SNPNB: analyzing neighboring-nucleotide biases on single nucleotide polymorphisms (SNPs)

SNP Formation Bias in the Murine Genome Provides Evidence for Parallel Evolution

In this study, we show novel DNA motifs that promote single nucleotide polymorphism (SNP) formation and are conserved among exons, introns, and intergenic DNA from mice (Sanger Mouse Genomes Project), human genes (1000 Genomes), and tumor-specific somatic mutations (data from TCGA). We further characterize SNPs likely to be very recent in origin (i.e., formed in otherwise congenic mice) and show enrichment for both synonymous and parallel DNA variants occurring under circumstances not attributable to purifying selection. The findings provide insight regarding SNP contextual bias and eukaryotic codon usage as strategies that favor long-term exonic stability. The study also furnishes new information concerning rates of murine genomic evolution and features of DNA mutagenesis (at the time of SNP formation) that should be viewed as "adaptive."

A matter of life or death: how microsatellites emerge in and vanish from the human genome

Microsatellites--tandem repeats of short DNA motifs--are abundant in the human genome and have high mutation rates. While microsatellite instability is implicated in numerous genetic diseases, the molecular processes involved in their emergence and disappearance are still not well understood. Microsatellites are hypothesized to follow a life cycle, wherein they are born and expand into adulthood, until their degradation and death. Here we identified microsatellite births/deaths in human, chimpanzee, and orangutan genomes, using macaque and marmoset as outgroups. We inferred mutations causing births/deaths based on parsimony, and investigated local genomic environments affecting them. We also studied birth/death patterns within transposable elements (Alus and L1s), coding regions, and disease-associated loci. We observed that substitutions were the predominant cause for births of short microsatellites, while insertions and deletions were important for births of longer microsatellites. Substitutions were the cause for deaths of microsatellites of virtually all lengths. AT-rich L1 sequences exhibited elevated frequency of births/deaths over their entire length, while GC-rich Alus only in their 3' poly(A) tails and middle A-stretches, with differences depending on transposable element integration timing. Births/deaths were strongly selected against in coding regions. Births/deaths occurred in genomic regions with high substitution rates, protomicrosatellite content, and L1 density, but low GC content and Alu density. The majority of the 17 disease-associated microsatellites examined are evolutionarily ancient (were acquired by the common ancestor of simians). Our genome-wide investigation of microsatellite life cycle has fundamental applications for predicting the susceptibility of birth/death of microsatellites, including many disease-causing loci.

Seq-SNPing: multiple-alignment tool for SNP discovery, SNP ID identification, and RFLP genotyping

Many sequence-alignment tools were developed to discover single nucleotide polymorphisms (SNPs) derived from resequencing in genomic regions. Whether an identified SNP is indeed a novel SNP or is already contained in dbSNP is often difficult to answer. Here, we describe a freely available software, Seq-SNPing, which is a Java-based software for SNP discovery, and ID identification and editing and visualizating of sequence alignments. It is easy to use, fast, and provides an accurate method for searching and organizing SNP IDs from multiple sequence inputs, thereby greatly facilitating genetic studies. Seq-SNPing provides SNP identification by selecting any range of unaligned or aligned sequences in sequences that are similar. SNP IDs in the National Center for Biotechnology Information (NCBI) or user-defined SNPs within a selected sequence can be identified by Seq-SNPing. Information needed for SNP-RFLP (restriction fragment length polymorphism) genotyping is provided, such as SNP-REs (restriction enzymes), the sequence trimmer, sequence finder, BLAST (Basic Local Alignment Search Tool), SNP-BLAST, UCSC BLAT (BLAST-like alignment tool), RE mining, antisequencer (Anti-seq), and T(m) (melting temperature)/GC% of selected sequence. The thresholds for SNP calling are adjustable by selecting the height of the peak for each nucleotide representative curve in the chromatogram. Therefore, Seq-SNPing can discover SNPs and identify SNP IDs in both sequence text and chromatogram files in a fast and reliable way. The software is fully compatible with Microsoft Windows. The program and user manual are available at http://bio.kuas.edu.tw/Seq-SNPing for download.

Candidate genes for schizophrenia: a survey of association studies and gene ranking

More than 500 genes have been reported with positive or negative association with schizophrenia. The wealth of this information, along with the complex nature of psychiatric disorders, provides a challenging but also unique opportunity for the investigation of molecular and cellular mechanisms in schizophrenia. In this study, we performed a comprehensive survey of the published association studies collected in the SchizophreniaGene database. We observed over time a strong trend for increases in the number of published reports, the number of studied genes, and the sample size of the studies. We also examined the studies, genes, and sample sizes in different ethnic populations and the distribution of these association studies and their employed markers among these susceptibility genes. We then selected and ranked candidate genes using a combined odds ratio method. The evaluation of this candidate gene set against sets selected by other methods suggested its utility in follow-up association studies and in further bioinformatics analysis. We also examined the functional biases of the selected genes.

A novel statistical method to estimate the effective SNP size in vertebrate genomes and categorized genomic regions

Background

The local environment of single nucleotide polymorphisms (SNPs) contains abundant genetic information for the study of mechanisms of mutation, genome evolution, and causes of diseases. Recent studies revealed that neighboring-nucleotide biases on SNPs were strong and the genome-wide bias patterns could be represented by a small subset of the total SNPs. It remains unsolved for the estimation of the effective SNP size, the number of SNPs that are sufficient to represent the bias patterns observed from the whole SNP data.

Results

To estimate the effective SNP size, we developed a novel statistical method, SNPKS, which considers both the statistical and biological significances. SNPKS consists of two major steps: to obtain an initial effective size by the Kolmogorov-Smirnov test (KS test) and to find an intermediate effective size by interval evaluation. The SNPKS algorithm was implemented in computer programs and applied to the real SNP data. The effective SNP size was estimated to be 38,200, 39,300, 38,000, and 38,700 in the human, chimpanzee, dog, and mouse genomes, respectively, and 39,100, 39,600, 39,200, and 42,200 in human intergenic, genic, intronic, and CpG island regions, respectively.

Conclusion

SNPKS is the first statistical method to estimate the effective SNP size. It runs efficiently and greatly outperforms the algorithm implemented in SNPNB. The application of SNPKS to the real SNP data revealed the similar small effective SNP size (38,000 – 42,200) in the human, chimpanzee, dog, and mouse genomes as well as in human genomic regions. The findings suggest strong influence of genetic factors across vertebrate genomes.

SNP@Domain: a web resource of single nucleotide polymorphisms (SNPs) within protein domain structures and sequences

The single nucleotide polymorphisms (SNPs) in conserved protein regions have been thought to be strong candidates that alter protein functions. Thus, we have developed SNP@Domain, a web resource, to identify SNPs within human protein domains. We annotated SNPs from dbSNP with protein structure-based as well as sequence-based domains: (i) structure-based using SCOP and (ii) sequence-based using Pfam to avoid conflicts from two domain assignment methodologies. Users can investigate SNPs within protein domains with 2D and 3D maps. We expect this visual annotation of SNPs within protein domains will help scientists select and interpret SNPs associated with diseases. A web interface for the SNP@Domain is freely available at and from .