It uses a haplotype-based strategy to identify reliable SNPs; it is optimized for the analysis of current RNA-seq data; but it can also be used on genomic DNA sequences derived from next-generation sequencing experiments. QualitySNPng does not require a sequenced reference genome and delivers reliable SNPs for di- as well as polyploid species. The tool features a user-friendly interface, multiple filtering options to handle typical sequencing errors, support for SAM and ACE files and interactive visualization. QualitySNPng produces high-quality SNP information that can be used directly in genotyping by sequencing approaches for application in QTL and genome-wide association mapping as well as to populate SNP arrays. The software can be used as a stand-alone application with a graphical user interface or as part of a pipeline system like Galaxy. Versions for Windows, Mac OS X and Linux, as well as the source code, are available from  Recent developments in sequencing technology have revolutionized genetic research, as vast amounts of sequencing data are now becoming available. From this data, single-nucleotide polymorphism (SNP) information can be extracted that is useful for genetic analysis, including quantitative trait locus (QTL) mapping and genome-wide association studies ,2. Althode novo assembled sequence alignments as input, it can also be used for species without a reference genome. The original QualitySNP was developed and optimized for Sanger sequenced expressed sequence tag (EST) data; however, the nature of DNA and RNA sequencing has changed drastically during the past 6 years, making an update necessary. Here, we present QualitySNPng that was specifically tuned to identify SNPs in data from the current next-generation sequencing (NGS) platforms. It features a graphical user interface (GUI), supports the popular SAM format (Since its publication, the QualitySNP pipeline for SNP detection in diploid and polyploidy species  has beenM format , generalM format . AdditioM format .de novo assemblers like CABOG (QualitySNPng takes as input a sequence alignment file in SAM  or ACE  format wke CABOG  and PCAPke CABOG . The Quake CABOG . The firke CABOG , is usedke CABOG . Also a The results of the SNP calling can be viewed directly using the GUI, and they are also saved in structured text files for later reference or further processing. The different contigs from the input sequence alignments are listed in a table showing the number of SNPs, the reads and the haplotypes. The haplotype count in the table is corrected for fragmented haplotypes by taking the maximal number of haplotypes that is found per SNP position. Fragmentation of haplotypes may occur and is caused by SNPs that are too far apart to be linked to one allele by a single-sequence read or a read pair, see A selected contig will show a window with the aligned reads and the SNPs indicated, a table with the haplotypes and their alleles per SNP position and a table showing the alleles for the different samples in the input data . For thiManual inspection of the read alignment together with the haplotype overview gives insight in the quality of the alignment, local read coverage and positions of the SNPs. Based on this visual inspection, one can decide to alter the stringency of the filter settings and rerun the SNP calling. The reads can be sorted on start position or per haplotype and can be viewed at different zoom levels.For the creation of a SNP array, marker SNPs can be selected and exported with flanking sequence of a specified length as a structured text file that can be imported into a standard spreadsheet program or an assay design program.To avoid problems in SNP scoring, we suggest selecting markers from contigs that have no more than the maximum expected number of haplotypes, i.e. two for diploid species, as contigs with more haplotypes may contain paralogous sequences. To further increase the chance of obtaining markers that will perform well on arrays, one could use the BLAST program  to elimiQualitySNPng was written in C++ using the Qt toolkit. The same executable file can be used interactively with the GUI, or as a command line tool for inclusion in analysis pipelines to be run on a compute server. The software can be compiled and runs on the Windows, Mac OS X and Linux operating systems. The output data are saved as CSV text files and can be reloaded for later analysis using QualitySNPng, or processed by custom scripts for further analysis.We believe there is a strong need for user-friendly software tools that allow biologists to directly analyse and visualize their data. QualitySNPng is a versatile tool that combines SNP detection and genotyping with interactive visualization of the results. The GUI with its pre-set filter options is easy to use and also highly configurable for specific needs. QualitySNPng is routinely used in-house for marker SNP identification in several projects . In one the Netherlands Genomics Initiative/Netherlands Organization for Scientific Research. Funding for open access charge: Wageningen University and Research Centre.The Netherlands Consortium for Systems Biology, which is part of Conflict of interest statement. None declared.


Streblospio benedicti, is an ideal system to study the evolutionary consequences of larval life history mode because the species displays a rare offspring dimorphism termed poecilogony, where females can produce either many small offspring or a few large ones. To further develop S. benedicti as a model system for studies of life history evolution, we apply 454 sequencing to characterize the transcriptome for embryos, larvae, and juveniles of this species, for which no genomic resources are currently available. Here we performed a de novo alignment of 336,715 reads generated by a quarter GS-FLX (Roche 454) run, which produced 7,222 contigs. We developed a novel approach for evaluating the site frequency spectrum across the transcriptome to identify potential signatures of selection. We also developed 84 novel single nucleotide polymorphism (SNP) markers for this species that are used to distinguish coastal populations of S. benedicti. We validated the SNPs by genotyping individuals of different developmental modes using the BeadXPress Golden Gate assay (Illumina). This allowed us to evaluate markers that may be associated with life-history mode.Next-generation sequencing technology is now frequently being used to develop genomic tools for non-model organisms, which are generally important for advancing studies of evolutionary ecology. One such species, the marine annelid  Planktotrophic species produce large numbers of tiny eggs where larvae feed in the plankton for weeks to months, potentially traveling 100 s of kilometers, resulting in extended ranges and high gene flow Lecithotrophic taxa produce fewer, larger eggs that are maternally endowed with enough energy to complete development; their non-feeding larvae have reduced dispersal, but higher survival. By influencing gene flow, larval type has sweeping micro- and macroevolutionary impacts on a lineage Investigating trade-offs in life history of marine taxa has greatly informed our understanding of macroevolutionary outcomes such as taxonomic diversification, geographic range size and rate of extinction . UnderstStreblospio benedicti is a particularly interesting species for exploring the evolution of contrasting life histories because there are two distinct yet heritable larval types that can occur together in coastal populations along the US East Coast poecilogonyS. benedicti can produce either hundreds of small eggs that develop into planktotrophic feeding larvae with a long development time (∼150 eggs of 60 µm diameter), or tens of large eggs that develop lecithotrophically, maturing quickly while feeding on maternally provided yolk (∼40 eggs of 100 µm diameter) . We have two main goals for this transcriptome analysis: (1) We address the utility of applying molecular population genetic indices to whole transcriptome data and assess the match of these inferences to known characteristics of the source population. Specifically, for a non-normalized transcriptome, we have the opportunity to investigate the ability of summary statistics based on the site frequency spectrum (SFS) to reflect the relative influences of evolutionary mechanisms such as historical demography and selection (reviewed in S. benedicti for subsequent studies.To develop 4 sequence submissions and 515 popsets for the Annelida compared to 1.4×106 protein and 1.2×104 popsets for the Arthropoda as of Feb 2011), we are as yet unable to annotate this transcriptome to the degree of other model systems; the genome for Capitella teleta has just been released (among the first for the Lophotrochozoa) but C. teleta is more than 400 million years divergent from S. benedictiGiven the paucity of genomic data for marine polychaetes , and ds-cDNA was prepared according to the SMARTer Pico PCR cDNA Synthesis Kit (Clontech). The cDNA library was not normalized prior to sequencing, and sequence data were generated using a quarter plate GS titanium chemistry FLX Next-gen (Roche 454) by the University of York Technology Facility.de novo in SeqMan sequence assembler (DNASTAR) using the default alignment settings . We determined this set of parameters was appropriate based on preliminary analyses with a range of parameter sets  that did not greatly change the number of quality contigs generated (results not shown).Sequence read files were aligned T) omputee value of 1.0 e−6T and F* categories.To identify potential signatures of selection across individual assembled contigs, we calculated summary statistics based on the SFS, which is the distribution of nucleotide frequencies at a large number of loci. We tested for contigs that represented outlier SFS patterns by calculating Tajima's D  for identified SNPs to the predicted MAF distribution for a population under neutrality. To do this, we initially found all SNPs from the transcriptome using PipeMeta a priori criteria. To design the most informative markers, we wanted to balance our choice of SNPs between highly conserved housekeeping genes and genes that are more typically associated with ecological and physiological function. For the latter category we were specifically interested in SNPs that occur in gene regions known to exhibit high protein diversity in natural populations  against the NCBI nucleotide database, and others with less informative BLAST hits, which may be due to omission of annelid genes from NCBI's database, or significant sequence divergence from taxa currently represented in GenBank. SNPs with low MAFs are less likely to be represented in the transcriptome, and therefore choosing SNPs based on the MAF in the SP sample will introduce ascertainment bias when applied to other populations Choosing SNPs in regions with good BLAST scores may bias our selection towards genes that are highly conserved across a diverse set of organisms and thus less variable in general. To mitigate ascertainment bias, we chose to balance the proportion of SNPs with good BLASTn hits  to determine all potential ORFs for each contig that contained one of the known SNPs. We determined the correct ORF by selecting the longest possible ORF and performing a BLASTx search on the resulting protein coding sequence and verified that it matched the initial nucleotide BLASTn result. When this was possible, and the SNP occurred within an open reading frame, the SNP was putatively scored as either a synonymous or non-synonymous substitution.We used BLASTn to determine whether the nucleotide substitution resulted in synonymous or non-synonymous changes in the protein sequence. We used NCBI's open reading frame (ORF) finder  and genotyped using the BeadXPress Golden Gate assay (Illumina) to verify the 96 SNPs. All 46 of the SP individuals are lecithotrophic and of the 50 BR individuals 17 are definitive lecithotrophs (which were observed releasing lecithotrophic larvae) and 33 are putative lecithotrophs (which appeared to be brooding lecithotrophic larvae that were not ready for release). These individuals were used to verify the 96 SNPs. Whole specimen genomic DNA was isolated as in rlequin 3.5 Genotypes were assigned and annotated using GenomeStudio (Illumina) with a default SNP call threshold of 0.30 . The call threshold is based on the distance of an individual read from the center of the SNP call cluster. We also evaluated a more stringent call threshold of 0.45, and while this significantly reduced the successful call rate for each SNP, it did not change the results of subsequent population analyses (data not shown). For the two populations genotyped and at all loci, we calculated observed and expected heterozygosity, as well as standard F-statistics, using AThe 454 pyrosequencing generated 336,715 reads of ∼400 bp average length. The SeqMan alignment produced 7,222 total contigs with an average contig length of 436 base pairs and 3.08× coverage. Singleton reads were excluded. PipeMeta found 2,817 SNPs  in total from the 7,222 contigs, although 6,940 contigs contained no nucleotide substitution SNPs. SNPs that failed the criterion of having no more than two possible nucleotides were excluded from our analysis.T values (i.e. had sufficient polymorphism and coverage), no DT statistics were significant relative to a null coalescent simulation model. The average DT for the transcriptome was −0.574 with a SD of 0.774, as opposed to previously analyzed mtCOI data from East and West Cost populations that had a DT of −1.98 T contigs (data not shown). We found no difference in F* except that 71 more contigs occurred in the positive tail of the distribution. The average F* across the data set was 0.51 (SD 0.92) and there was still no apparent difference in the distribution of GO terms for contigs with excessively positive and negative F*.Of the contigs that produced DT may be consistent with purifying or variable selection  between the observed and neutrally simulated values . There ie-value of less than 10−5). Because none of the contigs had significant DT values, and there was no difference in the functional categories assigned to genes that were in either tail of the DT distribution, we could not confidently assign contigs to putatively neutral or selective classes. Because we wanted to equally represent each of our MAF categories within the framework of our selection criteria, we selected 19 SNPs with a MAF between 30–35%, 24 between 36–40%, 31 between 41–45% and 22 between 46–50%.Application of our minimum depth and frequency criterion further reduced the number of candidate SNPs to 685. Finally, the Illumina primer quality scores designated 266 SNPs with an optimal rank (1) for successful primer design. Genes of functional interest, including developmental gut genes and heterozygous allozyme loci, were not represented in any of the contigs with a high probability  suggesting strong differentiation between the two populations. The FIT for all markers is 0.197 (p<0.001), which also indicates population differentiation, although the FIS (inbreeding coefficient within a population) is not significant. Significant nonrandom associations between loci pairs differed between the two populations. Of the 3570 pairwise comparisons between the 84 loci, in BR 1169 comparisons (32%) showed significant linkage disequilibrium, while in SP, only 184 loci (∼5%) were in significant linkage disequilibrium.Of the 96 SNPs originally chosen, ten of them (10.4%) produced no genotype calls in the assay and two were monomorphic and removed from subsequent analyses. The remaining 84 SNPs were validated [NCBI SRA SRA048717.1]. The average successful call rate for the remaining 84 SNPs was 87% (sd. 25%) of the individuals sampled, where 63 of the SNPs had call rates over 90% and an additional eleven were over 50% . Three aS. benedicti. Because our transcriptome was not normalized, the resultant frequency of site variants is an unbiased estimate of true allele frequencies in the total population, assuming that expression is not dependent on allelic identity. Our EST library was generated from one population, SP, and we therefore expected the SFS to reflect a relatively neutral DT . Therefore the expected fit to HWE will not change with increasing call stringency (data not shown). Interestingly, the proportion of genotyped individuals is not a good proxy for determining which SNPs will drop genotyping calls with increasing stringency. Instead, SNPs with a high expected heterozygosity (He>0.35) may be products of poor genotype assignments and generally drop out of the analysis when call stringency is increased.There are 32 SNPs that were not in HWE in one or both populations. While this could be due to a variety of evolutionary mechanisms at these markers, it is possible that these SNPs have been shifted from expected frequencies due to genotyping call error. When we increased the stringency of the call rate in Illumina's GenomeStudio from 0.30 (default) to 0.45, we found that many of these SNPs (55%) lost genotyping calls completely and 21% of the SNPs did not change their expected heterozygosity at all. Of the remaining SNPs whose expected heterozygosity did change slightly, the difference between observed and expected heterozygosity remained significantly different. This demonstrates that increasing the call rate threshold may remove some SNPs with poor calls, but it does not affect the fixation indices (FIt is important to note the transcriptome was generated from one lecithotrophic population (SP), while the SNP analysis used lecithotrophic individuals from two populations (SP and BR). Therefore the differences we see from this initial SNP analysis may only be due to population differences and not larval-type differences. It is notable that most SNPs have very different allele frequencies in both populations, although both populations are predominately the large-egg type . It is pThe evidence for genetic structure between the two populations suggests one of two possibilities. These populations may have become more evolutionarily divergent since their separation ∼100ya. However it is unlikely that significant divergence between the populations occurred in such a short time. It is far more likely that the West Coast introduction(s) occurred from a population on the East Coast that harbored existing genetic differences from the one studied. It is possible that a few individuals established West Coast populations, or that SP has undergone a bottleneck since introduction. This suggests that there may be more genetic structure on the US East Coast than previously suggested S. benedicti. The SNP markers developed here will significantly improve our ability to investigate life history trade-offs in this species.Using SFS statistics, it seems that a transcriptome with this amount of coverage does not allow for definitive inferences about population demographics without greater coverage or more accurate knowledge of sequencing errors. More informative inferences can be drawn from the SNP markers themselves. The majority of these SNP markers have a high genotyping call rate and will be useful in differentiating genetic structure between geographic and phenotypic populations of Table S1Categories and statistics for 84 SNPs. SNPs that did not meet the criterion for BLAST e- values are left blank. For substitution type, Syn is a putatively synonymous substitution and NS is a putatively nonsynonymous substitution. – designates when an allele is fixed. SNPs that have a He (heterozygosity) over 0.35 generally dropped out all genotyping scores when the call stringency was increased. * is p<0.05, ** is p<0.01, *** is p<0.001.(DOC)Click here for additional data file.


Panicum virgatum L.), a potentially high value biofuel feedstock crop, are currently underway throughout North America, the genetic tools for crop improvement in this species are still in the early stages of development. Identification of high-density molecular markers, such as single nucleotide polymorphisms (SNPs), that are amenable to high-throughput genotyping approaches, is the first step in a quantitative genetics study of this model biofuel crop species. We generated and sequenced expressed sequence tag (EST) libraries from thirteen diverse switchgrass cultivars representing both upland and lowland ecotypes, as well as tetraploid and octoploid genomes. We followed this with reduced genomic library preparation and massively parallel sequencing of the same samples using the Illumina Genome Analyzer technology platform. EST libraries were used to generate unigene clusters and establish a gene-space reference sequence, thus providing a framework for assembly of the short sequence reads. SNPs were identified utilizing these scaffolds. We used a custom software program for alignment and SNP detection and identified over 149,000 SNPs across the 13 short-read sequencing libraries (SRSLs). Approximately 25,000 additional SNPs were identified from the entire EST collection available for the species. This sequencing effort generated data that are suitable for marker development and for estimation of population genetic parameters, such as nucleotide diversity and linkage disequilibrium. Based on these data, we assessed the feasibility of genome wide association mapping and genomic selection applications in switchgrass. Overall, the SNP markers discovered in this study will help facilitate quantitative genetics experiments and greatly enhance breeding efforts that target improvement of key biofuel traits and development of new switchgrass cultivars.Although yield trials for switchgrass ( Panicum virgatumL.) is a perennial C4 warm-season grass native to North America, where it occurs naturally from 55° N latitude to deep into Mexico, mostly as a dominant species of the tall grass prairies. In North America, it has been used for more than 50 years for soil conservation, as a forage crop, and as an ornamental grass Switchgrass  and randomly amplified polymorphic DNA (RAPD) markers to develop genetic fingerprints for the existing cultivars Arabidopsis thalianaThe massive natural distribution and outcrossing life history of switchgrass suggest that the species most likely possesses high levels of genetic variation. Domestication efforts targeting improvement of the feedstock characteristics of switchgrass have only been in progress for the last two decades. Therefore, in many regards, switchgrass is an undomesticated forage grass that has held a dominant ecological role in large parts of the US prairie. This suggests that even registered cultivars are likely to have retained considerable allelic variation that could be utilized to improve the biofuel production potential and efficiency of this species. Conventional breeding efforts of switchgrass are time consuming and challenging. Marker-assisted breeding could reduce the cycle time of this perennial by severalfold. However, assuming it has characteristics similar to other highly outcrossing grasses, such as maize, the traits targeted for domestication/breeding are very likely to be controlled by hundreds of quantitative trait loci (QTL) with small effects Next generation sequencing platforms are transforming the way genomes are analyzed. Although SNP discovery using short-read sequence data is still in its early stages, several studies have already demonstrated that large numbers of high quality SNPs can be identified in a cost-effective manner using these data Here we describe how we coupled EST library and short-read sequencing approaches to discover over 149,000 SNPs in switchgrass. In the process, we developed a consensus-reference sequence of the switchgrass transcriptome of about 87.5 Mbs spanning the gene space of the switchgrass genome to anchor RRGL reads. Furthermore, we investigated population structure within our samples through PCA analysis on ∼4400 SNPs that had complete genotype information across all samples.Switchgrass cultivar seeds and clones were provided by MDC . A list of cultivars and libraries made is shown in Absolutely mRNA Purification Kit™ from Agilent  according to themanufacturer's instructions. Poly-A RNA purity and quantity was assessed with an Agilent Bioanalyzer™ . First-strand cDNA was generated using a Creator SMART cDNA Synthesis Kit™ , according to the manufacturer's protocol. For each cultivar's first-strand cDNA synthesis, 1 µg of poly-A RNA, SMART IV Oligo anda CDS-3M adapter  that incorporates asymmetric SfiI restriction enzyme sites (SfiIA and SfiIB) at the 5′ and 3′ ends of cDNA were used. First-strand cDNA was amplified by a long-distancePCR (LD-PCR) protocol with 15 PCR cycles: 94°C for 7 seconds, 66°C for 30 seconds, 72°C for 6 minutes. Normalization was accomplished by using a Trimmer-Direct cDNA Normalization Kit™  according to the manufacturer's protocol. Briefly, 1µg of amplified cDNA was purified with Qiagen PCR Purification Kit™ , precipitated with ethanol, and dissolved in nuclease free water. cDNA was mixed with 4x hybridization buffer, overlaid with mineral oil, denatured at 98°C for 3 minutes and allowed to renature at 68°C for 5 hours.Double stranded nuclease (DSN) treatment was performed as described in the Evrogen kit manual. The ssDNA fraction remaining after DSN treatment was amplified with primers M1 and M2 for 18 cycles , followed by digestion with SfiI restriction enzyme. After digestion, the library was size fractionatedto >0.5 kb. To create a normalized cDNA library, the digested cDNA was unidirectionally ligated into SfiI-digested pDNR-LIB vector for in situ amplification in bacteria. ElectroMax T1 DH10B cells were transformed with the ligation mixture by electroporation. Via colony counts, the titer of the original library was determined to be about 3×106 cfu/ml. Twenty-four colonies per transformation event were randomly picked and primers pDNR-LIB_forward andpDNR-LIB_reverse  were used to amplify and verify the inserts. Micro-titer plates in 384 well format were used for sequencing the vector inserts in picked colonies from both ends on ABI 3730 instruments  at JGI labs in Walnut Creek, CA.Switchgrass poly-A RNA was isolated from the total RNA using the ESTs from all libraries were processed through the JGI EST pipeline. ESTs were generated in pairs, using a 5′ and 3′ end-read from each cDNA clone. Common patterns at the ends of ESTs, such as vector and adaptor sequences, were identified and removed using a custom software tool developed internally at JGI. Clones were identified as “insertless” if more than 200 bases of vector sequence at the 5′ end or less than 100 bases of non-vector sequence remained in the sequence. Next, ESTs were trimmed for quality using a sliding window trimmer (window size  = 11 bases). Once the average quality score in the window was below the quality threshold , the EST was split and the longest remaining sequencewas retained as the trimmed EST sequence, unless less than 100 bases of high-quality sequence remained, in which case, the sequences were removed from further processing. In the next step, ESTs that contained poly-A or poly-T tails were trimmed andretained unless the remaining sequence was shorter than 100 base pairs, in which case they were discarded. In the following step, ESTs consisting of more than 50% low-complexity sequence  were also removed from the final set of processed ESTs. In cases where more than one read from the same clone in which the same direction existed, the longest high-quality read was retained.malign, a k-mer-based alignment tool Sister ESTs (paired-end reads) were categorized as follows: if one EST was insertless or a contaminant, then, by default, the second sister was categorized as the same and was discarded. However, when retained, each sister EST was treated separately for complexity and quality scores. Lastly, an annotational quality check was conducted by comparing the EST sequences with those in the GenBank nucleotide database to identify contaminants, i.e., non-desirable sequences such as those matching non-cellular and rRNA sequences. Once identified, those sequences were removed from the final set of processed ESTs. For clustering, ESTs were evaluated with Clusters may have more than one consensus sequence for various reasons, including alternative splicing, long-insert sequences, or errors in assembly. Cluster singlets are clusters of multiple reads from the same EST, whereas CAP3 singlets are single ESTs that had joined a cluster but, during cluster assembly, were isolated into a separate singlet consensus sequence. ESTs from each separate cDNA library were clustered and assembled separately and, subsequently, all of the ESTs for all cDNA libraries were clustered and assembled together. For cluster consensus sequence annotation, the consensus sequences were compared to Swissprot protein database using BLASTX and the annotations of the hits were reported.HpaII and reduced-representation libraries (RRLs) were prepared as described in Genomes of one sample from each of 13 cultivars were sampled as described previously. The DNA libraries were digested with the methylation-sensitive restriction enzyme panati suite of programs (http://panati.sourceforge.net/) Upon receipt of the sequences from JGI, each of the SRSLs was checked for reads that started with “CGG” sequence, the overhang left by the restriction enzyme upon digestion. Note that in cases where the second C of the CCGG recognition sequence is methylated, the enzyme cannot cut at the recognition sequence. Also, in cases where low-quality, broken molecules were included in the library, the read sequences will not contain the target overhang sequence. We also applied a prefilter to discard reads containing homopolymers larger than 16 bps. The resulting sequences were saved in fastq format and were analyzed with the panati works as follows:Briefly, panati-build” module, with user-defined options for “word-size,” i.e., the size of the words within the index, and “shift-size”, i.e., the length to shift the word window. Shift-size can be variable between 11 and 16 bps, where smaller shift-step size is slower but more accurate. We have used a 12 bps shift window.To enhance the search speed for mapping reads against the reference, the first step of the analysis process is building a sequence index from the reference sequence. This was implemented in the “The second step involves running the “fastq-qc” module on the SRSLs. This program takes in each read and begins to trim the sequence, starting at the 3′ end of the read, until a user-specified quality score threshold is reached. The minimum length required to include the trimmed read in the assembly can also be specified. Sequences are trimmed based on length and quality. For our specific case, we trimmed the 3′ end of the sequences until a minimum phred-like quality score of 10 or greater was established for the remaining sequence. After trimming, if the sequence size dropped below 12 bps, we discarded the read.The next step was performing an assembly/alignment of the reads against a sequence index using a banded-Smith-Waterman algorithm panati allows forscreening of the SNP positions for the ratio of reference reads to alternate reads, and minimum read quality at the SNP position. For switchgrass, we set the ratio of reference to alternate allele to 1:7 to allow for the influences of dosage effects that can be created due to polyploidy, and set a minimum quality threshold of aphred-like quality score of 20.The fourth step involved filtering the output of the previous step with user-defined criteria to improve the quality of the SNPs that would be reported from the final assembly, using a “combine-samples” program. Preliminary results indicated that the most important factorsaffecting the reliability of a SNP call were the number of reads that carry the alternate allele at the SNP position, as well as the total number of reads at that position. In an inbreeding species, detection of multiple alleles at the SNP position from a single library indicates to problems with assembly. However, switchgrass is highly heterozygous and is polyploid. Therefore, heterozygosity at aSNP position within libraries or, in other words, observation of a SNP within individual libraries is possible. To accommodate this characteristic of the species number of sequences required at the SNP position can be specified with two different run time flags: “-d”to specify the number of alternate read-carrying reads at the SNP position within a library, and “-l” to specify the number of alternate allele-carrying reads across the libraries. We called SNPs with various reliability thresholds for these options, but we are only reporting the results from the SNP callingwith a“–d 3–l 3” option, where we required at least three reads to carry the alternate allele at the SNP position. Further, panati, it filters reads that map to multiple locations. This feature of panati was used as a passive screen against influences of repetitive sequences during assembly.We did not actively screen for transposon and retro-transposon sequences; however, by virtue of the way the alignments and SNP calling are performed with panati parameters used for generating each result file were described with the files on the website.The resulting files were distributed from our website We called genotypes from the raw-read data as follows. A genotype was called only if the read depth for an individual at the SNP locus was greater than or equal to four reads. For the purpose of genotype calling, we treated the SNP data as if they were dominant-marker data. Individuals were called homozygous if they carried four or more reads for one allele and no reads for another allele. Individuals with four or more reads carrying both alleles were called heterozygous. SNPs with missing genotypes were excluded.Principal-components analysis (PCA) was performed using R statistical software package on the genotypes obtained as described above. A set of SNPs that had complete genotype information across all lines was used (4400 SNPs) for the analysis. The SNPs identified were re-coded numerically as 0 for reference allele, 2 for alternate allele and the ratio of reference allele to alternate allele for the heterozygote value.panati for this procedure, with two reads at the SNP position requirements  for calling a SNP.SNPs from the ESTs that were initially used to construct the unigene-cluster consensus sequences  were called by aligning the individual ESTs  against the reference sequence used for the SNP calling with SRSLs. We used panati is currently under review. The program is available upon on request by contacting MHW directly or the corresponding authors. The other software tools used for the study are available by contacting the corresponding authors.The manuscript on de novo assembly algorithm that can operate without a reference sequence to anchor the individual reads. Although several de novo assembly algorithms were released for public use in the last few years, The most significant challenge for SNP discovery in a species without a fully sequenced genome using short-read sequencing is the lack of a reliable Although it is possible to anchor some portion of short-read sequences to a related taxon for which a complete genome sequence is available, this is undesirable for SNP discovery. This is due mostly to confounding of the polymorphisms that discriminate species from each other (i.e. between species divergence) and are not segregating within the target species. This also manifests as an inability to identify the reference alleles (except for the rare occasion where a shared polymorphism exists between two species). Computationally, SNP discovery algorithms often prevent SNP calling against the reference sequence when no reads representing the reference allele are present in the assembly. Therefore, our initial objective for this project was to generate a within-species reference genome sequence, a DNA consensus sequence that would provide a scaffold to map short-read sequences for SNP discovery.See- Plant Material). Since the contribution of any individual cultivar to the EST sequence pool other than Kanlow was low, these sequences were not optimal for identifying differences between cultivars. However, they did provide sufficient coverage across the genome to allow us to createthe required scaffold sequence and to verify the SNP discovery procedure.There are many possible approaches for generating a genomic sequence scaffold , but the fastest, least expensive, and most popular method is to establish a transcriptome scaffold using EST libraries. The previous EST sequencing efforts by Tobias et al. ,See ). The output from the EST clustering of all of the libraries is available at http://www.maizegenetics.net/snp-discovery-in-switchgrassOur next challenge was optimization of the sequence-clustering algorithm and clustering parameters to address issues created by high levels of paralogy in a polyploid genome. The severity of this challenge was conditional on the nature of the polyploidy, i.e. auto-versus allo-, which created whole or partial genome duplications. By adjusting the EST clustering parameters, it was possible to empirically test stringency levels required for the assembly. The optimum threshold was where clusters did not include paralogous sequences, but also did not split allelic variants coming from the same locus. We tested a series of thresholds of percent identity to determine how a variation in this parameter affects clustering. We expected to see a steady increase in the number of clusters up to the level where alleles would start to split, at which point a spike in the total cluster numbers and a reduction in the average number of reads per cluster would occur. In fact, the rate of increase in the number of consensus sequences generated showed a considerable jump between the 98% and 99% identity thresholds (-- mapping efficiency). Since these sequences were identified as belonging to plant taxa [the possibility of cross-sample contamination during sequencing with another plant sample in the sequencing lab was disregarded], the 19% was deemed to be a portion of the switchgrass genome that wasn't covered with the EST libraries. For individual libraries, the largest reference coverage (at 1x sequence depth) was 17.9 Mbps (Shelter) and the smallest was 6.7 Mbp (WS4U) of total sequence. The SNP calling was restricted to the bases where at least three sequences carrying the alternate allele were observed (positions covered with at least 3x depth), and the ratio of reference to alternate alleles was no less than 1:7 and no more than 7∶1 . This crudely compensated for the possible dosage effects of mainly tetra- or octo- ploid genomes, and prevented SNP calling due to sequencing or assembly errors. Across the libraries, total reference-sequence coverage was 42.7 Mbps at 1x minimum depth and 25.4 Mbpsat 3x minimum depth (- library sizes), the depth level required for SNP calling. As expected, more SNPs per Kb were detected from the libraries with higher coverage (average  = 5.73 SNPs/Kb), and thus the contribution of each library to the total SNP pool varied based on its size. Two exceptions to this trend were WS8U (8.37 SNPs/Kb) and WS4U (5.96 SNPs/Kb), which displayed more SNPs per Kb than any of the other cultivars compared to their library sizes (- SNPs/Kb by library size)  The remaining 19% were not found within the reference sequences available (See - PCA for lines). No immediate patterns were detected from plotting the first two principle components, which together explained about 25% of the observed variance. PC1 explained about 14% of the observed variance, while PC2 explained the additional 11%.A small set of SNPswere identified in all 13 lines (4400 SNPs). These were used in a principal components analysis (PCA) to examine population structure in our sample , the overlap between SNPs was expected to be small since transcriptome coverage from a single tissue type is expected to be a small fraction of the genome. As expected, the EST sequences covered over 99% of the reference sequence (transcriptome assembly) at 1x coverage Table3,Until recently, large-scale marker discovery studies have usually concentrated on a small number of organisms with sequenced genomes. With decreasing costs for DNA sequencing and genotyping, coupled with improved NextGen sequencing technologies, we anticipate growing interest in moving rapidly toward large-scale marker discovery and, eventually, genomic selection (GS) studies in organisms for which relatively little genetic data currently exist. There are many orphaned crop species in need of genetic resources, and thousands of germplasm accessions in repositories worldwide that need their genomes indexed to accelerate breeding programs. In the present study, we provide a framework for rapidly and cost-effectively moving from few genetic resources to genome-wide characterization of a species. This would be particularly useful for species that are of cultural or niche-market interest, and that are unlikely to receive extensive funding.We generated 1.6 Gigabases (Gb) of DNA sequence data , and mapHpaII cut-site overhang. Although, on average, only about 25% of all reads contained intact CCG tags, overall, 42% of total reads library found a unique match against the reference sequence, and another 21% was mapped to multiple positions against the reference. This disparity may be due to loss of part or all of the CCG tags during library construction and sequencing. Another much simpler explanation may be that the mapping stringency is low- since we have allowed for 2 mismatches and upto 3 bp indels for mapping, summing up to a total of 20% mismatch allowance during mapping. This mapping stringency is lower than the stringencies used for most species for similar experiments.To determine the level of enrichment for genic regions, we calculated the read fractions in each library that contained the Overall, we were unable to use 58% of the reads  for the assembly. This indicates 41% of the total reads, or approximately 18 million25 bp reads, totaling about 451 Mbp were matched back to 29% of the reference at 1x or higher coverage.panati for several reasons: 1) it could incorporate unigene cluster reference sequences as opposed to requiring genome sequence data; 2) it could integrate both short-read sequence data as well as longer-read Sanger sequence data for SNP calling; 3) due to the high incidence of heterozygosity in the cultivars sequenced, it was necessary to have an algorithm that can call SNPs on reads from individual libraries; 4) due to the polyploid nature of switchgrass, a SNP calling algorithm with adjustable parameters for the expected frequency of the reference allele versus the alternate allele at SNP sites was required; 5) a scalable algorithm that could work for variable percent-identity levels across libraries was needed; and, 6) some of the published SNP-calling algorithms are not successful at distinguishing real SNPs from sequencing errors and, thus, resort to modifying the SNP-calling criteria based on quality scores and genotypic contingency tests  To make SNP calls, we used an in-house assembly and SNP calling algorithm named 2 = 0.89, R). Notable exceptions were WS8U and WS4U, two accessions that showed significant enrichment for diversity despite relatively small library sizes. WS8U diversity was similar to that of KY1625, despite the KY1625 library having 2.5x more reads, and WS4U diversity was similar to that of Blackwell, even though its library has about 4x more reads. This is likely due to the fact that WS4U and WS8U are diverse germplasm pools, each originating from a wide array of prairie-remnant populations with common DNA content (ploidy), i.e., WS4U plants are “tetraploids” with DNA content approximately 3.0 pg/nucleus; WS8U plants are “octoploids” with DNA content approximately 6.0 pg/nucleus As a measure of diversity, we calculated within and across library SNP detection rates. We observed a positive correlation between the total number of mapped reads per library (reference coverage) and the number of SNPs detected per kilobase pairs (Kbps) ), Cave-in-Rock would still have the lowest heterozygosity, with ∼5.5 SNPs per Kb, while Forestburg would be the most heterozygote, with 9.5 SNPs per Kb. That is about twice as high as what was observed in high diversity maize varieties.We used a subset of SNPs to examine switchgrass population structure with principal-components analysis. The first two principal components together explained 25% of the variance. We did not detect any clustering pattern that would indicate population structure within switchgrass. Since there was prior data suggesting that there are at least three distinct geographic clades – Central Great Plains, Northern Great Plains, and Eastern Savanna – within upland switchgrass germplasm, the lack of clustering observed in our data may be attributed to the relatively small sample size of these 13 genotypes, combined with the higher within-population variance in comparison to the between-population variance observed previously in switchgrass Table S1Depth and breadth wise coverage statistics by cultivar and across cultivars between 0x to 5x coverage. 0x designates the fraction of genome that is not covered. SNPs were called from the fraction that is either 3x in an individual library or 3x across libraries.(XLSX)Click here for additional data file.Table S2Names of the libraries that were used to tag reads from each library in the data files distributed from the project website at http://www.maizegenetics.net/snp-discovery-in-switchgrass.(XLSX)Click here for additional data file.Text S1GenBank SRA and dbEST accessions for the sequences generated for the project.(DOCX)Click here for additional data file.


As a fundamental genomic element, meiotic recombination hotspot plays important roles in life sciences. Thus uncovering its regulatory mechanisms has broad impact on biomedical research. Despite the recent identification of the zinc finger protein PRDM9 and its 13-mer binding motif as major regulators for meiotic recombination hotspots, other regulators remain to be discovered. Existing methods for finding DNA sequence motifs of recombination hotspots often rely on the enrichment of co-localizations between hotspots and short DNA patterns, which ignore the cross-individual variation of recombination rates and sequence polymorphisms in the population. Our objective in this paper is to capture signals encoded in genetic variations for the discovery of recombination-associated DNA motifs.Recently, an algorithm called “LDsplit” has been designed to detect the association between single nucleotide polymorphisms (SNPs) and proximal meiotic recombination hotspots. The association is measured by the difference of population recombination rates at a hotspot between two alleles of a candidate SNP. Here we present an open source software tool of LDsplit, with integrative data visualization for recombination hotspots and their proximal SNPs. Applying LDsplit on SNPs inside an established 7-mer motif bound by PRDM9 we observed that SNP alleles preserving the original motif tend to have higher recombination rates than the opposite alleles that disrupt the motif. Running on SNP windows around hotspots each containing an occurrence of the 7-mer motif, LDsplit is able to guide the established motif finding algorithm of MEME to recover the 7-mer motif. In contrast, without LDsplit the 7-mer motif could not be identified.cis-regulatory DNA sequence motifs stimulating meiotic recombination hotspots by screening and narrowing down to hotspot associated SNPs. It is the first computational method that utilizes the genetic variation of recombination hotspots among individuals, opening a new avenue for motif finding. Tested on an established motif and simulated datasets, LDsplit shows promise to discover novel DNA motifs for meiotic recombination hotspots.LDsplit is a software tool for the discovery of  In many species, recombination events are clustered in localized regions of a few kb long, called recombination hotspots. Understanding the regulatory mechanisms of recombination hotspots can shed light on birth defect diseases, molecular evolution, genome instability, etc. . Therefoet al. used the LDhat software to estimate the fine-scale genome-wide recombination hotspots from HapMap Phase II SNP data [trans-acting regulator of the locations and intensities of meiotic recombination hotspots in human and mouse [trans and cis-regulatory elements for meiotic recombination hotspots remain to be discovered. Some recent efforts have been made toward this goal [e.g. its detailed mechanism to mediate the location and intensity of meiotic recombination, is also under intense research [For the regulatory mechanisms of recombination hotspots, striking progress has been made recently, thanks to the high-throughput genomic technology and bioinformatics techniques. Myers SNP data . From thSNP data . Moreovend mouse -7. Striknd mouse ,9. Overand mouse  and the nd mouse . Therefohis goal -12. Moreresearch ,14.et al. carried out base-pair substitution screening on the genome of fission yeast to scan for DNA sequences responsible for the activities of hotspots [The discovery of DNA sequence motifs that stimulate meiotic recombination is crucial to the uncovering of the regulatory mechanisms of recombination hotspots. Several approaches have been developed for this purpose. The first approach is based on yeast mutagenesis. After genetically mapping the locations of meiotic recombination hotspots, Steiner hotspots . They idhotspots ,4. Its shotspots . Howeveret al. noted that the two SNPs associated with the DNA2 and NID1 hotspots discovered by sperm typing are located within the motifs (CCTCCCT and CCCCACCCC) after the motifs had been identified; however, information of sequence polymorphism did not contribute to the discovery of motifs. In extending the 7-mer motif to the degenerate 13-mer motif [et al. did compare the original and disrupt forms of motifs due to mutations, however they only compared motif occurrences in different hotspots in the human reference genome, rather than polymorphisms of the same motif occurrence and variations of intensities at the same hotspot (e.g. DNA2 and NID1) among different individuals. Therefore, in the above computational approach, genetic variation information encoded in the DNA sequence polymorphisms has not been fully used for motif discovery.It is desirable and challenging to design Bioinformatics algorithms to automatically detect degenerate motifs for recombination hotspots in large-scale. To this end, additional information is needed to increase the statistical power of motif detection. One type of such information is the association between sequence polymorphisms with the variation in recombination rates of a hotspot. Evidence of such association has been demonstrated by sperm typing. For instance, individuals with different alleles at the FG11 SNP located within the DNA2 hotspot have 20-fold difference in recombination rate of the DNA2 hotspot . A similer motif , Myers ecis-regulatory elements of these hotspots [e.g. about 200 consecutive SNPs from HapMap Phase 2 data) flanking a hotspot H, LDsplit first splits the haplotypes into two subsets according to alleles of a candidate SNP A . Then, LDsplit compares the population recombination rate of the hotspot H between the two subsets calculated by LDhat algorithm [A is located in a cis-regulatory element  of the hotspot H, then one allele of SNP A represents a mutation that disrupts the motif. As a result, the subset of haplotypes with that disruptive allele should have lower intensity of hotspot H than the other allele. This is the case for the sperm typing examples of DNA2 hotspot and NID1 hotspot. This method is based on the assumption that population genetics methods (e.g. LDhat) for estimation of recombination rate by inferring historical crossovers from LD patterns should be able to capture the extant difference of hotspot intensities between the two alleles, which has been supported by some evidence [cis-regulatory genomic loci associated with recombination hotspots [Recently we have designed an algorithm called LDsplit to detect DNA sequence polymorphisms associated with individual meiotic recombination hotspots, and thereby discover hotspots . Given algorithm . The ratevidence . Extensihotspots . Howevercis-regulatory genomic regions. First, we have implemented a user-friendly graphic interface (GUI) for the LDsplit algorithm. Compared with the previous implementation using Perl, this version is implemented in Java, which is more efficient and platform independent, running under Windows, Linux and Mac OS. It provides computational unsophisticated users from life sciences a convenient access to the software. More importantly, the integrative scientific visualization of genetic data  shows informative details about the genomic context of hotspots, which greatly facilitate explorative analysis of genetic factors of recombination hotspots. Far more than being incremental, this upgrading significantly enhances the usefulness and impact of LDsplit algorithm for the biomedical research. Second, to demonstrate the effectiveness of LDsplit, we carry out a focused analysis of SNPs falling inside the well known 7-mer motif CCTCCCT, which is the core of the binding motif of PRDM9. Running LDsplit on HapMap (phase 2) SNPs flanking the 7-mer motif, we observe that the original form of the 7-mer motif tends to have higher LD-based recombination rates than the disrupt form . This is evidence that, in spite of biased gene conversion, LDsplit is able to capture the allele-specific intensities of hotspots. Third, to validate LDsplit by demonstrating its ability to uncover the 7-mer motif, we picked haplotype windows of about 200 SNPs each flanking a hotspot containing an occurrence of the 7-mer motif CCTCCCT, and then ran LDsplit on each SNP window. From the output of LDsplit, SNPs with the most significant p-values are extended to short DNA sequences, which were fed into the motif-finding algorithm of MEME [i.e. without guide of LDsplit), none of the output motifs is similar to the 7-mer motif. This test on the well known 7-mer motif shows that LDsplit can narrow down to target cis-regulatory elements for meiotic recombination, which significantly increases the power of motif-finding algorithms like MEME [cis-regulatory motifs for recombination hotspots are yet unknown.In this paper we report a significant upgrading to the implementation of LDsplit algorithm, and also demonstrate that LDsplit can help discover DNA sequence motifs stimulating recombination by narrowing down to i.e. all chromosomes in one subpopulation have the same allele). Then it calls the rhomap program (which is designed to estimate variable recombination rates in the presence of hotspots) in the LDhat package (see [ρ) of hotspot strengths between the SNP alleles as (ρ0 − ρ1)/( ρ0 + ρ1), where ρ0 and ρ1 denote the strengths of the hotspot in two different subpopulations. The p-value of the hotspot-SNP association is estimated by comparing the observed Δρ with the null distribution of random Δρ simulated by permutation tests .The rationale of LDsplit is that, if a SNP is associated with a recombination hotspot, then between two alleles of the SNP the strengths of the hotspot are likely to be different. It is based on the assumption that historical recombination rate estimated by LD-based computational methods can approximate the extant recombination rate accurately. Based on this rationale, the LDsplit algorithm is designed as follows Figure . For eacage (see ) to estihttp://www.ntu.edu.sg/home/zhengjie/software/LDsplit.htm).The implementation of LDsplit consists of two stages. First, LDhat is called to calculate the recombination profiles for subpopulations split by SNP alleles and pseudo-population in permutation tests. Second, the p-values of hotspot-SNP associations are estimated and the recombination profiles of sub-populations are displayed. In the rest of this section, we will describe the two stages of LDsplit in detail. The source code and user manual of LDsplit are available for free download on its website ; (3) profiles of pseudo-populations from a random split of the input population. Meanwhile, the input SNP data in this stage consist of two text files, namely the sites file (consisting of haplotypes in FASTA format) and the locs file . Both types of files can be extracted from HapMap SNP database by cutting a window of haplotypes. LDsplit provides a friendly user interface to cut a window from HapMap data and generate the sites and locs files. In addition, the user can set the parameters involved in this stage . As LDhat is computationally costly, this stage of LDsplit usually takes a long time on a regular personal computer (e.g. a few hours for 180 haplotypes each of 200 SNPs). Hence it is better to batch this computation. The output of this stage can be exported to hard disk as a Java serialization file, which can be loaded back into LDsplit for analysis in the next stage.We apply LDhat to calculate recombination profiles for a window consisting of sequences of SNPs (or haplotypes). There are three types of recombination profiles: (1) the profile of the whole input population of haplotypes; (2) profiles of sub-populations of haplotypes each corresponding to an allele of a candidate SNP (Stage 2: Deriving hotspot-SNP associationse.g. 30%) in the window. For every candidate SNP, users can browse the recombination profiles of its two alleles  no less than a threshold , a forward-time population genetics simulation framework . In eachA simulated hotspot was located at the central point of haplotypes, and a causal SNP, whose alleles resulted in different probabilities of crossover at the recombination hotspot, was inserted at the position of 100 kb. In each generation, we randomly selected pairs of individuals as parents. Assuming one crossover event in a chromosome of 200 Mb per meiosis, in 200 kb the background probability of a crossover would be 0.001. When the hot allele of the causal SNP is present, the probability of a crossover would be increased to 0.01. In addition, the crossover position was chosen with the probability under normal distribution with mean at position 100 kb where the center of hotspot is located. At the end of simulation, a simulated population was exported, from which we randomly collected 10 subsets, each consisting of 90 individuals (180 haplotypes) as benchmark SNP data. The SNPs were extended with randomly generated DNA sequences, and artificial motifs were inserted spanning the causal SNPs. The SNP and DNA sequence data would then be fed into LDsplit and MEME to test if they are able to identify the motifs.cis-regulatory motifs of meiotic recombination hotspots, we will analyze the 7-mer motif of CCTCCCT, which has been established as the core binding motif of PRDM9. First, running LDsplit, we confirm that, when the 7-mer motif containing a SNP is disrupted by one allele of the SNP, its proximal hotspot would have lower intensity estimated from LD patterns. Second, we show that LDsplit can guide the discovery of the 7-mer motif by narrowing down to genomic regions proximal to motifs. Third, we also discuss the effects of biased gene conversion implicated in this study. Then, LDsplit is further validated through simulation studies. At the end, we outline directions for future work.As a case study to demonstrate the usefulness of LDsplit software for the discovery of As introduced in the background section of this paper, the DNA binding of PRDM9 protein regulates the locations and intensities of many recombination hotspots of human and mouse. Particularly, the 7-mer DNA sequence motif CCTCCCT is at the core of the 13-mer binding motif of PRDM9 in the human genome . Thus, tH that is located nearest to the candidate SNP A . In the following analysis, we will compare the recombination rates of hotspot H between two alleles of the SNP A, where one allele preserves the original form of the 7-mer motif, and the other allele disrupts the motif.First, we search in the human genome for all occurrences of the 7-mer motif that each contains a SNP . We have not observed any case that one occurrence of the 7-mer motif contains more than one SNPs. In our previous study [positive set in which the SNP allele preserving the original form of the 7-mer motif corresponds to a higher estimated recombination rate than the other allele; and the rest SNPs are in the negative set, i.e. the disrupting allele has the higher recombination rate. As shown in Figure Based on the estimated recombination rates from LDsplit, we can divide the above 70 SNPs into two categories: One is the i.e. from alleles that preserve the original motif and alleles that disrupt the motif). As shown in Figure To further investigate the disrupting effect of SNPs inside the 7-mer motif on the intensities of proximal hotspots using LD patterns, we compared recombination profiles between two SNP alleles as follows. For each of the 77 SNPs, we cut a window of haplotypes flanking the SNP as input to LDsplit, which will calculate the two recombination profiles from the subsets of haplotypes corresponding to two alleles of the SNP. One allele preserves the original form of the 7-mer motif of CCTCCCT, while the other allele disrupts the motif. Then, we aligned the 77 pairs of recombination profiles at the centers of the 15 proximal hotspots, and calculated the average recombination profiles of the two categories , minimum sites 10 and motif length between 10 to 20, etc. , and extended them into 101-base DNA sequences as input for MEME, which was run with the same parameters. To get stable results of comparison, we carried out such random tests for three groups Additional file From each of the aforementioned 70 SNPs inside the 7-mer motif, we extended to a window of 101 SNPs (with 50 SNPs on each side). Then we ran LDsplit on the haplotypes of the SNP window to get p-values of SNPs in the window. From the 70 windows, totally 332 SNPs were collected, each of which had a significant LDsplit p-value (p < 0.05). Given reference DNA sequences  and SNP physical locations in the reference genome, each of the 332 candidate SNPs was extended to a window of 101 DNA bases (50 bases on each side), by cutting the reference sequences flanking the candidate SNPs. Then, we ran the MEME program [e.g. motifs in the first row have the smallest E-value by MEME). The motifs on the left  flanking SNPs of significant association with hotspots, as detected by LDsplit. By contrast, most existing methods for finding motifs of recombination hotspots use DNA sequences under whole hotspots (2 kb – 10 kb long) as input ,4. In the.g. dogs [e.g. immunology related hotspots in the major histocompatibility complex (MHC) region.In the above, we have demonstrated the usefulness of LDsplit for the detection of recombination associated motifs, using the 7-mer core motif of PRDM9 as an example. Several recent papers suggest that PRDM9 may be responsible for more recombination hotspots in mammalian genomes than previously appreciated ,9,22. Ho.g. dogs ) or a pri.e. 180 haplotypes). The performance of LDsplit on the 50 datasets was consistent with our previous simulation tests [To evaluate the performance of LDsplit, we conducted a simulation study using a Python program based on simuPOP , which wNext, we tested whether the screening of SNPs by LDsplit can help find causal sequence motifs in the simulated data. From the 50 simulated datasets above, we collected totally 60 candidate SNPs with significant LDsplit p-values (p < 0.05), each of which was extended to a window of 101 DNA bases (50 bases on each side). Among the 60 DNA sequences, we inserted two randomly generated causal motifs “ACTATCGATCTAG” and “ATGATGTAATGGT” in locations where they span the causal SNPs, thereby modelling the causal effect of the motifs in stimulating meiotic recombination. In case the causal SNP is not included in the 60 SNPs due to false negative of LDsplit, the motif is not inserted. Then we ran MEME on the 60 DNA sequences to identify motifs, with the same setting of parameters of MEME as for finding the 7-mer motif of CCTCCCT. As shown in Figure et al.[et al. offers an explanation for the success of LDsplit in predicting the association between the DNA2 hotspot and FG11 SNP [While the idea of LDsplit appears simple, there are subtle caveats because a meiotic recombination hotspot tends to kill itself through biased gene conversion (BGC) ,27. Hellet al. suggesteet al.. Hence, FG11 SNP . AlthougFG11 SNP  highlighFG11 SNP .et al.[e.g. the DNA2-FG11 case).In this paper, we confirmed once gain the prediction of the mathematical model of Hellenthal et al.. As Figuet al., here wee.g. HapMap SNPs). Recently we have obtained significant speedup for LDhat algorithm by optimization and parallel computing [Since our presentation of LDsplit software in a recent conference , severalomputing . Second,omputing  for recee.g. DNA methylation and histone modification) play important roles in the regulation of recombination hotspots. Several recent studies have pointed to this connection [cis-regulatory DNA motifs.One implication of the discovery of PRDM9 is that epigenetic factors  DNA recombination, genomic evolution, disease gene mapping, and genome instability. In this paper, we demonstrated the usefulness and accuracy of LDsplit by testing on the 7-mer DNA motif of CCTCCCT which is bound to by PRDM9 and a well-known cis-regulator of recombination hotspots. We showed that SNP alleles disrupting the 7-mer motif have lower hotspot intensities estimated by LDsplit. Moreover, LDsplit is able to guide the discovery of the 7-mer motif and simulated motifs by the MEME software. Such a Bioinformatics approach is promising for the discovery of cis- and trans-regulators, to uncover the molecular machinery regulating recombination hotspots and genome stability.In this paper we presented LDsplit, an open source software tool for predicting Project name: LDsplit.Project home page: http://www.ntu.edu.sg/home/zhengjie/software/LDsplit.htm.Operating system(s): Windows, Linux, Mac OS.Programming language: Java.Other requirements: JDK, JFreechart package.License: GNU General Public License.Any restrictions to use by non-academics: None.BGC: Biased gene conversion; MAF: Minor allele frequency; SNP: Single nucleotide polymorphism; LD: Linkage disequilibrium; GUI: Graphical user interface.The authors declare that they have no competing interests.JZ and TM envisioned the idea behind the LDsplit algorithm. JZ, PY and JG implemented the LDsplit algorithm in a Java software tool and designed the graphical user interface. PY conducted computational experiments running and validating LDsplit, and MW collected data of DNA sequences and motif locations. PY, MW and JZ drafted the manuscript, with input from TM and CKK. All authors read and approved the final manuscript.Supplementary materials. The file of “Supplementary_materials.pdf” contains several figures and additional experimental results mentioned in this paper, including: (1) LDsplit’s confirmation of the association between FG11 SNP and DNA2 hotspot on chromosome 6 discovered by sperm typing; (2) the interface of LDsplit for loading input data and setting parameters; (3) 5 groups of top DNA sequence motifs found by MEME from DNA sequences flanking randomly selected SNPs.Click here for fileSNPs inside 7-mer core motif of PRDM9. Additional file Click here for fileDNA sequences for motif finding. Additional file Click here for file


Within- andacross-breed analyses estimated SNP allele substitution effects (ASEs) bygenomic best linear unbiased prediction (GBLUP) and variance components byrestricted maximum likelihood under an animal model incorporating a genomicrelationship matrix. GBLUP estimates of ASEs from the across-breed analysis weremoderately correlated (0.31–0.66) with those from the individualwithin-breed analyses, indicating that prediction equations for molecularestimates of breeding value developed from across-breed analyses should beeffective for genomic selection within breeds. We identified 79 genomic regionsassociated with WBSF in at least three breeds, but only eight were detected inall five breeds, suggesting that the within-breed analyses were underpowered,that different quantitative trait loci (QTL) underlie variation between breedsor that the BovineSNP50 SNP density is insufficient to detect common QTL amongbreeds. In the across-breed analysis, CAPN1 was followed byCAST as the most strongly associated WBSF QTL genome-wide,and associations with both were detected in all five breeds. We show that noneof the four commercialized CAST and CAPN1SNPdiagnostics are causal for associations with WBSF, and we putatively fine-mapthe CAPN1 causal mutation to a 4581-bp region. We estimate thatvariation in CAST and CAPN1 explains 1.02 and1.85% of the phenotypic variation in WBSF respectively.We performed a genome-wide association study for Warner–Bratzler shearforce (WBSF), a measure of meat tenderness, by genotyping 3360 animals from fivebreeds with 54 790 BovineSNP50 and 96 putative single-nucleotide polymorphisms(SNPs) within  Consumer assessment of beef quality, palatability and overall eating satisfaction issignificantly influenced by tenderness  on chromosome 7 and calpain 1, (mu/I) largesubunit (CAPN1) on chromosome 29 for Warner–Bratzler shear force (WBSF) measurements on the longissimus dorsimuscle on chromosomes 2, 4, 5, 7, 10, 11, 15, 20, 25 and 29 . Howevernderness . While pCAST andCAPN1 in order to identify the genomic regions most likely toharbour the causal variants influencing beef tenderness.To assist beef breeders to make efficient and large changes in tenderness, DNA assaysmust be developed that can reliably predict the genetic variation in tendernesswithout regard to the breed composition of an animal. To address this need, wegenotyped 3360 animals representing 114 half-sib families produced by the AmericanAngus Association (AAA), American Hereford Association (AHA), American SimmentalAssociation (ASA), American International Charolais Association (AICA) and the NorthAmerican Limousin Foundation (NALF) as part of the National Cattlemen's BeefAssociation (NCBA) sponsored Carcass Merit Project (CMP) to develop predictionequations for the implementation of genomic selection .All samples were genotyped using the Illumina BovineSNP50 BeadArray , qi =1 – pi, elements of theith column of M are2qi,qi −pi and−2pi forAA, AB and BB genotypesat autosomal and pseudoautosomal loci  of SNP ASEs in the across-breed analysis. For each of the regionstagged by these SNPs, we declared the region to harbour a QTL if at least threeSNPs from different within-breed analyses had ASEs ranked in the top 500. Whilelinkage disequilibrium (LD) decays to ∼0.1 within less than a 500-kbdistance within breeds of distantly related individuals  to identifypotential candidate genes for tenderness.CAST and a2.64-Mb region of BTA29 harbouring 93 SNPs spanning CAPN1 werefound to contain loci for which SNP ASEs ranked in the top 500 in thewithin-breed analyses. To allow haplotype-based analyses, we expanded theregions to 44 SNPs spanning 2.86 Mb for CAST and 100 SNPsspanning 3.12 Mb for CAPN1 (Table S3). We first analysed eachSNP individually by including allele effects  in β, in addition to thecontemporary group effects, and then we included haplotype effects for windowsof nine contiguous SNPs using phase information estimated by fastphase.The haplotype model was sequentially fit by sliding the nine SNP window througheach region one SNP at a time, and the statistics computed for each window wereassigned to the 5th SNP located at the centre of each window. In both cases, theanalysis was performed using the previously estimated variance components , the window producing the largest modelsum of squares does not necessarily result in the largestF-statistic or −log10P-value. To avoid this, we computedthe percentage of phenotypic variation explained by each window through theregion from the ratio of the window to phenotypic sums of squares, where thewindow sum of squares was estimated as the difference between model sum ofsquares including and excluding haplotype effects for the nine SNP window andthe phenotypic sum of squares was estimated as the total sum of squarescorrected for the mean and contemporary group sums of squares. This statisticidentifies the SNP window that explains the largest amount of variation in WBSFregardless of the number of haplotypes that are fit.A 1.48-Mb region of BTA7 harbouring 28 SNPs spanning mponents , and F-tN ×h, the number of phenotypes multiplied by the square root ofthe heritability, which is an estimate of the cumulative amount of additive geneticinformation in a sample of N unrelated individuals and was 468.3,451.5, 471.7, 85.4 and 143.5 in Angus, Hereford, Charolais, Limousin and Simmentalrespectively.We found large differences in the heritabilities of WBSF across the five breeds and wereCAST andCAPN1 loci with WBSF were the largest in the genome , presumably reflecting the very smallsample size for this breed. On the other hand, the CASTassociations were more variable among the breeds, being the most strongly associatedwith WBSF genome-wide in Hereford, ranking highly in Charolais and Limousin, butonly 234th and 208th in Angus and Simmental respectively. These results are likelydue to the fairly small sample sizes for the analysed breeds, but probably also mayreflect the different SNP densities within the two regions and differences in allelefrequencies at the SNPs and QTL across breeds. We accomplished a much higher SNPdensity in the region harbouring CAPN1 than CAST,and this suggests that we had insufficient SNPs to find at least one that was instrong LD with the causal mutations within CAST in all breeds.In the across-breed analysis, the use of the genomic relationship matrix corrects forthe stratification because of pedigree relatedness while accounting for the extentof background relatedness among the Angus and Continental breed groups because ofthe use of Angus dams to produce the crossbred Continental breed calves. In thisanalysis, the associations between the e genome , reflectAcross all 40 645 SNPs, the correlations between ASEs estimated within each of thebreeds varied from −0.02 to 0.04, indicating that models developed to predictgenomic breeding values within one breed will have very low accuracies in otherbreeds. This has previously been predicted using simulated data (P= 0.48) orbetween British and Continental breeds (P= 0.52) in theprobability of QTL detection for all 79 QTL or for the 42 QTL identified in onlythree breeds (P = 0.35 and 0.82 respectively). Clearlysample size, assay SNP density, constraints on SNP ranks and the size of regionsharbouring highly ranked SNP ASEs all impact the identification of putatively commonQTL. Of the 113 instances when the within-breed estimated SNP ASEs ranked>500, the average rank was only 2551, suggesting that the majority of theseregions harbour QTL that segregate in all breeds. Changing the minimum within-breedASE rank criterion to <1000 resulted in 17 of these QTL being detected in allfive breeds, 41 in four breeds and 21 in three breeds . Notwithstanding the poor resolution of QTL location mappedby linkage analysis, we also found support for all of the other previouslyidentified QTL. For example, in the across-breed analysis, QTL were identified withASE ranks <500 at 3 151 989 bp and at 6 831 955–7 086 105 bp (300 kbfrom MSTN) on BTA2. The first was supported by ASE ranks<500 for Angus and Charolais, but an ASE rank of 565 in Limousin. The secondwas supported by an ASE rank <500 in Charolais and ASE ranks <1000 inAngus, Limousin and Simmental. Thus, despite their proximity, these QTL are likelydistinct, and the concordance between our and previously published results suggeststhat the genetic architecture of meat tenderness is substantially less complex thanfor growth.We have previously found poor concordance between GWA and half-sib linkage analysesfor large-effect QTL underlying growth traits, even when large numbers (>50)of families with family sizes ranging from 20 to 224 half-sibs are analysed (datanot shown). Assuming that GWA analysis detects common variants, we would expect asignificant number of sires to be both heterozygous and detected to be segregatingfor a large-effect QTL; however, this largely depends on the underlying geneticarchitecture of the trait. is study  . M-calpainactivity has been associated with both meat tenderness and palatability measurements(FGF2) is an upstream regulator of heatshock protein B1 (HSPB1), which has been found to be negativelyrelated to WBSF , it was only the moststrongly associated CAST SNP within Hereford and Charolais, withstronger associations being detected for SNPs in the 5′ upstream region inAngus, Limousin and Simmental (Table S3). In fact, the haplotype analysis moves thelocation of the most significantly associated SNP window 83.7 kb upstream ofrs41255587 to be centred on rs43529872(−log10P = 8.78), and thisCAST window was found to explain the greatest amount ofphenotypic variation in WBSF in the across-breed  was most strongly associated with WBSF in Angus(−log10P = 9.70) andrs17872050 was the most strongly associated with WBSF inLimousin (−log10P = 3.23). However,rs42192103 was found to be slightly more strongly associatedwith WBSF than rs17812000 in the across-breed analysis(−log10P = 15.25 vs. 15.01), with anaverage ASE across breeds of 0.23 kg (Table S3). The amount of phenotypic variationexplained in the haplotype-based analyses again indicates that none of the testedSNPs are causal for effects on WBSF and that the strongest signal for associationwith WBSF was in the 8187-bp region from 44 062 694 to 44 070 881 in all five breeds(Table S3). The size of this region is sufficiently small to speculate that there isprobably only a single mutation in CAPN1 affecting WBSF in allBos t. taurus cattle breeds, and the across-breed haplotypeanalysis shown in Table S3 and Fig.).Among the SNP located within CAPN1, the higher SNP density achieved and the use ofacross-breed analysis, which erodes the patterns of LD within breeds, resolved thelikely location of the causal variant to a region of only 4581 bp.We conclusively demonstrate that none of the SNPs currently commercialized asdiagnostics for genetic merit are causal for their effects on WBSF .Furthermore, the design of these assays was facilitated by a community effort thatproduced more than 128.4X of genome sequence coverage on more than 80 animals, andSNP data from this work are now available in dbSNP. This project discovered 48.6million high-quality SNPs, which must include many of the causal variants underlyingquantitative variation in cattle, and it may be possible to impute genotypes at theresolution of the genome sequence (Despite the apparent reduced complexity of a trait such as meat tenderness relativeto growth, there appear to be a large number of QTL underlying variation in WBSF,and the identification of all of the mutations that underlie these QTL might appearto be intractable. However, recent developments in high-density SNP genotyping,high-throughput sequencing and genotype imputation suggest new strategies for therapid simultaneous identification of variants underlying quantitative traitsgenome-wide. We accomplished an average SNP spacing of 1139 bp for the 23 SNPsanalysed within


Salmonella Enteritidis is currently the world's leading cause of salmonellosis, in part because of its ability to contaminate the internal contents of eggs. Previous analyses have shown that it is an exceptionally clonal serotype, which nonetheless generates considerable phenotypic heterogeneity. Due to its clonality, whole genome analysis is required to find genetic determinants that contribute to strain heterogeneity of Salmonella Enteritidis. Comparative whole genome mutational mapping of two PT13a strains that varied in the ability to contaminate eggs and to form biofilm was achieved using a high-density tiling platform with primers designed from a PT4 reference genome. Confirmatory Sanger sequencing was used on each putative SNP identified by mutational mapping to confirm its presence and location as compared to the reference sequence. High coverage pyrosequencing was used as a supporting technology to review results.A total of 250 confirmed SNPs were detected that differentiated the PT13a strains. From these 250 SNPS, 247 were in the chromosome and 3 were in the large virulence plasmid. SNPs ranged from single base pair substitutions to a deletion of 215 bp. A total of 15 SNPs (3 in egg-contaminating PT13a 21046 and 12 in biofilm forming PT13a 21027) altered coding sequences of 16 genes. Pyrosequencing of the two PT13a subpopulations detected 8.9% fewer SNPs than were detected by high-density tiling. Deletions and ribosomal gene differences were classes of SNPs not efficiently detected by pyrosequencing.Salmonella enterica that impact the safety of the food supply. Results may also facilitate designing 2nd generation vaccines, because gene targets were identified that differentiate subpopulations with variant phenotypes. High-throughput genome sequencing platforms should be assessed for the ability to detect classes of SNPs equivalently, because each platform has different advantages and limits of detection.These results increase knowledge of evolutionary trends within  Salmonella enterica subspecies I serotype Enteritidis (S. Enteritidis) is a leading cause of salmonellosis worldwide . To. To17]. th most common Enteritidis pattern in the database (3 out of 676 - 0.44%). PT4 22079 was pattern JEGX01.0017 ARS    did not alter amino acid sequence  and neutral polar (np) amino acids were frequently substituted within the two chemical classes, but substitution between acidic and basic polar amino acids did not occur Table . BetweenMutational mapping of the two PT13a strains gave a prominent signal at a site in the reference genome known to include an ST64b-like bacteriophage, which defines the PT4 lineage. The absence of binding by the PT13a strains to the PT4 primers that hybridized ST64b-like sequence resulted in a run length (number of consecutive events) of more than 5500 consecutive mispriming events occurring in a window of approximately 47,000 bp. This large run of mispriming events has so far only been associated with the presence of a lysogenic bacteriophage.S. Enteritidis PT13a strains [S. Enteritidis PT4 for production of primers that would hybridize Fels2 sequence. To objectively assess whole genome data for the presence of sequence unique to test strains and absent in the reference, an interval map based on the average number of genes between SNPs was constructed from all data available in supplementary material  helped to locate the Fels2 bacteriophage in PT13a strains between S. Enteritidis gene (SEN) SEN2609 and SEN2624 of the PT4 genome. This location was confirmed by sequencing the beginning and end of the bacteriophage 3' to SEN2592 and 5' to SEN2623, respectively (data not shown); however, the complete bacteriophage sequence was not sequenced de novo. An additional benefit of interval mapping is that it appears to locate regions that have significant gaps in alignment defined by a gap of only 5 genes. This could help to locate even small inversions and gene transfers between serotypes. Otherwise, a strategy similar to interval mapping applied here is used elsewhere in software that depicts gene inversions and alignments in a graphing output (e. g. MAUVE); however, we needed finer detail to evaluate alignments to avoid missing inserted genes or non-coding sequence between the PT13a strains.The strategy used for comparison of genomes is inherently biased against detection of inserts in PT13a genomes compared to the PT4 reference genome, because no PT13a specific primers would have been generated from the PT4 genome. For example, DNA-DNA microarray hybridization had previously detected bacteriophage Fels2 in  strains ,11. HowedsdA, the 12 bp deletion in lrgB, and the 12 bp deletion in lysR (STM2912), all of which occurred in PT13a 21027, each had a run length of 4 that spanned a 21 bp window. The 11 bp deletion that introduced the read-through fusion of yjfK and yjfL of PT13a 21027 had a run length of 6 and a 35 bp window. The smallest deletion was 1 run length (1 mispriming event), which was confirmed to be a 1 bp deletion in the gene sefD of PT13a 21027. In a run, each mispriming event is separated by 7 bp due to the nature of the high-density tiling approach and the size of the primer overlap. Raw data showed that in some cases there were easily observed spectral evidence of deletions that varied between strains. This result indicates that larger deletions can be rapidly detected by observance of spectral signal alone. However, algorithms with a stringency of filtering for run length of 2 or greater will miss some deletions, and thus type II errors  are possible. In summary, rapid filtering of data for run lengths of 2 or greater will catch most deletions, but there is a risk that false negative results will occur and that the smallest deletions will be missed using filtering approaches that attempt to avoid confirmatory sequencing.A useful feature of whole genome analysis that would facilitate outbreak investigations is if raw data could be quickly gleaned for evidence of mutational events that would distinguish strains within serotypes. For this reason, we analyzed technical details of the size of window and mispriming events (run length) associated in the raw data with SNPs that were confirmed as resulting from a deletion. The 215 bp deletion in STM4551 of PT13a 21046 occurred within a 231 bp window as reported in raw data files. It was associated with 34 mispriming events, or a run length of 34. The 92 bp deletion in STY3762 of PT13a 21046 had a run length of 14 within a 91 bp window. The 10 bp deletion within Both PT13a strains had multiple SNPs that disrupted open reading frames (ORFs)  error, whereas comparative genome re-sequencing (CGS) was subject to Type II  error. Pyrosequencing was subject to Type II error for ribosomal gene base pair substitutions and deletions, but was otherwise accurate for calling base pair substitutions. Mutational mapping was overall the more cost-effective and accurate method for finding true polymorphisms that differentiated PT13a strains 21027 and 21046 as confirmed by sequencing f/r strains PT13a 21027, PT13a 21046 and PT4 22079. The cost of sequencing was greatly reduced by incorporating an approach that reduced genetic noise, namely triangulation of 3 genomes within the same serotype for virtual subtractive hybridization. Combining mutational mapping with pyrosequencing may eventually be the most cost effective and time efficient approach for SNP analysis of bacteria that have an exceptionally clonal population structure. Finding that pyrosequencing was excellent for detection of SNPs that involve basepair substitutions suggests that it may be a preferred method for epidemiological investigations, because it is amenable to being automated. However, if certain SNPs occur repeatedly within different subpopulations, then targeted sequencing may be more cost effective. For example, if epidemiological tracebacks could be accomplished using as few as 10 SNPs to track an outbreak strain, a baseline cost would be approximately $100 per strain ($10/SNP). It is not yet possible to complete whole genome sequencing for such a low cost, although it is required for the initial identification of SNPs useful for building analytical platforms. Application of these methods requires acknowledgement of method limitations and a strategy for overcoming deficiencies and skew in order to produce the highest quality databases.S. Enteritidis could evolve the ability to contaminate eggs. These results support the conclusion that S. Enteritidis has multiple evolutionary trajectories, involving multiple polymorphisms throughout the genome, which impart combinatorial complexity [S. Enteritidis to complete the infection pathway to the egg and ultimately to the consumer is an objective of future research.The relative contribution of each non-synonymous SNP to the pathway that results in egg contamination will be better understood as defined mutants within a similar genomic background are characterized. Knowledge of how combinations of genes aid organ colonization and growth will help to choose strains that are suitable for development of vaccines. The possibility that there is a vaccination strategy that prevents multiple serotypes from colonizing and growing in internal organs should be explored, because serotypes other than mplexity . FindingSalmonella enterica serovar Enteritidis generates an exceptional degree of strain heterogeneity while exhibiting a highly clonal population structure has been solved for the most part. S. Enteritidis appears to be undergoing evolution primarily at the level of the single nucleotide polymorphism. Even bacteriophage lineages share most chromosomal information outside of regions of lysogeny. This result indicates that exceptionally stringent methods for analysis of genetic variation are required for characterization of strains. This research supports concepts of evolution that have been previously published [S. Enteritidis to contaminate the internal contents of eggs produced by otherwise healthy hens.The conundrum of how ublished . LinkingS. Enteritidis. PT13a 21046 was shown to contaminate eggs, but it did not form biofilm; conversely, PT13a 21027 formed biofilm, but it did not contaminate eggs [S. Enteritidis is SEN plus a 4 digit number that indicates relative position within in the genome. SEN0001 is thrA and the last gene is SEN4356A, which overlaps thrA. Ribosomal genes are accessioned separately, from SEN_r001 to SEN_r022 for ribosomal RNA and SEN_t001 to SEN_t084 for transfer RNA [The PT4 22079 was an environmental isolate obtained from water downstream from a flock and traced back to a major outbreak of egg contamination that was historically associated with introduction of PT4 to the United States . The twoate eggs . PT4 220ate eggs  . Digested DNA was separated using the CHEF-DRII PFGE system as per the manufacturer's instructions . Electrophoresis was carried out for 19 h at 6 V, using 2.2 L of the buffer× Tris/borate/EDTA0.5 (TBE) at a temperature of 14°C, and an initial pulse time of 2.16 s followed by a final switch time of 63.8 s. BioNumerics software  was used to normalize the band patterns based on the molecular weight standards included on each gel.The PFGE patterns for PT13a 21027, PT13a 21046 and PT4 22079 were determined using standardized methods conducted by personnel trained in the method developed by the Centers for Disease Control as part of PulseNet ences.htm-32. BrieS. Enteritidis strains were grown in 10 ml of Brain Heart Infusion broth (BHI)  at 37°C for 16 hr. Bacterial cells were pelleted in a Sorvall RC5B Plus centrifuge at 5000 × g for 15 min in a Sorvall Super-lite SLA 600 TC rotor. For mutational mapping and associated re-sequencing services , total DNA was extracted using a Qiagen Genomic-tip 500/G kit following the protocol designated for bacteria. Precipitated DNA was dissolved in 150 ml of Tris-EDTA buffer  and stored at -20°C. For confirmatory sequencing from PCR-amplified product, total DNA was extracted using a Qiagen Genomic-tip 100/G kit following the protocol designated for bacteria . Precipitated DNA was dissolved in 200 ml of Tris-EDTA buffer  and stored at -20°C. Spectrophotometric readings were performed to ensure an OD260/280 ratio greater than 1.7 and a genomic DNA concentration of 1 μg/μl as required for CGS.Single colonies of Salmonella enterica has seven ribosomal regions that are highly similar and which can complicate assembly and annotation of libraries. For this reason, a high density tiling method was selected for first analysis over 454 sequencing, because it was less likely to produce assembly artifact due to its progressive nature. Whole genome analysis was divided into two phases as described by the provider of service . Briefly, the first phase is called mutational mapping. A set of 30-mer probes is computer generated from a reference sequence. For these analyses, the database of the S. Enteritidis PT4 reference genome from the Pathogen Sequencing Group at the Sanger Institute was used for generation of primers [AM933172) and DNA for the experimental protocol was from PT4 22079. The primer set is used to densely tile the test genome that has been labeled with a fluorescent signal. The probes overlap every 7 bases. For a genome that is approximately 5 million base pairs, approximately 1.42 million primers are required for tiling hybridization, with the service provider stating that chip capacity is about 1.3 million base pairs of genome and 385,000 probes. Our experiments required 4 chips to process the entire genome of S. Enteritidis, which is approximately 4.86 million base pairs. Genomic regions identified by mutational mapping are then fed into the second phase of analysis, which is referred to as targeted resequencing. Any location of the test DNA that has a suspected SNP is resequenced in the presence of all 4 nucleotides to see which one allows sequencing to progress. Since the nucleotides are labeled, it is possible to tell which nucleotide is incorporated and thus identity is established. primers  . To facilitate analysis, numerical signal data available as tab-format text files and readable with Notepad (Microsoft) were opened and transferred to multiple spreadsheets in 400 kb sections. 3 SNPs in the genes approach . All thrrrlC, rrlA and cyaA were again used as internal controls for assessing specificity and sensitivity of mutational mapping done in Phase I and confirmatory resequencing done in Phase II. All signals for the control SNPs generated during Phase II fell into the category "non-called ROI". This meant that Phase II re-sequencing technology resulted in many false negatives. Therefore, SNPs that fell into the "non-called ROI" category were analyzed by confirmatory PCR-based sequencing in forward and reverse directions.Phase II of CGS is an array-based re-sequencing process that identifies which single base pair is substituted for another and at what position the substitution or other change occurs in the reference genome. The known polymorphisms in S. Enteritidis PT4 whole genome sequence made publicly available by the Pathogen Sequencing Unit of the Sanger Institute (EMBL accession no.: AM933172) [Pairs of primers used to amplify DNA amplicons for confirmatory sequencing of putative polymorphisms were generated from the M933172) . Forward++, 2.5 U Taq enzyme , and 1μl template DNA. Single amplicon products were confirmed by gel electrophoresis. PCR products were purified using a Qiagen QIAquick PCR Purification kit and submitted for sequencing to the Eastern Regional Research Center . PCR amplicons were sequenced using an Applied Biosystems BigDye Terminator 1.1 reaction mix on an Applied Biosystems 3730 DNA Analyzer.The cycling conditions for an Applied Biosystems 2400 Gene Amp PCR system were determined individually for each primer pair and in general included denaturation at 95°C for 1 min, then 30 cycles of 95°C for 30 s, 60°C to 70°C for 30 s, and 72°C for 2 to 3 min. Each reaction contained 400 nM of each primer, 200 μM ACGT deoxynucleotide triphosphates (dNTPs), 1.5 mM Mg™ Assembler).Pyrosequencing (SeqWright) was performed according to the manufacturer's instructions following methods developed for the sequencing of bacteria with a genome of approximately 5 MB . At leasS. Typhimurium LT2 genome available at NCBI (GenBank: AE006468). If no match with similarity greater than 90% was found between the two serotypes, the BLAST search was first extended to other Salmonella enterica serotypes with available whole genome databases, then to all available Salmonella databases and finally to all proteobacteria in the gamma subdivision. Additional BLAST searches were conducted on other databases available at the Sanger Institute.Nucleotide and amino acid sequence alignment was done by the Clustal W method using software from DNASTAR. Default parameter settings were used. Nucleotide polymorphisms were located within the PT4 reference genome using the EditSeq and SeqMan programs. The nucleotide location of interest ± 150 bp of flanking DNA was used to conduct BLAST searches against the Salmonella genomes available as public databases from NCBI http://www.ncbi.nlm.nih.gov/sutils/genom_table.cgi and The Sanger Institute http://www.sanger.ac.uk/cgi-bin/blast/submitblast/Salmonella. Gene sequences were translated to amino acid sequences and aligned with DNAStar MegAlign software. Results are shown in Table S. Enteritidis and S. Typhimurium. Linkages to proteins and inclusion of some information about conservation of amino acid change, gene class and gene function is included in supplemental information  of . I serovar Enteritidis PT13a that differentiate subpopulations within variant pathotypes. This file is a searchable spreadsheet that lists each confirmed SNP according to its location in reference strain Salmonella enterica subsp. enterica serovar str. P125109NC_011294 (GenBank AM933172). An investigator can type in a gene name or label under the "find" function under "Edit" in Word Excel to see if the gene they are interested in contains a SNP. Column headings indicate specific information about each SNP, including if it is synonymous or non-synonymous. This is the file that will be updated in the event errors or additions are reported or discovered within the database.Click here for fileSalmonella Enteritidis PT13a (SEN) to the genome of Salmonella Typhimurium LT2 (STM)Interval map aligning SNPs of . This file was generated to search for gaps between genes that might indicate an insertion or deletion that was missed by the mutational mapping approach. Primers were generated only from the reference sequence. If the test sequence has additional DNA not present in the reference sequence, it could go unnoticed. Regions offset by 5 genes or greater suggest different gene contents. See text for further information.Click here for file