To overcome these challenges, procedures to map textual mentions of variations to database identifiers need to be developed.Most information on genomic variations and their associations with phenotypes are covered exclusively in scientific publications rather than in structured databases. These texts commonly describe variations using natural language; database identifiers are seldom mentioned. This complicates the retrieval of variations, associated articles, as well as information extraction, i.e. the association of them to unique database identifiers. Common pitfalls in the interpretation of single nucleotide polymorphism (SNP) mentions are highlighted and discussed. The developed normalization procedure achieves a precision of 98.1 % and a recall of 67.5% for unambiguous association of variation mentions with dbSNP identifiers on a text corpus based on 296 MEDLINE abstracts containing 527 mentions of SNPs.This article describes a workflow for normalization of variation mentions, http://www.scai.fraunhofer.de/snp-normalization-corpus.html.The annotated corpus is freely available at Comparable approaches usually focus on variations mentioned on the protein sequence and neglect problems for other SNP mentions. The results presented here indicate that normalizing SNPs described on DNA level is more difficult than the normalization of SNPs described on protein level. The challenges associated with normalization are exemplified with ambiguities and errors, which occur in this corpus. Differences in a single nucleotide between members of one species are referred to as single nucleotide polymorphism (SNP). SNPs are a subclass of sequence variations, encompassing single base exchanges, single base deletions and single base insertions. It is assumed that 90 % of all human sequence variants are SNPs [Sequence variations are changes of the genetic material, usually DNA, of an organism. They are important to increase the variance of the genetic pool of species but may also lead to severe hereditary diseases like Huntington disease, Cystic fibrosis or Hemophilia. Two terms are commonly distinguished when referring to variations on the DNA level: mutation and polymorphism. Polymorphism are alterations with a minor allele frequency of ≥ 1 % in a particular population. Variations with a lower frequency are usually called mutation. However, the term mutation is also often used to imply a deleterious effect of a sequence variation without any knowledge about the underlying frequency distribution. Throughout this publication we use the term are SNPs ,3. SNPs are SNPs -6. Some are SNPs -9 and arInformation on SNPs is covered in curated databases. Nevertheless, the wealth of information about the clinical impact of SNPs is contained in free text in the form of biomedical publications. At the moment, PubMed provides access to more than 19 million citations contained in MEDLINE. The described SNP mentions need to be interpreted to be valuable, either by a human curator alone or supported by a text mining system. This interpretation often requires the normalization of the SNP mention. By normalization we refer to the association of SNP mentions in text with their corresponding database identifiers, for instance from a sequence database such as dbSNP.The interpretation of SNP mentions is challenging due to ambiguous use of different nomenclatures, missing information in a publication or sloppiness in the description. Automated text mining methods are able to extract SNP mentions from text, but only few associate these with unique identifiers in SNP databases.SNP Data Sources, followed by a description of different problems of finding a database identifier for a SNP mention in Section Normalization Process. This section reviews the evolution of a human mutation nomenclature, common problems in named entity recognition, provenance and other problems. Subsequently previous approaches for automated extraction of variation mentions are discussed. The generation of a corpus and the implementation of our normalization algorithm is described in Section Methods and the relevancy of error types is estimated on this corpus in Section Results and Discussion.The main contribution of this paper is the description and analysis of these challenges and to provide background knowledge to either build such a system or to interpret SNP mentions in text. The paper is organized as follows: A brief summary of different SNP data sources is given in Section Detailed information about SNPs can be found in various databases like Online Mendelian Inheritance (OMIM) , jSNP 11, or thee. g. EntrezGene [All these databases have links to the dbSNP database , which itrezGene , GenBanktrezGene , the UnitrezGene , HapMap trezGene , EnsembltrezGene  or SNPedtrezGene .All these different sources contain valuable information like primer sequence, population frequency or information on the corresponding gene, but only little about the biomedical implication. This information is mostly covered in publications, which are stored in databases like MEDLINE. The National Center for Biotechnology Information (NCBI) provides references to 4487 articles for 24079 dbSNP entries. Due to high-throughput techniques like SNP-arrays , mass spIn the following, we describe characteristics regarding the process of finding database identifiers for free text mentions of human SNPs. More precisely, this work focuses on SNP substitutions in Homo sapiens with about 18 million entries (as of dbSNP version 128).Methods section.A general workflow for the automated extraction of SNP mentions from literature is illustrated in Figure e. g. hard copy, pdf, html), an automated machinery needs a uniform text representation that necessitates a preprocessing step (conversion of XML formats or extraction of plain text from full text documents). The mentions of SNPs in different nomenclatures or natural language need to be detected as well as the gene names. While this task is typically easily accomplished by a human, it is challenging for an automated system due to the huge amount of different complex formulations found in free text.In contrast to a human who typically perceives the provided document in the published form best  and their association, the normalization process is performed. This process is straight-forward for direct mentions of identifiers but highly problematic for other formulations.wild type, mutated allele, and location on the reference sequence is required. Further the underlying gene or protein needs to be identified. Whereas the terms wild type and mutated allele describe biological concepts, the rules to determine the position of a variation on a certain gene have changed recently. The following subsection describes the most important changes in the nomenclature for human mutations.The aim of SNP normalization is to correctly associate SNP mentions in text with unambiguous database identifiers. Thus, it is necessary to understand how these entities are typically described. To normalize a SNP the 1-antitrypsin Pittsburgh” [This section briefly summarizes some concepts of human mutation nomenclature and its evolution relevant for the normalization of SNP mentions. Prior to the first recommendation of a common nomenclature many different descriptions were arbitrarily used. For some alleles nicknames like “hemoglobin Crete” , “haemogtsburgh”  were comGenetic sequence changes occur in general on the DNA level. Variants located on an exon may be propagated to mRNA and may consequently lead to a change of the encoded polypeptide chain. Therefore, a SNP can be described on at least one of these three levels. However, SNPs are usually described on protein or DNA level. To distinguish between these two concepts, we use the terms protein sequence mutations (PSM) and nucleotide sequence mutations (NSM) . For ins“The Glu(298)– –>Asp  polymorphism of eNOS  has been related with cardiovascular disease.”All three SNP mentions can be further associated with the unambiguous dbSNP identifier rs1799983. For simplicity, the following sections are focusing on SNPs, but can be applied to many other types of variations analogously. However, the description of complex variations, like changes within duplications, can become rather complicated.In the first publications on this topic the idea that positions of PSM should always be deduced from the primary translation product was introduced ,32. ThisConsequently several suggestions to describe NSM have been made. In contrast to PSM, no intuitive start position exists. The first approach suggested to use the exact 5’ cap site as position +1. If the exact cap site is unknown, the most upstream known cDNA base is used as start point. When publishing these recommendations in 1993 and 1996 respectively, only few complete cDNA sequences were available and the human genome project was in its infancy. Therefore, it has been concluded that this system leads to an arbitrary numbering based on early sequence data. Bases upstream of the cap site should be consecutively numbered as –1, –2, –3 and so on. Numbering downstream follows the cDNA sequence, meaning that only exonic regions are consecutively numbered. Bases within an intronic region are described by two numbers. The first number is the base of the closest exon and the second is the relative distance to this base. This convention allows to describe intronic variants, without even knowing the exact length of the intronic region. The recommendation to describe NSMs on gene MECP2 using this first nomenclature are depicted in Figure Numbering based on genomic DNA is the most robust form of systematic nomenclature ,37. In tAntonarakis as well as Beutler et al. ,37 suggee. g. by regular expressions. Variations occurring in the 3’UTR are designated with a preceding asterisk (*) and the distance to the last base of the stop codon. Applying this nomenclature to our example variations results in c.–225C>A and c.26+2T>C. A visualization of the new recommendations applied on MECP2 is depicted in Figure The nomenclature updates ,39 introe. g. NM_005957.3:c.123G>T). These variation mentions can be easily extracted by using regular expression. The explicit statement of the used reference sequence facilitates the normalization to a unique identifier. Our running examples are now depicted as NG_007107.1(MECP2):c.–225C>A and NG_007107.1(MECP2):c.26+2T>C. In these two examples the gene is mentioned in brackets, because the used reference sequence NG_007107.1 covers two genes (MECP2 and IRAK1). The direct mention of the gene of interest in the expression is required to avoid ambiguity. For instance, the description NG_007107.1:c.1A>T may refer to a substitution on both genes. An increasing number of journals, like Human Mutation, insists on using the latest recommendations for a common human mutation nomenclature. The increasing complexity of guidelines makes the description of newly discovered mutations an error prone and time intensive process. Tools like Mutalyzer [In the most recent publication  rules inutalyzer  assist tutalyzer . More anutalyzer , which iFor NSMs an intuitive but important issue is that human genes often have more than one transcript variant and every transcript has its own unique exon/intron boundaries and start codon. Usually a NSM can be described with respect to all associated reference sequences. However, the description of a mutation may differ between two reference sequences due to the unique properties of a reference sequence. Explicitly mentioning the used reference sequence including a version number avoids this problem and is, therefore, recommended.e. g. Alanine, Arginine, Asparagine, and Aspartic acid or Threonine, Tryptophan and Tyrosine) are often wrongly abbreviated when using one letter abbreviations [The guidelines to describe variations have been recently changed -40. For viations . Also coviations .Even though the rules to calculate the position of a PSM remained unchanged since the first publication, two commonly used numbering variants have been reported:Signal, Transit, Peptide, Propeptide and, Var_seq of the corresponding UniProt entry.First, PSMs may be deduced from the mature protein instead of the precursor polypeptide. This is the case for the protein Lymphotoxin-alpha (LTA), where the precursor peptide contains a signaling sequence from position 1 to 34. After transport of LTA to the final destination, the signaling peptide is cleaved by a signal peptidase. This is the reason why some authors refer to position 35 as 1 and vice versa. This specific information is covered in the feature table of the corresponding UniProtKB entry P01374. This property has been described and used by Yip et al. [Initiator methionine of the respective UniProt entry P68871.The second problem is that some publications start to count one position after the leading amino acid. Therefore, some dbSNP entries have an offset of +1 compared to the textual description. This has been previously exemplified for the sickle cell allele Glu7Val located on Hemoglobin beta (HBB). Evidence for cleavage of the first amino acid can be found in the feature For the normalization of SNP mentions to sequence database identifiers, detection of SNP mention and the associated gene or protein names is crucial. For automated recognition, several tools have been proposed, both for gene and protein names -51 and vFor the normalization of SNP mentions, the location, wild type and mutated allele have to be known. Approaches relying on machine learning methods commonly identify wildtype, mutated allele and location separately ,57. ThesHowever, the creation of these tuples introduces a new error source, but the use of machine learning approaches to identify variation mentions in text has a substantial advantage: Approaches relying on regular expressions commonly extract only diallelic variations like L69K or 32C– –>T. However, for human triallelic SNPs (rs3091244), tetraallelic SNPs (rs293806), or even pentaallelic SNPs (rs1049092) have been observed. SNPs like 82C– –>T/G are currently only extracted by machine learning tools. Some authors describe only the observed allele of a variation like “52L” or the genotype like “–403 AT” or “–403 AA”. Even though these examples are not mutations, it is feasible to detect and normalize such mentions. Approaches detecting alleles and location separately provide more flexibility but the subsequent association introduces an additional error source.One kind of SNP mention found in biomedical text is the direct citation of a dbSNP identifier. In such cases, the SNP can be unambiguously identified and mapped to dbSNP. In a previous experiment , such mePrior to a normalization each detected SNP mention has to be correctly associated with the corresponding gene. This is necessary because the location of a SNP is described in relation to the corresponding gene. This is a difficult task even when only one single gene or protein is described in the text, because it is not guaranteed that this gene or protein is mentioned together with the SNP mentions in the text.Several methods for the association of gene or protein with the corresponding SNP have been proposed. Some consider sentence boundaries , while oGenomic information rapidly changes over time. This also includes the sequence of the genome and its annotation. In 2004 the sequence of the human genome covered already 99 % of the euchromatin sequence with an error rate of 0.01 ‰ . HoweverProblems of not being able to normalize a SNP mention because of database changes are hard to solve. Changes in the reference sequence as well as merging or renaming of identifiers are often not well documented. To circumvent these problems, the latest human mutation nomenclature advises authors to mention the accession number of the used reference sequence in front of the variation description. Adherence of this rule simplifies the normalization substantially, because the respective reference sequence is known.It has been previously mentioned by Antonarakis et al. , that thA SNP is always observable on both strands because of the structure of DNA. Therefore, the alleles can be described on any strand side. For example, the replacement of an adenine by a cytosine on one strand leads to a replacement of a thymine by a guanine on the anti-parallel strand. To avoid confusion in the textual description of variations, alleles are always described on the same strand as the reference sequence and cDNA sequences are usually on the same strand as the associated gene.In contrast, alleles in dbSNP may be arbitrarily located on any strand side. This is based on the build process of dbSNP. Submitted sequences obtain a unique and stable submission sequence (ss) number. The submitted sequence is aligned to the genome in question. Submission sequences describing the same SNP are merged into one single rs entry. A new rs entry is generated, if no ss previously covered this specific sequence variation. Regardless of the number of ss entries, every rs cluster has exactly one reference sequence. The reference sequence is always the longest submission sequence of each cluster and may be arbitrarily located on the sense or antisense strand. Therefore, the alleles are, depending on the alignment of the sequence to the genome, located on either strand. Due to this property, some alleles of dbSNP are on the opposite strand than the cDNA sequence. Information about the placement of a SNP on the current contig can be found online in the database table SNPContigLoc. Additionally information about the placement of a contig on the chromosome is contained in table ContigInfo.The shared alphabet between protein and nucleotide sequences introduces another problem of ambiguity. For example, the SNP A123T could describe a NSM or a PSM. Several rules to disambiguate PSM and NSM are described in . An apprAdditionally to the aforementioned problems, pitfalls that are obvious but not neglectable are reported: 1.) Some SNPs may not be contained in the SNP database of interest. This may have several reasons like a missing submission or a rather low minor allele frequency. 2.) SNPs reported in non coding regions are difficult to normalize as the current human mutation nomenclature covers these only in terms of genomic descriptions. Nevertheless, publications often describe these SNPs in relation to the closest gene, which is not covered in dbSNP. 3.) Not all needed information might be explicitly mentioned in the abstract. For example, the associated gene might be only mentioned in the full text article. 4.) Typos can basically appear in all types of descriptions  and may make the normalization infeasible.Manual extraction and normalization of SNP mentions is time consuming but feasible for specific domain topics. For example, the AlzGene database  containsCollecting such information on large scale requires automated methods due to the large amounts of published literature. Several text mining approaches for different purposes have been developed in the Life Science domain ,63. A tyMuteXt  is an eaThe tool MEMA  also useAn additional tool, solely concentrating on the extraction of non-synonymous variation mentions, is the freely available application MutationFinder . NonsynoAn alternative approach described by Yip et al.  focuses A different approach is OSIRIS V1.2 , which iThe usability of conditional random fields  to extraWhile regular expression-based recognition of dbSNP identifiers is a component of the previously mentioned system, the approach medRefSNP  focuses An approach to extract human kinase mutations is presented by Krallinger et al. . MutatioAll discussed approaches are able to identify variation mentions in scientific texts. Although a framework for the systematic analysis of mutation extraction systems exists , the resOnly OSIRIS and our approach associate extracted variations with unique SNP identifiers. Prior to the normalization of variations mentions with dbSNP, these approaches associate variation mentions with the corresponding gene or protein identifier. Therefore, these two approaches not only provide a normalization of variation mentions to dbSNP but also an association of variation mention to the corresponding gene/protein.To find and describe typical real world problems, a corpus consisting of SNP mentions associated with their dbSNP accession number mentioned in the text has been generated. Only few articles describe a variation in terms of natural language and the corresponding rs number. An initial list of 2,232 relevant articles has been received from dbSNP help desk. These abstracts were annotated by dbSNP and are known to mention at least one dbSNP identifier. The 2,232 abstracts were then automatically screened for SNP mentions using a modified version of MutationFinder.Human Mutation Nomenclatures. These modifications allow the detection of NSM mentions and are available in the supplementary material. The strict amino-acid alphabet of MutationFinder has been expanded to match ambiguous symbols like Xle, which can be used to describe the two amino acids leucine or isoleucine. Also different variations of termination symbols like term, amber or opal have been added to detect nonsense mutations. Additionally, regular expressions matching variation mentions using the latest recommendations for a human mutation nomenclature have been generated. The mutation mentions described on the homepage of the human genome variation society have been used for developing these regular expressions.Modifications encompass five regular expressions matching different NSM variants of the notations introduced in Section All regular expressions have been applied to the initial corpus of 2,232 articles. Extracted SNP mentions are then manually checked and associated with the corresponding dbSNP entry. SNPs missed by any of the regular expressions are also added into the corpus, if they could be associated with a dbSNP identifier. Identical descriptions were extracted only once per abstract. This procedure resulted in 527 variation/rs number pairs. From the 385 distinct rs numbers, 21 were found to use outdated dbSNP identifiers. These were replaced by the currently valid identifiers of dbSNP build 128.The main properties of our corpus are as follows:• The 527 SNP mentions can be separated into 283 PSM and 244 NSM.• 48 SNP mentions refer explicitly to the type of used reference sequence• 19 SNP mentions use the IVS concept to describe intronic variations• 17 PSM are ambiguous as they could be potentially interpreted as NSMFor each rs number we extract information about the associated gene, the position on the chromosome, known alleles, and the orientation of the SNP in relation to the associated gene. This information is extracted from a local copy of dbSNP. If the SNP is located in the coding region, the respective amino acid residues are also extracted from dbSNP. To allow for compensation of systematic differences in numbering, we follow the approach described by Yip et al. and parse information about post translational modifications from UniProtKB. Gene centric information, like exon/intron boundaries or the location of the start codon is extracted from Entrez Gene and ENSEMBL databases. Although both databases use the same genomic reference sequence, the boundaries for some transcripts (and their number) differ. Therefore, information about transcripts has been included from both databases. Gene name recognition and normalization to Entrez Gene and UniProt is performed using ProMiner. For evaluation we assume a perfect named entity recognition of gene names, by manually adding genes missed by ProMiner but required for subsequent SNP-normalization. This information can be gathered directly from the corpus, as dbSNP entries are associated with their corresponding Entrez Gene identifier. This allows a realistic assessment of the normalization procedure as the normalization algorithm is not influenced by the limited recall of a gene name recognition procedure. Subsequently, the algorithm retrieves for each SNP mention in the corpus a list of dbSNP candidates. The list of candidates is collected by retrieving all dbSNP entries associated with a gene contained in the article. In other words the SNP mention is associated with all genes mentioned in an article. The method iterates over all dbSNP candidates and performs a validation for the specific SNP mention.The normalization algorithm for one SNP mention and one dbSNP candidate is depicted in Figure 1.) For normalization of PSM the method first matches the extracted position against the dbSNP candidate. In case of a match the residues of the SNP mention are compared against the dbSNP residues. If both residues of the SNP mention are contained in the dbSNP candidate, the algorithm normalizes the mention. If the location of the SNP mention can not be validated, the algorithm incorporates knowledge about post translational modifications from UniProtKB. If any post translation modifications explains the difference in numbering the residues are validated. In case of a match the SNP mention is associated with the candidate. Otherwise the candidate is discarded.Human Mutation Nomenclatures depending on the information where the SNP is located . These counting variants are calculated for each reference sequence associated with the current candidate entry. The algorithm reverses alleles (e. g. A– >G becomes T– >C), if the dbSNP reference sequence is located on the opposite strand than the reference sequence. Again, the algorithm attempts to validate first the position of the SNP mention with the dbSNP candidate. If the location of the SNP mention complies with any of the counting variants the corresponding alleles are also compared.2.) For NSM normalization the normalization procedure is more sophisticated, as several counting variants have been introduced. For each dbSNP entry we rebuild all different counting variants as described in Section The algorithm does not distinguish between wildtype and mutated allele/residue as this information is usually based on the frequency in the observed population sample and does not necessarily resemble the recommendations for a human mutation nomenclature. Therefore, the normalization procedure checks if both alleles/residue are at the correct location, but allows changes in the directionality (e. g. A– >G becomes G– >A).Normalization Process was implemented as mentioned in Section Implementation details. The implementation correctly found 356 out of 527 variation/dbSNP pairs. The recall is 67.5% with a precision of 98.1 % . These 356 true positive SNPs can be further divided into 268 PSM and 88 NSM. Therefore, the recall is 94.7 % and 36.0 % for PSM and NSM respectively. Based on a manual inspection of each variation and the challenges described in the previous sections, typical problems and pitfalls are highlighted in the following.The general workflow described in Section initiator methionine. More information about these specific mutations is described in Table As previously described, two different counting variants for PSM are known. First, three PSMs with one digit lower than in the dbSNP entry have been found in the corpus. The reason for the different numbering is that the initiator amino acid is cleaved from the primary translation product. This leads to the observed difference of 1 between the textual description and the dbSNP entry. The corresponding authors were contacted and affirmed the deprecated property of the stated description. The authors used this description, because it is a long established term in the community. For all PSMs this information is covered in the UniProt feature Second, eight PSMs can be recovered using the “sequence annotation” feature information from UniProtKB. A complete list of recovered SNPs is depicted in Table As previously described, the dbSNP reference sequence can be located on the opposite strand than in the mentioned NSM. This property is observed seven times in the dataset. Therefore, the dbSNP entry describes the alleles on the opposite strand than the variation mention in the text. To successfully recover those variations one has to reverse the alleles contained in the dbSNP database. Detailed information for these NSMs is listed in Table Additionally to these systematic errors, the numeric patterns of five rs numbers are wrongly described in the text. A detailed description can be found in Table Independent from our corpus an error in the description of a SNP by Yoneyama et al.  has beenDue to the building process of dbSNP more than one rs entry for one SNP can be generated. This is the case when the flanking region of the ss entries substantially differs. In this case the genomic location for both dbSNP entries is the same but the dbSNP entries are not merged. This has been observed for two SNP mentions, which could be associated to both rs numbers. The entries are shown in Table During the analysis we observed that five dbSNP entries are not associated with the gene mentioned in the text. According to dbSNP the entries shown in Table The short form of 17 PSM is ambiguous in that it could also be interpreted as a NSM. Some examples are provided in Table From 244 NSM, 65 use a minus character, 30 mention the used reference sequence (c. or g.), and 19 are described by the IVS concept. Therefore 114 of 244 NSM could be previously classified as NSM. For the remaining NSM the normalization algorithm performs a NSM and PSM normalization.The distinction between wild type and mutated residue/allele is usually based on frequency in the specific population sample. However, the human variation nomenclature advises authors to use the nucleotide/residue of the reference sequence as wild type. This sometimes leads to a change in directionality between the alleles/residues described in the mutation mention and the corresponding database entry. This problem can be observed for 86 out of 244 (35.2%) NSM and 53 out of 283 (18.7%) PSM in our corpus. Examples for flipped wildtype and mutated alleles are provided in Table All problems observed in our corpus are summarized in Table 11Normalization and interpretation of SNP mentions is highly challenging for human readers as well as automated machineries. In this paper, we discussed several common pitfalls which have to be overcome for successful normalization of variation mentions to dbSNP. Most of them are generally valid and apply also for other SNP databases.In particular we depicted a number of observed real world examples based on a corpus of 527 SNP/dbSNP pairs. Using this corpus reveals non-systematic errors like permutation of numbers, which are hard to find. We demonstrate that our developed normalization algorithm produces precise results for both PSM and NSM. However, the recall for the normalization of PSM is substantially better (94.7 %) than for NSM (36.0 %). We believe that this might have several reasons: First, our approach currently incorporates no provenance information and uses only the latest gene annotations from ENSEMBL and Entrez Gene. Second, the early papers on mutation nomenclature pointed out, that the lack of a complete reference sequence might lead to an almost arbitrary position numbering and domain experts might stick to these deprecated references. Third, manual conversion of a SNP into any mutation nomenclature is, without any computational assistance like Mutalyzer, error prone. Finally, sequencing errors might lead to small but substantial differences in numbering. To overcome the limited recall for NSM normalization we plan to incorporate RefSeq transcripts, which are derived from GenBank and provide current and deprecated annotations for genes.http://www.scai.fraunhofer.de/snp-normalization-corpus.html.We believe the developed corpus will help to facilitate further development in the normalization of SNPs to dbSNP identifiers and will assist the community progress toward a common corpus useful for the systematic evaluation of grounding tools. The annotated corpus is available at • cDNA: Complementary DNA• gDNA: Genomic DNA• IVS: Intervening Sequence• Mutation: Refers to rare variants which often cause diseases and affect conserved residues in the protein sequence. Also used to refer to modified residues in a sequence after the experimental procedure of mutagenesis; NSM: Nucleotide Sequence Mutation• PSM: Protein Sequence Mutation; SNP: Single Nucleotide Polymorphisms or SNPs are DNA sequence variations in which a single nucleotide  is altered. SNPs are also referred as polymorphisms, natural variants, or common variants because they have a minor allele frequency in the population of at least 1 %. In contrast, rare variants have a minor allele frequency of less than 1 %. SNP mention: Textual description of a SNP• UTR: Untranslated Region• Variation: Any kind of short range sequence variation in the nucleotide sequence of the genome• Variation mention: Textual description of a variationThe authors declare that they have no competing interests.PET and RK wrote the main parts of this paper. PET annotated and analyzed the corpus and implemented the system. RK, LIF and CMF were involved in substantial discussions of aspects described here and revised this contribution. MHA critically revised the manuscript.


Eucalyptus species; however, limitations on water supply are affecting the biomass production and reducing the yield significantly [Eucalyptus were identified, as well as, a possible molecular marker associated to genomic regions related to plant response to water stress through AFLP technique [Forestry companies have occupied Northern and Northeastern of Brazil with plantations of ficantly .Tolerantficantly . Identifechnique  combinedechnique .E. grandis, E. urophylla, and their hybrids were selected according to their physiology. Specific primers were designed from homology sequences from Eucalyptus ESTs databank and amplification products submitted to sequencing which allowed the identification of SNPs and the genotyping of these plants. For the identification of the AFLP marker, contrasting DNA bulks were digested with restriction enzymes combination and routine protocols were followed. The selective amplification products were separated on 6% denaturing polyacrylamide gel and seen in the silver nitrate staining [Contrasting plants of staining .Seven SNPs were identified in a region of 1230 bp on NCED gene from which five were in codified regions and generated synonymous mutations. For the CMO gene, 49 SNP were identified in a region of 3885 bp, which 12 were in codified regions and 37 in UTRs and intron regions. Especially for these codified regions; 83,3% of the mutations were synonymous and 16,7% were non-synonymous. Through the genotyping of the SNPs, the NCED and CMO genes presented respectively, seven haplotypes with 15 different genotypes and 18 haplotypes with 16 diverse combinations. Nevertheless, CMO gene showed some unique genotypes for some species. Then, the genotyping of individuals by the allele-specific extension technique demonstrated to be efficient; moreover the SNPs primers designed can decrease costs and permit the genotyping of these mutations in large scale of contrasting populations to water deficit and in population studies. From the analysis of the DNA bulks, 50 AFLP primer combinations were tested from which 27 generated polymorphic bands between the bulks and just one primer combination was confirmed in all susceptible plants of the bulk by now. This fragment sequence will be compared and converted into PCR-based markers.


Malus using SNP data from 28 Malus genotypes. This array offers the prospect of high throughput genotyping and linkage map development for any given Malus progeny. To test the applicability of the array for mapping in diverse Malus genotypes, we applied the array to the construction of a SNP-based linkage map of an apple rootstock progeny.A whole-genome genotyping array has previously been developed for Malus SNP markers on the array, 1,823 (23.2%) were heterozygous in one of the two parents of the progeny, 1,007 (12.8%) were heterozygous in both parental genotypes, whilst just 2.8% of the 921 Pyrus SNPs were heterozygous. A linkage map spanning 1,282.2 cM was produced comprising 2,272 SNP markers, 306 SSR markers and the S-locus. The length of the M432 linkage map was increased by 52.7 cM with the addition of the SNP markers, whilst marker density increased from 3.8 cM/marker to 0.5 cM/marker. Just three regions in excess of 10 cM remain where no markers were mapped. We compared the positions of the mapped SNP markers on the M432 map with their predicted positions on the ‘Golden Delicious’ genome sequence. A total of 311 markers  mapped to positions that conflicted with their predicted positions on the ‘Golden Delicious’ pseudo-chromosomes, indicating the presence of paralogous genomic regions or mis-assignments of genome sequence contigs during the assembly and anchoring of the genome sequence.Of the 7,867 M. pumila to date. The data were generated rapidly in a high-throughput semi-automated pipeline, permitting significant savings in time and cost over linkage map construction using microsatellites. The application of the array will permit linkage maps to be developed for QTL analyses in a cost-effective manner, and the identification of SNPs that have been assigned erroneous positions on the ‘Golden Delicious’ reference sequence will assist in the continued improvement of the genome sequence assembly for that variety.We incorporated data for the 2,272 SNP markers onto the map of the M432 progeny and have presented the most complete and saturated map of the full 17 linkage groups of  Malus pumila Mill. (2n = 2x = 34), is a member of the Spireaoideae subfamily of Rosaceae, and is the fourth most economically-important fruit crop worldwide. As a consequence of the long juvenility phase of apple trees and of relatively high husbandry costs, the breeding and selection of novel apple rootstocks and scions is a time-consuming and costly procedure. Marker assisted selection (MAS) has the potential to increase the precision of apple breeding and an essential prerequisite to MAS is the production of high quality saturated genetic linkage maps to enable marker-trait associations to be made.The cultivated apple, Malus genome and almost complete coverage of the gene-space of the variety [The genome of the apple scion cultivar ‘Golden Delicious’ was recently sequenced and assembled into 17 pseudo-chromosomes by an international consortium , the ‘GoMalus progenies, but the high resolution anchoring orientation of the scaffolded sequence data to a reference linkage map [Malus varieties and species, and between Malus and Pyrus has recently been assessed [Malus progeny. In the study of Micheletti et al. [SNP) in a diverse selection of Malus germplasm. The investigation showed that SNPs identified within the ‘Golden Delicious’ sequence had an average transferability rate of 40.9% to Malus cultivars, with the lowest TSNP to the cultivar ‘Wagner’ (25.7%) from those tested. The transferability rate in rootstock germplasm was between 29.9% and 39.7%, whilst to an accession of Pyrus pyrifolia, the TSNP value was just 1.8%, demonstrating low cross-genera SNP transferability of the SNPs tested [The availability of data relating to single nucleotide polymorphisms (SNPs) from early in the development of the ‘Golden Delicious’ genome sequence meant that assays have been developed for screening segregating SNPs in apple mapping populations at relatively high throughput using the SNPlex and Golden Gate genotyping platforms ,4, permikage map . The degassessed , and esti et al. , SNPs idWagner’ 2.7% from Malus research community has developed an Infinium® II WGG genotyping array  for Malus and Pyrus using data from the re-sequencing of 27 Malus genotypes along with data from the ‘Golden Delicious’ genome sequence. The IRSC array contains a total of 7,867 Malus SNPs [Pyrus SNPs. The development of this array represents a milestone in the development of molecular genetics and genomics resources for Malus and offers the promise of rapid, low-cost, high-throughput genotyping for the purposes of linkage map construction and the genotyping of germplasm collections, that will facilitate future QTL and genome-wide association studies. However, reports are yet to emerge of the efficacy of such arrays for genotyping germplasm and mapping populations from divergent sources or from species related to those for which they were originally designed.Through international collaboration, led by the RosBREED initiative in the USA , the MalS-locus-specific markers, and 323  SSR markers [Malus genome. A comprehensive consensus linkage map of the progeny has been developed spanning 17 linkage groups (LGs), with an average marker density of one marker every 3.79 cM. The linkage map was partially anchored to the ‘Golden Delicious’ genome sequence, and a total of 47% of the sequence that was contained in metacontigs could be assigned positions on the M432 map.The M432 mapping progeny ,9 has beMalus genome sequence that could be directly related to regions of the M432 linkage map for the purposes of candidate gene identification and marker development following QTL analysis. We evaluated the SNP-based linkage map produced against the previously-published SSR-based linkage map of the population developed by Fernández-Fernández et al. [The aim of this investigation was to test the IRSC array in the M432 rootstock mapping progeny to determine its utility to genome-wide saturated map construction. Additionally, we aimed to increase the percentage of the z et al. . We compMalus SNPs and 921 Pyrus SNPs), 664 markers failed in both parental genotypes, 44 failed in the ‘M.27’ genotype and a further 51 markers failed in the ‘M.116’ genotype. A total of 5,078 markers were homozygous in both parental genotypes of the M432 progeny, and 95 revealed unexpected genotypes in the progeny given the parental genotypes. Of the remaining 2,856 markers 1,007 were heterozygous in both parents, 976 markers were heterozygous only in the ‘M.27’ parental genotype, and 873 markers were heterozygous only in ‘M.116’.GenTrain scores for all SNPs scored in the M432 progeny ranged from 0.043 to 0.961, with an average of 0.723. Cluster separation ranged from 0.004 to 1 with an average of 0.814. Of the 8,788 SNP markers contained on the IRSC array  revealed that the majority had poor cluster separation scores and did not cluster as expected despite having high GenTrain scores. In many cases, the clusters that were produced were composed of a number of smaller sub-clusters and of individual genotype calls that did not cluster tightly or that did not cluster in the expected region of the graph space and were shifted from either the 0 or the 1 axis  were heterozygous in one of the two parents of the progeny, and a further 998 (12.6%) were heterozygous in both parental genotypes. A total of 26 heterozygous SNPs were derived from the 921 Pyrus SNPs on the array, indicating a TSNP value of 2.82% transferability to Malus, which is within the range of values previously obtained by Micheletti et al. [Malus to Pyrus. The total percentage of heterozygous Malus SNPs (36%) was within the TSNP value range reported for the transferability of ‘Golden Delicious’ SNPs to rootstock varieties [SNP values for Malus cultivars should be broadly applicable to the number of SNPs expected to segregate in a given Malus genotype on the IRSC array.We have extended the previously published M432 genetic linkage map using SNP markers generated using the IRSC array, and increased marker saturation on the map from an average of one marker every 3.8 cM to one marker every 0.5 cM. Of the 7,867 Malus using EST data and concluded that the data supported a model of continuous small-scale duplication events, in addition to the recent whole genome duplication event in the lineage of the genus [Malus.Our investigation revealed a large number of SNPs putatively heterozygous and thus segregating in the M432 progeny, but for which genotype calls using GenomeStudio (Illumina) resulted in segregation patterns that could not be assigned a position on one of the 17 LGs of the M432 consensus map. A closer inspection of the genotype data in GenomeStudio (Illumina) revealed low cluster separation calls for all SNPs and a number of different causes for the discrepancies in these data, including many loci for which data were grouped into more than one sub-cluster in each genotype. Sub-clusters are most likely caused by probes detecting more than one locus, one of which contained an SNP or indel, or the detection of more than one allele containing SNPs within the probe sequence . Sanzol he genus . We suggMalus mapping progeny published to date. Previously, the IRSC array had been used to genotype the progeny derived from the cross ‘Royal Gala’ × ‘Granny Smith’ (RG × GS) which was reported by Chagné et al. [M. pumila mapping progeny.To our knowledge, this is the most extensive linkage map of a é et al. , althougé et al. . Of thosé et al. , it was S-locus, leading to the observed segregation distortion. Two other regions of distortion are probably the result of genes associated with the dwarfing phenotype in the cross, since some seedlings of the progeny displayed lethal and sub-lethal dwarf phenotypes. Efforts are on-going to characterise the M432 progeny and identify QTL associated with the dwarfing phenotype as well as a number of other traits including water-use efficiency.We identified a number of regions of the M432 map where segregation distortion was widespread and extensive. The most extreme example was at the bottom of LG17 where the self-incompatibility is located ,14. The Malus SNP markers mapped in the M432 progeny were compared to their physical positions on the ‘Golden Delicious’ genome sequence a high degree of colinearity was observed. Additionally, distinct regions of high and low recombination could be observed in a number of cases along the LGs/pseudochromosomes. The anchored portion of the ‘Golden Delicious’ genome from which the physical positions of the SNPs mapped in the M432 progeny were derived covers an estimated 71.2% of the ‘Golden Delicious’ genome and represents the majority of the gene-rich portion of the apple genome sequence [Caenorhabditis elegans[Oryza sativa[When the genetic positions of the 2,272 s elegans and Oryzza sativa. RegionsA total of 311 SNP markers  were located to positions on LGs that conflicted with the predicted positions of the SNPs on the ‘Golden Delicious’ pseudo-chromosomes [Malus genome. If the 13.7% of the markers mapped in the M432 progeny in this investigation that were identified as misplaced on the ‘Golden Delicious’ genome sequence of Velasco et al. [Malus cultivars.Human and animal genetics employ genetic information gained from individuals who are members of extended pedigrees for the identification of loci controlling complex genetic traits ,18. One Malus array to extend and saturate the SSR linkage map of the M432 mapping progeny. The use of the array enabled us to locate a total of 2,270 SNPs to the consensus linkage map at a fraction of the time and cost of developing a similar map using other experimental approaches, and to develop the most comprehensive saturated complete linkage map for a Malus mapping progeny to date. The mapping of the SNPs enabled us to assess the relative coverage and accuracy of the ‘Golden Delicious’ genome sequence and to identify a significant proportion (13.7%) of SNPs that had been erroneously located to the ‘Golden Delicious’ pseudochromosomes. Further mapping in additional progenies will help to characterise regions of the ‘Golden Delicious’ sequence that are incorrectly placed in the current assembly version, and assist in the continued improvement of the reference sequence for Malus.We have employed the IRSC The M432 rootstock progeny used in this investigation was first described by Evans et al. . It was Malus, and 921 possible Pyrus SNPs. Progeny DNA was assayed following the manufacturer’s recommendations. Briefly, a whole genome amplification reaction was performed, followed by denaturation and hybridization to the IRSC BeadChips (Illumina). Un-hybridized and non-specifically hybridized DNA was removed through washing, following which a single base extension reaction was performed to incorporate differentially labeled nucleotides at the SNP sites of each of the 8,788 probes of the BeadChip for each M432 genotype. BeadChips were then imaged and data were collected using the HiScan detection platform (Illumina) following Illumina-published standard operating procedures (http://www.illumina.com).The IRSC array, employing exclusively Illumina Infinium® II design probes and dual colour channel assays , that was described previously by Chagné et al.  was usedData generated for the 8,788 SNPs in the 140 seedlings and the two parents of the M432 progeny were scored using (Illumina) using a GenCall threshold of 0.15. Data were then exported into (Microsoft Inc.) for post scoring processing before data analysis. Prior to linkage analysis markers with a GenTrain score below 0.4 were excluded from the dataset. Following initial linkage analyses, all markers not mapping to one of the expected 17 LGs were visually inspected for accuracy, to determine data quality, and to resolve any errors created by automatic allele calling in (Illumina). Data for all SNPs showing putative segregation were discarded prior to mapping if one or both of the parental genotypes failed to amplify in the assay or if segregation data contained genotypes not expected from the parental genotypes.Markers were re-coded using genotype codes for linkage analysis using , according to their segregation type; AB × AA or AB × BB  were coded   and AA × AB or BB × AB  were coded   . Markers segregating AB × AB  were recoded   . SNP nomenclature in this investigation followed the dbSNP (EMBL) accession codes for each SNP on the array that has been deposited in the Genome Database for Rosaceae .Linkage mapping was performed with all SNP data and the SSR data of Fernández-Fernández et al. . Data foPhysical positions of all SNP markers were derived from their predicted positions on the ‘Golden Delicious’ genome sequence and were plotted as a function of genetic distances on the M432 consensus map. Mareymaps for each individual chromosome were produced using (Microsoft Inc.).The authors declare that they have no competing financial interests.LA carried out the experiments, analysed the data and co-authored the manuscript. FFF conceived the experiments, managed the populations and co-authored the manuscript. JJ analysed the data. EB carried out the experiments. KME conceived the experiments and critically evaluated the manuscript. RVio advised on the experiments. RVel managed the experiments. JMD advised on the experiments and critically evaluated the manuscript. MT conceived the experiments, managed the experiments, analysed the data and co-authored the manuscript. DJS conceived the experiments, managed the experiments, analysed the data and authored the manuscript. All authors read and approved the final manuscriptSegregation data for the 2,579 markers segregating in the M432 progeny. The 2,272 segregating IRSC array SNP markers mapped in the M432 population along with the 307 previously mapped SSR and S-locus markers, including the SNP name, the dbSNP (EMBL) accession code assigned to each on the IRSC array, the LG and map position of each mapped marker, the monogenic marker segregation ratios, associated chi-squared values and the predicted pseudo-chromosome positions of each marker on the ‘Golden Delicious’ genome sequence. Markers for which genetic and physical positions conflicted are highlighted in red.Click here for file


Litopenaeus vannamei (L. vannamei) generated from Illumina sequencing platform HiSeq 2000. One transcriptome of L. vannamei was obtained through sequencing on the RNA from larvae at mysis stage and its reference sequence was de novo assembled. The data from another transcriptome were downloaded from NCBI and the reads of the two transcriptomes were mapped separately to the assembled reference by BWA. SNP calling was performed using SAMtools. A total of 58,717 and 36,277 SNPs with high quality were predicted from the two transcriptomes, respectively. SNP calling was also performed using the reads of two transcriptomes together, and a total of 96,040 SNPs with high quality were predicted. Among these 96,040 SNPs, 5,242 and 29,129 were predicted as non-synonymous and synonymous SNPs respectively. Characterization analysis of the predicted SNPs in L. vannamei showed that the estimated SNP frequency was 0.21% (one SNP per 476 bp) and the estimated ratio for transition to transversion was 2.0. Fifty SNPs were randomly selected for validation by Sanger sequencing after PCR amplification and 76% of SNPs were confirmed, which indicated that the SNPs predicted in this study were reliable. These SNPs will be very useful for genetic study in L. vannamei, especially for the high density linkage map construction and genome-wide association studies.The application of next generation sequencing technology has greatly facilitated high throughput single nucleotide polymorphism (SNP) discovery and genotyping in genetic research. In the present study, SNPs were discovered based on two transcriptomes of  Litopenaeus vannamei (L. vannamei) is widely cultured in Asia, South and North America. According to the statistics on the global aquaculture production by FAO in 2011, the whole production of L. vannamei occupied 76% of the world penaeid shrimp production. In order to understand the molecular mechanism for desired traits, molecular genetics research such as linkage map construction L. vannamei is an important and worldwide shrimp species in aquaculture, available genetic markers in public database for this species was limited.L. vannameiSingle nucleotide polymorphisms (SNPs) are the most abundant type of DNA sequence polymorphism which has been proved to be useful in genetic studies L. vannamei, transcriptome sequencing was applied in shrimp larvae, TSV-infected and non-infected individuals using next-generation sequencing technique L. vannamei till present.Next generation sequencing technologies have made high throughput SNP discovery feasible for non-model species L. vannamei and the characterization of these SNPs was analyzed. The predicted SNPs in this study will be very helpful for further genetic studies in L. vannamei, especially for the high density linkage map construction and genome wide association studies.In the present study, SNPs were predicted by analysis of reads from two transcriptomes of L. vannamei larvae at developmental stage of mysis were collected from Guangtai Marine breeding company in Hainan province, China. They were frozen in liquid nitrogen immediately and stored in −80°C before RNA isolation. Total RNA was isolated using RNAiso Plus  following manufacturer’s protocol. The RNA amounts and quality was estimated using NanoDrop 1000 spectrophotometer . Sequencing of RNA extracted from mysis larvae was conducted in BGI  using the paired-end RNA-Seq method Hundreds of L. vannamei at 20 days post spawning, which was deposited in NCBI with the Sequence Read Archive (SRA) accession number of SRR346404 The reads of 100 larvae of de novo assembled using Trinity http://bio-bwa.sourceforge.net/) with the default settings except for no gap tolerance. The software package SAMtools (http://samtools.sourceforge.net/) was used to convert sequence alignment/map (SAM) file to sorted binary alignment/map (BAM) file The reads from M transcriptome were Mapped reads ratio (MRR) to the reference in each dataset was calculated by applying flagstat command of SAMtools software to the BAM file. SNP frequency was calculated by dividing the total length of reference by total number of SNPs. Transition verse transversion ratio was calculated by analyzing each type of DNA substitution. SNP depth (DP) and Minor Allele Frequency were also extract from the result file of SAMtools. SNP classification was obtained by a Perl script used in previous study http://wego.genomics.org.cn) in WEGO native format to plotting GO annotation results. KEGG pathways were assigned to unigenes containing SNPs using the online KEGG Automatic Annotation Server (KAAS) (http://www.genome.jp/tools/kaas/) The unigenes containing SNPs were annotated using Basic Local Alignment Search Tool (BLAST) against to NCBI non-redundant (nr) database by BLASTX. The BLAST results were utilized by Blast2GO to annotate the unigenes with GO terms of biological processes, molecular functions, and cellular components http://www.mbio.ncsu.edu/BioEdit/bioedit.html).In order to validate the accuracy of SNPs prediction, 50 SNPs were randomly selected for SNP validation using DNA as templates. Primers were designed to amplify the flanking sequence of selected SNPs using Primer 3 with fragment length of 200 bp. Primers were synthesized in Sangon Biotech . Eight DNA pools made by 96 individuals were used as templates for amplification. The amplified PCR products were sequenced by ABI Prism 3730 sequencer  and sequencing results were analyzed by BioEdit version 7.0.5.3 . A total of 24,680,276 high quality reads with 90 bp were obtained from P transcriptome .De novo assembly of the independent short reads from M transcriptome generates 66,215 unigenes with an average length of 705 bp. A total of 86.83% and 71.70% of the short reads from M and P transcriptome were mapped to the reference separately . Within the identified SNPs, more transitions substitution (66.8%) were found than transversion substitution (33.2%) . In termAs the read depth in SNPs position was closely related to the prediction accuracy of SNPs Minor allele frequency (MAF) was calculated based on the sequence data. As only SNPs with MAF more than 0.05 are regarded as true SNPs, those with more than 0.05 MAF were analyzed. SNPs with MAF range from 0.45–0.5 accounts for 60% of the total SNPs .As shown in SNP distribution among unigenes is important when considering the marker density and genome coverage using SNP marker Among the 25,071 unigenes containing SNPs, 13,591 unigenes (54%) had significant hit to the protein in the non-redundant (nr) database. The unigenes were annotated by the corresponding top best BLASTx hit. After the Gene Ontology annotation, 12,978 unigenes (52%) were assigned with one or more GO ID. The plotted GO annotations of these annotated unigenes were shown in The unigenes with higher SNP frequency (more than 0.014) were separately extracted from the annotated file and their GO annotation were plotted together with those of total SNPs . Genes iKEGG analysis showed that 10,803  unigens containing 23,553 SNPs could be annotated by KEGG database. These unigenes could be assigned to 254 KEGG pathways . The topAmong the 50 primer pairs designed for SNP validation, 34 could amplify target sequences. Within these amplified sequences, 26 SNPs were validated . The estL. vannamei through transcriptome sequencing. As next generation sequencing could enable the deep and efficient probing of transcriptome L. vannamei, two sets of transcriptome data were used in this study. A total of 58,717 and 36,277 SNPs with high quality were predicted by using these two sets of data separately. Using the reads of two transcriptomes together, we detected 96,040 SNPs including 20,225 SNPs which was not detected by using the two transcriptomes separately. Heterozygosity and homozygosity analysis of these 20,225 SNPs indicate most of these SNPs were polymorphic in each transcriptome.The purpose of the present study is to develop a large amount of convinced and gene specific SNPs for L. vannamei may endow a moderately high genetic diversity. Similar reports were published in Streblospio benedicti where population differentiation were observed between different populations The overlap analysis of SNPs discovered by two transcriptomes separately showed that only half of the SNPs were identical. The identical SNPs may refer to SNPs which could be easily transferred in this species. This result indicated that L. vannamei is one per 476 bp, which is lower than European hake (1/137 bp) MAF is a relative estimation of true allele frequency in the population Read depth is a key parameter affecting the predicting accuracy of SNPs L. vannamei.Another advantage of SNP discovery using transcriptome data is to find the SNPs directly associated with interested traits, such as disease resistance or growth advantages. Researchers primarily focused on non-synonymous coding SNPs (nsSNPs) as those SNPs might influence the protein activity directly. Reports on human genome wide association studies (GWAS) showed that the synonymous SNPs might play the same role as those nsSNPs The SNP frequency in each unigene was calculated in this study. The SNP frequencies ranged from one per 1.4 kbp to one per 26 bp. The high polymorphic unigenes detected in this study could be used in the population diversity or population differentiation analysis. We arbitrarily categorized unigenes with SNP frequency over 0.014 as higher polymorphic genes. Gene Ontology analysis showed that some components tend to be less polymorphic.Among 50 primers used for SNP validation, 34 primer pairs could amplified target sequence. As we used genome DNA for validation, primers may locate in boundary of exon and intron which resulted in failed amplification. Another reason for failed amplification may be large size intron insert between primer pair. We also found that the quality score of SNPs influenced the validation result. The accuracy for SNPs with quality score over 90 was higher than that with quality score over 20. It was reported previously that when selecting SNPs in genetic studies, we should consider the allele frequency of the SNPs L. vannmei were used for SNP discovery. A total of 96,040 high quality SNPs were predicted using the reads of the two transcriptomes together. Within those SNPs, approximately half could be annotated. The read depth and MAF analysis showed these predicted SNPs were accurate and common in this species. Besides, a moderately high genetic diversity and high heterozygosity in L. vannmei were found through characteristic analysis of SNPs. Overall, the SNPs predicted in this study will be useful in the genome wide association studies and whole genome selection studies.In this study, next generation sequencing reads from two transcriptomes of Table S1KEGG pathway of annotated SNPs.(XLSX)Click here for additional data file.Table S2The SNPs validated by Sanger sequencing.(XLSX)Click here for additional data file.


The computer code used for obtaining these ratings through a Web interface is provided. To further enhance the usefulness of this resource, ancillary measures obtained from other sources are also provided for each pair. These measures include associate production norms, contextual relatedness in terms of latent semantic analysis distance, total number of letters, phrase-level usage frequency, and word-level usage frequency summed across the words in each pair. Results of correlation and regression analyses are also provided for a quantitative description of the stimulus set. A subset of these stimuli was used to identify neural correlates of successful conceptual combination Graves, Binder, Desai, Conant, & Seidenberg,  noun–noun pairs. Half of these pairs  are reversed versions of the other half  contains supplementary material, which is available to authorized users. A growing body of work has examined the cognitive mechanisms underlying how concepts are combined  and not meaningful when reversed (house lake) were used in a functional magnetic resonance imaging (fMRI) study of the neural correlates of successful conceptual combination , or 249,500, potential phrases. A large database of human-generated text from Internet-based USENET groups  were recruited from the psychology student participant pool at the University of Wisconsin–Madison and provided informed consent. For participant tracking and to award course credit, each participant was given a World Wide Web address to which to point a browser and a random tracking number that ended in a number between 1 and 5. This determined which of the five sets the participant was given to rate. The order of presentation of phrases within each set was rerandomized for each participant. Upon entering the Web address, participants were given instructions for making their ratings using the full range of a 5-point scale  as follows:Please read each phrase, then judge how meaningful it is as a single concept, using a scale from 0 to 4 as follows: If the phrase makes no sense, the appropriate rating is 0. If the phrase makes some sense, the appropriate rating is 2. If the phrase makes complete sense, the appropriate rating is a 4. Please indicate your response by clicking on the button to the left of the number. Please consider the full range of the scale when making your ratings.Participants , the fox mask, 2 (makes some sense), the computer programmer, 4 (makes complete sense). After entering the tracking number and clicking “submit,” the ratings page was displayed. Upon completing the ratings and clicking “submit,” the data were appended to a master file that resided on a laboratory file server.The following examples were given as anchor points: SDs) from the mean were eliminated, as were data from 1 participant who failed to complete a majority of the ratings. This resulted in elimination of data from 1 to 2 participants per list, or 8 out of 150 participants. Each phrase was rated by a minimum of 26 participants. A full list of the noun–noun pairs, along with all ancillary data, is provided as an Excel file in the Ratings were checked for outlier status on a per-participant basis. Since each participant saw only one of five lists, mean ratings for each item were calculated within list and checked for correlation with each participant’s ratings. Data from any participant giving ratings with a correlation more than 2 standard deviations . A word frequency measure was obtained for each phrase by log10-transforming the frequency of each word in the phrase as it appears in CELEX  or surface properties (number of letters). The LSA measure reported here was calculated as the cosine distance between vectors representing two words in high-dimensional semantic space, with higher numbers reflecting ostensibly greater semantic or contextual relatedness  and reversed (less meaningful) pairs were strongly and reliably different, with forward phrases having a mean meaningfulness rating of 2.83 and reversed order phrases having a mean rating of 1.23  is between meaningfulness and the phrase-level Google-derived frequency estimate , supporting the common practice of log-transforming frequency values. The only correlation with meaningfulness that did not reach significance was for the sum of the individual word frequencies, as derived from CELEX . This correlation was surprising, since number of letters is not considered to be a variable related to meaning . This suggests that letter length bears a relationship to meaningfulness that is different from that of variables such as whole-phrase frequency or distance in semantic space that are more straightforwardly related to semantics.Total number of letters (letter length) for each pair showed a modest but reliable correlation with meaningfulness ratings , followed by summed word frequency , and letter length . The direction of the summed word frequency result is perhaps surprising in that having lower frequency constituent words predicted higher meaningfulness ratings. This result however, along with the letter length result, should be interpreted with caution. Although they were statistically reliable (at p < .0001), having a large number of observations can inflate the chances of finding results that, while statistically reliable, show effects small enough to be considered negligible. Indeed, the R2 values for summed word frequency (.108) and letter length (.002) suggest that together they explain only about 11% of the variance in meaningfulness ratings.In addition to individual pairwise correlations, multiple linear regression analyses were performed to examine the degree to which the ancillary variables uniquely contribute to predicting the meaningfulness ratings. This was done for the subset of 2,144  phrases for which association measures were available. The influence on meaningfulness ratings of the following five factors was examined: association, LSA distance, letter length, phrase frequency, and summed word frequency Table . These vß = .46, p < .0001). This was followed by phrase frequency , with letter length being the least predictive, although still reliable . Adding category to the regression model revealed an effect of LSA distance  that was not present in the previous model. This suggests that, if left unmodeled, the effect of phrase category overwhelms the effect of LSA distance. LSA distance may also be particularly sensitive to within-category variance in meaningfulness ratings.An additional regression analysis was performed that was identical to the previous one, except that phrase category  was added to the analysis to test whether or not the other factors predicted continuous meaningfulness ratings beyond that explained by a priori phrase category Table . This molake house) or reversed (house lake) order succeeded in that it led to a bimodal distribution of meaningfulness values  in the regression analysis of meaningfulness ratings Table , LSA disFor investigators preparing to use these phrases, it may be worth noting that several of the constituent words are repeated a number of times across phrases, as described above in the Method section. This offers the option of choosing a subset of the phrases containing unique words. Alternatively, one could take advantage of the fact that, although several of the constituent words repeat, each phrase in the corpus is unique. This would, for example, allow investigators to choose phrases that elicit word-level repetition priming , and frequency of phrase occurrence based on number of hits from the Google Internet search engine (similar to the phrase-frequency measure used in the present study). Analyses revealed phrase frequency and familiarity to be highly correlated at either r = .60 or .50, depending on the set of phrases being considered.While this stimulus set is, to our knowledge, the largest of its kind, Wisniewski and Murphy  collecteESM 1(XLSX 340 kb)ESM 2(ZIP 21.5 kb)Below is the link to the electronic supplementary material.


Bos indicus-influenced breeds: Brahman cattle and Tropical Composites. Two QTL regions on this chromosome were identified in both breeds as strongly associated with scrotal circumference measurements, a reproductive trait previously shown to be useful for selection of young bulls. Scrotal circumference is genetically correlated with early age at puberty in both male and female offspring. These QTL were located at positions 69–77 and 81–92 Mb respectively, large areas each to which a significant number of potential candidate genes were mapped.Previous genome-wide association studies have identified significant regions of the X chromosome associated with reproductive traits in two Tex11) were associated (P < 0.001) with scrotal circumference in both breeds, and associations with percentage of normal sperm cells were also observed (P < 0.05). Evidence for recent selection was found as Tex11 SNP form a haplotype segment of Bos taurus origin that is retained within Brahman and Tropical Composite cattle with greatest reproductive potential.To further characterise these regions, a bioinformatic approach was undertaken to identify novel non-synonymous SNP within the QTL regions of interest in Brahman cattle. After SNP discovery, we used conventional molecular assay technologies to perform studies of two candidate genes in both breeds. Non-synonymous SNP mapped to Testis-expressed gene 11 (Tex11 in male fertility, warranting further in-depth molecular characterisation.Association of non-synonymous SNP presented here are a first step to functional genetic studies. Bovine species may serve as a model for studying the role of  Presenc regions ,3. Howev regions -6. For e regions -9. Tropi regions -12. ImprFertility of individual sires is an important production and economic trait affecting reproductive performance of the whole herd. However, fertility is not an easily defined trait, comprising a variety of important heritable traits for selection of early maturing animals . WhereasA limitation in the use of SC and PNS as selection tools is that these reproductive traits cannot be measured before bulls reach 12–24 months of age. Time constraint can limit the attractiveness of these traits as selection tools. Identifying genetic markers associated with these traits would potentially overcome this limitation as they can be measured in DNA samples extracted from blood at birth.Recent genome-wide association studies (GWAS) in Brahman and Tropical Composite cattle has resulted in the identification of a number of genomic regions associated with SC, PNS and other reproductive traits -30. FortAR) and testis expressed 11 (Tex11), were investigated for their association with SC and PNS measurements in Brahman and Tropical Composite populations. Androgen Receptor was chosen for its proximity to the QTL peak and its well-studied roles during embryogenesis and puberty for male phenotype development and sexual maturation [Tex11 gene is also in the same QTL region in chromosome X [These confirmed QTL regions warrant further investigation and in the current study, two candidate genes, androgen receptor  gene with reproductive parameters and uterine infection in dairy cattle [Bos taurus or Bos indicus origin. Haplotypes from across the entire X chromosome were contrasted with Tex11 haplotypes to verify the origin of haplotypes and search for evidence of recent selection.The aim of this study was to characterise the variations in these genes including 5′UTR, test their association with male reproductive traits and investigate the origin of favourable haplotypes. Precedence for the importance of polymorphisms in the 5′UTR gene region include SNP within this region of the bovine growth hormone receptor gene, which has a marked effect on beef production traits , and they cattle . IdentifBos taurus UMD3.1 Bovine Genome Assembly on chromosome X between positions 60,168,532 and 93,999,806 bp. This region has previously been shown to contain 2 QTL associated with SC, PNS and other reproductive traits in both Brahman and Tropical Composite bulls [Genome sequences from 16 Brahman bulls, sires of the studied population, were initially compared to the published te bulls -30. Usinte bulls , 139,409AR genomic region equaling an average of 1 SNP per 380 bp. No SNP were identified within the coding region of the gene, although many SNP or short indels are present surrounding the intron-exon boundaries. For Tex11 530 SNP were identified across ~187 kb equaling an average of 1 SNP per 352 bp. Three previously unreported nsSNP were identified within exons 1, 10 and 23. It has previously been estimated that 20–30% of nsSNP affect protein function [Bioinformatic analysis initially identified 555 SNP across the ~210 kb region of the function ,38. The ::CC, 5AR TTAA and 5ARTTAALONG  previously identified bioinformatically were represented within the Brahman population. Furthermore, the sequencing exercise identified 3 markers (2 SNP and 1 indel) that were not observed in bioinformatic analyses  of Brahman bulls were characterised by direct sequencing with haplotype frequencies of 5ARAR 5′UTR polymorphism may be changes to transcription factor binding sites across this region, as demonstrated through in silico studies  and a SNP in intron 4 of the AR gene (AR_In4) , differences in the frequency of Bos taurus haplotypes were observed  and animals carrying the A allele had 1.33% on average (ranging from 0.6% to 3%). For AR_In4, 18% and 3% of Bos taurus haplotypes were observed for those animals with G and A alleles respectively. For Tex11_g297d 16% and 3% of Bos taurus haplotypes were the average for bulls with G and A alleles respectively.Across the entire genome or entire chromosome X, cattle carrying the favourable Tex11_r38k allele for SC had 2.6% of IGF1), gonadotropin-releasing hormone receptor (GNRHR) and testicular luteinizing hormone receptor (LHR) were associated with age at puberty in Angus male cattle, in Polish Holstein-Friesian cattle [Tex11 and AR as regulators of puberty onset in bovine bulls.Several studies have been published on the role of polymorphisms in other candidate genes associated to pubertal traits in cattle. For example, polymorphisms in insulin like growth factor 1 . However, when animals were grouped on the basis of allelic variants at the four SNP , striking differences were found in the average frequency of Bos taurus haplotypes between these groups  compared to those bulls carrying the A allele at these loci (only 1.33% on average). A similar but less pronounced effect was observed for AR_In4 (18% compared to 3%) and Tex11_g297d (16% compared to 3%). The increase in the proportion of Bos taurus haplotypes at approximately 70–100 Mb on chromosome X encompasses the candidate genes and the previously characterised QTL region associated with SC, PNS and other reproductive traits in both Brahman and Tropical Composite bulls. This suggests that this region of Bos taurus origin may have been retained in Brahmans and Tropical Composites through positive selection due to its favourable phenotypic outcomes including larger SC related to younger age at puberty and higher fertility.Previous studies have estimated that Australian Brahman cattle may contain up to 10% n traits . Upon exAR and Tex11) were investigated for their association with SC and PNS measurements, recognised fertility traits in Brahman and Tropical Composite bulls. These genes were selected for this study because of their position, which mapped to reported QTL [AR and in particular Tex11 in male reproduction in cattle. These two genes emerge as strong candidate genes for explaining SC and PNS variation in tropically adapted cattle breeds such as Brahman and Tropical Composite, with varying degrees of taurine and indicine content. Evidence for recent positive selection of a favourable Bos taurus haplotype was shown and potential causative mutations were described, such as the nsSNP in Tex11 and the 5’UTR SNP in AR that might affect SRY transcription factor binding sites. Knowledge about causative mutations merits confirmation and functional studies. These causative mutations can be used to improve animal breeding and selection as well as being potential models for male fertility issues in mammals.In this study, two putative candidate genes  breeding experiment to investigate genetics associated with whole herd profitability ,45-48. DTraits utilized in this study are components of male fertility: scrotal circumference (SC) at 12 (SC12), 18 (SC18) and 24 (SC24) months and percentage of normal sperm (PSN) at 18 (PNS18) and 24 (PNS24) months. These traits were measured at the bull breeding soundness evaluations (BBSE). Scrotal circumference was measured in centimetres using a standard metal tape . As parthttp://www.illumina.com/software/genome_analyzer_software.ilmn), where reads were matched to the UMD3.1 Bovine Genome Assembly. A single BAM format file was created for each sire.As part of the larger Beef CRC project, the genomes of sixteen Brahman bulls were sequenced at CSIRO Animal, Food and Health Sciences in St Lucia (unpublished data). These bulls were from the cohort of 55 sires used to generate the population used in GWAS analyses -28,30,42Bos taurus UMD3.1 Bovine Genome Assembly. Variant calls were outputted in VCF format. Variant Effect Predictor (VEP) was used to predict the functional consequences of detected variants [In silico prediction of DNA transcription factor binding sites was performed using TFSEARCH [Using Samtools , raw genvariants . In siliTFSEARCH .AR gene, DNA samples were selected from 17 bulls representing the extreme phenotypes for SC. Sequencing was performed for 10 animals with the highest SC measurements, high SC, and 7 animals with the lowest SC measurements, low SC. Primer design used the Bos taurus reference gene sequence (ENSBTAG00000022255). Primer pairs were designed to amplify a number of intronic regions immediately up- or downstream of known exons, with a full list of primer pairs in Additional file The AR gene is highly conserved across populations, with no nsSNP detected by bioinformatic analyses. Classic PCR and sequencing methodologies across exon-intron boundaries were used to confirm novel non-coding SNP that may be experimentally valuable for further characterisation of this region in studied populations. For sequencing of the 0C for 2 mins, 35 cycles of 94°C for 10 s, primer-dependent annealing temperature . DNA sequence data were analyzed using Sequencher™ 4.1 .PCR amplification was performed in an Eppendorf Master Cycler Gradient EP-S thermal cycler. PCR reactions (25 μl) contained 50–100 ng DNA, 1x GoTaq® PCR master mix  and 0.5 μM of each primer. The reaction profile used was 94 For a number of PCR products, deletions predicated the need for cloning of products prior to sequencing. When necessary, PCR products from individual animals were excised from the gel and purified using the QIAquick gel extraction kit . PCR products were then cloned into pGem-Teasy vector . Putative positive clones were sequenced with M13 forward and reverse primers.AR gene (AR_In4) located in the fourth intron, as well as 3 novel nsSNP spanning the Tex11 gene . Primers and probes were developed using the Custom TaqMan® Array Design Tool, and are listed in Additional file Custom TaqMan® assays were developed for a novel non-coding SNP in the Genotyping was performed by allelic discrimination using custom TaqMan® SNP genotyping assays, following the manufacturer's instructions. Briefly, 5 μl PCR reactions were carried out containing 2.5 μl of TaqMan® Universal PCR Master Mix , 10 ng of DNA template and 0.25 μl of TaqMan® assay primers and FAM/VIC labelled probes by Applied Biosystems as Assays-by-Design™ . All thermal cycling experiments were performed in 384 well plates on a Gene Amp 9700 (Applied Biosystems). Amplification conditions consisted of 50°C for 2 min, 95°C for 10 min followed by 40 cycles of 95°C for 30 s and 60°C for 1 min, and finally 25°C until removed from the thermal cycler. End-point reads were then performed on the Applied Biosystems ViiA™ 7 Real-Time PCR System, and allelic discrimination analysis was performed using ViiA™ 7RUO software .The association of each SNP with SC12, SC18, SC24, PNS18 and PNS24 was examined for genotyped Brahman bulls using a mixed model analysis of variance with ASREML software . The anayi represents the phenotypic measurement for the ith animal, X is the incidence matrix relating fixed effects in β with observations in y, Z is the incidence matrix relating to random additive polygenic effects of animal in μ with observations in y and Sj is the observed animal genotype for the jth SNP , αj is the estimated SNP effect, lastly ei is the random residual effect. The same fixed effects were used for each trait. These fixed effects included contemporary group  and a second term that was the interaction of year and month of birth.Where j-th SNP was estimated according to the formula j-th SNP estimated across the entire population, aj is the estimated additive effect of the j-th SNP on the trait under analysis, and σg2 is the REML estimate of the (poly-) genetic variance for the trait.The percentage of the genetic variance accounted by the Bos taurus or Bos indicus in origin depending on their relative frequency in a reference population. In brief, haplotypes of 10 consecutive SNP were built and their frequencies calculated in 1,105 Brahman bulls (reference population for Bos indicus) and 3,666 Angus, Hereford, Murray Grey and Shorthorn bulls (reference population for Bos taurus) of the Beef CRC. The probability that each segment was of Bos taurus origin (bi) was calculated using the following formula:The origin of haplotypes on the X chromosome was determined by using the method described by Bolormaa et al. . This mepBti is the frequency of haplotype segment i in Bos taurus animals and pBii is the frequency of haplotype segment i in Bos indicus (Brahman) animals. Segments were classified as Bos taurus if bi > 0.6 and Bos indicus if bi < 0.4. Haplotype segments with bi between 0.4 and 0.6 were classified as undetermined origin.Where Bos taurus alleles across the genome or X chromosome in animals carrying the G allele of the Tex11_r38k SNP, the percentage of Bos taurus in each animal was estimated using either all 50 k autosomal SNP or all X chromosome SNP from the 50 k chip, using Admixture [Bos taurus animals  and 1,032 Brahman bulls, all extracted from The Beef CRC database, with 50 k genotyping detailed before [Bos taurus proportion was then calculated and used to compare animals with the A or G alleles of Tex11_r38k.To determine whether there was a general increase in the proportion of dmixture . The datd before ,30,54. Thttp://www.1000bullgenomes.com/).New SNP were submitted to NCBI and the list of SS reference numbers is provided in the additional material section  through subset of X chromosome (628). Mean and Standard deviation of estimated Click here for fileForward and reverse primers used to amplify the intron-exon boundaries, 5′UTR and 3′UTR regions of the AR gene.Click here for fileSupplementary list of SS reference numbers for new polymorphisms as provided by NCBI.Click here for file


Arabidopsis halleri (Brassicaceae). The allele frequency estimates of the pooled population samples  were determined after mapping Illumina reads to (i) the publicly available, high-quality reference genome of a closely related species  and (ii) our own de novo draft genome assembly of A. halleri. We then pyrosequenced nine selected SNPs using the same individuals from each population, resulting in a total of 540 samples. Our results show a highly significant and accurate relationship between pooled and individually determined allele frequencies, irrespective of the reference genome used. Allele frequencies differed on average by less than 4%. There was no tendency that either the Pool-Seq or the individual-based approach resulted in higher or lower estimates of allele frequencies. Moreover, the rather high coverage in the mapping to the two reference genomes, ranging from 55 to 284x, had no significant effect on the accuracy of the Pool-Seq. A resampling analysis showed that only very low coverage values (below 10-20x) would substantially reduce the precision of the method. We therefore conclude that a pooled re-sequencing approach is well suited for analyses of genetic variation in natural populations.Sequencing of pooled samples (Pool-Seq) using next-generation sequencing technologies has become increasingly popular, because it represents a rapid and cost-effective method to determine allele frequencies for single nucleotide polymorphisms (SNPs) in population pools. Validation of allele frequencies determined by Pool-Seq has been attempted using an individual genotyping approach, but these studies tend to use samples from existing model organism databases or DNA stores, and do not validate a realistic setup for sampling natural populations. Here we used pyrosequencing to validate allele frequencies determined by Pool-Seq in three natural populations of  During the past few years, the use of next-generation sequencing technologies  has draRecently, Futschik and Schlötterer  demonstrHence, studies using Pool-Seq imply that their SNP allele frequency estimates are accurate, often without validation of these allele frequency estimates. Several studies have tried to address this topic in the recent past and validated allele frequencies from pooled samples by genotyping single individuals from within the pools , we compared the allele frequencies at nine SNPs located within six different genes using Illumina Pool-Seq data with the allele frequencies from individual genotyping using pyrosequencing of the same 20 samples per population . The allele frequencies from the Pool-Seq were determined using two different mapping approaches. We first mapped reads to a closely related species with a high-quality reference genome , and second to our own de novo draft assembly of the A. halleri genome. Our results show that estimates of allele frequencies measured in the pooled samples are highly accurate, independent of the reference genome used for mapping, and that a Pool-Seq approach is therefore well suited for population genomic analyses of natural populations. In the present study, we filled this gap and used pyrosequencing ,26 of inArabidopsis halleri (L.) is a member of the Brassicaceae and closely related to the model species A. thaliana. It occurs in a wide range of habitats, including mountain slopes, forest margins and rocky crevices [crevices . The percrevices  and has crevices  and heavcrevices ,34 due tA. halleri is not protected or endangered in Switzerland. Genomic DNA was extracted from dried leaves using the DNeasy Plant Kit (Qiagen). The quality of the extractions was tested using 1.5% agarose gels stained with GelRed (Biotum) and a Nanodrop 8000 (Thermo Scientific). The precise double stranded DNA concentration of individual samples was quantified using a Qubit fluorometer .We sampled 20 individuals in each of three natural populations in the southern Swiss Alps: in Brusio , Chironico , and Castasegna . A sampling permission for these locations was not required, because they do not represent nature reserves and Per population, one pooled sample with a total of 7 µg of RNA-free genomic high-quality DNA was prepared with equimolar concentrations of all 20 individual samples. Library preparations  and genome sequencing  were performed by GATC Biotech . To correct for channel bias, the three tagged populations were distributed among two full channels on one flow cell. After sequencing, forward and reverse reads were trimmed with Cutadapt , removinA. thaliana reference genome , usi, usiA. t, synchr using the software Velvet [A. halleri genome assembly, which was then used as a new reference to re-map the reads from the three study populations as described above, with one exception, namely that the mismatch parameter was lowered to 5%. SNPs were called as described above. The gene sequences of the six genes of interest  during a process of finding highly differentiated SNPs in genes , 1 U of AmpliTaq Gold DNA Polymerase (Life Technologies) and 1x buffer. An initial polymerase activation step of 5 min at 95°C was followed by 50 cycles of 15 s at 95°C, 30 s at the primer-specific annealing temperature , 0.2 µM of each primer  estimated on the basis of Pool-Seq data and population allele frequencies determined from individual samples genotyped by pyrosequencing. The major allele was defined as the allele that showed the highest frequency from the reads of all three populations combined. Therefore, for single populations, MAF can be below 0.5. The regression included 27 (3 populations × 9 SNPs) comparisons. Since we were testing technical aspects, we considered all comparisons as independent detection events, although they are biologically not independent. As we were mainly interested in the deviation from the ideal 1:1 ratio of the two estimates of allele frequency, we did not transform the data when determining the intercept, slope and R2-value of the regression. However, for the calculation of a p-value for the regression, we used arcsine square root-transformed allele frequencies. To check if Pool-Seq generally under- or overestimates allele frequencies, we performed a paired t-test on the 27 comparisons using arcsine square root-transformed allele frequencies. Both the linear regression and the paired t-test were performed for all 27 comparisons and for the comparisons without missing values only (amplification and/or sequencing failures in the pyrosequencing process). This allowed us to estimate the effect of missing values on the results. Finally, we tested the effect of sequencing coverage on the accuracy of the estimates of allele frequency by using a correlation analysis (Pearson's r) with the coverage as the explaining variable and the absolute difference between the two estimates of allele frequency as the dependent variable.The following analyses were performed in IBM SPSS Statistics 21 for both mapping approaches, i.e. using the allele frequencies derived from mapping to A. thaliana reference genome were used. With an in-house script in R 3.0.1 [R2 of the linear regressions for each simulated coverage.As the coverage analysis described above only included SNPs with high coverage, we used a resampling approach to test the  effect of low coverage on the accuracy of the Pool-Seq allele frequency estimates. For this analysis, only reads that mapped to the  R 3.0.1 , we randA. halleri genome (average of 104x per population). We unambiguously mapped 18.1% of the A. halleri reads to the A. thaliana reference genome. Using a minimum minor allele count of four, we identified over 2 million SNPs among the three populations. The coverage at the nine SNPs selected for the validation  could be identified in the contigs of the de novo assembly  assembly . The covThe eight pyrosequencing assays  developeA. thaliana reference genome by individual pyrosequencing was successful . Excluding comparisons with missing values from the pyrosequencing led to a very similar result using linear regressions  and the paired t-test . The coverage, ranging from 55 to 269x, had no significant effect on the accuracy of the Pool-Seq, expressed as difference between the two methods .The validation of the estimates of allele frequency from pooled samples mapped against the ccessful . The avede novo assembled reads were highly congruent with those described above. On average, allele frequencies differed by 3.9% ± 0.8 SE, with a maximum of 16.8% (R2 of 0.979 and was highly significant (p < 0.001). The paired t-test showed no systematic difference between the methods . The same was the case for the analyses excluding SNPs with missing values from the pyrosequencing . Finally, the coverage (69-284x) had no significant impact  on the accuracy of the Pool-Seq.The results using the allele frequency estimates based on the of 16.8% . The linR2 of 0.95 in a linear regression, a minimum coverage of 11x was needed, whereas for R2 = 0.99 a coverage of 38x was sufficient  has increased in the last few years, for example to estimate SNP allele frequencies of whole populations e.g., ,5,7,8), ,7,8, 5,7on A. halleri show a good match of the individually determined allele frequencies and the ones estimated by Pool-Seq, irrespective of whether paired-end reads were mapped to a closely related genome  or our own de novo draft assembly of A. halleri . Our findings are in concordance with previous studies that have verified Pool-Seq results  in order to cover rare variants. However, the resampling analysis to evaluate the effect of low coverage shows that, in our case, the Pool-Seq is already quite accurate at much lower coverage (10-20x), at least in terms of regression parameters and allele frequency differences  exist, is unlikely to result from the different platforms used. It rather points to general problems with the template DNA or mapping procedure. As we checked the quality of each DNA sample before pooling, the former is rather unlikely. Therefore, one possibility is that some individuals were so polymorphic in the specific region that (i) their reads did not map to the right position in the mapping process (thereby influencing the development of the pyrosequencing assays), and (ii) PCR and sequencing primers could not bind to the template DNA.A. thaliana [Arabidopsis thaliana and A. halleri are estimated to have diverged approximately 5 to 18 million years ago, have different chromosome numbers, and the A. halleri genome is about twice the size of the A. thaliana genome [A. halleri reads to the A. thaliana genome, which resulted in highly accurate allele frequency estimates for the SNPs of interest. Both mapping procedures represent interesting approaches for Pool-Seq studies using non-model organisms. While mapping to a closely related species is a quick and straightforward method, assembling an own draft genome leads to a higher proportion of mapped reads that can be used for further analyses. Analyses of sequence data can be strongly facilitated by the availability of a high-quality reference genome of a close relative, like thaliana  used in a genome . NeverthA. thaliana: in the three out of 27 comparisons where the minor allele per population represents a singleton, the allele frequency determined by Pool-Seq differed max. 0.4% from the allele frequency determined by pyrosequencing. Moreover, in five out of six comparisons in which loci were inferred to be monomorphic within a population based on pyrosequencing, this was confirmed by Pool-Seq. These results strongly support the accuracy of the pooling approach, as long as pipetting and DNA quantification errors can be held at a minimum, and the coverage is sufficient in comparison to the pool size and to the threshold of read number needed to accept an allele during SNP calling.It was beyond the scope of this study to test the effect of pool size on the accuracy of allele frequency estimates of the populations. However, it is clear that increasing the number of individual samples in a pool will decrease the contribution of pipetting and DNA content measurements to the overall error rate . On the As a consequence of its quantitative nature, the pyrosequencing method used for the validation of the Pool-Seq can also itself be applied to accurately determine allele frequencies in pooled samples ,29. TherIn conclusion, our validation study shows that allele frequency estimates derived from Pool-Seq can be highly reliable and accurate. The sampling design we used in this study is a typical example for studies investigating population genomics or local adaptation of natural populations . We thusFile S1A. halleri .Mapped consensus sequences of  This zip file contains the mapped consensus sequences of the six genes containing the nine validated SNPs, whose positions are given in Table 2. These consensus sequences were compiled from the population specific sequences  and were used for designing the SNP arrays.(ZIP)Click here for additional data file.File S2A. halleri de novo assembly containing the validated SNPs.Contigs of the  This zip file contains the contigs covering the nine validated SNPs, whose positions are given in Table 2. The contigs were assembled using the Brusio population.(ZIP)Click here for additional data file.