ALBERT

Description: Background: Recently, the Bayesian method becomes more popular for analyzing high dimensional gene expression data as it allows us to borrow information across different genes and provides powerful estimators for evaluating gene expression levels. It is crucial to develop a simple but efficient gene selection algorithm for detecting differentially expressed (DE) genes based on the Bayesian estimators. Results: In this paper, by extending the two-criterion idea of Chen et al. (Chen M-H, Ibrahim JG, Chi Y-Y. A new class of mixture models for differential gene expression in DNA microarray data. J Stat Plan Inference. 2008;138:387–404), we propose two new gene selection algorithms for general Bayesian models and name these new methods as the confident difference criterion methods. One is based on the standardized differences between two mean expression values among genes; the other adds the differences between two variances to it. The proposed confident difference criterion methods first evaluate the posterior probability of a gene having different gene expressions between competitive samples and then declare a gene to be DE if the posterior probability is large. The theoretical connection between the proposed first method based on the means and the Bayes factor approach proposed by Yu et al. (Yu F, Chen M-H, Kuo L. Detecting differentially expressed genes using alibrated Bayes factors. Statistica Sinica. 2008;18:783–802) is established under the normal-normal-model with equal variances between two samples. The empirical performance of the proposed methods is examined and compared to those of several existing methods via several simulations. The results from these simulation studies show that the proposed confident difference criterion methods outperform the existing methods when comparing gene expressions across different conditions for both microarray studies and sequence-based high-throughput studies. A real dataset is used to further demonstrate the proposed methodology. In the real data application, the confident difference criterion methods successfully identified more clinically important DE genes than the other methods. Conclusion: The confident difference criterion method proposed in this paper provides a new efficient approach for both microarray studies and sequence-based high-throughput studies to identify differentially expressed genes.

Description: Background: Plant organ segmentation from 3D point clouds is a relevant task for plant phenotyping and plant growth observation. Automated solutions are required to increase the efficiency of recent high-throughput plant phenotyping pipelines. However, plant geometrical properties vary with time, among observation scales and different plant types. The main objective of the present research is to develop a fully automated, fast and reliable data driven approach for plant organ segmentation. Results: The automated segmentation of plant organs using unsupervised, clustering methods is crucial in cases where the goal is to get fast insights into the data or no labeled data is available or costly to achieve. For this we propose and compare data driven approaches that are easy-to-realize and make the use of standard algorithms possible. Since normalized histograms, acquired from 3D point clouds, can be seen as samples from a probability simplex, we propose to map the data from the simplex space into Euclidean space using Aitchisons log ratio transformation, or into the positive quadrant of the unit sphere using square root transformation. This, in turn, paves the way to a wide range of commonly used analysis techniques that are based on measuring the similarities between data points using Euclidean distance. We investigate the performance of the resulting approaches in the practical context of grouping 3D point clouds and demonstrate empirically that they lead to clustering results with high accuracy for monocotyledonous and dicotyledonous plant species with diverse shoot architecture. Conclusion: An automated segmentation of 3D point clouds is demonstrated in the present work. Within seconds first insights into plant data can be deviated – even from non-labelled data. This approach is applicable to different plant species with high accuracy. The analysis cascade can be implemented in future high-throughput phenotyping scenarios and will support the evaluation of the performance of different plant genotypes exposed to stress or in different environmental scenarios.

Description: Background: Tumorigenesis is an evolutionary process by which tumor cells acquire mutations through successive diversification and differentiation. There is much interest in reconstructing this process of evolution due to its relevance to identifying drivers of mutation and predicting future prognosis and drug response. Efforts are challenged by high tumor heterogeneity, though, both within and among patients. In prior work, we showed that this heterogeneity could be turned into an advantage by computationally reconstructing models of cell populations mixed to different degrees in distinct tumors. Such mixed membership model approaches, however, are still limited in their ability to dissect more than a few well-conserved cell populations across a tumor data set. Results: We present a method to improve on current mixed membership model approaches by better accounting for conserved progression pathways between subsets of cancers, which imply a structure to the data that has not previously been exploited. We extend our prior methods, which use an interpretation of the mixture problem as that of reconstructing simple geometric objects called simplices, to instead search for structured unions of simplices called simplicial complexes that one would expect to emerge from mixture processes describing branches along an evolutionary tree. We further improve on the prior work with a novel objective function to better identify mixtures corresponding to parsimonious evolutionary tree models. We demonstrate that this approach improves on our ability to accurately resolve mixtures on simulated data sets and demonstrate its practical applicability on a large RNASeq tumor data set. Conclusions: Better exploiting the expected geometric structure for mixed membership models produced from common evolutionary trees allows us to quickly and accurately reconstruct models of cell populations sampled from those trees. In the process, we hope to develop a better understanding of tumor evolution as well as other biological problems that involve interpreting genomic data gathered from heterogeneous populations of cells.

Description: Background: Understanding the architecture and function of RNA molecules requires methods for comparing and analyzing their tertiary and quaternary structures. While structural superposition of short RNAs is achievable in a reasonable time, large structures represent much bigger challenge. Therefore, we have developed a fast and accurate algorithm for RNA pairwise structure superposition called SETTER and implemented it in the SETTER web server. However, though biological relationships can be inferred by a pairwise structure alignment, key features preserved by evolution can be identified only from a multiple structure alignment. Thus, we extended the SETTER algorithm to the alignment of multiple RNA structures and developed the MultiSETTER algorithm. Results: In this paper, we present the updated version of the SETTER web server that implements a user friendly interface to the MultiSETTER algorithm. The server accepts RNA structures either as the list of PDB IDs or as user-defined PDB files. After the superposition is computed, structures are visualized in 3D and several reports and statistics are generated. Conclusion: To the best of our knowledge, the MultiSETTER web server is the first publicly available tool for a multiple RNA structure alignment. The MultiSETTER server offers the visual inspection of an alignment in 3D space which may reveal structural and functional relationships not captured by other multiple alignment methods based either on a sequence or on secondary structure motifs.

Description: Background: Today’s modern research of B and T cell antigen receptors (the immunoglobulins (IG) or antibodies and T cell receptors (TR)) forms the basis for detailed analyses of the human adaptive immune system. For instance, insights in the state of the adaptive immune system provide information that is essentially important in monitoring transplantation processes and the regulation of immune suppressiva. In this context, algorithms and tools are necessary for analyzing the IG and TR diversity on nucleotide as well as on amino acid sequence level, identifying highly proliferated clonotypes, determining the diversity of the cell repertoire found in a sample, comparing different states of the human immune system, and visualizing all relevant information. Results: We here present IMEX, a software framework for the detailed characterization and visualization of the state of human IG and TR repertoires. IMEX offers a broad range of algorithms for statistical analysis of IG and TR data, CDR and V-(D)-J analysis, diversity analysis by calculating the distribution of IG and TR, calculating primer efficiency, and comparing multiple data sets. We use a mathematical model that is able to describe the number of unique clonotypes in a sample taking into account the true number of unique sequences and read errors; we heuristically optimize the parameters of this model. IMEX uses IMGT/HighV-QUEST analysis outputs and includes methods for splitting and merging to enable the submission to this portal and to combine the outputs results, respectively. All calculation results can be visualized and exported. Conclusion: IMEX is an user-friendly and flexible framework for performing clonality experiments based on CDR and V-(D)-J rearranged regions, diversity analysis, primer efficiency, and various different visualization experiments. Using IMEX, various immunological reactions and alterations can be investigated in detail. IMEX is freely available for Windows and Unix platforms at http://bioinformatics.fh-hagenberg.at/immunexplorer/.

Description: Background: Membrane proteins represent over 25 % of human protein genes and account for more than 60 % of drug targets due to their accessibility from the extracellular environment. The increasing number of available crystal structures of these proteins in the Protein Data Bank permits an initial estimation of their structural properties.DescriptionWe have developed two web servers—TMalphaDB for α-helix bundles and TMbetaDB for β-barrels—to analyse the growing repertoire of available crystal structures of membrane proteins. TMalphaDB and TMbetaDB permit to search for these specific sequence motifs in a non-redundant structure database of transmembrane segments and quantify structural parameters such as ϕ and ψ backbone dihedral angles, χ 1 side chain torsion angle, unit bend and unit twist. Conclusions: The structural information offered by TMalphaDB and TMbetaDB permits to quantify structural distortions induced by specific sequence motifs, and to elucidate their role in the 3D structure. This specific structural information has direct implications in homology modeling of the growing sequences of membrane proteins lacking experimental structure. TMalphaDB and TMbetaDB are freely available at http://lmc.uab.cat/TMalphaDB and http://lmc.uab.cat/TMbetaDB.

Description: Background: Scoring DNA sequences against Position Weight Matrices (PWMs) is a widely adopted method to identify putative transcription factor binding sites. While common bioinformatics tools produce scores that can reflect the binding strength between a specific transcription factor and the DNA, these scores are not directly comparable between different transcription factors. Other methods, including p-value associated approaches (Touzet H, Varré J-S. Efficient and accurate p-value computation for position weight matrices. Algorithms Mol Biol. 2007;2(1510.1186):1748–7188), provide more rigorous ways to identify potential binding sites, but their results are difficult to interpret in terms of binding energy, which is essential for the modeling of transcription factor binding dynamics and enhancer activities. Results: Here, we provide two different ways to find the scaling parameter λ that allows us to infer binding energy from a PWM score. The first approach uses a PWM and background genomic sequence as input to estimate λ for a specific transcription factor, which we applied to show that λ distributions for different transcription factor families correspond with their DNA binding properties. Our second method can reliably convert λ between different PWMs of the same transcription factor, which allows us to directly compare PWMs that were generated by different approaches. Conclusion: These two approaches provide computationally efficient ways to scale PWM scores and estimate the strength of transcription factor binding sites in quantitative studies of binding dynamics. Their results are consistent with each other and previous reports in most of cases.

Description: Background: Biological pathways are descriptive diagrams of biological processes widely used for functional analysis of differentially expressed genes or proteins. Primary data analysis, such as quality control, normalisation, and statistical analysis, is often performed in scripting languages like R, Perl, and Python. Subsequent pathway analysis is usually performed using dedicated external applications. Workflows involving manual use of multiple environments are time consuming and error prone. Therefore, tools are needed that enable pathway analysis directly within the same scripting languages used for primary data analyses. Existing tools have limited capability in terms of available pathway content, pathway editing and visualisation options, and export file formats. Consequently, making the full-fledged pathway analysis tool PathVisio available from various scripting languages will benefit researchers. Results: We developed PathVisioRPC, an XMLRPC interface for the pathway analysis software PathVisio. PathVisioRPC enables creating and editing biological pathways, visualising data on pathways, performing pathway statistics, and exporting results in several image formats in multiple programming environments.We demonstrate PathVisioRPC functionalities using examples in Python. Subsequently, we analyse a publicly available NCBI GEO gene expression dataset studying tumour bearing mice treated with cyclophosphamide in R. The R scripts demonstrate how calls to existing R packages for data processing and calls to PathVisioRPC can directly work together. To further support R users, we have created RPathVisio simplifying the use of PathVisioRPC in this environment. We have also created a pathway module for the microarray data analysis portal ArrayAnalysis.org that calls the PathVisioRPC interface to perform pathway analysis. This module allows users to use PathVisio functionality online without having to download and install the software and exemplifies how the PathVisioRPC interface can be used by data analysis pipelines for functional analysis of processed genomics data. Conclusions: PathVisioRPC enables data visualisation and pathway analysis directly from within various analytical environments used for preliminary analyses. It supports the use of existing pathways from WikiPathways or pathways created using the RPC itself. It also enables automation of tasks performed using PathVisio, making it useful to PathVisio users performing repeated visualisation and analysis tasks. PathVisioRPC is freely available for academic and commercial use at http://projects.bigcat.unimaas.nl/pathvisiorpc.

Description: Background: Detecting and quantifying isoforms from RNA-seq data is an important but challenging task. The problem is often ill-posed, particularly at low coverage. One promising direction is to exploit several samples simultaneously. Results: We propose a new method for solving the isoform deconvolution problem jointly across several samples. We formulate a convex optimization problem that allows to share information between samples and that we solve efficiently. We demonstrate the benefits of combining several samples on simulated and real data, and show that our approach outperforms pooling strategies and methods based on integer programming. Conclusion: Our convex formulation to jointly detect and quantify isoforms from RNA-seq data of multiple related samples is a computationally efficient approach to leverage the hypotheses that some isoforms are likely to be present in several samples. The software and source code are available at http://cbio.ensmp.fr/flipflop.

Description: Background: The cascade computer model (CCM) was designed as a machine-learning feature platform for prediction of drug diffusivity from the mucoadhesive formulations. Three basic models (the statistical regression model, the K nearest neighbor model and the modified version of the back propagation neural network) in CCM operate sequentially in close collaboration with each other, employing the estimated value obtained from the afore-positioned base model as an input value to the next-positioned base model in the cascade.The effects of various parameters on the pharmacological efficacy of a female controlled drug delivery system (FcDDS) intended for prevention of women from HIV-1 infection were evaluated using an in vitro apparatus “Simulant Vaginal System” (SVS). We used computer simulations to explicitly examine the changes in drug diffusivity from FcDDS and determine the prognostic potency of each variable for in vivo prediction of formulation efficacy. The results obtained using the CCM approach were compared with those from individual multiple regression model. Results: CCM significantly lowered the percentage mean error (PME) and enhanced r 2 values as compared with those from the multiple regression models. It was noted that CCM generated the PME value of 21.82 at 48169 epoch iterations, which is significantly improved from the PME value of 29.91 % at 118344 epochs by the back propagation network model. The results of this study indicated that the sequential ensemble of the classifiers allowed for an accurate prediction of the domain with significantly lowered variance and considerably reduces the time required for training phase. Conclusion: CCM is accurate, easy to operate, time and cost-effective, and thus, can serve as a valuable tool for prediction of drug diffusivity from mucoadhesive formulations. CCM may yield new insights into understanding how drugs are diffused from the carrier systems and exert their efficacies under various clinical conditions.

Description: Background: In many domains, scientists build complex simulators of natural phenomena that encode their hypotheses about the underlying processes. These simulators can be deterministic or stochastic, fast or slow, constrained or unconstrained, and so on. Optimizing the simulators with respect to a set of parameter values is common practice, resulting in a single parameter setting that minimizes an objective subject to constraints. Results: We propose algorithms for post optimization posterior evaluation (POPE) of simulators. The algorithms compute and visualize all simulations that can generate results of the same or better quality than the optimum, subject to constraints. These optimization posteriors are desirable for a number of reasons among which are easy interpretability, automatic parameter sensitivity and correlation analysis, and posterior predictive analysis. Our algorithms are simple extensions to an existing simulation-based inference framework called approximate Bayesian computation. POPE is applied two biological simulators: a fast and stochastic simulator of stem-cell cycling and a slow and deterministic simulator of tumor growth patterns. Conclusions: POPE allows the scientist to explore and understand the role that constraints, both on the input and the output, have on the optimization posterior. As a Bayesian inference procedure, POPE provides a rigorous framework for the analysis of the uncertainty of an optimal simulation parameter setting.

Description: Background: Estimating the phylogenetic position of bacterial and archaeal organisms by genetic sequence comparisons is considered as the gold-standard in taxonomy. This is also a way to identify the species of origin of the sequence. The quality of the reference database used in such analyses is crucial: the database must reflect the up-to-date bacterial nomenclature and accurately indicate the species of origin of its sequences.DescriptionleBIBI QBPP is a web tool taking as input a series of nucleotide sequences belonging to one of a set of reference markers (e.g., SSU rRNA, rpoB, groEL2) and automatically retrieving closely related sequences, aligning them, and performing phylogenetic reconstruction using an approximate maximum likelihood approach. The system returns a set of quality parameters and, if possible, a suggested taxonomic assigment for the input sequences. The reference databases are extracted from GenBank and present four degrees of stringency, from the “superstringent” degree (one type strain per species) to the loosely parsed degree (“lax” database). A set of one hundred to more than a thousand sequences may be analyzed at a time. The speed of the process has been optimized through careful hardware selection and database design. Conclusion: leBIBI QBPP is a powerful tool helping biologists to position bacterial or archaeal sequence commonly used markers in a phylogeny. It is a diagnostic tool for clinical, industrial and environmental microbiology laboratory, as well as an exploratory tool for more specialized laboratories. Its main advantages, relatively to comparable systems are: i) the use of a broad set of databases covering diverse markers with various degrees of stringency; ii) the use of an approximate Maximum Likelihood approach for phylogenetic reconstruction; iii) a speed compatible with on-line usage; and iv) providing fully documented results to help the user in decision making.

Description: Background: Despite the tremendous drop in the cost of nucleotide sequencing in recent years, many research projects still utilize sequencing of pools containing multiple samples for the detection of sequence variants as a cost saving measure. Various software tools exist to analyze these pooled sequence data, yet little has been reported on the relative accuracy and ease of use of these different programs. Results: In this manuscript we evaluate five different variant detection programs—The Genome Analysis Toolkit (GATK), CRISP, LoFreq, VarScan, and SNVer—with regard to their ability to detect variants in synthetically pooled Illumina sequencing data, by creating simulated pooled binary alignment/map (BAM) files using single-sample sequencing data from varying numbers of previously characterized samples at varying depths of coverage per sample. We report the overall runtimes and memory usage of each program, as well as each program’s sensitivity and specificity to detect known true variants. Conclusions: GATK, CRISP, and LoFreq all gave balanced accuracy of 80 % or greater for datasets with varying per-sample depth of coverage and numbers of samples per pool. VarScan and SNVer generally had balanced accuracy lower than 80 %. CRISP and LoFreq required up to four times less computational time and up to ten times less physical memory than GATK did, and without filtering, gave results with the highest sensitivity. VarScan and SNVer had generally lower false positive rates, but also significantly lower sensitivity than the other three programs.

Description: Background: Spirulina (Arthrospira) platensis is the only cyanobacterium that in addition to being studied at the molecular level and subjected to gene manipulation, can also be mass cultivated in outdoor ponds for commercial use as a food supplement. Thus, encountering environmental changes, including temperature stresses, is common during the mass production of Spirulina. The use of cyanobacteria as an experimental platform, especially for photosynthetic gene manipulation in plants and bacteria, is becoming increasingly important. Understanding the mechanisms and protein-protein interaction networks that underlie low- and high-temperature responses is relevant to Spirulina mass production. To accomplish this goal, high-throughput techniques such as OMICs analyses are used. Thus, large datasets must be collected, managed and subjected to information extraction. Therefore, databases including (i) proteomic analysis and protein-protein interaction (PPI) data and (ii) domain/motif visualization tools are required for potential use in temperature response models for plant chloroplasts and photosynthetic bacteria.DescriptionsA web-based repository was developed including an embedded database, SpirPro, and tools for network visualization. Proteome data were analyzed integrated with protein-protein interactions and/or metabolic pathways from KEGG. The repository provides various information, ranging from raw data (2D-gel images) to associated results, such as data from interaction and/or pathway analyses. This integration allows in silico analyses of protein-protein interactions affected at the metabolic level and, particularly, analyses of interactions between and within the affected metabolic pathways under temperature stresses for comparative proteomic analysis. The developed tool, which is coded in HTML with CSS/JavaScript and depicted in Scalable Vector Graphics (SVG), is designed for interactive analysis and exploration of the constructed network. SpirPro is publicly available on the web at http://spirpro.sbi.kmutt.ac.th. Conclusions: SpirPro is an analysis platform containing an integrated proteome and PPI database that provides the most comprehensive data on this cyanobacterium at the systematic level. As an integrated database, SpirPro can be applied in various analyses, such as temperature stress response networking analysis in cyanobacterial models and interacting domain-domain analysis between proteins of interest.

Description: Background: The knowledge of the spatial organisation of the chromatin fibre in cell nuclei helps researchers to understand the nuclear machinery that regulates dna activity. Recent experimental techniques of the type Chromosome Conformation Capture (3c, or similar) provide high-resolution, high-throughput data consisting in the number of times any possible pair of dna fragments is found to be in contact, in a certain population of cells. As these data carry information on the structure of the chromatin fibre, several attempts have been made to use them to obtain high-resolution 3d reconstructions of entire chromosomes, or even an entire genome. The techniques proposed treat the data in different ways, possibly exploiting physical-geometric chromatin models. One popular strategy is to transform contact data into Euclidean distances between pairs of fragments, and then solve a classical distance-to-geometry problem. Results: We developed and tested a reconstruction technique that does not require translating contacts into distances, thus avoiding a number of related drawbacks. Also, we introduce a geometrical chromatin chain model that allows us to include sound biochemical and biological constraints in the problem. This model can be scaled at different genomic resolutions, where the structures of the coarser models are influenced by the reconstructions at finer resolutions. The search in the solution space is then performed by a classical simulated annealing, where the model is evolved efficiently through quaternion operators. The presence of appropriate constraints permits the less reliable data to be overlooked, so the result is a set of plausible chromatin configurations compatible with both the data and the prior knowledge. Conclusions: To test our method, we obtained a number of 3d chromatin configurations from hi-c data available in the literature for the long arm of human chromosome 1, and validated their features against known properties of gene density and transcriptional activity. Our results are compatible with biological features not introduced a priori in the problem: structurally different regions in our reconstructions highly correlate with functionally different regions as known from literature and genomic repositories.

Description: Background: The traditional method used to estimate tree biomass is allometry. In this method, models are tested and equations fitted by regression usually applying ordinary least squares, though other analogous methods are also used for this purpose. Due to the nature of tree biomass data, the assumptions of regression are not always accomplished, bringing uncertainties to the inferences. This article demonstrates that the Data Mining (DM) technique can be used as an alternative to traditional regression approach to estimate tree biomass in the Atlantic Forest, providing better results than allometry, and demonstrating simplicity, versatility and flexibility to apply to a wide range of conditions. Results: Various DM approaches were examined regarding distance, number of neighbors and weighting, by using 180 trees coming from environmental restoration plantations in the Atlantic Forest biome. The best results were attained using the Chebishev distance, 1/d weighting and 5 neighbors. Increasing number of neighbors did not improve estimates. We also analyze the effect of the size of data set and number of variables in the results. The complete data set and the maximum number of predicting variables provided the best fitting. We compare DM to Schumacher-Hall model and the results showed a gain of up to 16.5 % in reduction of the standard error of estimate. Conclusion: It was concluded that Data Mining can provide accurate estimates of tree biomass and can be successfully used for this purpose in environmental restoration plantations in the Atlantic Forest. This technique provides lower standard error of estimate than the Schumacher-Hall model and has the advantage of not requiring some statistical assumptions as do the regression models. Flexibility, versatility and simplicity are attributes of DM that corroborates its great potential for similar applications.

Description: Background: Motivated by the general need to identify and classify species based on molecular evidence, genome comparisons have been proposed that are based on measuring mostly Euclidean distances between Chaos Game Representation (CGR) patterns of genomic DNA sequences. Results: We provide, on an extensive dataset and using several different distances, confirmation of the hypothesis that CGR patterns are preserved along a genomic DNA sequence, and are different for DNA sequences originating from genomes of different species. This finding lends support to the theory that CGRs of genomic sequences can act as graphic genomic signatures. In particular, we compare the CGR patterns of over five hundred different 150,000 bp genomic sequences spanning one complete chromosome from each of six organisms, representing all kingdoms of life: H. sapiens (Animalia; chromosome 21), S. cerevisiae (Fungi; chromosome 4), A. thaliana (Plantae; chromosome 1), P. falciparum (Protista; chromosome 14), E. coli (Bacteria - full genome), and P. furiosus (Archaea - full genome). To maximize the diversity within each species, we also analyze the interrelationships within a set of over five hundred 150,000 bp genomic sequences sampled from the entire aforementioned genomes. Lastly, we provide some preliminary evidence of this method’s ability to classify genomic DNA sequences at lower taxonomic levels by comparing sequences sampled from the entire genome of H. sapiens (class Mammalia, order Primates) and of M. musculus (class Mammalia, order Rodentia), for a total length of approximately 174 million basepairs analyzed. We compute pairwise distances between CGRs of these genomic sequences using six different distances, and construct Molecular Distance Maps, which visualize all sequences as points in a two-dimensional or three-dimensional space, to simultaneously display their interrelationships. Conclusion: Our analysis confirms, for this dataset, that CGR patterns of DNA sequences from the same genome are in general quantitatively similar, while being different for DNA sequences from genomes of different species. Our assessment of the performance of the six distances analyzed uses three different quality measures and suggests that several distances outperform the Euclidean distance, which has so far been almost exclusively used for such studies.

Description: Background: Next-generation sequencing (NGS) has greatly facilitated metagenomic analysis but also raised new challenges for metagenomic DNA sequence assembly, owing to its high-throughput nature and extremely short reads generated by sequencers such as Illumina. To date, how to generate a high-quality draft assembly for metagenomic sequencing projects has not been fully addressed. Results: We conducted a comprehensive assessment on state-of-the-art de novo assemblers and revealed that the performance of each assembler depends critically on the sequencing depth. To address this problem, we developed a pipeline named InteMAP to integrate three assemblers, ABySS, IDBA-UD and CABOG, which were found to complement each other in assembling metagenomic sequences. Making a decision of which assembling approaches to use according to the sequencing coverage estimation algorithm for each short read, the pipeline presents an automatic platform suitable to assemble real metagenomic NGS data with uneven coverage distribution of sequencing depth. By comparing the performance of InteMAP with current assemblers on both synthetic and real NGS metagenomic data, we demonstrated that InteMAP achieves better performance with a longer total contig length and higher contiguity, and contains more genes than others. Conclusions: We developed a de novo pipeline, named InteMAP, that integrates existing tools for metagenomics assembly. The pipeline outperforms previous assembly methods on metagenomic assembly by providing a longer total contig length, a higher contiguity and covering more genes. InteMAP, therefore, could potentially be a useful tool for the research community of metagenomics.

Description: Background: Conventional pairwise sequence comparison software algorithms are being used to process much larger datasets than they were originally designed for. This can result in processing bottlenecks that limit software capabilities or prevent full use of the available hardware resources. Overcoming the barriers that limit the efficient computational analysis of large biological sequence datasets by retrofitting existing algorithms or by creating new applications represents a major challenge for the bioinformatics community. Results: We have developed C libraries for pairwise sequence comparison within diverse architectures, ranging from commodity systems to high performance and cloud computing environments. Exhaustive tests were performed using different datasets of closely- and distantly-related sequences that span from small viral genomes to large mammalian chromosomes. The tests demonstrated that our solution is capable of generating high quality results with a linear-time response and controlled memory consumption, being comparable or faster than the current state-of-the-art methods. Conclusions: We have addressed the problem of pairwise and all-versus-all comparison of large sequences in general, greatly increasing the limits on input data size. The approach described here is based on a modular out-of-core strategy that uses secondary storage to avoid reaching memory limits during the identification of High-scoring Segment Pairs (HSPs) between the sequences under comparison. Software engineering concepts were applied to avoid intermediate result re-calculation, to minimise the performance impact of input/output (I/O) operations and to modularise the process, thus enhancing application flexibility and extendibility. Our computationally-efficient approach allows tasks such as the massive comparison of complete genomes, evolutionary event detection, the identification of conserved synteny blocks and inter-genome distance calculations to be performed more effectively.

Description: Background: Selective pressures at the DNA level shape genes into profiles consisting of patterns of rapidly evolving sites and sites withstanding change. These profiles remain detectable even when protein sequences become extensively diverged. A common task in molecular biology is to infer functional, structural or evolutionary relationships by querying a database using an algorithm. However, problems arise when sequence similarity is low. This study presents an algorithm that uses the evolutionary rate at codon sites, the dN/dS (ω) parameter, coupled to a substitution matrix as an alignment metric for detecting distantly related proteins. The algorithm, called BLOSUM-FIRE couples a newer and improved version of the original FIRE ( F unctional I nference using R ates of E volution) algorithm with an amino acid substitution matrix in a dynamic scoring function. The enigmatic hepatitis B virus X protein was used as a test case for BLOSUM-FIRE and its associated database EvoDB. Results: The evolutionary rate based approach was coupled with a conventional BLOSUM substitution matrix. The two approaches are combined in a dynamic scoring function, which uses the selective pressure to score aligned residues. The dynamic scoring function is based on a coupled additive approach that scores aligned sites based on the level of conservation inferred from the ω values. Evaluation of the accuracy of this new implementation, BLOSUM-FIRE, using MAFFT alignment as reference alignments has shown that it is more accurate than its predecessor FIRE. Comparison of the alignment quality with widely used algorithms (MUSCLE, T-COFFEE, and CLUSTAL Omega) revealed that the BLOSUM-FIRE algorithm performs as well as conventional algorithms. Its main strength lies in that it provides greater potential for aligning divergent sequences and addresses the problem of low specificity inherent in the original FIRE algorithm. The utility of this algorithm is demonstrated using the Hepatitis B virus X (HBx) protein, a protein of unknown function, as a test case. Conclusion: This study describes the utility of an evolutionary rate based approach coupled to the BLOSUM62 amino acid substitution matrix in inferring protein domain function. We demonstrate that such an approach is robust and performs as well as an array of conventional algorithms.

Description: Background: In structural bioinformatics, there is an increasing interest in identifying and understanding the evolution of local protein structures regarded as key structural or functional protein building blocks. A central need is then to compare these, possibly short, fragments by measuring efficiently and accurately their (dis)similarity. Progress towards this goal has given rise to scores enabling to assess the strong similarity of fragments. Yet, there is still a lack of more progressive scores, with meaningful intermediate values, for the comparison, retrieval or clustering of distantly related fragments. Results: We introduce here the Amplitude Spectrum Distance (ASD), a novel way of comparing protein fragments based on the discrete Fourier transform of their C α distance matrix. Defined as the distance between their amplitude spectra, ASD can be computed efficiently and provides a parameter-free measure of the global shape dissimilarity of two fragments. ASD inherits from nice theoretical properties, making it tolerant to shifts, insertions, deletions, circular permutations or sequence reversals while satisfying the triangle inequality. The practical interest of ASD with respect to RMSD, RMSD d , BC and TM scores is illustrated through zinc finger retrieval experiments and concrete structure examples. The benefits of ASD are also illustrated by two additional clustering experiments: domain linkers fragments and complementarity-determining regions of antibodies. Conclusions: Taking advantage of the Fourier transform to compare fragments at a global shape level, ASD is an objective and progressive measure taking into account the whole fragments. Its practical computation time and its properties make ASD particularly relevant for applications requiring meaningful measures on distantly related protein fragments, such as similar fragments retrieval asking for high recalls as shown in the experiments, or for any application taking also advantage of triangle inequality, such as fragments clustering.ASD program and source code are freely available at: http://www.irisa.fr/dyliss/public/ASD/.

Description: Background: Identifying periodically expressed genes across different processes (e.g. the cell and metabolic cycles, circadian rhythms, etc) is a central problem in computational biology. Biological time series may contain (multiple) unknown signal shapes of systemic relevance, imperfections like noise, damping, and trending, or limited sampling density. While there exist methods for detecting periodicity, their design biases (e.g. toward a specific signal shape) can limit their applicability in one or more of these situations. Methods: We present in this paper a novel method, SW1PerS, for quantifying periodicity in time series in a shape-agnostic manner and with resistance to damping. The measurement is performed directly, without presupposing a particular pattern, by evaluating the circularity of a high-dimensional representation of the signal. SW1PerS is compared to other algorithms using synthetic data and performance is quantified under varying noise models, noise levels, sampling densities, and signal shapes. Results on biological data are also analyzed and compared. Results: On the task of periodic/not-periodic classification, using synthetic data, SW1PerS outperforms all other algorithms in the low-noise regime. SW1PerS is shown to be the most shape-agnostic of the evaluated methods, and the only one to consistently classify damped signals as highly periodic. On biological data, and for several experiments, the lists of top 10% genes ranked with SW1PerS recover up to 67% of those generated with other popular algorithms. Moreover, the list of genes from data on the Yeast metabolic cycle which are highly-ranked only by SW1PerS, contains evidently non-cosine patterns (e.g. ECM33, CDC9, SAM1,2 and MSH6) with highly periodic expression profiles. In data from the Yeast cell cycle SW1PerS identifies genes not preferred by other algorithms, hence not previously reported as periodic, but found in other experiments such as the universal growth rate response of Slavov. These genes are BOP3, CDC10, YIL108W, YER034W, MLP1, PAC2 and RTT101. Conclusions: In biological systems with low noise, i.e. where periodic signals with interesting shapes are more likely to occur, SW1PerS can be used as a powerful tool in exploratory analyses. Indeed, by having an initial set of periodic genes with a rich variety of signal types, pattern/shape information can be included in the study of systems and the generation of hypotheses regarding the structure of gene regulatory networks.

Description: Background: Two component systems (TCS) are signalling complexes manifested by a histidine kinase (receptor) and a response regulator (effector). They are the most abundant signalling pathways in prokaryotes and control a wide range of biological processes. The pairing of these two components is highly specific, often requiring costly and time-consuming experimental characterisation. Therefore, there is considerable interest in developing accurate prediction tools to lessen the burden of experimental work and cope with the ever-increasing amount of genomic information. Results: We present a novel meta-predictor, MetaPred2CS, which is based on a support vector machine. MetaPred2CS integrates six sequence-based prediction methods: in-silico two-hybrid, mirror-tree, gene fusion, phylogenetic profiling, gene neighbourhood, and gene operon. To benchmark MetaPred2CS, we also compiled a novel high-quality training dataset of experimentally deduced TCS protein pairs for k-fold cross validation, to act as a gold standard for TCS partnership predictions. Combining individual predictions using MetaPred2CS improved performance when compared to the individual methods and in comparison with a current state-of-the-art meta-predictor. Conclusion: We have developed MetaPred2CS, a support vector machine-based metapredictor for prokaryotic TCS protein pairings. Central to the success of MetaPred2CS is a strategy of integrating individual predictors that improves the overall prediction accuracy, with the in-silico two-hybrid method contributing most to performance. MetaPred2CS outperformed other available systems in our benchmark tests, and is available online at http://metapred2cs.ibers.aber.ac.uk, along with our gold standard dataset of TCS interaction pairs.

Description: Background: Technological advances have enabled the analysis of very small amounts of DNA in forensic cases. However, the DNA profiles from such evidence are frequently incomplete and can contain contributions from multiple individuals. The complexity of such samples confounds the assessment of the statistical weight of such evidence. One approach to account for this uncertainty is to use a likelihood ratio framework to compare the probability of the evidence profile under different scenarios. While researchers favor the likelihood ratio framework, few open-source software solutions with a graphical user interface implementing these calculations are available for practicing forensic scientists. Results: To address this need, we developed Lab Retriever, an open-source, freely available program that forensic scientists can use to calculate likelihood ratios for complex DNA profiles. Lab Retriever adds a graphical user interface, written primarily in JavaScript, on top of a C++ implementation of the previously published R code of Balding. We redesigned parts of the original Balding algorithm to improve computational speed. In addition to incorporating a probability of allelic drop-out and other critical parameters, Lab Retriever computes likelihood ratios for hypotheses that can include up to four unknown contributors to a mixed sample. These computations are completed nearly instantaneously on a modern PC or Mac computer. Conclusions: Lab Retriever provides a practical software solution to forensic scientists who wish to assess the statistical weight of evidence for complex DNA profiles. Executable versions of the program are freely available for Mac OSX and Windows operating systems.

Description: Background: One of the most important application spectrums of transcriptomic data is cancer phenotype classification. Many characteristics of transcriptomic data, such as redundant features and technical artifacts, make over-fitting commonplace. Promising classification results often fail to generalize across datasets with different sources, platforms, or preprocessing. Recently a novel differential network rank conservation (DIRAC) algorithm to characterize cancer phenotypes using transcriptomic data. DIRAC is a member of a family of algorithms that have shown useful for disease classification based on the relative expression of genes. Combining the robustness of this family’s simple decision rules with known biological relationships, this systems approach identifies interpretable, yet highly discriminate networks. While DIRAC has been briefly employed for several classification problems in the original paper, the potentials of DIRAC in cancer phenotype classification, and especially robustness against artifacts in transcriptomic data have not been fully characterized yet. Results: In this study we thoroughly investigate the potentials of DIRAC by applying it to multiple datasets, and examine the variations in classification performances when datasets are (i) treated and untreated for batch effect; (ii) preprocessed with different techniques. We also propose the first DIRAC-based classifier to integrate multiple networks. We show that the DIRAC-based classifier is very robust in the examined scenarios. To our surprise, the trained DIRAC-based classifier even translated well to a dataset with different biological characteristics in the presence of substantial batch effects that, as shown here, plagued the standard expression value based classifier. In addition, the DIRAC-based classifier, because of the integrated biological information, also suggests pathways to target in specific subtypes, which may enhance the establishment of personalized therapy in diseases such as pediatric AML. In order to better comprehend the prediction power of the DIRAC-based classifier in general, we also performed classifications using publicly available datasets from breast and lung cancer. Furthermore, multiple well-known classification algorithms were utilized to create an ideal test bed for comparing the DIRAC-based classifier with the standard gene expression value based classifier. We observed that the DIRAC-based classifier greatly outperforms its rival. Conclusions: Based on our experiments with multiple datasets, we propose that DIRAC is a promising solution to the lack of generalizability in classification efforts that uses transcriptomic data. We believe that superior performances presented in this study may motivate other to initiate a new aline of research to explore the untapped power of DIRAC in a broad range of cancer types.

Description: Background: Searching for two-dimensional (2D) structural similarities is a useful tool to identify new active compounds in drug-discovery programs. However, as 2D similarity measures neglect important structural and functional features, similarity by 2D might be underestimated. In the present study, we used combined 2D and three-dimensional (3D) similarity comparisons to reveal possible new functions and/or side-effects of known bioactive compounds. Results: We utilised more than 10,000 compounds from the SuperTarget database with known inhibition values for twelve different anti-cancer targets. We performed all-against-all comparisons resulting in 2D similarity landscapes. Among the regions with low 2D similarity scores are inhibitors of vascular endothelial growth factor receptor (VEGFR) and inhibitors of poly ADP-ribose polymerase (PARP). To demonstrate that 3D landscape comparison can identify similarities, which are untraceable in 2D similarity comparisons, we analysed this region in more detail. This 3D analysis showed the unexpected structural similarity between inhibitors of VEGFR and inhibitors of PARP. Among the VEGFR inhibitors that show similarities to PARP inhibitors was Vatalanib, an oral “multi-targeted” small molecule protein kinase inhibitor being studied in phase-III clinical trials in cancer therapy. An in silico docking simulation and an in vitro HT universal colorimetric PARP assay confirmed that the VEGFR inhibitor Vatalanib exhibits off-target activity as a PARP inhibitor, broadening its mode of action. Conclusion: In contrast to the 2D-similarity search, the 3D-similarity landscape comparison identifies new functions and side effects of the known VEGFR inhibitor Vatalanib.

Description: Background: Sequencing technologies provide a wealth of details in terms of genes, expression, splice variants, polymorphisms, and other features. A standard for sequencing analysis pipelines is to put genomic or transcriptomic features into a context of known functional information, but the relationships between ontology terms are often ignored. For RNA-Seq, considering genes and their genetic variants at the group level enables a convenient way to both integrate annotation data and detect small coordinated changes between experimental conditions, a known caveat of gene level analyses. Results: We introduce the high throughput data integration tool, htsint, as an extension to the commonly used gene set enrichment frameworks. The central aim of htsint is to compile annotation information from one or more taxa in order to calculate functional distances among all genes in a specified gene space. Spectral clustering is then used to partition the genes, thereby generating functional modules. The gene space can range from a targeted list of genes, like a specific pathway, all the way to an ensemble of genomes. Given a collection of gene sets and a count matrix of transcriptomic features (e.g. expression, polymorphisms), the gene sets produced by htsint can be tested for ‘enrichment’ or conditional differences using one of a number of commonly available packages. Conclusion: The database and bundled tools to generate functional modules were designed with sequencing pipelines in mind, but the toolkit nature of htsint allows it to also be used in other areas of genomics. The software is freely available as a Python library through GitHub at https://github.com/ajrichards/htsint.

Description: Background: In the past decade, the identification of gene co-expression has become a routine part of the analysis of high-dimensional microarray data. Gene co-expression, which is mostly detected via the Pearson correlation coefficient, has played an important role in the discovery of molecular pathways and networks. Unfortunately, the presence of systematic noise in high-dimensional microarray datasets corrupts estimates of gene co-expression. Removing systematic noise from microarray data is therefore crucial. Many cleaning approaches for microarray data exist, however these methods are aimed towards improving differential expression analysis and their performances have been primarily tested for this application. To our knowledge, the performances of these approaches have never been systematically compared in the context of gene co-expression estimation. Results: Using simulations we demonstrate that standard cleaning procedures, such as background correction and quantile normalization, fail to adequately remove systematic noise that affects gene co-expression and at times further degrade true gene co-expression. Instead we show that a global version of removal of unwanted variation (RUV), a data-driven approach, removes systematic noise but also allows the estimation of the true underlying gene-gene correlations. We compare the performance of all noise removal methods when applied to five large published datasets on gene expression in the human brain. RUV retrieves the highest gene co-expression values for sets of genes known to interact, but also provides the greatest consistency across all five datasets. We apply the method to prioritize epileptic encephalopathy candidate genes. Conclusions: Our work raises serious concerns about the quality of many published gene co-expression analyses. RUV provides an efficient and flexible way to remove systematic noise from high-dimensional microarray datasets when the objective is gene co-expression analysis. The RUV method as applicable in the context of gene-gene correlation estimation is available as a BioconductoR-package: RUVcorr.

Description: Background: The detection of the glomeruli is a key step in the histopathological evaluation of microscopic images of the kidneys. However, the task of automatic detection of the glomeruli poses challenges owing to the differences in their sizes and shapes in renal sections as well as the extensive variations in their intensities due to heterogeneity in immunohistochemistry staining.Although the rectangular histogram of oriented gradients (Rectangular HOG) is a widely recognized powerful descriptor for general object detection, it shows many false positives owing to the aforementioned difficulties in the context of glomeruli detection. Results: A new descriptor referred to as Segmental HOG was developed to perform a comprehensive detection of hundreds of glomeruli in images of whole kidney sections. The new descriptor possesses flexible blocks that can be adaptively fitted to input images in order to acquire robustness for the detection of the glomeruli. Moreover, the novel segmentation technique employed herewith generates high-quality segmentation outputs, and the algorithm is assured to converge to an optimal solution. Consequently, experiments using real-world image data revealed that Segmental HOG achieved significant improvements in detection performance compared to Rectangular HOG. Conclusion: The proposed descriptor for glomeruli detection presents promising results, and it is expected to be useful in pathological evaluation.

Description: Background: Numerous methods are available to profile several epigenetic marks, providing data with different genome coverage and resolution. Large epigenomic datasets are then generated, and often combined with other high-throughput data, including RNA-seq, ChIP-seq for transcription factors (TFs) binding and DNase-seq experiments. Despite the numerous computational tools covering specific steps in the analysis of large-scale epigenomics data, comprehensive software solutions for their integrative analysis are still missing. Multiple tools must be identified and combined to jointly analyze histone marks, TFs binding and other -omics data together with DNA methylation data, complicating the analysis of these data and their integration with publicly available datasets. Results: To overcome the burden of integrating various data types with multiple tools, we developed two companion R/Bioconductor packages. The former, methylPipe, is tailored to the analysis of high- or low-resolution DNA methylomes in several species, accommodating (hydroxy-)methyl-cytosines in both CpG and non-CpG sequence context. The analysis of multiple whole-genome bisulfite sequencing experiments is supported, while maintaining the ability of integrating targeted genomic data. The latter, compEpiTools, seamlessly incorporates the results obtained with methylPipe and supports their integration with other epigenomics data. It provides a number of methods to score these data in regions of interest, leading to the identification of enhancers, lncRNAs, and RNAPII stalling/elongation dynamics. Moreover, it allows a fast and comprehensive annotation of the resulting genomic regions, and the association of the corresponding genes with non-redundant GeneOntology terms. Finally, the package includes a flexible method based on heatmaps for the integration of various data types, combining annotation tracks with continuous or categorical data tracks. Conclusions: methylPipe and compEpiTools provide a comprehensive Bioconductor-compliant solution for the integrative analysis of heterogeneous epigenomics data. These packages are instrumental in providing biologists with minimal R skills a complete toolkit facilitating the analysis of their own data, or in accelerating the analyses performed by more experienced bioinformaticians.

Description: Background: The characterization of proteins in families and subfamilies, at different levels, entails the definition and use of class labels. When the adscription of a protein to a family is uncertain, or even wrong, this becomes an instance of what has come to be known as a label noise problem. Label noise has a potentially negative effect on any quantitative analysis of proteins that depends on label information. This study investigates class C of G protein-coupled receptors, which are cell membrane proteins of relevance both to biology in general and pharmacology in particular. Their supervised classification into different known subtypes, based on primary sequence data, is hampered by label noise. The latter may stem from a combination of expert knowledge limitations and the lack of a clear correspondence between labels that mostly reflect GPCR functionality and the different representations of the protein primary sequences. Results: In this study, we describe a systematic approach, using Support Vector Machine classifiers, to the analysis of G protein-coupled receptor misclassifications. As a proof of concept, this approach is used to assist the discovery of labeling quality problems in a curated, publicly accessible database of this type of proteins. We also investigate the extent to which physico-chemical transformations of the protein sequences reflect G protein-coupled receptor subtype labeling. The candidate mislabeled cases detected with this approach are externally validated with phylogenetic trees and against further trusted sources such as the National Center for Biotechnology Information, Universal Protein Resource, European Bioinformatics Institute and Ensembl Genome Browser information repositories. Conclusions: In quantitative classification problems, class labels are often by default assumed to be correct. Label noise, though, is bound to be a pervasive problem in bioinformatics, where labels may be obtained indirectly through complex, many-step similarity modelling processes. In the case of G protein-coupled receptors, methods capable of singling out and characterizing those sequences with consistent misclassification behaviour are required to minimize this problem. A systematic, Support Vector Machine-based method has been proposed in this study for such purpose. The proposed method enables a filtering approach to the label noise problem and might become a support tool for database curators in proteomics.

Description: Background: Copy number variations are important in the detection and progression of significant tumors and diseases. Recently, Whole Exome Sequencing is gaining popularity with copy number variations detection due to low cost and better efficiency. In this work, we developed VEGAWES for accurate and robust detection of copy number variations on WES data. VEGAWES is an extension to a variational based segmentation algorithm, VEGA: Variational estimator for genomic aberrations, which has previously outperformed several algorithms on segmenting array comparative genomic hybridization data. Results: We tested this algorithm on synthetic data and 100 Glioblastoma Multiforme primary tumor samples. The results on the real data were analyzed with segmentation obtained from Single-nucleotide polymorphism data as ground truth. We compared our results with two other segmentation algorithms and assessed the performance based on accuracy and time. Conclusions: In terms of both accuracy and time, VEGAWES provided better results on the synthetic data and tumor samples demonstrating its potential in robust detection of aberrant regions in the genome.

Description: Background: In the last decade, a great number of methods for reconstructing gene regulatory networks from expression data have been proposed. However, very few tools and datasets allow to evaluate accurately and reproducibly those methods. Hence, we propose here a new tool, able to perform a systematic, yet fully reproducible, evaluation of transcriptional network inference methods. Results: Our open-source and freely available Bioconductor package aggregates a large set of tools to assess the robustness of network inference algorithms against different simulators, topologies, sample sizes and noise intensities. Conclusions: The benchmarking framework that uses various datasets highlights the specialization of some methods toward network types and data. As a result, it is possible to identify the techniques that have broad overall performances.

Description: Background: We are creating software for agent-based simulation and visualization of bio-molecular processes in bacterial and eukaryotic cells. As a first example, we have built a 3-dimensional, interactive computer model of an Escherichia coli bacterium and its associated biomolecular processes. Our illustrative model focuses on the gene regulatory processes that control the expression of genes involved in the lactose operon. Prokaryo, our agent-based cell simulator, incorporates cellular structures, such as plasma membranes and cytoplasm, as well as elements of the molecular machinery, including RNA polymerase, messenger RNA, lactose permease, and ribosomes. Results: The dynamics of cellular ’agents’ are defined by their rules of interaction, implemented as finite state machines. The agents are embedded within a 3-dimensional virtual environment with simulated physical and electrochemical properties. The hybrid model is driven by a combination of (1) mathematical equations (DEQs) to capture higher-scale phenomena and (2) agent-based rules to implement localized interactions among a small number of molecular elements. Consequently, our model is able to capture phenomena across multiple spatial scales, from changing concentration gradients to one-on-one molecular interactions.We use the classic gene regulatory mechanism of the lactose operon to demonstrate our model’s resolution, visual presentation, and real-time interactivity. Our agent-based model expands on a sophisticated mathematical E. coli metabolism model, through which we highlight our model’s scientific validity. Conclusion: We believe that through illustration and interactive exploratory learning a model system like Prokaryo can enhance the general understanding and perception of biomolecular processes. Our agent-DEQ hybrid modeling approach can also be of value to conceptualize, illustrate, and—eventually—validate cell experiments in the wet lab.

Description: Background: The alignment of multiple protein sequences is one of the most commonly performed tasks in bioinformatics. In spite of considerable research and efforts that have been recently deployed for improving the performance of multiple sequence alignment (MSA) algorithms, finding a highly accurate alignment between multiple protein sequences is still a challenging problem. Results: We propose a novel and efficient algorithm called, MSAIndelFR, for multiple sequence alignment using the information on the predicted locations of IndelFRs and the computed average log–loss values obtained from IndelFR predictors, each of which is designed for a different protein fold. We demonstrate that the introduction of a new variable gap penalty function based on the predicted locations of the IndelFRs and the computed average log–loss values into the proposed algorithm substantially improves the protein alignment accuracy. This is illustrated by evaluating the performance of the algorithm in aligning sequences belonging to the protein folds for which the IndelFR predictors already exist and by using the reference alignments of the four popular benchmarks, BAliBASE 3.0, OXBENCH, PREFAB 4.0, and SABRE (SABmark 1.65). Conclusions: We have proposed a novel and efficient algorithm, the MSAIndelFR algorithm, for multiple protein sequence alignment incorporating a new variable gap penalty function. It is shown that the performance of the proposed algorithm is superior to that of the most–widely used alignment algorithms, Clustal W2, Clustal Omega, Kalign2, MSAProbs, MAFFT, MUSCLE, ProbCons and Probalign, in terms of both the sum–of–pairs and total column metrics.

Description: Background: Molecular interactions between proteins and DNA molecules underlie many cellular processes, including transcriptional regulation, chromosome replication, and nucleosome positioning. Computational analyses of protein-DNA interactions rely on experimental data characterizing known protein-DNA interactions structurally and biochemically. While many databases exist that contain either structural or biochemical data, few integrate these two data sources in a unified fashion. Such integration is becoming increasingly critical with the rapid growth of structural and biochemical data, and the emergence of algorithms that rely on the synthesis of multiple data types to derive computational models of molecular interactions.DescriptionWe have developed an integrated affinity-structure database in which the experimental and quantitative DNA binding affinities of helix-turn-helix proteins are mapped onto the crystal structures of the corresponding protein-DNA complexes. This database provides access to: (i) protein-DNA structures, (ii) quantitative summaries of protein-DNA binding affinities using position weight matrices, and (iii) raw experimental data of protein-DNA binding instances. Critically, this database establishes a correspondence between experimental structural data and quantitative binding affinity data at the single basepair level. Furthermore, we present a novel alignment algorithm that structurally aligns the protein-DNA complexes in the database and creates a unified residue-level coordinate system for comparing the physico-chemical environments at the interface between complexes. Using this unified coordinate system, we compute the statistics of atomic interactions at the protein-DNA interface of helix-turn-helix proteins. We provide an interactive website for visualization, querying, and analyzing this database, and a downloadable version to facilitate programmatic analysis. Conclusions: This database will facilitate the analysis of protein-DNA interactions and the development of programmatic computational methods that capitalize on integration of structural and biochemical datasets. The database can be accessed at http://ProteinDNA.hms.harvard.edu.

Description: Background: Available methods to simulate nucleotide or amino acid data typically use Markov models to simulate each position independently. These approaches are not appropriate to assess the performance of combinatorial and probabilistic methods that look for coevolving positions in nucleotide or amino acid sequences. Results: We have developed a web-based platform that gives a user-friendly access to two phylogenetic-based methods implementing the Coev model: the evaluation of coevolving scores and the simulation of coevolving positions. We have also extended the capabilities of the Coev model to allow for the generalization of the alphabet used in the Markov model, which can now analyse both nucleotide and amino acid data sets. The simulation of coevolving positions is novel and builds upon the developments of the Coev model. It allows user to simulate pairs of dependent nucleotide or amino acid positions. Conclusions: The main focus of our paper is the new simulation method we present for coevolving positions. The implementation of this method is embedded within the web platform Coev-web that is freely accessible at http://coev.vital-it.ch/, and was tested in most modern web browsers.

Description: Background: Identification of one or several disease causing variant(s) from the large collection of variants present in an individual is often achieved by the sequential use of heuristic filters. The recent development of whole exome sequencing enrichment designs for several non-model species created the need for a species-independent, fast and versatile analysis tool, capable of tackling a wide variety of standard and more complex inheritance models. With this aim, we developed “Mendelian”, an R-package that can be used for heuristic variant filtering. Results: The R-package Mendelian offers fast and convenient filters to analyze putative variants for both recessive and dominant models of inheritance, with variable degrees of penetrance and detectance. Analysis of trios is supported. Filtering against variant databases and annotation of variants is also included. This package is not species specific and supports parallel computation. We validated this package by reanalyzing data from a whole exome sequencing experiment on intellectual disability in humans. In a second example, we identified the mutations responsible for coat color in the dog. This is the first example of whole exome sequencing without prior mapping in the dog. Conclusion: We developed an R-package that enables the identification of disease-causing variants from the long list of variants called in sequencing experiments. The software and a detailed manual are available at https://github.com/BartBroeckx/Mendelian.

Description: Background: Identification of bacteria may be based on sequencing and molecular analysis of a specific locus such as 16S rRNA, or a set of loci such as in multilocus sequence typing. In the near future, healthcare institutions and routine diagnostic microbiology laboratories may need to sequence the entire genome of microbial isolates. Therefore we have developed Reads2Type, a web-based tool for taxonomy identification based on whole bacterial genome sequence data. Results: Raw sequencing data provided by the user are mapped against a set of marker probes that are derived from currently available bacteria complete genomes. Using a dataset of 1003 whole genome sequenced bacteria from various sequencing platforms, Reads2Type was able to identify the species with 99.5 % accuracy and on the minutes time scale. Conclusions: In comparison with other tools, Reads2Type offers the advantage of not needing to transfer sequencing files, as the entire computational analysis is done on the computer of whom utilizes the web application. This also prevents data privacy issues to arise. The Reads2Type tool is available at http://www.cbs.dtu.dk/~dhany/reads2type.html.

Description: Background: In recent years, hyperspectral microscopy techniques such as infrared or Raman microscopy have been applied successfully for diagnostic purposes. In many of the corresponding studies, it is common practice to measure one and the same sample under different types of microscopes. Any joint analysis of the two image modalities requires to overlay the images, so that identical positions in the sample are located at the same coordinate in both images. This step, commonly referred to as image registration, has typically been performed manually in the lack of established automated computational registration tools. Results: We propose a corresponding registration algorithm that addresses this registration problem, and demonstrate the robustness of our approach in different constellations of microscopes. First, we deal with subregion registration of Fourier Transform Infrared (FTIR) microscopic images in whole-slide histopathological staining images. Second, we register FTIR imaged cores of tissue microarrays in their histopathologically stained counterparts, and finally perform registration of Coherent anti-Stokes Raman spectroscopic (CARS) images within histopathological staining images. Conclusions: Our validation involves a large variety of samples obtained from colon, bladder, and lung tissue on three different types of microscopes, and demonstrates that our proposed method works fully automated and highly robust in different constellations of microscopes involving diverse types of tissue samples.

Description: Background: The number of γH2AX foci per nucleus is an accepted measure of the number of DNA double-strand breaks in single cells. One of the experimental techniques for γH2AX detection in cultured cells is immunofluorescent labelling of γH2AX and nuclei followed by microscopy imaging and analysis. Results: In this study, we present the algorithm FoCo for reliable and robust automatic nuclear foci counting in single cell images. FoCo has the following advantages with respect to other software packages: i) the ability to reliably quantify even densely distributed foci, e.g., on images of cells subjected to radiation doses up to 10 Gy, ii) robustness of foci quantification in the sense of suppressing out-of-focus background signal, and iii) its simplicity. FoCo requires only 5 parameters that have to be adjusted by the user. Conclusions: FoCo is an open-source user-friendly software with GUI for individual foci counting, which is able to produce reliable and robust foci quantifications even for low signal/noise ratios and densely distributed foci.

Description: Background: Analysis of single cells in their native environment is a powerful method to address key questions in developmental systems biology. Confocal microscopy imaging of intact tissues, followed by automatic image segmentation, provides a means to conduct cytometric studies while at the same time preserving crucial information about the spatial organization of the tissue and morphological features of the cells. This technique is rapidly evolving but is still not in widespread use among research groups that do not specialize in technique development, perhaps in part for lack of tools that automate repetitive tasks while allowing experts to make the best use of their time in injecting their domain-specific knowledge. Results: Here we focus on a well-established stem cell model system, the C. elegans gonad, as well as on two other model systems widely used to study cell fate specification and morphogenesis: the pre-implantation mouse embryo and the developing mouse olfactory epithelium. We report a pipeline that integrates machine-learning-based cell detection, fast human-in-the-loop curation of these detections, and running of active contours seeded from detections to segment cells. The procedure can be bootstrapped by a small number of manual detections, and outperforms alternative pieces of software we benchmarked on C. elegans gonad datasets. Using cell segmentations to quantify fluorescence contents, we report previously-uncharacterized cell behaviors in the model systems we used. We further show how cell morphological features can be used to identify cell cycle phase; this provides a basis for future tools that will streamline cell cycle experiments by minimizing the need for exogenous cell cycle phase labels. Conclusions: High-throughput 3D segmentation makes it possible to extract rich information from images that are routinely acquired by biologists, and provides insights — in particular with respect to the cell cycle — that would be difficult to derive otherwise.

Description: Background: Inferring gene regulatory network (GRN) has been an important topic in Bioinformatics. Many computational methods infer the GRN from high-throughput expression data. Due to the presence of time delays in the regulatory relationships, High-Order Dynamic Bayesian Network (HO-DBN) is a good model of GRN. However, previous GRN inference methods assume causal sufficiency, i.e. no unobserved common cause. This assumption is convenient but unrealistic, because it is possible that relevant factors have not even been conceived of and therefore un-measured. Therefore an inference method that also handles hidden common cause(s) is highly desirable. Also, previous methods for discovering hidden common causes either do not handle multi-step time delays or restrict that the parents of hidden common causes are not observed genes. Results: We have developed a discrete HO-DBN learning algorithm that can infer also hidden common cause(s) from discrete time series expression data, with some assumptions on the conditional distribution, but is less restrictive than previous methods. We assume that each hidden variable has only observed variables as children and parents, with at least two children and possibly no parents. We also make the simplifying assumption that children of hidden variable(s) are not linked to each other. Moreover, our proposed algorithm can also utilize multiple short time series (not necessarily of the same length), as long time series are difficult to obtain. Conclusions: We have performed extensive experiments using synthetic data on GRNs of size up to 100, with up to 10 hidden nodes. Experiment results show that our proposed algorithm can recover the causal GRNs adequately given the incomplete data. Using the limited real expression data and small subnetworks of the YEASTRACT network, we have also demonstrated the potential of our algorithm on real data, though more time series expression data is needed.

Description: Background: Protein function in eukaryotic cells is often controlled in a cell cycle-dependent manner. Therefore, the correct assignment of cellular phenotypes to cell cycle phases is a crucial task in cell biology research. Nuclear proteins whose localization varies during the cell cycle are valuable and frequently used markers of cell cycle progression. Proliferating cell nuclear antigen (PCNA) is a protein which is involved in DNA replication and has cell cycle dependent properties. In this work, we present a tool to identify cell cycle phases and in particular, sub-stages of the DNA replication phase (S-phase) based on the characteristic patterns of PCNA distribution. Single time point images of PCNA-immunolabeled cells are acquired using confocal and widefield fluorescence microscopy. In order to discriminate different cell cycle phases, an optimized processing pipeline is proposed. For this purpose, we provide an in-depth analysis and selection of appropriate features for classification, an in-depth evaluation of different classification algorithms, as well as a comparative analysis of classification performance achieved with confocal versus widefield microscopy images. Results: We show that the proposed processing chain is capable of automatically classifying cell cycle phases in PCNA-immunolabeled cells from single time point images, independently of the technique of image acquisition. Comparison of confocal and widefield images showed that for the proposed approach, the overall classification accuracy is slightly higher for confocal microscopy images. Conclusion: Overall, automated identification of cell cycle phases and in particular, sub-stages of the DNA replication phase (S-phase) based on the characteristic patterns of PCNA distribution, is feasible for both confocal and widefield images.

Description: Background: Several methods exist for the prediction of precursor miRNAs (pre-miRNAs) in genomic or sRNA-seq (small RNA sequences) data produced by NGS (Next Generation Sequencing). One key information used for this task is the characteristic hairpin structure adopted by pre-miRNAs, that in general are identified using RNA folders whose complexity is cubic in the size of the input. The vast majority of pre-miRNA predictors then rely on further information learned from previously validated miRNAs from the same or a closely related genome for the final prediction of new miRNAs. With this paper, we wished to address three main issues. The first was methodological and aimed at obtaining a more time-efficient predictor, however without losing in accuracy which represented a second issue. We indeed aimed at better predicting miRNAs at a genome scale, but also from sRNAseq data where in some cases, notably of plants, the current folding methods often infer the wrong structure. The third issue is related to the fact that it is important to rely as little as possible on previously recorded examples of miRNAs. We therefore also sought a method that is less dependent on previous miRNA records. Results: As concerns the first and second issues, we present a novel alternative to a classical folder based on a thermodynamic Nearest-Neighbour (NN) model for computing the free energy and predicting the classical hairpin structure of a pre-miRNA. We show that the free energies thus computed correlate well with those of RNAfold. This novel method, called Mirinho, has quadratic instead of cubic complexity and is much more efficient also in practice. When applied to sRNAseq data of plants, it gives in general better results than classical folders. On the third issue, we show that Mirinho, which uses as only knowledge the length of the loops and stem-arms and the free energy of the pre-miRNA hairpin, compares well with algorithms that require more information. The results, obtained with different datasets, are indeed similar to those of other approaches with which such a comparison was possible. These needed to be publicly available softwares that could be used on a large input. In some cases, Mirinho is even better in terms of sensitivity or precision. Conclusion: We provide a simpler and much faster method with very reasonable sensitivity and precision, which can be applied without special adaptation to the prediction of both animal and plant pre-miRNAs, using as input either genomic sequences or sRNA-seq data.

Description: Background: The Electron Microscopy DataBank (EMDB) is growing rapidly, accumulating biological structural data obtained mainly by electron microscopy and tomography, which are emerging techniques for determining large biomolecular complex and subcellular structures. Together with the Protein Data Bank (PDB), EMDB is becoming a fundamental resource of the tertiary structures of biological macromolecules. To take full advantage of this indispensable resource, the ability to search the database by structural similarity is essential. However, unlike high-resolution structures stored in PDB, methods for comparing low-resolution electron microscopy (EM) density maps in EMDB are not well established. Results: We developed a computational method for efficiently searching low-resolution EM maps. The method uses a compact fingerprint representation of EM maps based on the 3D Zernike descriptor, which is derived from a mathematical series expansion for EM maps that are considered as 3D functions. The method is implemented in a web server named EM-SURFER, which allows users to search against the entire EMDB in real-time. EM-SURFER compares the global shapes of EM maps. Examples of search results from different types of query structures are discussed. Conclusions: We developed EM-SURFER, which retrieves structurally relevant matches for query EM maps from EMDB within seconds. The unique capability of EM-SURFER to detect 3D shape similarity of low-resolution EM maps should prove invaluable in structural biology.

Description: Background: Both differential expression (DE) and differential co-expression (DC) analyses are appreciated as useful tools in understanding gene regulation related to complex diseases. The performance of integrating DE and DC, however, remains unexplored. Results: In this study, we proposed a novel analytical approach called DECODE ( D iffer e ntial Co -expression and D ifferential E xpression) to integrate DC and DE analyses of gene expression data. DECODE allows one to study the combined features of DC and DE of each transcript between two conditions. By incorporating information of the dependency between DC and DE variables, two optimal thresholds for defining substantial change in expression and co-expression are systematically defined for each gene based on chi-square maximization. By using these thresholds, genes can be categorized into four groups with either high or low DC and DE characteristics. In this study, DECODE was applied to a large breast cancer microarray data set consisted of two thousand tumor samples. By identifying genes with high DE and high DC, we demonstrated that DECODE could improve the detection of some functional gene sets such as those related to immune system, metastasis, lipid and glucose metabolism. Further investigation on the identified genes and the associated functional pathways would provide an additional level of understanding of complex disease mechanism. Conclusions: By complementing the recent DC and the traditional DE analyses, DECODE is a valuable methodology for investigating biological functions of genes exhibiting disease-associated DE and DC combined characteristics, which may not be easily revealed through DC or DE approach alone.DECODE is available at the Comprehensive R Archive Network (CRAN): http://cran.r-project.org/web/packages/decode/index.html.

Description: Background: Identifying protein complexes is an essential task for understanding the mechanisms of proteins in cells. Many computational approaches have thus been developed to identify protein complexes in protein-protein interaction (PPI) networks. Regarding the information that can be adopted by computational approaches to identify protein complexes, in addition to the graph topology of PPI network, the consideration of functional information of proteins has been becoming popular recently. Relevant approaches perform their tasks by relying on the idea that proteins in the same protein complex may be associated with similar functional information. However, we note from our previous researches that for most protein complexes their proteins are only similar in specific subsets of categories of functional information instead of the entire set. Hence, if the preference of each functional category can also be taken into account when identifying protein complexes, the accuracy will be improved. Results: To implement the idea, we first introduce a preference vector for each of proteins to quantitatively indicate the preference of each functional category when deciding the protein complex this protein belongs to. Integrating functional preferences of proteins and the graph topology of PPI network, we formulate the problem of identifying protein complexes into a constrained optimization problem, and we propose the approach DCAFP to address it. For performance evaluation, we have conducted extensive experiments with several PPI networks from the species of Saccharomyces cerevisiae and Human and also compared DCAFP with state-of-the-art approaches in the identification of protein complexes. The experimental results show that considering the integration of functional preferences and dense structures improved the performance of identifying protein complexes, as DCAFP outperformed the other approaches for most of PPI networks based on the assessments of independent measures of f-measure, Accuracy and Maximum Matching Rate. Furthermore, the function enrichment experiments indicated that DCAFP identified more protein complexes with functional significance when compared with approaches, such as PCIA, that also utilize the functional information. Conclusions: According to the promising performance of DCAFP, the integration of functional preferences and dense structures has made it possible to identify protein complexes more accurately and significantly.

Description: Background: Weighted and un-weighted protein residue networks can predict key functional residues in proteins based on the closeness centrality C and betweenness centrality B values for each residue. A static snapshot of the protein structure, and a cutoff distance, are used to define edges between the network nodes. In this work we apply the weighted network approach to study the β-Lactamase Inhibitory Protein (BLIP). Joint recurrences extracted from molecular dynamics MD trajectory positions of the protein residue carbon alpha atoms are used to define edge weights between nodes, and no cutoff distance is used. The results for B and C from our approach are compared with those extracted from an un-weighted network, and a weighted network that uses interatomic contacts to define edge weights between nodes, respectively. Results: The joint recurrence weighted network approach performs well in pointing out key protein residues. Furthermore, it seems to emphasize residues with medium to high relative solvent accessibility that lie in loop regions between secondary structure elements of the protein. Conclusions: Protein residue networks that use joint recurrences extracted from molecular dynamics simulations of a solvated protein perform well in pointing to hotspot residues and hotspot clusters. This approach uses no distance cutoff threshold, and does not exclude any interactions between the residues, including water-mediated interactions.

Description: Background: High-throughput technologies such as flow and mass cytometry have the potential to illuminate cellular networks. However, analyzing the data produced by these technologies is challenging. Visualization is needed to help researchers explore this data. Results: We developed a web-based software program, NetworkPainter, to enable researchers to analyze dynamic cytometry data in the context of pathway diagrams. NetworkPainter provides researchers a graphical interface to draw and “paint” pathway diagrams with experimental data, producing animated diagrams which display the activity of each network node at each time point. Conclusion: NetworkPainter enables researchers to more fully explore multi-parameter, dynamical cytometry data.

Description: Background: The Immunoglobulins (IG) and the T cell receptors (TR) play the key role in antigen recognition during the adaptive immune response. Recent progress in next-generation sequencing technologies has provided an opportunity for the deep T cell receptor repertoire profiling. However, a specialised software is required for the rational analysis of massive data generated by next-generation sequencing. Results: Here we introduce tcR, a new R package, representing a platform for the advanced analysis of T cell receptor repertoires, which includes diversity measures, shared T cell receptor sequences identification, gene usage statistics computation and other widely used methods. The tool has proven its utility in recent research studies. Conclusions: tcR is an R package for the advanced analysis of T cell receptor repertoires after primary TR sequences extraction from raw sequencing reads. The stable version can be directly installed from The Comprehensive R Archive Network (http://cran.r-project.org/mirrors.html). The source code and development version are available at tcR GitHub (http://imminfo.github.io/tcr/) along with the full documentation and typical usage examples.

Description: Background: One aspect in which RNA sequencing is more valuable than microarray-based methods is the ability to examine the allelic imbalance of the expression of a gene. This process is often a complex task that entails quality control, alignment, and the counting of reads over heterozygous single-nucleotide polymorphisms. Allelic imbalance analysis is subject to technical biases, due to differences in the sequences of the measured alleles. Flexible bioinformatics tools are needed to ease the workflow while retaining as much RNA sequencing information as possible throughout the analysis to detect and address the possible biases. Results: We present AllelicImblance, a software program that is designed to detect, manage, and visualize allelic imbalances comprehensively. The purpose of this software is to allow users to pose genetic questions in any RNA sequencing experiment quickly, enhancing the general utility of RNA sequencing. The visualization features can reveal notable, non-trivial allelic imbalance behavior over specific regions, such as exons. Conclusions: The software provides a complete framework to perform allelic imbalance analyses of aligned RNA sequencing data, from detection to visualization, within the robust and versatile management class, ASEset.

Description: When constructing new biomarker or gene signature scores for time-to-event outcomes, the underlying aims are to develop a discrimination model that helps to predict whether patients have a poor or good prognos...

Description: Massive biological datasets are generated in different locations all over the world. Analysis of these datasets is required in order to extract knowledge that might be helpful for biologists, physicians and ph...

Description: Physical mapping of DNA with restriction enzymes allows for the characterization and assembly of much longer molecules than is feasible with sequencing. However, assemblies of physical map data are sensitive t...

Description: Protein–protein interaction networks are receiving increased attention due to their importance in understanding life at the cellular level. A major challenge in systems biology is to understand the modular str...

Description: Abnormalities in glycan biosynthesis have been conclusively related to various diseases, whereas the complexity of the glycosylation process has impeded the quantitative analysis of biochemical experimental da...

Description: The predictive nature of the primate sensorimotor systems, for example the smooth pursuit system and their ability to compensate for long delays have been proven by many physiological experiments. However, few...

Description: DNA methylation is an important regulator of gene expression and chromatin structure. Methylated DNA immunoprecipitation sequencing (MeDIP-Seq) is commonly used to identify regions of DNA methylation in eukary...

Description: The Gene Ontology (GO) is a dynamic, controlled vocabulary that describes the cellular function of genes and proteins according to tree major categories: biological process, molecular function and cellular com...

Description: Cellular respiration is a catabolic pathway for producing adenosine triphosphate (ATP) and is the most efficient process through which cells harvest energy from consumed food. When cells undergo cellular respi...

Description: Current volumes of sequence data can lead to large numbers of hits identified on a search, typically in the range of 10s to 100s of thousands. It is often quite difficult to tell from these raw results whether...

Description: Biomedical information and knowledge, structural and non-structural, stored in different repositories can be semantically connected to form a hybrid knowledge network. How to compute relatedness between concep...

Description: The planted (l, d) motif search (PMS) is an important yet challenging problem in computational biology. Pattern-driven PMS algorithms usually use k out of t input sequences as reference sequences to generate cand...

Description: Finding highly relevant articles from biomedical databases is challenging not only because it is often difficult to accurately express a user’s underlying intention through keywords but also because a keyword-...

Description: Given a set of biallelic molecular markers, such as SNPs, with genotype values encoded numerically on a collection of plant, animal or human samples, the goal of genetic trait prediction is to predict the quan...

Description: Clustering is a common technique used by molecular biologists to group homologous sequences and study evolution. There remain issues such as how to cluster molecular sequences accurately and in particular how ...

Description: Major Histocompatibility Complex (MHC) or Human Leukocyte Antigen (HLA) Class I molecules bind to peptide fragments of proteins degraded inside the cell and display them on the cell surface. We are interested ...

Description: Comparing phenotypes of heterogeneous cell populations from multiple biological conditions is at the heart of scientific discovery based on flow cytometry (FC). When the biological signal is measured by the av...

Description: Next generation sequencing technology has enabled characterization of metagenomics through massively parallel genomic DNA sequencing. The complexity and diversity of environmental samples such as the human gut...

Description: The interplay among genetic, environment and epigenetic variation is not fully understood. Advances in high-throughput genotyping methods, high-density DNA methylation detection and well-characterized sample c...

Description: A standard procedure in many areas of bioinformatics is to use a multiple sequence alignment (MSA) as the basis for various types of homology-based inference. Applications include 3D structure modelling, prote...

Description: Integrative analysis of multi-omics data is becoming increasingly important to unravel functional mechanisms of complex diseases. However, the currently available multi-omics datasets inevitably suffer from mi...

Description: The growing complexity of biological experiment design based on high-throughput RNA sequencing (RNA-seq) is calling for more accommodative statistical tools. We focus on differential expression (DE) analysis u...

Description: Discovery of microRNAs (miRNAs) relies on predictive models for characteristic features from miRNA precursors (pre-miRNAs). The short length of miRNA genes and the lack of pronounced sequence features complica...

Description: Comparative analysis of whole genome sequence data from closely related prokaryotic species or strains is becoming an increasingly important and accessible approach for addressing both fundamental and applied ...

Description: TopHat is a popular spliced junction mapper for RNA sequencing data, and writes files in the BAM format – the binary version of the Sequence Alignment/Map (SAM) format. BAM is the standard exchange format for ...

Description: SomaLogic’s SOMAscan™ assay platform allows the analysis of the relative abundance of over 1300 proteins directly from biological matrices such as blood plasma and serum. The data resulting from the assay is p...

Description: MicroRNAs (miRNAs) impact various biological processes within animals and plants. They complementarily bind target mRNAs, effecting a post-transcriptional negative regulation on mRNA level. The investigation o...

Description: Endocytosis is regarded as a mechanism of attenuating the epidermal growth factor receptor (EGFR) signaling and of receptor degradation. There is increasing evidence becoming available showing that breast canc...

Description: Genomics and proteomics are nowadays the dominant techniques for novel biomarker discovery. However, histopathology images contain a wealth of information related to the tumor histology, morphology and tumor-h...

Description: Several techniques have been tailored to the quantification of microRNA expression, including hybridization arrays, quantitative PCR (qPCR), and high-throughput sequencing. Each of these has certain strengths ...

Description: Malaria infection is still widespread in some parts of the world and threatens the lives of millions of people every year. Vaccines, especially oral vaccines are considered to be effective in reducing the burd...

Description: Assays that are capable of detecting genome-wide chromatin interactions have produced massive amount of data and led to great understanding of the chromosomal three-dimensional (3D) structure. As technology be...

Description: The Cancer Genome Atlas (TCGA) is a pool of molecular data sets publicly accessible and freely available to cancer researchers anywhere around the world. However, wide spread use is limited since an advanced k...

Description: G-DOC Plus is a data integration and bioinformatics platform that uses cloud computing and other advanced computational tools to handle a variety of biomedical BIG DATA including gene expression arrays, NGS and m...

Description: The development of high-throughput sequencing technologies has revolutionized the field of microbial ecology via the sequencing of phylogenetic marker genes (e.g. 16S rRNA gene amplicon sequencing). Denoising,...

Description: Computing alignments between two or more sequences are common operations frequently performed in computational molecular biology. The continuing growth of biological sequence databases establishes the need for...

Description: Intronless genes are a significant characteristic of prokaryotes. Systematic identification and annotation are primary and crucial steps for determining the functions of intronless genes and understanding thei...

Description: All species live in complex ecosystems. The structure and complexity of a microbial community reflects not only diversity and function, but also the environment in which it occurs. However, traditional ecologi...

Description: Background: Feature extraction (FE) is difficult, particularly if there are more features than samples, as small sample numbers often result in biased outcomes or overfitting. Furthermore, multiple sample classes often complicate FE because evaluating performance, which is usual in supervised FE, is generally harder than the two-class problem. Developing sample classification independent unsupervised methods would solve many of these problems. Results: Two principal component analysis (PCA)-based FE, specifically, variational Bayes PCA (VBPCA) was extended to perform unsupervised FE, and together with conventional PCA (CPCA)-based unsupervised FE, were tested as sample classification independent unsupervised FE methods. VBPCA- and CPCA-based unsupervised FE both performed well when applied to simulated data, and a posttraumatic stress disorder (PTSD)-mediated heart disease data set that had multiple categorical class observations in mRNA/microRNA expression of stressed mouse heart. A critical set of PTSD miRNAs/mRNAs were identified that show aberrant expression between treatment and control samples, and significant, negative correlation with one another. Moreover, greater stability and biological feasibility than conventional supervised FE was also demonstrated. Based on the results obtained, in silico drug discovery was performed as translational validation of the methods. Conclusions: Our two proposed unsupervised FE methods (CPCA- and VBPCA-based) worked well on simulated data, and outperformed two conventional supervised FE methods on a real data set. Thus, these two methods have suggested equivalence for FE on categorical multiclass data sets, with potential translational utility for in silico drug discovery.

Description: Background: This article provides an overview of the first BioASQ challenge, a competition on large-scale biomedical semantic indexing and question answering (QA), which took place between March and September 2013. BioASQ assesses the ability of systems to semantically index very large numbers of biomedical scientific articles, and to return concise and user-understandable answers to given natural language questions by combining information from biomedical articles and ontologies. Results: The 2013 BioASQ competition comprised two tasks, Task 1a and Task 1b. In Task 1a participants were asked to automatically annotate new PubMed documents with MeSH headings. Twelve teams participated in Task 1a, with a total of 46 system runs submitted, and one of the teams performing consistently better than the MTI indexer used by NLM to suggest MeSH headings to curators. Task 1b used benchmark datasets containing 29 development and 282 test English questions, along with gold standard (reference) answers, prepared by a team of biomedical experts from around Europe and participants had to automatically produce answers. Three teams participated in Task 1b, with 11 system runs. The BioASQ infrastructure, including benchmark datasets, evaluation mechanisms, and the results of the participants and baseline methods, is publicly available. Conclusions: A publicly available evaluation infrastructure for biomedical semantic indexing and QA has been developed, which includes benchmark datasets, and can be used to evaluate systems that: assign MeSH headings to published articles or to English questions; retrieve relevant RDF triples from ontologies, relevant articles and snippets from PubMed Central; produce “exact” and paragraph-sized “ideal” answers (summaries). The results of the systems that participated in the 2013 BioASQ competition are promising. In Task 1a one of the systems performed consistently better from the NLM’s MTI indexer. In Task 1b the systems received high scores in the manual evaluation of the “ideal” answers; hence, they produced high quality summaries as answers. Overall, BioASQ helped obtain a unified view of how techniques from text classification, semantic indexing, document and passage retrieval, question answering, and text summarization can be combined to allow biomedical experts to obtain concise, user-understandable answers to questions reflecting their real information needs.

Description: Background: Recent advances in synthetic biology have provided tools to efficiently construct complex DNA molecules which are an important part of many molecular biology and biotechnology projects. The planning of such constructs has traditionally been done manually using a DNA sequence editor which becomes error-prone as scale and complexity of the construction increase. A human-readable formal description of cloning and assembly strategies, which also allows for automatic computer simulation and verification, would therefore be a valuable tool. Results: We have developed pydna, an extensible, free and open source Python library for simulating basic molecular biology DNA unit operations such as restriction digestion, ligation, PCR, primer design, Gibson assembly and homologous recombination. A cloning strategy expressed as a pydna script provides a description that is complete, unambiguous and stable. Execution of the script automatically yields the sequence of the final molecule(s) and that of any intermediate constructs. Pydna has been designed to be understandable for biologists with limited programming skills by providing interfaces that are semantically similar to the description of molecular biology unit operations found in literature. Conclusions: Pydna simplifies both the planning and sharing of cloning strategies and is especially useful for complex or combinatorial DNA molecule construction. An important difference compared to existing tools with similar goals is the use of Python instead of a specifically constructed language, providing a simulation environment that is more flexible and extensible by the user.

Description: Background: Bisulphite sequencing enables the detection of cytosine methylation. The sequence of the methylation states of cytosines on any given read forms a methylation pattern that carries substantially more information than merely studying the average methylation level at individual positions. In order to understand better the complexity of DNA methylation landscapes in biological samples, it is important to study the diversity of these methylation patterns. However, the accurate quantification of methylation patterns is subject to sequencing errors and spurious signals due to incomplete bisulphite conversion of cytosines. Results: A statistical model is developed which accounts for the distribution of DNA methylation patterns at any given locus. The model incorporates the effects of sequencing errors and spurious reads, and enables estimation of the true underlying distribution of methylation patterns. Conclusions: Calculation of the estimated distribution over methylation patterns is implemented in the R Bioconductor package MPFE. Source code and documentation of the package are also available for download at http://bioconductor.org/packages/3.0/bioc/html/MPFE.html.

Description: Background: Chip-seq experiments are becoming a standard approach for genome-wide profiling protein-DNA interactions, such as detecting transcription factor binding sites, histone modification marks and RNA Polymerase II occupancy. However, when comparing a ChIP sample versus a control sample, such as Input DNA, normalization procedures have to be applied in order to remove experimental source of biases. Despite the substantial impact that the choice of the normalization method can have on the results of a ChIP-seq data analysis, their assessment is not fully explored in the literature. In particular, there are no diagnostic tools that show whether the applied normalization is indeed appropriate for the data being analyzed. Results: In this work we propose a novel diagnostic tool to examine the appropriateness of the estimated normalization procedure. By plotting the empirical densities of log relative risks in bins of equal read count, along with the estimated normalization constant, after logarithmic transformation, the researcher is able to assess the appropriateness of the estimated normalization constant. We use the diagnostic plot to evaluate the appropriateness of the estimates obtained by CisGenome, NCIS and CCAT on several real data examples. Moreover, we show the impact that the choice of the normalization constant can have on standard tools for peak calling such as MACS or SICER. Finally, we propose a novel procedure for controlling the FDR using sample swapping. This procedure makes use of the estimated normalization constant in order to gain power over the naive choice of constant (used in MACS and SICER), which is the ratio of the total number of reads in the ChIP and Input samples. Conclusions: Linear normalization approaches aim to estimate a scale factor, r, to adjust for different sequencing depths when comparing ChIP versus Input samples. The estimated scaling factor can easily be incorporated in many peak caller algorithms to improve the accuracy of the peak identification. The diagnostic plot proposed in this paper can be used to assess how adequate ChIP/Input normalization constants are, and thus it allows the user to choose the most adequate estimate for the analysis.

Description: Background: Electronic medical record (EMR) systems have become widely used throughout the world to improve the quality of healthcare and the efficiency of hospital services. A bilingual medical lexicon of Chinese and English is needed to meet the demand for the multi-lingual and multi-national treatment. We make efforts to extract a bilingual lexicon from English and Chinese discharge summaries with a small seed lexicon. The lexical terms can be classified into two categories: single-word terms (SWTs) and multi-word terms (MWTs). For SWTs, we use a label propagation (LP; context-based) method to extract candidates of translation pairs. For MWTs, which are pervasive in the medical domain, we propose a term alignment method, which firstly obtains translation candidates for each component word of a Chinese MWT, and then generates their combinations, from which the system selects a set of plausible translation candidates. Results: We compare our LP method with a baseline method based on simple context-similarity. The LP based method outperforms the baseline with the accuracies: 4.44% Acc1, 24.44% Acc10, and 62.22% Acc100, where AccN means the top N accuracy. The accuracy of the LP method drops to 5.41% Acc10 and 8.11% Acc20 for MWTs. Our experiments show that the method based on term alignment improves the performance for MWTs to 16.22% Acc10 and 27.03% Acc20. Conclusions: We constructed a framework for building an English-Chinese term dictionary from discharge summaries in the two languages. Our experiments have shown that the LP-based method augmented with the term alignment method will contribute to reduction of manual work required to compile a bilingual sydictionary of clinical terms.