Journal of Bioinformatics and Computational Biology

In recent times, zebrafish has garnered lot of popularity as model organism to study human cancers. Despite high evolutionary divergence from humans, zebrafish develops almost all types of human tumors when induced. However, mechanistic details of tumor formation have remained largely unknown. Present study is aimed at analysis of repertoire of kinases in zebrafish proteome to provide insights into various cellular components. Annotation using highly sensitive remote homology detection methods revealed "substantial expansion" of Ser/Thr/Tyr kinase family in zebrafish compared to humans, constituting over 3% of proteome. Subsequent classification of kinases into subfamilies revealed presence of large number of CAMK group of kinases, with massive representation of PIM kinases, important for cell cycle regulation and growth. Extensive sequence comparison between human and zebrafish PIM kinases revealed high conservation of functionally important residues with a few organism specific variations. There are about 300 PIM kinases in zebrafish kinome, while human genome codes for only about 500 kinases altogether. PIM kinases have been implicated in various human cancers and are currently being targeted to explore their therapeutic potentials. Hence, in depth analysis of PIM kinases in zebrafish has opened up new avenues of research to verify the model organism status of zebrafish.

In the systems biology field, algorithms for structural identification of ordinary differential equations (ODEs) have mainly focused on fixed model spaces like S-systems and/or on methods that require sufficiently good data so that derivatives can be accurately estimated. There is therefore a lack of methods and software that can handle more general models and realistic data.

We present ODEion, a software module for structural identification of ODEs. Main characteristic features of the software are:

• The model space is defined by arbitrary user-defined functions that can be nonlinear in both variables and parameters, such as for example chemical rate reactions.

• ODEion implements computationally efficient algorithms that have been shown to efficiently handle sparse and noisy data. It can run a range of realistic problems that previously required a supercomputer.

• ODEion is easy to use and provides SBML output.

We describe the mathematical problem, the ODEion system itself, and provide several examples of how the system can be used.

Available at: http://www.odeidentification.org.

Predicting the sub-cellular localization of proteins is an important task in bioinformatics, for which many standard prediction tools are available. While these tools are powerful in general and capable of predicting protein localization for the most common compartments, their performance strongly depends on the organism of interest. More importantly, there are special compartments, such as the apicoplast of apicomplexan parasites, for which these tools cannot provide a prediction at all. In the absence of a highly conserved targeting signal, even motif searches may not be able to provide a lead for the accurate prediction of protein localization for a compartment of interest. In order to approach difficult cases of that kind, we propose an alternative method that complements existing approaches by using a more targeted protein sequence model. Moreover, our method makes use of (weighted) measures for time series comparison. To demonstrate its performance, we use this method for predicting localization in special compartments of three different species, for which existing methods yield only sub-optimal results. As shown experimentally, our method is indeed capable of producing reliable predictions of sub-cellular localization for difficult cases, i.e. if training data is scarce and a potential protein targeting signal may not be well conserved.

To determine the 3D conformation of proteins is a necessity to understand their functions or interactions with other molecules. It is commonly admitted that, when proteins fold from their primary linear structures to their final 3D conformations, they tend to choose the ones that minimize their free energy. To find the 3D conformation of a protein knowing its amino acid sequence, bioinformaticians use various models of different resolutions and artificial intelligence tools, as the protein folding prediction problem is a NP complete one. More precisely, to determine the backbone structure of the protein using the low resolution models (2D HP square and 3D HP cubic), by finding the conformation that minimizes free energy, is intractable exactly. Both proofs of NP-completeness and the 2D prediction consider that acceptable conformations have to satisfy a self-avoiding walk (SAW) requirement, as two different amino acids cannot occupy a same position in the lattice. It is shown in this document that the SAW requirement considered when proving NP-completeness is different from the SAW requirement used in various prediction programs, and that they are different from the real biological requirement. Indeed, the proof of NP completeness and the predictions in silico consider conformations that are not possible in practice. Consequences of this fact are investigated in this research work.

We propose and implement a method to obtain all duplicated sequences (repeats) from a chromosome or whole genome. Unlike existing approaches our method makes it possible to simultaneously identify and classify repeats into super, local, and non-nested local maximal repeats. Computation verification demonstrates that maximal repeats for a genome of several gigabases can be identified in a reasonable time, enabling us to identified these maximal repeats for any sequenced genome. The algorithm used for the identification relies on enhanced suffix array data structure to achieve practical space and time efficiency, to identify and classify the maximal repeats, and to perform further post-processing on the identified duplicated sequences. The simplicity and effectiveness of the implementation makes the method readily extendible to more sophisticated computations. Maxmers can be exhaustively accounted for in few minutes for genome sequences of dozen megabases in length and in less than a day or two for genome sequences of few gigabases in length. One application of duplicated sequence identification is to the study of duplicated sequence length distributions, which our found to exhibit for large lengths a persistent power-law behavior. Variation of estimated exponents of this power law are studied among different species and successive assembly release versions of the same species. This makes the characterization of the power-law regime of sequenced genomes via maximal repeats identification and classification, an important task for the derivation of models that would help us to elucidate sequence duplication and genome evolution.

The GPCR genes have a variety of exon–intron structures even though their proteins are all structurally homologous. We have examined all human GPCR genes with at least two functional protein isoforms, totaling 199, aiming to gain an understanding of what may have contributed to the large diversity of the exon–intron structures of the GPCR genes. The 199 genes have a total of 808 known protein splicing isoforms with experimentally verified functions. Our analysis reveals that 1301 (80.6%) adjacent exon–exon pairs out of the total of 1,613 in the 199 genes have either exactly one exon skipped or the intron in-between retained in at least one of the 808 protein splicing isoforms. This observation has a statistical significance p-value of 2.051762 * e^-09, assuming that the observed splicing isoforms are independent of the exon–intron structures. Our interpretation of this observation is that the exon boundaries of the GPCR genes are not randomly determined; instead they may be selected to facilitate specific alternative splicing for functional purposes.

Non-negative matrix factorization (NMF) has proven to be a useful decomposition technique for multivariate data, where the non-negativity constraint is necessary to have a meaningful physical interpretation. NMF reduces the dimensionality of non-negative data by decomposing it into two smaller non-negative factors with physical interpretation for class discovery. The NMF algorithm, however, assumes a deterministic framework. In particular, the effect of the data noise on the stability of the factorization and the convergence of the algorithm are unknown. Collected data, on the other hand, is stochastic in nature due to measurement noise and sometimes inherent variability in the physical process. This paper presents new theoretical and applied developments to the problem of non-negative matrix factorization (NMF). First, we generalize the deterministic NMF algorithm to include a general class of update rules that converges towards an optimal non-negative factorization. Second, we extend the NMF framework to the probabilistic case (PNMF). We show that the Maximum a posteriori (MAP) estimate of the non-negative factors is the solution to a weighted regularized non-negative matrix factorization problem. We subsequently derive update rules that converge towards an optimal solution. Third, we apply the PNMF to cluster and classify DNA microarrays data. The proposed PNMF is shown to outperform the deterministic NMF and the sparse NMF algorithms in clustering stability and classification accuracy.

Residual Dipolar Couplings (RDCs) are a source of NMR data that can provide a powerful set of constraints on the orientation of inter-nuclear vectors, and are quickly becoming a larger part of the experimental toolset for molecular biologists. However, few reliable protocols exist for the determination of protein backbone structures from small sets of RDCs. DynaFold is a new dynamic programming algorithm designed specifically for this task, using minimal sets of RDCs collected in multiple alignment media. DynaFold was first tested utilizing synthetic data generated for the N–H, C_α–H_α, and C–N vectors of 1BRF, 1F53, 110M, and 3LAY proteins, with up to ±1 Hz error in three alignment media, and was able to produce structures with less than 1.9 Å of the original structures. DynaFold was then tested using experimental data, obtained from the Biological Magnetic Resonance Bank, for proteins PDBID:1P7E and 1D3Z using RDC data from two alignment media. This exercise yielded structures within 1.0 Å of their respective published structures in segments with high data density, and less than 1.9 Å over the entire protein. The same sets of RDC data were also used in comparisons with traditional methods for analysis of RDCs, which failed to match the accuracy of DynaFold's approach to structure determination.

Outer membrane proteins (OMPs) play critical roles in many cellular processes and discriminating OMPs from other types of proteins is very important for OMPs identification in bacterial genomic proteins. In this study, a method SSEA_SVM is developed using secondary structure element alignment and support vector machine. Moreover, a novel kernel function is designed to utilize secondary structure information in the support vector machine classifier. A benchmark dataset, which consists of 208 OMPs, 673 globular proteins, and 206 α-helical membrane proteins, is used to evaluate the performance of SSEA_SVM. A high accuracy of 97.7% with 0.926 MCC is achieved while SSEA_SVM is applied to discriminating OMPs and non-OMPs. In comparison with existing methods in the literature, SSEA_SVM is also highly competitive. We suggest that SSEA_SVM is a much more promising method to identify OMPs in genomic proteins. A web server that implements SSEA_SVM is freely available at http://bioinfo.tmmu.edu.cn/SSEA_SVM/.

Reconstruction of signaling pathways is crucial for understanding cellular mechanisms. A pathway is represented as a path of a signaling cascade involving a series of proteins to perform a particular function. Since a protein pair involved in signaling and response have a strong interaction, putative pathways can be detected from protein–protein interaction (PPI) networks. However, predicting directed pathways from the undirected genome-wide PPI networks has been challenging. We present a novel computational algorithm to efficiently predict signaling pathways from PPI networks given a starting protein and an ending protein. Our approach integrates topological analysis of PPI networks and semantic analysis of PPIs using Gene Ontology data. An advanced semantic similarity measure is used for weighting each interacting protein pair. Our distance-wise algorithm iteratively selects an adjacent protein from a PPI network to build a pathway based on a distance condition. On each iteration, the strength of a hypothetical path passing through a candidate edge is estimated by a local heuristic. We evaluate the performance by comparing the resultant paths to known signaling pathways on yeast. The results show that our approach has higher accuracy and efficiency than previous methods.

Phage display can rapidly discover peptides binding to any given target; thus, it has been widely used in basic and applied research. Each round of panning consists of two basic processes: Selection and amplification. However, recent studies have showed that the amplification step would decrease the diversity of phage display libraries due to different propagation capacity of phage clones. This may induce phages with growth advantage rather than specific affinity to appear in the final experimental results. The peptides displayed by such phages are termed as propagation-related target-unrelated peptides (PrTUPs). They would mislead further analysis and research if not removed. In this paper, we describe PhD7Faster, an ensemble predictor based on support vector machine (SVM) for predicting clones with growth advantage from the Ph.D.-7 phage display peptide library. By using reduced dipeptide composition (ReDPC) as features, an accuracy (Acc) of 79.67% and a Matthews correlation coefficient (MCC) of 0.595 were achieved in 5-fold cross-validation. In addition, the SVM-based model was demonstrated to perform better than several representative machine learning algorithms. We anticipate that PhD7Faster can assist biologists to exclude potential PrTUPs and accelerate the finding of specific binders from the popular Ph.D.-7 library. The web server of PhD7Faster can be freely accessed at http://immunet.cn/sarotup/cgi-bin/PhD7Faster.pl.

A promising alternative for unraveling the principles under which the dynamic interactions among genes lead to cellular phenotypes relies on mathematical and computational models at different levels of abstraction, from the molecular level of protein-DNA interactions to the system level of functional relationships among genes. This review article presents, under a bottom–up perspective, a hierarchy of approaches to modeling gene regulatory network dynamics, from microscopic descriptions at the single-molecule level in the spatial context of an individual cell to macroscopic models providing phenomenological descriptions at the population-average level. The reviewed modeling approaches include Molecular Dynamics, Particle-Based Brownian Dynamics, the Master Equation approach, Ordinary Differential Equations, and the Boolean logic abstraction. Each of these frameworks is motivated by a particular biological context and the nature of the insight being pursued. The setting of gene network dynamic models from such frameworks involves assumptions and mathematical artifacts often ignored by the non-specialist. This article aims at providing an entry point for biologists new to the field and computer scientists not acquainted with some recent biophysically-inspired models of gene regulation. The connections promoting intuition between different abstraction levels and the role that approximations play in the modeling process are highlighted throughout the paper.