Microbial forensics is important in tracking the source of a pathogen, whether the disease is a naturally occurring outbreak or part of a criminal investigation.SNP and PCR-RFLP Optimization) software to perform a comprehensive, whole-genome analysis to forensically discriminate multiple sequences is presented. Tools for the optimization of forensic typing using Single Nucleotide Polymorphism (SNP) and PCR-Restriction Fragment Length Polymorphism (PCR-RFLP) analyses across multiple isolate sequences of a species are described. The PCR-RFLP analysis includes prediction and selection of optimal primers and restriction enzymes to enable maximum isolate discrimination based on sequence information. SPR Opt calculates all SNP or PCR-RFLP variations present in the sequences, groups them into haplotypes according to their co-segregation across those sequences, and performs combinatoric analyses to determine which sets of haplotypes provide maximal discrimination among all the input sequences. Those set combinations requiring that membership in the fewest haplotypes be queried (i.e. the fewest assays be performed) are found. These analyses highlight variable regions based on existing sequence data. These markers may be heterogeneous among unsequenced isolates as well, and thus may be useful for characterizing the relationships among unsequenced as well as sequenced isolates. The predictions are multi-locus. Analyses of mumps and SARS viruses are summarized. Phylogenetic trees created based on SNPs, PCR-RFLPs, and full genomes are compared for SARS virus, illustrating that purported phylogenies based only on SNP or PCR-RFLP variations do not match those based on multiple sequence alignment of the full genomes.A method and SPR Opt (.This is the first software to optimize the selection of forensic markers to maximize information gained from the fewest assays, accepting whole or partial genome sequence data as input. As more sequence data becomes available for multiple strains and isolates of a species, automated, computational approaches such as those described here will be essential to make sense of large amounts of information, and to guide and optimize efforts in the laboratory. The software and source code for SPR Opt is publicly available and free for non-profit use at  Microbial forensics and epidemiology is important in tracking the source of a pathogen, whether the disease is a naturally occurring outbreak or part of a criminal investigation. Polymorphisms among isolates or strains provide information as to the origin, phylogenetic relationships, or transmission patterns of those isolates . The 200Increasing availability of genomic sequence data makes it possible to predict regions of a genome that display variation among strains or isolates . In the The needs described above demand a full knowledge of all the SNPs and fragment length polymorphisms  that distinguish known isolates. SNPs and PCR-RFLP analyses have been used extensively in genotyping for forensic and epidemiological applications -10. AlthSNP PCR-RFLP Optimization). To our knowledge, this is the first comprehensive, automated, computational tool that performs the following four steps:To address this need, we have developed an automated forensic pipeline for SNP and PCR-RFLP optimization, called SPR Opt (1) identifies all SNPs or PCR-RFLP variations in a set of input sequences that can be as long as whole microbial genomes,2) groups the co-segregating markers into haplotypes,3) performs combinatoric analysis on the haplotypes to generate multi-locus solutions to maximally discriminate each of the input sequences from the others,4) uses simulated annealing to find the best solutions using the smallest total number of haplotypes  to discriminate all the input genomes to the maximum degree possible.Both SNP and PCR-RFLP solutions computed by the SPR Opt software include multiple loci when necessary for maximal sequence discrimination. In this paper, input sequences may also be referred to as genomes. However, the input need not be complete genomes; it can be gene sequences or other fragments from a number of isolates. Here a SNP is considered to be a single polymorphic base surrounded by conserved upstream and downstream sequence . The lenSPR Opt also predicts PCR-RFLP assays to discriminate the input sequences. Those primer pair and restriction enzyme combinations to maximally discriminate the input sequences are identified. For the PCR step, it is assumed that primer pairs must be conserved among all the input sequences. The resulting amplicons are examined for length polymorphisms or sequence variations that would result in differences in fragment length distributions after restriction digest among the multiple input sequences (RFLP). This software elucidates all PCR-RFLP detectable variations, whether they are caused by insertions or deletions of non-repeated sequence, tandem repeats, non-tandem repeats, microsatellites, mutations that alter a restriction site, and so on.For both SNP and PCR-RFLP analyses, some conservation is required among the input sequences surrounding the variable site, whether it be conserved bases up and downstream of the SNP, or conserved primers surrounding the fragment length polymorphism or RFLP. This ensures that there are not false negatives because the surrounding sequence was not amplified or otherwise detected. For example, detection of SNPs may be performed using a microarray with oligos chosen so that the central base is a SNP, and there are 4 oligos for each conserved surrounding sequence, each representing the SNP position filled with A, T, C, or G, respectively. If there is not a hybridization signal from any of the four oligos, then this serves as an alert that there may be a problem with the reaction conditions or the sample. In contrast, if conserved oligos (sequences surrounding the SNPs) are not used, then it is unclear whether the sample is a variant without that particular SNP, or whether the sample is degraded, the reaction conditions unsuitable, or the target species is not present.Here it is assumed that a haplotype is a group of markers (SNP or PCR-RFLP) that segregate in the same pattern across the genomes. Any single marker in a given haplotype indicates the identities of the other markers in this haplotype, so that every marker within the haplotype need not be examined. Here it is not assumed that the markers within a given haplotype cluster spatially in any pattern within a given sequence. That is, markers in the same haplotype may be distributed evenly or randomly, and do not necessarily occur within blocks of contiguous sequence. Haplotypes do not necessarily correlate with "hotspots" of mutation. Markers in the same haplotype may be likely to be linked, located in close proximity and inherited as a group, but it is not assumed that this is the case. Indeed, if markers in the same haplotype are separated in the genome, it may indicate that a recombination event has occurred. Alternatively, this pattern could be a result of selection or chance. Such an investigation of how positional information of markers in the same haplotype may indicate recombination is beyond the scope of this paper.Although the first part of this software depends on locating the SNP or PCR-RFLP sites (step 1 above), the "backend" (steps 2–4 above) of this computational forensics pipeline depends only on the haplotypes. Thus, while the tools are currently coded to work for the SNP or PCR-RFLP analyses described here, any type of marker such as microarray hybridization patterns, microsatellite markers, or even chemical or physical features that differ among isolates could be categorized into haplotypes and fed into the back end of the combinatorial analyses described below.One question that is investigated here is that of how well phylogenetic trees based on the haplotype splits of PCR-RFLPs or SNPs correspond with those generated from full genome sequence alignments or phenotypic differences. The results presented indicate that trees built solely on forensic markers do not closely match those based on full genome alignments. This finding has important implications for tree generation and interpretation in the absence of sufficient genomic sequence information, since empirical forensic techniques are often used for tree prediction. A second analysis presented is the frequency that different restriction enzymes result in fragment length polymorphisms for the SARS and mumps data.The impacts of this software are the following: 1) The software provides computational guidance as to an optimal set of assays for genotyping the isolates, particularly helpful when large numbers of sequences or genomes are available. 2) All the SNP or PCR-RFLP variations in the available data are found and grouped into haplotypes. This includes identification of the sequence surrounding a SNP that will be useful in designing the assay , or primer prediction and restriction enzyme selection. If the available sequence data is limited, there is no guarantee that the primers identified will be found in unsequenced strains, although requiring conservation among the sequenced isolates increases the chances of conservation among unsequenced isolates as well..The application of SPR Opt is illustrated for two viruses for which multiple genomes are publicly available. The software and source code is publicly available and free for non-profit use at . The main results are summarized in Tables The output files listed in Table A comparison of the SARS phylograms created using full genome sequences, SNPs, or PCR-RFLPs Figures , 3, 4, 5Table PCR without restriction digestion was inferior to PCR-RFLP and SNPs for genome discrimination of the viruses examined, although in additional analyses of bacterial genomes, PCR without digestion was adequate for complete genome discrimination (unpublished). When it is possible to choose from a very large number of restriction enzymes, PCR-RFLP may enable a greater level of discrimination than SNP analysis, illustrated by the result that the genomes can be subdivided into more, smaller unresolved clusters using PCR-RFLP than using SNPs. However, this is not always the case: for example the two SARS genomes PUMC03 and Sino1-11 can be discriminated using SNPs but not using PCR-RFLP. If only a small number of restriction enzymes are available, then analyses using SPR Opt indicate that SNPs outperform PCR-RFLP (unpublished). In most cases, either SNPs or PCR-RFLPs can discriminate most genomes, with minor differences in the exact unresolved clusters predicted. In these cases, one may need to use both techniques in order to discern different sets of similar genomes.Contrasting the phylogenetic trees generated from full genomes, SNPs, or PCR-RFLPs suggests that caution is needed in assessing genome divergence based on forensic/epidemiological data in lieu of full genome sequences, since the isolates predicted to be most divergent may differ across the three measures of variation. In addition, if only the subset of SNPs determined to be optimal for forensic discrimination are queried, phylogenetic trees based on this information may not be representative of the true phylogenic relationships, and may result in particularly poor estimation of branching structure and branch lengths.Highly heterogeneous viruses like many of the single-stranded RNA viruses  must be subdivided into clades or other sub-groupings of genetically similar strains even before this software can be applied. Lack of conserved sequence upstream and downstream of a SNP, or lack of conserved primers for PCR amplification, may thwart attempts at SNP or PCR-RFLP discovery at the species level. Instead, one requires subgroups of genomes that are sufficiently similar to locate conserved regions surrounding the forensically informative sites. A future version of the software under development will employ a less restrictive definition of SNPs, allowing some variation surrounding the SNP position.In addition to SNP and PCR-RFLP analysis like the examples presented here for a given target species, there are many questions one could address with the aid of this software. For example, the character haplotypes and genome groups generated by SPR Opt contain a wealth of information. If there are differences in virulence, host range, or other interesting phenotypic traits, one may examine the list of genome groups to see if there are any whose genome membership corresponds with the phenotypic variation. If so, these may be interesting regions for further biological investigation. For example, in which genes do the corresponding character haplotypes land? Do the nucleotide variations translate into protein sequence differences? One could also take a complementary approach, asking whether SNPs or PCR-RFLPs are clustered in certain genes or intergenic regions. It would also be interesting to examine whether or not SNP locations within a species correspond to regions of relative inter-specific conservation or variation. As mentioned in the introduction, the distribution of co-segregating markers in a given haplotype across the genome sequence might be used to look for evidence of recombination events or correlated selection on multiple genes, as might be observed if genes are in the same pathway or affected by the same environmental factors.In conclusion, bioinformatic software called SPR Opt is described to optimize SNP and PCR-RFLP analyses in order to provide the maximum amount of genotyping information from the fewest assays at the bench. SPR Opt requires as input a set of sequences and their multiple sequence alignment. This software not only predicts the variable sites based on input sequence data, but also groups these into co-segregating haplotypes and provides guidance as to the ways in which these may maximally discriminate genomes using the fewest possible assays. These are computationally challenging problems that are solved using a bit vector intersection approach to determine sets of haplotypes to maximally discriminate each input genome, as well as parallel simulated annealing to select a subset of the many possible solutions that will enable users to extract the most information from the fewest forensic tests. Analyses of two viruses were presented, and a number of potential investigations using this software are suggested. This is the first comprehensive tool to optimize the selection of forensic markers to maximize information gained from the fewest assays, accepting whole or partial genome sequence data as input. As more sequence data becomes available for multiple strains and isolates of a species, automated, computational approaches such as those described here will be essential to make sense of large amounts of information, and to guide and optimize efforts in the laboratory.The required input to the software is a fasta-formatted file of all the input genomes and a multiple sequence alignment. A consensus "gestalt" is automatically constructed from the multiple sequence alignment of the input genomes, in which conserved bases are indicated as letters  and dots indicate positions where all input sequences do not agree . Where tmin_len_upstream bases and conserved downstream sequence of length min_len_downstream bases. By conserved sequence, it is meant that the sequence is identical among all input genomes. If the sequence surrounding a SNP occurs more than once or is absent in any of the input genomes (checking both the plus and minus strands), the position is deleted from consideration as a SNP. A polymorphic base differs in one or more of the input genomes. If min_len_downstream and min_len_upstream are greater than zero, then this is a strict definition of SNPs. Insertions or deletions of even a single base are not considered SNPs. The parameters min_len_upstream and min_len_downstream are user-specified. The software finds SNPs using the conservation gestalt; in the conservation gestalt, any dot surrounded by at least min_len_upstream and min_len_downstream letters is a candidate SNP. The sequences indicated by the letters upstream and downstream of the candidate SNP are stored. Using regular expression pattern matching in PERL, the location of the SNP is determined in each of the input genomes. If an exact match to the conserved sequence surrounding the SNP does not occur exactly once in each of the input sequences, then the SNP and its surrounding sequence is eliminated from further consideration. Thus, the SNP is defined by its conserved surrounding sequence, and the position of a SNP within a genome may differ among the genomes.A SNP is defined to be a single polymorphic base surrounded by conserved upstream sequence of length min_len_downstream or min_len_upstream equal to 0, and thus pick up more regions, including those that may be the beginning of insertion/deletion variations among genotypes. This might be a desired approach for the Single Base Extension assay, since only conservation immediately 5' of the variable position matters [If a less restrictive definition of a SNP is required, it is possible to set either (but not both)  matters .min_len_upstream and min_len_downstream are too short, then sequence surrounding the SNP is likely to be repeated within a given genome. Since each of the repeats could have a different character at the SNP position, if these candidates are not eliminated then it is not possible to distinguish the case of a sample containing a mixture of multiple genotypes from the case of a sample containing a single strain that has multiple occurrences of the sequence surrounding a candidate SNP position with different bases filling the variable position. In contrast, if min_len_upstream and min_len_downstream are too long, then SNP variation may be overlooked because candidate SNPs are surrounded by an insufficient number of conserved bases. If a microarray chip platform using oligos of length 25 bases is to be used for the assay, then setting min_len_upstream and min_len_downstream equal to 12 will predict 25-mers with the central base position the SNP.The choice of the length requirement for the surrounding conserved sequence is very important. If This software excludes degenerate bases  indicated within a given genome from consideration as SNPs. Degenerate bases may be due to polymorphic populations within a given strain or to low quality sequencing, and thus do not deliver a confident characterization of differences between strains.A PCR-RFLP variation occurs if PCR amplicons or the fragments that result after restriction digest of such amplicons have a different length distribution among the input genomes. Fragment length distributions differ if they have different numbers of fragments or if any of the fragments differ in length.num_restriction_enzymes may be set equal to 0, 1, 2, or 3, as will be described below. Length variation must be detectable by electrophoresis, since there is a limit to the precision to which lengths can be determined. Here, if all the fragments in each of the genomes are less than 50 bases long, or if there are no differences in fragment lengths among any of the genomes that are at least precision bases, then the amplicon+enzyme combination is not considered to be sufficiently variable.The fragments are generated by first determining amplicons in which the forward and reverse primers are conserved among all the input genomes. If no restriction digest is to be applied, then an amplicon must differ in length among the input genomes. With restriction digest, the distribution of fragment lengths after cutting the amplicon with restriction enzymes must differ among the genomes. The parameter max_amplicon_length, to have at least one variable position , and are chosen using a sliding window moving at least jump bases from the start of the previous window. If jump is too large, PCR-RFLP sites may be missed, and if jump is too small, the same insertion/deletion variation may be counted more than once if it is contained within more than one pair of primers. However, all instances of counting the same variable site will appear within the same haploblock, so a simple examination of the markers within a given haploblock sorted by position should indicate those that query the same site as a result of using too small a value for the jump parameter. The software may also be run several times with different values of jump, and the largest value of jump that still provides the greatest discrimination among genomes may be selected.In order to find candidate regions for PCR-RFLP variation, sequence fragments that may contain a PCR-RFLP marker are selected from the conservation gestalt. These are chosen to be just over the Second, conserved primer pairs are selected from these fragments using MIT's primer3 software with user-specified parameters. Third, genomes are searched for an exact match to each primer pair on both the plus and minus (reverse complemented) strands, and those primer pairs are discarded in which 1) one or both primers are absent from any of the genomes, 2) one or both primers occur more than once in any of the genomes (with no mismatches), or 3) the forward and reverse primers are too far apart to reliably generate an amplicon in one or more of the genomes. In the analyses here, it is assumed that any distance longer than 1200 bases is too long for amplification, which is reasonable if a short elongation time in the PCR thermocycle is to be used. This value can be changed in the source code.num_restriction_enzymes. Simulated cutting is performed computationally using the regular expression pattern matching function in Perl, and it is assumed that cuts occur in all locations where a given enzyme sequence occurs .Amplicons can be cut by 0, 1, 2 or 3 enzymes simultaneously , as a user-specified option num_restriction_enzymes = 1, then a given PCR product may be digested by only one enzyme at a time before electrophoresis or other empirical measurement of fragment lengths. That is, digestion of the original PCR products with many alternative enzymes may be performed as long as each digestion is followed by its own fragment length measurement. The final solution guiding how to discriminate all the input sequences may involve a number of different enzymes. But when num_restriction_enzymes = 1, digestion is always done only with one enzyme at a time before fragment lengths are measured.If the number of restriction enzymes is set to 0, then in the output files the restriction enzyme is indicated as "NONE", and the PCR amplicons must vary in length among the sequences without any restriction digest. If num_restriction_enzymes = 2 or 3, then digestion can occur with combinations of 2 or 3 enzymes, respectively, before the measurement of fragment lengths occurs after each digestion with a given combination of enzymes. Enzyme combinations should be examined by the user to make sure the buffers and reaction conditions are compatible for all the enzymes in the combination, as the software does not assess enzyme compatibility. Although setting num_restriction_enzymes = 2 or 3 is allowed, it is not clear that users ever need to use these options, since with num_restriction_enzymes = 1, many different enzymes may contribute to the total solution of discriminating all the input genomes, as long as they are not applied simultaneously. All results reported here were computed using num_restriction_enzymes = 1.If The restriction enzymes to be considered are specified by the user. Currently, the software is not implemented for non-palindromic restriction enzymes, although this could be added as a minor modification if required.SNPs_all or FLPs_all, respectively. "FLP" refers to Fragment Length Polymorphisms, whether the polymorphisms result from PCR amplification or restriction digest length differences. The total number of SNPs is counted as the number of positions that are variable. For PCR-RFLPs, the software reports the total number of unique combinations of primer pair sequences and restriction enzyme(s) that yield variation in fragment length distributions among the genomes, as well as the number of unique fragment length distributions (summed across primer pairs and restriction enzyme combinations).First, the software calculates all SNPs or all PCR-RFLPs, as defined above. These are listed in the files The second computation the software performs is to divide the SNPs (or PCR-RFLPs) into co-segregating groups of markers called character haplotypes. That is, any two variable markers that distinguish the same set of genomes, and thus provide equivalent forensic/epidemiological information, are considered members of the same character haplotype. The genomes that co-segregate for a particular character haplotype is called the genome group. Genomes are members of multiple genome groups that may overlap in membership, and there may be genome groups that are proper subsets of other genome groups. For easy association, the genome group identification number is the same as the character haplotype identification number. The algorithm to generate the list of character haplotypes and genome groups works in the following way: at each variable site, genomes are grouped by the marker identity  each genome contains at that locus. Every time a new clustering pattern of genomes occurs, the cluster of genomes defines a new genome group, and the marker is stored in the character haplotype associated with that genome group. If a clustering pattern has already been observed at a locus previously examined, then that allele is added to the list of markers for the associated character haplotype.character_haplotypes contains a listing of the SNPs or PCR-RFLPs contained in each character haplotype, and the file genome_groups gives the associated genome groups. Inspection of these files is useful in additional analyses of sequence clusters (e.g. phylogenetic tree construction based on number of SNPs or PCR-RFLPs supporting a given relationship) or group-level assays .The file The third part of the software computes all sets of 1 or more character haplotype(s) that will maximally discriminate each genome. Each genome requires the testing of one or more polymorphic sites to pull it out from the other genomes. Thus, each solution set to resolve a given genome contains one to many haplotypes (a multi-locus solution). There may be many alternative solution sets for every genome, each of which provide the same information, and it is up to the user to select the one that works the best on the chosen platform.To find a set of character haplotypes to uniquely discriminate one genome from the rest requires that the intersection of the genomes across the associated genome groups is that single target genome. For example, if genome group 1 (associated with haplotype 1) contains genomes A, B, and C, and genome group 2 (associated with haplotype 2) contains genomes A and D, then the set of haplotypes 1 and 2 can uniquely discriminate genome A. That is, if in an unknown sample, one found a SNP that was included in haplotype 1 and another SNP that was included in haplotype 2, then it would be concluded that the unknown sample was like A, and not B, C, or D.100C3 = 161,700 tests. We represent each genome group as a bit vector of 1's and 0's indicating the membership or exclusion, respectively, of each of the genomes in that genome group. Then all genome groups containing only a single genome (each of the markers in the associated character haplotype uniquely identifies that genome) are reported, as well as all genome group combinations of size = number_combinations_to_test and fewer in which the bitwise AND (intersection) is a single genome. The bit vector approach was the fastest method that was tested by the authors. Thus, all intersections of sets of genome groups are examined, and for each of the input genomes, a search is made for those in which the intersection is uniquely that genome. The maximum number of genome groups per combination that is tested in a multi-locus solution set is the parameter number_combinations_to_test. Thus, combinatorics are performed on number haplotypessCnumber_combinations_to_test total combinations.The combinatoric demands of this step may be substantial. For example, if there are 100 genome groups, then examining the intersections of all possible combinations of 3 genome groups requires i for genome A is represented as the list others.If there is not an intersection that contains uniquely the target genome, the intersections that contain the fewest other genomes in addition to the target genome is reported as the solution set for that target. Thus, if the target in question cannot be uniquely discriminated, the most specific level to which it can be discerned is output. The other genomes in solution set number_combinations_to_test must be set at the low value of 2 in order for the combinatoric step to finish in a reasonable amount of time (hours or less). First, if there are two preliminary solutions i ≠ j where others ≠ others, new combinatorics are performed on only the union of the genome groups that comprise all preliminary solutions for the genome A under consideration. Since the number of genome groups making up all preliminary solutions for the specific genome A is a small subset of the total number of genome groups, testing all possible combinations (not just combinations up to a given size) computes rapidly. This generates new solution sets, each of which contains more genome groups than the preliminary solution sets. Each of these new solution sets for the given genome has an identical list of others(A), so that the index i may be dropped.When there are many genomes and many genome groups, it may be necessary to take two additional steps to find the combinations to maximally discriminate sequences. For example, there may be hundreds of genome groups, so that the others(A), if the size of others(B) < size of others(A), then one should be able to discriminate genome A to a higher level (with fewer others(A)), if more genome groups are included in the solution set. In this case, the program cycles through all genome groups, adding the first genome group encountered to the solution sets for A that contain genome A but not genome B. Although taking the first acceptable genome group may not be the best ), this method is fast and does give a good solution in the many test cases examined. The list of others(A) is then recomputed with the new genome group included in the solution set, and this process is repeated for all genomes in the list others(A). We repeat this procedure for all the genomes that cannot be uniquely discriminated. This generates final solution set(s) for each genome that may contain many more genome groups than the original combinatoric size number_combinations_to_test.A second step is sometimes required to find maximally discriminating multi-locus combinations in situations with large numbers of genomes and genome groups. For genome B that is in the list of others with others for all i ≠ j. The second step is computed automatically based on results of comparing others(A) with others(B) for all genomes A and B. These extra steps are rarely needed, since in most cases, there are few enough genome groups to select a sufficiently large value of number_combinations_to_test to find optimal solution sets with the fewest possible genome groups for each genome, rather than to find acceptable  solution sets containing more than number_combinations_to_test genome groups. These steps were required for analyses of SARS virus. All solution sets for each genome are given in the file all_discriminating_sets.The first step described above is performed automatically based on results of comparing lists of pare_groups that can be set to 1. If pare_groups = 1 then the algorithm finds the cutoff number of genomes per genome group below which there are genome groups that together contain all the input genomes. Then only the haplotypes that contain fewer than this cutoff number of genomes are considered in the combinatoric steps described above. This option may result in orders of magnitude improvement in algorithm speed and memory for situations in which there are a large number of genome groups.There is a third option for speeding up the combinatorics of the process of finding sets of genome groups that maximally discriminate each genome: this is to exclude consideration of those genome groups with many genomes from the combinatoric calculations. This step yields a set of "pared groups". Since genome groups with many genomes provide the poorest discrimination among those genomes, it is reasonable to only consider the genome groups with the fewest genomes. This is an optional parameter When genomes cannot be uniquely distinguished at the single genome level, the maximally discriminating genome groups are referred to as unresolved clusters. For complete isolate-level discrimination of all the input genomes, the number of unresolved clusters must equal the number of genomes.Finally, the combinations of solution sets for each genome are found that enable the testing of the fewest total character haplotypes (i.e. that are associated with the fewest genome groups). For the example shown in Figure 30 combinations, far too many to do an exhaustive search for the global optimum solution that minimizes the number of character haplotypes to be examined. Therefore, simulated annealing was used to search for approximately optimal solutions using the Metropolis algorithm. Forty parallel processes of simulated annealing were run, and from these the best solutions were selected. The best score is the minimum number of haplotypes that must be examined. The file sim_anneal_results_summary lists the combinations of set numbers (each set is a set of haplotypes) for each genome that enable the fewest haplotypes to be tested overall. Each row is a different solution from simulated annealing that has the same score, that is, each row contains a list of set numbers, one set for each genome, characterizing the combination of sets. The set numbers correspond to the set numbers given in the file all_discriminating_sets, and are not the same as the genome group or character haplotype numbers. Many of these simulated annealing solutions are very similar to one another. For test cases examined in which there were fewer than 107 possible set combinations, we verified that simulated annealing predicted the true global optimum.When many of the genomes can be discriminated by multiple alternative solution sets, the number of possible set combinations across all the genomes is the product of the number of solution sets for each genome. Unpublished analyses show that the number of possible set combinations can skyrocket to over 10min_len_upstream = 7 and min_len_downstream = 7. The minimum length of conserved upstream and downstream bases was selected to be 7 because it enabled a finer level of genome discrimination than did 12-mers .For the analyses described below, the following parameter values were used for SNP analyses: max_amplicon_length = 1000. Amplicons of length 900–1000 bases are specified as preferred, although shorter amplicons are allowed if longer ones cannot be found. Other parameters are: jump = 500 when num_restriction_enzymes = 1, jump = 200 when num_restriction_enzymes = 0, and precision = 5. Primer3 parameters in the file p3.params.pcr.primers as well as a file listing the restriction enzymes used in the computations are available for download at .For the PCR-RFLP analyses described here, the SNP and PCR-RFLP analyses were performed using SPR Opt on 102 genomes of severe acute respiratory syndrome (SARS) virus (~30 Kb) and 17 genomes of mumps virus (~15 Kb) that were publicly available at the time the analyses were done. The Genbank genome identification information for these is given in Three phylogenetic trees for SARS virus were constructed based on 1) a full genome multiple sequence alignment, 2) SNPs, and 3) PCR-RFLPs. Newick trees were created using the PHYLIP software package ,15, usinTo calculate a distance matrix from the genome group data, first, each genome group was weighted by the number of SNPs or the number of PCR primers (for PCR-RFLP) contained in the associated character haplotype. For PCR-RFLPs, the weight was not increased if two or more alternative enzymes cutting the same amplicon (i.e. from the same set of primers) gave the same pattern of genome segregation, since in the runs described, there were many possible enzymes that provided the same information about genome relationships after cutting a single amplicon, and this might artificially increase the weight. After the weights were calculated, then for all possible pairs of genomes, each genome group was examined, and each time a genome group contained both of the genomes, the weight of that genome group was subtracted from the total distance score between that pair of genomes. After all the pairwise distances were calculated, a constant equal to the minimum (negative) distance was subtracted from each pairwise distance, so that none of the distances would be negative. The distance matrices for each of the SNP and the PCR-RFLP analyses were used in the PHYLIP neighbor program. The resulting phylogenetic trees (phylograms) were drawn using a web interface . A phyloTo verify that this method of creating a distance matrix from the genome group data provides an accurate picture for the SNP data, a distance matrix was created from a traditional SNP matrix. In the SNP matrix, columns correspond to the SNPs, and rows correspond to the isolates. This matrix, containing every SNP in the data set, was used in the dnadist algorithm of PHYLIP using the maximum likelihood option. The tree generated was identical to that created using the algorithm described above calculating distances from the genome groups.FLPs_all (Table The total number of times that each restriction enzyme created a different distribution of fragment lengths was summed across all amplicons in the PCR-RFLP analyses. This was done simply by counting the frequency of occurrence of each enzyme in the file ll Table .SPR Opt: SNP and PCR-RFLP OptimizationSNP: Single Nucleotide PolymorphismPCR-RFLP: Polymerase Chain Reaction-Restriction Fragment Length PolymorphismSNG conceived of the project, developed much of the software, and drafted the manuscript. MCW created the code for performing set combinatorics using bit vectors, assisted with the code for distributed simulated annealing, and bundled the code for distribution.Description of additional data files provided at Digest*organism_name NoDigest*organism_name SNP*organism_name The * refers to the files indicated in Table .Lists of unresolved clusters. Click here for fileDescription of additional data files provided at Digest*organism_name NoDigest*organism_name SNP*organism_name The * refers to the files indicated in Table .List of genomes used in the analyses. Click here for file


Macaca mulatta genome. We estimate that over 60% of the SNPs will be of high frequency and useful for mapping QTLs, genetic management, and studies of individual relatedness, whereas other less frequent SNPs may be useful as population specific markers for ancestry identification. We have created a web resource called MamuSNP to view the SNPs and associated information online. This resource will also be useful for researchers using a wide variety of Macaca species in their research.We developed a novel method for identifying SNPs widely distributed throughout the coding and non-coding regions of a genome. The method uses large-scale parallel pyrosequencing technology in combination with bioinformatics tools. We used this method to generate approximately 23,000 candidate SNPs throughout the  Macaca mulatta) are a widely used and valuable model for biomedical research and understanding the etiology of human diseases. With the completion of a draft sequence of the rhesus macaque genome M. mulatta genome has already begun Rhesus macaques  DNA sequences. Due to the nature of the sequencing method, these sequences are distributed randomly across the entire genome. This wide range of coverage makes it advantageous to discover unlinked common SNPs in coding and non-coding regions by screening one or only a few animals. This method of SNP identification is unique because it can be used to identify SNPs distant from any genes. Therefore, this method is useful for identifying regulatory regions located in genomic deserts far from a gene, but which still contribute to variation in a phenotype e.g. .http://mamusnp.ucdavis.edu. Updated SNP frequencies in populations as well as additional verified SNPs will be routinely uploaded to the website.Here we use large-scale parallel pyrosequencing from 454 Life Science™ to generate approximately 23,000 candidate SNPs throughout the rhesus macaque genome; the candidate SNPs are distributed across all 20 autosomes and the X chromosome. Each candidate SNP is mapped to an exact location on the current draft of the genome (07/06) and flanking DNA sequence for each SNP is provided so that primers can be easily designed to assay the SNP. Over 1,500 SNPs were sequenced at least twice through the pyosequencing process and a subset of these SNPs have been confirmed via Sanger sequencing in a panel of animals from geographically diverse origins. All candidate SNPs and associated information can be accessed at the website Macaca fascicularis, are becoming an important non-human primate model and may soon outnumber rhesus macaques in biomedical research. Doxiadis et al. MamuSNP website may also be polymorphic in cynomolgus macaques and other closely related primates. Although these SNPs would exhibit a significant ascertainment bias in cynomolgus macaques, they may still be useful as markers for QTL analyses.Cynomolgus macaques , The average read length received from 454 Life Science™ was 103.92 bases. The DNA sequence was of high quality except within regions of homopolymer tracts, a known limitation of pyrosequencing. We obtained 339,967 reads from a single run. These reads were blasted against a repeat masked version of the macaque genome. Of the reads 42% exhibited a high match score with the macaque genome. It is likely that the remaining reads aligned to repeat masked regions of the genome or represent artifacts or DNA sequence from exogenous organisms in the original blood sample from which DNA was extracted. Of the total number of reads, 58% did not match the macaque genome with a ≥98% identity, 20% exhibited a perfect match with the DNA sequence from the macaque genome and 8% contained the information for candidate SNPs . After fWe were able to confirm the presence of 26 high-frequency SNPs (63.4%) out of the 41 loci sequenced in a pooled sample of four rhesus macaques . Five ofMamuSNP, can be accessed at http://mamusnp.ucdavis.edu. MamuSNP supports queries on one or more 454 sequence IDs to create a summary report with 454 sequences, relative positions of each base pair, quality scores in a table format and with mismatch sequences high-lighted by a different color. The details of BLAST outputs including BLAST scores and the sequence alignments with reference are also shown in the report. The query feature is a useful tool for verifying computational results and SNP candidate selection. Since queries concerning chromosome number and map positions are supported, this tool is particularly useful to search for novel SNPs in a particular genomic region of interest, for screening possible cases of overlaps between 454 sequences within a genomic region and for the retrieval of sequences flanking the target template. The latter facilitates primer design for the experimental verification of candidate SNPs.It is a non-trivial task to manage and analyze hundreds of thousands of sequences and their associate computational results, especially in a complete genome context. We created a database that has integrated 454 Life Science™ DNA sequences, their corresponding quality scores, BLAST outputs, rhesus macaque genome sequences and their corresponding Sanger quality scores. The database is built on top of MySQL with a web interface that allows dynamic queries through Perl CGI (Common Gateway Interface). The website for this database dubbed M. mulatta blood sample Sch00R1684 using a QiaAmp Blood Extraction Mini Kit. The three extracts were pooled, purified, and concentrated using a Millipore Microcon YM-100 centrifugal Filter Device. Five micrograms of concentrated extract (at>300 ng/ul) was submitted to 454 Life Sciences in Branford, CT for pyrosequencing.We chose to pyrosequence an animal from western China to maximize diversity when compared to the draft sequence from a rhesus macaque of Indian ancestry. Sample Sch00R1684 was obtained from Sichuan Province, China and the provenience of this sample was confirmed through mitochondrial DNA (mtDNA) sequencing (see Genbank Accession # DQ373245: http://www.genome.wustl.edu/genome.cgi?GENOME = Macaca%20mulatta&SECTION = assemblies) from the Baylor College of Human Medicine. Prior to the search, repetitive sequences and low quality regions were masked out of the database at Baylor. The alignment was performed with NCBI's BLASTN variant of the BLASTALL program All DNA sequences once received from 454 were blasted against the MMUL 1.0 genome assembly  were removed from further analysis. Only alignments with ≥98% identity or only one mismatch, with the second alignment scoring worst than the first were accepted for analysis. An identity match of 98% was selected to allow one or two mismatches on a single sequence. To control for pyrosequencing read errors in homopolymeric regions due to non-linear light response http://www.ncbi.nlm.nih.gov/projects/genome/seq/BlastGen/BlastGen.cgi?taxid = 9544). Fragments that matched multiple regions of the genome were discarded to avoid complications during primer-design and genotyping. Forty-one SNP loci in forty-one independent fragments were chosen for Sanger sequencing confirmation. After identifying the candidate loci, 400 bp of flanking sequence on either side of the SNP were retrieved using the ‘Sequence Query’ feature of the MamuSNP web interface (http://mamusnp.ucdavis.edu); these sequences were used to generate oligonucleotide primers.We screened a subset of candidate SNPs using Sanger sequencing of a pooled sample. The putative SNPs were first verified and checked for paralogous sequences by comparing the 454-generated fragment sequence to the rhesus macaque genome, using NCBI's rhesus macaque genome megaBLAST resource. Click here for additional data file.


GPX1, GPX2, GPX3, GPX4, TXNRD1, and SEPP1) known to contribute to antioxidant host defenses, a re-sequence analysis was conducted across these genes with particular interest directed at the coding regions, intron-exon borders and flanking untranslated regions (UTR) for each gene in an 102 individual population representative of 4 major ethnic groups found within the United States.There are at least 25 human selenoproteins, each characterized by the incorporation of selenium into the primary sequence as the amino acid selenocysteine. Since many selenoproteins have antioxidant properties, it is plausible that inter-individual differences in selenoprotein expression or activity could influence risk for a range of complex diseases, such as cancer, infectious diseases as well as deleterious responses to oxidative stressors like cigarette smoke. To capture the common genetic variants for 6 important selenoprotein genes (GPX1 locus (3p21) there was evidence for positive selection. Strong confirmatory evidence for recent positive selection at the genomic region 3p21 in Asian populations is provided by data from the International HapMap project.For 5 of the genes there was no strong evidence for selection according to the expectations of the neutral equilibrium model of evolution; however, at the GPX1 locus.The SNPs and fine haplotype maps described in this report will be valuable resources for future functional studies, for population specific genetic studies designed to comprehensively explore the role of selenoprotein genetic variants in the etiology of various human diseases, and to define the forces responsible for a recent selective sweep in the vicinity of the  Increasing data suggests that selenium deficiency is a risk factor for certain cancers, neurodegenerative disorders and complications from diabetes -4. SelenSEPP1), thioredoxin reductase 1 (TXNRD1), and 4 selenium containing glutathione peroxidase genes, GPX1, GPX2, GPX3 and GPX4 . Eac. Eac64].GPX1 (NM_000581), GPX2 (NM_002083), GPX3 (NM_002084), GPX4 (NM_002085), TXNRD1 (NM_003330), and SEPP1 (NM_005410). SNPs 5' of the ATG are represented as a negative number relative to the first base of the start codon; SNP 3' of the stop are represented as a positive number relative to the last base of the stop codon; SNPs within an intron are represented as intron number plus the number of bases from the first base of the start of the intron; SNPs within an exon are represented as either synonymous (SYN) or non-synonymous (NSYN) and the amino acid position is provided. In the case of GPX1, the resequencing in the 5' direction extended into the coding region of a neighboring gene, ras homolog gene family member A (RHOA). Of note, there is an uncharacterized phylogenetically conserved transcript (BC039102) overlapping the 3' end of SEPP1 in an antisense orientation. Resequencing at the SEPP1 locus was expanded to include putative exons and the promoter region corresponding to this antisense transcript because of the possibility that overlapping transcripts might post-transcriptionally regulate each other's expression [TXNRD1 exhibits alternative splicing at the 5' end. Our re-sequencing corresponded to the exons of TXNRD1 transcript variant 1 (NM_003330) and also included the published promoter region, which is conserved between the mouse and human[For the purpose of mapping SNPs and primer locations we used the May 2004 assembly of the human genome . Genomic sequences between the most 5' forward PCR primer and the most 3' reverse PCR primer were obtained using the UCSC In-Silico PCR program . The locpression ,68. TXNRand human.T) and Fu and Li's (DF and F) statistics [FST) for all population pairs using GENEPOP on the Web, developed from the Genepop DOS versions 3.3/3.4[FST is the proportion of the total genetic variance contained in a subpopulation (s) relative to the total genetic variance (t). Values can range from 0 to 1. High FST implies a considerable degree of differentiation among populations. GENEPOP is a population genetics software package originally designed by Michel Raymond and Francois Rousset, at the Laboratiore de Genetique et Environment, Montpellier, France. Transformation of data from prettybase format to GENEPOP format was facilitated by using the perl script report_prettybase.pl written by Fares Z. Najar (revised by James D. White) at the Advanced Center for Genome Technology, University of Oklahoma. To confirm recent positive selection at the GPX1 locus, we used Haplotter to query the results of a scan for positive selection in the human genome developed using SNP data from the International HapMap project [To compare the sequence diversity between genes, the heterozygosity per nucleotide site was estimated by calculating nucleotide diversity (π) and the population mutation parameter (Θ) . To deteatistics ,38. The atistics . PHASED atistics ,72. PHASs 3.3/3.4. FST is  project ,41,74. T project .2 threshold 0.8 and LOD threshold for multi-marker tests 3.0. Of note, htSNPs are selected on a block-by-block basis; therefore, the end set of htSNPs is not necessarily the most parsimonious one for the entire data set, but is more likely to capture variation in a new, larger data set that was not observed in the initial data set. Non-synonymous SNPs and SECIS region SNPs with a minimum rare allele frequency of ≥0.05 were force included as tagged SNPs.For each gene the most probable PHASED haplotype was determined, as described above, using only those SNPs that had a minimum rare allele frequency of ≥0.05. Using the Chimp BLAT Search at the UCSC Genome Bioinformatics Site, we aligned the human locus of interest and the corresponding locus from the chimp genome (Nov. 2003 assembly) to infer a chimp haplotype . LD (D')SEPP1 locus and participated in the revision of the manuscript. UP is the principal investigator for an NIH intramural research award that provided funding for sequencing reagents. She contributed to the conception of the project, study design and the revision of the manuscript. All authors read and approved the final manuscript.CBF conceived the idea for the study and is the principal investigator on an NIH K22 research award that provided partial funds for the re-sequencing. He oversaw and contributed to all aspects of the re-sequencing project, helped organize genotype data, performed all genetic analyses and authored the manuscript. KA contributed to all technical aspects of the project, performed most of the PCR and DNA sequencing and organized the genotype database. SJC helped develop the SNP500Cancer control population and database, provided input into study design and contributed to the revision of the manuscript. He is the director of the NIH core genotyping facility where sequencing reactions were run. HFM performed PCR amplification and re-sequencing of the Genotype Frequencies and Hardy-Weinberg Equilibrium (HWE) Calculations for Single Nucleotide Polymorphisms (SNPs) at the GPX1 Locus. Genotype frequencies and HWE calculations are provided for each of the 4 ethnic subpopulations, AA (n = 24), CA (n = 31), HI (n = 23), and PR . RS# refers to the SNPs reference cluster ID, a unique SNP ID assigned by dbSNP[ by dbSNP. Genotyp by dbSNP. LocatioClick here for fileGenotype Frequencies and Hardy-Weinberg Equilibrium (HWE) Calculations for Single Nucleotide Polymorphisms (SNPs) at the GPX2 Locus. Genotype frequencies and HWE calculations are provided for each of the 4 ethnic subpopulations, AA (n = 24), CA (n = 31), HI (n = 23), and PR . RS# refers to the SNPs reference cluster ID, a unique SNP ID assigned by dbSNP[ by dbSNP. Genotyp by dbSNP. LocatioClick here for fileGenotype Frequencies and Hardy-Weinberg Equilibrium (HWE) Calculations for Single Nucleotide Polymorphisms (SNPs) at the GPX3 Locus. Genotype frequencies and HWE calculations are provided for each of the 4 ethnic subpopulations, AA (n = 24), CA (n = 31), HI (n = 23), and PR . RS# refers to the SNPs reference cluster ID, a unique SNP ID assigned by dbSNP[ by dbSNP. Genotyp by dbSNP. LocatioClick here for fileGenotype Frequencies and Hardy-Weinberg Equilibrium (HWE) Calculations for Single Nucleotide Polymorphisms (SNPs) at the GPX4 Locus. Genotype frequencies and HWE calculations are provided for each of the 4 ethnic subpopulations, AA (n = 24), CA (n = 31), HI (n = 23), and PR . RS# refers to the SNPs reference cluster ID, a unique SNP ID assigned by dbSNP[ by dbSNP. Genotyp by dbSNP. LocatioClick here for fileGenotype Frequencies and Hardy-Weinberg Equilibrium (HWE) Calculations for Single Nucleotide Polymorphisms (SNPs) at the SEPP1 Locus. Genotype frequencies and HWE calculations are provided for each of the 4 ethnic subpopulations, AA (n = 24), CA (n = 31), HI (n = 23), and PR . RS# refers to the SNPs reference cluster ID, a unique SNP ID assigned by dbSNP[ by dbSNP. Genotyp by dbSNP. LocatioClick here for fileGenotype Frequencies and Hardy-Weinberg Equilibrium (HWE) Calculations for Single Nucleotide Polymorphisms (SNPs) at the TXNRD1 Locus. Genotype frequencies and HWE calculations are provided for each of the 4 ethnic subpopulations, AA (n = 24), CA (n = 31), HI (n = 23), and PR . RS# refers to the SNPs reference cluster ID, a unique SNP ID assigned by dbSNP[ by dbSNP. Genotyp by dbSNP. LocatioClick here for fileMost Probable PHASED Haplotypes at the GPX1 Locus Determined Using Only Those SNPs With a Minimum Rare Allele Frequency of ≥ 0.05. Haplotype frequencies are provided for the combined SNP500 DNA population (n = 102), and for each of the 4 ethnic subpopulations, AA (n = 24), CA (n = 31), HI (n = 23), and PR . RS# refers to the SNPs reference cluster ID, a unique SNP ID assigned by dbSNP[ by dbSNP. LocatioClick here for fileMost Probable PHASED Haplotypes at the GPX2 Locus Determined Using Only Those SNPs With a Minimum Rare Allele Frequency of ≥ 0.05. Haplotype frequencies are provided for the combined SNP500 DNA population (n = 102), and for each of the 4 ethnic subpopulations, AA (n = 24), CA (n = 31), HI (n = 23), and PR . RS# refers to the SNPs reference cluster ID, a unique SNP ID assigned by dbSNP[ by dbSNP. LocatioClick here for fileMost Probable PHASED Haplotypes at the GPX3 Locus Determined Using Only Those SNPs With a Minimum Rare Allele Frequency of ≥ 0.05. Haplotype frequencies are provided for the combined SNP500 DNA population (n = 102), and for each of the 4 ethnic subpopulations, AA (n = 24), CA (n = 31), HI (n = 23), and PR . RS# refers to the SNPs reference cluster ID, a unique SNP ID assigned by dbSNP[ by dbSNP. LocatioClick here for fileMost Probable PHASED Haplotypes at the GPX4 Locus Determined Using Only Those SNPs With a Minimum Rare Allele Frequency of ≥ 0.05. Haplotype frequencies are provided for the combined SNP500 DNA population (n = 102), and for each of the 4 ethnic subpopulations, AA (n = 24), CA (n = 31), HI (n = 23), and PR . RS# refers to the SNPs reference cluster ID, a unique SNP ID assigned by dbSNP[ by dbSNP. LocatioClick here for fileMost Probable PHASED Haplotypes at the SEPP1 Locus Determined Using Only Those SNPs With a Minimum Rare Allele Frequency of ≥ 0.05. Haplotype frequencies are provided for the combined SNP500 DNA population (n = 102), and for each of the 4 ethnic subpopulations, AA (n = 24), CA (n = 31), HI (n = 23), and PR . RS# refers to the SNPs reference cluster ID, a unique SNP ID assigned by dbSNP[ by dbSNP. LocatioClick here for fileMost Probable PHASED Haplotypes at the TXNRD1 Locus Determined Using Only Those SNPs With a Minimum Rare Allele Frequency of ≥ 0.05. Haplotype frequencies are provided for the combined SNP500 DNA population (n = 102), and for each of the 4 ethnic subpopulations, AA (n = 24), CA (n = 31), HI (n = 23), and PR . RS# refers to the SNPs reference cluster ID, a unique SNP ID assigned by dbSNP[ by dbSNP. LocatioClick here for fileEstimates for linkage disequilibrium (LD) and location of major haplotype blocks across 6 selenoprotein loci, stratified by ethnic subpopulation. Pair wise plots (D') across 6 selenoprotein loci based on genotype data obtained from re-sequencing DNA samples from individuals of AA (n = 24), CA (n = 31), HI (n = 23) and PR heritage from the SNP500 DNA population. Re-sequenced genes include a) GPX1, b) GPX2, c) GPX3, d) GPX4, e) SEPP1, and f) TXNRD1. SNP identifiers are indicated on the abscissas. Numbers within cells correspond to LD values (D'). The LD color scheme is stratified according to the logarithm of the odds (LOD) score and D': LOD <2 (white for D'<1 and blue for D' = 1) or LOD >2 (shades of pink/red for D'<1 and bright red for D' = 1). Haplotype blocks were created using the algorithm of Gabriel et al, Science 2002 [nce 2002 . 95% conClick here for filePCR Primer Pairs. This file provides the name, location and sequence for PCR primers used in the resequencing project.Click here for fileGPX1 Prettybase File. This file is in prettybase format and provides SNP location in the reference sequence, SNP500 sample ID number, and genotype calls for each identified SNP at the GPX1 locus.Click here for fileGPX2 Prettybase File. This file is in prettybase format and provides SNP location in the reference sequence, SNP500 sample ID number, and genotype calls for each identified SNP at the GPX2 locus.Click here for fileGPX3 Prettybase File. This file is in prettybase format and provides SNP location in the reference sequence, SNP500 sample ID number, and genotype calls for each identified SNP at the GPX3 locus.Click here for fileGPX4 Prettybase File. This file is in prettybase format and provides SNP location in the reference sequence, SNP500 sample ID number, and genotype calls for each identified SNP at the GPX4 locus.Click here for fileSEPP1 Prettybase File. This file is in prettybase format and provides SNP location in the reference sequence, SNP500 sample ID number, and genotype calls for each identified SNP at the SEPP1 locus.Click here for fileTXNRD1 Prettybase File. This file is in prettybase format and provides SNP location in the reference sequence, SNP500 sample ID number, and genotype calls for each identified SNP at the TXNRD1 locus.Click here for fileRegions Covered in Resequencing. This file provides the information on the regions of the reference sequence for which we were able to get good quality sequence data, allowing identification of gaps in sequence coverage or regions that could not be resequenced.Click here for fileGPX1 Genomic Sequence. This file provides the chromosomal location and genomic DNA sequence for GPX1 and is the reference used map the location of SNPs in the prettybase file and PCR primer pair file.Click here for fileGPX2 Genomic Sequence. This file provides the chromosomal location and genomic DNA sequence for GPX2 and is the reference used map the location of SNPs in the prettybase file and PCR primer pair file.Click here for fileGPX3 Genomic Sequence. This file provides the chromosomal location and genomic DNA sequence for GPX3 and is the reference used map the location of SNPs in the prettybase file and PCR primer pair file.Click here for fileGPX4 Genomic Sequence. This file provides the chromosomal location and genomic DNA sequence for GPX4 and is the reference used map the location of SNPs in the prettybase file and PCR primer pair file.Click here for fileSEPP1 Genomic Sequence. This file provides the chromosomal location and genomic DNA sequence for SEPP1 and is the reference used map the location of SNPs in the prettybase file and PCR primer pair file.Click here for fileTXNRD1 Genomic Sequence. This file provides the chromosomal location and genomic DNA sequence for TXNRD1 and is the reference used map the location of SNPs in the prettybase file and PCR primer pair file.Click here for file


Pediocin-like bacteriocins, ribosomally-synthesized antimicrobial peptides, are generally coexpressed with cognate immunity proteins in order to protect the bacteriocin-producer from its own bacteriocin. As a step for understanding the mode of action of immunity proteins, we determined the crystal structure of PedB, a pediocin-like immunity protein conferring immunity to pediocin PP-1.The 1.6 Å crystal structure of PedB reveals that PedB consists of an antiparallel four-helix bundle with a flexible C-terminal end. PedB shows structural similarity to an immunity protein against enterocin A (EntA-im) but some disparity to an immunity protein against carnobacteriocin B2 (ImB2) in both the C-terminal conformation and the local structure constructed by α3, α4, and their connecting loop. Structure-inspired mutational studies reveal that deletion of the last seven residues of the C-terminus of PedB almost abolished its immunity activity.The fact that PedB, EntA-im, and ImB2 share a four-helix bundle structure strongly suggests the structural conservation of this motif in the pediocin-like immunity proteins. The significant difference in the core structure and the C-terminal conformation provides a structural basis for the classification of pediocin-like immunity proteins. Our mutational study using C-terminal-shortened PedBs and the investigation of primary sequence of the C-terminal region, propose that several polar or charged residues in the extreme C-terminus of PedB which is crucial for the immunity are involved in the specific recognition of pediocin PP-1. Listeria spp. [Bacteriocins are ribosomally-synthesized antimicrobial peptides produced by bacteria. Most bacteriocins are generally synthesized as prepeptides and can be classified into several classes depending on their post-translational processing and the mode of action -3. Type ria spp. ,9. NMR sria spp. -12: the ria spp. ,14, whilria spp. ,15,16.In general, type IIa bacteriocins are coexpressed with cognate immunity proteins (pediocin-like immunity proteins) composed of 88~115 amino acids, in order to protect the organism from the antimicrobial activity of its own bacteriocin -21. It iPediococcus spp. is a representative of the type IIa bacteriocins. Among pediocins, pediocin PA-1 and its operon of Pediococcus acidilactici PAC1.0 is the most well known [pedA, pedB, pedC, and pedD) from Pediococcus pentosaceus CBT-8, which is isolated from the Korean traditional fermentative food, kimchi. Based on the remarkable degree of sequence identity (~99%) of PedB of P. pentosaceus to that of P. acidilactici, we regarded PedB as the immunity protein against pediocin PP-1. PedB is a small positively charged protein with 112 amino acids and belongs to subgroup A. No transmembrane helices are predicted from sequence analysis. To provide a structural insight into the function of PedB, we determined the three dimensional structure of PedB at 1.6 Å resolution and suggested the basis for the structural classification of pediocin-like immunity proteins, and examined the role of the C-terminal end of PedB.Pediocin secreted by ll known ,29. ReceP. acidilactici (only one conservative substitution (Glu for Asp) at a position of 104 exists between two PedB proteins for pediocin PP-1 and pediocin PA-1) . Because the pedB gene doesn't have its own promoter, we used the promoter of sodA encoding Mn-containing superoxide dismutase in Bacillus subtilis [L. sakei harboring the pedB gene was about 26 times less susceptible compared to the strain possessing only the sodA promoter, indicating that PedB successfully acted as the immunity protein to pediocin PP-1  Figure . To inve4 strain  and testsubtilis ,31. L. s Figures  &4. Howeα atoms).The native PedB and selenomethionine-substituted PedB mutant (L24M) (See Methods) were crystallized under similar conditions. The initial model was built according to the phased electron density map obtained by using anomalous scattering differences of selenium atoms in the selenomethionyl L24M crystal. A final model for native PedB was refined subsequently to 1.6 Å resolution against native data. The native PedB crystals contain one molecule in the asymmetric unit. Out of the total 112 amino acids in native PedB, residues 7–93 are visible in the crystal structure. No significant structural difference was observed between the native and mutant PedB  of the second helix of PedB and EntA-im forms a 3 Figures . The mos Figures . Two hyd Figures  & 3a. As1–93, PedB1–96, PedB1–99, PedB1–102, PedB1–105, and PedB1–108) into L. sakei and tested the susceptibility to pediocin PP-1.The fact that PedB and EntA-im have almost identical four-helix bundle structures but their cognate bacteriocins are not identical Figure , led us 1–108) resulted in severe loss of immunity compared to the wild type PedB, and deletion of seven residues (PedB1–105) almost completely abolished the activity. Since the flexible C-terminal end is not involved in the folding of PedB, the deletion cannot disturb the core structure of PedB. Actually, in the far-UV spectral region (190~250 nm), the CD spectra of PedB variants were highly similar to that of the wild type PedB  share a four helix bundle structure. In addition, the subgroup B immunity proteins, such as sakacin P and piscicolin 126 , are alsP. pentosaceus CBT8. L. sakei NCFB 2714 containing a plasmid with or without inserted PedB variants was used as an indicator strain for assaying immunity activity against pediocin PP-1. For overexpression and selenomethionine-labelling, E. coli strain BL21 (DE3) and B834 (DE3) (Novagen) were used respectively. P. pentosaceus and L. sakei were grown in MRS broth (Difco) at 30°C, and E. coli strains were grown in Luria-Bertani medium at 37°C. Chloramphenicol (Sigma) was used at 5 μg ml-1 for L. sakei and 34 μg ml-1 for E. coli. Ampicillin (Sigma) was used at 100 μg ml-1.Pediocin PP-1 was produced by pedB gene under the control of the promoter of sodA, DNA fragments containing the pedB ORF were amplified by polymerase chain reaction (PCR) using two primers possessing NdeI and BamHI site, respectively, and cloned into pGEMTeasy (Promega) generating pGEMT-pedB. EcoRI-BamHI fragment of pGEMT-pedB was ligated into pGEX4T-1, generating pGEX-pedB. The sodA promoter was amplified by PCR using B. subtilis genomic DNA as a template and cloned into pGEMTeasy, generating pGEMT-psodA. This plasmid was digested with SalI-NdeI and inserted into the pGEX-pedB, generating pGEX-psodA:pedB. SalI-BamHI fragment of this plasmid was ligated into pSET5s [SalI-EcoRI fragment of pGEMT-psodA was also cloned to pSET5s, generating pSET-psodA. To construct plasmids for shortened PedB variants, each PedB variant was amplified by PCR with proper primer pairs containing stop codon at positions of 94, 97, 100, 103, 106, and 109, respectively. And then, 0.35 kb NdeI-BamHI fragments of the PCR products were exchanged with pedB in pGEX-psodA:pedB. Finally, SalI-BamHI fragment of these plasmids were moved to pSET5s.In order to construct a plasmid expressing the wild type o pSET5s , generatL. sakei using Bio-Rad gene pulser as described by Aukrust et al. [L. sakei was made electrocompetent as follows. Cells were grown up to OD600 of approximately 0.5 in MRS broth with 2% (w/v) glycine, and then were washed twice with 1 mM MgCl2 followed by washing twice with 30% (w/v) polyethylene glycol 1500.The expression constructs were electroporated into the t et al. . BrieflyE. coli and purified as described previously [E. coli B834 (DE3) (Novagen) strain was used as a host for plasmid transformation. The selenomethionyl PedB-L24M protein was purified by an identical procedure to that for the wild-type PedB. The purified SeMet PedB-L24M was concentrated to approximately 10 mg ml-1 for crystallization.PedB was expressed in eviously . To ensuP62; a = b = 62.2 and c = 39.9 Å, a monomer in asymmetric unit) were grown and a 1.6 Å data set was obtained with a Bruker Proteum 300 CCD at Beamline 6B at Pohang Light Source as described previously [Native PedB crystals  and refined with CNS [Rfree of 0.276 and Rwork of 0.256 in the resolution range of 20-1.9 Å. The native PedB structure was solved by molecular replacement with CNS using the L24M structure as a search model, and refined to Rfree of 0.225 and Rwork of 0.192 in the resolution range of 20-1.6 Å. The refinement statistics for the native PedB structure are summarized in Table PROCHECK [The selenomethionyl PedB-L24M crystals were produced under the condition containing 2.4 M ammonium sulfate, 0.3 M sodium chloride, and 0.1 M MES (pH 6.0). These crystals belonged to space group am suite . The str RESOLVE . Initialwith CNS  to an RfPROCHECK . The RamKav, where Kav = (Ve - Vo)/(Vt - Vo): Ve, elution volume; Vo, void volume; Vt, total bed volume. Kav of PedB determined by using the same column was compared to the profile of protein standards: bovine serum albumin (67 kDa), ovalbumin (43 kDa), chymotrypsinogen A (25 kDa), and ribonuclease A (13.7 kDa).Purified recombinant PedB and low molecular mass standards (Amersham Biosciences) were applied on a Superdex 75 16/60 column (Amersham Biosciences) preequilibrated with 20 mM Tris-HCl (pH 8.0) containing 150 mM NaCl. A standard curve was generated by plotting the logarithm of molecular mass of standard proteins against their E. coli, each PedB variant was amplified by PCR with proper primer pairs containing stop codon at positions of 94, 97, 100, 103, 106, and 109, respectively. And then, 0.35 kb BamHI-SalI fragments of the PCR products were cloned into pGEX-4T-1. Each PedB variant was expressed and purified by an identical procedure to that for the wild type PedB.To construct plasmids for expression of shortened PedB variants in All of the CD experiments were performed on a Jasco J-715 spectropolarimeter using a 0.2 cm path-length cell, with a 1 nm bandwidth and 4 s response time. Far- and near-UV CD spectra were collected from 250 to 190 nm and from 360 to 240 nm, respectively, with a scan speed of 100 nm/min and 1 nm step resolution. Three individual scans were added and averaged. A buffer containing 20 mM Tris-HCl (pH 7.4) and 150 mM NaCl was used for CD spectra.P. pentosaceus containing pediocin PP-1. After the ammonium sulphate was added to the culture supernatant of P. pentosaceus to 80% saturation, the precipitate was dissolved in 10 mM sodium acetate pH 5.0. When this ammonium sulphate-precipitated fermentate was applied to the spot test against lawns of Listeria monocytogenes and Listeria innocua on agar media, it showed strong inhibitory effect  was defined as the concentration of bacteriocin that inhibited growth of the indicator strain by 50%. The MICs presented here are the results of three to four independent measurements. Transformed indicator strains were grown in the presence of 5 μg ml-1 chloramphenicol to maintain the plasmids in the cells. The immunity activity of PedB was calculated by comparing MIC values for a strain expressing the PedB variants with those for the strain possessing only the sodA promoter.Bacteriocin activity was measured using a microtiter plate assay system as described by Nissen-Meyer et al. . BrieflyIK carried out the protein expression, purification, and crystallization experiments, participated in structure determination, construction of PedB-expressing plasmids, and bacteriocin assay, and wrote the initial version of the manuscript. MK carried out the site-directed mutagenesis, mutant purification, crystallization and participated in data analysis, structure determination, and electroporation. JK participated in the electroporation and bacteriocin assay. HY participated in data analysis and design of this study. SC and SK conceived the project, supervised the project, participated in its design and coordination, and helped to draft the manuscript. All authors read and approved the final manuscript.Detection of pediocin PP-1 activity in the absence and presence of PedB. The ammonium sulphate-precipitated fermentate was applied to the spot test against lawns of L. innocua and L. monocytogenes on agar media.Click here for file


Single nucleotide polymorphisms (SNPs) have been increasingly utilized to investigate somatic genetic abnormalities in premalignancy and cancer. LOH is a common alteration observed during cancer development, and SNP assays have been used to identify LOH at specific chromosomal regions. The design of such studies requires consideration of the resolution for detecting LOH throughout the genome and identification of the number and location of SNPs required to detect genetic alterations in specific genomic regions. Our study evaluated SNP distribution patterns and used probability models, Monte Carlo simulation, and real human subject genotype data to investigate the relationships between the number of SNPs, SNP HET rates, and the sensitivity (resolution) for detecting LOH. We report that variances of SNP heterozygosity rate in dbSNP are high for a large proportion of SNPs. Two statistical methods proposed for directly inferring SNP heterozygosity rates require much smaller sample sizes (intermediate sizes) and are feasible for practical use in SNP selection or verification. Using HapMap data, we showed that a region of LOH greater than 200 kb can be reliably detected, with losses smaller than 50 kb having a substantially lower detection probability when using all SNPs currently in the HapMap database. Higher densities of SNPs may exist in certain local chromosomal regions that provide some opportunities for reliably detecting LOH of segment sizes smaller than 50 kb. These results suggest that the interpretation of the results from genome-wide scans for LOH using commercial arrays need to consider the relationships among inter-SNP distance, detection probability, and sample size for a specific study. New experimental designs for LOH studies would also benefit from considering the power of detection and sample sizes required to accomplish the proposed aims. More than 99% of each person's genome is identical to everyone else's. Many of the differences involve single base pairs, termed single nucleotide polymorphisms (SNPs). SNPs are used as genetic markers to facilitate identification of disease-causing genes, as well as in cancer studies by aiding in determining which regions of the genome may be lost (LOH) or amplified during neoplastic progression. One drawback to SNPs is their low informativity: a SNP is only informative if it is polymorphic on the two different alleles found on each chromosome of a pair; and if there is not an informative SNP in the region of genome of interest, it is impossible to detect alterations occurring there through LOH. A common solution to this problem is to use arrays containing hundreds of thousands of SNPs to ensure adequate coverage, but for many studies this is prohibitive on a cost and sample amount basis. In addition, SNP distribution itself can constrain the size of loss that can be reliably detected at the population level. We examined the relationship between chromosome loss sizes and detection probability of LOH genome-wide. The study provides useful information for researchers designing LOH-related studies and evaluating results obtained from such studies. Single nucleotide polymorphisms (SNPs) are common DNA sequence variations and have been widely investigated for their roles in disease causation  or assochttp://www.ncbi.nlm.nih.gov/SNP), the heterozygosity (HET) rates estimated for a substantial number of SNPs have large estimated variances, likely due to small sample sizes, among other reasons [Detection of LOH requires SNPs to be heterozygous . In the largest public SNP database, dbSNP  directly by population allele frequencies [These results indicate that a significant number of the SNPs in dbSNP have large estimated variances, which would not provide enough precise information for designing studies requiring the accurate estimation of SNP HET rates . Traditionally, for diallelic alleles with quencies . This merh(1 − rh))/(N − 1), where N is the sample size. N = 100), the confidence interval or CVs are quite large for all values of HET rates listed, particularly for lower HET rates . With 500 subjects tested, the estimated CI and variance are small, but such a sample size is prohibitively large for many studies.Using hypothetical parameters for true HET rates and sample sizes, we first show the relationships among true HET rates, estimated HET rates, their estimated variances, and sample sizes using the score method with continuity correction  . The varrsh versus a prespecified HET rate value rh0 . With a given power and sample size n, we have:We introduce two different approaches to deal with the unrealistically large sample size requirement. In using SNPs to evaluate LOH in a specific chromosomal region, it is desirable that the HET rates of selected SNPs used in the region be higher than a specific value to increase the probability that at least one SNP will be informative for each patient. Therefore, the question is to test the statistical hypothesis for the HET rate of a specific SNP βZ, where αZ and βZ are the 100(1−α)th and 100(1−β)th percentile of the standard normal distribution.To get sample size, we have: H0 is reasonably small in most cases; e.g., when rh0 = 0.2, and rsh = 0.35, only 50–72 subjects need to be tested. However, when a SNP HET rate rsh is near the desired threshold value rh0, the required sample size becomes much larger.Using h using the hypothesis 0: h = hH0 versus H11: h = h, (h1< h0). The likelihood ratio isα, and type II error  level β, (power = 1 − β ), it has been shown that sample testing should continue if lnln) < lnln(λ) reaches or passes beyond the two bounds, then sample testing should stop. The hypothesis H0 will be accepted when ln ln(λ), or H0 will be rejected and H1 accepted when ln(λ) ≥ lnh,0h1, α, β, and the underlying HET rate h of a specific SNP. In the SPRT approach, for fixed h, α, and β, the ASN (average sample number) depends on h0 and h1. h0 = 0.3, and h1 = 0.2 against various true (sample) SNP HET rates h. For example, if the true SNP HET rates under testing are h = 0.4 or above, approximately 15 to 40 subjects need to be tested, on average, to make a decision on whether h = h0, and, under the most optimistic situations, only four subjects are necessary to determine the HET rate regarding hypothesis H0. Depending on the goals of a study, the SPRT method could be used to significantly reduce the testing sample size required for SNP HET rate inference  level th (0 < th ≤ 0.5), then k SNPs are needed such that 1 − (1 − th)k ≥ threshold  to guarantee at least one or more heterozygous SNP will be in the lost segment. However, th is not constant across all SNPs (k SNPs are needed to have:k) to satisfy α level  which guarantees that the left-hand-side of α) 100% of the time. The probability density distribution of k is shown in k) is 10, and for a SNP HET rate ≥0.4, the required number of SNPs (k) is 9 .We also examined the number of SNPs needed for reliable detection of LOH for random chromosomal regions of a specific length assuming the SNP HET rate distribution shown in all SNPs . TherefoNP rates , Monte CGiven the non-random distribution pattern of SNP HET rates in the genome, the next obvious question is how long (in base pairs) must a random chromosomal segment be to contain one or more heterozygous SNPs so that LOH is detected with a high probability . Based on HapMap data, we used three approaches to ascertain this relationship, including simulation using the fitted dbSNP HET rate distribution pattern in s be the size (in nucleotide base pairs) of the DNA being lost on a chromosome, d the distance (in nucleotide base pairs) between two SNPs (inter-SNP distance), and heth the SNP HET rate, assuming the SNPs to be evenly distributed. If s ≤ d, the probability of the lost DNA segment containing a SNP can be estimated as p = d = phhetp . The probability that at least one SNP is heterozygous can be estimated as dp = 1 − (1 − heth)k. The relationships are shown in Many publications ,18–20 ha = phhet A. When sFinally, we used a bootstrap method to randomly sample the heterozygous SNPs on Chromosomes 1, 3, 9, and 17 genotype data within a 500 kb window in two human subjects from the HapMap database. Using SNPs for LOH detection is of great value for chromosomal instability studies and cancer risk prediction, but a better understanding of the resolution of the technique and how to select an informative panel of SNPs for a given application is needed. The variances of SNP HET rates are large for a large number of SNPs. In most cases, this is likely to be due to the small sample sizes used for estimation of allele frequencies in most cases. Differences in ethnic groups might also contribute to the variance of averaged HET rates. Relatively large sample sizes are needed to accurately estimate SNP HET rates using traditional methods. In order to reduce sample size for practical use, we presented two statistical methods that could be used to determine the number of individuals in the population that would need to be examined to determine if a SNP HET rate was above or below a specified threshold. The Monte Carlo simulation was performed on SNPs in dbSNP with HET rate estimation values ≤0.5 as well as all SNPs, with essentially no change in the conclusion of the study . Only 0.r2), the estimation of r2 and variance of r2 themselves are subject to the effects of sample size and evolutionary history of specific SNPs [r2 should be considered when r2 are used for inferring SNP HET rates if a study has stringent requirements .Based on specific study goals or technologies, more study specific methods such as truncated SPRT schemes  could pofic SNPs . TherefoWe did not distinguish coding and non-coding regions of the genome in this simulation since Cargill et al.  reportedp > 0.95 or 0.99) detecting LOH of segment sizes smaller than 50 kb (Our study showed that a region of LOH greater than 200 kb could be detected with high probability (>90%), with losses smaller than 50 kb having a substantially lower detection probability when using all SNPs currently in the HapMap database . Higher an 50 kb . Finallyan 50 kb ; howeverUsing dbSNP and HapMap data, this study evaluated the distribution of SNP HET rates and resolution of LOH genome wide. The results of this study have two important implications that might improve design and interpretation of future genome wide LOH screens of cancers and premalignant tissues. First, retrospective review of previous genome-wide LOH screens indicate that technology limitations  used in the experiments could have missed significant numbers of LOH events that were below the resolution of the SNP array –31. By uLOH has been frequently proposed as a candidate biomarker for cancer risk prediction. The ability to detect an LOH event will depend on informativity, SNP density, and the size of the LOH event. Our results could improve sample size calculations for design of future LOH studies. If one would like to detect the effect of an LOH event on the risk of progression to cancer, then the sample size depends on the LOH detection probability. For example, in a study with a 1:5 ratio of cases and controls, a minimum detectable relative risk of the LOH of 5, a statistical detection power 0.9, and an LOH prevalence rate of 30% among informative subjects, at least 23 cases and 117 controls will be needed if the LOH detection probability is 100% (large region loss or high density of informative SNPs). However, if the LOH detection probability is 0.7 or 0.3, for example, , then at least 44 cases and 190 controls or 116 cases and 468 controls will be needed, respectively.All the results obtained in this analysis are based on the assumption that heterozygous SNPs are required for detection of LOH. New technologies are emerging that could be used to detect chromosome copy number changes (including deletion) using homozygous SNPs with a reasonably high accuracy ,34. Howeftp://ftp.ncbi.nih.gov/snp/organisms/human_9606/database/organism_data/). HapMap SNP data for the human genome were downloaded from the HapMap Web site (July 2006 release) (http://www.hapmap.org/genotypes/). We only used the CEU population (Utah residents with Northern and Western European ancestry) data from HapMap. Our methods can easily be extended to other ethnic group data. The estimated SNP HET rates >0.5 were dropped from the analysis of HET rate distribution. The estimated variances for SNP HET rates were directly obtain from dbSNP.The data for SNPs HET rates were downloaded from dbSNP (build 126)  which guarantees that the left-hand-side of α) 100% of the time (Data from dbSNP were used to summarize the HET rate distribution pattern of SNPs  and evalthe time . The simthe time  was donethe time  red lineTo examine the spatial pattern of LOH detection probability along a chromosome , we chos