs)) and are considered to be faster and more cost-effective than experimental procedures. A major challenge in computational SNP discovery is distinguishing allelic variation from sequence variation between paralogous sequences, in addition to recognizing sequencing errors. For the majority of the public EST sequences, trace or quality files are lacking which makes detection of reliable SNPs even more difficult because it has to rely on sequence comparisons only.We have developed a new algorithm to detect reliable SNPs and insertions/deletions (indels) in EST data, both with and without quality files. Implemented in a pipeline called QualitySNP, it uses three filters for the identification of reliable SNPs. Filter 1 screens for all potential SNPs and identifies variation between or within genotypes. Filter 2 is the core filter that uses a haplotype-based strategy to detect reliable SNPs. Clusters with potential paralogs as well as false SNPs caused by sequencing errors are identified. Filter 3 screens SNPs by calculating a confidence score, based upon sequence redundancy and quality. Non-synonymous SNPs are subsequently identified by detecting open reading frames of consensus sequences (contigs) with SNPs. The pipeline includes a data storage and retrieval system for haplotypes, SNPs and alignments. QualitySNP's versatility is demonstrated by the identification of SNPs in EST datasets from potato, chicken and humans. and as Additional files.QualitySNP is an efficient tool for SNP detection, storage and retrieval in diploid as well as polyploid species. It is available for running on Linux or UNIX systems. The program, test data, and user manual are available at  Sequence variation in the genomic DNA of individuals of the same species or related species are typically single nucleotide polymorphisms (SNP) or small insertions/deletions (indels) ,2. BecauThere are several strategies, both experimental and computational for SNP discovery. Experimental SNP discovery often consists of a number of laborious steps that make this process complex and expensive . The comThe major drawback of all these computational approaches is that they do not provide a good way to distinguish allelic variation from sequence variation between paralogous sequences. In addition, they do not recognize sequencing errors very well, leading to the frequent occurrence of false positives ,16,18. OIn this paper we describe a new method  that uses a haplotype-based strategy to detect reliable synonymous and non-synonymous SNPs from public EST data without the requirement of trace/quality files or genomic sequence data. Haplotypes in this context represent the different alleles of a gene in a dataset. The haplotype reconstruction is based on a mathematical algorithm. QualitySNP's versatility is demonstrated by the identification of SNPs in EST datasets from potato, chicken and humans.Homo sapiens) were used. The Single Nucleotide Polymorphism database (dbSNP) was downloaded from NCBI (Jun 3th 2006) to our local machine.For potato, two datasets were used in our study. One dataset was from the EMBL database (version 79), containing 83,565 ESTs with tissue information of the potato variety Kennebec. The other was from the Potato Gene Index of TIGR database (data of Dec 7th 2004) containing 87,637 reads of potato ESTs with quality files, which was used to evaluate the quality of public potato ESTs and the performance of our SNP discovery pipeline. Function annotation information of the potato ESTs was obtained from the TIGR Gene Index  and UniGTo detect non-synonymous and synonymous SNPs, the UniProt database (version of Feb 28th 2005)  was usedThe pipeline consists of five steps: 1) EST assembling using cross_match for removing vectors and CAP3 for sequence clustering, 2) analysis of the alignment information to select clusters with at least 4 EST members, 3) SNP detection and distinguishing variations between or within genotypes, 4) distinction between non-synonymous and synonymous SNPs using FASTY, and 5) transferring the final results into a SNP database .Sij per polymorphic site between candidate sequence i and all current members of one potential haplotype is defined asFirstly, the similarity j is one polymorphic site of the sequence i, k is one current member of the potential haplotype, and m is the total number of current members in the potential haplotype. sij(k) expresses whether or not the nucleotide at polymorphic site j of sequence i is the same as that of member k in the haplotype, whereas dij(k) expresses whether it is different: when the nucleotide at site j in sequence i is the same as that in sequence k, sij(k) is set to 1 and dij(k) is set to 0; when the nucleotides are different, sij(k) is set to 0 and dij(k) is set to 1. If sequence k has no information at site j both sij(k) and dij(k) are set to 0. Sij is the similarity of sequence i to all current members in the potential haplotype on site j; Dij is the dissimilarity between them. When Sij is more than 0.75, sequence i is considered to match the haplotype on site j, so Sij is set to 1 and Dij is set to 0. When Sij as calculated from [i to the potential haplotype with confidence, so Sij is set to 0 and Dij is set to 1. When both Sij and Dij are set to 0.where Si of sequence i and the potential haplotype of all polymorphic sites is defined asSecondly, the similarity n is the total number of all potential polymorphic sites of sequence i. When Si is more than 0.8, sequence i is considered to belong to this haplotype. If both Si is set to 0.0.where Sets containing paralogous sequences can be expected to contain more polymorphisms than sets with only allelic sequences. A method based on the number and frequency of polymorphisms may therefore separate paralogs from alleles. However, some EST clusters show a larger than average number of SNPs because some genes or regions of genes evolve more rapidly . These S(a) Remove all potential haplotypes consisting of only one sequence: these are probably of poor quality .(b) Calculate the number of potential SNPs defining each haplotype, i.e. the number of potential SNP sites where all sequences in all other haplotypes contain the same nucleotide and only the current haplotype has a different nucleotide.(c) Normalize this number of SNPs defining each potential haplotype:snpi  is the number of potential SNPs defining haplotype i, and ahap is the number of all haplotypes after removing poor quality haplotypes (a).where (d) Calculate the standard deviation of the normalized number of potential SNPs among these haplotypes:In theory, the value of D can range from 0 to infinite. In our study, in 98% of the clusters the value of D ranged from 0 to 1. With increasing D-value the variation in number of SNPs among haplotypes is larger, and there is a higher probability of paralogs in the cluster. The value of D can therefore be used to identify clusters with a low probability of containing paralogs. Following from its definition D-value can only be used to distinguish paralogous clusters if at least three haplotypes are identified in those clusters.mahap) is defined as the number of haplotypes with a major allelic nucleotide on one polymorphic site, and the minor allele haplotype score (mihap) is the number of haplotypes with the minor allelic nucleotide. The formulas are as follows:In addition to using a redundancy-based criterion , another more stringent selection is used in the algorithm. The selection is a combination of two measures: major allele haplotype score and minor allele haplotype score. The major allele is the allele occurring in the majority of the sequences in a cluster, while the other is called the minor allele. The major allele haplotype score ; hbi and lbi are the number of sequences with the minor allelic nucleotide represented in high quality and low quality regions; wh and wl are the weight values for the high quality and low quality regions; hci is the number of sequences in the haplotype i with information at the polymorphic site. When more than 75% of the members in one haplotype have the same major or minor allelic nucleotide, mahapi or mihapi is increased by 1; otherwise they remain the same, as in this case the correct nucleotide on this site of the haplotype can not be assigned easily. Note that in "Haplotype reconstruction" in filter 2, we allowed for some discrepancies between haplotype members. When both mahap and mihap are greater than 1 in the cluster, each of major and minor allele occurs in at least one haplotype and the SNP therefore can be considered to be reliable.SNP patterns and SNP blocks are also defined in filter 2. SNP patterns are those SNPs with the same pattern of allele distribution over the haplotypes; they are determined as in the autoSNP program . SNP bloThe third filter calculates a confidence score for every putative SNP according to the number of occurrences of each allele in high (HQ) and low (LQ) quality regions. In standard sequencing procedures the beginnings and the ends of the sequence are generally of lower quality than the rest of the sequence, and therefore are likely to contain more sequencing errors. We used potato ESTs with quality scores from the TIGR database to establish the boundaries of HQ and LQ for sequences (see Results), and these were used as default settings in our program.Based on the HQ and LQ, the confidence score of each allele is calculated according to the score rules as defined in figure If quality files of sequences are available, one additional filtering step is used to screen for reliable SNPs. In this filter "SNP quality" is calculated as the smaller value of the average quality scores of major and minor alleles per polymorphic site in a cluster. In our study we used a minimum (PHRED) score value of 20.For the detection of non-synonymous SNPs (nsSNP), synonymous SNPs and SNP in UTR, two strategies can be used: alignment with reference protein sequences or ORF prediction using programs such as ESTscan. In our approach, the first method is used; FASTY was chosen as the tool to search the protein database rather than BLASTX, because it allows for frameshifts within codons  and prodAll files containing relevant SNP information are transferred to the database. An SQL script for SNP database creation and data loading is produced automatically by the pipeline. The data in this database can be made accessible through the use of a web server. PHP scripts for generating web pages are supplied together with the code of the pipeline. The PHP script allows for easy retrieval of SNP information from the database, and BLAST searching .The new pipeline for SNP detection  presented here distinguishes itself from other programs mainly in the approach it takes for detecting sequencing errors and paralogous sequences. The source code and the manual of the program are freely available for academic use , see Add, 3, an sSij, formula [In the first step, 83,565 potato ESTs were assembled into 10,670 clusters  of the  number of SNPs per haplotype to identify clusters that probably contain paralogs (See formula ). In ordTo evaluate the paralog identification routine of QualitySNP, 15 human UniGene datasets from NCBI and the human genomic sequence from UCSC were analyzed. QualitySNP was executed on these UniGene datasets individually after clustering by CAP3. The clusters with a D-value higher than 0.6 (default setting) were considered as clusters containing paralogs. For most clusters identified by QualitySNP as paralogous clusters, the consensus sequences (as generated by CAP3) were compared to the human genomic sequence by the BLAT server of UCSC. For 49 of the 62 (79%) presumed paralogous clusters the consensus sequences picked up multiple loci in the genome (with similarity setting 90% for 90% of the whole sequence)  were verified by the BLAT analysis against the human genome.mahap and mihap, the default value of the weight values for HQ (wh) or LQ (wl) were set to 1.0, but these can be adjusted according to the data quality. For example, if sequences in low quality regions are very bad, the parameters can be set to wl = 0.5 and wh = 1.0. In filter 3 confidence scores are calculated .The quality of the SNP data was further improved by taking the quality of the sequence data into account. This was demonstrated with the potato EST dataset, Figure Using the default settings the major and minor allele haplotype score were calculated and this resulted in a selection of 17,745 reliable SNPs from the potato EST dataset from a total of 31,815. An additional 1,020 SNPs with confidence scores less than 2 were dropped from the set of SNPs which left 16,725 reliable bi-SNPs including 1815 indels. The ratio of transitions (C for T or A for G) and transversion  was 1.9 (9853/5057), the frequency of reliable SNPs was one SNP per 224 nucleotides, and the frequency of indels was one per 2,070 nucleotides (further statistic information is presented at our website ).The accuracy of the determination of SNP reliability from sequence data only was evaluated by using another potato EST dataset from TIGR that included quality files. In this dataset 21,240 potential SNPs were detected by filter 1, of these 7,971 reliable SNPs were identified by filter 2, and 6,431 were attributed a high confidence score,  by filter 3. The SNP quality score was calculated by an additional filter from quality files . The distribution of potential (filter 1), reliable (filter 2) and HCS (filter 3) SNPs over the PHRED quality scores is shown in figure Depending on the research context, users of the pipeline may have an interest in predicting non-synonymous SNPs. This is in particular the case for studies involving protein structure and functional domains, where SNPs might affect the function of a protein. The UniProt database was searched with the consensus sequences of 2651 clusters of potato dataset . FASTY identified 2167 81.7%) contigs with an open reading frame (ORF) that matched entries in the UniProt database, including 102 contigs which were corrected for frameshifts. This indicates that the UniProt had sufficient coverage to act as reference protein database for potato. Using the FASTY results, 10,354 reliable SNPs were identified in protein-encoding regions, 34% of these being nsSNP, which is similar to the results obtained by other authors: 35% in chicken ESTs 1.7% cont.Reliable SNPs as identified by QualitySNP were validated by experimental data for the potato dataset. For this, we used 37 amplified and sequenced loci containing 60 SNPs and one indel identified by QualitySNP in the potato variety Kennebec . Three SNPs turned out to be false, and for 8 SNPs the resequencing data was not conclusive, most likely due to the tetraploid nature of potato. The remaining 50 SNPs as well as the indel were confirmed, demonstrating the reliability of the SNPs identified by Quality SNP.Up to now, PolyBayes is considered to be one of the best SNP detection programs. However, the program needs the availability of the EST trace files or quality files or the genomic sequence in order to perform its task. This limits the usability of PolyBayes and similar programs, such as PolyFreq  to casesQualitySNP and AutoSNP were used to identify SNPs in nineteen individual human UniGene datasets. For each cluster of EST sequences, the consensus sequence was used to BLAST against SNP loci of dbSNP . Each SNP from dbSNP that occurs in the consensus sequence was determined by finding the perfect match of its sequence context in the consensus sequence. An SNP is considered to be confirmed when the SNP locus (approx. 60nt) matches the consensus sequence for 90% or more and the SNP identity (location and substitution) is confirmed. The results are summarized in Table We present here a SNP identification pipeline called QualitySNP that uses a haplotype-based strategy and reconstructs haplotypes at the start of the SNP identification process. This haplotype-based strategy makes full use of redundancy in sequences by clustering them, and in doing so not only reduces the influence of sequencing errors, but also removes poor quality sequences which otherwise would be identified as a haplotype with one single sequence. In the haplotype-based strategy, we eliminate SNPs that can be due to random and/or systematical sequencing errors (resulting from the sequencing strategy) or reverse transcriptase errors.Once haplotypes have been defined and classified, it is possible to choose which SNPs will be used to diagnose the haplotype present in a genotype. Haplotype-based analysis of SNPs is more informative than analysis based on individual SNPs only, and is therefore more powerful in analyzing association with phenotypes .et al. [About 96% of the clusters obtained from EST sequence data of potato were predicted to contain four or less haplotypes Table , which set al.  that cloThe identification of paralogs is an important problem in SNP detection, especially in large contigs, which are more likely to contain paralogs and random errors ,16. MostParalogous sequences are generally less similar than allelic sequences. This property can be used to identify clusters that are likely to contain paralogs. POLYBAYES is a Bayesian method using the dissimilarity rate of paralogs and the polymorphism rate as input to calculate the probability that ESTs represent paralogs . HoweverFor our potato dataset, with the threshold D-value set to 0.6, 14% of the clusters with a high probability of containing paralogs were excluded from SNP detection. For the chicken dataset the D-value threshold set to 0.9, leaving 6% clusters with potentially paralogous sequences. The higher D-value threshold for the chicken dataset and the lower numbers of false positive and false negative clusters are most likely the result of the better quality of chicken sequence data compared to the potato data. In addition, the differences between chicken and potato may be partly accounted for by the fact that the potato genome is likely to contain more paralogous genes than the chicken genome, as gene duplication events in potato have occurred more frequently than in the chicken genome. Indeed, the paralog content of the chicken genome is relatively low even compared to the human genome .The study with the human UniGene datasets Table  demonstrmihap/mahap calculations and settings and using the High Confidence Scores which effectively eliminates most of the SNPs identified in presumably low quality sequence regions . These steps include 1) the The reliability of SNPs produced by QualitySNP is illustrated by the fact that nearly all of the 52 potato SNPs (49) and an indel that we were able to evaluate by sequencing were indeed confirmed. In addition, our validation of the SNP output of QualitySNP using human EST and SNP data demonstrates that QualitySNP outperforms autoSNP, producing a higher number as well as more reliable SNPs than autoSNP. The percentage of SNPs confirmed by comparison to dbSNP may appear relatively low (35%). However, dbSNP is a public-domain archive for a broad collection of simple genetic polymorphisms (from NCBI), and although the number of SNPs in dbSNP increases everyday, it does not cover most of the SNPs present in the human genome. Therefore, it is likely that a number of true SNPs will not find a match in the dbSNPs, and therefore can not be confirmed.QualitySNP includes a retrieval system that allows the user to extract additional useful information from the analysis. For instance, information about the nature of the SNPs (synonymous or non-synonymous) can be made part of the output. The SNP output can be modified by changing the reference genotype, and the D-value setting can be used to adjust the stringency with which paralogous clusters are detected and excluded. This may be very useful when focusing on a specific gene family where alleles of different paralogous sequences need to be identified. Statistics concerning the number of different types of SNPs and clusters can be included in the output. Searching parameters include the contig reference number, GenBank/EMBL/DDBJ accession number of ESTs, and UniGene ID; output options include SNP information, alignment information, EST function annotation information and ORF information of the contig. The SNP retrieval system based on the potato data is available at the website .In conclusion, QualitySNP works at least as well as, and in cases outperforms currently available methods, without the drawbacks of some of them, such as the necessity to provide a genomic sequence or sequence quality files. However, if quality files are available, this information can also be used by QualitySNP. By using a haplotype-based strategy, QualitySNP not only predicts reliable SNPs but also identifies haplotypes, and thus can be used in EST-based genotyping.Another advantage of QualitySNP over other programs for SNP detection in nucleotide databases is the availability of a retrieval system that can output various kinds of data. Although QualitySNP can be used as a SNP detection tool with default settings, it can also be used for instance to examine specific clusters of genes, or to find nsSNPs in candidate genes.BV, JL and JT identified the need to develop the program, initiated the project, and designed its basic functionality. JT designed the algorithm and wrote the source code. All authors contributed to the overall design and feature requirements, and participated in the drafting of the manuscript and approved the final version.QualitySNP. The source code of QualitySNP; The file is unpacked by using the command "gunzip QualitySNP.tar.gz", and then use "tar -xvf QualitySNP.tar" on a Unix/Linux computer.Click here for filetestQualitySNP. A dataset for testing QualitySNP;The file is unpacked by using the command "gunzip testQualitySNPseq.tar.gz", and then use "tar -xvf testQualitySNPseq.tar" on a Unix/Linux computer.Click here for fileQualitySNP manual. The manual of QualitySNPClick here for file


Picea glauca [Moench] Voss), a softwood tree of major economic importance.High-throughput genotyping technologies represent a highly efficient way to accelerate genetic mapping and enable association studies. As a first step toward this goal, we aimed to develop a resource of candidate Single Nucleotide Polymorphisms (SNP) in white spruce , and the contig depth and length. SNP detection in 3' and 5' reads from the same clones revealed a level of inconsistency between overlapping sequences as low as 1%. A subset of 245 predicted SNPs were verified through the independent resequencing of genomic DNA of a genotype also used to prepare cDNA libraries. The validation rate reached a maximum of 85% for SNPs predicted with either PSNP ≥ 0.95 or ≥ 0.99. A total of 9,310 SNPs were detected by using PSNP ≥ 0.95 as a criterion. The SNPs were distributed among 3,590 contigs encompassing an array of broad functional categories, with an overall frequency of 1 SNP per 700 nucleotide sites. Experimental and statistical approaches were used to evaluate the proportion of paralogous SNPs, with estimates in the range of 8 to 12%. The 3,789 coding SNPs identified through coding region annotation and ORF prediction, were distributed into 39% nonsynonymous and 61% synonymous substitutions. Overall, there were 0.9 SNP per 1,000 nonsynonymous sites and 5.2 SNPs per 1,000 synonymous sites, for a genome-wide nonsynonymous to synonymous substitution rate ratio (Ka/Ks) of 0.17.A white spruce SNP resource encompassing 12,264 SNPs was constructed from a set of 6,459 contigs derived from Expressed Sequence Tags (EST) and by using the bayesian-based statistical software We integrated the SNP data in the ForestTreeDB database along with functional annotations to provide a tool facilitating the choice of candidate genes for mapping purposes or association studies. Arabidopsis [Large-scale sequencing projects offer a possibility for low cost SNP discovery since sequence variants can be found computationally by analysing the redundancy in sequence databases. For example, this approach has facilitated SNP-based genetic mapping in human ,2, Arabibidopsis -5, and rbidopsis ,7. Follobidopsis ,9.Phrap, false positives were avoided by applying several filters to remove low quality sequences [PolyBayes to determine a confidence score for each SNP detected in a cluster of ESTs [PolyBayes uses a priori information about the average pairwise difference between paralogous sequences to calculate a posteriori, the probability that a sequence is native by comparison to a reference sequence from the EST cluster. Thus, sequences classified as paralogous in one EST cluster are excluded from the SNP detection procedure. Then, based on the alignment of the ESTs within the cluster, another Bayesian calculation generates the probability that a variant at a given location of a multiple alignment represents a true polymorphism as opposed to a sequencing error. This calculation takes into account the alignment depth, the base calls in each of the sequences, the associated base quality values, the base composition in the region, and the expected a priori rate of polymorphism of the species under investigation. This approach was shown to be adequate for SNP prediction in human [The first-generation SNP detection software was derived from tools used to help processing sequence data. After the clustering and alignment of ESTs with equences . Such apequences . A majorequences  or sequeequences . Indeed,equences ,13. Otheequences . They we of ESTs . PolyBayin human , sugarcain human , soybeanin human , and pinin human .The development of SNP markers in plant species will contribute to the understanding of crop evolutionary history, to the positional cloning of QTLs, and to marker-assisted breeding (reviewed in ). EspeciPicea glauca) [in silico detection of SNPs in the EST alignments using the PolyBayes software and describe the factors that affect the automated SNP detection. The quality of the in silico approach was further assessed by the independent sequencing of a subset of genomic DNA fragments. We demonstrate how this analysis was used to define a set of highly reliable predicted SNPs according to their probability scores. Contigs may also contain paralogous sequences which may cause the detection of non allelic SNPs. Parameters for limiting this bias can be set in the prediction software PolyBayes, but methods for a posteriori assessment of this source of variation remain to be defined. We took advantage of our SNP dataset to evaluate the proportion of paralogous SNPs in our white spruce gene index using statistical and experimental approaches. Two general approaches were also applied to delineate translated regions and distinguish synonymous from nonsynonymous SNPs. To facilitate the choice of candidate genes to be mapped, this white spruce SNP resource was integrated into a publicly available database ForestTreeDB, which also includes functional annotations of the gene index.As part of our research integrating functional and structural genomics analyses in white spruce ( glauca) , we have glauca) . Here, wPolyBayes software. Predicted SNP analyses have frequently been conducted based on a number of aligned sequence reads. We have conducted large-scale EST sequencing with both 3' and 5' reads from the same cDNA clones and obtained a large number of cDNAs with of overlapping reads in the same contig [We estimated SNP diversity and distribution parameters in 6,459 contigs each derived from sequences of at least two cDNA clones with the e contig . We thusSNP ≥ 0.10. The overlapping sequences represented an error rate or false prediction rate of less than 1%. The low level of false prediction indicates that the sequences were generated with a high accuracy and that the initial filtering using a Phred score of 20 appears to be sufficiently stringent to eliminate the majority of experimental errors.We exploited the fact that sequence reads in opposite directions from the same clone overlapped in 4,395 contigs, in order to verify whether SNPs were detected in these redundant sequences. Indeed, differences detected within same-clone overlaps would not represent real SNPs but experimental errors from reverse transcription or sequencing. The total available alignment length of these overlapping sequences was 4,755,154 nucleotides. In total, there were only 288 positions  where reads from the same clone conflicted, and 74% of these positions were uncalled or ambiguous bases. Among the remaining 75 putative false substitutions, we observed a few cases where the discrepancy in one clone was repeated in another clone at the same site in the contig. After removing these repeated discrepancies, we detected 69 sites that conflicted between overlapping reads from the same clone, out of the overall 7,555 SNPs detected with probability score PSNP) depends on the a priori expected SNP frequency (termed p-prior in the formula generating PSNP) [a posteriori; therefore, we tested its influence on the frequency of predicted SNPs  . The p-ps Figure . There wPinus pinaster [Picea glauca, based upon a total of 9,253 nucleotide sites sampled for eight nuclear genes  [Pinus taeda (0.016) [Pinus pinaster (0.0097) [SNP ≥ 0.95; Figure Pinus pinaster, LeDantec et al. (2004) reported a SNP rate of 1 per 660 nucleotide sites by using p-prior = 0.01 and PSNP ≥ 0.99, and analysing contigs containing 4 reads or more [P. glauca became 1 SNP per 689 nucleotide sites, somewhat closer to that reported for P. pinaster, when using the same probability levels and considering only contigs of 4 clones or more. Our rate rate further increased to 1 SNP per 492 nucleotide sites when considering SNPs with PSNP ≥ 0.95 . Our analyses also showed that 55.6% to 61.5% the contigs included at least one SNP predicted when varying PSNP from ≥ 0.95 to ≥ 0.10  ,31. It i (0.016)  and for (0.0097) . With p- or more . The ratin silico using PolyBayes (minimum PSNP = 0). The majority of in silico detected SNPs (55.4 %) were detected at PSNP ≥ 0.99, and most of the remaining SNPs were detected by decreasing the detection stringency to 0.80 . Indeed, several SNP resources were developed by using PolyBayes to detect SNPs and only SNP detected with score PSNP ≥ 0.99 were considered [SNP ≥ 0.99 would eliminate 44.6% of the in silico detected SNPs from our data set. [PolyBayes to detect SNPs in maize ESTs. They determined that the PSNP score as an important parameter and chose to display all the SNPs in their database, irrespective of their PSNP scores, to avoid discarding too many SNPs. However, the validity of the various polymorphisms was not assessed experimentally. We felt it was important to strike a balance between the cost of genotyping and the desire to achieve a high SNP discovery rated, thus, we experimentally tested the in silico detected SNPs for various levels of PSNP scores.The collection of nsidered ,18. Howet.Figure . Useche . (2001)  used Polin silico with a range of PSNP values were selected to amplify the genomic sequences. We were able to specifically amplify the single expected sequence for 112 contigs, encompassing 245 in silico detected SNPs. A total of 43,286 nucleotides corresponding to these 112 genes were sequenced and were visually inspected by aligning the trace files [see in silico detected SNPs that were also detected in the genomic DNA sequence analysis  reported a validation rate of 83% for SNPs detected with a PSNP ≥ 0.99 in pine [SNP ≥ 0.95 rather than of PSNP ≥ 0.99, we increased the number of SNPs by 2,514 out of a total of 12,264 SNPs detected in silico using PolyBayes.For the purpose of validation, fragments of spruce genomic DNA were amplified and sequenced from the same source material as the previously conducted EST sequencing . We carriles see  for deta reports ,18. SimiPolyBayes or other computational prediction methods have been conducted only sparingly, and for a few other genomes. PolyBayes was used to predict SNPs in human ESTs; a subset of 36 SNPs detected with a PSNP score ≥ 0.40 was experimentally verified by screening four populations, leading to a confirmation rate of 56% [in silico in the human genome by data-mining procedures were experimentally analysed by a pooled DNA sequencing approach, showing that 80% of the detected SNPs were found polymorphic in three ethnic groups [in silico using several approaches and it was not possible to attribute a performance level to PolyBayes specifically [Such experimental validations of SNPs detected by e of 56% . On a lac groups . In anotifically .SNP ≥ 0.95. The validation rate increased to 92% when only considering contigs derived from 2 or 3 clones. The SNPs falsely predicted by PolyBayes represent either experimental errors in the ESTs or differences between paralogous sequences erroneously assembled into a single contig. The experimental error rate was previously shown to be very low, from the analysis of overlapping sequence reads in opposite directions from the same clone. On the other hand, the higher confirmation rate observed with contigs containing fewer clones would indicate that putative paralogs are indeed present in the contigs, especially in those comprised of several cDNA clones, in spite of the stringency of the parameters used in the contig assembly. When the complete genome is available to help delineate orthologs and paralogs, the confirmation rate of in silico detected SNPs tends to be higher than that observed in our study. For example, in a SNP detection project conducted in Arabidopsis, the confirmation rate was 96% for in silico detected SNPs [in silico SNP detection in genomes that are incompletely sequenced, before the information is used for more advanced genetic analyses. In the following section, we examine two methods to estimate the proportion of paralogous SNPs among in silico detected SNPs and show that most of the SNPs falsely predicted by PolyBayes represent differences between paralogous sequences.We obtained an experimental validation rate 85% (137 out of 161) for SNPs detected in our white spruce contigs at PA number of contigs were likely to contain paralogous sequences, so we were interested in evaluating the proportion of paralogous versus orthologous SNPs in the dataset. We use the term 'paralogous SNPs' to refer to non allelic SNPs which likely result from the misassembly of paralogous, and 'orthologous SNPs' for allelic differences occurring between reads truly derived from the same gene. The paralogous SNPs are to a great extent undesirable for population or association genetics studies and in genetic mapping. To estimate the proportion of paralogous SNPs, we followed two approaches.in silico detected SNPs where a set of 112 contigs was re-sequenced from genomic DNA of the single genotype PG653. The same set of contigs was re-sequenced from genomic DNA of a haploid white spruce megagametophyte, with 43,286 nucleotides determined and visually inspected by aligning the trace files. The presence of superimposed peaks in sequences from haploid DNA indicates the presence of 'paralogous SNPs' We found 19 SNPs (out 161) that corresponded to multiple peaks in the sequences obtained from haploid megagametophyte DNA, among the in silico detected SNPs used for validation, with genomic DNA of PG653 (see above), which represents a proportion of paralogous SNPs of 12%. If we only consider the SNPs detected in silico with PSNP ≥ 0.95, the proportion of paralogous SNPs decreases slightly to 10.5%.We first used the previous validation experiment of in silico SNP frequencies, as described in Methods. It relies on a comparison of the frequency of SNPs in contig sequences derived only from the single genotype PG653, and the frequency of SNPs in the total population sampled. When paralogous sequences are intermixed with orthologous sequences, the proportion of segregating sites k is biased upward by a quantity corresponding to ε. ε is a constant independent of the number of genotypes analysed, while k will increase with the number of haploid genomes analysed, thus resulting in a reduction of ε relative to k. This principle can be used to estimate ε. For this purpose, only SNPs with PSNP ≥ 0.95 and contigs containing 10 clones or more were considered, in order to sample as many distinct haploid genomes as possible while sampling a reasonable number of contigs and SNPs. When sequences corresponding to contigs obtained from PG653 only were considered, 43 SNPs were detected from a cumulative length of 36,191 nucleotide sites, for a k value of 1/842 or 0.00119. When sequences from all genotypes were considered for the same contigs, 1,532 SNPs were detected from a cumulative length of 507,528 nucleotide sites, and k was higher with a value of 1/331 or 0.00302. Then, the estimated proportion of «paralogous SNPs» in the dataset, ε, was obtained by solving the set of equations (4) and (5) in Methods and for two sampling scenarios delineated therein. The ε values obtained were 0.000238 and 0.000517, respectively, corresponding to proportions of paralogous SNPs of 8% and 17% in the entire dataset. As explained in Methods, the second scenario, which assumes a sampling size of 24 haploid genomes, is less likely than the first scenario of sampling 10 distinct haploid genomes per contig, given that very few contigs would be characterized by a number of distinct clones approaching 24, the maximum number of haploid genomes corresponding to the 12 distinct genotypes used to obtain EST sequences. Thus, the value of 8% should be more realistic than the value of 17%.The second method used is based on population genetics theory and relies entirely on estimates of PolyBayes were not experimental errors, but that the criteria used to assemble the ESTs and run PolyBayes were stringent enough to avoid a wide occurrence of paralogous SNPs. To our knowledge, these are the first experimental and statistical estimates of proportions of paralogous SNPs in SNP datasets derived from EST contigs. The statistical procedure outlined above offers a simple way to estimate the proportion of paralogous SNPs for a given set of contig assembly parameters, as long as EST sequences from a single genotype can be traced back. However, this procedure may not be suitable when the reference genotype has been genetically manipulated.These ε estimates agree well with the proportion of paralogous SNPs obtained from experimental validation using haploid megagametophyte DNA (rate of 10 to 12%). These estimates indicate that most of the false positives predicted by in silico detection with PolyBayes (Table SNP ≥ 0.95) increased from 37.5 % to 93%, for contigs derived from 2 and 10 clones respectively. . The SNP frequency ranged from one per 1,184 nucleotide to one per 361 nucleotide sites in contigs from two to 10 or more clones, respectively (Table SNP ≥ 0.95) per redundant nucleotide site. A redundant site is one where information for more than one sequence is available in the contig. We thus developed a Perl script to only count the number of redundant nucleotide sites involved in the SNP detection within each contig, thus excluding non redundant nucleotide sites, which were only represented by one sequence in the contig alignment. With this approach, the SNP frequency range from one per 712 redundant nucleotide to one per 331 redundant nucleotide sites for contigs of two or 10 and more clones, respectively. We thus detected one SNP per 511 redundant nucleotide sites, across all the contigs with 2 or more clones . The first one was based on similarities with protein sequences in public databases and the second one was based on blastx program was used to compare translations of the spruce contigs with protein sequences from the Uniref100 database and the Arabidopsis TAIR database. The alignments with homologous sequences at the protein level enabled us to localize conserved coding regions and to determine the frame of the coding sequences within boundaries defined by sequence conservation. If a SNP site lay within the boundaries of the alignment, then it was considered as belonging to a coding region conserved across species. If the SNP lay outside of these boundaries, we could not characterize the nature of the SNP. Out of the 3,590 contigs containing SNPs (PSNP ≥ 0.95), 3,160 contigs had a match with an Arabidopsis protein or a protein from Uniref100 with blastx . with only 9 detected by Diogenes trained with Pinaceae sequences only. We combined the ORFs detected by blastx and Diogenes and we were able to localize an ORF for a total of 3,374 out of the 3,590 snp'ed contigs. An ORF was assigned to 88% of the contigs based on blastx matches alone, whereas the combination of both blast and Diogenes increased the assignments to 94%. The assignments were distributed as follows: i) 3,160 ORFs with a blastx hit in Uniref100 (including 20 sequences with a match in the Arabidopsis proteome but not in UniRef100); ii) 225 ORFs predicted by Diogenes-Brassiceae; and iii) 9 ORFs specifically predicted by Diogenes-Pinaceae.A second method based on ORF prediction was used to pursue two objectives. It enabled us to identify putative ORFs among the 430 orphan contigs and to classify the SNPs as coding or not coding without any assumptions regarding sequence similarity. ORFs were predicted by using the datasets . We analDiogenes wherever possible using, in preference over the boundaries of the conserved regions detected by blastx. Boundaries of coding regions delineated by similarity searches (blastx) were avoided because they could have introduced an estimation bias related to conserved regions. We compared the blastx and the Diogenes results to annotate the coding regions based on ORF predictions and developed and improved dataset to classify nonsynonymous and synonymous SNPs and estimate their frequencies. The improved dataset was still comprised of 3,374 ORFs but distributed as follows: i) 2,823 ORFs predicted by Diogenes-Brassiceae ii) 9 ORFs specifically predicted by Diogenes-Pinaceae, iii) 533 regions similar to known proteins identified by blastx searches against Uniref100 but without any ORF predicted by Diogenes, and iv) 9 regions similar to Arabidopsis proteins identified with blastx but without any ORF predicted with Diogenes.In order to classify synonymous and nonsynonymous SNPs, we used the boundaries of the ORFs predicted by blastx or Diogenes), the ratio of synonymous to nonsynonymous substitutions was about 1.5:1. This ratio is lower than the ratio of 2:1 found in Arabidopsis by Schmid et al. (2003) [Ls) and nonsynonymous sites (La) [Ka/Ks) was 0.17. Such a ratio far below 1 indicates that, on average, white spruce ORFs are under strong purifying selection. Using the ORFs delineated by the blastx search or predicted by Diogenes had no effect on the estimation of SNP frequencies, indicating that there was no bias associated with the delineation of coding regions using blastx. Similar Ka/Ks ratios based on smaller numbers of genes have been estimated in Arabidopsis. The white spruce "genome-wide" Ka/Ks ratio (0.17) is identical to the mean Ka/Ks of 0.17 estimated from 23 nuclear genes in Arabidopsis [Ka/Ks ratio of 0.207 obtained from 242 genes in A. thaliana [Ka/Ks values. Similar variation was also found in the present study (data not shown).We thus classified 3,789 coding SNPs, distributed into 39.8% nonsynonymous and 60.2% synonymous substitutions (Table tes (La)  were detbidopsis , and it Diogenes, we classify 221 SNPs (among the 430) contigs with no match in Uniref100. In this subset of 221 SNPs, the rate of synonymous substitutions was 6.18 SNPs per 1,000 sites and that of nonsynonymous substitutions was 1.10 SNP per 1,000 sites, which are slightly higher rates than the overall dataset., but the overall Ka/Ks ratio remained about the same at 0.18. This result was expected, as the discovery of additional SNPs by in silico methods should not affect the overall balance between nonsynonymous and synonymous SNPs.By combining several approaches to delineate ORFs we were able to increase the number of coding SNPs by 13%. With in silico. These public query pages are available to mine the data at . Each SNP is described by its location on the contig sequence, including the appropriate strand. The possible bases of the SNP are represented by the IUPAC nucleotide ambiguity code. An "evidence code" indicates the SNP status as "predicted", "validated," or "not validated". The number of distinct clones that support each SNP location is retained, as well as the analytical parameters used for the prediction, including the p_prior value and the confidence level of the in silico detection (PSNP). Several computational approaches were used to explore the putative functions of the sequences recovered in the snp'ed contig dataset the results were incorporated in the ForestTreeDB database. The analyses included sequence similarity searches against several databases and Hidden Markov Models searches with the models available in the PFAM protein families database, as described previously [blastx hit in UniRef100 . Among the diverse molecular function categories, 1,943 annotations  were associated to 1,388 snp'ed contigs (one contig may be associated to more than one GO term). The wide diversity of predicted functions among the snp'ed contigs. indicates that dataset could be useful in diverse association studies drawing upon genes from diverse protein functions.To facilitate the retrieval of snp'ed contigs according to their functional annotations, the white spruce SNP data was uploaded into ForestTreeDB, a database unifying EST data and sequence annotations. ForestTreeDB includes tables containing data related to 12,264 white spruce SNPs detected eviously . Approxia priori information on predicted gene polymorphisms along with predicted protein functions. ForestTreeDB interfaces enable researchers to visualize and retrieve sequences, contig compositions, sequence similarity search results and prediction by PolyBayes in a parsed format. Figure Among other applications, ForestTreeDB is particularly useful in the context of candidate gene approaches, as it provides Picea glauca cDNA libraries were prepared from a diverse tissues sampled to maximize the diversity of the isolated genes. Details about the sampled tissues are available on the Arborea web site [Picea glauca to incorporate more genotype diversity. Details of the preparation of cDNA libraries and EST sequencing methods previously presented elsewhere [A total of 17 web site . Nine lilsewhere .Phred score above 20. The majority of contigs, namely 73.4%, were derived from both 3' and 5' reads; they had an average length of 1,078 nucleotide and represented completely sequenced inserts. A total 20.4% of the contigs were derived from the 3' reads and only 6.2% were from the 5' reads alone. The 5' and 3'contigs were slightly shorter, with mean lengths 803 and 823 nucleotides, respectively. The EST sequences were deposited in the dbEST section of the GenBank database. The contig sequences, the contig composition, blast reports, and some statistics about the sequence dataset are available at our web site [All of the clones were randomly chosen and sequenced from the 3' end . A subseweb site .in silico detected SNPs involved PCR amplification and sequencing of genomic DNA corresponding to EST contigs. To minimize the risk of amplifying multiple members of the same multigenic family when amplifying genomic DNA corresponding to EST contigs, we chose contigs with a small number of hits against the more extensively sequenced pine transcriptome (Pinus Gene Index Release 4)[Arabidopsis proteins against which they had a match. We thus anchored one of the two primers (or both) upstream of the ATG or downstream of the STOP codons to target the less conserved untranslated regions. For the contigs in which ATG or STOP codons could not be predicted, we selected sequence regions that were not conserved with known sequences to anchor at least one of the two PCR primers. Primers for PCR amplification and sequencing were designed by using the Primer3 software [blast [2, 200 μM of each dNTP, 200 μM of both 5' and 3' primers and 1.0 unit of Platinum Taq DNA Polymerase . About 5–20 ng of genomic DNA was used as template. A peltier Thermal Cycler  was used, with the following thermal cycling program : 4 min at 94°C, followed by 35 cycles of 30 s at 94°C, 30 s at annealing temperature optimized between 54 and 60°C for each pair of primers, and 1 min at 72°C, followed by 10 min at 72°C. Each PCR product was directly sequenced in both directions with a Perkin-Elmer ABI 3700 XL DNA sequencer , using BigDye Terminator cycle sequencing kit (version 3.1). Contigs were constructed with the seqmerge program in the GCG package .Experimental validation of the elease 4). We assusoftware  installee [blast  against Phred base calling software version 0.020425.c [Phred quality values less than 20 were considered to be ambiguous in this experiment and were assigned base N. Quality trimming and vector filtering  were done using the software gstVF4 [Phrap (version 0.990319) [Phrap contigs were evaluated for chimeric sequences, and reassembled so as to minimize chimeric tendencies. Final assembly threshold for phrap were -minmatch 50 -minscore 100. This procedure produced better quality contigs prior to running PolyBayes. For the SNP prediction, the PolyBayes software version 3.0 [SNP provided by PolyBayes with several cutoffs for PSNP. PolyBayes outputs were analysed with in-house scripts written in Perl.EST sequences were processed with locally-developed system at the Center for Computational Genomics and Bioinformatics, University of Minnesota, for contig quality control, as was previously described in detail . Sequence gstVF4 . Process.990319) . Phrap csion 3.0  was run The following procedure was used to estimate the proportion of paralogous versus orthologous SNPs in the SNP dataset. In the absence of paralogous SNPs, it can be shown thatk = aθ     (1)k is the proportion of segregating sites, those harboring a SNP, and θ = 4Neμ, Ne being the effective population size and μ, the mutation rate per site per generation, andwhere a = 1 + 1/2 + ... + 1/(n-1)     (2)n is the number of sequences sampled per gene . When pk = aθ + ε     (3)Thus, ε could be estimated by solving the following set of equations:kPG653 = θ + ε     (4)kpop = aθ + ε     (5)kPG653 and kpop correspond to the number of segregating sites in sequences from the single genotype PG653 and from the population sample of 12 genotypes, respectively. In both cases, the number of sites sampled corresponded to the cumulative length of sequences of redundant nucleotide sites, those represented in at least two clones, and we used only SNPs with PSNP ≥ 0.95. Because contigs with a small number of clones are biased downward in terms of sampling SNP diversity  to (4). When considering all 12 genotypes and equation (5), lower and higher bound scenarios were considered. A first scenario assumed a sampling size of 10, given that contigs with 10 or more sequences were analysed, thus corresponding to an a value of 3.733 in equation (5). We also tested a second scenario, by assuming a sampling size of 24 corresponding to the number of haploid complements used to obtain EST sequences, with corresponding a value of 2.929 in equation (5). This scenario is less likely, given that very few contigs had a number of sequences in excess of 30. Then, the proportion of paralogous SNPs in the entire dataset could simply be obtained by the ratio of ε over kpop.where Phrap contigs were analysed by blastx comparisons [Arabidopsis protein dataset retrieved from the TAIR web site [Diogenes software [blastx output and the Diogenes output . To sort the SNPs into coding and non coding classes, a Perl program was developed to extract from the contig sequence the coding regions, to split them into triplets based on the frame information and then to deduce the codons based on the strand information. To determine whether the SNP induced an amino acid change, the CodonTable from Bioperl was incorporated into the main script.parisons  against parisons , Arabidoweb site , and theweb site . We extrweb site ,48 assocsoftware , which wNP: coordination of bioinformatics activities, data analysis, preparation of the manuscript; LP: data analysis, EST assembly, programming; CP: sequence annotation, gene ontology assignment, databasing; JM: supervision and management of the sequencing project, preparation of the manuscript; JB: supervision of the SNP project, data analysis, preparation of the manuscript. All authors approved the manuscript.Experimentally verified SNPs. Locations and sequences of the PCR primers, SNP location and prediction scores, presence of SNPs in the sequenced PCR fragments, functional annotation of the contigs.Click here for file


Single nucleotide polymorphisms (SNPs) as defined here are single base sequence changes or short insertion/deletions between or within individuals of a given species. As a result of their abundance and the availability of high throughput analysis technologies SNP markers have begun to replace other traditional markers such as restriction fragment length polymorphisms (RFLPs), amplified fragment length polymorphisms (AFLPs) and simple sequence repeats (SSRs or microsatellite) markers for fine mapping and association studies in several species. For SNP discovery from chromatogram data, several bioinformatics programs have to be combined to generate an analysis pipeline. Results have to be stored in a relational database to facilitate interrogation through queries or to generate data for further analyses such as determination of linkage disequilibrium and identification of common haplotypes. Although these tasks are routinely performed by several groups, an integrated open source SNP discovery pipeline that can be easily adapted by new groups interested in SNP marker development is currently unavailable.SNP discovery Pipeline with additional features for identification of common haplotypes within a sequence tagged site  and GenBank (-dbSNP) submissions. This tool was applied for analyzing sequence traces from diverse soybean genotypes to discover over 10,000 SNPs. This package was developed on UNIX/Linux platform, written in Perl and uses a MySQL database. Scripts to generate a user-friendly web interface are also provided with common queries for preliminary data analysis. A machine learning tool developed by this group for increasing the efficiency of SNP discovery is integrated as a part of this package as an optional feature. The SNP-PHAGE package is being made available open source at .We developed SNP-PHAGE  submissions. SNP selection and visualization are aided through a user-friendly web interface. This tool is useful for analyzing sequence tagged sites (STSs) of genomic sequences, and this software can serve as a starting point for groups interested in developing SNP markers. When undertaking polymorphism discovery, the selection of an optimal tool depends on the nature of input sequences. For sequences that are derived from individual clones where there is no heterogeneity, SNP discovery can be accomplished by comparing sequence information. When relatively few reads are available, PolyBayes in combiFor large-scale targeted SNP discovery from PCR amplified sequence tagged sites containing potential sequence variation(s) using a set of flanking primers, PolyPhred  is widelA high-throughput polymorphism discovery project involves the analysis and management of different types of data including sequences, polymorphisms, genotypes, and haplotypes, and, hence, all of the analytical steps need to be automated and the information stored in a relational database. Bioperl  is a ricThe SNP-PHAGE software pipeline is run through a combination of UNIX/Linux command line and web browser interfaces. It was implemented in Perl and uses standard open source modules such as Bioperl . SNP-PHASNP-PHAGE software has three components:a) Relational database schema and tables for storing information about the STS, sample chromatograms and genotypes, consensus sequence, and polymorphisms and haplotypes;b) Scripts for analyzing chromatograms by executing the programs described above in a given sequence , parsing the program outputs for putative SNPs, and making database entries.c) Implementation of a web interface for viewing putative SNPs; adding new SNPs; validating SNPs; and editing consensus sequence and generating a report containing confirmed SNPs. The genotype output is of the highest quality base from duplicate samples and/or sequencing reads from both forward and reverse directions. Haplotypes present in each STS are also determined.A flow-chart containing data processing and analysis steps in SNP-PHAGE is presented in Figure SNP-PHAGE software was implemented with the following features.• A standard chromatogram naming convention is required to facilitate sequence assembly by following the St. Louis convention described in the Phrap documentation  for easy• Phred was used for base calling because it provides quality scores along with the base calls. While our internal test runs (not reported) with the demo version of the TraceTuner commercial software provided similar results as Phred for high quality bases, the two base-calling software packages gave variable results with low quality bases. Neither software was consistently superior.• Phrap was used for sequence assembly of chromatograms from STSs, because it has more flexible parameters than CAP3  to force• The SNP discovery pipeline includes: 1) PolyBayes, which uses a Bayesian inference engine to calculate the probability that a given site is polymorphic primarily optimized for detecting SNPs among homozygous individuals; and 2) PolyPhred, which is optimized for detecting SNPs in heterozygous individuals. PolyBayes software has been used to accurately predict SNPs in humans with EST sequences aligned to finished and working-draft quality genomic sequences. It can • Chromatograms from several STSs can be combined into a batch for automated analysis. The output files from PolyBayes and PolyPhred containing the candidate polymorphisms are parsed and entries are made to a relational database.• A web interface provides access to search or browse options through a list of STSs to analyze and validate candidate polymorphisms Figure . Additio• SNP validation can be performed for local or global alignments with candidate SNPs visualized from a web based graphical interface similar to the Consed  output s• The SNP-PHAGE software currently has a limitation in viewing the underlying trace files. Hence, heterozygous SNPs marked by PolyPhred have to be visualized using the Consed interface. A software update to incorporate this feature will be released at a later date.• The results generated from a machine learning algorithm along with the multiple features used in the analysis are displayed to aid the decision making process.• Candidate polymorphisms can be either confirmed or eliminated from the web interface after manual inspection. These expert-confirmed polymorphisms are then used to create new database entries of validated SNPs, to assign a genotype for each sample, to identify haplotypes within an STS, and to make database entries to facilitate dbSNP submissions.in-silico predicted SNPs in soybean using EST data (manuscript in preparation).SNP-PHAGE has been applied to amplified STSs of soybean to discover SNPs . FollowiSNP-PHAGE is designed to identify a set of putative SNPs by applying PolyBayes and PolyPhred. PolyBayes and PolyPhred were optimized for different SNP detection scenarios (homozygous and heterozygous SNPs) and inclusion of both packages in the software pipeline enhances flexibility. Depending on the species or DNA/RNA source considered, one of the algorithms may be more appropriate. Application of machine learning in polymorphism discovery was shown to improve the efficiency of SNP discovery by 85% with SNPs predicted by PolyBayes. Inclusion of machine learning inference along with a visual interface for easy and quick visualization of SNPs is likely to also improve the efficiency of SNP validation.SNP-PHAGE accounts for differences in SNPs detected by PolyBayes and PolyPhred. When a SNP is called by both algorithms, genotypes generated from PolyPhred are used, because it finds both homozygous and heterozygous bases. PolyBayes marks each position in a multiple indel as a separate SNP, whereas PolyPhred combines the multiple indels into a single SNP, as is required for dbSNP submission. The SNP positions in such multiple indels from PolyBayes can be flagged during SNP validation so that they can be combined into a single indel in subsequent analysis. The consensus sequence at SNP positions depends on the individuals genotyped, because bases in those positions are derived from the most frequently observed genotypes. For indels, the change is more striking to the user, because it can offset other SNP positions in the consensus sequence.SNP-PHAGE can, in principle, be extended to work with other UNIX/Linux based SNP detection packages such as novoSNP  and SNPdThe interface allows the user to deal with some exceptional cases encountered during the expert SNP validation such as (i) identification of a high quality SNP not called by the prediction programs, (2) poor consensus sequence trimming, and (3) an incorrect consensus base at the SNP position. The interface allows for the addition of the SNP, along with the entry of the associated genotype into the database, custom consensus sequence trimming, and editing of consensus sequence bases.SNP-PHAGE is a simple, user friendly package for automated high-throughput polymorphism discovery. The package is provided with an installation guide and user manual to assist in deployment and implementation of this application. Additional pre-requisites for installing this application are minimal and only require those needed for base calling, sequence assembly and polymorphism detection.Polymorphism discovery and validation requires a balance between sensitivity  and specificity . However, these requirements may vary for individual projects. Projects driven by the need to reduce sequencing costs may wish to investigate all putative SNPs thus allowing for false positives; whereas other targeted projects such as those using SNPs as a biomarker for disease validation cannot tolerate false positives. The polymorphism detection programs attempt to fulfill these requirements by ranking SNPs by a score/probability value. The number of false positive SNPs predicted by PolyBayes and PolyPhred has been shown to be reduced by additionally considering the alignment and quality of the neighboring bases (NQS ). The moSNP-PHAGE provides the basic framework for SNP detection and validation. Other supplementary features and functions are being considered for addition to the interface. These additional features will be provided as updates in future with notifications by email for all registered users. This open source resource is intended to be further improved through user suggestions and encourages participation through provision of their custom scripts to be included in SNP-PHAGE. Some of these features planned will require availability of additional data such as whole genome sequence to provide genome coordinates, EST sequence to annotate the location of intron and exon sites, and protein sequence for annotation as synonymous vs. non-synonymous SNPs. Other scripts that are being customized for incorporation into the SNP-PHAGE include trio validation, estimation of θ and Fst values  and dataProject name: SNP-PHAGESoftware: Additional file 1(Check the project home page for more recent versions)Test dataset: Additional file 2Project home page: Operating system: UNIX/LinuxProgramming language: PerlOther requirements: Phred, Phrap, Consed, MySQL and Perl modules from CPAN listed in the project documentation .License: Open GPLAny restrictions to use by non-academics: NoneLM, JG dealt with the computational aspects in the implementationIC, DH and PC performed the sequencing, SNP data generation and suggested important features.CVT provided overall guidance for this projectAll authors read and approved the final manuscript.SNP-PHAGE software package. This compressed file contains all scripts required to create a SNP processing pipeline and a web interface for data analysis and visualization that is powered by a backend relational database.Click here for fileTest dataset. This compressed file contains test data for recreating the demo website. It contains the database table entries and chromatogram files to test for proper installation of all functionalities provided.Click here for file


A variety of diseases are caused by chromosomal abnormalities such as aneuploidies , microdeletions, microduplications, and uniparental disomy. High density single nucleotide polymorphism (SNP) microarrays provide information on chromosomal copy number changes, as well as genotype (heterozygosity and homozygosity). SNP array studies generate multiple types of data for each SNP site, some with more than 100,000 SNPs represented on each array. The identification of different classes of anomalies within SNP data has been challenging.We have developed SNPscan, a web-accessible tool to analyze and visualize high density SNP data. It enables researchers (1) to visually and quantitatively assess the quality of user-generated SNP data relative to a benchmark data set derived from a control population, (2) to display SNP intensity and allelic call data in order to detect chromosomal copy number anomalies (duplications and deletions), (3) to display uniparental isodisomy based on loss of heterozygosity (LOH) across genomic regions, (4) to compare paired samples , and (5) to generate a file type for viewing SNP data in the University of California, Santa Cruz (UCSC) Human Genome Browser. SNPscan accepts data exported from Affymetrix Copy Number Analysis Tool as its input. We validated SNPscan using data generated from patients with known deletions, duplications, and uniparental disomy. We also inspected previously generated SNP data from 90 apparently normal individuals from the Centre d'Étude du Polymorphisme Humain (CEPH) collection, and identified three cases of uniparental isodisomy, four females having an apparently mosaic X chromosome, two mislabelled SNP data sets, and one microdeletion on chromosome 2 with mosaicism from an apparently normal female. These previously unrecognized abnormalities were all detected using SNPscan. The microdeletion was independently confirmed by fluorescence in situ hybridization, and a region of homozygosity in a UPD case was confirmed by sequencing of genomic DNA..SNPscan is useful to identify chromosomal abnormalities based on SNP intensity  and heterozygosity data (including regions of LOH and some cases of UPD). The program and source code are available at the SNPscan website  A single nucleotide polymorphism (SNP) is a variation in a DNA sequence that occurs in an appreciable portion of the population. High density SNP microarrays provide information on the copy number of individual SNPs, based on measurement of the intensity of hybridized genomic DNA fragments to the microarray. These microarrays also provide genotype information on the state of heterozygosity or homozygosity at each SNP position. SNP arrays facilitate studies of chromosomal copy number change, large-scale linkage analysis, and whole genome association studies.A variety of platforms for SNP-based genotyping are commercially available . Most rep) values across one chromosome, as well as genotype scores and p values as regions of LOH. Several recent reports describe the visualization of SNP array data using this tool or similar plots of intensity ratios and/or log10 p values (y-axis) versus chromosomal position (x-axis) . BAC clones were obtained from Roswell Park Cancer Institute . 1 μg of BAC DNA was labelled by nick translation with a kit  including incorporation of Spectrum Orange dUTP. The BAC DNA was separated from the reaction mixture, co-precipitated with highly repetitive DNA, denatured, pre-annealed, then hybridized in situ to metaphase cells.Lymphoblastoid cells were obtained from the Kennedy Krieger Institute  or the Coriell Cell Repository . Cells were grown in RPMI medium, treated with colcemid (a mitotic spindle inhibitor), and metaphase spreads were incubated with chromosome-specific telomere probes  and labelled BAC clones. Cells were counterstained with 4',6-diamidino-2-phenylindole (DAPI) to visualize nuclei. Fluorescent signals were visualized using a Zeiss AxioSkop equipped with epifluorescence, appropriate filter sets, and a computer-assisted FISH capture system and software for producing high resolution images.Genomic DNA samples from five apparently normal cases  and one case suspected to have UPD (NA12874) were purchased from Coriell Cell Repositories. Genomic DNA (100 ng) was PCR amplified  using standard conditions including 30 to 35 cycles using denaturation , annealing , and extension . The eight sets of oligonucleotide primers were as follows : 1p1, 5'-CTCTGTGCAAGGTGTGAGGA-3' and 5'-ATGGCCCAAGGTCACATAAA-3'; 1p2, 5'-TTGAAACACTTCACAAAAGATGTG-3' and 5'-GTGCTCCTGGGAGAACTCAG-3'; 1p3, 5'-CCCAGTGCCATTATTACACTCA-3' and 5'-ATAGGGGCTCTGCACCTTTC-3'; 1p4, 5'-TGTATTGTTGGATTTGGTTTGC-3' and 5'-AGTCCCAGATGGGTTCACTG-3'; 1q1, 5'-TGTCTTCCAAAACGCACTTG-3' and 5'-AGCCCATCACGTCATATTCC-3'; 1q2, 5'-GGGGGTATCAGAGGCAATTT-3' and 5'-AGTGAAGAGCTCCTGCCTTG-3'; 1q3, 5'-CATCCGTGAGAATGGAAACC-3' and 5'-ATGAGGTCCATGCAGGAAAA-3'; 1q4, 5'-CAGGCAGGCTTTGACTCTTC-3' and 5'-CCCTAGAAACAGCTCCCAAA-3'. PCR products were electrophoresed on a 1.5% agarose gel, purified , and sequenced . Eight sets of primers were designed to span 2 or 3 SNPs  in <500 base pair regions of chromosome 1p (where UPD is not expected) and 1q.Project name: SNPscanProject home page: Operating system(s): Platform independent websiteProgramming languages: Perl, HTML, R v2.1Other requirements: Data input is from CNATv2.0 or v2.1 (Affymetrix).License: GNU GPLAny restrictions to use by non-academics: none. The source code is downloadable from the website.CEPH, Centre d'Étude du Polymorphisme Humain; CGH, comparative genomic hybridization; CNAT, Copy Number Analysis Tool; DAPI, 4',6-diamidino-2-phenylindole; FISH, fluorescence in situ hybridization; GDAS, GeneChip DNA Analysis Software; LOH, loss of heterozygosity; Mb, megabase; PDF, portable document format; PNG, portable network graphics; PS, PostScript; SNP, single nucleotide polymorphism; TIFF, tagged image file format; UCSC, University of California, Santa Cruz; UPD, uniparental disomy; WIG, wiggle track.JT designed the SNPscan algorithm, developed the KKISNP tools, and implemented the SNPscan website. He identified novel chromosomal anomalies in the apparently normal 90 individuals discussed in this paper via SNPscan and performed data analysis in R. YY performed the FISH studies and sequencing of genomic DNA, and contributed to writing the manuscript. GHT made intellectual contributions to the design and interpretation of the experiments and contributed to the writing of the manuscript. IR contributed to the data analysis and software development. JP conceived of the study and contributed to data analysis, development of the SNPscan website, and writing the manuscript. All authors read and approved the final manuscript.


Loss of heterozygosity (LOH) of chromosomal regions bearing tumor suppressors is a key event in the evolution of epithelial and mesenchymal tumors. Identification of these regions usually relies on genotyping tumor and counterpart normal DNA and noting regions where heterozygous alleles in the normal DNA become homozygous in the tumor. However, paired normal samples for tumors and cell lines are often not available. With the advent of oligonucleotide arrays that simultaneously assay thousands of single-nucleotide polymorphism (SNP) markers, genotyping can now be done at high enough resolution to allow identification of LOH events by the absence of heterozygous loci, without comparison to normal controls. Here we describe a hidden Markov model-based method to identify LOH from unpaired tumor samples, taking into account SNP intermarker distances, SNP-specific heterozygosity rates, and the haplotype structure of the human genome. When we applied the method to data genotyped on 100 K arrays, we correctly identified 99% of SNP markers as either retention or loss. We also correctly identified 81% of the regions of LOH, including 98% of regions greater than 3 megabases. By integrating copy number analysis into the method, we were able to distinguish LOH from allelic imbalance. Application of this method to data from a set of prostate samples without paired normals identified known regions of prevalent LOH. We have developed a method for analyzing high-density oligonucleotide SNP array data to accurately identify of regions of LOH and retention in tumors without the need for paired normal samples. A key event in the generation of many cancers is loss of heterozygosity (LOH) of chromosomal regions containing tumor suppressor genes, whereby one parent's version of the tumor suppressor is lost. As we develop a better understanding of the molecular mechanisms that generate different cancers, a description of the LOH events underlying these cancers is forming an important part of their classification. Generally, detection of LOH relies on comparison of the tumor's genome to the normal genome of the individual. Unfortunately, for many tumors, including most experimental models of cancer, the normal genome is not available. Therefore, the authors have developed a hidden Markov model-based method that evaluates the probability of LOH at all sites throughout the genome, based on high-resolution genotyping of only the tumor. They were able to achieve high levels of accuracy, specifically by taking into account the haplotype block structure of the genome. Application of this method to a set of 34 prostate cancer samples allowed the authors to identify the locations of the known and suspected tumor suppressor genes that are targeted by LOH. Loss of heterozygosity (LOH) refers to change from a state of heterozygosity in a normal genome to a homozygous state in a paired tumor genome. LOH is most often regarded as a mechanism for disabling tumor suppressor genes (TSGs) during the course of oncogenesis ,2. AlthoSingle nucleotide polymorphisms (SNPs) are the most common genetic variation in the human genome and can be used to search for germline genetic contributions to disease. To that end, oligonucleotide SNP arrays have been developed to simultaneously genotype thousands of SNP markers across the human genome –9. The dTraditionally, LOH analyses require the comparison of the genotypes of the tumor and its normal germline counterpart. However, for cell line, xenograft, leukemia, and archival samples, paired normal DNA is often unavailable. Current generations of SNP arrays provide high enough marker density to make it feasible to identify regions of LOH by the absence of heterozygous loci , rather than by comparison to the paired normal. For example, the homozygosity mapping of deletions method was developed to use highly polymorphic microsatellite markers to identify regions of hemizygous deletion in unpaired tumor cell lines , and a sWe approached this problem by developing a hidden Markov model (HMM) to infer LOH. HMMs are appropriate for inferring the unobserved underlying states that give rise to an observed chain of data, using multiple sources of information. They have been used to model biological data in diverse applications such as sequence analysis –20, linkThe components of a HMM are the unobserved states, the observed measurements, the emission probabilities connecting these two, the transition probabilities between the unobserved states, and the initial probabilities of the states at the beginning of the chain . To infeFor a SNP under the RET state, we observed Het calls with a probability equal to the heterozygosity rate of each SNP, which we estimated from normal samples see . For a SP0(RET) and P0(LOSS) = 1 −P0(LOSS), specify the probabilities of RET and LOSS for the p-terminal marker on a chromosome. They also specify the probabilities of the RET and LOSS states for any marker, if no other information exists for that marker. Assuming Het markers are observed in regions of LOSS only as a result of genotyping or mapping errors, the observed proportion of Het markers in a tumor sample is P0(RET) × average heterozygosity rate + P0(LOSS) × SNP error rate. As the SNP error rate is small the second term can be omitted. Therefore we estimated P0(RET) by dividing the proportion of Het markers by the average heterozygosity rate of SNPs in the population.These probabilities, denoted by θ as the probability that the state of the first marker does not inform the state of the second . Empirically, nearby markers tend to have the same LOSS or RET state, while distant markers do not. To capture this observation we calculate θ using an increasing function θ = (1−ed−2), where d is the physical distance (in the unit of 100 megabases [Mb] ≈ 1 morgan) between the two adjacent SNP markers. With probability 1 − θ, the two markers have the same LOH state. Therefore, the marker-specific transition probabilities of the second marker being LOSS given the LOH state of the first marker are:These probabilities describe the dependence between the LOH states of adjacent markers. For any two adjacent SNP markers, we first defined d, instead of estimating it as in the instability-selection model, but this does not affect the method performance (see below). In addition, the empirical transition frequencies estimated from observed LOH calls in paired normal and tumor samples agreed well with the transition probabilities estimated by this model .With these methods in place, we next applied the basic HMM to 100 K SNP array data from two prostate cancers and two lung cancer cell lines along with paired normal DNA, which were not included in the 10 K dataset. Here, the number of noninformative regions inferred as LOSS increased significantly B. When nAs indicated above, within a region of LD, the observed genotype of any marker depends not only on the underlying LOH state, but also on the genotypes of the adjacent markers  to four states: Hom LOSS, Het LOSS, Hom RET, and Het RET. Here Hom and Het represent the SNP marker's genotype in the unobserved normal sample. For example, “Hom LOSS” indicates that the SNP is homozygous in normal and LOH in tumor. The state “Hom LOSS”, “Het LOSS”, and “Hom RET” will result in homozygous genotype calls in the tumor unless genotyping or mapping error occurs, so the emission probability of the Hom genotype from these three states is set to (1 – SNP error rate). The state “Het RET” will result in a heterozygous SNP call in the tumor unless a genotyping or mapping error happens, so the emission probability of the Hom genotype from this state is set to the SNP error rate. The emission probability of the Het genotype is 1 minus that of the Hom genotype.i by P(iU+1 = Hom|Ui = Hom) and P(iU+1 = Het|Ui = Het). P(iU+1 = Het|Ui = Hom) and P(iU+1 = Hom|Ui = Het) are one minus the previous two probabilities, respectively. When there were not enough data to estimate these probabilities at a marker, the SNP-specific heterozygosity rate was estimated from the reference set and used as the unconditional probabilities—e.g., replacing P(iU+1 = Het|iU = Hom) by P(iU+1 = Het), the heterozygosity rate of marker i + 1. Next, we built the transition probabilities by combining the above genotype dependence probabilities with the probability of an LOH state change. We denote the underlying LOH state of marker i by iVi,U where iU is either Hom or Het and iV is either LOSS or RET. Suppose the current SNP i is in the “Hom LOSS” state while the next SNP i + 1 is in the “Het RET” state. For this to happen, two independent events must occur: a homozygous genotype is followed by a heterozygous genotype in the normal with the probability P(iU+1 = Het|iU = Hom) estimated as above, and the LOH state changes from LOSS to RET in the tumor with the probability P(iV+1 = RET|iV = LOSS) as specified in the transition probability of the basic HMM. The transition probability from “Hom LOSS” to “Het RET” is then the product of these two probabilities. In general, the transition probability of going from LOH state iViU to i+1Vi+1U is P(iU+1iV+1|iViU) = P(iU+1|iU)P(iV+1|iV).The transition probabilities now reflect both the probability of a state change from RET to LOSS (LOH state), and a state change from Het to Hom (genotype state). We estimated genotype dependencies as the probability, for each SNP marker, of the next adjacent SNP marker toward q-arm being Hom (or Het), given the current SNP marker being Hom (or Het), in a reference set of normal samples see . We denoWith the addition of the initial probabilities (which are the same as the basic HMM), the HMM parameters were fully specified and the forward-backward algorithm was used to obtain the probability of the LOH state being LOSS (either “Hom LOSS” or “Het LOSS”) for every SNP, given all the observed SNP calls along one chromosome of a tumor sample. Application of the LD-HMM to the 100 K dataset of normals, in place of the basic HMM, reduced the frequency of loss calls from 4.7% to 1.5% of markers C. LikewiWe posited that the remaining regions of falsely inferred LOH resulted from three specific deficiencies of the LD-HMM. First, regions of LD might be present in a relatively small subset of patients . Across To validate these results, we extended the analysis to a set of 100 K data obtained from two lung cancer cell lines and six gliomas with paired normals, that had not been used in any of our prior analyses. Here, the sensitivity and specificity of the HC/LD-HMM were 98.7% and 99.3%, respectively  and S1C.Given the importance of taking into account haplotype block structure, which is known to vary between ethnic groups , we sougConversely, the haplotype correction method relies on the ability to match specific haplotypes present in the tumors to those same haplotypes in the reference set. One might expect, therefore, that use of the JHC samples as the reference set for haplotype correction would result in poorer specificity than use of a Caucasian reference set. That is in fact the case, with the specificity of the HC/LD-HMM rising to only 98.3% when JHC samples were used for the haplotype correction, rather than the 99.0% when Caucasian samples were used .d from 50 Mb to 200 Mb changed the LOH inferences of only 2% of SNP markers. These results suggest that the basic and LD-HMMs should be able to provide accurate LOH inferences in datasets that have different error rates, heterozygosity rates, or LOH-retention transition frequencies from the sample sets presented here, and that the set of reference samples used for determination of allele frequencies and dependencies need not be larger than 60 individuals.The methods described above rely on the empirical estimates of a number of the parameters used in the initial, emission, and transition probabilities of the HMM. To assess whether the tumor-only inference methods were unduly influenced by these estimates, we tested the performance of the basic and LD-HMMs as we varied these parameters. Specifically, the accuracy of the model results, as judged against observed LOH in the paired tumor/normal data, changed by less than 0.3% as the SNP error rate was varied from 0.1% to 1% (10 K array). Moreover, when the SNP-specific heterozygosity rates were replaced by an average heterozygosity rate that was varied from 0.1 to 0.5 (10 K array) or from 0.1 to 0.27 (100 K array), the accuracy of the model results changed by less than 5% and 0.5%, respectively. Likewise, use of 60 versus 89 reference samples (from the JHC reference set) affected model accuracy by less than 0.1%. We also found that varying the scaling factor n and in the reference set of normal samples, the probability with which a window of n homozygous SNPs occurs. Using this, one could determine a threshold number t: If a tumor contained a string of more than t homozygous SNPs, that region is likely to be suffering LOH.If the HMM is robust to parameter specifications, the question naturally arises, Why institute an HMM-based approach that requires these parameters, rather than a more simplistic approach? The most obvious simple approach is to calculate, for all We applied such an approach  to our training set of 100 K data, and found that it does not match the specificity of the HC/LD-HMM for thresholds at which reasonable sensitivities are reached . InspectThe value of this HMM-based approach appears to be that it can straightforwardly integrate multiple sources of information, including SNP-specific heterozygosity rates, haplotype block structure, and genotyping error rates, to generate a local probability of LOH. It appears that each of these sources of information is necessary to obtain the highest sensitivity and specificity. Undoubtedly, other approaches may be devised to integrate these sources of information, but such approaches are likely to have a similar complexity to the HMM-based methods.The above analyses suggest that the HMM-based methods are robust for inferring LOH on a per marker basis. We next asked whether the HC/LD-HMM was equally effective in detecting regions of LOH and whether detection of such regions was influenced by their size. To this end, we compared the ability of the tumor-only LOH analysis to identify LOH regions observed from comparing paired normal and tumor samples . Here, wAs mentioned in the introduction, LOH arises due to complete loss of one allele through hemizygous deletion (copy loss) or through gene duplication . On the other hand, heterozygous loci can erroneously be assigned a homozygous genotype in settings of allele specific amplification . This will occur whether or not LOH is determined using paired normals, and may present paradoxical results, with recurrently amplified oncogenes seen as potential TSGs. To address this issue we determined the copy number at each SNP locus using the probe level signal intensity data  and corrModels of human cancer including xenografts and cell lines rarely are accompanied by paired normal samples. The utility of such models may be enhanced if we can ascertain the patterns of LOH in such models and relate them to those seen in actual human tumors. To this end, we next asked whether the HC/LD-HMM could detect regions of common LOH using 11 K SNP array data from 34 prostate cell lines, xenografts, and metastases where the corresponding normal DNA was unavailable . We first scored each SNP by averaging the probability of LOH over all 34 samples . These dWe have developed an HMM-based method to infer the probability of LOH events from tumor samples without matched normals. The method utilizes several sources of information, including intermarker distances, SNP genotyping and mapping error rates, and haplotype information. LOH inferences using only tumor samples agree well with LOH patterns determined by analysis of tumor/normal pairs in two different array types (10 K and 100 K), three different tissue types , and in both cell lines and tumors, in test and in validation datasets. The inferences are robust to model parameter specifications. LOH is resolved to about 3 Mb or 100 SNPs in 100 K array data. This method makes it feasible to use SNP array technology to map LOH in tumor samples for which normal DNA is unavailable. Given that genotyping paired normal samples constitutes up to half the cost of LOH mapping experiments, this method also makes it feasible to perform these experiments at a much lower cost per sample, at the expense of slightly reduced accuracy.One advantage of a model-based approach over the existing tumor-only LOH inference methods ,16 is itAt higher SNP densities, where the haplotype structure of the human genome becomes relevant, an approach that considers the dependence among multiple SNPs in a region of LD is necessary in addition to the LD-HMM. We used a haplotype correction that compared regions of inferred putative LOH to a set of reference normal samples to reduced false LOH inference. This method works best if the reference samples have similar haplotypes to the tumor sample. Use of reference samples from a different ethnic group tends not to decrease the sensitivity of the method, but can substantially decrease its specificity.False designation of regions of LOH due to allelic imbalance may lead to paradoxical results, with recurrently amplified oncogenes seen as potential TSGs. SNP arrays, by providing signal intensity along with genotyping data, allow such regions to be identified. We can thus integrate these data to exclude regions of putative LOH with high copy numbers as likely due to allelic imbalance. At the interpretive level, our finding that LOH is often copy-neutral suggests that LOH and copy loss should be considered independently when predicting the presence of a TSG, and may best be used in conjoined analyses.NKX3.1 locus is more prevalent among real tumors and xenografts than among cell lines, LOH at the p53 locus is more prevalent among xenografts than among real tumors or cell lines, and LOH at the Rb locus is equally prevalent in all three groups , accounting for the haplotype structure of the human genome in the analysis of these data will be of greater import. The methods described herein should be readily extensible to both the higher density arrays and to the increasingly detailed information describing the haplotype structure of the human genome. The software package, dChipSNP, is freely available at We used data from Early Access 10 K, Mapping 10 K, and 100 K SNP arrays   interrogating, respectively, 10,044, 11,555, and 116,204 SNP loci on all chromosomes except Y, with an average intermarker distance of 210 kb (11 K array) and 23.6 kb (100 K array) and average heterozygosity rate of 0.38 (11 K array) and 0.27 (100 K array) . 10 K arThe heterozygosity rates for each SNP and the dependence information between the genotypes of neighboring SNPs were estimated from sets of normal samples; the haplotype correction was also performed against separate sets of normal samples see . All thehttp://www.affymetrix.com) and the UCSC human genome assembly (http://genome.ucsc.edu).dChipSNP ,28 was uFigure S1NumHom was applied using window sizes of 33 (with and without haplotype correction) and 50. Data are displayed as in (99 KB PDF)Click here for additional data file.Protocol S1(135 KB DOC)Click here for additional data file.Table S1The proportion of LOSS and RET markers identified in paired tumor/normal data, that were identified correctly in unpaired tumors in the 10 K dataset (A), 100 K training dataset (B), and 100 K validation dataset (C).(53 KB DOC)Click here for additional data file.Table S2Ground truth was considered to be the results of a HMM applied to paired tumor/normal data.(32 KB DOC)Click here for additional data file.Table S3The number and proportion of SNP Markers in the 100 K Validation Dataset with LOSS or RET in Tumor/Normal Pairs, inferred as LOSS or RET by the LD-HMM (A) and HC/LD-HMM (B), using reference samples from alternative ethnicities.(35 KB DOC)Click here for additional data file.Table S4(28 KB DOC)Click here for additional data file.Table S5The percentage of LOH regions identified in 10 K data from tumor/normal pairs that were also identified by the HC/LD-HMM applied to the unpaired tumors, according to the size of the region (A) or number of SNPs present (B).(33 KB DOC)Click here for additional data file.Table S6(36 KB DOC)Click here for additional data file.