Complete Linkage Clustering Research Articles

Model selection for minimum‐diameter partitioning

The minimum-diameter partitioning problem (MDPP) seeks to produce compact clusters, as measured by an overall goodness-of-fit measure known as the partition diameter, which represents the maximum dissimilarity between any two objects placed in the same cluster. Complete-linkage hierarchical clustering is perhaps the best-known heuristic method for the MDPP and has an extensive history of applications in psychological research. Unfortunately, this method has several inherent shortcomings that impede the model selection process, such as: (1) sensitivity to the input order of the objects, (2) failure to obtain a globally optimal minimum-diameter partition when cutting the tree at K clusters, and (3) the propensity for a large number of alternative minimum-diameter partitions for a given K. We propose that each of these problems can be addressed by applying an algorithm that finds all of the minimum-diameter partitions for different values of K. Model selection is then facilitated by considering, for each value of K, the reduction in the partition diameter, the number of alternative optima, and the partition agreement among the alternative optima. Using five examples from the empirical literature, we show the practical value of the proposed process for facilitating model selection for the MDPP.

A methodology to assess the intrinsic discriminative ability of a distance function and its interplay with clustering algorithms for microarray data analysis

BackgroundClustering is one of the most well known activities in scientific investigation and the object of research in many disciplines, ranging from statistics to computer science. Following Handl et al., it can be summarized as a three step process: (1) choice of a distance function; (2) choice of a clustering algorithm; (3) choice of a validation method. Although such a purist approach to clustering is hardly seen in many areas of science, genomic data require that level of attention, if inferences made from cluster analysis have to be of some relevance to biomedical research.ResultsA procedure is proposed for the assessment of the discriminative ability of a distance function. That is, the evaluation of the ability of a distance function to capture structure in a dataset. It is based on the introduction of a new external validation index, referred to as Balanced Misclassification Index (BMI, for short) and of a nontrivial modification of the well known Receiver Operating Curve (ROC, for short), which we refer to as Corrected ROC (CROC, for short). The main results are: (a) a quantitative and qualitative method to describe the intrinsic separation ability of a distance; (b) a quantitative method to assess the performance of a clustering algorithm in conjunction with the intrinsic separation ability of a distance function. The proposed procedure is more informative than the ones available in the literature due to the adopted tools. Indeed, the first one allows to map distances and clustering solutions as graphical objects on a plane, and gives information about the bias of the clustering algorithm with respect to a distance. The second tool is a new external validity index which shows similar performances with respect to the state of the art, but with more flexibility, allowing for a broader spectrum of applications. In fact, it allows not only to quantify the merit of each clustering solution but also to quantify the agglomerative or divisive errors due to the algorithm.ConclusionsThe new methodology has been used to experimentally study three popular distance functions, namely, Euclidean distance d2, Pearson correlation dr and mutual information dMI. Based on the results of the experiments, we have that the Euclidean and Pearson correlation distances have a good intrinsic discrimination ability. Conversely, the mutual information distance does not seem to offer the same flexibility and versatility as the other two distances. Apparently, that is due to well known problems in its estimation. since it requires that a dataset must have a substantial number of features to be reliable. Nevertheless, taking into account such a fact, together with results presented in Priness et al., one receives an indication that dMI may be superior to the other distances considered in this study only in conjunction with clustering algorithms specifically designed for its use. In addition, it results that K-means, Average Link, and Complete link clustering algorithms are in most cases able to improve the discriminative ability of the distances considered in this study with respect to clustering. The methodology has a range of applicability that goes well beyond microarray data since it is independent of the nature of the input data. The only requirement is that the input data must have the same format of a "feature matrix". In particular it can be used to cluster ChIP-seq data.

Exploration of the antioxidative defense system to characterize chickpea genotypes showing differential response towards water deficit conditions

The present investigation was carried out to characterize genotypic variability in chickpea for water deficit tolerance by exploring the antioxidative defense system and seedling growth. Twenty nine chickpea genotypes including cultivars and advanced lines were grown under control and water deficit conditions induced by adding 3 % mannitol. The genotypes showed differential response in seedling growth under water deficit conditions. The activities of catalase (CAT) and superoxide dismutase (SOD) were observed to be differentially expressed in the roots of various genotypes, under control and water deficit conditions. The contents of H2O2, malondialdehyde (MDA) and proline were also observed to be variable in the roots of all the genotypes, under control and water deficit conditions. Stress tolerance index for the various parameters, viz, CAT and SOD activity, H2O2, MDA and proline content, root length, shoot length and their biomass was determined and the level of stress resistance calculated. The genotypes which showed increased activities of CAT and SOD, decreased contents of H2O2 and MDA together with least affected seedling growth under water deficit conditions exhibited higher stress resistance capacity. Multivariate principal component analysis for all the parameters affected under water deficit conditions, grouped the genotypes into three clusters having different (high, moderate and low) levels of stress resistance. Complete linkage clustering grouped these genotypes into two major clusters-I and II. The genotypes present in sub–sub cluster ‘A1’ and sub cluster ‘B’ of major cluster-I have been observed to possess high stress resistance levels for respective parameters. It can thus be concluded that chickpea genotypes exhibiting increased stress resistance levels in relation to SOD and CAT activities, H2O2 and MDA contents and seedling growth would have higher stress tolerance under water deficit conditions.

Analysis of Agglomerative Clustering

The diameter $k$-clustering problem is the problem of partitioning a finite subset of $\mathbb{R}^d$ into $k$ subsets called clusters such that the maximum diameter of the clusters is minimized. One early clustering algorithm that computes a hierarchy of approximate solutions to this problem (for all values of $k$) is the agglomerative clustering algorithm with the complete linkage strategy. For decades, this algorithm has been widely used by practitioners. However, it is not well studied theoretically. In this paper, we analyze the agglomerative complete linkage clustering algorithm. Assuming that the dimension $d$ is a constant, we show that for any $k$ the solution computed by this algorithm is an $O(\log k)$-approximation to the diameter $k$-clustering problem. Our analysis does not only hold for the Euclidean distance but for any metric that is based on a norm. Furthermore, we analyze the closely related $k$-center and discrete $k$-center problem. For the corresponding agglomerative algorithms, we deduce an approximation factor of $O(\log k)$ as well.

Conformational and functional analysis of molecular dynamics trajectories by Self-Organising Maps

BackgroundMolecular dynamics (MD) simulations are powerful tools to investigate the conformational dynamics of proteins that is often a critical element of their function. Identification of functionally relevant conformations is generally done clustering the large ensemble of structures that are generated. Recently, Self-Organising Maps (SOMs) were reported performing more accurately and providing more consistent results than traditional clustering algorithms in various data mining problems. We present a novel strategy to analyse and compare conformational ensembles of protein domains using a two-level approach that combines SOMs and hierarchical clustering.ResultsThe conformational dynamics of the α-spectrin SH3 protein domain and six single mutants were analysed by MD simulations. The Cα's Cartesian coordinates of conformations sampled in the essential space were used as input data vectors for SOM training, then complete linkage clustering was performed on the SOM prototype vectors. A specific protocol to optimize a SOM for structural ensembles was proposed: the optimal SOM was selected by means of a Taguchi experimental design plan applied to different data sets, and the optimal sampling rate of the MD trajectory was selected. The proposed two-level approach was applied to single trajectories of the SH3 domain independently as well as to groups of them at the same time. The results demonstrated the potential of this approach in the analysis of large ensembles of molecular structures: the possibility of producing a topological mapping of the conformational space in a simple 2D visualisation, as well as of effectively highlighting differences in the conformational dynamics directly related to biological functions.ConclusionsThe use of a two-level approach combining SOMs and hierarchical clustering for conformational analysis of structural ensembles of proteins was proposed. It can easily be extended to other study cases and to conformational ensembles from other sources.

A novel clustering method via nucleotide-based Fourier power spectrum analysis

A novel clustering method is proposed to classify genes or genomes. This method uses a natural representation of genomic data by binary indicator sequences of each nucleotide (adenine (A), cytosine (C), guanine (G), and thymine (T)). Afterwards, the discrete Fourier transform is applied to these indicator sequences to calculate spectra of the nucleotides. Mathematical moments are calculated for each of these spectra to create a multidimensional vector in a Euclidean space for each gene or genome sequence. Thus, each gene or genome sequence is realized as a geometric point in the Euclidean space. Finally, pairwise Euclidean distances between these points (i.e. genome sequences) are calculated to cluster the gene or genome sequences. This method is applied to three sets of data. The first is 34 strains of coronavirus genomic data, the second is 118 of the known strains of Human rhinovirus (HRV), and the third is 30 bacteria genomes. The distance matrices are computed based on the three sets, showing the distances from each point to the others. We used the complete linkage clustering algorithm to build phylogenetic trees to indicate how the distances among these sequence correspond to the evolutionary relationship among these sequences. This genome representation provides a powerful and efficient method to classify genomes and is much faster than the widely acknowledged multiple sequence alignment method.

Neotropical and North American Vaccinioideae (Ericaceae) share their mycorrhizal Sebacinales - an indication for concerted migration?

Neotropical Vaccinioideae (Ericaceae) are evolutionarily rather young and presumably of Northern Hemisphere origin. Vaccinioideae are highly dependent on their mycorrhizal symbionts and Sebacinales (basidiomycetes) were previously found to be the dominant mycobionts of Andean Clade Vaccinioideae (Neotropical Vaccinieae). We were interested to see whether the North American Vaccinioideae reached the Neotropics with their mycobionts or whether they acquired new, local Sebacinales.We investigated Sebacinales of 58 individuals of Vaccinioideae from Ecuador, Panama and North America to examine whether mycobionts of each region are distantly or closely related.We isolated the ITS of the ribosomal nuclear DNA in order to infer a molecular phylogeny of Sebacinales and to determine Molecular Operational Taxonomic Units (MOTUs). MOTU delimitation was based on a 3% threshold of ITS variability and conducted with complete linkage clustering. The analyses revealed that most Sebacinales from Ecuador, Panama and North America are closely related and that two MOTUs out of 33 have a distribution ranging from the Neotropics to the Pacific Northwest of North America. The data suggest that Neotropical and temperate Vaccinioideae of North America share their Sebacinales communities and that plants and fungi migrated together.

Robust computational analysis of rRNA hypervariable tag datasets.

Next-generation DNA sequencing is increasingly being utilized to probe microbial communities, such as gastrointestinal microbiomes, where it is important to be able to quantify measures of abundance and diversity. The fragmented nature of the 16S rRNA datasets obtained, coupled with their unprecedented size, has led to the recognition that the results of such analyses are potentially contaminated by a variety of artifacts, both experimental and computational. Here we quantify how multiple alignment and clustering errors contribute to overestimates of abundance and diversity, reflected by incorrect OTU assignment, corrupted phylogenies, inaccurate species diversity estimators, and rank abundance distribution functions. We show that straightforward procedural optimizations, combining preexisting tools, are effective in handling large () 16S rRNA datasets, and we describe metrics to measure the effectiveness and quality of the estimators obtained. We introduce two metrics to ascertain the quality of clustering of pyrosequenced rRNA data, and show that complete linkage clustering greatly outperforms other widely used methods.

Cladistic analysis of Egyptian horse flies (Diptera: Tabanidae) based on morphological data

The family Tabanidae is one of the important families of superfamily Tabanoidea, having medical and veterinary importance. In Egypt, there is no strict cladistic approach on tabanid flies phylogeny yet. The 20 available Egyptian tabanids under 2 subfamilies are analyzed cladistically. Cladistic analysis is based on 91 morphological characters depending on Single linkage, UPGMA, Complete linkage clustering methods (Cophenetic correlation value). This produces a well-resolved and firmly supported phylogenetic hypothesis on the generic relationships. Based on our phylogenetic results, the revised classification of examined taxa is consistent with the conventional classification.

Compare various combinations of similarity coefficients and clustering methods for Olea europaea sativa

The aim of the study was to compared the genetic similarity coefficients (Jaccard, Dice, Simple Matching) and different clustering methods (UPGMA, WPGMA, Single Linkage and Complete Linkage) combinations for cultivated olives. A total of 12 samples, Gemlik, Manzanilla, Edremit, Domat, Uslu and Memecik cultivars were screened with RAPD-PCR analysis by Operon random primers OP-Q kit. The closest samples based on their genetic similarity values have been found as ‘Edremit and Gemlik 5’ and the most distant ones have been found as ‘Manzanilla 1 and Gemlik 3’ in both of Dice and Jaccard coefficients; whereas, in simple-matching coefficient, the closest samples based on their genetic similarities have been determined as Edremit and Gemlik 5 and the most distant ones have been determined as Manzanilla 1 and Gemlik 3 and also Manzanilla 1 and Gemlik 4. The results from Mantel test of original matrices show that the correlation between Jaccard and Dice similarity matrices was high and significant (0.9971). UPGMA clustering for Dice coefficient was given a highest cophenetic correlation as 0.9571 and Complete linkage clustering for Simple Matching was yielded a lowest correlation value as 0.8992. The results obtained from consensus indices shown that Consensus fork index was found (

Ironing out the wrinkles in the rare biosphere through improved OTU clustering

Deep sequencing of PCR amplicon libraries facilitates the detection of low-abundance populations in environmental DNA surveys of complex microbial communities. At the same time, deep sequencing can lead to overestimates of microbial diversity through the generation of low-frequency, error-prone reads. Even with sequencing error rates below 0.005 per nucleotide position, the common method of generating operational taxonomic units (OTUs) by multiple sequence alignment and complete-linkage clustering significantly increases the number of predicted OTUs and inflates richness estimates. We show that a 2% single-linkage preclustering methodology followed by an average-linkage clustering based on pairwise alignments more accurately predicts expected OTUs in both single and pooled template preparations of known taxonomic composition. This new clustering method can reduce the OTU richness in environmental samples by as much as 30–60% but does not reduce the fraction of OTUs in long-tailed rank abundance curves that defines the rare biosphere.

The rare biosphere: sorting out fact from fiction

Over the past 25 years, microbiologists have employed the occurrence of DNA sequences as proxies for the presence of different kinds of organisms in microbial communities. These culture-independent investigations described new dimensions of diversity, identified novel candidate phyla, and redefined habitable ranges for single-cell organisms. The recent introduction of massively-parallel sequencing technology significantly increased estimates of microbial diversity from molecular-based studies. Matching SSU pyrotags to a reference rRNA database or clustering tags in a taxon-independent manner to identify Operational Taxonomic Units (OTUs) suggests that taxonomic richness in marine, terrestrial, and both the human and mouse microbiomes exceeds all prior estimates of microbial diversity. The occurrence of rare sequences in these data sets correspond to low abundance taxa that comprise the 'rare biosphere'. Numerous theories and mechanisms that could account for the existence and persistence of rare biosphere members compete with explanations that invoke sequencing or clustering artifacts. Even with sequencing error rates below 0.005 per nucleotide position, the common method of generating OTUs (i.e. multiple sequence alignment and complete-linkage clustering) significantly increases the number of predicted OTUs and inflates richness estimates. The use of a novel Single Linkage Preclustering (SLP) strategy applied to short hypervariable regions of ribosomal RNAs accurately identified the predicted complexity of 'mock' microbial communities with a known number of rRNA operons. The strategy initially identifies sequences that are likely to have arisen by error using nearest neighbor clustering of pairwise sequence distances. The most abundant sequence for each precluster and the number of sequences in the precluster define inputs to average neighbor clustering using MOTHUR. When applied to sequences obtained from multiple microbial communities, the OTU-based descriptions of microbial population structures under different ecological regimes, and the global distribution patterns of OTUs reinforce credibility of the 'rare biosphere' as revealed through deep sequencing efforts.

IgG autoantibody to brain beta tubulin III associated with cytokine cluster-II discriminate cerebral malaria in central India.

BackgroundThe main processes in the pathogenesis of cerebral malaria caused by Plasmodium falciparum involved sequestration of parasitized red blood cells and immunopathological responses. Among immune factors, IgG autoantibodies to brain antigens are increased in P. falciparum infected patients and correlate with disease severity in African children. Nevertheless, their role in the pathophysiology of cerebral malaria (CM) is not fully defined. We extended our analysis to an Indian population with genetic backgrounds and endemic and environmental status different from Africa to determine if these autoantibodies could be either a biomarker or a risk factor of developing CM.Methods/Principal FindingsWe investigated the significance of these self-reactive antibodies in clinically well-defined groups of P. falciparum infected patients manifesting mild malaria (MM), severe non-cerebral malaria (SM), or cerebral malaria (CM) and in control subjects from Gondia, a malaria epidemic site in central India using quantitative immunoprinting and multivariate statistical analyses. A two-fold complete-linkage hierarchical clustering allows classifying the different patient groups and to distinguish the CM from the others on the basis of their profile of IgG reactivity to brain proteins defined by PANAMA Blot. We identified beta tubulin III (TBB3) as a novel discriminant brain antigen in the prevalence of CM. In addition, circulating IgG from CM patients highly react with recombinant TBB3. Overall, correspondence analyses based on singular value decomposition show a strong correlation between IgG anti-TBB3 and elevated concentration of cluster-II cytokine (IFNγ, IL1β, TNFα, TGFβ) previously demonstrated to be a predictor of CM in the same population.Conclusions/SignificanceCollectively, these findings validate the relationship between antibody response to brain induced by P. falciparum infection and plasma cytokine patterns with clinical outcome of malaria. They also provide significant insight into the immune mechanisms associated to CM by the identification of TBB3 as a new disease-specific marker and potential therapeutic target.

CEBPA Methylation as a Prognostic Biomarker in Adult Patients with De Novo AML.

Abstract 1569Poster Board I-593Acute myeloid leukemia (AML) is a clonal disorder characterized by acquired genetic abnormalities in hematopoietic progenitors. CCAAT/enhancer binding protein α (C/EBPα) is a basic leucine zipper transcription factor that regulates differentiation-dependent genes during granulocyte differentiation. CEBPA mutations have been observed in several human malignancies. Epigenetic modification of the distal CEBPA promoter region (-1422 to -896) has been reported to result in down-regulation of CEBPA expression in lung cancer, pancreatic cancer, and head and neck squamous cell carcinoma. Apart from solid tumors, epigenetic modification of CEBPA in AML has also been reported. Chim et al. reported aberrant CEBPA core promoter methylation in a small proportion (2.85%) of patients with AML. Wouters et al. identified a subgroup of AML patients (1.4%) in which the CEBPA gene was silenced in association with CEBPA core promoter methylation. Hackanson et al. observed CEBPA methylation in the distal promoter region in 51% of selected AML patients. Due to the selection of differing patient populations and CEBPA promoter regions for analysis in past studies, the clinical significance of CEBPA methylation in AML remains unclear. To evaluate the CEBPA methylation status of patients with AML, 193 patients with de novo AML were evaluated for CEBPA methylation by bisulfite sequencing and MSP methods. A substantial proportion of patients were noted to have some degree of heterogeneous methylation in the distal region (-1423 to -1121), but not in the proximal region (-1121 to -896) or in the core region (-141 to +103), of the CEBPA promoter. Only one patient was noted to have broad methylation from the distal region to the core of the CEBPA promoter, and extending into exon1 (-25 to +103). This patient was a 61-year-old female with M1 subtype AML, a normal karyotype and an absence of CEBPA, NPM1 or FLT3 mutations. We further correlated the CEBPA methylation levels with CEBPA transcript levels in leukemic cells from 12 patients with AML and found a negative correlation (coefficient of correlation = -0.722, P = 0.017). To clarify the correlation between CEBPA methylation and clinical features in AML, quantitative MassARRAY analyses for CEBPA methylation were performed. Among the 193 patients studied, methylation levels ranged from 0.073 to 0.971 with a median value of 0.229, compared to normal bone marrow cells (NBM) with levels ranging from 0.207 to 0.269 (n=5, median: 0.218) and mature leukocytes (NPB) which ranged from 0.144 to 0.201 (n=5, median 0.161). Using complete linkage clustering, we divided the patients into two groups: one with high CEBPA methylation levels (≥ 0.55, n = 28), and the other with low methylation levels (< 0.55, n = 165). Clinical and laboratory features of these two groups are explored. Gene alteration analysis revealed NPM1 mutation was mutually exclusive with CEBPA methylation (P = 0.004), so was similar trend in CEBPA mutation. Of the 28 patients with high CEBPA methylation, only one patient had a CEBPA mutation. immunophenotyping work revealed that leukemic blasts from patients with high CEBPA methylation were predominately negative for HLA-DR (P = 0.005) and CD56 (P = 0.015) expression. Of the 140 patients receiving standard induction therapy, 110 (79.3%) patients achieved CR. With a median follow-up of 17.5 months, patients with high CEBPA methylation demonstrated a greater probability of survival (median >60 months vs. 20 months, P = 0.020) than those with low CEBPA methylation. In addition, in the subgroup of AML patients without favorable karyotypes as well as NPM1 and CEBPA mutations, patients with high CEBPA methylation (n = 14) still had better five-year survival (median 34 months vs. 11 months, P = 0.048) than those with low CEBPA methylation. Intriguingly, among the cytogenetically normal AML patients, except those with CEBPA and NPM1 mutations, the five-year survival of patients with high CEBPA methylation (n=8) was remarkably better than those with low CEBPA methylation (n=19; median >60 months vs. 11 months, P = 0.002). These results suggest that, in addition to current cytogenetic and molecular markers, CEBPA methylation status could emerge as a prognostic factor for survival advantage in AML patients. DisclosuresNo relevant conflicts of interest to declare.

Gene-targeted-metagenomics reveals extensive diversity of aromatic dioxygenase genes in the environment

Understanding the relationship between gene diversity and function for important environmental processes are major ecological research goals. We applied gene-targeted-metagenomics and pyrosequencing to aromatic dioxygenase genes to obtain greater sequence depth than possible by other methods. A PCR primer set designed to target a 524 bp region that confers substrate specificity of biphenyl dioxygenases yielded 2000 and 604 sequences from 5′ and 3′ ends of the PCR products, respectively, that passed our validity criteria. Sequence alignment showed three known conserved residues as well as another seven conserved residues not previously reported. Ninety-five and 41% of the valid sequences were assigned to 22 and 3 novel clusters in that they did not include any previously reported sequences at 0.6 distance by Complete Linkage Clustering for the sequenced regions. The greater diversity revealed by this gene-targeted approach provides deeper insights into genes potentially important in environmental processes to better understand their ecology, functional differences and evolutionary origins. We also provide criteria for primer design for this approach as well as guidance for data processing of diverse functional genes since gene databases for most genes of environmental relevance are limited.

Genetic Variability ofSchistosoma Japontcum(Katsorada, 1904) Intermediate HostsOncomelania hupensis(Gredler, 1881) (Gastropoda: Rissooidea)

Abstract. Schistosoma japonicum in China has been one of the most serious public problems. Subspecies of the pomatiopsid snail species Oncomelania hupensis is well known as the intermediate S. japonicum host. We used the simple sequence repeat anchored PCR (SSR-PCR) to assess the systematic position of the snail populations from the northwestern part of Guangxi Province, and to assess the degree to which SSR-PCR derived relationships are congruent with the allozymes, COI, and AFLPs results. For 19 populations studied, the genetic distance D ranged from 0.00 to 0.73, with an average value 0.22 ± 0.013. The cophenetic correlation coefficient for the Complete-link cluster, Single-link cluster, UPGMA cluster and NJ cluster was 0.932, 0.906, 0.939 and 0.733, respectively. The goodness of fit for these clusters except NJ cluster was very good. These results were very similar to the results of the AFLPs analysis data. The patterns of genetic differentiation are basically consistent with geographic distribution a...

패스파인더 네트워크 분석에 의한 ASIST Proceedings 인용흐름 연구

In this study, pathfinder network analysis has been carried out to identify subject domains of documents which cited articles in the ASIST Proceedings. This represents how articles in the ASIST Proceedings are flowed and used in what subjects areas. For this analysis, 240 documents were selected through a search of the Scopus database. The complete linkage clustering method was used to draw out 16 clusters from 240 documents. Through MDS and pathfinder network analysis, knowledge networks of clusters have been produced. As a result, articles in the ASIST Proceedings relating to knowledge management, bibliometrics, information retrieval and digital libraries have been cited actively by other publications. The most frequent citation flow type of ASIST proceedings was citation from proceedings(ASIST) to reviews(ARIST), via journals, and the most popular subject areas related to documents were bibliometrics.

Performance comparison of gene family clustering methods with expert curated gene family data set in Arabidopsis thaliana

With the exponential growth of genomics data, the demand for reliable clustering methods is increasing every day. Despite the wide usage of many clustering algorithms, the accuracy of these algorithms has been evaluated mostly on simulated data sets and seldom on real biological data for which a "correct answer" is available. In order to address this issue, we use the manually curated high-quality Arabidopsis thaliana gene family database as a "gold standard" to conduct a comprehensive comparison of the accuracies of four widely used clustering methods including K-means, TribeMCL, single-linkage clustering and complete-linkage clustering. We compare the results from running different clustering methods on two matrices: the E-value matrix and the k-tuple distance matrix. The E-value matrix is computed based on BLAST E-values. The k-tuple distance matrix is computed based on the difference in tuple frequencies. The TribeMCL with the E-value matrix performed best, with the Inflation parameter (=1.15) tuned considerably lower than what has been suggested previously (=2). The single-linkage clustering method with the E-value matrix was second best. Single-linkage clustering, K-means clustering, complete-linkage clustering, and TribeMCL with a k-tuple distance matrix performed reasonably well. Complete-linkage clustering with the k-tuple distance matrix performed the worst.

Comparative Analysis of Selenocysteine Machinery and Selenoproteome Gene Expression in Mouse Brain Identifies Neurons as Key Functional Sites of Selenium in Mammals

Although dietary selenium (Se) deficiency results in phenotypes associated with selenoprotein depletion in various organs, the brain is protected from Se loss. To address the basis for the critical role of Se in brain function, we carried out comparative gene expression analyses for the complete selenoproteome and associated biosynthetic factors. Using the Allen Brain Atlas, we evaluated 159 regions of adult mouse brain and provided experimental analyses of selected selenoproteins. All 24 selenoprotein mRNAs were expressed in the mouse brain. Most strikingly, neurons in olfactory bulb, hippocampus, cerebral cortex, and cerebellar cortex were exceptionally rich in selenoprotein gene expression, in particular in GPx4, SelK, SelM, SelW, and Sep15. Over half of the selenoprotein genes were also expressed in the choroid plexus. A unique expression pattern was observed for one of the highly expressed selenoprotein genes, SelP, which we suggest to provide neurons with Se. Cluster analysis of the expression data linked certain selenoproteins and selenocysteine machinery genes and suggested functional linkages among selenoproteins, such as that between SelM and Sep15. Overall, this study suggests that the main functions of selenium in mammals are confined to certain neurons in the brain.

Identifying chickpea homoclimes using the APSIM chickpea model

Chickpea (Cicer arietinum L.) has been traditionally grown in India but is a relatively new export crop in Australia where its cultivation is expanding into new areas. The objective of this study was to identify homoclimes (i.e. similar chickpea-growing environments) in the major chickpea-growing areas of the 2 countries, using the Agricultural Production Systems Simulator (APSIM) chickpea model. The model, which processes climatic, soil, and plant information on a daily time step, was first validated and then used to simulate flowering, maturity, and grain yield of Amethyst, a mid-season cultivar, and Barwon, a full-season cultivar, on low (100 mm), medium (150 mm), and high (190 mm) water-holding capacity soils, using historical climatic data of 67 Australian and 24 Indian locations. The mean of annual outputs of flowering, maturity, and grain yield of the 2 cultivars on 3 soils was then clustered using Ward’s hierarchical complete linkage clustering procedure. At a 90% level of similarity, all the locations could be grouped into 6 homoclime clusters. The Australian locations appeared more diverse as they were present in all the clusters, whereas the Indian locations were present only in clusters 1, 2, and 6. While there were clear geographical patterns of spread of these clusters, in Australia they were not entirely related to latitude. The cluster 1 and 2 locations, which represent the largest chickpea-growing area in Australia, had homoclime locations in common with northern India. The clustering of locations appeared generally consistent with the known adaptation of chickpea in different environments of the 2 countries and therefore suggests that the methodology could be potentially used for complementing conventional approaches of introducing or exchanging germplasm, as well as determining appropriateness of breeding/testing sites.