Temporal graphical models for cross-species gene regulatory network discovery

Many genes and biological processes function in similar ways across different species. Cross-species gene expression analysis, as a powerful tool to characterize the dynamical properties of the cell, has found a number of applications, such as identifying a conserved core set of cell cycle genes. However, to the best of our knowledge, there is limited effort on developing appropriate techniques to capture the causality relations between genes from time-series microarray data across species. In this paper, we present hidden Markov random field regression with L(1) penalty to uncover the regulatory network structure for different species. The algorithm provides a framework for sharing information across species via hidden component graphs and is able to incorporate domain knowledge across species easily. We demonstrate our method on two synthetic datasets and apply it to discover causal graphs from innate immune response data.     

Validation of candidate gene markers for marker-assisted selection of potato cultivars with improved tuber quality

Tuber yield, starch content, starch yield and chip color are complex traits that are important for industrial uses and food processing of potato. Chip color depends on the quantity of reducing sugars glucose and fructose in the tubers, which are generated by starch degradation. Reducing sugars accumulate when tubers are stored at low temperatures. Early and efficient selection of cultivars with superior yield, starch yield and chip color is hampered by the fact that reliable phenotypic selection requires multiple year and location trials. Application of DNA-based markers early in the breeding cycle, which are diagnostic for superior alleles of genes that control natural variation of tuber quality, will reduce the number of clones to be evaluated in field trials. Association mapping using genes functional in carbohydrate metabolism as markers has discovered alleles of invertases and starch phosphorylases that are associated with tuber quality traits. Here, we report on new DNA variants at loci encoding ADP-glucose pyrophosphorylase and the invertase Pain-1, which are associated with positive or negative effect with chip color, tuber starch content and starch yield. Marker-assisted selection (MAS) and marker validation were performed in tetraploid breeding populations, using various combinations of 11 allele-specific markers associated with tuber quality traits. To facilitate MAS, user-friendly PCR assays were developed for specific candidate gene alleles. In a multi-parental population of advanced breeding clones, genotypes were selected for having different combinations of five positive and the corresponding negative marker alleles. Genotypes combining five positive marker alleles performed on average better than genotypes with four negative alleles and one positive allele. When tested individually, seven of eight markers showed an effect on at least one quality trait. The direction of effect was as expected. Combinations of two to three marker alleles were identified that significantly improved average chip quality after cold storage and tuber starch content. In F1 progeny of a single-cross combination, MAS with six markers did not give the expected result. Reasons and implications for MAS in potato are discussed.     

Efficient combination of multiple word models for improved sequence comparison

MOTIVATION: Studies of efficient and sensitive sequence comparison methods are driven by a need to find homologous regions of weak similarity between large genomes. RESULTS: We describe an improved method for finding similar regions between two sets of DNA sequences. The new method generalizes existing methods by locating word matches between sequences under two or more word models and extending word matches into high-scoring segment pairs (HSPs). The method is implemented as a computer program named DDS2. Experimental results show that DDS2 can find more HSPs by using several word models than by using one word model. AVAILABILITY: The DDS2 program is freely available for academic use in binary code form at http://bioinformatics.iastate.edu/aat/align/align.html and in source code form from the corresponding author.     

Dinucleotide controlled null models for comparative RNA gene prediction

Background  

Comparative prediction of RNA structures can be used to identify functional noncoding RNAs in genomic screens. It was shown recently by Babaket al. [BMC Bioinformatics. 8:33] that RNA gene prediction programs can be biased by the genomic dinucleotide content, in particular those programs using a thermodynamic folding model including stacking energies. As a consequence, there is need for dinucleotide-preserving control strategies to assess the significance of such predictions. While there have been randomization algorithms for single sequences for many years, the problem has remained challenging for multiple alignments and there is currently no algorithm available.     

A method for cross-species gene expression analysis with high-density oligonucleotide arrays

DNA microarrays have been widely used in gene expression analysis of biological processes. Due to a lack of sequence information, the applications have been largely restricted to humans and a few model organisms. Presented within this study are results of the cross-species hybridization with Affymetrix human high-density oligonucleotide arrays or GeneChip® using distantly related mammalian species; cattle, pig and dog. Based on the unique feature of the Affymetrix GeneChip® where every gene is represented by multiple probes, we hypothesized that sequence conservation within mammals is high enough to generate sufficient signals from some of the probes for expression analysis. We demonstrated that while overall hybridization signals are low for cross-species hybridization, a few probes of most genes still generated signals equivalent to the same-species hybridization. By masking the poorly hybridized probes electronically, the remaining probes provided reliable data for gene expression analysis. We developed an algorithm to select the reliable probes for analysis utilizing the match/mismatch feature of GeneChip®. When comparing gene expression between two tissues using the selected probes, we found a linear correlation between the cross-species and same-species hybridization. In addition, we validated cross-species hybridization results by quantitative PCR using randomly selected genes. The method shown herein could be applied to both plant and animal research.     

Developing optimal prediction models for cancer classification using gene expression data

Microarrays can provide genome-wide expression patterns for various cancers, especially for tumor sub-types that may exhibit substantially different patient prognosis. Using such gene expression data, several approaches have been proposed to classify tumor sub-types accurately. These classification methods are not robust, and often dependent on a particular training sample for modelling, which raises issues in utilizing these methods to administer proper treatment for a future patient. We propose to construct an optimal, robust prediction model for classifying cancer sub-types using gene expression data. Our model is constructed in a step-wise fashion implementing cross-validated quadratic discriminant analysis. At each step, all identified models are validated by an independent sample of patients to develop a robust model for future data. We apply the proposed methods to two microarray data sets of cancer: the acute leukemia data by Golub et al. and the colon cancer data by Alon et al. We have found that the dimensionality of our optimal prediction models is relatively small for these cases and that our prediction models with one or two gene factors outperforms or has competing performance, especially for independent samples, to other methods based on 50 or more predictive gene factors. The methodology is implemented and developed by the procedures in R and Splus. The source code can be obtained at http://hesweb1.med.virginia.edu/bioinformatics.     

An improved Bayesian network method for reconstructing gene regulatory network based on candidate auto selection

Background

The reconstruction of gene regulatory network (GRN) from gene expression data can discover regulatory relationships among genes and gain deep insights into the complicated regulation mechanism of life. However, it is still a great challenge in systems biology and bioinformatics. During the past years, numerous computational approaches have been developed for this goal, and Bayesian network (BN) methods draw most of attention among these methods because of its inherent probability characteristics. However, Bayesian network methods are time consuming and cannot handle large-scale networks due to their high computational complexity, while the mutual information-based methods are highly effective but directionless and have a high false-positive rate.

Results

To solve these problems, we propose a Candidate Auto Selection algorithm (CAS) based on mutual information and breakpoint detection to restrict the search space in order to accelerate the learning process of Bayesian network. First, the proposed CAS algorithm automatically selects the neighbor candidates of each node before searching the best structure of GRN. Then based on CAS algorithm, we propose a globally optimal greedy search method (CAS + G), which focuses on finding the highest rated network structure, and a local learning method (CAS + L), which focuses on faster learning the structure with little loss of quality.

Conclusion

Results show that the proposed CAS algorithm can effectively reduce the search space of Bayesian networks through identifying the neighbor candidates of each node. In our experiments, the CAS + G method outperforms the state-of-the-art method on simulation data for inferring GRNs, and the CAS + L method is significantly faster than the state-of-the-art method with little loss of accuracy. Hence, the CAS based methods effectively decrease the computational complexity of Bayesian network and are more suitable for GRN inference.

     

Large-scale prokaryotic gene prediction and comparison to genome annotation

MOTIVATION: Prokaryotic genomes are sequenced and annotated at an increasing rate. The methods of annotation vary between sequencing groups. It makes genome comparison difficult and may lead to propagation of errors when questionable assignments are adapted from one genome to another. Genome comparison either on a large or small scale would be facilitated by using a single standard for annotation, which incorporates a transparency of why an open reading frame (ORF) is considered to be a gene. RESULTS: A total of 143 prokaryotic genomes were scored with an updated version of the prokaryotic genefinder EasyGene. Comparison of the GenBank and RefSeq annotations with the EasyGene predictions reveals that in some genomes up to approximately 60% of the genes may have been annotated with a wrong start codon, especially in the GC-rich genomes. The fractional difference between annotated and predicted confirms that too many short genes are annotated in numerous organisms. Furthermore, genes might be missing in the annotation of some of the genomes. We predict 41 of 143 genomes to be over-annotated by >5%, meaning that too many ORFs are annotated as genes. We also predict that 12 of 143 genomes are under-annotated. These results are based on the difference between the number of annotated genes not found by EasyGene and the number of predicted genes that are not annotated in GenBank. We argue that the average performance of our standardized and fully automated method is slightly better than the annotation.     

Fine mapping and candidate gene prediction of a major QTL for kernel number per ear in maize

Kernel number per ear (KNE) is a maize yield component and an important breeding target for improving grain yield. As a complex quantitative trait, KNE has been assumed to be controlled by a set of quantitative trait loci (QTLs) with minor effects. Identification and genetic evaluation of these QTLs are prerequisites for improving KNE with a molecular breeding approach. In this study, we developed the chromosome segment introgression line SL19-41, which exhibited 95.60% recovery of the Ye478 background and showed a higher KNE and grain yield. The plant architecture and flowering time of SL19-41 were not significantly different from those of Ye478. We employed introgression line (IL)-derived mapping populations and identified a major QTL, KNE4, which is partially dominant. KNE4 was validated in a backcross population and a set of sub-introgression lines and was delimited to a 440-kb genomic region in Bin4.07. An allele included in the introgression fragment had a synergistic effect, noticeably increasing KNE and showing the potential to improve KNE in Ye478. Subsequently, the association between sequence polymorphism in the QTL interval and KNE variation revealed a putative candidate gene that encoded a long-chain acyl-CoA synthetase responsible. This result provides an available locus for the molecular improvement of KNE and for the isolation of functional genes underlying this QTL.     

An improved parameter estimation and comparison for soft tissue constitutive models containing an exponential function

Motivated by the well-known result that stiffness of soft tissue is proportional to the stress, many of the constitutive laws for soft tissues contain an exponential function. In this work, we analyze properties of the exponential function and how it affects the estimation and comparison of elastic parameters for soft tissues. In particular, we find that as a consequence of the exponential function there are lines of high covariance in the elastic parameter space. As a result, one can have widely varying mechanical parameters defining the tissue stiffness but similar effective stress–strain responses. Drawing from elementary algebra, we propose simple changes in the norm and the parameter space, which significantly improve the convergence of parameter estimation and robustness in the presence of noise. More importantly, we demonstrate that these changes improve the conditioning of the problem and provide a more robust solution in the case of heterogeneous material by reducing the chances of getting trapped in a local minima. Based upon the new insight, we also propose a transformed parameter space which will allow for rational parameter comparison and avoid misleading conclusions regarding soft tissue mechanics.     

A comparison of probe-level and probeset models for small-sample gene expression data

Background  

Statistical methods to tentatively identify differentially expressed genes in microarray studies typically assume larger sample sizes than are practical or even possible in some settings.     

致病基因的定位候选克隆

基因组研究的迅猛发展,使我们有必要重新审视致病基因克隆的各种策略与技术,以及人类基因组研究在致病基因克隆中的作用。定位候选克隆基因策略强调充分利用已知的细胞遗传学、医学遗传学、分子遗传学、分子生物学和生物化学知识,特别是人类基因组研究的最新成果,综合功能克隆、定位克隆与传统候选基因研究的策略,分离鉴定致病基因。今天的定位克隆已几乎不再需要染色体步移,甚至有可能避开cDNA筛选。     

COMPARE, a multi-organism system for cross-species data comparison and transfer of information

MOTIVATION: COMPARE is a multi-organism web-based resource system designed to easily retrieve, correlate and interpret data across species. The COMPARE interface provides access to a wide array of information including genomic structure, expression data, annotations, pathways and literature links for human and three widely studied animal models (zebrafish, Drosophila and mouse). A consensus ortholog-finding pipeline combining several ortholog prediction methods allows accurate comparisons of data across species and has been utilized to transfer information from well studied organisms to more poorly annotated ones. AVAILABILITY: http://compare.ibdml.univ-mrs.fr.     

Conserved co-expression for candidate disease gene prioritization

Background  

Genes that are co-expressed tend to be involved in the same biological process. However, co-expression is not a very reliable predictor of functional links between genes. The evolutionary conservation of co-expression between species can be used to predict protein function more reliably than co-expression in a single species. Here we examine whether co-expression across multiple species is also a better prioritizer of disease genes than is co-expression between human genes alone.     

Self-assembled terplexes for targeted gene delivery with improved transfection

To improve transfection efficiency and to incorporate target ligands to the gene delivery systems, heparin and heparin-biotin were introduced to complexes of polyamidoamine dendrimer and DNA (PAMAM/DNA) via electrostatic interactions to form self-assembled PAMAM/DNA/heparin and PAMAM/DNA/heparin-biotin terplexes, respectively. The self-assembled terplexes were characterized by agarose gel electrophoresis and particle size analysis. The MTT assay indicated that, after incorporation of heparin and heparin-biotin, the terplexes exhibited decreased cytotoxicity. In addition, as compared with PAMAM/DNA and PAMAM/DNA/heparin complexes, the PAMAM/DNA/heparin-biotin complexes exhibited much higher cellular uptake into HeLa cells due to the specific interactions between biotin and biotin receptors on HeLa cells, which led to the enhanced transfection activity. The PAMAM/DNA/heparin-biotin complexes would be a promising targeting gene delivery system.