bluuebunny/crossref_metadata_2025_split huggingface dataset:
Local protein structure prediction using discriminative models
Authors: Sander Oliver , Sommer Ingolf , Lengauer Thomas 
Published: January 2006

Abstract

Background
In recent years protein structure prediction methods using local structure information have shown promising improvements. The quality of new fold predictions has risen significantly and in fold recognition incorporation of local structure predictions led to improvements in the accuracy of results.
We developed a local structure prediction method to be integrated into either fold recognition or new fold prediction methods. For each local sequence window of a protein sequence the method predicts probability estimates for the sequence to attain particular local structures from a set of predefined local structure candidates.
The first step is to define a set of local structure representatives based on clustering recurrent local structures. In the second step a discriminative model is trained to predict the local structure representative given local sequence information.

Results
The step of clustering local structures yields an average RMSD quantization error of 1.19 Å for 27 structural representatives (for a fragment length of 7 residues). In the prediction step the area under the ROC curve for detection of the 27 classes ranges from 0.68 to 0.88.

Conclusion
The described method yields probability estimates for local protein structure candidates, giving signals for all kinds of local structure. These local structure predictions can be incorporated either into fold recognition algorithms to improve alignment quality and the overall prediction accuracy or into new fold prediction methods.
An approach for clustering gene expression data with error information
Authors: Tjaden Brian 
Published: January 2006

AbstractBackgroundClustering of gene expression patterns is a well-studied technique for elucidating trends across large numbers of transcripts and for identifying likely co-regulated genes. Even the best clustering methods, however, are unlikely to provide meaningful results if too much of the data is unreliable. With the maturation of microarray technology, a wealth of research on statistical analysis of gene expression data has encouraged researchers to consider error and uncertainty in their microarray experiments, so that experiments are being performed increasingly with repeat spots per gene per chip and with repeat experiments. One of the challenges is to incorporate the measurement error information into downstream analyses of gene expression data, such as traditional clustering techniques.ResultsIn this study, a clustering approach is presented which incorporates both gene expression values and error information about the expression measurements. Using repeat expression measurements, the error of each gene expression measurement in each experiment condition is estimated, and this measurement error information is incorporated directly into the clustering algorithm. The algorithm, CORE (Clustering Of Repeat Expression data), is presented and its performance is validated using statistical measures. By using error information about gene expression measurements, the clustering approach is less sensitive to noise in the underlying data and it is able to achieve more accurate clusterings. Results are described for both synthetic expression data as well as real gene expression data fromEscherichia coliandSaccharomyces cerevisiae.ConclusionThe additional information provided by replicate gene expression measurements is a valuable asset in effective clustering. Gene expression profiles with high errors, as determined from repeat measurements, may be unreliable and may associate with different clusters, whereas gene expression profiles with low errors can be clustered with higher specificity. Results indicate that including error information from repeat gene expression measurements can lead to significant improvements in clustering accuracy.
In silico discovery of human natural antisense transcripts
Authors: Li Yuan-Yuan , Qin Lei , Guo Zong-Ming , Liu Lei , Xu Hao , Hao Pei , Su Jiong , Shi Yixiang , He Wei-Zhong , Li Yi-Xue 
Published: January 2006

Abstract

Background
Several high-throughput searches for ppotential natural antisense transcripts (NATs) have been performed recently, but most of the reports were focused on cis type. A thorough in silico analysis of human transcripts will help expand our knowledge of NATs.

Results
We have identified 568 NATs from human RefSeq RNA sequences. Among them, 403 NATs are reported for the first time, and at least 157 novel NATs are trans type. According to the pairing region of a sense and antisense RNA pair, hNATs are divided into 6 classes, of which about 87% involve 5' or 3' UTR sequences, supporting the regulatory role of UTRs. Among a total of 535 NAT pairs related with splice variants, 77.4% (414/535) have their pairing regions affected or completely eliminated by alternative splicing, suggesting significant relationship of alternative splicing and antisense-directed regulation. The extensive occurrence of splice variants in hNATs and other multiple pairing patterns results in a one-to-many relationship, allowing the formation of complex regulation networks. Based on microarray data from Stanford Microarray Database, two hNAT pairs were found to display significant inverse expression patterns before and after insulin injection.

Conclusion
NATs might carry out more extensive and complex functions than previously thought. Combined with endogenous micro RNAs, hNATs could be regarded as a special group of transcripts contributing to the complex regulation networks.
Discovery of time-delayed gene regulatory networks based on temporal gene expression profiling
Authors: Li Xia , Rao Shaoqi , Jiang Wei , Li Chuanxing , Xiao Yun , Guo Zheng , Zhang Qingpu , Wang Lihong , Du Lei , Li Jing , Li Li , Zhang Tianwen , Wang Qing K 
Published: January 2006

Abstract

Background
It is one of the ultimate goals for modern biological research to fully elucidate the intricate interplays and the regulations of the molecular determinants that propel and characterize the progression of versatile life phenomena, to name a few, cell cycling, developmental biology, aging, and the progressive and recurrent pathogenesis of complex diseases. The vast amount of large-scale and genome-wide time-resolved data is becoming increasing available, which provides the golden opportunity to unravel the challenging reverse-engineering problem of time-delayed gene regulatory networks.

Results
In particular, this methodological paper aims to reconstruct regulatory networks from temporal gene expression data by using delayed correlations between genes, i.e., pairwise overlaps of expression levels shifted in time relative each other. We have thus developed a novel model-free computational toolbox termed TdGRN (Time-delayed Gene Regulatory Network) to address the underlying regulations of genes that can span any unit(s) of time intervals. This bioinformatics toolbox has provided a unified approach to uncovering time trends of gene regulations through decision analysis of the newly designed time-delayed gene expression matrix. We have applied the proposed method to yeast cell cycling and human HeLa cell cycling and have discovered most of the underlying time-delayed regulations that are supported by multiple lines of experimental evidence and that are remarkably consistent with the current knowledge on phase characteristics for the cell cyclings.

Conclusion
We established a usable and powerful model-free approach to dissecting high-order dynamic trends of gene-gene interactions. We have carefully validated the proposed algorithm by applying it to two publicly available cell cycling datasets. In addition to uncovering the time trends of gene regulations for cell cycling, this unified approach can also be used to study the complex gene regulations related to the development, aging and progressive pathogenesis of a complex disease where potential dependences between different experiment units might occurs.
Approximation properties of haplotype tagging
Authors: Vinterbo Staal A , Dreiseitl Stephan , Ohno-Machado Lucila 
Published: January 2006

AbstractBackgroundSingle nucleotide polymorphisms (SNPs) are locations at which the genomic sequences of population members differ. Since these differences are known to follow patterns, disease association studies are facilitated by identifying SNPs that allow the unique identification of such patterns. This process, known as haplotype tagging, is formulated as a combinatorial optimization problem and analyzed in terms of complexity and approximation properties.ResultsIt is shown that the tagging problem is NP-hard but approximable within 1 + ln((n2-n)/2) fornhaplotypes but not approximable within (1 -ε) ln(n/2) for anyε> 0 unless NP ⊂ DTIME(nlog logn).A simple, very easily implementable algorithm that exhibits the above upper bound on solution quality is presented. This algorithm has running timeO("Equation missing"(2m-p+ 1)) ≤O(m(n2-n)/2) wherep≤ min(n,m) fornhaplotypes of sizem. As we show that the approximation bound is asymptotically tight, the algorithm presented is optimal with respect to this asymptotic bound.ConclusionThe haplotype tagging problem is hard, but approachable with a fast, practical, and surprisingly simple algorithm that cannot be significantly improved upon on a single processor machine. Hence, significant improvement in computatational efforts expended can only be expected if the computational effort is distributed and done in parallel.
Critical values and variation in type I error along chromosomes in the COGA dataset using the applied pseudo-trait method
Authors: Papanicolaou George J , Justice Cristina M , Kovac Illija M , Sorant Alexa JM , Wilson Alexander F 
Published: January 2006

Abstract

Background
By analyzing a "pseudo-trait," a trait not linked or associated with any of the markers tested, the distribution of the test statistic under the null hypothesis can provide the critical value for the appropriate percentile of the distribution. In addition, the anecdotal observation that p-values tend to be more significant near the telomeres was investigated.

Results
The applied pseudo-trait (APT) method was applied to the Affymetrix and Illumina SNPs in the Collaborative Study on the Genetics of Alcoholism dataset to determine appropriate critical values for regression of offspring on mid-parent (ROMP) and Haseman-Elston association and linkage analyses, investigating the occurrence of type I errors in different chromosomal locations, and the extent to which the critical values obtained depend on the type of pseudo-trait used.

Conclusion
On average, the 5 percentile critical values obtained for this study were less than the expected 0.05. The distribution of p-values does not seem to depend on chromosomal position for ROMP association analysis methods, but does in some cases for Haseman-Elston linkage analysis. Results vary with different pseudo-traits.
A variance component analysis on recombination rate in the COGA pedigrees
Authors: Wang Lin , Xu Xin 
Published: January 2006

Abstract
We estimated the crossover frequency in 1,232 gametes from 356 subjects in pedigrees from the Collaborative Study on the Genetics on Alcoholism. We examined the effect of covariates including age, ethnicity, and years with ALDX1 on recombination rate, and found a positive correlation between recombination rate and years with ALDX1. By variance-component analysis, we estimated the heritability of recombination rate to be around 0.5, and provided suggestive evidence for a locus linked to recombination rate.
Linkage and association analysis in pedigrees from different populations
Authors: Beyene Joseph , Yan Jun , Greenwood Celia MT 
Published: January 2006

Abstract
Using the Genetic Analysis Workshop 14 simulated datasets we carried out nonparametric linkage analyses and applied a log-linear method for analysis of case-parent-triad data with stratification on parental mating type. We proposed and applied a random effect modelling approach to explore the impact of population heterogeneity on tests of association between genetic markers and disease status. The estimated genetic effect may appear to be strongly significant in one population but nonsignificant in another population, leading to confusion about interpretation. However, when results are interpreted in the light of a random effects model, both studies may be making similar statements about a genetic effect that varies depending on environment and background.
Fine mapping – 19th century style
Authors: Molitor John , Zhao Keyan , Marjoram Paul 
Published: January 2006

Abstract

Background
There is great interest in the use of computationally intensive methods for fine mapping of marker data. In this paper we develop methods based upon ideas originally proposed 100 years ago in the context of spatial clustering.

Methods
We use spatial clustering of haplotypes as a low-dimensional surrogate for the unobserved genealogy underlying a set of genotype data. In doing so we hope to avoid the computational complexity inherent in explicitly modelling details of the ancestry of the sample, while at the same time capturing the key correlations induced by that ancestry at a much lower computational cost.

Results
We benchmark our methods using the simulated Genetic Analysis Workshop 14 data, using 100 replicates of 4 phenotypes to indicate the power of our method. When a functional mutation relating to a trait is actually present, we find evidence for that mutation in 97 out of 100 replicates, on average.

Conclusion
Our results show that our method has the ability to accurately infer the location of functional mutations from unphased genotype data.
Application of family-based association testing to assess the genotype-phenotype association involved in complex traits using single-nucleotide polymorphisms
Authors: Wang Ming-Hsi , Guo Mitchell , Shugart Yin Y 
Published: January 2006

Abstract

Background
We used the FBAT (family-based association test) software to test for association between 300 individual single-nucleotide polymorphisms and P1 (a latent trait of Kofendred Personality Disorder) in 100 simulated replicates of the Aipotu population. Using the Genetic Analysis Workshop 14 dataset, we calculated the power of FBAT to detect linkage disequilibrium on chromosome 3 (D2). Also, we calculated the false-positive rate on chromosome 1, which contains a true locus (D1) but no linkage disequilibrium was simulated between the trait and all the surrounding single-nucleotide polymorphisms.

Results
We were able to detect the associations between phenotype P1 and three adjacent markers B03T3056 (average p-value = 0.0002), B03T3057 (average p-value = 0.00072), and B03T3058 (average p-value = 0.0038) with power of 98%, 87%, 71% on chromosome 3, respectively. The overall false positve rate to detect association was 0.06 on chromosome 1.

Conclusion
The power to detect a significant association in 100 nuclear families affected with the latent trait of Kofendred Personality Disorder by using FBAT was reasonable (based on 100 replicates). In the future, we will compare the performance of FBAT with alternative approaches, such as using FBAT-generalized estimating equations methods to test for association in families affected with complex traits.
Comparison of the power between microsatellite and single-nucleotide polymorphism markers for linkage and linkage disequilibrium mapping of an electrophysiological phenotype
Authors: Lin Hsiu-Fen , Juo Suh-Hang Hank , Cheng Rong 
Published: January 2006

Abstract
We performed linkage and linkage disequilibrium (LD) mapping analyses to compare the power between microsatellite and single nucleotide polymorphism (SNP) markers. Chromosome-wide analyses were performed for a quantitative electrophysiological phenotype, ttth1, on chromosome 7. Multipoint analysis of microsatellite markers using the variance component (VC) method showed the highest LOD score of 4.20 at 162 cM, near D7S509 (163.7 cM). Two-point analysis of SNPs using the VC method yielded the highest LOD score of 3.98 in the Illumina SNP data and 3.45 in the Affymetrix SNP data around 152–153 cM. In family-based single SNP and SNP haplotype LD analysis, we identified seven SNPs associated with ttth1. We searched for any potential candidate genes in the location of the seven SNPs. The SNPs rs1476640 and rs768055 are located in the FLJ40852 gene (a hypothetical protein), and SNP rs1859646 is located in the TAS2R5 gene (a taste receptor). The other four SNPs are not located in any known or annotated genes. We found the high density SNP scan to be superior to microsatellites because it is effective in downstream fine mapping due to a better defined linkage region. Our study proves the utility of high density SNP in genome-wide mapping studies.
Haplotype-sharing analysis using Mantel statistics for combined genetic effects
Authors: Beckmann Lars , Fischer Christine , Obreiter Markus , Rabes Michael , Chang-Claude Jenny 
Published: January 2006

Abstract
We applied a new approach based on Mantel statistics to analyze the Genetic Analysis Workshop 14 simulated data with prior knowledge of the answers. The method was developed in order to improve the power of a haplotype sharing analysis for gene mapping in complex disease. The new statistic correlates genetic similarity and phenotypic similarity across pairs of haplotypes from case-control studies. The genetic similarity is measured as the shared length between haplotype pairs around a genetic marker. The phenotypic similarity is measured as the mean corrected cross-product based on the respective phenotypes. Cases with phenotype P1 and unrelated controls were drawn from the population of Danacaa. Power to detect main effects was compared to the X
2-test for association based on 3-marker haplotypes and a global permutation test for haplotype association to test for main effects. Power to detect gene × gene interaction was compared to unconditional logistic regression. The results suggest that the Mantel statistics might be more powerful than alternative tests.
Real-time PCR quantification of human complement C4A and C4B genes
Authors: Szilagyi Agnes , Blasko Bernadett , Szilassy Denes , Fust George , Sasvari-Szekely Maria , Ronai Zsolt 
Published: January 2006

AbstractBackgroundThe fourth component of human complement (C4), an essential factor of the innate immunity, is represented as two isoforms (C4A and C4B) in the genome. Although these genes differ only in 5 nucleotides, the encoded C4A and C4B proteins are functionally different. Based on phenotypic determination, unbalanced production of C4A and C4B is associated with several diseases, such as systemic lupus erythematosus, type 1 diabetes, several autoimmune diseases, moreover with higher morbidity and mortality of myocardial infarction and increased susceptibility for bacterial infections. Despite of this major clinical relevance, only low throughput, time and labor intensive methods have been used so far for the quantification of C4A and C4B genes.ResultsA novel quantitative real-time PCR (qPCR) technique was developed for rapid and accurate quantification of the C4A and C4B genes applying a duplex, TaqMan based methodology. The reliable, single-step analysis provides the determination of the copy number of the C4A and C4B genes applying a wide range of DNA template concentration (0.3–300 ng genomic DNA). The developed qPCR was applied to determine C4A and C4B gene dosages in a healthy Hungarian population (N = 118). The obtained data were compared to the results of an earlier study of the same population. Moreover a set of 33 samples were analyzed by two independent methods. No significant difference was observed between the gene dosages determined by the employed techniques demonstrating the reliability of the novel qPCR methodology. A Microsoft Excel worksheet and a DOS executable are also provided for simple and automated evaluation of the measured data.ConclusionThis report describes a novel real-time PCR method for single-step quantification of C4A and C4B genes. The developed technique could facilitate studies investigating disease association of different C4 isotypes.
Does tumorigenesis select for or against mutations of the DNA repair-associated genes BRCA2 and MRE11?: Considerations from somatic mutations in microsatellite unstable (MSI) gastrointestinal cancers
Authors: van der Heijden Michiel S , Brody Jonathan R , Elghalbzouri-Maghrani Elhaam , Zdzienicka Malgorzata Z , Kern Scott E 
Published: January 2006

Abstract

Background
The BRCA2 and MRE11 proteins participate in the repair of double-strand DNA breaks by homologous recombination. Germline BRCA2 mutations predispose to ovarian, breast and pancreatic cancer, while a germline MRE11 mutation is associated with an ataxia telangiectasia-like disorder. Somatic mutations of BRCA2 are rare in typical sporadic cancers. In tumors having microsatellite instability (MSI), somatic truncating mutations in a poly [A] tract of BRCA2 are reported on occasion.

Results
We analyzed gastrointestinal MSI cancers by whole gene BRCA2 sequencing, finding heterozygous truncating mutations in seven (47%) of 15 patients. There was no cellular functional defect in RAD51 focus-formation in three heterozygously mutated lines studied, although other potential functions of the BRCA2 protein could still be affected. A prior report of mutations in primary MSI tumors affecting the IVS5-(5–15) poly [T] tract of the MRE11 gene was confirmed and extended by analysis of the genomic sequence and protein expression in MSI cancer cell lines. Statistical analysis of the published MRE11 mutation rate in MSI tumors did not provide evidence for a selective pressure favoring biallelic mutations at this repeat.

Conclusion
Perhaps conflicting with common suspicions, the data are not compatible with selective pressures during tumorigenesis promoting the functional loss of BRCA2 and MRE11 in MSI tumors. Instead, these data fit closely with an absence of selective pressures acting on BRCA2 and MRE11 gene status during tumorigenesis.
Segregation analysis of apolipoprotein A1 levels in families of adolescents: A community-based study in Taiwan
Authors: Chien Kuo-Liong , Chen Wei J , Hsu Hsiu-Ching , Su Ta-Chen , Chen Ming-Fong , Lee Yuan-Teh 
Published: January 2006

Abstract

Background
Apolipoprotein (Apo) A1 is a protective factor for cardiovascular events. This study aimed to perform complex segregation analyses of Apo A1 levels in families of adolescents systematically ascertained from the junior high school students in a rural community. Both siblings and parents of the adolescent probands were recruited for the study. Apo A1 concentrations were measured by turbidimetric immunoassay methods. After adjustment for gender, age, body mass index, smoking and drinking status, residual values of Apo A1 were subjected to subsequent analyses.

Results
Significant mother-father and parent-offspring correlations were found. Commingling analyses indicated that a four-component distribution model was needed to account for the Apo A1 variation. Segregation analysis using regressive models revealed that the best-fit model of Apo A1 was a model of environmental effect plus familial correlation (heritability = 23.9%), in which a significant mother-father correlation existed. Models containing major gene effect could be rejected.

Conclusion
These results suggest that variations of Apo A1 levels in the normal range, especially during adolescence, are likely to be influenced by multiple factors without significant contribution from major genes.