Journal of Computational Biology

In RNA-Seq studies, there are still many challenges for reliably assembling transcripts. Both genome-guided and de novo methods always produce too many false transcripts because of known and unknown factors. Therefore, the postassembly quality filtering is necessary before performing downstream analyses. Here, we present an automatic and efficient tool of dbHT-Trans for filtering the protein-encoding transcripts assembled by RNA-Seq. For each candidate transcript, we first deduced all potential open reading frames and translated them into amino acid sequences. By searching against the reference protein database, a transcript would be predicted a false one when it has no homologous sequence. Using this method, it is expected to filter out the falsely assembled transcripts of protein-encoding genes. Application of dbHT-Trans to the annotated transcriptome of mouse revealed that the sensitivity was almost 90% for recalling protein-encoding transcripts. After this quality filtering, the numbers of assembled genes became more consistent between Cufflinks and Trinity tools. To significantly decrease the data storage, we transformed all intermediate data into descriptive metadata and stored by the MySQL database, which will be utilized by downstream analyses in a real-time style. The source codes, example data, and manual of dbHT-Trans are freely available on the GitHub repository.

Ionizing-radiation-resistant bacteria (IRRB) are important in biotechnology. In this context, in silico methods of phenotypic prediction and genotype–phenotype relationship discovery are limited. In this work, we analyzed basal DNA repair proteins of most known proteome sequences of IRRB and ionizing-radiation-sensitive bacteria (IRSB) in order to learn a classifier that correctly predicts this bacterial phenotype. We formulated the problem of predicting bacterial ionizing radiation resistance (IRR) as a multiple-instance learning (MIL) problem, and we proposed a novel approach for this purpose. We provide a MIL-based prediction system that classifies a bacterium to either IRRB or IRSB. The experimental results of the proposed system are satisfactory with 91.5% of successful predictions.

DynDom3D is a program for the analysis of domain movements in multimeric proteins. Its inputs are two structure files that indicate a possible domain movement, but the onus has been on the user to process the files so that there is the necessary one-to-one equivalence between atoms in the two atom lists. This is often a prohibitive task to carry out manually, which has limited the application of DynDom3D. Here we report on a webserver with a preprocessor that automatically creates an equivalence between atoms using sequence alignment methods. The processed structure files are passed to DynDom3D and the results are presented on a webpage that includes molecular graphics for easy visualization.

Following burgeoning genomic and transcriptomic sequencing data, biochemical and molecular biology groups worldwide are implementing high-throughput cloning and mutagenesis facilities in order to obtain a large number of soluble proteins for structural and functional characterization. Since manual primer design can be a time-consuming and error-generating step, particularly when working with hundreds of targets, the automation of primer design process becomes highly desirable. HTP-OligoDesigner was created to provide the scientific community with a simple and intuitive online primer design tool for both laboratory-scale and high-throughput projects of sequence-independent gene cloning and site-directed mutagenesis and a Tm calculator for quick queries.

Advances in DNA sequencing technologies have enabled large cancer sequencing studies, collecting somatic mutation data from a large number of cancer patients. One of the main goals of these studies is the identification of all cancer genes—genes associated with cancer. Its achievement is complicated by the extensive mutational heterogeneity of cancer, due to the fact that important mutations in cancer target combinations of genes (i.e., pathways). Recently, the pattern of mutual exclusivity among mutations in a cancer pathway has been observed, and methods that find significant combinations of cancer genes by detecting mutual exclusivity have been proposed. A key question in the analysis of mutual exclusivity is the computation of the minimum number of samples required to reliably find a meaningful set of mutually exclusive mutations in the data, or conclude that there is no such set. In general, the problem of determining the sample complexity, or the number of samples required to identify significant combinations of features, of genomic problems is largely unexplored.

In this work we propose a framework to analyze the sample complexity of problems that arise in the study of genomic datasets. Our framework is based on tools from combinatorial analysis and statistical learning theory that have been used for the analysis of machine learning and probably approximately correct (PAC) learning. We use our framework to analyze the problem of the identification of cancer pathways through mutual exclusivity analysis. We analytically derive matching upper and lower bounds on the sample complexity of the problem, showing that sample sizes much larger than currently available may be required to identify all the cancer genes in a pathway. We also provide two algorithms to find a cancer pathway from a large genomic dataset. On simulated and cancer data, we show that our algorithms can be used to identify cancer pathways from large genomic datasets.

The coefficient of intrinsic dependence (CID) is capable of determining associations among variables without making distributional or functional assumptions regarding random variables. In this study, we developed the partial coefficient of intrinsic dependence (pCID) to facilitate the step-by-step selection of variables that are relevant to a target variable. The strategy of selecting relevant variables using the CID along with the pCID can eliminate interference from other relevant variables. From simulation results, we observed that the proposed method is more sensitive to curvilinearity and more specific to linearity than the combination of Pearsons correlation coefficient and the partial correlation coefficient (PCC/pPCC). This property may provide the opportunity to index different levels of curvilinearity according to CID/pCID outcomes. In practice trials conducted using publicly available microarray data, the CID/pCID procedure successfully identified cold-responsive genes related to three C-repeat binding factors, and was especially effective at identifying some sample-specific gene–gene interactions. Therefore, the proposed strategy may be beneficial in meta-analysis to distinguish general forms of relationships from the noise.

The dynamics of gene transcription is tightly regulated in eukaryotes. Recent experiments have revealed various kinds of transcriptional dynamics, such as RNA polymerase II pausing, that involves regulation at the transcription initiation stage, and the choice of different regulation pattern is closely related to the physiological functions of the target gene. Here we consider a simplified model of transcription initiation, a process including the assembly of transcription complex and the pausing and releasing of the RNA polymerase II. Focusing on the collective behaviors of a population level, we explore the potential regulatory functions this model can offer. These functions include fast and synchronized response to environmental change, or long-term memory about the transcriptional status. As a proof of concept we also show that, by selecting different control mechanisms cells can adapt to different environments. These findings may help us better understand the design principles of transcriptional regulation.