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A nearly complete sequential resonance assignment is a key factor leading to successful protein structure determination via NMR spectroscopy. Assuming the availability of a set of NMR spectral peak lists, most of the existing assignment algorithms first use the differences between chemical shift values for common nuclei across multiple spectra to provide the evidence that some pairs of peaks should be assigned to sequentially adjacent amino acid residues in the target protein. They then use these connectivities as constraints to produce a sequential assignment. At various levels of success, these algorithms typically generate a large number of potential connectivity constraints, and it grows exponentially as the quality of spectral data decreases. A key observation used in our sequential assignment program, CISA, is that chemical shift residual signature information can be used to improve the connectivity determination, and thus to dramatically decrease the number of predicted connectivity constraints. Fewer connectivity constraints lead to less ambiguities in the sequential assignment. Extensive simulation studies on several large test datasets demonstrated that CISA is efficient and effective, compared to three most recently proposed sequential resonance assignment programs RANDOM, PACES, and MARS.

Background: Gene expression microarray is a powerful technology for genetic profiling diseases and their associated treatments. Such a process involves a key step of biomarker identification, which are expected to be closely related to the disease. A most important task of these identified genes is that they can be used to construct a classifier which can effectively diagnose disease and even recognize the disease subtypes. Binary classification, for example, diseased or healthy, in microarray data analysis has been successful, while multi-class classification, such as cancer subtyping, remains challenging., Results: We target on the challenging multi-class classification in microarray data analysis, especially on the cancer subtyping using gene expression microarray. We present a novel class discrimination strength vector to represent individual genes and introduce a new measurement to quantify the class discrimination strength difference between two genes. Such a new distance measure is employed in gene clustering, and subsequently the gene cluster information is exploited to select a set of genes which can be used to construct a sample classifier. We tested our method on four real cancer microarray datasets each contains multiple subtypes of cancer patients. The experimental results show that the constructed classifiers all achieved a higher classification accuracy than the previously best classification results obtained on these four datasets. Additional tests show that the selected genes by our method are less correlated and they all contribute statistically significantly to the more accurate cancer subtyping., Conclusion: The proposed novel class discrimination strength vector is a better representation than the gene expression vector, in the sense that it can be used to effectively eliminate highly correlated but redundant genes for classifier construction. Such a method can build a classifier to achieve a higher classification accuracy, which is demonstrated via cancer subtyping.

The success in backbone resonance sequential assignment is fundamental to three dimensional protein structure determination via Nuclear Magnetic Resonance (NMR) spectroscopy. Such a sequential assignment can roughly be partitioned into three separate steps: grouping resonance peaks in multiple spectra into spin systems, chaining the resultant spin systems into strings, and assigning these strings to non-overlapping consecutive amino acid residues in the target protein. Separately dealing with these three steps has been adopted in many existing assignment programs, and it works well on protein NMR data with close-to-ideal quality, while only moderately or even poorly on most real protein datasets, where noises as well as data degeneracies occur frequently. We propose in this work to partition the sequential assignment not by physical steps, but only virtual steps, and use their outputs to cross validate each other. The novelty lies in the places, where the ambiguities at the grouping step will be resolved in finding the highly confident strings at the chaining step, and the ambiguities at the chaining step will be resolved by examining the mappings of strings at the assignment step. In this way, all ambiguities at the sequential assignment will be resolved globally and optimally. The resultant assignment program is called Graph-based Approach for Sequential Assignment (GASA), which has been compared to several recent similar developments including PACES, RANDOM, MARS, and RIBRA. The performance comparisons with these works demonstrated that GASA is more promising for practical use.

The avian influenza virus (AIV) has eight genomic segments (hemagglutinin [HA], neuraminidase [NA], RNA polymerase subunit A [PA], RNA polymerase subunit B1 [PB1], RNA polymerase subunit B2 [PB2], nucleoprotein [NP], nonstructural gene [NS], and matrix protein [M]). The genetic reassortments, recombinations, and mutations lead to a rapid emergence of novel genotypes of the AIVs during their evolution. These emerging viruses provide a large reservoir for pandemic strains. Here we describe a novel computational strategy for genetic reassortment identification. In contrast to the traditional phylogenetic approaches, our method views the genotypes through the modules in networks. Genetic segments with short phylogenetic distance are grouped into modules. Our method is not limited to the number of sequences. We applied this method in reassortment identification of NP segments in H5N1 AIVs. We identified two new potential reassortments for H5N1 AIVs beyond the reported genotypes in literature.

In this paper, a new representation is presented for the Maximum Quartet Consistency (MQC) problem, where solving the MQC problem becomes searching for an ultrametric matrix that satisfies a maximum number of given quartet topologies. A number of structural properties of the MQC problem in this new representation are characterized through formulating into answer set programming, a recent powerful logic programming tool for modeling and solving search problems. Using these properties, a number of optimization techniques are proposed to speed up the search process. The experimental results on a number of simulated data sets suggest that the new representation, combined with answer set programming, presents a unique perspective to the MQC problem.

Automatically identifying frequent composite patterns in DNA sequences is an important task in bioinformatics, especially when all the basic elements (or monad patterns) of a composite pattern are weak. In this paper, we compare one straightforward approach to assemble the monad patterns into composite patterns to two other rather complex approaches. Both our theoretical analysis and empirical results show that this overlooked straightforward method can be several orders of magnitude faster. Furthermore, different from the previous understandings, the empirical results show that the runtime superiority among the three approaches is closely related to the insignificance of the monad patterns.

Background: The information of RNA local secondary structures (LSSs) can help retrieve biologically important motifs and study functions of RNA molecules. Most of the current RNA secondary structure prediction tools are not suitable for RNA LSS prediction on the genome scale due to high computational complexity., Methods: We developed a new computer package Rnall based on a dynamic programming technique, which scans an RNA sequence with a sliding window and extracts all RNA LSSs with sizes no larger than the window size using the nearest neighbor thermodynamic parameters. The worst case running time of Rnall is O(W(3)L), where W is the window size and L is the query sequence length. In practice we observed a running time of O(W(2)L). We further introduced the concept of energy landscape for illustrating RNA LSS, which may facilitate RNA motif mining on the genomic scale., Results: Rnall shows better prediction accuracy than two other prediction tools Lfold and Quickfold. Rnall is also applied to scan for RNA LSSs in three genomes, and the prediction maps well with known RNA motifs., Conclusions: Rnall is designed for RNA LSS prediction and together with the energy landscape, it has unique features that could be used for RNA structural motif mining. Rnall is freely available for download at http://digbio.missouri.edu/~wanx/Rnall or http://www.sysbio.muohio.edu/Rnall.

Microarray gene expression data often contains multiple missing values due to various reasons. However, most of gene expression data analysis algorithms require complete expression data. Therefore, accurate estimation of the missing values is critical to further data analysis. In this paper, an Iterated Local Least Squares Imputation (ILLSimpute) method is proposed for estimating missing values. Two unique features of ILLSimpute method are: ILLSimpute method does not fix a common number of coherent genes for target genes for estimation purpose, but defines coherent genes as those within a distance threshold to the target genes. Secondly, in ILLSimpute method, estimated values in one iteration are used for missing value estimation in the next iteration and the method terminates after certain iterations or the imputed values converge. Experimental results on six real microarray datasets showed that ILLSimpute method performed at least as well as, and most of the time much better than, five most recent imputation methods.

Background: Microarray data analysis is notorious for involving a huge number of genes compared to a relatively small number of samples. Gene selection is to detect the most significantly differentially expressed genes under different conditions, and it has been a central research focus. In general, a better gene selection method can improve the performance of classification significantly. One of the difficulties in gene selection is that the numbers of samples under different conditions vary a lot., Results: Two novel gene selection methods are proposed in this paper, which are not affected by the unbalanced sample class sizes and do not assume any explicit statistical model on the gene expression values. They were evaluated on eight publicly available microarray datasets, using leave-one-out cross-validation and 5-fold cross-validation. The performance is measured by the classification accuracies using the top ranked genes based on the training datasets., Conclusion: The experimental results showed that the proposed gene selection methods are efficient, effective, and robust in identifying differentially expressed genes. Adopting the existing SVM-based and KNN-based classifiers, the selected genes by our proposed methods in general give more accurate classification results, typically when the sample class sizes in the training dataset are unbalanced.

The success in backbone resonance sequential assignment is fundamental to protein three dimensional structure determination via NMR spectroscopy. Such a sequential assignment can roughly be partitioned into three separate steps, which are grouping resonance peaks in multiple spectra into spin systems, chaining the resultant spin systems into strings, and assigning strings of spin systems to non-overlapping consecutive amino acid residues in the target protein. Separately dealing with these three steps has been adopted in many existing assignment programs, and it works well on protein NMR data that is close to ideal quality, while only moderately or even poorly on most real protein datasets, where noises as well as data degeneracy occur frequently. We propose in this work to partition the sequential assignment not into physical steps, but only virtual steps, and use their outputs to cross validate each other. The novelty lies in the places where the ambiguities in the grouping step will be resolved in finding the highly confident strings in the chaining step, and the ambiguities in the chaining step will be resolved by examining the mappings of strings in the assignment step. In such a way, all ambiguities in the sequential assignment will be resolved globally and optimally. The resultant assignment program is called GASA, which was compared to several recent similar developments RIBRA, MARS, PACES and a random graph approach. The performance comparisons with these works demonstrated that GASA might be more promising for practical use.

This paper proposes a novel statistical evaluation model for automated protein NMR sequential resonance assignment. It can be bound to any assignment program and provides confidences for the whole output assignment and each individual mapping. A simulation study on a set of four proteins shows that the statistical evaluation results are informative.

A new method called Complete Composition Vector (CCV), which is a collection of Composition Vectors (CV), is described to infer evolutionary relationships between species using their complete genomic sequences. Such a method bypasses the complexity of performing multiple sequence alignments and avoids the ambiguity of choosing individual genes for species tree construction. It is expected to effectively retain the rich evolutionary information contained in the whole genomic sequence. The method was applied to infer the evolutionary footprints for several datasets that have been previously studied. The final phylogenies were built by an improved clustered Neighbour-Joining method. The generated phylogenetic trees are highly consistent with taxonomy hierarchy and previous studies, with some biologically interesting disagreements.

Existing HIV-1 genotyping systems require a computationally expensive phase of multiple sequence alignments and the alignments must have a sufficiently high quality for accurate genotyping. This is particularly a challenge when the number of strains is large. Here we propose a whole genome composition distance (WGCD) to measure the evolutionary closeness between two HIV-1 whole genomic RNA sequences, and use that measure to develop an HIV-1 genotyping system. Such a WGCD-based genotyping system avoids multiple sequence alignments and does not require any pre-knowledge about the evolutionary rates. Experimental results showed that the system is able to correctly identify the known subtypes, sub-subtypes, and individual circulating recombinant forms.

Protein NMR peak assignment refers to the process of assigning a group of "spin systems" obtained experimentally to a protein sequence of amino acids. The automation of this process is still an unsolved and challenging problem in NMR protein structure determination. Recently, protein NMR peak assignment has been formulated as an interval scheduling problem (ISP), where a protein sequence P of amino acids is viewed as a discrete time interval I (the amino acids on P one-to-one correspond to the time units of I), each subset S of spin systems that are known to originate from consecutive amino acids from P is viewed as a "job" j(s), the preference of assigning S to a subsequence P of consecutive amino acids on P is viewed as the profit of executing job j(s) in the subinterval of I corresponding to P, and the goal is to maximize the total profit of executing the jobs (on a single machine) during I. The interval scheduling problem is max SNP-hard in general; but in the real practice of protein NMR peak assignment, each job j(s) usually requires at most 10 consecutive time units, and typically the jobs that require one or two consecutive time units are the most difficult to assign/schedule. In order to solve these most difficult assignments, we present an efficient 13/7-approximation algorithm for the special case of the interval scheduling problem where each job takes one or two consecutive time units. Combining this algorithm with a greedy filtering strategy for handling long jobs (i.e., jobs that need more than two consecutive time units), we obtain a new efficient heuristic for protein NMR peak assignment. Our experimental study shows that the new heuristic produces the best peak assignment in most of the cases, compared with the NMR peak assignment algorithms in the recent literature. The above algorithm is also the first approximation algorithm for a nontrivial case of the well-known interval scheduling problem that breaks the ratio 2 barrier.

In NMR protein structure determination, after the resonance peaks have been identified and chemical shifts from peaks across multiple spectra have been grouped into spin systems, associating these spin systems to their host residues is the key toward the success of structural information extraction and thus the key to the success of the structure calculation. To achieve accurate enough structure calculation, a near complete and accurate assignment is a prerequisite. There are two pieces of information that can be used into the assignment, one of which is the adjacency information among the spin systems and the other is the signature information of the spin systems. The signature information reflects the fact that, generally speaking, for one type of amino acid residing in a specific local structural environment, the chemical shifts for the atoms inside the amino acid fall into some very narrow distinct ranges. In most of the existing work, normal distributions are assumed with means and standard deviations statistically collected from the available data. In this paper, we followed a simple yet effective histogram-based way to estimate for every spin system the probability that its host is a certain type of amino acid residing in a certain type of secondary structure. We used two combinations of chemical shifts to demonstrate the effectiveness of this type of histogram-based scoring schemes.

NMR resonance assignment is one of the key steps in solving an NMR protein structure. The assignment process links resonance peaks to individual residues of the target protein sequence, providing the prerequisite for establishing intra- and inter-residue spatial relationships between atoms. The assignment process is tedious and time-consuming, which could take many weeks. Though there exist a number of computer programs to assist the assignment process, many NMR labs are still doing the assignments manually to ensure quality. This paper presents a new computational method based on the combination of a suite of algorithms for automating the assignment process, particularly the process of backbone resonance peak assignment. We formulate the assignment problem as a constrained weighted bipartite matching problem. While the problem, in the most general situation, is NP-hard, we present an efficient solution based on a branch-and-bound algorithm with effective bounding techniques using two recently introduced approximation algorithms. We also devise a greedy filtering algorithm for reducing the search space. Our experimental results on 70 instances of (pseudo) real NMR data derived from 14 proteins demonstrate that the new solution runs much faster than a recently introduced (exhaustive) two-layer algorithm and recovers more correct peak assignments than the two-layer algorithm. Our result demonstrates that integrating different algorithms can achieve a good tradeoff between backbone assignment accuracy and computation time.

NMR resonance peak assignment is one of the key steps in solving an NMR protein structure. The assignment process links resonance peaks to individual residues of the target protein sequence, providing the prerequisite for establishing intra- and inter-residue spatial relationships between atoms. The assignment process is tedious and time-consuming, which could take many weeks. Though there exist a number of computer programs to assist the assignment process, many NMR labs are still doing the assignments manually to ensure quality. This paper presents (1) a new scoring system for mapping spin systems to residues, (2) an automated adjacency information extraction procedure from NMR spectra, and (3) a very fast assignment algorithm based on our previous proposed greedy filtering method and a maximum matching algorithm to automate the assignment process. The computational tests on 70 instances of (pseudo) experimental NMR data of 14 proteins demonstrate that the new score scheme has much better discerning power with the aid of adjacency information between spin systems simulated across various NMR spectra. Typically, with automated extraction of adjacency information, our method achieves nearly complete assignments for most of the proteins. The experiment shows very promising perspective that the fast automated assignment algorithm together with the new score scheme and automated adjacency extraction may be ready for practical use.

Protein three-dimensional structure prediction through threading approach has been extensively studied and various models and algorithms have been proposed. In order to further explore ways to improve accuracy and efficiency of the threading process, this paper investigates the effectiveness of a new method: protein threading via linear programming. Based on the contact map model of protein 3D structure, we formulate the protein threading problem as a large scale integer programming problem, then relax to a linear programming problem, and finally solve the integer program by a branch-and-bound method. The final solution is optimal with respect to energy functions incorporating pairwise interaction and allowing variable gaps. The algorithm has been implemented as software package RAPTOR--RApid Protein Threading predictOR. Experimental results for fold recognition show that RAPTOR significantly outperforms other programs at the fold similarity level. The RAPTOR webserver is at http://www.cs.uwaterloo.ca/-j3xu/RAPTOR_form.htm.

NMR resonance assignment is one of the key steps in solving an NMR protein structure. The assignment process links resonance peaks to individual residues of the target protein sequence, providing the prerequisite for establishing intra- and inter-residue spatial relationships between atoms. The assignment process is tedious and time-consuming, which could take many weeks. Though there exist a number of computer programs to assist the assignment process, many NMR labs are still doing the assignments manually to ensure quality. This paper presents a new computational method based on our recent work towards automating the assignment process, particularly the process of backbone resonance peak assignment. We formulate the assignment problem as a constrained weighted bipartite matching problem. While the problem, in the most general situation, is NP-hard, we present an efficient solution based on a branch-and-bound algorithm with effective bounding techniques and a greedy filtering algorithm for reducing the search space. Our experimental results on 70 instances of (pseudo) real NMR data derived from 14 proteins demonstrate that the new solution runs much faster than a recently introduced (exhaustive) two-layer algorithm and recovers more correct peak assignments than the two-layer algorithm.