Barcode Server: A Visualization-Based Genome Analysis System

We have previously developed a computational method for representing a genome as a barcode image, which makes various genomic features visually apparent. We have demonstrated that this visual capability has made some challenging genome analysis problems relatively easy to solve. We have applied this capability to a number of challenging problems, including (a) identification of horizontally transferred genes, (b) identification of genomic islands with special properties and (c) binning of metagenomic sequences, and achieved highly encouraging results. These application results inspired us to develop this barcode-based genome analysis server for public service, which supports the following capabilities: (a) calculation of the k-mer based barcode image for a provided DNA sequence; (b) detection of sequence fragments in a given genome with distinct barcodes from those of the majority of the genome, (c) clustering of provided DNA sequences into groups having similar barcodes; and (d) homology-based search using Blast against a genome database for any selected genomic regions deemed to have interesting barcodes. The barcode server provides a job management capability, allowing processing of a large number of analysis jobs for barcode-based comparative genome analyses. The barcode server is accessible at http://csbl1.bmb.uga.edu/Barcode.     

Accurate recognition of cis-regulatory motifs with the correct lengths in prokaryotic genomes

We present a new computational method for solving a classical problem, the identification problem of cis-regulatory motifs in a given set of promoter sequences, based on one key new idea. Instead of scoring candidate motifs individually like in all the existing motif-finding programs, our method scores groups of candidate motifs with similar sequences, called motif closures, using a P-value, which has substantially improved the prediction reliability over the existing methods. Our new P-value scoring scheme is sequence length independent, hence allowing direct comparisons among predicted motifs with different lengths on the same footing. We have implemented this method as a Motif Recognition Computer (MREC) program, and have extensively tested MREC on both simulated and biological data from prokaryotic genomes. Our test results indicate that MREC can accurately pick out the actual motif with the correct length as the best scoring candidate for the vast majority of the cases in our test set. We compared our prediction results with two motif-finding programs Cosmo and MEME, and found that MREC outperforms both programs across all the test cases by a large margin. The MREC program is available at http://csbl.bmb.uga.edu/~bingqiang/MREC1/.     

CINPER: An Interactive Web System for Pathway Prediction for Prokaryotes

We present a web-based network-construction system, CINPER (CSBL INteractive Pathway BuildER), to assist a user to build a user-specified gene network for a prokaryotic organism in an intuitive manner. CINPER builds a network model based on different types of information provided by the user and stored in the system. CINPER’s prediction process has four steps: (i) collection of template networks based on (partially) known pathways of related organism(s) from the SEED or BioCyc database and the published literature; (ii) construction of an initial network model based on the template networks using the P-Map program; (iii) expansion of the initial model, based on the association information derived from operons, protein-protein interactions, co-expression modules and phylogenetic profiles; and (iv) computational validation of the predicted models based on gene expression data. To facilitate easy applications, CINPER provides an interactive visualization environment for a user to enter, search and edit relevant data and for the system to display (partial) results and prompt for additional data. Evaluation of CINPER on 17 well-studied pathways in the MetaCyc database shows that the program achieves an average recall rate of 76% and an average precision rate of 90% on the initial models; and a higher average recall rate at 87% and an average precision rate at 28% on the final models. The reduced precision rate in the final models versus the initial models reflects the reality that the final models have large numbers of novel genes that have no experimental evidences and hence are not yet collected in the MetaCyc database. To demonstrate the usefulness of this server, we have predicted an iron homeostasis gene network of Synechocystis sp. PCC6803 using the server. The predicted models along with the server can be accessed at http://csbl.bmb.uga.edu/cinper/.     

pDAWG: An Integrated Database for Plant Cell Wall Genes

We have recently developed a database, pDAWG, focused on information related to plant cell walls. Currently, pDAWG contains seven complete plant genomes, 12 complete algal genomes, along with computed information for individual proteins encoded in these genomes of the following types: (a) carbohydrate active enzyme (CAZy) family information when applicable; (b) phylogenetic trees of cell wall-related CAZy family proteins; (c) protein structure models if available; (d) physical and predicted interactions among proteins; (e) subcellular localization; (f) Pfam domain information; and (g) homology-based functional prediction. A querying system with a graphical interface allows a user to quickly compose information of different sorts about individual genes/proteins and to display the composite information in an intuitive manner, facilitating comparative analyses and knowledge discovery about cell wall genes. pDAWG can be accessed at http://csbl1.bmb.uga.edu/pDAWG/.     

A framework for generalized subspace pattern mining in high-dimensional datasets

Background

A generalized notion of biclustering involves the identification of patterns across subspaces within a data matrix. This approach is particularly well-suited to analysis of heterogeneous molecular biology datasets, such as those collected from populations of cancer patients. Different definitions of biclusters will offer different opportunities to discover information from datasets, making it pertinent to tailor the desired patterns to the intended application. This paper introduces ‘GABi’, a customizable framework for subspace pattern mining suited to large heterogeneous datasets. Most existing biclustering algorithms discover biclusters of only a few distinct structures. However, by enabling definition of arbitrary bicluster models, the GABi framework enables the application of biclustering to tasks for which no existing algorithm could be used.

Results

First, a series of artificial datasets were constructed to represent three clearly distinct scenarios for applying biclustering. With a bicluster model created for each distinct scenario, GABi is shown to recover the correct solutions more effectively than a panel of alternative approaches, where the bicluster model may not reflect the structure of the desired solution. Secondly, the GABi framework is used to integrate clinical outcome data with an ovarian cancer DNA methylation dataset, leading to the discovery that widespread dysregulation of DNA methylation associates with poor patient prognosis, a result that has not previously been reported. This illustrates a further benefit of the flexible bicluster definition of GABi, which is that it enables incorporation of multiple sources of data, with each data source treated in a specific manner, leading to a means of intelligent integrated subspace pattern mining across multiple datasets.

Conclusions

The GABi framework enables discovery of biologically relevant patterns of any specified structure from large collections of genomic data. An R implementation of the GABi framework is available through CRAN (http://cran.r-project.org/web/packages/GABi/index.html).

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0355-5) contains supplementary material, which is available to authorized users.     

Identification of Bicluster Regions in a Binary Matrix and Its Applications

Biclustering has emerged as an important approach to the analysis of large-scale datasets. A biclustering technique identifies a subset of rows that exhibit similar patterns on a subset of columns in a data matrix. Many biclustering methods have been proposed, and most, if not all, algorithms are developed to detect regions of “coherence” patterns. These methods perform unsatisfactorily if the purpose is to identify biclusters of a constant level. This paper presents a two-step biclustering method to identify constant level biclusters for binary or quantitative data. This algorithm identifies the maximal dimensional submatrix such that the proportion of non-signals is less than a pre-specified tolerance δ. The proposed method has much higher sensitivity and slightly lower specificity than several prominent biclustering methods from the analysis of two synthetic datasets. It was further compared with the Bimax method for two real datasets. The proposed method was shown to perform the most robust in terms of sensitivity, number of biclusters and number of serotype-specific biclusters identified. However, dichotomization using different signal level thresholds usually leads to different sets of biclusters; this also occurs in the present analysis.     

Integration of sequence-similarity and functional association information can overcome intrinsic problems in orthology mapping across bacterial genomes

Existing methods for orthologous gene mapping suffer from two general problems: (i) they are computationally too slow and their results are difficult to interpret for automated large-scale applications when based on phylogenetic analyses; or (ii) they are too prone to making mistakes in dealing with complex situations involving horizontal gene transfers and gene fusion due to the lack of a sound basis when based on sequence similarity information. We present a novel algorithm, Global Optimization Strategy (GOST), for orthologous gene mapping through combining sequence similarity and contextual (working partners) information, using a combinatorial optimization framework. Genome-scale applications of GOST show substantial improvements over the predictions by three popular sequence similarity-based orthology mapping programs. Our analysis indicates that our algorithm overcomes the intrinsic issues faced by sequence similarity-based methods, when orthology mapping involves gene fusions and horizontal gene transfers. Our program runs as efficiently as the most efficient sequence similarity-based algorithm in the public domain. GOST is freely downloadable at http://csbl.bmb.uga.edu/~maqin/GOST.     

A general framework for biclustering gene expression data

A large number of biclustering methods have been proposed to detect patterns in gene expression data. All these methods try to find some type of biclusters but no one can discover all the types of patterns in the data. Furthermore, researchers have to design new algorithms in order to find new types of biclusters/patterns that interest biologists. In this paper, we propose a novel approach for biclustering that, in general, can be used to discover all computable patterns in gene expression data. The method is based on the theory of Kolmogorov complexity. More precisely, we use Kolmogorov complexity to measure the randomness of submatrices as the merit of biclusters because randomness naturally consists in a lack of regularity, which is a common property of all types of patterns. On the basis of algorithmic probability measure, we develop a Markov Chain Monte Carlo algorithm to search for biclusters. Our method can also be easily extended to solve the problems of conventional clustering and checkerboard type biclustering. The preliminary experiments on simulated as well as real data show that our approach is very versatile and promising.     

Identification of coherent patterns in gene expression data using an efficient biclustering algorithm and parallel coordinate visualization

Background  

The DNA microarray technology allows the measurement of expression levels of thousands of genes under tens/hundreds of different conditions. In microarray data, genes with similar functions usually co-express under certain conditions only [1]. Thus, biclustering which clusters genes and conditions simultaneously is preferred over the traditional clustering technique in discovering these coherent genes. Various biclustering algorithms have been developed using different bicluster formulations. Unfortunately, many useful formulations result in NP-complete problems. In this article, we investigate an efficient method for identifying a popular type of biclusters called additive model. Furthermore, parallel coordinate (PC) plots are used for bicluster visualization and analysis.     

Phylogenetic comparative assembly

Background

Biclustering is an important analysis procedure to understand the biological mechanisms from microarray gene expression data. Several algorithms have been proposed to identify biclusters, but very little effort was made to compare the performance of different algorithms on real datasets and combine the resultant biclusters into one unified ranking.

Results

In this paper we propose differential co-expression framework and a differential co-expression scoring function to objectively quantify quality or goodness of a bicluster of genes based on the observation that genes in a bicluster are co-expressed in the conditions belonged to the bicluster and not co-expressed in the other conditions. Furthermore, we propose a scoring function to stratify biclusters into three types of co-expression. We used the proposed scoring functions to understand the performance and behavior of the four well established biclustering algorithms on six real datasets from different domains by combining their output into one unified ranking.

Conclusions

Differential co-expression framework is useful to provide quantitative and objective assessment of the goodness of biclusters of co-expressed genes and performance of biclustering algorithms in identifying co-expression biclusters. It also helps to combine the biclusters output by different algorithms into one unified ranking i.e. meta-biclustering.     

BiC2PAM: constraint-guided biclustering for biological data analysis with domain knowledge

Background

Biclustering has been largely used in biological data analysis, enabling the discovery of putative functional modules from omic and network data. Despite the recognized importance of incorporating domain knowledge to guide biclustering and guarantee a focus on relevant and non-trivial biclusters, this possibility has not yet been comprehensively addressed. This results from the fact that the majority of existing algorithms are only able to deliver sub-optimal solutions with restrictive assumptions on the structure, coherency and quality of biclustering solutions, thus preventing the up-front satisfaction of knowledge-driven constraints. Interestingly, in recent years, a clearer understanding of the synergies between pattern mining and biclustering gave rise to a new class of algorithms, termed as pattern-based biclustering algorithms. These algorithms, able to efficiently discover flexible biclustering solutions with optimality guarantees, are thus positioned as good candidates for knowledge incorporation. In this context, this work aims to bridge the current lack of solid views on the use of background knowledge to guide (pattern-based) biclustering tasks.

Methods

This work extends (pattern-based) biclustering algorithms to guarantee the satisfiability of constraints derived from background knowledge and to effectively explore efficiency gains from their incorporation. In this context, we first show the relevance of constraints with succinct, (anti-)monotone and convertible properties for the analysis of expression data and biological networks. We further show how pattern-based biclustering algorithms can be adapted to effectively prune of the search space in the presence of such constraints, as well as be guided in the presence of biological annotations. Relying on these contributions, we propose BiClustering with Constraints using PAttern Mining (BiC2PAM), an extension of BicPAM and BicNET biclustering algorithms.

Results

Experimental results on biological data demonstrate the importance of incorporating knowledge within biclustering to foster efficiency and enable the discovery of non-trivial biclusters with heightened biological relevance.

Conclusions

This work provides the first comprehensive view and sound algorithm for biclustering biological data with constraints derived from user expectations, knowledge repositories and/or literature.

     

PicXAA: greedy probabilistic construction of maximum expected accuracy alignment of multiple sequences

Accurate tools for multiple sequence alignment (MSA) are essential for comparative studies of the function and structure of biological sequences. However, it is very challenging to develop a computationally efficient algorithm that can consistently predict accurate alignments for various types of sequence sets. In this article, we introduce PicXAA (Probabilistic Maximum Accuracy Alignment), a probabilistic non-progressive alignment algorithm that aims to find protein alignments with maximum expected accuracy. PicXAA greedily builds up the multiple alignment from sequence regions with high local similarities, thereby yielding an accurate global alignment that effectively grasps the local similarities among sequences. Evaluations on several widely used benchmark sets show that PicXAA constantly yields accurate alignment results on a wide range of reference sets, with especially remarkable improvements over other leading algorithms on sequence sets with local similarities. PicXAA source code is freely available at: http://www.ece.tamu.edu/∼bjyoon/picxaa/.     

A polynomial time biclustering algorithm for finding approximate expression patterns in gene expression time series

Background  

The ability to monitor the change in expression patterns over time, and to observe the emergence of coherent temporal responses using gene expression time series, obtained from microarray experiments, is critical to advance our understanding of complex biological processes. In this context, biclustering algorithms have been recognized as an important tool for the discovery of local expression patterns, which are crucial to unravel potential regulatory mechanisms. Although most formulations of the biclustering problem are NP-hard, when working with time series expression data the interesting biclusters can be restricted to those with contiguous columns. This restriction leads to a tractable problem and enables the design of efficient biclustering algorithms able to identify all maximal contiguous column coherent biclusters.     

libSRES: a C library for stochastic ranking evolution strategy for parameter estimation

SUMMARY: Estimation of kinetic parameters in a biochemical pathway or network represents a common problem in systems studies of biological processes. We have implemented a C library, named libSRES, to facilitate a fast implementation of computer software for study of non-linear biochemical pathways. This library implements a (mu, lambda)-ES evolutionary optimization algorithm that uses stochastic ranking as the constraint handling technique. Considering the amount of computing time it might require to solve a parameter-estimation problem, an MPI version of libSRES is provided for parallel implementation, as well as a simple user interface. libSRES is freely available and could be used directly in any C program as a library function. We have extensively tested the performance of libSRES on various pathway parameter-estimation problems and found its performance to be satisfactory. AVAILABILITY: The source code (in C) is free for academic users at http://csbl.bmb.uga.edu/~jix/science/libSRES/     

Genome Sequence of Aggregatibacter actinomycetemcomitans Serotype c Strain D11S-1

Aggregatibacter actinomycetemcomitans is a major etiological agent of periodontitis. Here we report the complete genome sequence of serotype c strain D11S-1, which was recovered from the subgingival plaque of a patient diagnosed with generalized aggressive periodontitis.Aggregatibacter actinomycetemcomitans is a major etiologic agent of human periodontal disease, in particular aggressive periodontitis (12). The natural population of A. actinomycetemcomitans is clonal (7). Six A. actinomycetemcomitans serotypes are distinguished based on the structural and serological characteristics of the O antigen of LPS (6, 7). Three of the serotypes (a, b, and c) comprise >80% of all strains, and each serotype represents a distinct clonal lineage (1, 6, 7). Serotype c strain D11S-1 was cultured from a subgingival plaque sample of a patient diagnosed with generalized aggressive periodontitis. The complete genome sequencing of the strain was determined by 454 pyrosequencing (10), which achieved 25× coverage. Assembly was performed using the Newbler assembler (454, Branford, CT) and generated 199 large contigs, with 99.3% of the bases having a quality score of 40 and above. The contigs were aligned with the genome of the sequenced serotype b strain HK1651 (http://www.genome.ou.edu/act.html) using software written in house. The putative contig gaps were then closed by primer walking and sequencing of PCR products over the gaps. The final genome assembly was further confirmed by comparison of an in silico NcoI restriction map to the experimental map generated by optical mapping (8). The genome structure of the D11S-1 strain was compared to that of the sequenced strain HK1651 using the program MAUVE (2, 3). The automated annotation was done using a protocol similar to the annotation engine service at The Institute for Genomic Research/J. Craig Venter Institute with some local modifications. Briefly, protein-coding genes were identified using Glimmer3 (4). Each protein sequence was then annotated by comparing to the GenBank nonredundant protein database. BLAST-Extend-Repraze was applied to the predicted genes to identify genes that might have been truncated due to a frameshift mutation or premature stop codon. tRNA and rRNA genes were identified by using tRNAScan-SE (9) and a similarity search to our in-house RNA database, respectively.The D11S-1 circular genome contains 2,105,764 nucleotides, a GC content of 44.55%, 2,134 predicted coding sequences, and 54 tRNA and 19 rRNA genes (see additional data at http://expression.washington.edu/bumgarnerlab/publications.php). The distribution of predicted genes based on functional categories was similar between D11S-1 and HK1651 (http://expression.washington.edu/bumgarnerlab/publications.php). One hundred six and 86 coding sequences were unique to strain D11S-1 and HK1651, respectively (http://expression.washington.edu/bumgarnerlab/publications.php). Genomic islands were identified based on annotations for strain HK1651 and based on manual inspection of contiguous D11S-1 specific DNA regions with G+C bias (http://expression.washington.edu/bumgarnerlab/publications.php). Among 12 identified genomics islands, 5 (B, C, D, E and G; cytolethal distending toxin gene cluster, tight adherence gene cluster, O-antigen biosynthesis and transport gene cluster, leukotoxin gene cluster, and lipoligosaccharide biosynthesis enzyme gene, respectively) correspond to islands 2 to 5 and 8 of strain HK1651 (http://www.oralgen.lanl.gov/) (5). Island F (∼5 kb) is homologous to a portion of the 12.5-kb island 7 in HK1651. Five genomic islands (H to L) were unique to strain D11S-1. The remaining island (A) is a fusion of genomic islands 1 and 6, in strain HK1651. The genome of D11S-1 is largely in synteny with the genome of the sequenced serotype b strain HK1651 but contained several large-scale genomic rearrangements.Strain D11S-1 harbors a 43-kb bacteriophage and two plasmids of 31 and 23 kb (http://expression.washington.edu/bumgarnerlab/publications.php). Excluding an ∼9-kb region of low homology, the phage showed >90% nucleotide sequence identity with AaΦ23 (11). A 49-bp attB site (11) was identified at coordinates 2,024,825 to 2,024,873. The location of the inserted phage was identified in the optical map of strain D11S-1 and further confirmed by PCR amplification and sequencing of the regions flanking the insertion site. A closed circular form of the phage was also detected in strain D11S-1 by PCR analysis of the phage ends. The 23-kb plasmid is homologous to pVT745 (92% nucleotide identities). The 31-kb plasmid is a novel plasmid. It has significant homologies in short regions (<2 kb) to Haemophilus influenzae biotype aegyptius plasmid pF1947 and other plasmids.