MOTIVATION: Taxonomic classification of 16S ribosomal RNA gene amplicon is an efficient and economic approach in microbiome analysis. 16S rRNA sequence databases like SILVA, RDP, EzBioCloud and HOMD used in downstream bioinformatic pipelines have limitations on either the sequence redundancy or the delay on new sequence recruitment. To improve the 16S rRNA gene-based taxonomic classification, we merged these widely used databases and a collection of novel sequences systemically into an integrated resource. RESULTS: MetaSquare version 1.0 is an integrated 16S rRNA sequence database. It is composed of more than 6 million sequences and improves taxonomic classification resolution on both long-read and short-read methods. AVAILABILITY AND IMPLEMENTATION: Accessible at https://hub.docker.com/r/lsbnb/metasquare_db and https://github.com/lsbnb/MetaSquare. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: A new emerged cancer treatment utilizes intrinsic immune surveillance mechanism that is silenced by those malicious cells. Hence, studies of tumor infiltrating lymphocyte populations (TILs) are key to the success of advanced treatments. In addition to laboratory methods such as immunohistochemistry and flow cytometry, in silico gene expression deconvolution methods are available for analyses of relative proportions of immune cell types. RESULTS: Herein, we used microarray data from the public domain to profile gene expression pattern of twenty-two immune cell types. Initially, outliers were detected based on the consistency of gene profiling clustering results and the original cell phenotype notation. Subsequently, we filtered out genes that are expressed in non-hematopoietic normal tissues and cancer cells. For every pair of immune cell types, we ran t-tests for each gene, and defined differentially expressed genes (DEGs) from this comparison. Equal numbers of DEGs were then collected as candidate lists and numbers of conditions and minimal values for building signature matrixes were calculated. Finally, we used v -Support Vector Regression to construct a deconvolution model. The performance of our system was finally evaluated using blood biopsies from 20 adults, in which 9 immune cell types were identified using flow cytometry. The present computations performed better than current state-of-the-art deconvolution methods. CONCLUSIONS: Finally, we implemented the proposed method into R and tested extensibility and usability on Windows, MacOS, and Linux operating systems. The method, MySort, is wrapped as the Galaxy platform pluggable tool and usage details are available at https://testtoolshed.g2.bx.psu.edu/view/moneycat/mysort/e3afe097e80a .

Due to advancement in next-generation sequencing (NGS) technology, ultra-large datasets have been generated for further studies. These datasets contain a large amount of erroneous data. It is a computational problem to detect and correct the errors to improve performance of future applications, e.g., de novo assembly. In this article, we present a MapReduce-based algorithm, CloudEC, to correct sequencing errors in NGS data. We use five datasets and GAGE benchmark to compare efficiency and efficacy of CloudEC with CloudRS and error correction module of ALLPATHS-LG. Experiments show that CloudEC is 1.6 times faster than CloudRS. The corrected reads are then assembled by Velvet assembler, and the assembled results are examined by GAGE benchmark for correctness of assembly. It is shown that the correct N50 of the assembled contigs with CloudEC as the error corrector is larger than that with CloudRS as the error corrector, and is comparable to that with ALLPATHS-LG as the error corrector.

This paper deals with the problem of pose estimation (i.e., estimating position and orientation of an moving target) for real-time visual servoing, where the vision hardware is assumed to have severely limited measurement capability. In other words, we aim to compensate the slow sensor dynamics in industrial machine vision systems. The common approach is to predict the present target motion by propagating the delayed estimates with the target dynamics. Such method is sometimes problematic since the target motion characteristics (i.e., target dynamics) may change from one visual servoing task to another. Therefore, this paper presents a method which is able to estimate the target pose as well as learn the target dynamics. We apply the Expectation-Maximization algorithm to simultaneously solve the pose estimation problem and the target dynamics modeling problem. Several techniques including the extended Kalman filter/smoother, the block coordinate descent method, and the convex optimization method are utilized to address this problem. The effectiveness of the proposed algorithm is demonstrated experimentally on a 6-DOF industrial robot.

This paper discusses the control strategies for robot manipulators to track moving targets based on realtime vision guidance. The work is motivated by some new demands of vision guided robot manipulators in which the workpieces being manipulated are in complex motion and the widely adopted look-then-move control strategy cannot give satisfactory performance. In order to serve industrial applications, the limited sensing and actuation capabilities have to be considered properly. In our work, a cascade control structure is introduced. The sensor and actuator limits are dealt with by consecutive modules in the controller respectively. In particular, the kinematic visual servoing (KVS) module is discussed in detail. It is a kinematic controller that generates reference trajectory in real-time. Sliding control is used to give a basic Jacobian-based design. Constrained optimal control is applied to address the actuator limits. validation is conducted through simulation and experiment.

This paper discusses the problem of planning well conditioned trajectories for learning a class of nonlinear models such as the imaging model of a camera and the multibody dynamic model of a robot. In such model learning problems, the model parameters can be linearly decoupled from system variables in the feature space. The learning accuracy and robustness against measurement noise and unmodeled response depend largely on the condition number of the data matrix. A new method is proposed to plan well conditioned trajectories efficiently by using low-discrepancy sequences and matrix subset selection. Application examples show promising results.

BACKGROUND: Many research results show that the biological systems are composed of functional modules. Members in the same module usually have common functions. This is useful information to understand how biological systems work. Therefore, detecting functional modules is an important research topic in the post-genome era. One of functional module detecting methods is to find dense regions in Protein-Protein Interaction (PPI) networks. Most of current methods neglect confidence-scores of interactions, and pay little attention on using gene expression data to improve their results. RESULTS: In this paper, we propose a novel hub-attachment based method to detect functional modules from confidence-scored protein interactions and expression profiles, and we name it HUNTER. Our method not only can extract functional modules from a weighted PPI network, but also use gene expression data as optional input to increase the quality of outcomes. Using HUNTER on yeast data, we found it can discover more novel components related with RNA polymerase complex than those existed methods from yeast interactome. And these new components show the close relationship with polymerase after functional analysis on Gene Ontology. CONCLUSION: A C++ implementation of our prediction method, dataset and supplementary material are available at http://hub.iis.sinica.edu.tw/Hunter/. Our proposed HUNTER method has been applied on yeast data, and the empirical results show that our method can accurately identify functional modules. Such useful application derived from our algorithm can reconstruct the biological machinery, identify undiscovered components and decipher common sub-modules inside these complexes like RNA polymerases I, II, III.

BACKGROUND: Nucleic acid hybridization, a fundamental technique in molecular biology, can be modified into very effective and sensitive methods for detecting particular targets mixed with millions of non-target sequences. Therefore, avoiding cross-hybridization is the most crucial issue for developing diagnostic methods based on hybridization. RESULTS: To develop a probe with a high discriminating power, this study constructed a web service, the Unique Probe Selector (UPS), for customized probe design. The UPS service integrates a probe design mechanism and a scoring system for evaluating the performance of probe annealing and the uniqueness of a probe in a user-defined genetic background. Starting from an intuitive web interface, the UPS accepts a query with single or multiple sequences in fasta format. The best probe(s) for each sequence can be downloaded from result pages in a fasta or .csv format with a summary of probe characteristics. The option "Unique probe within group" selects the most unique probe for each target sequence with low probability to hybridize to the other sequences in the same submitted query. The option "Unique probe in the specific organism" devises probes for each submitted sequence to identify its target among selected genetic backgrounds based on Unigene. CONCLUSION: The UPS evaluates probe-to-target hybridization under a user-defined condition in silico to ensure high-performance hybridization and minimizes the possibility of non-specific reactions. UPS has been applied to design human arrays for gene expression studies and to develop several small arrays of gene families that were inferred as molecular signatures of cancer typing/staging or pathogen signatures. Notably, UPS is freely accessible at http://array.iis.sinica.edu.tw/ups/.

BACKGROUND: The recent increase in the use of high-throughput two-hybrid analysis has generated large quantities of data on protein interactions. Specifically, the availability of information about experimental protein-protein interactions and other protein features on the Internet enables human protein-protein interactions to be computationally predicted from co-evolution events (interolog). This study also considers other protein interaction features, including sub-cellular localization, tissue-specificity, the cell-cycle stage and domain-domain combination. Computational methods need to be developed to integrate these heterogeneous biological data to facilitate the maximum accuracy of the human protein interaction prediction. RESULTS: This study proposes a relative conservation score by finding maximal quasi-cliques in protein interaction networks, and considering other interaction features to formulate a scoring method. The scoring method can be adopted to discover which protein pairs are the most likely to interact among multiple protein pairs. The predicted human protein-protein interactions associated with confidence scores are derived from six eukaryotic organisms--rat, mouse, fly, worm, thale cress and baker's yeast. CONCLUSION: Evaluation results of the proposed method using functional keyword and Gene Ontology (GO) annotations indicate that some confidence is justified in the accuracy of the predicted interactions. Comparisons among existing methods also reveal that the proposed method predicts human protein-protein interactions more accurately than other interolog-based methods.

BACKGROUND: Proteins control and mediate many biological activities of cells by interacting with other protein partners. This work presents a statistical model to predict protein interaction networks of Drosophila melanogaster based on insight into domain interactions. RESULTS: Three high-throughput yeast two-hybrid experiments and the collection in FlyBase were used as our starting datasets. The co-occurrences of domains in these interactive events are converted into a probability score of domain-domain interaction. These scores are used to infer putative interaction among all available open reading frames (ORFs) of fruit fly. Additionally, the likelihood function is used to estimate all potential protein-protein interactions. All parameters are successfully iterated and MLE is obtained for each pair of domains. Additionally, the maximized likelihood reaches its converged criteria and maintains the probability stable. The hybrid model achieves a high specificity with a loss of sensitivity, suggesting that the model may possess major features of protein-protein interactions. Several putative interactions predicted by the proposed hybrid model are supported by literatures, while experimental data with a low probability score indicate an uncertain reliability and require further proof of interaction.Fly-DPI is the online database used to present this work. It is an integrated proteomics tool with comprehensive protein annotation information from major databases as well as an effective means of predicting protein-protein interactions. As a novel search strategy, the ping-pong search is a naïve path map between two chosen proteins based on pre-computed shortest paths. Adopting effective filtering strategies will facilitate researchers in depicting the bird's eye view of the network of interest. Fly-DPI can be accessed at http://flydpi.nhri.org.tw. CONCLUSION: This work provides two reference systems, statistical and biological, to evaluate the reliability of protein interaction. First, the hybrid model statistically estimates both experimental and predicted protein interaction relationships. Second, the biological information for filtering and annotation itself is a strong indicator for the reliability of protein-protein interaction. The space-temporal or stage-specific expression patterns of genes are also critical for identifying proteins involved in a particular situation.

We implemented a statistical model into our protein interaction database for validation of two-hybrid assays of Helicobacter pylori, and prediction of putative protein interactions not yet discovered experimentally. To present the enormous amount of experimental and inferred protein interaction networking maps, the H.pylori Database of Protein Interactomes (hp-DPI) is developed with a succinct yet comprehensive visualization tool integrated with annotation from Genbank, GO, and KEGG. hp-DPI is first built with, but not limited to, H.pylori protein interactions and is expected to naturally include other organisms' protein interacting relationships in the future.