Accurate detection and genotyping of SNPs utilizing population sequencing data

The detection of single nucleotide polymorphisms (SNPs) is of great significance in the early diagnosis of diseases and the rational use of drugs. Thus, a novel biosensor based on the quenching effect of fluorescence-embedded SYBR Green I (SG) dye and graphene oxide (GO) was introduced in this study. The probe DNA forms a double helix structure with perfectly complementary DNA (pcDNA) and 15 single-base mismatch DNA (smDNA) respectively. SG is highly intercalated with perfectly complementary dsDNA (pc-dsDNA) and exhibits strong fluorescence emission. Single-base mismatch dsDNA (SNPs) has a loose double-stranded structure and exhibits poor SG intercalation and low fluorescence sensing. At this time, the sensor still showed poor SNP discrimination. GO has a strong effect on single-stranded DNA (ssDNA), which can reduce the fluorescence response of probe DNA and eliminate background interference. And competitively combined with ssDNA in SNPs, quenching the fluorescence of SG/SNP, while the fluorescence value of pc-dsDNA was retained, increasing the signal-to-noise ratio. At this time, the sensor has obtained excellent SNP resolution. Different SNPs detect different intensities of fluorescence in the near-infrared region to evaluate the sensor's identification of SNPs. The experimental parameters such as incubation time, incubation temperature and salt concentration were optimized. Under optimal conditions, 1 nM DNA with 0–10 nM linear range and differentiate 5% SNP were achieved. The detection method does not require labeling, is low cost, simple in operation, exhibits high SNP discrimination and can be distinguished by SNP at room temperature.

We present Sprite, a new high-performance data analysis pipeline for detecting single nucleotide polymorphisms (SNPs) in the human genome. A SNP detection pipeline for next-generation sequencing data uses several software tools, including tools for read alignment, processing alignment output, and SNP identification. We target end-to-end scalability and I/O efficiency in Sprite by merging tools in this pipeline and eliminating redundancies. For a benchmark human whole-genome sequencing data set, Sprite takes less than 50 min on 16 nodes of the TACC Stampede supercomputer. A key component of our optimized pipeline is parsnip, a new parallel method and software tool for SNP detection. We find that the quality of results obtained by parsnip (sensitivity and precision using high-confidence variant calls as ground truth) is comparable to state-of-the-art SNP-calling software. A prototype implementation of Sprite is available at sprite-psu.sourceforge.net.

DNA single-nucleotide polymorphism (SNP) detection has attracted much attention due to mutation-related diseases. Various fluorescence methods for SNP detection have been proposed and many are already in use. However, fluorescence enhancement for signal-on SNP identification without label modification still remains a challenge. Here, we find that the abasic site (AP site) in a DNA duplex can be developed as a binding pocket favorable for the occurrence of the excited-state intramolecular proton transfer (ESIPT) of a 3-hydroxyflavone, fisetin, which is used as a proof of concept for effective SNP identification. Fisetin binding at the AP site is highly selective for target thymine or cytosine facing the AP site by observation of a drastic increase in the ESIPT emission band. In addition, the target recognition selectivity based on this ESIPT process is not affected by flanking bases of the AP site. The binding selectivity of fisetin at the AP site is also confirmed by measurements of fluorescence resonance energy transfer, emission lifetime and DNA melting. The fluorescent signal-on sensing for SNP based on this fluorophore is substantially advantageous over the previously used fluorophores such as the AP site-specific signal-off organic ligands with a similar fluorescing mechanism before and after binding to DNA with hydrogen bonding interaction. We expect that this approach will be employed to develop a practical SNP detection method by locating an AP site toward a target and employing an ESIPT probe as readout.

Single nucleotide polymorphisms (SNPs) constitute the most common types of genetic variations in the human genome. A number of SNPs have been linked to the development of life threatening diseases including cancer, cardiovascular diseases and neurodegenerative diseases. The ability for ultrasensitive and accurate detection of low abundant disease-related SNPs in bodily fluids (e.g. blood, serum, etc.) holds a significant value in the development of non-invasive future biodiagnostic tools. Over the past two decades, nanomaterials have been utilized in a myriad of biosensing applications due to their ability of detecting extremely low quantities of biologically important biomarkers with high sensitivity and accuracy. Of particular interest is the application of such technologies in the detection of SNPs. The use of various nanomaterials, coupled with different powerful signal amplification strategies, has paved the way for a new generation of ultrasensitive SNP biodiagnostic assays. Over the past few years, several ultrasensitive SNP biosensors capable of detecting specific targets down to the ultra-low regimes (ca. aM and below) and therefore holding great promises for early clinical diagnosis of diseases have been developed. This mini review will highlight some of the most recent, significant advances in nanomaterial-based ultrasensitive SNP sensing technologies capable of detecting specific targets on the attomolar (10(-18) M) regime or below. In particular, the design of novel, powerful signal amplification strategies that hold the key to the ultrasensitivity is highlighted.

SNP discovery in associating genetic variation with human disease phenotypes

Detection of nucleic acids and single nucleotide polymorphisms (SNPs) is of pivotal importance in biology and medicine. Given that the biological effect of SNPs often is enhanced in combination with other SNPs, multiplexed SNP detection is desirable. We show proof of concept of the multiplexed detection of SNPs based on the template-directed native chemical ligation (NCL) of PNA-probes carrying a metal tag allowing detection using ICP-MS. For the detection of ssDNA oligonucleotides (30 bases), two probes, one carrying the metal tag and a second one carrying biotin for purification, are covalently ligated. The methodological limit of detection is of 29 pM with RSD of 6.7% at 50 pM (n=5). Detection of SNPs is performed with the combination of two sets of reporter probes. The first probe set targets the SNP, and its yield is compared with a second set of probes targeting a neighboring sequence. The assay was used to simultaneously differentiate between alleles of three SNPs at 5-nM concentration.

DNA sequence alignment and single-nucleotide polymorphism (SNP) detection are two important tasks in genomics research. A common genome resequencing analysis workflow is to first perform sequence alignment and then detect SNPs among the aligned sequences. In practice, the performance bottleneck in this workflow is usually the intermediate result I/O due to the separation of the two components, especially when the in-memory computation has been accelerated, e.g., by graphics processors. To address this bottleneck, we propose to integrate the two tasks tightly so as to eliminate the I/O of intermediate results in the workflow. Specifically, we make the following three changes for the tight integration: (1) we adopt a partition-based approach so that the external sorting of alignment results, which was required for SNP detection, is eliminated; (2) we perform customized compression on alignment results to reduce memory footprint; and (3) we move the computation of a global matrix from SNP detection to sequence alignment to save a file scan. We have developed a GPU-accelerated system that tightly integrates sequence alignment and SNP detection. Our results with human genome data sets show that our GPU-acceleration of individual components in the traditional workflow improves the overall performance by 18 times and that the tight integration further improves the performance of the GPU-accelerated system by 2.3 times.

As the amount of re-sequencing genome data grows, minimizing the execution time of an analysis is required. For this purpose, recent computing systems have been adopting both high-performance coprocessors and host processors. However, there are few applications that efficiently utilize these heterogeneous computing resources. This problem equally refers to the work of single nucleotide polymorphism (SNP) detection, which is one of the bottlenecks in genome data processing. In this paper, we propose a method for speeding up an SNP detection by enhancing the utilization of heterogeneous computing resources often used in recent high-performance computing systems. Through the measurement of workload in the detection procedure, we divide the SNP detection into several task groups suitable for each computing resource. These task groups are scheduled using a window overlapping method. As a result, we improved upon the speedup achieved by previous open source applications by a magnitude of 10.

The objectives of this study were to develop breed-specific single nucleotide polymorphisms (SNPs) in five pig breeds sequenced with Illumina's Genome Analyzer and to investigate their usefulness for breed assignment purposes. DNA pools were prepared for Duroc, Landrace, Large White, Pietrain and Wild Boar. The total number of animals used for sequencing was 153. SNP discovery was performed by aligning the filtered reads against Build 7 of the pig genome. A total of 313,964 high confidence SNPs were identified and analysed for the presence of breed-specific SNPs (defined in this context as SNPs for which one of the alleles was detected in only one breed). There were 29,146 putative breed-specific SNPs identified, of which 4441 were included in the PorcineSNP60 beadchip. Upon re-examining the genotypes obtained using the beadchip, 193 SNPs were confirmed as being breed specific. These 193 SNPs were subsequently used to assign an additional 490 individuals from the same breeds, using the sequenced individuals as reference populations. In total, four breed assignment tests were performed. Results showed that for all methods tested 99% of the animals were correctly assigned, with an average probability of assignment of at least 99.2%, indicating the high utility of breed-specific markers for breed assignment and traceability. This study provides a blueprint for the way next-generation sequencing technologies can be used for the identification of breed-specific SNPs, as well as evidence that these SNPs may be a powerful tool for breed assignment and traceability of animal products to their breeds of origin.

Many association studies provide the relationship between single nucleotide polymorphisms (SNPs), diseases and cancers, without giving a SNP ID, however. Here, we developed the SNP ID-info freeware to provide the SNP IDs within inputting genetic and physical information of genomes. The program provides an "SNP-ePCR" function to generate the full-sequence using primers and template inputs. In "SNPosition," sequence from SNP-ePCR or direct input is fed to match the SNP IDs from SNP fasta-sequence. In "SNP search" and "SNP fasta" function, information of SNPs within the cytogenetic band, contig position, and keyword input are acceptable. Finally, the SNP ID neighboring environment for inputs is completely visualized in the order of contig position and marked with SNP and flanking hits. The SNP identification problems inherent in NCBI SNP BLAST are also avoided. In conclusion, the SNP ID-info provides a visualized SNP ID environment for multiple inputs and assists systematic SNP association studies. The server and user manual are available at http://bio.kuas.edu.tw/snpid-info.

Alzheimer's disease (AD) is one of the most serious neurodegenerative diseases in the world and has a strong genetic predisposition. At present, there is still no effective method for the early diagnosis and prevention of AD. Accumulating evidence shows the association of several loci with AD risk, such as apolipoprotein E (APOE) and translocase of outer mitochondrial membrane 40 (TOMM40). However, for routine disease diagnosis in clinics, genotype detection methods based on gene sequencing technology are time-consuming and excessively costly. Thus, in this study, we developed a high-sensitivity, low-cost, and convenient single nucleotide polymorphism(SNP) detection assay method based on allele-specific quantitative polymerase chain reaction (AS-qPCR) technology, which can be used to determine the SNP genotype in APOE and TOMM40. A total of 40 patients were recruited from the outpatient department of the memory clinic of Dongzhimen Hospital, Beijing University of Chinese Medicine. The SNP detection assay method includes three steps. First, positive plasmids with different genotypes (TT/CC/TC) in APOE rs429358, rs7412, and TOMM40 rs11556505 were prepared. Second, 3'-T/3'-C primers were designed to amplify these positive plasmids for each SNP site. Finally, we calculated the log10 of the copy number ratio for each positive plasmid, and the genotype interpretation interval was established. Based on this method, we investigated whether the SNPs in 40 patients could be accurately calculated using AS-qPCR technology. The accuracy of SNP detection was verified by PCR-Pooling sequencing. The results showed that SNP genotypes assessed by AS-qPCR technology corresponded perfectly to the results obtained by conventional DNA sequencing. We have developed a genotype detection method for AD based on AS-qPCR, which can be performed easily, rapidly, accurately, and at low cost. The method will contribute to the early diagnosis of patients with late-onset Alzheimer's and the detection of large clinical samples in the future.

Massively parallel sequencing (MPS), since its debut in 2005, has transformed the field of genomic studies. These new sequencing technologies have resulted in the successful identification of causal variants for several rare Mendelian disorders. They have also begun to deliver on their promise to explain some of the missing heritability from genome-wide association studies (GWAS) of complex traits. We anticipate a rapidly growing number of MPS-based studies for a diverse range of applications in the near future. One crucial and nearly inevitable step is to detect SNPs and call genotypes at the detected polymorphic sites from the sequencing data. Here, we review statistical methods that have been proposed in the past five years for this purpose. In addition, we discuss emerging issues and future directions related to SNP detection and genotype calling from MPS data.

409 Background: The X-ray repair cross-complementing protein 1 (XRCC1) gene plays an important role in DNA damage repair; and the R194W and R399Q single nucleotide polymorphisms (SNPs) are associated with attenuated DNA repair activity. Our objective was to determine whether XRCC1 SNPs were associated with response to neoadjuvant chemoradiation therapy (CRT) in rectal cancer patients. Methods: We conducted a prospective multicenter phase II trial to investigate the effect of additional chemotherapy with a longer CRT-to-surgery interval on treatment response in patients with rectal cancer. Paired normal and rectal cancer tissues were prospectively collected from enrolled patients (n=110). DNA was extracted from normal rectal mucosa and analyzed for XRCC1 R194W and R399Q by polymerase chain reaction and sequencing. Each SNP was compared to response to CRT. Response to CRT was determined by a central pathologist. Results: Of the 110 patients, 25% (n=27) achieved a pathologic complete response (pCR). We examined the XRCC1 R194W SNP and observed that the allelic frequency of patients with the R/R genotype was 84% (n=92), the R/W genotype was 14% (n=16), the W/W genotype was 1% (n=2). By Fisher's Exact test, patients harboring either the R/W or W/W genotype, had a significantly higher rate of pCR compared to patients with the R/R genotype (44% vs. 21%, respectively; p=0.04). We then examined the R399Q SNP and observed that the allelic frequency of patients with the R/R genotype was 34% (n=37), the R/Q genotype was 49% (n=54), and the Q/Q genotype was 17% (n=19). In contrast, the detection of XRCC1 R399Q SNPs was not associated with pCR. Conclusions: We demonstrate that XRCC1 R194W SNP is associated with increased rate of pCR in our prospective multi-center trial. Although the detection of XRCC1 R399Q SNP is not associated with pCR, the detection of XRCC1 SNPs that predict treatment response may help identify patients who are unlikely to respond to CRT and therefore may potentially avoid treatment-associated morbidities. No significant financial relationships to disclose.

Assessment of genetic diversity, detection of strain-specific single nucleotide polymorphisms and identification of the Bangladesh and Vietnam strain of Channa striata by PCR-RFLP analysis of the mitochondrial COI gene fragment

BackgroundAmplicon re-sequencing based on the automated Sanger method remains popular for detection of single nucleotide polymorphisms (SNPs) and insertion-deletion polymorphisms (InDels) for a spectrum of genetics applications. However, existing software tools for detecting intra-individual SNPs and InDels in direct amplicon sequencing of diploid samples are insufficient in analyzing single traces and their accuracy is still limited.ResultsWe developed a novel computation tool, named DiSNPindel, to improve the detection of intra-individual SNPs and InDels in direct amplicon sequencing of a diploid. Neither reference sequence nor additional sample was required. Using two real datasets, we demonstrated the usefulness of DiSNPindel in its ability to improve largely the true SNP and InDel discovery rates and reduce largely the missed and false positive rates as compared with existing detection methods.ConclusionsThe software DiSNPindel presented here provides an efficient tool for intra-individual SNP and InDel detection in diploid amplicon sequencing. It will also be useful for identification of DNA variations in expressed sequence tag (EST) re-sequencing.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0790-y) contains supplementary material, which is available to authorized users.