Results: We apply a new method for rapid structure comparison based on Simplicial Neighborhood Analysis of Protein Packing (SNAPP) to the diverse structural classification of proteins (SCOP) /β-class of protein folds. The procedure identifies inter-residue packing motifs shared by protein pairs from different folds. A threshold of 0.67 Å RMSD for all atoms of corresponding residues ensures inclusion of only highly significant similarities comparable with those observed for identical catalytic residues in homologues. Many tertiary packing motifs are shared among the three classical Rossmannoid folds, as well as thousands of other motifs that occur in at least two distinct folds. Merging of neighboring packing motifs facilitated recognition of larger, recurrent substructures or cores. The anti-codon-binding domain of an archeal aminoacyl-tRNA synthetase (aaRS) was discovered to possess a packed core in which eight identical amino acid residues are within 0.55 Å RMSD of the comparable structure in the FixJ receiver, a member of the Rossmannoid family that also includes the CheY signaling protein and flavodoxin-like proteins. Further investigation identified close variants of this core in five other Rossmannoid folds, including a functionally relevant core in Class Ia aminoacyl-tRNA synthetases. Although it is possible that the two essentially identical cores in the ProRS anti-codon-binding domain and the FixJ receiver converged to the same structure, the consensus core obtained from the structural and sequence alignments suggests that all the implicated protein folds descended from a simpler ancestral protein in which this core provided nucleotide binding and proto-allosteric functions.
Availability: Programs are available at
Implementation: Programs were written in Perl and c and run under Linux.
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Results: We have developed a new software package, STAMP, for comparative metagenomics that supports best practices in analysis and reporting. Examination of a pair of iron mine metagenomes demonstrates that deeper biological insights can be gained using statistical techniques available in our software. An analysis of the functional potential of ‘Candidatus Accumulibacter phosphatis’ in two enhanced biological phosphorus removal metagenomes identified several subsystems that differ between the A.phosphatis stains in these related communities, including phosphate metabolism, secretion and metal transport.
Availability: Python source code and binaries are freely available from our website at
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Results: We systematically analyzed how the short-read mapping tools MAQ, Bowtie, Burrows-Wheeler alignment tool (BWA), Novoalign and RazerS perform on simulated datasets that contain indels and evaluated how indels affect error rates in SNV detection. We implemented a simple algorithm to compute the equivalent indel region eir, which can be used to process the alignments produced by the mapping tools in order to perform indel calling. Using simulated data that contains indels, we demonstrate that indel detection works well on short-read data: the detection rate for microindels (<4 bp) is >90%. Our study provides insights into systematic errors in SNV detection that is based on ungapped short sequence read alignments. Gapped alignments of short sequence reads can be used to reduce this error and to detect microindels in simulated short-read data. A comparison with microindels automatically identified on the ABI Sanger and Roche 454 platform indicates that microindel detection from short sequence reads identifies both overlapping and distinct indels.
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Results: We developed three implementations of a probabilistic Binomial mixture model, called SNVMix, designed to infer SNVs from NGS data from tumors to address this problem. The first models allelic counts as observations and infers SNVs and model parameters using an expectation maximization (EM) algorithm and is therefore capable of adjusting to deviation of allelic frequencies inherent in genomically unstable tumor genomes. The second models nucleotide and mapping qualities of the reads by probabilistically weighting the contribution of a read/nucleotide to the inference of a SNV based on the confidence we have in the base call and the read alignment. The third combines filtering out low-quality data in addition to probabilistic weighting of the qualities. We quantitatively evaluated these approaches on 16 ovarian cancer RNASeq datasets with matched genotyping arrays and a human breast cancer genome sequenced to >40x (haploid) coverage with ground truth data and show systematically that the SNVMix models outperform competing approaches.
Availability: Software and data are available at
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Results: Here, we have developed an alignment-free and visual approach to analyze sequence relationships. We use the number of shared n-grams between sequences as a measure of sequence similarity and rearrange the resulting affinity matrix applying a spectral technique. Heat maps of the affinity matrix are employed to identify and visualize clusters of related sequences or outliers, while n-gram-based dot plots and conservation profiles allow detailed analysis of similarities among selected sequences. Using this approach, we have identified signatures of domain shuffling in an otherwise poorly characterized family, and homology clusters in another. We conclude that this approach may be generally useful as a framework to analyze related, but highly divergent protein sequences. It is particularly useful as a fast method to study sequence relationships prior to much more time-consuming multiple sequence alignment and phylogenetic analysis.
Availability: A software implementation (MOSAIC) of the framework described here can be downloaded from
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Results: We created benchmark data set of structural domain assignments in the CATH database and a corresponding set of Hidden Markov Model-based domain predictions. Using these, we demonstrate that by considering all possible combinations of matches using the HCF approach, we achieve much higher prediction accuracy than the standard BMC method. We also show that it is essential to allow overlapping domain matches to a query in order to identify correct domain assignments. Furthermore, we introduce a straightforward and effective protocol for resolving any overlapping assignments, and producing a single set of non-overlapping predicted domains.
Availability and implementation: The new approach will be used to determine MDAs for UniProt and Ensembl, and made available via the Gene3D website:
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Results: In this article we develop an approach (termed Cascleave) to predict both classical (i.e. following a P1 Asp) and non-typical caspase cleavage sites. When using local sequence-derived profiles, Cascleave successfully predicted 82.2% of the known substrate cleavage sites, with a Matthews correlation coefficient (MCC) of 0.667. We found that prediction performance could be further improved by incorporating information such as predicted solvent accessibility and whether a cleavage sequence lies in a region that is most likely natively unstructured. Novel bi-profile Bayesian signatures were found to significantly improve the prediction performance and yielded the best performance with an overall accuracy of 87.6% and a MCC of 0.747, which is higher accuracy than published methods that essentially rely on amino acid sequence alone. It is anticipated that Cascleave will be a powerful tool for predicting novel substrate cleavage sites of caspases and shedding new insights on the unknown caspase-substrate interactivity relationship.
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Results: We have developed image segmentation and registration methods to identify and extract spatial gene expression patterns from RNA in situ hybridization experiments of Drosophila embryos. These methods allow us to normalize and extract expression information for 78 621 images from 3724 genes across six time stages. The similarity between gene expression patterns is computed using four scoring metrics: mean squared error, Haar wavelet distance, mutual information and spatial mutual information (SMI). We additionally propose a strategy to calculate the significance of the similarity between two expression images, by generating surrogate datasets with similar spatial expression patterns using a Monte Carlo swap sampler. On data from an early development time stage, we show that SMI provides the most biologically relevant metric of comparison, and that our significance testing generalizes metrics to achieve similar performance. We exemplify the application of spatial metrics on the well-known Drosophila segmentation network.
Availability: A Java webstart application to register and compare patterns, as well as all source code, are available from:
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Results: We apply GPR with uncertain measurement times to a microarray dataset of mRNA expression for the hair-growth cycle in mouse back skin, predicting both profile shapes and biological times for each replicate. The predicted time shifts show high consistency with independently obtained morphological estimates of relative development. We also show that the method systematically reduces prediction error on out-of-sample data, significantly reducing the mean squared error in a cross-validation study.
Availability: Matlab code for GPR with uncertain time shifts is available at
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Results: We have developed a simple, but powerful Bayesian hierarchical model for ChIP-chip data via a hidden Ising model. Metropolis within Gibbs sampling algorithm is used to simulate from the posterior distribution of the model parameters. The proposed model naturally incorporates the spatial dependency of the data, and can be used to analyze data with various genomic resolutions and sample sizes. We illustrate the method using three publicly available datasets and various simulated datasets, and compare it with three closely related methods, namely TileMap HMM, tileHMM and BAC. We find that our method performs as well as TileMap HMM and BAC for the high-resolution data from Affymetrix platform, but significantly outperforms the other three methods for the low-resolution data from Agilent platform. Compared with the BAC method which also involves MCMC simulations, our method is computationally much more efficient.
Availability: A software called iChip is freely available at
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The effect size measure c=P(T1<T0), i.e. the probability that a person randomly chosen from group G1 dies earlier than a person from G0, is independent of the proportional hazards (PH) assumption. Here we consider its generalization to continuous data c' and investigate the suitability of c' for gene selection.
Results: Under PH, c' is most efficiently estimated by COX. Under NPH, c' can be obtained by weighted Cox regression (WHE) or a novel method, concordance regression (CON). The least biased and most stable estimates were obtained by CON. We propose to use c' as summary measure of effect size to rank genes irrespective of different types of NPH and censoring patterns.
Availability: WHE and CON are available as R packages.
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Results: We extend previous contributions that suggest that fractional mass information allows targeted fragmentation of analytes of interest. We introduce a non-linear Random Forest classification and a discrete mapping approach, which can be trained to discriminate among arbitrary fractional mass patterns for an arbitrary number of classes of analytes. These methods can be used to increase fragmentation efficiency for specific subsets of analytes or to select suitable fragmentation technologies on-the-fly. We show that theoretical generalization error estimates transfer into practical application, and that their quality depends on the accuracy of prior distribution estimate of the analyte classes. The methods are applied to two real-world proteomics datasets.
Availability: All software used in this study is available from
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Methods: We report PHYLOSTRAT, a new method that corrects for population stratification by combining phylogeny constructed from SNP genotypes and principal coordinates from multi-dimensional scaling (MDS) analysis. This hybrid approach efficiently captures both discrete and admixed population structures.
Results: By extensive simulations, the analysis of a synthetic genome-wide association dataset created using data from the Human Genome Diversity Project, and the analysis of a lactase-height dataset, we show that our method can correct for population stratification more efficiently than several existing population stratification correction methods, including EIGENSTRAT, a hybrid approach based on MDS and clustering, and STRATSCORE , in terms of requiring fewer random SNPs for inference of population structure. By combining the flexibility and hierarchical nature of phylogenetic trees with the advantage of representing admixture using MDS, our hybrid approach can capture the complex population structures in human populations effectively.
Software Availability: Codes can be downloaded from
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Results: The proposed graph-based approach generates a robust GSN for the training process of genetic network construction. The RVM-based ensemble model that combines AdaBoost and reduced-feature yields improved performance on large-scale learning problems with massive missing values in comparison to Naïve Bayes.
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Results: Results from leave-one-out cross-validation show that a recall and precision of ~0.8 can be achieved for a subset of 13 transformation rules. Therefore, it is possible to optimize precision without compromising recall. We are currently integrating the results into an experimental version of the UM-PPS server.
Availability: The program is freely available on the web at
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Results: Through a simulation study using data models and analysis of real microarray data, we show that (i) for small samples the root mean square differences of the estimated and true metrics are considerable; (ii) even for large samples, there is only weak correlation between the true and estimated metrics; and (iii) generally, there is weak regression of the true metric on the estimated metric. For classification rules, we consider linear discriminant analysis, linear support vector machine (SVM) and radial basis function SVM. For error estimation, we consider resubstitution, three kinds of cross-validation and bootstrap. Using resampling, we show the unreliability of some published ROC results.
Availability: Companion web site at
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Results: We characterized the effect of multiple SNPs under various models using our proposed importance measure in random forests, which uses maximal conditional chi-square (MCC) as a measure of association between a SNP and the trait conditional on other SNPs. Based on this importance measure, we employed a permutation test to estimate empirical P-values of SNPs. Our method was compared to a univariate test and the permutation test using the Gini and permutation importance. In simulation, the proposed method performed consistently superior to the other methods in identifying of risk SNPs. In a GWAS of age-related macular degeneration, the proposed method confirmed two significant SNPs (at the genome-wide adjusted level of 0.05). Further analysis showed that these two SNPs conformed with a heterogeneity model. Compared with the existing importance measures, the MCC importance measure is more sensitive to complex effects of risk SNPs by utilizing conditional information on different SNPs. The permutation test with the MCC importance measure provides an efficient way to identify candidate SNPs in GWAS and facilitates the understanding of the etiology between genetic variants and complex diseases.
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Availability: C++ source code and user manual are available for download from
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Results: This article introduces a new software suite for the comparison, manipulation and annotation of genomic features in Browser Extensible Data (BED) and General Feature Format (GFF) format. BEDTools also supports the comparison of sequence alignments in BAM format to both BED and GFF features. The tools are extremely efficient and allow the user to compare large datasets (e.g. next-generation sequencing data) with both public and custom genome annotation tracks. BEDTools can be combined with one another as well as with standard UNIX commands, thus facilitating routine genomics tasks as well as pipelines that can quickly answer intricate questions of large genomic datasets.
Availability and implementation: BEDTools was written in C++. Source code and a comprehensive user manual are freely available at
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iMotifs is a graphical motif analysis environment that allows visualization of annotated sequence motifs and scored motif hits in sequences. It also offers motif inference with the sensitive NestedMICA algorithm, as well as overrepresentation and pairwise motif matching capabilities. All of the analysis functionality is provided without the need to convert between file formats or learn different command line interfaces.
The application includes a bundled and graphically integrated version of the NestedMICA motif inference suite that has no outside dependencies. Problems associated with local deployment of software are therefore avoided.
Availability: iMotifs is licensed with the GNU Lesser General Public License v2.0 (LGPL 2.0). The software and its source is available at
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Using the sequence structure of 10.4 base DNA repeat presented in our previous works and nucleosome core DNA sequences database, we have derived the complete nucleosome DNA bendability matrix of Caenorhabditis elegans.
We have developed a web server named FineStr that allows users to upload genomic sequences in FASTA format and to perform a single-base-resolution nucleosome mapping on them.
Availability: FineStr server is freely available for use on the web at
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Availability: The software and data produced in this work are freely available for academic use at
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Availability: This error analysis framework is written in Perl and runs on Mac OS and Linux/Unix systems. The filter, documentation and sample Excel files for quality analysis are available at
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Availability: The SwissVar portal is available at
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Results: We designed and implemented a new algorithm, Burrows-Wheeler Aligner's Smith-Waterman Alignment (BWA-SW), to align long sequences up to 1 Mb against a large sequence database (e.g. the human genome) with a few gigabytes of memory. The algorithm is as accurate as SSAHA2, more accurate than BLAT, and is several to tens of times faster than both.
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Results: Our framework for longer range interactions is described as a k-mer order model. We succeeded in applying our model to the specific problem of secondary structure prediction, to be used as an additional layer on top of existing methods. We achieved our goal of making the predictions more realistic and protein like, and remarkably this also improved the overall performance. We improve the Segment OVerlap (SOV) score by 1.8%, but more importantly we radically improve the probability of the real sequence given a prediction from an average of 0.271 per residue to 0.385. Crucially, this improvement is obtained using no additional information.
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Results: We propose a novel approach that uses available quantitative information directly for classification rather than first using a regression scheme. It uses a new type of loss function called weighted biased regression. Application of this method to four widely studied datasets of the CoEPrA contest (Comparative Evaluation of Prediction Algorithms,
Availability: A stand alone application is available at the webpage
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Results:
Availability: The C source code of
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Results: In cross-validation experiments on two benchmark datasets from the Catalytic Site Atlas and CATRES resources containing a total of 437 manually curated enzymes spanning 487 SCOP families, D
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Results: We established that by our coarse-grained measures for change, predictions and observations were similar, suggesting that our results were not biased by prediction mistakes. Changes in secondary structure and disorder predictions were linearly proportional to the change in sequence. Surprisingly, neither the content nor the length distribution for the predicted secondary structure changed substantially. Regions with long disorder behaved differently in that significantly fewer such regions were predicted after a few mutation steps. Our findings suggest that the formation of regular secondary structure is an intrinsic feature of random amino acid sequences, while the formation of long-disordered regions is not an intrinsic feature of proteins with disordered regions. Put differently, helices and strands appear to be maintained easily by evolution, whereas maintaining disordered regions appears difficult. Neutral mutations with respect to disorder are therefore very unlikely.
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Results: We device an approach called RapidShapes that computes the shapes above a specified probability threshold T by generating a list of promising shapes and constructing specialized folding programs for each shape to compute its share of Boltzmann probability. This aims at a heuristic improvement of runtime, while still computing exact probability values.
Conclusion: Evaluating this approach and several substrategies, we find that only a small proportion of shapes have to be actually computed. For an RNA sequence of length 400, this leads, depending on the threshold, to a 10–138 fold speed-up compared with the previous complete method. Thus, probabilistic shape analysis has become feasible in medium-scale applications, such as the screening of RNA transcripts in a bacterial genome.
Availability: RapidShapes is available via
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Methods: Our approach for improving 0 estimation is to modify an existing simple method by introducing artificial censoring to P-values. In a comprehensive simulation study and the applications to experimental datasets, we compare our method with eight existing estimation methods.
Results: The simulation study confirms that our method can clearly improve the estimation performance. Compared with the existing methods, our method can generally provide a relatively accurate estimate with relatively small variance. Using experimental microarray datasets, we also demonstrate that our method can generally provide satisfactory estimates in practice.
Availability: The R code is freely available at
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Results: We show that a model based on the physical distance between neurons can explain the C.elegans neural network structure, specifically, we demonstrate that a simple model based on a geometrical synapse formation probability and the inhibition of short coherent cycles can explain the properties of the C.elegans' neural network. We suggest this model as an initial framework to discuss neural network generation and as a first step toward the development of models for more advanced creatures. In order to measure the circle frequency in the network, a novel graph-theory circle length measurement algorithm is proposed.
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Results: We developed a protocol based on brief, timed infusion of 13C isotope-enriched substrates for the tricarboxylic acid (TCA) cycle followed by quick freezing of tissue biopsies. NMR measurements of tissue extracts were used for flux estimation based on a computational model of carbon transitions between TCA cycle metabolites and related amino acids. To this end, we developed a computational framework in which metabolic systems can be flexibly assembled, simulated and analyzed. Flux parameters were quantified from NMR multiplets by a partial grid search followed by repeated Nelder–Mead optimizations implemented on a computer grid. We implemented a model of the TCA cycle and showed by extensive simulations that the timed infusion protocol reliably quantitates multiple fluxes. Experimental validation of the method was done in vivo on hearts of anesthetized pigs under two different conditions: basal state (n = 7) and cardiac stress caused by infusion of dobutamine (n = 7). About nine tissue samples (40–200 mg dry-weight) were taken per heart. TCA cycle flux was 6.11 ± 0.28 (SEM) µmol/min · gdw at baseline versus 9.29 ± 1.03 µmol/min · gdw for dobutamine stress. Oxygen consumption calculated from the TCA cycle flux and from ‘gold standard’ blood gas-based measurements were close, correlating with r=0.88 (P < 10–4). Spatial heterogeneity in metabolic fluxes is detectable amongst the small samples. We propose that our novel isotope snapshot methodology is suitable for flux measurements in biopsies in vivo.
Availability: Non-profit organizations will, upon request, be granted a non-exclusive license to use the software for internal research and teaching purposes at no charge. A web interface for using the software on our computer grid is available under
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Results: We build a corpus for organism disambiguation where every occurrence of protein/gene entity is manually tagged with a species ID, and evaluate a number of methods on it. Promising results are obtained by training a machine learning model on syntactic parse trees, which is then used to decide whether an entity belongs to the model organism denoted by a neighbouring species-indicating word (e.g. yeast). The parser-based approaches are also compared with a supervised classification method and results indicate that the former are a more favorable choice when domain portability is of concern. The best overall performance is obtained by combining the strengths of syntactic features and supervised classification.
Availability: The corpus and demo are available at
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Results: We have combined the expressiveness of rules with the mathematical rigor of Bayesian networks (BNs) to develop and evaluate a Bayesian rule learning (BRL) system. This system utilizes a novel variant of the K2 algorithm for building BNs from the training data to provide probabilistic scores for IF-antecedent-THEN-consequent rules using heuristic best-first search. We then apply rule-based inference to evaluate the learned models during 10-fold cross-validation performed two times. The BRL system is evaluated on 24 published ‘omic’ datasets, and on average it performs on par or better than other readily available rule learning methods. Moreover, BRL produces models that contain on average 70% fewer variables, which means that the biomarker panels for disease prediction contain fewer markers for further verification and validation by bench scientists.
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Availability: BamView and Artemis are freely available (under a GPL licence) for download (for MacOSX, UNIX and Windows) at:
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Results: We present a fast, free and powerful, open source R package,
Availability: The R-package
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Availability: Free access at
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Availability: This algorithm is available as a free web service for the scientific community at
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Availability and implementation: PSiFR is freely available for use on the web at
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Availability: PyRosetta is a stand-alone package available at
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Availability:
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Results: The implementation of MDR for GPUs (MDRGPU) includes core features of the widely used Java software package, MDR. This GPU implementation allows for large-scale analysis of epistasis at a dramatically lower cost than the standard CPU-based implementations. As a proof-of-concept, we applied this software to a genome-wide study of sporadic amyotrophic lateral sclerosis (ALS). We discovered a statistically significant two-SNP classifier and subsequently replicated the significance of these two SNPs in an independent study of ALS. MDRGPU makes the large-scale analysis of epistasis tractable and opens the door to statistically rigorous testing of interactions in genome-wide datasets.
Availability: MDRGPU is open source and available free of charge from
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Availability: NEMO is a stand-alone Java application available for Windows and Linux platforms. The program is distributed under the creative commons licence and can be freely downloaded from
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Availability: MSMSpdbb was developed in C++ using the Qt libraries (Nokia) and licensed under the GNU General Public License version 2. MSMSpdbb is freely available, and its installation files, instructions for use and additional documentation can be found at the MPR web site
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Availability and Implementation: dbTEU is freely available at
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Availability: Xper2 software can be freely downloaded at
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Availability: The database can be accessed through
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Results: We have developed ConceptGen, a web-based gene set enrichment and gene set relation mapping tool that is streamlined and simple to use. ConceptGen offers over 20 000 concepts comprising 14 different types of biological knowledge, including data not currently available in any other gene set enrichment or gene set relation mapping tool. We demonstrate the functionalities of ConceptGen using gene expression data modeling TGF-beta-induced epithelial-mesenchymal transition and metabolomics data comparing metastatic versus localized prostate cancers.
Availability: ConceptGen is part of the NIH's National Center for Integrative Biomedical Informatics (NCIBI) and is freely available at
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Results: Our approach, correlation matrix diagonal segmentation (CMDS), identifies RCNAs based on a between-chromosomal-site correlation analysis. Directly using the raw intensity ratio data from all samples and adopting a diagonal transformation strategy, CMDS substantially reduces computational burden and can obtain results very quickly from large datasets. Our simulation indicates that the statistical power of CMDS is higher than that of single-sample CNA calling based two-step approaches. We applied CMDS to two real datasets of lung cancer and brain cancer from Affymetrix and Illumina array platforms, respectively, and successfully identified known regions of CNA associated with EGFR, KRAS and other important oncogenes. CMDS provides a fast, powerful and easily implemented tool for the RCNA analysis of large-scale data from cancer genomes.
Availability: The R and C programs implementing our method are available at
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Results: By exploiting the vast protein sequence and protein structure data available, we have brought secondary-structure prediction closer to the expected theoretical limit. When tested by a leave-one-out cross validation on a non-redundant set of PDB cutting at 30% sequence identity containing 5860 protein chains, the overall per-residue accuracy for secondary-structure prediction, i.e. Q3 is 82.9%. The overall per-residue accuracy for three- and eight-state Shape Strings are 85.1 and 71.5%, respectively. We have also benchmarked our program with the latest version of PSIPRED for secondary structure prediction and our program predicted 0.3% better in Q3 when tested on 2241 chains with the same training set. For Shape Strings, we compared our method with a recently published method with the same dataset and definition as used by that method. Our program predicted at 2.2% better in accuracy for three-state Shape Strings. By quantitatively investigating the effect of data base size on 1D structure prediction we show that the accuracy increases by ~1% with every doubling of the database size.
Availability: The program is available for download at
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Results: We analyzed the complete genomes of 90 prokaryotes. We found that most oligonucleotides (pentanucleotides and higher) do not have a reverse complement in close proximity in the genomic sequences. Combined with further analysis, we conclude that the contribution of the widespread stem-loop potential of single-stranded genomic DNA to the formation and maintenance of strand symmetry would be very limited, at least for higher-order oligonucleotides. Therefore, other possible causes for strand symmetry must be taken into account to a deeper degree.
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Results: First, we estimated the prior variance of a gene expression by pooling variances obtained from mixture modeling of large samples in the public microarray database. Then, using the prior variance, we identified DEGs in small sample-sized test datasets using the Baldi's framework. For benchmark study, we generated test datasets having several samples from relatively large datasets. Our proposed method outperformed other benchmark methods in terms of detecting gold-standard DEGs from the test datasets. The results may be a challenging evidence for usage of public microarray databases in microarray data analysis.
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Results: We present a generative statistical model and associated inference methods that handle read mapping uncertainty in a principled manner. Through simulations parameterized by real RNA-Seq data, we show that our method is more accurate than previous methods. Our improved accuracy is the result of handling read mapping uncertainty with a statistical model and the estimation of gene expression levels as the sum of isoform expression levels. Unlike previous methods, our method is capable of modeling non-uniform read distributions. Simulations with our method indicate that a read length of 20–25 bases is optimal for gene-level expression estimation from mouse and maize RNA-Seq data when sequencing throughput is fixed.
Availability: An initial C++ implementation of our method that was used for the results presented in this article is available at
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Results: To model non-independence of variables (e.g. correlated gene expressions) while alleviating the problem with the large number of unknown parameters associated with a general non-diagonal covariance matrix, we generalize the mixture of factor analyzers to that with penalization, which, among others, can effectively realize variable selection. We use simulated data and real microarray data to illustrate the utility and advantages of the proposed method over several existing ones.
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Results: Using pairwise dissimilarities among predictor variables, one obtains a univariate configuration of these covariates. This is interpreted as variable ordination that defines the domain of a suitable function space, thus leading to the FEM of the high-dimensional data. The embedding may then be followed by functional logistic regression for the classification of high-dimensional multivariate data as an example for downstream analysis. The resulting functional classification is evaluated on several published gene expression array datasets and a mass spectrometric data, and is shown to compare favorably with various methods that have been employed previously for the classification of these high-dimensional gene expression profiles.
Availability: The implementation of FEM and Classification via Functional Embedding (CFEM) as described in this article was done with the PACE package written in Matlab. The latest version of PACE is publicly accessible at
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Results: Towards this end, we generated multiple parameter-based, synthetic datasets—derived from real data—that contain somatic copy number aberrations (CNAs) of various lengths and frequencies over germline single nucleotide polymorphisms (SNPs) genome-wide. Gold-standard causal sub-regions were assigned within these CNAs, followed by an assessment of ADT's ability to detect these sub-regions. Results indicate that ADT possesses high sensitivity and specificity in large sample sizes across most parameter cases, including those that more closely reflect existing SNP and CNA cancer data.
Availability: ADT is implemented in the Java software HADiT and can be downloaded through the SVN repository (via Develop-> Code->SVN Browse) at:
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Results: Here, we propose a novel integrated approach that uses network topology to predict biological pathways. We integrated four types of biological evidence (protein–protein interaction, genetic interaction, domain–domain interaction and semantic similarity of Gene Ontology terms) to generate a functionally associated network. This network was then used to develop a new pathway finding algorithm to predict biological pathways in yeast. Our approach discovered 195 biological pathways and 31 functionally redundant pathway pairs in yeast. By comparing our identified pathways to three public pathway databases (KEGG, BioCyc and Reactome), we observed that our approach achieves a maximum positive predictive value of 12.8% and improves on other predictive approaches. This study allows us to reconstruct biological pathways and delineates cellular machinery in a systematic view.
Availability: The method has been implemented in Perl and is available for downloading from
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Results: In this article, we describe a novel constraint-based method called RobustKnock that predicts gene deletion strategies that lead to the over-production of chemicals of interest, by accounting for the presence of competing pathways in the network. We describe results of applying RobustKnock to Escherichia coli's metabolic network towards the production of various chemicals, demonstrating its ability to provide more robust predictions than those obtained via current state-of-the-art methods.
Availability: An implementation of RobustKnock is available via
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Results: We present Bisque, a web-based platform specifically designed to provide researchers with organizational and quantitative analysis tools for 5D image data. Users can extend Bisque with both data model and analysis extensions in order to adapt the system to local needs. Bisque's extensibility stems from two core concepts: flexible metadata facility and an open web-based architecture. Together these empower researchers to create, develop and share novel bioimage analyses. Several case studies using Bisque with specific applications are presented as an indication of how users can expect to extend Bisque for their own purposes.
Availability: Bisque is web based, cross-platform and open source. The system is also available as software-as-a-service through the Center of Bioimage Informatics at UCSB.
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Results: jORCA is a desktop client aimed at facilitating seamless integration of Web Services. It does so by making a uniform representation of the different web resources, supporting scalable service discovery, and automatic composition of workflows. Usability is at the top of the jORCA agenda; thus it is a highly customizable and extensible application that accommodates a broad range of user skills featuring double-click invocation of services in conjunction with advanced execution-control, on the fly data standardization, extensibility of viewer plug-ins, drag-and-drop editing capabilities, plus a file-based browsing style and organization of favourite tools. The integration of bioinformatics Web Services is made easier to support a wider range of users.
Availability and Implementation: jORCA binaries and extended documentation are freely available at
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Results: Here we present GWAS Analyzer, a database driven web-based tool that integrates genotype and phenotype data, association analysis results and genomic annotations from multiple public resources. GWAS Analyzer contains features for browsing these interrelated data, exploring phenotypic values by family or genotype, and filtering association results based on multiple criteria. The utility of the tool has been demonstrated by a genome-wide association study of human in vitro susceptibility to bacterial infection. GWAS Analyzer facilitated management of large sets of phenotype and genotype data, analysis of phenotypic variation and heritability, and most importantly, generation of a refined set of candidate single nucleotide polymorphisms (SNPs). The tool revealed a SNP that was experimentally validated to be associated with increased cell death among Salmonella infected HapMap cell lines.
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Availability: The software described in this manuscript, named cnD for copy number detector, is free and released under the GPL. The program is implemented in the D programming language using the Tango library. Source code and pre-compiled binaries are available at
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Availability: The source code is freely available for download at
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Availability: The COPS-Benchmark service is available at
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Availability:
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Availability: LD-Plus is freely available for non-commercial research institutions. For full details see
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Availability: A web-computing service WebPARE is publicly available at:
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Availability: GonadSAGE is at
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