KEGG(Kyoto Encyclopedia of Genes and Genomes)〔和文〕 (特集 ゲノム医学の現在と未来--基礎と臨床) -- (データベース)

The Sorting Intolerant from Tolerant (SIFT) algorithm predicts the effect of coding variants on protein function. It was first introduced in 2001, with a corresponding website that provides users with predictions on their variants. Since its release, SIFT has become one of the standard tools for characterizing missense variation. We have updated SIFT’s genome-wide prediction tool since our last publication in 2009, and added new features to the insertion/deletion (indel) tool. We also show accuracy metrics on independent data sets. The original developers have hosted the SIFT web server at FHCRC, JCVI and the web server is currently located at BII. The URL is http://sift-dna.org (24 May 2012, date last accessed).

SIFT web server: predicting effects of amino acid substitutions on proteins

We present a DNA library preparation method that has allowed us to reconstruct a high-coverage (30×) genome sequence of a Denisovan, an extinct relative of Neandertals. The quality of this genome allows a direct estimation of Denisovan heterozygosity indicating that genetic diversity in these archaic hominins was extremely low. It also allows tentative dating of the specimen on the basis of “missing evolution” in its genome, detailed measurements of Denisovan and Neandertal admixture into present-day human populations, and the generation of a near-complete catalog of genetic changes that swept to high frequency in modern humans since their divergence from Denisovans.

https://science.sciencemag.org/content/sci/338/6104/222.full.pdf

A high-coverage genome sequence from an archaic Denisovan individual

MicroRNAs in Stress Signaling and Human Disease

The rate at which nonsynonymous single nucleotide polymorphisms (nsSNPs) are being identified in the human genome is increasing dramatically owing to advances in whole-genome/whole-exome sequencing technologies. Automated methods capable of accurately and reliably distinguishing between pathogenic and functionally neutral nsSNPs are therefore assuming ever-increasing importance. Here, we describe the Functional Analysis Through Hidden Markov Models (FATHMM) software and server: a species-independent method with optional species-specific weightings for the prediction of the functional effects of protein missense variants. Using a model weighted for human mutations, we obtained performance accuracies that outperformed traditional prediction methods (i.e., SIFT, PolyPhen, and PANTHER) on two separate benchmarks. Furthermore, in one benchmark, we achieve performance accuracies that outperform current state-of-the-art prediction methods (i.e., SNPs&GO and MutPred). We demonstrate that FATHMM can be efficiently applied to high-throughput/large-scale human and nonhuman genome sequencing projects with the added benefit of phenotypic outcome associations. To illustrate this, we evaluated nsSNPs in wheat (Triticum spp.) to identify some of the important genetic variants responsible for the phenotypic differences introduced by intense selection during domestication. A Web-based implementation of FATHMM, including a high-throughput batch facility and a downloadable standalone package, is available at http://fathmm.biocompute.org.uk.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/humu.22225

Predicting the functional, molecular, and phenotypic consequences of amino acid substitutions using hidden Markov models.

Nonsynonymous single nucleotide polymorphisms (nsSNPs) are prevalent in genomes and are closely associated with inherited diseases. To facilitate identifying disease-associated nsSNPs from a large number of neutral nsSNPs, it is important to develop computational tools to predict the nsSNP's phenotypic effect (disease-associated versus neutral). nsSNPAnalyzer, a web-based software developed for this purpose, extracts structural and evolutionary information from a query nsSNP and uses a machine learning method called Random Forest to predict the nsSNP's phenotypic effect. nsSNPAnalyzer server is available at http://snpanalyzer.utmem.edu/.

/pdf/nssnpanalyzer-identifying-disease-associated-nonsynonymous-5at1f7abtq.pdf

nsSNPAnalyzer : identifying disease-associated nonsynonymous single nucleotide polymorphisms

Motivation: There has been great expectation that the knowledge of an individual's genotype will provide a basis for assessing susceptibility to diseases and designing individualized therapy. Non-synonymous single nucleotide polymorphisms (nsSNPs) that lead to an amino acid change in the protein product are of particular interest because they account for nearly half of the known genetic variations related to human inherited diseases. To facilitate the identification of disease-associated nsSNPs from a large number of neutral nsSNPs, it is important to develop computational tools to predict the phenotypic effects of nsSNPs.

Results: We prepared a training set based on the variant phenotypic annotation of the Swiss-Prot database and focused our analysis on nsSNPs having homologous 3D structures. Structural environment parameters derived from the 3D homologous structure as well as evolutionary information derived from the multiple sequence alignment were used as predictors. Two machine learning methods, support vector machine and random forest, were trained and evaluated. We compared the performance of our method with that of the SIFT algorithm, which is one of the best predictive methods to date. An unbiased evaluation study shows that for nsSNPs with sufficient evolutionary information (with not <10 homologous sequences), the performance of our method is comparable with the SIFT algorithm, while for nsSNPs with insufficient evolutionary information (<10 homologous sequences), our method outperforms the SIFT algorithm significantly. These findings indicate that incorporating structural information is critical to achieving good prediction accuracy when sufficient evolutionary information is not available.

Availability: The codes and curated dataset are available at http://compbio.utmem.edu/snp/dataset/

Contact: [email protected]

Supplementary information: The curated dataset is available at http://compbio.utmem.edu/snp/dataset/

/pdf/prediction-of-the-phenotypic-effects-of-non-synonymous-2zk8mpq1bk.pdf

Prediction of the phenotypic effects of non-synonymous single nucleotide polymorphisms using structural and evolutionary information

Polymorphism in microRNA Target Site (PolymiRTS) database is a collection of naturally occurring DNA variations in putative microRNA target sites. PolymiRTSs may affect gene expression and cause variations in complex phenotypes. The database integrates sequence polymorphism, phenotype and expression microarray data, and characterizes PolymiRTSs as potential candidates responsible for the quantitative trait locus (QTL) effects. It is a resource for studying PolymiRTSs and their implications in phenotypic variations. PolymiRTS database can be accessed at http://compbio.utmem. edu/miRSNP/.

/pdf/polymirts-database-linking-polymorphisms-in-microrna-target-1e2lvhcbub.pdf

PolymiRTS Database: linking polymorphisms in microRNA target sites with complex traits

Recent studies on transcriptional control of gene expression have pinpointed the importance of long-range interactions and three-dimensional organization of chromatins within the nucleus. Distal regulatory elements such as enhancers may activate transcription over long distances; hence, their action must be restricted within appropriate boundaries to prevent illegitimate activation of non-target genes. Insulators are DNA elements with enhancer-blocking and/or chromatin-bordering functions. In vertebrates, the versatile transcription regulator CCCTC-binding factor (CTCF) is the only identified trans-acting factor that confers enhancer-blocking insulator activity. CTCF-binding sites were found to be commonly distributed along the vertebrate genomes. We have constructed a CTCF-binding site database (CTCFBSDB) to characterize experimentally identified and computationally predicted CTCF-binding sties. Biological knowledge and data from multiple resources have been integrated into the database, including sequence data, genetic polymorphisms, function annotations, histone methylation profiles, gene expression profiles and comparative genomic information. A web-based user interface was implemented for data retrieval, analysis and visualization. In silico prediction of CTCF-binding motifs is provided to facilitate the identification of candidate insulators in the query sequences submitted by users. The database can be accessed at http://insulatordb.utmem.edu/

/pdf/ctcfbsdb-a-ctcf-binding-site-database-for-characterization-1sfyo8eq7n.pdf

CTCFBSDB: a CTCF-binding site database for characterization of vertebrate genomic insulators

Bayesian network modeling is a promising approach to define and evaluate gene expression circuits in diverse tissues and cell types under different experimental conditions. The power and practicality of this approach can be improved by restricting the number of potential interactions among genes and by defining causal relations before evaluating posterior probabilities for billions of networks. A newly developed genetical genomics method that combines transcriptome profiling with complex trait analysis now provides strong constraints on network architecture. This method detects those chromosomal intervals responsible for differences in mRNA expression using quantitative trait locus (QTL) mapping. We have developed an efficient Bayesian approach that exploits the genetical genomics method to focus computational effort on the most plausible gene modulatory networks. We exploit a dense marker map for a genetic reference population (GRP) that consists of 32 BXD strains of mice made by intercrossing two progenitor strains— C57BL/6J and DBA/2J. These progenitors differ at 1.3 million known single nucleotide polymorphisms (SNPs), all of which can be exploited to estimate the probability that a gene contains functional polymorphisms that segregate within the GRP. We constructed 66 candidate networks that include all the candidate modulator genes located in the 209 statistically significant trans-acting QTL regions. SNPs that distinguish between the two progenitor strains were used to further winnow the list of candidate modulators. Bayesian network was then used to identify the genetic modulatory relations that best explain the microarray data.

/pdf/inferring-gene-transcriptional-modulatory-relations-a-1klpbqthee.pdf

Lei Bao

Papers

nsSNPAnalyzer : identifying disease-associated nonsynonymous single nucleotide polymorphisms

Prediction of the phenotypic effects of non-synonymous single nucleotide polymorphisms using structural and evolutionary information

PolymiRTS Database: linking polymorphisms in microRNA target sites with complex traits

CTCFBSDB: a CTCF-binding site database for characterization of vertebrate genomic insulators

Inferring gene transcriptional modulatory relations: a genetical genomics approach