SNP (Single Nucleotide Polymorphism), the most common genetic variations between human beings, is believed to be a promising way towards personalized medicine. As more and more research on SNPs are being conducted, non-standard nomenclatures may generate potential problems. The most serious issue is that researchers cannot perform cross referencing among different SNP databases. This will result in more resources and time required to track SNPs. It could be detrimental to the entire academic community.Universal Automated SNP Identification System) is a web-based server for SNP nomenclature standardization and translation at DNA level. Three utilities are available. They are UASIS Aligner, Universal SNP Name Generator and SNP Name Mapper. UASIS maps SNPs from different databases, including dbSNP, GWAS, HapMap and JSNP etc., into an uniform view efficiently using a proposed universal nomenclature and state-of-art alignment algorithms. UASIS is freely available at http://www.uasis.tk with no requirement of log-in.UASIS (UASIS is a helpful platform for SNP cross referencing and tracking. By providing an informative, unique and unambiguous nomenclature, which utilizes unique position of a SNP, we aim to resolve the ambiguity of SNP nomenclatures currently practised. Our universal nomenclature is a good complement to mainstream SNP notations such as rs# and HGVS guidelines. UASIS acts as a bridge to connect heterogeneous representations of SNPs. SNP, or Single Nucleotide Polymorphism, is defined as a bi-allele polymorphism at a single base with a frequency of more than 1% in the population . Around http://www.ncbi.nlm.nih.gov/SNP/), has collected 38,077,719 SNPs (rs#'s) for Homo sapiens to date . The amount of data has been growing significantly. In addition, there are many more SNP databases, either public or private, that are used for pharmacogenetic research. An universal nomenclature is critical for clear, unequivocal and effective communication. However, it is widely recognized that heterogeneity of SNP nomenclatures and notations has complicated the process [With the publication of the Human Genome Project (HGP) and emergence of next generation high-throughput sequencing techniques, there has been an explosion of data available for public use. SNP databases such as dbSNP , GWAS  has recommended widely-used guidelines for mutation notation, researchers of each laboratory have strong emotional attachment to their own naming system . Researchttp://www.hgvs.org/mutnomen/). However, the sole existence of the guidelines by themselves is not sufficient. The standardization of SNP identification is far from complete [Unambiguous and correct descriptions of SNPs in databases and in the literature are of utmost importance, not in the least since mistakes and uncertainties may lead to undesired errors in clinical diagnosis. HGVS nomenclature guidelines were proposed in as early as 1998  then extcomplete ,12,14.It is clear that dbSNP is becoming a major center for deposition of SNPs from various sources. The SNP nomenclature of dbSNP, rs#, is unique, clear and stable. It has been widely adopted and heavily referenced in the literature. JSNP, GWAS, HapMap and PharmGKB provide corresponding rs# when displaying their own records. We highly respect its authority.It is noted that overlapping of SNPs is very low (around 1%) among recognized databases . JSNP re"<Accession Number>.<version number>(<Gene symbol>):<sequence type>.<mutation>". However, it is not universally applied as a standard, since it is complex and not unique. Table Another candidate is HGVS mutation nomenclature guidelines, which are largely adopted by researchers and enforced by some journals. The format is like What SNPs have been discovered on gene CHR1 ? or What diseases have been found closely associated to rs28942082? HGVS nomenclature is searchable and informative, but suffers from complexity and non-unique feature.Current SNP nomenclatures, including rs#, are mostly arbitrary combination of letters and digits maintained by manual curation. The major problem is that they are not informative and only available within a single database. Automatic ways of mapping SNPs based on their names are rare. One way is to perform searching in available databases separately, and then compare the obtained records manually. For example, given only SNP names, we are unable to answer these kind of simple questions: With different nomenclatures, it is difficult to cross reference SNPs among the various databases. Research based on the data only from one SNP database will lead to an incomplete compilation of variants and inadequate genomic analysis. For researchers who track SNPs through literature scanning, it is very difficult to gain a global picture from overwhelming publications since SNPs are not uniformly searchable in the literature. It is also not possible to search by position or polymorphism information. That could be a tough data mining challenge, which consumes considerable resources and time. From the discussion above, we believe that the existing SNP nomenclatures do not provide a universal standard.Tremendous efforts have been made to keep SNP data uniformly. Besides the continuous development of HGVS nomenclature guidelines, SNP databases are integrating data from more sources.GWAS, previously HGVbaseG2P, is one of the largest SNP databases ,24. It gSome applications focus on retrieving SNPs fulfilling certain criteria such as locus and haplotype tagging. SNPper is web-based platform to search and export SNP records from dbSNP . TAMAL . We believe our nomenclature is a good complement to rs# and HGVS, acting as a bridge connecting various databases, including private and unpublished ones. A system of nomenclature has to strike a compromise between the convenience and simplicity required for everyday use and the need for adequate definition of the concepts involved [From the discussions above, it is clear that dbSNP is an important database that cannot be ignored by any application. However, it does take considerable effort to translate nomenclatures among the SNP databases. To overcome the shortcomings of rs# and HGVS nomenclatures, we propose a universal nomenclature and UASIS  for annotating novel SNPs and cross-referencing among databases instantaneously. There are utility tools available, i.e., UASIS Aligner and Universal SNP Name Generator. For newly discovered SNPs, UASIS aligner performs efficient sequence alignment and checks whether the polymorphism has been deposited in main databases, including GWAS, dbSNP, JSNP and HapMap. In addition, for each mutation, UASIS provides an identifier based on our proposed nomenclature as described above. These identifiers can be used immediately and instantaneously. In this way, researchers are free to map SNPs among various nomenclatures. More databases like PharmGKB are currently in the process of being integrated into UASIS. Universal SNP Name Generator and SNP Name Mapper take in information of a SNP and perform cross-checking among main databases.UASIS is a web-based server system . Figure Users upload flanking sequences of SNPs explicitly or by uploading a file in FASTQ or FASTA format. They could choose underlying alignment tool, which chromosome to align, and how many mismatches allowed according to query characteristics. Meanwhile, they are able to specify advanced options manually. The human reference genome used is based on HG19, downloaded from UCSC  and Illumina (a few hundreds base pairs). Bowtie and BWA are sufficient to perform long read alignments if there are just a few mismatches. Bowtie supports queries up to 1024bp . Mah et Query sequences are uploaded and aligned to reference human genome by executing Bowtie or BWA. Then UASIS checks whether the query SNP exists in dbSNP, GWAS, JSNP or HapMap by inspecting the allele position. UASIS is very responsive since the alignment tools are efficient."none" message will be displayed for that database. Results in SAM format can be downloaded for further analysis. Figure Alignments will be listed in tabular form, including query id, allele position, alleles, UASIS identifier, dbSNP rs#, GWAS id, JSNP id, HapMap id. Given the polymorphism position, we are able to obtain corresponding identifiers recorded in dbSNP, JSNP and HapMap. If no record is found in a database, a To evaluate the accuracy and efficiency of UASIS, we conducted experiments on simulated and real SNPs with length 35, 76, 128 and 512bp, and performed cross-checking between dbSNP and JSNP. CPU time on a quad core of a 2.4 GHz Xeon E5620 processor with 16G RAM and accuracy in percentage are evaluated (see Table ftp://ftp.ncbi.nih.gov/snp/organisms/human_9606/rs_fasta/) and JSNP (http://snp.ims.u-tokyo.ac.jp/map/Dump/). For Bowtie, we use the options "-best -k 2 -v 3", meaning that it will report at most two hits allowing three mismatches in decreasing quality order. And for BWA, the options are "-n 3 -o 3", meaning that the edit distance is at most three and there are at most three gaps.94771 reads were simulated from the human genome (Build 37.1) using MetaSim  package For both dataset, all three tools were found to show reliability. As the read length grows, the accuracy improves. Bowtie generated higher error rate since it does not support gapped alignment. But Bowtie was very efficient, taking less than 4 seconds to process.UASIS is also introduced briefly on CBAS-SYMBIO 2010 held in Singapore. Approximately 30 people outside UASIS group have tested it.Similar to Mutalyzer , our gen"rs3897" from dbSNP. If the input name is not valid, a "None" message will be displayed. Figure The SNP Name Mapper performs similar task to Name Generator. However, it is more suitable for researchers who have some SNPs at hand, and would like to know what related works have been done in the literature. Users are required to provide an existing SNP name from certain database. For example, Universal Automated SNP Identification System) proposes an informative, unique and unambiguous nomenclature that serves as a good complement to the present methods of identifying SNPs. The universal nomenclature is important for naming newly discovered or unpublished SNPs. The most significant advantage is that it provides a bridge to cross reference SNP identifiers among various databases. UASIS Aligner is an utility to perform pairwise sequence alignment and cross referencing in real time (<20s). Through Universal Name Generator and SNP Name Mapper, SNPs from dbSNP, GWAS, JSNP and HapMap can be mapped to one another. More databases are being integrated into UASIS. UASIS not only helps to achieve uniform notation of SNPs in the literature, but also aid in determining accurate SNP genotypes and haplotypes.Differing SNP nomenclatures have been a large concern for a long period. UASIS Project name: UASIS : Ubuntu Server 10.04, kernel 2.6.32Programming language: C++ and PHP web interface, could be assessed by various browsers, including Molliza Firefox, Chrome, Internet Explorer, etcDatabase: MYSQL 5.1.41, storing 68382797 SNP records from dbSNP and JSNPUASIS focuses on the standardization of single nucleotide polymorphisms. Currently we do not handle more complicated variations, including reversions, deletion/insertions of multiple bases, rearrangements and CNVs (Copy Number Variations) . BecauseThe authors declare that they have no competing interests.Dr. Poo and Dr. Mah proposed the novel idea of universal nomenclature, reviewed and compared various SNP nomenclatures. Mr. Cai completed the universal nomenclature, developed the web server system, conducted the experiments and drafted this paper.


Meiotic recombination events tend to cluster into narrow spans of a few kilobases long, called recombination hotspots. Such hotspots are not conserved between human and chimpanzee and vary between different human ethnic groups. At the same time, recombination hotspots are heritable. Previous studies showed instances where differences in recombination rate could be associated with sequence polymorphisms.In this work we developed a novel computational approach, LDsplit, to perform a large-scale association study of recombination hotspots with genetic polymorphisms. LDsplit was able to correctly predict the association between the FG11 SNP and the DNA2 hotspot observed by sperm typing. Extensive simulation demonstrated the accuracy of LDsplit under various conditions. Applying LDsplit to human chromosome 6, we found that for a significant fraction of hotspots, there is an association between variations in intensity of historical recombination and sequence polymorphisms. From flanking regions of the SNPs output by LDsplit we identified a conserved 11-mer motif GGNGGNAGGGG, whose complement partially matches 13-mer CCNCCNTNNCCNC, a critical motif for the regulation of recombination hotspots.Our result suggests that computational approaches based on historical recombination events are likely to be more powerful than previously anticipated. The putative associations we identified may be a promising step toward uncovering the mechanisms of recombination hotspots. Meiotic recombination is an important cellular process. Errors in meiotic recombination can result in chromosomal abnormalities that underlie diseases and aneuploidy ,2. A maiThere are three methods for estimating recombination rates. Sperm-typing is an experimental method that allows the recombination rate for an individual man to be measured . It has Using the above methods, extensive variation in recombination hotspots has been observed across species, implying that hotspots evolve rapidly ,17. Despcis- and trans-acting elements that jointly influence hotspot activity [This interplay between conservation and variability has been difficult to model. One model explaining the rapid evolution of recombination hotspots is the biased transmission of non-hotspot alleles, as a result of which a hotspot tends to disappear ,25. Thisactivity ,30.et al. [et al. [et al. [One approach to correlating recombination with sequence features is to divide the genome into regions of high recombination rates  and low recombination rates , and then measure the correlation by comparing the enrichment for candidate elements in jungles and deserts. Using this method and LD-based historical recombination hotspots in human, Myers et al.  observed [et al.  observed [et al.  extended [et al. ,14,33. Net al. [RNF212 gene, a putative ortholog of the ZHP-3 gene in Caenorhabditis elegans whose functions are involved in recombination and chiasma formation. Chowdhury et al. [RNF212 gene, and also found differences in sequence polymorphisms associated with male and female recombination.Pedigree-based methods have been used to search for sequence polymorphisms associated with genome-wide recombination phenotype. Kong et al.  identifiy et al.  identifitrans- and cis-factors of recombination hotspots. Using a PCR-based method on mouse germ-lines, Baudat and de Massy [trans-acting element that activates by 2,000-fold the recombination activity of a hotspot near the Psmb9 gene in the mouse major histocompatibility complex, as well as a cis-acting element that represses the hotspot. By comparing crossover rates in very short regions among different males using sperm genotyping experiments, Jeffreys and Neumann [Prdm9 gene is uniquely expressed in early meiosis, capable of trimethylation of histone H3 lysine 4, and has a role in infertility and double-strand break repair [trans-acting protein for recombination hotspots of human and mouse [et al. [Prdm9 locus has a strong effect on sperm hotspot activity. However, since the 13-mer motif occurs in only about 40% of human hotspots [Prdm9 gene can explain only about 18% of variation in human recombination phenotype [Molecular experimental approaches have also been used to predict de Massy  identifi Neumann ,35 ident Neumann . It is kk repair . Recentlnd mouse -39. Impo [et al.  demonstrhotspots  and the henotype , it is uet al. [et al. as well as other comparisons between LD patterns and sperm typing observations [In this work we investigated whether SNP population data, such as that in the HapMap database, could be used to uncover associations between differences in hotspot strength and sequence polymorphisms. Hellenthal et al.  argued trvations  highlighWe developed a simple method called LDsplit that divides the population of chromosomes into two subpopulations by SNP alleles , estimates the recombination rates for both subpopulations of chromosomes, and compares the difference between these rates to the difference expected by chance. To correct for potential bias due to different allelic backgrounds, we standardized the hotspot difference of each hotspot-SNP pair by the empirical distribution of SNPs with the same minor allele frequency (MAF) in a chromosome.First, running on HapMap SNP data, LDsplit was able to uncover the known association between the FG11 SNP and the DNA2 hotspot , with thq-values are available in Additional file Our results suggested that LD-based computational methods for associating sequence polymorphisms with recombination hotspots are likely to be more powerful than previously anticipated. Moreover, the putative associations that we identified using LDsplit would be an important step toward uncovering regulatory mechanisms of recombination hotspots. The hotspot-SNP pairs in chromosome 6 of the HapMap CHB + JPT population and their LDsplit ρ = 4Ner for each segment , and the recombination activity of a segment is measured by the product of ρ and physical length of the segment. The recombination activity of a hotspot, also called hotspot 'strength', was then measured by the sum of recombination activities of the segments that the hotspot spans. Since the actual level of hotspot strength in each chromosome is unknown, we used the difference of historical hotspot activities between the two subpopulations as a proxy for the current hotspot differences between the subpopulations . Let ρ0 and ρ1 denote the strengths of the same hotspot of two different subpopulations, then the difference of recombination activities between the two subpopulations, denoted Δρ, is defined as (ρ0 - ρ1)/(ρ0 + ρ1), that is, the difference of hotspot strengths normalized by the sum. To measure the significance of a hotspot-SNP association, we estimated the P-value of the alternative hypothesis that the observed Δρ is non-zero, using permutation tests . In computing P-value, we assumed that the Δρ from the random split should be normally distributed around zero. We used the Shapiro test to filter out the hotspots that violated this assumption. However, we observed that hotpots with non-normal distributions of random Δρ typically contain a few 'outlier' chromosomes. We developed a method to identify such outlier chromosomes  and observed that after their removal from the population, the distribution of Δρ often passed the normality test.We first provide an overview of the LDsplit approach. Technical details of the approach are provided in the Materials and methods section. For each candidate SNP, LDsplit divides the population of chromosomes into two subpopulations: one subpopulation containing chromosomes having allele 0 of this SNP, and the other subpopulation having allele 1. If the SNP is associated with the hotspot, then different alleles of the SNP may putatively correspond to different levels of recombination activities in the hotspot. For example, while one allele could enhance the hotspot, the other allele could suppress it. Using the LDhat method we estimated the population recombination rate ρ as the ρ of the more frequent allele minus the ρ of the rare allele. Then, for each hotspot-SNP pair, we estimated the expectation, denoted E(Δρ), and standard deviation of Δρ, denoted SD(Δρ), from the empirical distribution of those SNPs with equal MAF values from the chromosome that contains the hotspot-SNP pair. Then, the standardized version of hotspot difference is defined as (Δρ - E(Δρ))/SD(Δρ). We applied the same standardization to the permutation data, and obtained the standardized P-values.There might be a potential bias in estimating differences in recombination rates as a result of the frequency difference between the two alleles of a SNP. The allele with lower frequency tends to be younger and its subpopulation is likely to have stronger LD around the SNP than the allele with higher frequency . Moreoveet al. [We first tested if LDsplit was able to correctly predict a hotspot-SNP association that had been shown to exist by sperm typing experiments , namely et al.  used theP < 0.000447) and the YRI (P < 0.0235) populations. In the CEU population, the association is not statistically significant, but the T allele still has a higher population recombination rate than the C allele, consistent with those in the other populations . Therefore, we identified the outlier chromosomes and removed them from the corresponding populations. After the removal of the outlier chromosomes, we observed: (1) the distribution of Δρ passed the normality test; (2) the association between FG11 and DNA2 in the CHB + JPT population became even more significant, and the association in the YRI population also became significant . Running on simulated SNP data, LDsplit calculated for SNPs with MAF ≥ 0.3  the We tested different values of key parameters, namely the positions of causal SNPs and hot allele frequencies at the beginning and the end of the simulation  using the ldSelect program [P-values. By these criteria, we counted true positive (TP) SNPs as the number of tag SNPs that are both true and positive, and similarly for false positive (FP), true negative (TN) and false negative (FN) SNPs. The sensitivity, specificity, and positive predictive value (PPV) are TP/(TP + FN), TN/(TN + FP) and TP/(TP + FP), respectively. Note that we inserted only one causal SNP while there were usually much more non-causal SNPs, which might amplify the effect of false positives in the calculation of the PPV. For each population, we assessed the above measures of performance among haplotype samples. The average performance of LDsplit on these populations is shown in Table We then evaluated the performance of LDsplit as follows. First, we measured how likely LDsplit was to predict the hot and cold alleles of the causal SNP. If the hotspot strength in the subpopulation of the hot allele was bigger than that of the cold allele, we counted it as a correct prediction of direction. We report the proportion of correct predictions in the samples of a population as a measure of performance. Second, we tested if the LDsplit  program , and fro blocks r ≥ 0.8 usIn the above simulation, we assumed that the causal SNP was produced by a single mutation event that split the coalescent tree into two subtrees. We consider these simulations to be run under 'normal' conditions. In addition, we tested the robustness of LDsplit under some unusual conditions. The first case is recurrent mutation at the causal SNP. During evolution, multiple mutation events were allowed to occur at the causal SNP after its birth, and its mutation rate was specified to be ten times higher than the background rate. As shown in Table P-values of associations, we assumed that the distribution of random Δρ (that is Δρ of random splits into two subpopulations) could be reasonably approximated by the normal distribution. For each hotspot, we estimated the distribution of Δρ based on 200 random splits. We rejected hotspots with non-normal distributions of random Δρ , and were left with 781 hotspots.Encouraged by the results for the sperm typing case study and the simulation, we performed a large-scale analysis. First, we identified a list of recombination hotspots from the SNP data for chromosome 6 of the CHB + JPT population of the HapMap dataset, phase II, from which we filtered out hotspots of weak intensity compared to the background . In this way we identified 5,149 hotspots. As mentioned in the outline of LDsplit, to estimate the For each selected hotspot, we considered all SNPs that were within a distance of 200 SNPs on either side of the hotspot and with an MAF of at least 0.3. The lower bound of the MAF value was needed for an accurate estimation of the recombination rate for each subpopulation.P-value to a q-value [q-values to be no more than α, the expected proportion of false positives (FDR) is also no more than α.In this study, as in most genome-wide studies where the number of features tested is typically more than tens of thousands, an important concern is multiple testing. To achieve a balance between the number of false positives and the number of true positives, we used the false discovery rate (FDR). The FDR is defined as the expected proportion of false positives among those features claimed to be significant . In addi q-value . SpecifiP-values and q-values for these artificial hotspot-SNP pairs, in one-to-one correspondence with the real pairs. As shown in the histograms of real and random P-values  was not by chance. As shown in Table To test further if these hotspot-SNP pairs could have been selected by chance, we simulated the null model  as follows. For each hotspot-SNP pair tested in the real case, we randomly divided the population into two subpopulations whose sizes were equal to the sizes of the real case. Then we calculated q < 0.01) measured by: (1) the physical distance (in kilobases) from the SNP to the center of the hotspot; and (2) the number of SNPs between the candidate SNP and the proximal boundary  of the hotspot. Figure Next, we studied the distribution of the hotspot-SNP distances of significant hotspot-SNP pairs , we call the hotspot-SNP pair 'derived-cold' (or 'derived-hot'). The ancestral states of HapMap SNPs were obtained from dbSNP and alignment between human and chimpanzee genomes . Supposeq-values with signals of selective sweep estimated using iHS scores from Haplotter [q-values in Figure R2, which measures the fraction of variance explained, is mostly less than 0.01. The strongest correlation is when SNPs are inside hotspots and the derived allele is cold, with R2 = 0.00602. Therefore, most signals of hotspot differences in LDsplit cannot be explained by selective sweep.Some of the hotspot differences might also be caused by the extended haplotype block created by selective sweep at one allele. To estimate the confounding effect between LDsplit and selection, we correlated the LDsplit aplotter . For a SFrom the large scale analysis, we identified a list of candidate SNPs associated with recombination hotspots in chromosome 6 of the human genome. In this section, we analyze these SNPs in search of genomic features that might be associated with the regulation of recombination hotspots. After controlling for confounding effects such as hotspot-SNP distance and LD blocks, we selected 498 candidate SNPs and 604 control SNPs . The goal was to identify genomic features that preferentially occur near candidate SNPs but not control SNPs.P = 0.0616, Wilcoxon test).First, we searched for conserved motifs near candidate SNPs. The SNPs were extended on both sides to flanking windows of 90 bases long. Running MEME on candidate and control windows, respectively, we identified three motifs in candidate windows and two motifs in control windows. The first two motifs in candidate windows are C-rich and T-rich sequences, and are similar or approximately complementary to the two motifs in the control windows (data not shown). The third 11-mer motif Figure  preferenNext, we searched for genomic elements that overlap with windows around candidate SNPs. To catch more complete information, we extended SNPs to windows of 200 bases long. Using the intersection operation of the UCSC genome browser, we counted the proportions of candidate and control windows that overlap with a certain genomic element, and assessed the significance of enrichment by Fisher's test. Of the 20 genomic elements , while (TG)n and MIR3 occur more frequently near control SNPs (P = 0.0268).Overall, there is no difference in enrichment of repeats between candidate and control SNPs in general .q-values output by LDsplit , but none of the other elements is significantly enriched or depleted  than either of the CEU or YRI populations . Moreover, the CEU and the YRI samples have a trio family structure, which may make it more difficult for LDhat to accurately estimate the recombination rates. Another reason might be the difference in demographic history.In this work, we demonstrate that the variations in strengths of recombination hotspots could be associated with sequence polymorphisms, and we propose a method called LDsplit to map such associations based on LD patterns in HapMap data. Previous work suggested that it is difficult, if not impossible, to uncover allele-specific recombination hotspots from LD patterns. However, LDsplit was able to correctly predict the association of the FG11 SNP with the DNA2 hotspot in the MHC class II region that had been directly observed by sperm typing experiments. Moreover, we carried out forward simulations of causal SNPs of recombination hotspots, and tested the performance of LDsplit on the simulated data. Despite BGC, the performance of LDsplit turned out to be reasonably good, implying that the extant hot alleles tend to experience more historical crossovers than cold alleles. Then we applied LDsplit to chromosome 6 of the CHB + JPT population and observed widespread associations of sequence polymorphisms with hotspots unlikely to occur by chance. Taking into account the ancestral states of SNPs, we showed that LDsplit is not confounded by the artifact of different allelic backgrounds or selective sweeps. From flanking regions of the SNPs identified by LDsplit with significant association with hotspots, we found a conserved 11-mer motif, whose complement partially matches the 13-mer CCNCCNTNNCCNC, a critical motif for the regulation of recombination hotspots. This result not only confirms previous work ,37 aboutGiven the aforementioned restrictions, the LDsplit method is not expected to uncover all associations. Nevertheless, the agreement between the LDsplit prediction and sperm typing result suggests that LDsplit is promising in connecting historical recombination with the extant phenotype of a recombination hotspot. The SNPs predicted by LDsplit may co-segregate with some evolutionarily inheritable factors that regulate the increase and decrease of recombination rates. Therefore, this work should provide an important step towards understanding the regulatory mechanism behind recombination hotspot activity.We used a subset of the phased data of HapMap phase II, release 22, which consists of 90 JPT and 90 CHB samples of chromosome 6. In total, there are 176,352 SNPs, out of which 56,510 have a MAF ≥ 0.3. We used the latter SNPs for association with hotspots .Recombination rate profiles were estimated using the program 'interval' from the LDhat package version 2.1 . Hotspotρ. Let ρ0 and ρ1 represent the hotspot strengths of the two subpopulations, and define their difference by:Given a hotspot detected from a combined population , we first fixed the boundaries of the hotspot region. Then we estimated the recombination rates of the region for the two subpopulations separately. The two subpopulations could be either true or pseudo. To measure the difference of a hotspot between two subpopulations, we calculated the strength of the hotspot in each subpopulation, denoted by ρ - E(Δρ))/SD(Δρ), where the expectation E(Δρ) and standard deviation SD(Δρ) were estimated from the empirical distribution of SNPs with equal MAF values from the chromosome that contains the SNP in question.Then, we standardized the hotspot difference as . Therefore, the test on the sampled hotspots showed that there should be no significant difference between the two types of permutations with regard to P-values. However, we reported the more conservative results for an FDR  of 0.01 rather than the customary 0.05, taking into account that the FDR might be slightly underestimated using the 50/50 permutation tests.Due to the formidable computational cost of the permutation tests, we reused the permutation data that one of the authors (PPK) had generated previously - over a month long computation on the NIH Biowulf cluster. In those permutations the chromosomes were divided into two random sets of equal size, and homologous chromosomes of the same person were always permuted together. In the current study, however, we allowed our subpopulations to be of different sizes . Furthermore homologous chromosomes of the same individual could be separated. Thus, our permutation test should ideally have considered all possible partition sizes with the smaller partition at least 30%. To test if this difference of permutations would cause artifacts, we randomly sampled ten hotspots and calculated ρ at a hotspot deviates significantly from the normal distribution, we identified outlier chromosomes using a side score defined as follows. For each permutation, we kept track of which subpopulation the chromosome belongs to. If it is in the subpopulation with the higher hotspot strength, it gains a credit of |Δρ|; otherwise, it loses by |Δρ|. The side-score for a chromosome is the sum of the gains and losses, that is:To deal with the cases when the random Δρi is positive if the chromosome is in the hotter subpopulation in the ith permutation, and negative otherwise. The side-scores of most chromosomes were distributed normally, except for a few outliers. We used Grubbs' method to detect the chromosomes with outlier side-scores, and estimated the P-value of being an outlier using the student's t-distribution.where ΔOur simulation program was developed using Python 2.6 and based on simuPOP (version 1.0.3), an open source framework for forward simulation of population genetics . We simuA hotspot was inserted with its center at 100 kb, and a causal SNP, whose alleles determined different recombination rates at the hotspot, was inserted at various positions . In each generation, we randomly picked pairs of individuals of opposite sex as parents. For each parent we simulated the meiosis, specifically focusing on the process of recombination, as follows. If both alleles at the causal SNP were cold, then there was no hotspot, and the crossover position was uniformly distributed along the 200 kb. For simplicity, we assumed that there was one crossover in a chromosome of 200 Mb per meiosis; thus, in 200 kb the probability was 0.001. If the causal SNP was heterozygous, then the probability of crossover was increased by ten times the background rate; if both alleles at the causal SNP were hot, then the probability increased by 20 times. Moreover, the crossover position was simulated under normal distribution centered at the position of 100 kb where the hotspot center is located. We also simulated BGC by repairing a small region  from the chromosome that initiates a double-strand break by copying from the other chromosome. At the center of the tract was the breakpoint of the crossover, and the tract length was simulated in Gaussian distribution with mean equal to 500. To simulate severe BGC, we increased the mean tract length to 10 kb. After meiosis, the parents transmitted their gamete chromosomes to the next generation.At the beginning of evolution, the causal SNP had either the cold or hot allele fixed in the population. Then a derived allele was introduced by a mutation event and invaded the population by some evolutionary force. The frequency of the hot allele in the last generation was specified as a simulation parameter. Changes in the hot allele frequency followed a linear trajectory, dictated by the reject-sampling algorithm . If the Some hotspots tend to be near each other, and it is computationally costly to estimate recombination rates. Thus, to avoid redundant splits for closely located hotspots, we searched for association using sliding windows centered at candidate SNPs. Note that we may estimate different recombination rates for the same segment in the overlapping region of two sliding windows, due to the difference of non-overlapping SNPs. We resolved this issue by setting each sliding window to span 500 segments and discarding recombination rates of 50 segments at both ends of the window. As we observed, the estimation of the recombination rate of a segment usually depended on no more than 100 SNPs surrounding it.q ≤ 0.01 as candidates and SNPs with q > 0.5 as controls. Then, to correct for redundancy of linked SNPs, we clustered SNPs into LD blocks using the ldSelect program [r2 ≥ 0.8. To control for hotspot-SNP distance, for each hotspot with at least one candidate LD block, we selected control blocks so that the distances between the block centers and the hotspot center were closest to the corresponding distance of the candidate block. For each candidate block, we picked two control blocks, if available, for a more complete coverage of background signals. Then, we selected the SNP from each candidate LD block with the smallest q-value as the tag candidate SNP, and similarly from the control blocks. The resulting 498 candidate and 604 control tag SNPs were compared in the following analysis.To search for genomic features associated with candidate SNPs, we first selected candidate and control SNPs as follows. From the output of LDsplit on chromosome 6 of the HapMap JPT + CHB population, we chose split SNPs with  program  with r2 First, we extracted 90-base DNA sequences around the candidate and control tag SNPs from human genome reference NCBI b36.2, and uploaded them onto the MEME web server ,53 to seBGC: biased gene conversion; FDR: false discovery rate; LD: linkage disequilibrium; MAF: minor allele frequency; PPV: positive predictive value; SNP: single nucleotide polymorphism.JZ, PPK, DRC, and TMP designed the study. JZ implemented all algorithms and performed all computations with the exception of generating genome scale permutation data, which was done by PPK. JZ, PPK, DRC, and TMP analyzed the results. JZ and TMP wrote the paper with input from PPK and DRC.rs (reference SNP ID) number of SNPs and LDsplit P-values and q-valuesA tab-delimited table in which each row is a hotspot-SNP pair in our original dataset, and columns are positions of hotspots and SNPs, .Click here for fileSource code for the LDsplit program and simulation along with a user's manual.Click here for fileFigures S1 to S4 and Tables S1 to S6.Click here for file


Identifying SNPs is important for accurate quantification of methylation levels and for identification of allele-specific epigenetic events such as imprinting. We have developed a model-based bisulfite SNP caller, Bis-SNP, that results in substantially better SNP calls than existing methods, thereby improving methylation estimates. At an average 30× genomic coverage, Bis-SNP correctly identified 96% of SNPs using the default high-stringency settings. The open-source package is available at  Cytosine methylation of DNA plays an important role in mammalian gene regulation, chromatin structure and imprinting during normal development and the development of pathological conditions such as cancer. With the dramatic increase in throughput made possible by next-generation DNA sequencing technologies, sodium bisulfite conversion followed by massively parallel sequencing (Bisulfite-seq) has become an increasingly popular method for investigating epigenetic profiles in the human genome (reviewed in ). SeveraBisulfite treatment of DNA converts unmethylated cytosines to uracils, which are replaced by thymines during amplification. This dramatic change to sequence composition necessitates specialized software for almost all sequence analysis tasks. Typically, the first step in processing high-throughput sequencing data is to map and align each read to the correct location in the reference genome (genome mapping), and a number of powerful tools have been developed to map bisulfite-converted reads (reviewed in ). The necis effects on methylation, perhaps due to the presence of key regulatory motifs [Accurate SNP calling at the positions immediately surrounding a cytosine is equally important. Those nucleotides lying one or two positions 3' of the cytosine are particularly critical, as they are subject to the specificity of particular methyltransferases. These methyltransferase-specific context positions can be organism or cell type specific. In mammals, CpG dinucleotides are often highly methylated in most cell types, while CpA dinucleotides have much lower methylation levels and are cell type restricted ,15. In py motifs . Heterozy motifs  and impoy motifs -22.Despite the great interest in Bisulfite-seq and the availability of a number of tools for genomic mapping, no adequate software exists for SNP calling . In ordedirectional bisulfite-seq protocol [Non-directional protocols  have been used [A key property of some bisulfite-related protocols is that G nucleotides on the strand opposing a C are not affected by conversion. This strand-specificity principle has been exploited in order to distinguish bisulfite conversion from C>T SNPs . The Illprotocol . Non-direen used , but havad hoc way. The Salk Institute group filtered out cytosines which did not have one or more unconverted Cs on the C-strand, but this approach can result in lost information about completely unmethylated cytosines  [The inherent directionality of Illumina Bisulfite-seq has thus far been used only in a limited and ulation) ,29. Our ulation) . A thirdulation) . Importaulation) . Others ulation) ,20,21. SBis-SNP, that is based on methods that have proven successful in non-bisulfite SNP calling [Here, we describe a probabilistic SNP caller,  calling ,13. Bis- calling . Bis-SNP calling , which t.bam format), which can be generated by popular Bisulfite-seq mapping programs such as MAQ, Bismark, BSMAP, PASH, or Novoalign (reviewed in [.bam) files.The two primary steps in the Bis-SNP workflow are outlined in Figure iewed in ). This aiewed in  and any X, and estimate these as a group separately from T>T, A>T, or G>T. The effect is that we can effectively recalibrate base call quality scores for all except the X nucleotide, improving our ability to accurately identify SNPs. Importantly, we are able to improve SNP calling at cytosines by recalibrating 'G-strand' Gs that are complementary to the cytosine.The Bis-SNP model relies on the accuracy of base quality scores, which are initially estimated by the instrument-specific base caller. However, these initial base scores do not accurately represent true error probabilities, which are highly dependent on local sequence context . In the .bed or .wig file, and a separate output file is generated for each cytosine context specified by the user on the command line. Example cytosine contexts are CG, CH, or CHH . The .wig output contains the methylation percentage for each methylated cytosine, while the .bed format also contains the number of C/T reads the percentage is based on, plus the strand of each cytosine relative to the reference genome. For SNPs, Bis-SNP can return a Variant Calling Format (.vcf) file, which contains all SNP calls and likelihood scores in addition to methylation percentages.The user can choose among several output files. For methylation levels, Bis-SNP can return a standard UCSC G), as shown in Figure A and B). The prior probability of each genotype, π(G), is determined using population data from dbSNP (including 1000 genomes data) similar to SOAPsnp [AB, is expressed as Pr(D|G = AB) and is the product of observing the base call at each individual read j . As described below, Pr(jD|G = AB) is calculated according to the strand of read j and several bisulfite-specific parameters, β,α and γ . In the case of Bisulfite-seq, this is true for A:T genotypes but not C:G. For C:G genotypes, the probability of observing a T depends on the strand of the read, the methylation state, and the efficiency of bisulfite conversion. Reads on the G-strand opposite the cytosine are treated with the normal GATK model. Reads on the C-strand use an alternate model that considers C>T substitutions as either potential errors or bisulfite conversions . The probability of observing a bisulfite conversion event depends on both the underlying methylation state and bisulfite conversion errors. While none of these are observed directly, they are included in the model as variables β,α and γ as described in Equation 5 in the Methods section.In the GATK non-bisulfite SNP calling model, the probability of observing a base call different from the presumed genotype underconversion, while a methylated C that is converted to T is referred to as an overconversion. The underconversion rate, α, is often estimated using either a spike in control [γ, which is set to 0% by default. In the future, this could be estimated by spiking in fully-methylated control DNA.After bisulfite treatment, an unmethylated C that fails to get converted to a T is referred to as an  control  or the u control . This raβ, which we describe next.The percentage of methylated reads at a given cytosine position can vary widely. Since C reads and T reads yield more information about the presence of a C>T SNP than T reads, the locus-specific methylation rate can strongly influence SNP calling. In mammalian genomes, CpG methylation levels are multimodal, with various classes of functional elements having distinct methylation patterns. At least four different classes exist with mean methylation rates ranging from around 0% to over 80% ,24. Furtnaive estimate for β where the probability of a read being methylated or unmethylated at any particular cytosine position was 0.5. Second, we used context-specific estimates which were determined in a two-round procedure as follows. In the first round, naive estimates were used as described above, and the resulting SNP calls were used along with dbSNP to select a set of high-confidence non-SNP homozygous cytosines (probability>99.99%). These homozygous cytosines were used to estimate average methylation levels for a set of cytosine sequence contexts that could be specified on the Bis-SNP command line . In the third and final estimation method, β was estimated for each cytosine locus individually using the number of C and T reads . The latter two methods performed substantially better than naive estimation, so those are the only two discussed below. We evaluated accuracy using an actual whole-genome Bisulfite-seq dataset from a normal  human colon mucosa sample published previously by our lab [We evaluated Bis-SNP calling accuracy for each of the three different methylation estimation methods  using a novel bisulfite-sequencing approach [The potential applications of Bisulfite-seq in basic biology and medicine are broad, and Bis-SNP can be used for the majority of Bisulfite-seq experimental designs including Whole-Genome Bisulfite-Seq (WGBS), Reduced Representation Bisulfite-Seq (RRBS), and customizable genome selection methods. While we have focused on human studies, Bis-SNP can output methylation levels split up according to user-defined cytosine contexts, which makes it applicable to analysis of approach .methQTLs, reviewed in [An intriguing potential use of Bisulfite-seq and Bis-SNP is the study of genome-wide associations between SNPs and DNA methylation patterns (i.e. iewed in ). While ProperlyPaired field set.Reads with mapping quality scores less than 30 and those mapped to multiple genomic regions were removed, as are PCR duplicates . For paired-end reads, we remove read pairs that do not have the RealignerTargetCreator to count mismatch number but not count thymine as a mismatch when the reference genome position is cytosine. After we create a potential indel interval, we realign using a modified version of GATK's IndelRealigner. PCR duplicate reads are marked after indel realignment.We use GATK to perform local multiple sequence realignment and sequence recalibration mostly as described . Since mCountVariantWalker and TableRecalibrationWalker classes. The algorithm first tabulates empirical mismatches to the reference at all loci not known to vary in the population . These counts are categorized by their reported instrument-reported quality score (R) and position (cycle) within the read (C). In tabulating mismatches, we do not count thymine as a mismatch when the reference genome position is cytosine .For base quality recalibration, we modify the GATK algorithm to account for bisulfite conversion by extending the GATK By default, only positions with a recalibrated Base Calling Quality Score of greater than 5 are used for SNP calling. This quality cutoff can be set using a command line parameter  represent the base calls at a particular genomic position i that is covered by r sequencing reads. We then calculate the posterior probability by (1) as in GATK:We begin with the bayesian likelihood model of GATK , and maG is the underlying diploid genotype, AB, with A and B being the two parental alleles. π(G) is a genotype prior probability for observing the given genotype based on the genotype of the reference genome and population frequencies, the same as discussed in Table Pr(D) is defined as the sum over all possible genotypes ∑AB π(AB) Pr (D|AB), but is the same in each case and can generally be ignored since we are concerned with likelihood ratios. We assume that each of the two alleles are equally likely to be sequenced, and calculate the overall likelihood of D as the product of all individual reads (2),(3):Here, ε that the position is an error, which is used in the following calculation.The following steps are shown for single-end sequences. For paired end sequences, the first end is treated as described, but the second end is reverse complemented before performing these calculations (because the Illumina second end is the complementary strand of the same template as the first end). This changes G>A bisulfite substitutions, which occur on the second end, to the actual C>T substitutions present on the bisulfite-converted template. The recalibrated base quality scores are on a phred scale which represents the probability a), thymine (t), bisulfite conversion does not apply and the probability estimation is straightforward as shown for t:When the underlying allele is adenine . When the underlying allele is a c or a g, however, the probabilities are strand-specific since bisulfite conversion only affects one strand in the directional Bisulfite-seq protocol , as well as the bisulfite conversion efficiency (α and γ). Bisulfite treatment converts all unmethylated cytosines to thymine, but in practice it is not 100% efficient [α is the estimated frequency of unmethylated cytosines which are not converted . By default, α is set to 0.0025 but can be specified by the user. We also include a γ parameter for over-conversion, i.e. the rate at which methylated cytosines are converted. Although this is not routinely measured in practice, it could be estimated by including an enzymatically methylated control DNA [γ is set to 0 but can be specified by the user. The full likelihood calculation for cytosines is as follows:Here, l Figure . The proEquation . When thEquation . When thd in DNA . In thistrol DNA , or a seB = c results in a very high probability of seeing a t+ (a 't' read on the C-strand), but a very low probability of seeing a t- (an 'a' read on the G-strand). The genotype best Gwith the highest posterior probability Pr(G|D) is chosen, and the final output score is the odds ratio between the best (bestG) and the second best (nextbestG), as in Equation (6). In practice, we optimize execution by evaluating only the subset of the 10 possible diploid genotypes that are possible given the sequences read.The key to these calculations is that reads on the same strand as the inferred cytosine allele (denoted with +) are treated differently than reads from the opposite strand (denoted with -). As expected based on the example in Figure α and γ typically vary by less than 1%, so the critical parameter included in Equation 5 is the methylation rate β. Since this rate varies by genomic context, organism, and even cell type, we allow the user to specify the possible contexts as a set of n nucleotides sequences specified by their IUPAC degeneracy codes . In mammalian genomes where typically only the single base 3' of the cytosine is considered relevant, the user would specify CG and CH (the Bis-SNP default). For Arabidopsis, one might specify CG, CHH, and CHG. Any arbitrary number of 5' and 3' bases may be specified in order to accommodate the full range of Bisulfite-seq assays. For instance a CCGG pattern could be specified for MspI restriction sites inherent to the RRBS protocol  is created for each cytosine context specified by the user. For each cytosine determined to have the particular sequence context, the percent methylated (the number of C reads on the C-strand divided by the number of C or T reads on the C-strand) is output as the score field. To aid in statistical analysis, a second field contains the total number of C/T reads.Non-conversion of unmethylated Cs is known to preferentially affect the 5' end of Illumina-generated reads, most likely driven by the re-annealing of sequences adjacent to the fully methylated sequence adapters during bisulfite conversion. We control for this using a 5' non-conversion filter as implemented in our earlier work . For eacUsing the approach of GATK, we apply additional quality filters before SNP calling to avoid known sources of false positives. SNPs found in clusters (two or more within a ten-base-pair window) were filtered out. SNPs with coverage depth above 120, Strand Bias(SB) score more than -0.02, or Quality by Depth(QD) less than 1.0 are filtered out. All of these parameters are configurable . If BAM contains Mapping Quality scores, suspicious regions are filtered out when greater than 10% of aligned reads (minimum of 40 reads) have mapping quality of 0.Bisulfite sequencing can have higher strand biases since high bisulfite concentration can lead to DNA degradation when the depurination step causes random strand breaks ,43. We cz reads at each individual nucleotide locus. The following formula is used, where N is the mean coverage of total dataset before downsampling (32× in this case), n is the desired downsampling coverage, and m is the actual coverage at the particular locus.We downsampled the human colon mucosa Bisulfite-seq dataset into different mean coverages using GATK, which randomly picks G-strand, while at other positions we combine the two strands to get read counts. After these filters, we use a K cutoff which can vary from 0-10 and apply the K-allele threshold as follows. For positions with n passing reads where n is less than 10, we require that each of the two alleles have at least K reads. For positions where n is greater than 10, we require at least K of 7 reads and at least 10%, and excluded all C/T SNPs. The UCLA group [The K-allele method was used to identify heterozygous SNPs as a generalization of described methods ,30, bothha group  used a sbisReadMapper version 1 [genomePrep.pl to preprocess the reference genome and extract cytosine position in each chromosome. The built in read mapper could not handle our large BAM file, so we circumvented the mapping step and used the BAM files directly as input. This is not a standard part of the bisReadMapper package, and required us to divide our BAM alignment files to separate reads aligning to the forward strand of the reference genome from those aligning to the reverse strand. We used the following bisReadMapper parameters: allC=1; length=75; snp=dbsnp135.rod; alignMode=S; qualBase=33; trim3=0; trim5=0; refDir=/path/to/GenomePreparationProcessedDir/We downloaded ersion 1 . We firsin silico (Cs converted to Ts). Input reads are filtered by their criteria: (1) Base calls at the examined SNP site and three flanking positions on either side needed to have a minimum Base Quality score of 15. (2) If a certain base was present in more than 20% of reads on one strand, its reverse complement needed to be present on at least 20% of the reads on the opposing strand. Only positions passing these two criteria were analyzed. Base Quality scores were used to weight the nucleotide count contributions to the nucleotide frequency matrix. This matrix was normalized, multiplied by the read count to get final nucleotide number matrix in each location . The Fisher exact test was applied to each nucleotide in each of the alleles . Two p-values of each allele were multiplied together for each of ten possible genotypes and then normalized. The SNPs were selected out when (1) The best genotype was 10 times more than the next most likely genotype, (2) the SNP was in reported in dbSNP, and (3) had at least 10× read depth.The Shoemaker  method wgenome_methylation_bismark2bedGraph.pl to extract cytosines. Default settings were used.We downloaded Bismark-0.50 . We convk% of reads on the C-strand to be C or T, and k% of the reads on the G-strand to be G. The default setting  data generated at USC by the TCGA (The Cancer Genome Atlas) USC-JHU Epigenome Characterization Center. Data is unpublished, but available for download via the UCSC Cancer Genomics Hub (CG-Hub ). The luOne hundred-base pair paired-end sequence datasets from two independent mouse samples were used. We downCpG: dinucleotide sequencing consisting of a cytosine followed by guanine; CpH: cytosine followed by an H nucleotide ; SNP: Single-nucleotide polymorphisms; WGBS: Whole-Genome Bisulfite-Seq; RRBS: Reduced Representation Bisulfite Sequencing; BSPP: Bisulfite Padlock Probes; ENCODE: ENCyclopedia Of DNA Elements; TCGA: The Cancer Genome Atlas; GATK: Genome Analysis Toolkit; VCF: Variant Calling Format; FDR: False Discovery Rate; IUPAC: International Union of Pure and Applied Chemistry; GWAS: Genome-Wide Association Study; BAM: Binary version of the Sequence Alignment/Map (SAM) format; SB: Strand Bias; QD: Quality by Depth.The authors declare that they have no competing interests.YL, PWL, and BPB conceived and designed the study. YL and BPB conceived the statistical approach with input from KDS. YL implemented Bis-SNP and all other computational tools. BPB and YL wrote the manuscript, with input from KS and PWL. All authors have read and approved the manuscript for publication.Detecting heterozygous C/T single nucleotide polymorphisms from Bisulfite-seq data. Hypothetical bisulfite-seq data with all labels as in Figure Click here for fileBis-SNP error frequencies at C:T heterozygous SNPs. The data for heterozygous SNP calling in Figure Click here for fileBis-SNP executable, utility scripts, and User Manual. We suggest that the user download the most recent version of these files directly from [tly from .Click here for fileBis-SNP source code. We suggest that the user download the most recent version of these files directly from [tly from .Click here for file


A challenge within the context of cryptic species is the delimitation of individual species within the complex. Statistical parsimony network analytics offers the opportunity to explore limits in situations where there are insufficient species-specific morphological characters to separate taxa. The results also enable us to explore the spread in taxa that have invaded globally.Bemisia tabaci cryptic species complex, the analysis revealed 28 networks plus 7 unconnected individual haplotypes. Of the networks, 24 corresponded to the putative species identified using the rule set devised by Dinsdale et al. (2010). Only two species proposed in Dinsdale et al. (2010) departed substantially from the structure suggested by the analysis. The analysis of the two invasive members of the complex, Mediterranean (MED) and Middle East – Asia Minor 1 (MEAM1), showed that in both cases only a small number of haplotypes represent the majority that have spread beyond the home range; one MEAM1 and three MED haplotypes account for >80% of the GenBank records. Israel is a possible source of the globally invasive MEAM1 whereas MED has two possible sources. The first is the eastern Mediterranean which has invaded only the USA, primarily Florida and to a lesser extent California. The second are western Mediterranean haplotypes that have spread to the USA, Asia and South America. The structure for MED supports two home range distributions, a Sub-Saharan range and a Mediterranean range. The MEAM1 network supports the Middle East - Asia Minor region.Using a 657 bp portion of mitochondrial cytochrome oxidase 1 from 352 unique haplotypes belonging to the The network analyses show a high level of congruence with the species identified in a previous phylogenetic analysis. The analysis of the two globally invasive members of the complex support the view that global invasion often involve very small portions of the available genetic diversity. An enduring challenge within the context of cryptic species is the delimitation of individual species within the complex Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae). In their recent review, De Barro et al. (2011) B. tabaci was a complex species composed of biotypes or a complex composed of multiple species. They concluded that separation of different members of the B. tabaci complex using morphological or biological traits was not possible and that all reliable separations were being made on the basis of molecular data, primarily mtCOI. In particular, they noted that the Bayesian phylogenetic analyses of mtCOI haplotypes by Boykin et al. (2007) B. tabaci. Here, they observed that the separation identified in Dinsdale et al (2010) B. tabaci was composed of at least 24 separate species while Hu et al. (2011) B. tabaci species is that of Dinsdale et al. (2010) Since the early 1990s there has been the ongoing debate as to taxonomic identity of the whitefly, B. tabaci species complex, this is of considerable interest in regards to two members of the complex, Middle East-Asia Minor 1  and Mediterranean  which have become global invaders through the trade in ornamentals We have applied statistical parsimony network analysis using TCS to explore whether the approach supports the one used by Dinsdale et al. (2010) Statistical parsimony analysis of the 352 unique haplotypes revealed the presence of 35 distinct networks , 2. Of tOf the 660 MEAM1 sequences, 601 (0.911) came from the invaded range and 59 (0.089) from the home range. Furthermore, of the 80 unique haplotypes, 40 came from the home range, 44 from the invaded range, four from both, 36 from the home range only and 40 from the invaded range only. Of the 374 MED sequences, 153 (0.409) came from the invaded range and 221 (0.591) from the home range. Of the 86 unique haplotypes, 76 came from the home range, 12 from the invaded range, four from both, 72 from the home range only and eight from the invaded range only.For MED, the search of GenBank identified 86 unique haplotypes. Of these 23 (MED-Hap1 to MED-Hap23) were represented by at least two sequences while the remaining 63 were represented by a single sequence , Fig. 3 The global pattern of spread beyond this assumed home range is more complex than for MEAM1. Of the 85 haplotypes considered, 14 have spread outside this region. In the case of the USA, three haplotypes have been recorded. MSy  were represented by at least two sequences while the remaining 69 were represented by a single sequence , Fig. 4.The results Φst analysis and AMOVA support there being a complete genetic break among the 35 independent networks  and so pB. tabaci which move around the world as a consequence of trade B. tabaci diversity is mtCOI and both a phylogenetic and network based analysis of this data provided similar outcomes that agree with available mating compatibility data. Future research could further consider the analysis of this dataset with a range of species distinctiveness measures to further explore the challenge of species delimitation and the applicability of the approaches used to date. The addition of genomic data will no doubt add further to our understanding of species level separation in this complex.The statistical parsimony analysis showed that of the 352 unique haplotypes, all but 15 belonged to the network that corresponded to one of the 24 putative species identified in Dinsdale et al. (2010) B. tabaci complex shows very marked phylogeographic structure B. tabaci cryptic species complex follow similar patterns. In both cases only a small number of haplotypes represent the majority of haplotypes that have spread beyond the home ranges; one haplotype in the case of MEAM1 and three in the case of MED account for >80% of the GenBank records in the invaded ranges. Furthermore, in both cases, the one haplotype for MEAM1 and two of the three for MED form hubs to which most invader haplotypes connect directly. The analysis of the haplotype data strongly suggests a role for Israel as the source of MEAM1 that has invaded most parts of the world whereas the pattern of invasion for MED suggests two sources. The first is the eastern Mediterranean where a haplotype found only in this part of MED's home range has been detected only the USA where it occurs primarily in Florida and to a lesser extent California. The second are haplotypes that occur in the western Mediterranean which have spread not only to the USA, but also to Asia and South America. It is also of some interest that in the case of MED 12 out of 86 haplotypes have spread beyond the assumed home range whereas for MEAM1 44 of the 80 haplotypes identified occurred outside the home range. Given that MEAM1 appears to have first begun to invade globally in the mid 1990s, around 10 years before MED began its global invasion, one might interpret this result in the context of the increasing body of knowledge that is accumulating in regards to global invasions which suggests that over time, invaded ranges accumulate an increasing proportion of the home range genetic diversity The results are based on a partial single maternally inherited gene (mtCOI) and what data there is supports a high level of congruence between phylogenetic relationships derived from mitochondrial and genomic DNA The structure of the MED network supports the existence of two distributions within the home range. The first is a Sub-Saharan range and the second a Mediterranean range. The significance of this is unclear, but one possible explanation is that MED evolved in Sub-Saharan Africa and then spread to the eastern Mediterranean and from there to the western Mediterranean. There is also the suggestion that MED has moved back to Sub-Saharan Africa from the Mediterranean region, possibly via Sudan and that this shift is coincident with the global spread of invasive MED that has been documented over the past 10 years The MEAM1 network is a little simpler to interpret and supports the view that it originated in the Middle East - Asia Minor region. There is also a case to suggest the involvement of Israel in the global spread of MEAM1, but this needs to be tempered in the light of the low level of sampling across the home range relative to the invaded range. However, all the haplotypes that have spread globally and have a connection back to the home range, do so via Israel. Of the countries in the home range Israel has by far the most developed ornamental plant export industry which accounts for a third of Israel's agricultural exports and ornamental plants are the primary means by which MEAM1 has been spread globally Networks are bidirectional in nature and so cannot directly show a hierarchical relationship. However, in some cases the pattern of the network can be used to propose a directional relationship. In our case the structure of the MEAM1 network supports the argument for a Middle East - Asia Minor origin Another feature of the MEAM1 network is the radiation of nodes about the Jap1 node. Here, when the branch containing haplotypes that originated only in the presumed home range are removed, of the 52 remaining nodes, 39 nodes were within one node of Jap1. Of these, 32 are known to occur only in locations outside the home range. There are several possible explanations. Firstly, this proliferation may be due to sequencing read errors. We cannot check for this as the trace files are largely unavailable. Secondly, they may simply reflect a difference in sampling effort with greater effort being apportioned to areas that have been invaded by MEAM1. This is quite plausible as the bulk of the literature pertaining to this pest come from countries where the pest has invaded.B. tabaci cryptic species complex. In so doing it provides an insight into the patterns of global spread of two highly invasive members of the complex. Studies comparing mtCOI variation in home and invaded ranges have found that depending on the example, genetic diversity in the invaded range may be less, equivalent or exceed that observed in the home range The data and analyses presented here provide the first detailed consideration of the network relationship between different members of the B. tabaci mtCOI sequences. This yielded a dataset of 2325 sequences. Sequences were then aligned using ClustalX 1.83 GAAAATTAGAGGTATTT and terminated at the 3′ end with TCCTTTCTTCTTCTGCGGT; a 657 bp fragment. Sequences with gaps, stop codons or shorter than 610 bp were removed yielding a final dataset of 1436 sequences of which 352 were unique. The consensus sequences from Dinsdale et al. (2010) GenBank was searched for all B. tabaci mtCOI haplotypes from Dinsdale et al. (2010) We used the 198 unique B. tabaci complex MEAM1 and MED. In GenBank there were 660 MEAM1 sequences and 374 MED sequences and for each sequence the collection country location was then recorded. Duplicate haplotypes were identified using TCS There are two globally invasive members of the To identify limits to gene flow among these putative cryptic species, pairwise Φst values were calculated Table S1The 352 unique mitochondrial cytochrome oxidase 1 haplotypes used in the network analysis. The GenBank accession number for each haplotype is provided along with the country of origin. The haplotype identifier refers to haplotypes belonging to MEAM1 and MED where there were at least two identical sequences in GenBank; the haplotype identifiers are used in (DOCX)Click here for additional data file.