Mining for single nucleotide polymorphisms and insertions/deletions in maize expressed sequence tag data

We have developed a computer based method to identify candidate single nucleotide polymorphisms (SNPs) and small insertions/deletions from expressed sequence tag data. Using a redundancy-based approach, valid SNPs are distinguished from erroneous sequence by their representation multiple times in an alignment of sequence reads. A second measure of validity was also calculated based on the cosegregation of the SNP pattern between multiple SNP loci in an alignment. The utility of this method was demonstrated by applying it to 102,551 maize (Zea mays) expressed sequence tag sequences. A total of 14,832 candidate polymorphisms were identified with an SNP redundancy score of two or greater. Segregation of these SNPs with haplotype indicates that candidate SNPs with high redundancy and cosegregation confidence scores are likely to represent true SNPs. This was confirmed by validation of 264 candidate SNPs from 27 loci, with a range of redundancy and cosegregation scores, in four inbred maize lines. The SNP transition/transversion ratio and insertion/deletion size frequencies correspond to those observed by direct sequencing methods of SNP discovery and suggest that the majority of predicted SNPs and insertion/deletions identified using this approach represent true genetic variation in maize.     

SEAN: SNP prediction and display program utilizing EST sequence clusters

SEAN is an application that predicts single nucleotide polymorphisms (SNPs) using multiple sequence alignments produced from expressed sequence tag (EST) clusters. The algorithm uses rules of sequence identity and SNP abundance to determine the quality of the prediction. A Java viewer is provided to display the EST alignments and predicted SNPs.     

Discovery of single nucleotide polymorphisms in Lycopersicon esculentum by computer aided analysis of expressed sequence tags

Single nucleotide polymorphisms (SNPs) are useful for characterizing allelic variation, for genome-wide mapping, and as a tool for marker-assisted selection. Discovery of SNPs through de novo sequencing is inefficient within cultivated tomato (Lycopersicon esculentum Mill.) because the polymorphism rate is more than ten-fold lower than the sequencing error rate. The availability of expressed sequence tag (EST) data has made it feasible to discover putative SNPs in silico prior to experimental verification. By exploiting redundancy among EST data available for different varieties among 148,373 tomato ESTs, we have identified candidate SNPs for use within cultivated germplasm pools. 1,245 contigs having three EST sequences of Rio Grande and three EST sequences of TA496 were used for SNP discovery. We detected 1 SNP for every 8,500 bases analyzed, with 101 candidate SNPs in 44 genes identified. Sixty-six SNPs could be recognized by restriction enzymes, and subsequent experimental verification using restriction digestion or CEL I digestion confirmed 83% of the putative polymorphisms tested. SNPs between TA496 and Rio Grande have a high probability (53%) of detecting polymorphisms between other L. esculentum varieties. Twenty-six SNPs in 18 unigenes were mapped to specific chromosomes. Two SNPs, LEOH23 and LEOH37, were shown to be linked to quantitative trait loci contributing to fruit color within elite breeding populations. These results suggest that the growing databases of DNA sequence will yield information that facilitates improvement within the germplasm pools that have contributed to productive modern varieties.     

Snipping polymorphisms from large EST collections in barley (<Emphasis Type="Italic">Hordeum vulgare </Emphasis>L.)

The public EST (expressed sequence tag) databases represent an enormous but heterogeneous repository of sequences, including many from a broad selection of plant species and a wide range of distinct varieties. The significant redundancy within large EST collections makes them an attractive resource for rapid pre-selection of candidate sequence polymorphisms. Here we present a strategy that allows rapid identification of candidate SNPs in barley (Hordeum vulgare L.) using publicly available EST databases. Analysis of 271,630 EST sequences from different cDNA libraries, representing 23 different barley varieties, resulted in the generation of 56,302 tentative consensus sequences. In all, 8171 of these unigene sequences are members of clusters with six or more ESTs. By applying a novel SNP detection algorithm (SNiPpER) to these sequences, we identified 3069 candidate inter-varietal SNPs. In order to verify these candidate SNPs, we selected a small subset of 63 present in 36 ESTs. Of the 63 SNPs selected, we were able to validate 54 (86%) using a direct sequencing approach. For further verification, 28 ESTs were mapped to distinct loci within the barley genome. The polymorphism information content (PIC) and nucleotide diversity () values of the SNPs identified by the SNiPpER algorithm are significantly higher than those that were obtained by random sequencing. This demonstrates the efficiency of our strategy for SNP identification and the cost-efficient development of EST-based SNP-markers.The first two authors contributed equally to this work     

Confirming single nucleotide polymorphisms from expressed sequence tag datasets derived from three cattle cDNA libraries

Using the Phred/Phrap/Polyphred/Consed pipeline established in the National Livestock Research Institute of Korea, we predicted candidate coding single nucleotide polymorphisms (cSNPs) from 7,600 expressed sequence tags (ESTs) derived from three cDNA libraries (liver, M. longissimus dorsi, and intermuscular fat) of Hanwoo (Korean native cattle) steers. From the 7,600 ESTs, 829 contigs comprising more than two EST reads were assembled using the Phrap assembler. Based on the contig analysis, 201 candidate cSNPs were identified in 129 contigs, in which transitions (69%) outnumbered transversions (31%). To verify whether the predicted cSNPs are real, 17 SNPs involved in lipid and energy metabolism were selected from the ESTs. Twelve of these were confirmed to be real while five were identified as artifacts, possibly due to expressed sequence tag sequence error. Further analysis of the 12 verified cSNPs was performed using the program BLASTX. Five were identified as nonsynonymous cSNPs, five were synonymous cSNPs, and two SNPs were located in 3'-UTRs. Our data indicated that a relatively high SNP prediction rate (71%) from a large EST database could produce abundant cSNPs rapidly, which can be used as valuable genetic markers in cattle.     

SNP mining porcine ESTs with MAVIANT, a novel tool for SNP evaluation and annotation

MOTIVATION: Single nucleotide polymorphisms (SNPs) analysis is an important means to study genetic variation. A fast and cost-efficient approach to identify large numbers of novel candidates is the SNP mining of large scale sequencing projects. The increasing availability of sequence trace data in public repositories makes it feasible to evaluate SNP predictions on the DNA chromatogram level. MAVIANT, a platform-independent Multipurpose Alignment VIewing and Annotation Tool, provides DNA chromatogram and alignment views and facilitates evaluation of predictions. In addition, it supports direct manual annotation, which is immediately accessible and can be easily shared with external collaborators. RESULTS: Large-scale SNP mining of polymorphisms bases on porcine EST sequences yielded more than 7900 candidate SNPs in coding regions (cSNPs), which were annotated relative to the human genome. Non-synonymous SNPs were analyzed for their potential effect on the protein structure/function using the PolyPhen and SIFT prediction programs. Predicted SNPs and annotations are stored in a web-based database. Using MAVIANT SNPs can visually be verified based on the DNA sequencing traces. A subset of candidate SNPs was selected for experimental validation by resequencing and genotyping. This study provides a web-based DNA chromatogram and contig browser that facilitates the evaluation and selection of candidate SNPs, which can be applied as genetic markers for genome wide genetic studies. AVAILABILITY: The stand-alone version of MAVIANT program for local use is freely available under GPL license terms at http://snp.agrsci.dk/maviant. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

Single-nucleotide polymorphisms detected in expressed sequence tags of melon (Cucumis melo L.).

A search was performed for single-nucleotide polymorphisms (SNP) and short insertions-deletions (indels) in 34 melon (Cucumis melo L.) expressed sequence tag (EST) fragments between two distantly related melon genotypes, a group Inodorus 'Piel de sapo' market class breeding line T111 and the Korean accession PI 161375. In total, we studied 15 kb of melon sequence. The average frequency of SNPs between the two genotypes was one every 441 bp. One indel was also found every 1666 bp. Seventy-five percent of the polymorphisms were located in introns and the 3'untranslated regions. On average, there were 1.26 SNPs plus indels per amplicon. We explored three different SNP detection systems to position five of the SNPs in a melon genetic map. Three of the SNPs were mapped using cleaved amplified polymorphic sequence (CAPS) markers, one SNP was mapped using the single primer extension reaction with fluorescent-labelled dideoxynucleotides, and one indel was mapped using polyacrilamide gel electrophoresis separation. The discovery of SNPs based on ESTs and a suitable system for SNP detection has broad potential utility in melon genome mapping.     

SNP Discovery with EST and NextGen Sequencing in Switchgrass (Panicum virgatum L.)

Although yield trials for switchgrass (Panicum virgatum L.), a potentially high value biofuel feedstock crop, are currently underway throughout North America, the genetic tools for crop improvement in this species are still in the early stages of development. Identification of high-density molecular markers, such as single nucleotide polymorphisms (SNPs), that are amenable to high-throughput genotyping approaches, is the first step in a quantitative genetics study of this model biofuel crop species. We generated and sequenced expressed sequence tag (EST) libraries from thirteen diverse switchgrass cultivars representing both upland and lowland ecotypes, as well as tetraploid and octoploid genomes. We followed this with reduced genomic library preparation and massively parallel sequencing of the same samples using the Illumina Genome Analyzer technology platform. EST libraries were used to generate unigene clusters and establish a gene-space reference sequence, thus providing a framework for assembly of the short sequence reads. SNPs were identified utilizing these scaffolds. We used a custom software program for alignment and SNP detection and identified over 149,000 SNPs across the 13 short-read sequencing libraries (SRSLs). Approximately 25,000 additional SNPs were identified from the entire EST collection available for the species. This sequencing effort generated data that are suitable for marker development and for estimation of population genetic parameters, such as nucleotide diversity and linkage disequilibrium. Based on these data, we assessed the feasibility of genome wide association mapping and genomic selection applications in switchgrass. Overall, the SNP markers discovered in this study will help facilitate quantitative genetics experiments and greatly enhance breeding efforts that target improvement of key biofuel traits and development of new switchgrass cultivars.     

Applications of single nucleotide polymorphisms in crop genetics

The discovery of single nucleotide polymorphisms (SNPs) and insertions/deletions, which are the basis of most differences between alleles, has been simplified by recent developments in sequencing technology. SNP discovery in many crop species, such as corn and soybean, is relatively straightforward because of their high level of intraspecific nucleotide diversity, and the availability of many gene and expressed sequence tag (EST) sequences. For these species, direct readout of SNP haplotypes is possible. Haplotype-based analysis is more informative than analysis based on individual SNPs, and has more power in analyzing association with phenotypes. The elite germplasm of some crops may have been subjected to bottlenecks relatively recently, increasing the amount of linkage disequilibrium (LD) present and facilitating the association of SNP haplotypes at candidate gene loci with phenotypes. Whole-genome scans may help identify genome regions that are associated with interesting phenotypes if sufficient LD is present. Technological improvements make the use of SNP and indel markers attractive for high-throughput use in marker-assisted breeding, EST mapping and the integration of genetic and physical maps.     

猪的全基因组水平SNP研究现状

SNP（single nucleotide polymorphism,单核苷酸多态）在猪基因组中的分布极其广泛,平均分布间隔为300～400 bp,相关数据库收录已达55万条。猪基因组测序已取得实质性进展,大规模搜索发现基因组及EST（expressed sequence tag）序列中的SNP已展开,应用于猪全基因组水平的SNP芯片已建立。在此基础上,基于猪SNP标记的遗传图谱绘制、QTL（quantitative trait loci）定位、遗传多样性检测及全基因组关联分析等也都相继出现。     

Association between fatty acid compositions and genotypes of FABP4 and LXR-alpha in Japanese Black cattle

Background

Single nucleotide polymorphisms (SNPs) and small insertions or deletions (indels) are the most common type of polymorphisms and are frequently used for molecular marker development. Such markers have become very popular for all kinds of genetic analysis, including haplotype reconstruction. Haplotypes can be reconstructed for whole chromosomes but also for specific genes, based on the SNPs present. Haplotypes in the latter context represent the different alleles of a gene. The computational approach to SNP mining is becoming increasingly popular because of the continuously increasing number of sequences deposited in databases, which allows a more accurate identification of SNPs. Several software packages have been developed for SNP mining from databases. From these, QualitySNP is the only tool that combines SNP detection with the reconstruction of alleles, which results in a lower number of false positive SNPs and also works much faster than other programs. We have build a web-based SNP discovery and allele detection tool (HaploSNPer) based on QualitySNP.

Results

HaploSNPer is a flexible web-based tool for detecting SNPs and alleles in user-specified input sequences from both diploid and polyploid species. It includes BLAST for finding homologous sequences in public EST databases, CAP3 or PHRAP for aligning them, and QualitySNP for discovering reliable allelic sequences and SNPs. All possible and reliable alleles are detected by a mathematical algorithm using potential SNP information. Reliable SNPs are then identified based on the reconstructed alleles and on sequence redundancy.

Conclusion

Thorough testing of HaploSNPer (and the underlying QualitySNP algorithm) has shown that EST information alone is sufficient for the identification of alleles and that reliable SNPs can be found efficiently. Furthermore, HaploSNPer supplies a user friendly interface for visualization of SNP and alleles. HaploSNPer is available from http://www.bioinformatics.nl/tools/haplosnper/.     

Automated SNP Detection in Expressed Sequence Tags: Statistical Considerations and Application to Maritime Pine Sequences

We developed an automated pipeline for the detection of single nucleotide polymorphisms (SNPs) in expressed sequence tag (EST) data sets, by combining three DNA sequence analysis programs: Phred, Phrap and PolyBayes. This application requires access to the individual electrophoregram traces. First, a reference set of 65 SNPs was obtained from the sequencing of 30 gametes in 13 maritime pine (Pinus pinaster Ait.) gene fragments (6671 bp), resulting in a frequency of 1 SNP every 102.6 bp. Second, parameters of the three programs were optimized in order to retrieve as many true SNPs, while keeping the rate of false positive as low as possible. Overall, the efficiency of detection of true SNPs was 83.1%. However, this rate varied largely as a function of the rare SNP allele frequency: down to 41% for rare SNP alleles (frequency < 10%), up to 98% for allele frequencies above 10%. Third, the detection method was applied to the 18498 assembled maritime pine (Pinus pinaster Ait.) ESTs, allowing to identify a total of 1400 candidate SNPs, in contigs containing between 4 and 20 sequence reads. These genetic resources, described for the first time in a forest tree species, were made available at http://www.pierroton.inra/genetics/Pinesnps. We also derived an analytical expression for the SNP detection probability as a function of the SNP allele frequency, the number of haploid genomes used to generate the EST sequence database, and the sample size of the contigs considered for SNP detection. The frequency of the SNP allele was shown to be the main factor influencing the probability of SNP detection.     

Mining single-nucleotide polymorphisms from hexaploid wheat ESTs.

Single-nucleotide polymorphisms (SNPs) represent a new form of functional marker, particularly when they are derived from expressed sequence tags (ESTs). A bioinformatics strategy was developed to discover SNPs within a large wheat EST database and to demonstrate the utility of SNPs in genetic mapping and genetic diversity applications. A collection of > 90000 wheat ESTs was assembled into contiguous sequences (contigs), and 45 random contigs were then visually inspected to identify primer pairs capable of amplifying specific alleles. We estimate that homoeologue sequence variants occurred 1 in 24 bp and the frequency of SNPs between wheat genotypes was 1 SNP/540 bp (theta = 0.0069). Furthermore, we estimate that one diagnostic SNP test can be developed from every contig with 10-60 EST members. Thus, EST databases are an abundant source of SNP markers. Polymorphism information content for SNPs ranged from 0.04 to 0.50 and ESTs could be mapped into a framework of microsatellite markers using segregating populations. The results showed that SNPs in wheat can be discovered in ESTs, validated, and be applied to conventional genetic studies.     

A pipeline for high throughput detection and mapping of SNPs from EST databases

Single nucleotide polymorphisms (SNPs) represent the most abundant type of genetic variation that can be used as molecular markers. The SNPs that are hidden in sequence databases can be unlocked using bioinformatic tools. For efficient application of these SNPs, the sequence set should be error-free as much as possible, targeting single loci and suitable for the SNP scoring platform of choice. We have developed a pipeline to effectively mine SNPs from public EST databases with or without quality information using QualitySNP software, select reliable SNP and prepare the loci for analysis on the Illumina GoldenGate genotyping platform. The applicability of the pipeline was demonstrated using publicly available potato EST data, genotyping individuals from two diploid mapping populations and subsequently mapping the SNP markers (putative genes) in both populations. Over 7000 reliable SNPs were identified that met the criteria for genotyping on the GoldenGate platform. Of the 384 SNPs on the SNP array approximately 12% dropped out. For the two potato mapping populations 165 and 185 SNPs segregating SNP loci could be mapped on the respective genetic maps, illustrating the effectiveness of our pipeline for SNP selection and validation.     

Benefit-of-doubt (BOD) scoring: A sequencing-based method for SNP candidate assessment from high to medium read number data sets

Identification of single nucleotide polymorphisms (SNPs) is a key element in sequence-based genetic analysis. Next generation sequencing offers a cost-effective basis to generate the necessary, large sequence data sets, and bioinformatic methods are being developed to process sequencing machine readouts. We were interested in detection of SNPs in a 350 kb region of an EMS-mutagenized Arabidopsis chromosome 3. The region was selectively analyzed using PCR-generated, overlapping fragments for Solexa sequencing. The ensuing reads provided a high coverage and were processed bioinformatically. In order to assess the SNP candidates obtained with a frequently used alignment program and SNP caller, we developed an additional method that allows the identification of high confidence SNP loci. The method can easily be applied to complete genome sequence data of sufficient coverage.     

DigiPINS: a database for vertebrate exonic single nucleotide polymorphisms and its application to cancer association studies

Single nucleotide polymorphisms (SNPs), which are the most abundant form of genetic variations in numerous organisms, have emerged as important tools for the study of complex genetic traits and deciphering of genome evolution. High-throughput genome sequencing projects worldwide provide an unprecedented opportunity for whole-genome SNP analysis in a variety of species. To facilitate SNP discovery in vertebrates, we have developed a web-based, user-friendly, and fully automated application, DigiPINS, for genome-wide identification of exonic SNPs from EST data. Currently, the database can be used to the mining of exonic SNPs in six complete genomes (Homo sapiens, Mus musculus, Rattus norvegicus, Canis familiaris, Gallus gallus and Danio rerio). In addition to providing information on sequence conservation, DigiPINS allows compilation of comprehensive sets of polymorphisms within cancer candidate genes or identification of novel cancer markers, making it potentially useful for cancer association studies. The DigiPINS server is available via the internet at http://pbil.univ-lyon1.fr/gem/DigiPINS/query_DigiPINS.php.