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PubMed2006

Longitudinal and repeated cross-sectional cluster-randomization designs using mixed effects regression for binary outcomes: bias and coverage of frequentist and Bayesian methods.

As medical applications for cluster randomization designs become more common, investigators look for guidance on optimal methods for estimating the effect of group-based interventions over time. This study examines two distinct cluster randomization designs: (1) the repeated cross-sectional design in which centres are followed over time but patients change, and (2) the longitudinal design in which individual patients are followed over time within treatment clusters. Simulations of each study design stipulated a multiplicative treatment effect (on the log odds scale), between 5 and 15 clusters in each of two treatment arms, and followed over two time periods. Estimation options included linear mixed effects models using restricted maximum likelihood (REML), generalized estimating equations (GEE), mixed effects logistic regression using both penalized quasi likelihood (PQL) and numerical integration, and Bayesian Monte Carlo analysis. For the repeated cross-sectional designs, most methods performed well in terms of bias and coverage when clusters were numerous (30) and variability across clusters of baseline risk and treatment effect was modest. With few clusters (two groups of five) and higher variability, only the Bayesian methods maintained coverage. In the longitudinal designs, the common methods of REML, GEE, or PQL performed poorly when compared to numerical integration, while Bayesian methods demonstrated less bias and better coverage for estimates of both log odds ratios and risk differences. The performance of common statistical tools for the analysis of cluster randomization designs depends heavily on the precise design, the number of clusters, and the variability of baseline outcomes and treatment effects across centres.

Bayes Theorem↗
PubMed2004

Multiple genetic processes result in heterogeneous rates of evolution within the major cluster disease resistance genes in lettuce.

Resistance Gene Candidate2 (RGC2) genes belong to a large, highly duplicated family of nucleotide binding site-leucine rich repeat (NBS-LRR) encoding disease resistance genes located at a single locus in lettuce (Lactuca sativa). To investigate the genetic events occurring during the evolution of this locus, approximately 1.5- to 2-kb 3' fragments of 126 RGC2 genes from seven genotypes were sequenced from three species of Lactuca, and 107 additional RGC2 sequences were obtained from 40 wild accessions of Lactuca spp. The copy number of RGC2 genes varied from 12 to 32 per genome in the seven genotypes studied extensively. LRR number varied from 40 to 47; most of this variation had resulted from 13 events duplicating two to five LRRs because of unequal crossing-over within or between RGC2 genes at one of two recombination hot spots. Two types of RGC2 genes (Type I and Type II) were initially distinguished based on the pattern of sequence identities between their 3' regions. The existence of two types of RGC2 genes was further supported by intron similarities, the frequency of sequence exchange, and their prevalence in natural populations. Type I genes are extensive chimeras caused by frequent sequence exchanges. Frequent sequence exchanges between Type I genes homogenized intron sequences, but not coding sequences, and obscured allelic/orthologous relationships. Sequencing of Type I genes from additional wild accessions confirmed the high frequency of sequence exchange and the presence of numerous chimeric RGC2 genes in nature. Unlike Type I genes, Type II genes exhibited infrequent sequence exchange between paralogous sequences. Type II genes from different genotype/species within the genus Lactuca showed obvious allelic/orthologous relationships. Trans-specific polymorphism was observed for different groups of orthologs, suggesting balancing selection. Unequal crossover, insertion/deletion, and point mutation events were distributed unequally through the gene. Different evolutionary forces have impacted different parts of the LRR.

Alleles↗
PubMedDate not supplied

[System of distributed storage and analysis of genomic information].

A distributed computing system is developed to search and analyze genetic databases using parallel computing technologies. Queries are processed by a local network PC cluster. A universal task and data exchange format is developed for effective query processing. A multilevel hierarchic task batching procedure is elaborated to generate multiple subtasks and distribute them over cluster units under dynamic priority levels and with dynamic distribution of replicated source data subbases. Primary source data preparation and generation of annotation word indices are used to significantly reduce query processing time.

Databases, Genetic↗
PubMed2005

CLAGen: a tool for clustering and annotating gene sequences using a suffix tree algorithm.

Most multiple gene sequence alignment methods rely on conventions regarding the score of a multiple alignment in pairwise fashion. Therefore, as the number of sequences increases, the runtime of sequencing expands exponentially. In order to solve the problem, this paper presents a multiple sequence alignment method using a linear-time suffix tree algorithm to cluster similar sequences at one time without pairwise alignment. After searching for common subsequences, cross-matching common subsequences were generated, and sometimes inexact matching was found. So, a procedure aimed at masking the inexact cross-matching pairs was suggested here. In addition, BLAST was combined with a clustering tool in order to annotate the clusters generated by suffix tree clustering. The proposed method for clustering and annotating genes consists of the following steps: (1) construction of a suffix tree; (2) searching and overlapping common subsequences; (3) grouping subsequence pairs; (4) masking cross-matching pairs; (5) clustering gene sequences; (6) annotating gene clusters by the BLAST search. The performance of the proposed system, CLAGen, was successfully evaluated with 42 gene sequences in a TCA cycle (a citrate cycle) of bacteria. The system generated 11 clusters and found the longest subsequences of each cluster, which are biologically significant.

Algorithms↗
PubMed2000

The SYSTERS protein sequence cluster set.

The SYSTERS (short for SYSTEmatic Re-Searching) protein sequence cluster set consists of the classification of all sequences from SWISS-PROT and PIR into disjoint protein family clusters and hierarchically into superfamily and subfamily clusters. The cluster set can be searched with a sequence using the SSMAL search tool or a traditional database search tool like BLAST or FASTA. Additionally a multiple alignment is generated for each cluster and annotated with domain information from the Pfam database of protein domain families. A taxonomic overview of the organisms covered by a cluster is given based on the NCBI taxonomy. The cluster set is available for querying and browsing at http://www.dkfz-heidelberg. de/tbi/services/cluster/systersform

Amino Acid Sequence↗
PubMed2004

Determining the number of clusters by sampling with replacement.

A split-sample replication criterion originally proposed by J. E. Overall and K. N. Magee (1992) as a stopping rule for hierarchical cluster analysis is applied to multiple data sets generated by sampling with replacement from an original simulated primary data set. An investigation of the validity of this bootstrap procedure was undertaken using different combinations of the true number of latent populations, degrees of overlap, and sample sizes. The bootstrap procedure enhanced the accuracy of identifying the true number of latent populations under virtually all conditions. Increasing the size of the resampled data sets relative to the size of the primary data set further increased accuracy. A computer program to implement the bootstrap stopping rule is made available via a referenced Web site.

Cluster Analysis↗
PubMed1996

Characterizations of diverse residue clusters in protein three-dimensional structures.

We present new methods for identifying and analyzing statistically significant residue clusters that occur in three-dimensional (3D) protein structures. Residue clusters of different kinds occur in many contexts. They often feature the active site (e.g., in substrate binding), the interface between polypeptide units of protein complexes, regions of protein-protein and protein-nucleic acid interactions, or regions of metal ion coordination. The methods are illustrated with 3D clusters centering on four themes. (i) Acidic or histidine-acidic clusters associated with metal ions. (ii) Cysteine clusters including coordination of metals such as zinc or iron-sulfur structures, cysteine knots prominent in growth factors, multiple sets of buried disulfide pairings that putatively nucleate the hydrophobic core, or cysteine clusters of mostly exposed disulfide bridges. (iii) Iron-sulfur proteins and charge clusters. (iv) 3D environments of multiple histidine residues. Study of diverse 3D residue clusters offers a new perspective on protein structure and function. The algorithms can aid in rapid identification of distinctive sites, suggest correlations among protein structures, and serve as a tool in the analysis of new structures.

Cysteine↗
PubMed2002

Heterodisulfide reductase from Methanothermobacter marburgensis contains an active-site [4Fe-4S] cluster that is directly involved in mediating heterodisulfide reduction.

Heterodisulfide reductases (HDRs) from methanogenic archaea are iron-sulfur flavoproteins or hemoproteins that catalyze the reversible reduction of the heterodisulfide (CoM-S-S-CoB) of the methanogenic thiol coenzymes, coenzyme M (CoM-SH) and coenzyme B (CoB-SH). In this work, the ground- and excited-state electronic properties of the paramagnetic Fe-S clusters in Methanothermobacter marburgensis HDR have been characterized using the combination of electron paramagnetic resonance and variable-temperature magnetic circular dichroism spectroscopies. The results confirm multiple S=1/2 [4Fe-4S](+) clusters in dithionite-reduced HDR and reveal spectroscopically distinct S=1/2 [4Fe-4S](3+) clusters in oxidized HDR samples treated separately with the CoM-SH and CoB-SH cosubstrates. The active site of HDR is therefore shown to contain a [4Fe-4S] cluster that is directly involved in mediating heterodisulfide reduction. The catalytic mechanism of HDR is discussed in light of the crystallographic and spectroscopic studies of the related chloroplast ferredoxin:thioredoxin reductase class of disulfide reductases.

Archaeal Proteins↗
PubMed2005

Factors associated with non-attendance at pediatric subspecialty asthma clinics.

BACKGROUND: Children with Medicaid insurance are less likely to receive subspecialty asthma care than non-Medicaid patients. However, it is not clear if such disparities are due to non-attendance at scheduled visits by patients. OBJECTIVE: To determine factors associated with non-attendance at scheduled visits for pediatric subspecialty asthma care. DESIGN/METHODS: We conducted a cross-sectional study of children with scheduled visits at three asthma clinics during a 12-month period. Our outcome of interest was patient non-attendance for a scheduled visit, controlling for age, gender, new patient status, type of insurance, severity of illness, distance to clinic, clinic site, month, and weekday of scheduled visit. We used logistic regression for multivariate comparisons and controlled for clustering effects for children with multiple scheduled visits. RESULTS: There were 1236 scheduled visits for 857 unique patients. Median age: 7 years (IQR 3-11); median distance traveled: 24 miles (IQR 13-41); 20% had Medicaid insurance. The non-attendance rate was 8%. In multivariate analysis, Medicaid insurance (OR 2.33, 95% CI 1.45-3.74) and visits scheduled in September-December (3.26, 2.08-5.11) were associated with non-attendance. CONCLUSIONS: Children with Medicaid insurance are less likely to attend scheduled visits for subspecialty asthma care, controlling for seasonal variation. Programs designed to address disparities in pediatric asthma outcomes regarding subspecialist care may improve their effectiveness by addressing barriers to attendance and anticipating poor attendance in the fall season.

Appointments and Schedules↗
PubMed2005

Illnesses you have to fight to get: facts as forces in uncertain, emergent illnesses.

Chronic fatigue syndrome and multiple chemical sensitivity are two clusters of illnesses that are pervaded by medical, social and political uncertainty. This article examines how facts are talked about and experienced in struggles over these emergent, contested illnesses in the US. Based principally on a large archive of internet newsgroup postings, and also on fieldwork and on published debates, it finds that (1) sufferers describe their experiences of being denied healthcare and legitimacy through bureaucratic categories of exclusion as dependent upon their lack of biological facts; (2) institutions manage these exclusions rhetorically through exploiting the open-endedness of science to deny efficacy to new facts; (3) collective patient action responds by archiving the systematic nature of these exclusions and developing counter-tactics. The result is the maintenance of these very expensive struggles for all involved.

Fatigue Syndrome, Chronic↗
PubMed1988

Multiple sclerosis: updated risks for relatives.

Two important characteristics of multiple sclerosis (MS) are familial clustering and a variable age of onset. There is increasing evidence for a genetically influenced susceptibility in MS. Because of this, patients and their relatives are increasingly asking about the risk for relatives of developing MS. In the MS Clinic in Vancouver, genetic histories are taken routinely for all patients and are updated annually. Patients do not attend the clinic specifically to participate in genetic studies, which could result in over-representation of familial cases. Data were available for 815 MS index cases and 11,345 of their relatives. Age-specific MS risks were calculated for first-, second-, and third-degree relatives of probands and are presented in an easy-reference format. In general, first-degree relatives of probands have a risk that is 30-50 times greater than the 0.1% risk for the general population.

British Columbia↗
PubMed2007

Mitochondrial DNA variants in Bulgarian patients affected by multiple sclerosis.

The occurrence of multiple sclerosis (MS) in subjects clustering to a particular mitochondrial DNA (mtDNA) haplogroup/haplotype or carrying mtDNA mutations associated with Leber's hereditary optic neuropathy (LHON) has suggested that mitochondrial genome may contribute to susceptibility to MS. In the present study, 58 unrelated Bulgarian patients with relapsing remitting form of MS and 104 randomly selected healthy individuals were analysed for the presence of 14 mtDNA polymorphisms determining major European haplogroups as well as three (4216, 14 798, 13 708) secondary LHON mutations. Restriction enzyme analysis used to screen patients and controls for the common haplogroup-associated polymorphisms showed that each of these changes was present in MS patients at a similar frequency to control subjects. However, 21 of the 58 patients (36.2%) were positive for T4 216C mutation, while only 11.3% of the controls carried this secondary LHON base change (P < 0.01; OR = 4.38). Our finding indicated that 4216C base substitution could be considered as a predisposing marker for MS and supported the hypothesis that particular mtDNA variants could contribute to genetic susceptibility of MS, and merits further investigation.

Adult↗
PubMed2006

EMD: an ensemble algorithm for discovering regulatory motifs in DNA sequences.

BACKGROUND: Understanding gene regulatory networks has become one of the central research problems in bioinformatics. More than thirty algorithms have been proposed to identify DNA regulatory sites during the past thirty years. However, the prediction accuracy of these algorithms is still quite low. Ensemble algorithms have emerged as an effective strategy in bioinformatics for improving the prediction accuracy by exploiting the synergetic prediction capability of multiple algorithms. RESULTS: We proposed a novel clustering-based ensemble algorithm named EMD for de novo motif discovery by combining multiple predictions from multiple runs of one or more base component algorithms. The ensemble approach is applied to the motif discovery problem for the first time. The algorithm is tested on a benchmark dataset generated from E. coli RegulonDB. The EMD algorithm has achieved 22.4% improvement in terms of the nucleotide level prediction accuracy over the best stand-alone component algorithm. The advantage of the EMD algorithm is more significant for shorter input sequences, but most importantly, it always outperforms or at least stays at the same performance level of the stand-alone component algorithms even for longer sequences. CONCLUSION: We proposed an ensemble approach for the motif discovery problem by taking advantage of the availability of a large number of motif discovery programs. We have shown that the ensemble approach is an effective strategy for improving both sensitivity and specificity, thus the accuracy of the prediction. The advantage of the EMD algorithm is its flexibility in the sense that a new powerful algorithm can be easily added to the system.

Algorithms↗
PubMed2004

Fuzzy J-Means and VNS methods for clustering genes from microarray data.

MOTIVATION: In the interpretation of gene expression data from a group of microarray experiments that include samples from either different patients or conditions, special consideration must be given to the pleiotropic and epistatic roles of genes, as observed in the variation of gene coexpression patterns. Crisp clustering methods assign each gene to one cluster, thereby omitting information about the multiple roles of genes. RESULTS: Here, we present the application of a local search heuristic, Fuzzy J-Means, embedded into the variable neighborhood search metaheuristic for the clustering of microarray gene expression data. We show that for all the datasets studied this algorithm outperforms the standard Fuzzy C-Means heuristic. Different methods for the utilization of cluster membership information in determining gene coregulation are presented. The clustering and data analyses were performed on simulated datasets as well as experimental cDNA microarray data for breast cancer and human blood from the Stanford Microarray Database. AVAILABILITY: The source code of the clustering software (C programming language) is freely available from Nabil.Belacel@nrc-cnrc.gc.ca

Algorithms↗
PubMed1998

Clusters of histologic characteristics in children with infratentorial neuroglial tumors. The Childhood Brain Tumor Consortium.

Five quantitative histologic factors, differing linear combinations of 26 reliably recognized histologic features, account for much of the histologic variance in 1068 children with infratentorial neuroglial tumors in the Childhood Brain Tumor Consortium (CBTC) database. In this study, we used the scores on the Spongy, Proliferative, Ring, Fibrillary, and Nuclear factors in cluster analyses and identified 11 clusters of children's tumors. Each had statistically significant differences in histology and relative histologic homogeneity. Three clusters had ependymoma-like histologic features; 4 had astrocytoma-like features; and 4 had primitive neuroectodermal-like (PNET or medulloblastoma) features. Each cluster had a unique high/low mean factor score pattern. Multiple operative and other clinical features characterized the three groups of clusters. We used Kaplan-Meier survival models to test for differences in survival among clusters and proportional hazards survival models to adjust for associated covariates. Among the 'ependymoma' clusters the 5 year survival probability ranged from 0.25 to 0.54. Among the 4 'astrocytoma' clusters, 5 year survival probability ranged from 0.59 to 0.94. The 5 year survival probability for the 'medulloblastoma' clusters ranged from 0.20 to 0.44. Within the three groups, clusters had differing covariates associated with survival. The tumor clusters identified in this study ensure relatively homogeneous histologic subsets. The five factor scores of a child's tumor provide the basis for finding the cluster nearest to that tumor. We propose that this tumor clustering strategy be employed for selection of children and for analyses of therapeutic clinical trials.

Astrocytoma↗
PubMed2006

Persistence of cooperatively stabilized signaling clusters drives T-cell activation.

Antigen recognition triggers the recruitment of the critical adaptor protein SLP-76 to small macromolecular clusters nucleated by the T-cell receptor (TCR). These structures develop rapidly, in parallel with TCR-induced increases in tyrosine phosphorylation and cytosolic calcium, and are likely to contribute to TCR-proximal signaling. Previously, we demonstrated that these SLP-76-containing clusters segregate from the TCR and move towards the center of the contact interface. Neither the function of these clusters nor the structural requirements governing their persistence have been examined extensively. Here we demonstrate that defects in cluster assembly and persistence are associated with defects in T-cell activation in the absence of Lck, ZAP-70, or LAT. Clusters persist normally in the absence of phospholipase C-gamma1, indicating that in the absence of a critical effector, these structures are insufficient to drive T-cell activation. Furthermore, we show that the critical adaptors LAT and Gads localize with SLP-76 in persistent clusters. Mutational analyses of LAT, Gads, and SLP-76 indicated that multiple domains within each of these proteins contribute to cluster persistence. These data indicate that multivalent cooperative interactions stabilize these persistent signaling clusters, which may correspond to the functional complexes predicted by kinetic proofreading models of T-cell activation.

Adaptor Proteins, Signal Transducing↗