BACKGROUND: One of the main challenges when analyzing complex metagenomics data is the fact that large amounts of information need to be presented in a comprehensive and easy-to-navigate way. In the process of analyzing FASTQ sequencing data, visualizing which organisms are present in the data can be useful, especially with metagenomics data or data suspected to be contaminated. Here, we describe the development and application of a command-line tool, Keanu, for visualizing and exploring sample content in metagenomics data. We developed Keanu as an interactive tool to make viewing complex data easier. RESULTS: Keanu, a tool for exploring sequence content, helps a user to understand the presence and abundance of organisms in a sample by analyzing alignments against a database that contains taxonomy data and displaying them in an interactive web page. The content of a sample can be presented either as a collapsible tree, with node size indicating abundance, or as a bilevel partition graph, with arc size indicating abundance. Here, we illustrate how Keanu works by exploring shotgun metagenomics data from a sample collected from a bluff that contained paleosols and a krotovina in an alpine site in Ft. Greely, Alaska. CONCLUSIONS: Keanu provides a simple means by which researchers can explore and visualize species present in sequence data generated from complex communities and environments. Keanu is written in Python and is freely available at https://github.com/IGBB/keanu .

The tenth annual conference of the MidSouth Computational Biology and Bioinformatics Society (MCBIOS 2013), "The 10th Anniversary in a Decade of Change: Discovery in a Sea of Data", took place at the Stoney Creek Inn & Conference Center in Columbia, Missouri on April 5-6, 2013. This year's Conference Chairs were Gordon Springer and Chi-Ren Shyu from the University of Missouri and Edward Perkins from the US Army Corps of Engineers Engineering Research and Development Center, who is also the current MCBIOS President (2012-3). There were 151 registrants and a total of 111 abstracts (51 oral presentations and 60 poster session abstracts).

BACKGROUND: Pathway alterations reflected as changes in gene expression regulation and gene interaction can result from cellular exposure to toxicants. Such information is often used to elucidate toxicological modes of action. From a risk assessment perspective, alterations in biological pathways are a rich resource for setting toxicant thresholds, which may be more sensitive and mechanism-informed than traditional toxicity endpoints. Here we developed a novel differential networks (DNs) approach to connect pathway perturbation with toxicity threshold setting. METHODS: Our DNs approach consists of 6 steps: time-series gene expression data collection, identification of altered genes, gene interaction network reconstruction, differential edge inference, mapping of genes with differential edges to pathways, and establishment of causal relationships between chemical concentration and perturbed pathways. A one-sample Gaussian process model and a linear regression model were used to identify genes that exhibited significant profile changes across an entire time course and between treatments, respectively. Interaction networks of differentially expressed (DE) genes were reconstructed for different treatments using a state space model and then compared to infer differential edges/interactions. DE genes possessing differential edges were mapped to biological pathways in databases such as KEGG pathways. RESULTS: Using the DNs approach, we analyzed a time-series Escherichia coli live cell gene expression dataset consisting of 4 treatments (control, 10, 100, 1000 mg/L naphthenic acids, NAs) and 18 time points. Through comparison of reconstructed networks and construction of differential networks, 80 genes were identified as DE genes with a significant number of differential edges, and 22 KEGG pathways were altered in a concentration-dependent manner. Some of these pathways were perturbed to a degree as high as 70% even at the lowest exposure concentration, implying a high sensitivity of our DNs approach. CONCLUSIONS: Findings from this proof-of-concept study suggest that our approach has a great potential in providing a novel and sensitive tool for threshold setting in chemical risk assessment. In future work, we plan to analyze more time-series datasets with a full spectrum of concentrations and sufficient replications per treatment. The pathway alteration-derived thresholds will also be compared with those derived from apical endpoints such as cell growth rate.

Structural analysis over well studied transcriptional regulatory networks indicates that these complex networks are made up of small set of reoccurring patterns called motifs. While information theoretic approaches have been immensely popular, these approaches rely on inferring the regulatory networks by aggregating pair-wise interactions. In this paper, we propose novel structure based information theoretic approaches to infer transcriptional regulatory networks from the microarray expression data. The core idea is to go beyond pair-wise interactions and consider more complex structures as found in motifs. While this increases the network inference complexity over pair-wise interaction based approaches, it achieves much higher accuracy and yet is scalable to genome-level inference. Detailed performance analyses based on benchmark precision and recall metrics on the known Escherichia coli's transcriptional regulatory network indicates that the accuracy of the proposed algorithms is consistently higher in comparison to popular algorithms such as context likelihood of relatedness (CLR), relevance networks (RN) and GEneNetwork Inference with Ensemble of trees (GENIE3). In the proposed approaches the size of structures was limited to three node cases (any node and its two parents). Analysis on a smaller network showed that the performance of the algorithm improved when more complex structures were considered for inference, although such higher level structures may be computationally challenging to infer networks at the genome scale.

Proteins search along DNA for targets (e.g. transcription initiation sequences) through a combination of sliding, jumping, and intersegment transfers, wherein the sliding process proceeds until the protein-DNA complex dissociates. As such, we propose a diffusion-reaction model of the sliding phase of a total search process, and study the effect of varying reaction rate on the detailed search kinetics. With increasing dissociation rate, only the "fastest" proteins survive to explore downstream sequences, but at the price of increasing rarity, allowing for fluctuations to dominate the behavior at late times and long distances. Predictions of this model for the total search time agree with experimental estimates across an order of magnitude, providing bounds on dissociation rates that suggest a balance is established between maximizing downstream exploration of the DNA while minimizing the number of rare trajectories to guarantee the greatest chance of a successful search.

Most of the popular approaches towards gene regulatory networks inference e.g., Dynamic Bayesian Networks, Probabilistic Boolean Networks etc. are computationally complex and can only be used to infer small networks. While high-throughput experimental methods to monitor gene expression provide data for thousands of genes, these methods cannot fully utilize the entire spectrum of generated data. With the advent of information theoretic approaches in the last decade, the inference of larger regulatory networks from high throughput microarray data has become possible. Not all information theoretic approaches are scalable though; only methods that infer networks considering pair-wise interactions between genes such as, relevance networks, ARACNE and CLR to name a few, can be scaled upto genome-level inference. ARACNE and CLR attempt to improve the inference accuracy by pruning false edges, and do not bring in newer true edges. REVEAL is another information theoretic approach, which considers mutual information between multiple genes. As it goes beyond pair wise interactions, this approach was not scalable and could only infer small networks. In this paper, we propose two algorithms to improve the scalability of REVEAL by utilizing a transcription factor list (that can be predicted from the gene sequences) as prior knowledge and implementing time lags to further reduce the potential transcription factors that may regulate a gene. Our proposed S-REVEAL algorithms can infer larger networks with higher accuracy than the popular CLR algorithm.

BACKGROUND: Gene Regulatory Networks (GRNs) provide integrated views of gene interactions that control biological processes. Many public databases contain biological interactions extracted from experimentally validated literature reports, but most furnish only information for a few genetic model organisms. In order to provide a bioinformatic tool for researchers who work with non-model organisms, we developed RefNetBuilder, a new platform that allows construction of putative reference pathways or GRNs from expressed sequence tags (ESTs). RESULTS: RefNetBuilder was designed to have the flexibility to extract and archive pathway or GRN information from public databases such as the Kyoto Encyclopedia of Genes and Genomes (KEGG). It features sequence alignment tools such as BLAST to allow mapping ESTs to pathways and GRNs in model organisms. A scoring algorithm was incorporated to rank and select the best match for each query EST. We validated RefNetBuilder using DNA sequences of Caenorhabditis elegans, a model organism having manually curated KEGG pathways. Using the earthworm Eisenia fetida as an example, we demonstrated the functionalities and features of RefNetBuilder. CONCLUSIONS: The RefNetBuilder provides a standalone application for building reference GRNs for non-model organisms on a number of operating system platforms with standard desktop computer hardware. As a new bioinformatic tool aimed for constructing putative GRNs for non-model organisms that have only ESTs available, RefNetBuilder is especially useful to explore pathway- or network-related information in these organisms.

BACKGROUND: Proteins search along the DNA for targets, such as transcription initiation sequences, according to one-dimensional diffusion, which is interrupted by micro- and macro-hopping events and intersegmental transfers that occur under close packing conditions. RESULTS: A one-dimensional diffusion-reaction model in the form of difference-differential equations is proposed to analyze the nonequilibrium protein sliding kinetics along a segment of bacterial DNA. A renormalization approach is used to derive an expression for the mean first-passage time to arrive at sites downstream of the origin from the occupation probabilities given by the individual transport equations. Monte Carlo simulations are employed to assess the validity of the proposed approach, and all results are interpreted within the context of bacterial transcription. CONCLUSIONS: Mean first-passage times decrease with increasing reaction rates, indicating that, on average, surviving proteins more rapidly locate downstream targets than their reaction-free counterparts, but at the price of increasing rarity. Two qualitatively different screening regimes are identified according to whether the search process operates under "small" or "large" values for the dissociation rate of the protein-DNA complex. Lower bounds are placed on the overall search time for varying reactive conditions. Good agreement with experimental estimates requires the reaction rate reside near the transition between both screening regimes, suggesting that biology balances a need for rapid searches against maximum exploration during each round of the sliding phase.

The Eighth Annual Conference of the MidSouth Computational Biology and Bioinformatics Society (MCBIOS’2011) was held in College Station, Texas on April 1-2, 2011. The Conference General Chair was Ulisses Braga-Neto, the MCBIOS President for the 2010-2011 term, from Texas A&M University. There were nearly 200 registrants and 140 abstracts were submitted, divided into 48 oral presentation abstracts and 92 poster session abstracts.

BACKGROUND: A number of models and algorithms have been proposed in the past for gene regulatory network (GRN) inference; however, none of them address the effects of the size of time-series microarray expression data in terms of the number of time-points. In this paper, we study this problem by analyzing the behaviour of three algorithms based on information theory and dynamic Bayesian network (DBN) models. These algorithms were implemented on different sizes of data generated by synthetic networks. Experiments show that the inference accuracy of these algorithms reaches a saturation point after a specific data size brought about by a saturation in the pair-wise mutual information (MI) metric; hence there is a theoretical limit on the inference accuracy of information theory based schemes that depends on the number of time points of micro-array data used to infer GRNs. This illustrates the fact that MI might not be the best metric to use for GRN inference algorithms. To circumvent the limitations of the MI metric, we introduce a new method of computing time lags between any pair of genes and present the pair-wise time lagged Mutual Information (TLMI) and time lagged Conditional Mutual Information (TLCMI) metrics. Next we use these new metrics to propose novel GRN inference schemes which provides higher inference accuracy based on the precision and recall parameters. RESULTS: It was observed that beyond a certain number of time-points (i.e., a specific size) of micro-array data, the performance of the algorithms measured in terms of the recall-to-precision ratio saturated due to the saturation in the calculated pair-wise MI metric with increasing data size. The proposed algorithms were compared to existing approaches on four different biological networks. The resulting networks were evaluated based on the benchmark precision and recall metrics and the results favour our approach. CONCLUSIONS: To alleviate the effects of data size on information theory based GRN inference algorithms, novel time lag based information theoretic approaches to infer gene regulatory networks have been proposed. The results show that the time lags of regulatory effects between any pair of genes play an important role in GRN inference schemes.

BACKGROUND: The Quail Genomics knowledgebase (http://www.quailgenomics.info) has been initiated to share and develop functional genomic data for Northern bobwhite (Colinus virginianus). This web-based platform has been designed to allow researchers to perform analysis and curate genomic information for this non-model species that has little supporting information in GenBank. DESCRIPTION: A multi-tissue, normalized cDNA library generated for Northern bobwhite was sequenced using 454 Life Sciences next generation sequencing. The Quail Genomics knowledgebase represents the 478,142 raw ESTs generated from the sequencing effort in addition to assembled nucleotide and protein sequences including 21,980 unigenes annotated with meta-data. A normalized MySQL relational database was established to provide comprehensive search parameters where meta-data can be retrieved using functional and structural information annotation such as gene name, pathways and protein domain. Additionally, blast hit cutoff levels and microarray expression data are available for batch searches. A Gene Ontology (GO) browser from Amigo is locally hosted providing 8,825 unigenes that are putative orthologs to chicken genes. In an effort to address over abundance of Northern bobwhite unigenes (71,384) caused by non-overlapping contigs and singletons, we have built a pipeline that generates scaffolds/supercontigs by aligning partial sequence fragments against the indexed protein database of chicken to build longer sequences that can be visualized in a web browser. CONCLUSION: Our effort provides a central repository for storage and a platform for functional interrogation of the Northern bobwhite sequences providing comprehensive GO annotations, meta-data and a scaffold building pipeline. The Quail Genomics knowledgebase will be integrated with Japanese quail (Coturnix coturnix) data in future builds and incorporate a broader platform for these avian species.

The seventh annual Midsouth Computational Biology and Bioinformatics (MCBIOS) conference took place February 19 and 20, 2010 at Arkansas State University in Jonesboro, AR, presided over by Daniel Berleant, this year’s President of MCBIOS. Keynote speakers were Elaine Ostrander of NIH, renowned for her work on dog genomics, Clayton Naeve, CIO of St. Jude Children's Research Hospital, and Robert Cottingham, Leader of the Computational Biology and Bioinformatics Group, Oak Ridge National Laboratory. The record-breaking attendance exceeded 200 participants which necessitated parallel talk sessions for the first time. Student oral presentation award winners were Heidi Pagan (first place) of Mississippi State University (MSU), Juliet Tang (second place) of MSU, and Aleksandra Markovets (third) of Mississippi Valley State University. In addition, a record number of posters were also presented. Due to the number of posters, student poster awards were given in two categories, Biological Focus and Computational Focus. Winners were N. Platt and V. Chaitankar (1st place), G. Cooper and L. Pillai (2nd), and M. Ammari and C. Gearheart (3rd). Reflecting the integration of these foci in the field, winners in one category frequently scored high in the other category as well. MCBIOS is also pleased to have this year acquired legal status as a non-profit organization.

MOTIVATION: An important contribution to the Gene Ontology (GO) project is to develop tools that facilitate the creation, maintenance and use of ontologies. Several tools have been created for communicating and using the GO project. However, a limitation with most of these tools is that they suffer from lack of a comprehensive search facility. We developed a web application, GOfetcher, with a very comprehensive search facility for the GO project and a variety of output formats for the results. GOfetcher has three different levels for searching the GO: 'Quick Search', 'Advanced Search' and 'Upload Files' for searching. The application includes a unique search option which generates gene information given a nucleotide or protein accession number which can then be used in generating GO information. The output data in GOfetcher can be saved into several different formats; including spreadsheet, comma-separated values and the extensible markup language (XML) format. The database is available at http://mcbc.usm.edu/gofetcher/.

Microarray technology is widely applied to address complex scientific questions. However, there remain fundamental issues of how to design experiments to ensure that the resulting data enables robust statistical analysis. Interwoven loop design has several advantages over other designs. However it suffers in the complexity of design. We have implemented an online web application which allows users to find optimal loop designs for two-color microarray experiments. Given a number of conditions (such as treatments or time points) and replicates, the application will find the best possible design of the experiment and output experimental parameters. It is freely available from http://mcbc.usm.edu/iloop.

BACKGROUND: The regulation of gene expression is achieved through gene regulatory networks (GRNs) in which collections of genes interact with one another and other substances in a cell. In order to understand the underlying function of organisms, it is necessary to study the behavior of genes in a gene regulatory network context. Several computational approaches are available for modeling gene regulatory networks with different datasets. In order to optimize modeling of GRN, these approaches must be compared and evaluated in terms of accuracy and efficiency. RESULTS: In this paper, two important computational approaches for modeling gene regulatory networks, probabilistic Boolean network methods and dynamic Bayesian network methods, are compared using a biological time-series dataset from the Drosophila Interaction Database to construct a Drosophila gene network. A subset of time points and gene samples from the whole dataset is used to evaluate the performance of these two approaches. CONCLUSION: The comparison indicates that both approaches had good performance in modeling the gene regulatory networks. The accuracy in terms of recall and precision can be improved if a smaller subset of genes is selected for inferring GRNs. The accuracy of both approaches is dependent upon the number of selected genes and time points of gene samples. In all tested cases, DBN identified more gene interactions and gave better recall than PBN.

BACKGROUND: Eisenia fetida, commonly known as red wiggler or compost worm, belongs to the Lumbricidae family of the Annelida phylum. Little is known about its genome sequence although it has been extensively used as a test organism in terrestrial ecotoxicology. In order to understand its gene expression response to environmental contaminants, we cloned 4032 cDNAs or expressed sequence tags (ESTs) from two E. fetida libraries enriched with genes responsive to ten ordnance related compounds using suppressive subtractive hybridization-PCR. RESULTS: A total of 3144 good quality ESTs (GenBank dbEST accession number EH669363-EH672369 and EL515444-EL515580) were obtained from the raw clone sequences after cleaning. Clustering analysis yielded 2231 unique sequences including 448 contigs (from 1361 ESTs) and 1783 singletons. Comparative genomic analysis showed that 743 or 33% of the unique sequences shared high similarity with existing genes in the GenBank nr database. Provisional function annotation assigned 830 Gene Ontology terms to 517 unique sequences based on their homology with the annotated genomes of four model organisms Drosophila melanogaster, Mus musculus, Saccharomyces cerevisiae, and Caenorhabditis elegans. Seven percent of the unique sequences were further mapped to 99 Kyoto Encyclopedia of Genes and Genomes pathways based on their matching Enzyme Commission numbers. All the information is stored and retrievable at a highly performed, web-based and user-friendly relational database called EST model database or ESTMD version 2. CONCLUSION: The ESTMD containing the sequence and annotation information of 4032 E. fetida ESTs is publicly accessible at http://mcbc.usm.edu/estmd/.

BACKGROUND: Cell culture systems are useful in studying toxicological effects of chemicals such as Hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX), however little is known as to how accurately isolated cells reflect responses of intact organs. In this work, we compare transcriptional responses in livers of Sprague-Dawley rats and primary hepatocyte cells after exposure to RDX to determine how faithfully the in vitro model system reflects in vivo responses. RESULTS: Expression patterns were found to be markedly different between liver tissue and primary cell cultures before exposure to RDX. Liver gene expression was enriched in processes important in toxicology such as metabolism of amino acids, lipids, aromatic compounds, and drugs when compared to cells. Transcriptional responses in cells exposed to 7.5, 15, or 30 mg/L RDX for 24 and 48 hours were different from those of livers isolated from rats 24 hours after exposure to 12, 24, or 48 mg/Kg RDX. Most of the differentially expressed genes identified across conditions and treatments could be attributed to differences between cells and tissue. Some similarity was observed in RDX effects on gene expression between tissue and cells, but also significant differences that appear to reflect the state of the cell or tissue examined. CONCLUSION: Liver tissue and primary cells express different suites of genes that suggest they have fundamental differences in their cell physiology. Expression effects related to RDX exposure in cells reflected a fraction of liver responses indicating that care must be taken in extrapolating from primary cells to whole animal organ toxicity effects.