Development of joint application strategies for two microbial gene finders

MOTIVATION: As a starting point in annotation of bacterial genomes, gene finding programs are used for the prediction of functional elements in the DNA sequence. Due to the faster pace and increasing number of genome projects currently underway, it is becoming especially important to have performant methods for this task. RESULTS: This study describes the development of joint application strategies that combine the strengths of two microbial gene finders to improve the overall gene finding performance. Critica is very specific in the detection of similarity-supported genes as it uses a comparative sequence analysis-based approach. Glimmer employs a very sophisticated model of genomic sequence properties and is sensitive also in the detection of organism-specific genes. Based on a data set of 113 microbial genome sequences, we optimized a combined application approach using different parameters with relevance to the gene finding problem. This results in a significant improvement in specificity while there is similarity in sensitivity to Glimmer. The improvement is especially pronounced for GC rich genomes. The method is currently being applied for the annotation of several microbial genomes. AVAILABILITY: The methods described have been implemented within the gene prediction component of the GenDB genome annotation system.     

Towards patterns tree of gene coexpression in eukaryotic species

MOTIVATION: Cellular pathways behave coordinated regulation activity, and some reported works also have affirmed that genes in the same pathway have similar expression pattern. However, the complexity of biological systems regulation actually causes expression relationships between genes to display multiple patterns, such as linear, non-linear, local, global, linear with time-delayed, non-linear with time-delayed, monotonic and non-monotonic, which should be the explicit representation of cellular inner regulation mechanism in mRNA level. To investigate the relationship between different patterns, our work aims to systematically reveal gene-expression relationship patterns in cellular pathways and to check for the existence of dominating gene-expression pattern. By a large scale analysis of genes expression in three eukaryotic species, Saccharomyces cerevisiae, Caenorhabditis elegans and Human, we constructed gene coexpression patterns tree to systematically and hierarchically illustrate the different patterns and their interrelations. RESULTS: The results show that the linear is the dominating expression pattern in the same pathway. The time-shifted pattern is another important relationship pattern. Many genes from the different pathway also present coexpression patterns. The non-linear, non-monotonic and time-delayed relationship patterns reflect the remote interactions between the genes in cellular processes. Gene coexpression phenomena in the same pathways are diverse in different species. Genes in S.cerevisiae and C.elegans present strong coexpression relationships, especially in C.elegans, coexpression is more universal and stronger due to its special array of genes. However in Human, gene coexpression is not apparent and the human genome involves more complicated functional relationships. In conclusion, different patterns corresponding to different coordinating behaviors coexist. The patterns trees of different species give us comprehensive insight and understanding of genes expression activity in the cellular society.     

Comparative analysis of various gene finders specific to Caenorhabditis elegans genome

Computational gene prediction and identifying alternatively spliced isoforms have always been a challenging task. In this paper, we describe the performance of three gene/exon finding programmes namely Fex, Gen view2 and Gene builder capable of predicting open reading frames or exons for a given set of sequences from C. elegans genome. The predicted exons were compared with the 'sequencing consortium' identified exons and degree of consensus among them is discussed. We found that exon prediction by Fex was similar to the consortium prediction as compared to Gen view2 and Gene builder results. Interestingly, some exons (six exons in five genes) predicted positive only by Fex and not by the 'sequencing consortium' are found at the C. elegans EST database. This data is critical for further debate and discussion on gene finding in C. elegans.     

Computer programs for eukaryotic gene prediction

Seven popular programs for gene prediction in eukaryotic organisms are described and evaluated on the basis of availability for in-house and on-line use and prediction accuracy. This report outlines generally applicable approaches to computational gene prediction and known limitations in this field.     

Automatic term list generation for entity tagging

MOTIVATION: Many entity taggers and information extraction systems make use of lists of terms of entities such as people, places, genes or chemicals. These lists have traditionally been constructed manually. We show that distributional clustering methods which group words based on the contexts that they appear in, including neighboring words and syntactic relations extracted using a shallow parser, can be used to aid in the construction of term lists. RESULTS: Experiments on learning lists of terms and using them as part of a gene tagger on a corpus of abstracts from the scientific literature show that our automatically generated term lists significantly boost the precision of a state-of-the-art CRF-based gene tagger to a degree that is competitive with using hand curated lists and boosts recall to a degree that surpasses that of the hand-curated lists. Our results also show that these distributional clustering methods do not generate lists as helpful as those generated by supervised techniques, but that they can be used to complement supervised techniques so as to obtain better performance. AVAILABILITY: The code used in this paper is available from http://www.cis.upenn.edu/datamining/software_dist/autoterm/     

Exploiting single-molecule transcript sequencing for eukaryotic gene prediction

We develop a method to predict and validate gene models using PacBio single-molecule, real-time (SMRT) cDNA reads. Ninety-eight percent of full-insert SMRT reads span complete open reading frames. Gene model validation using SMRT reads is developed as automated process. Optimized training and prediction settings and mRNA-seq noise reduction of assisting Illumina reads results in increased gene prediction sensitivity and precision. Additionally, we present an improved gene set for sugar beet (Beta vulgaris) and the first genome-wide gene set for spinach (Spinacia oleracea). The workflow and guidelines are a valuable resource to obtain comprehensive gene sets for newly sequenced genomes of non-model eukaryotes.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-015-0729-7) contains supplementary material, which is available to authorized users.     

Interpolated Markov models for eukaryotic gene finding.

Computational gene finding research has emphasized the development of gene finders for bacterial and human DNA. This has left genome projects for some small eukaryotes without a system that addresses their needs. This paper reports on a new system, GlimmerM, that was developed to find genes in the malaria parasite Plasmodium falciparum. Because the gene density in P. falciparum is relatively high, the system design was based on a successful bacterial gene finder, Glimmer. The system was augmented with specially trained modules to find splice sites and was trained on all available data from the P. falciparum genome. Although a precise evaluation of its accuracy is impossible at this time, laboratory tests (using RT-PCR) on a small selection of predicted genes confirmed all of those predictions. With the rapid progress in sequencing the genome of P. falciparum, the availability of this new gene finder will greatly facilitate the annotation process.     

Integrated computer system for regulating eukaryotic gene expression

The review describes several modules of the GeneExpress integrated computer system concerning the regulation of gene expression in eukaryotes. Approaches to the presentation of experimental data in databases are considered. The employment of GeneExpress in computer analysis and modeling of the organization and function of genetic systems is illustrated with examples. GeneExpress is available at http://wwwmgs.bionet.nsc.ru/mgs/gnw/.     

Pseudoabsence generation strategies for species distribution models

Background

Species distribution models require selection of species, study extent and spatial unit, statistical methods, variables, and assessment metrics. If absence data are not available, another important consideration is pseudoabsence generation. Different strategies for pseudoabsence generation can produce varying spatial representation of species.

Methodology

We considered model outcomes from four different strategies for generating pseudoabsences. We generating pseudoabsences randomly by 1) selection from the entire study extent, 2) a two-step process of selection first from the entire study extent, followed by selection for pseudoabsences from areas with predicted probability <25%, 3) selection from plots surveyed without detection of species presence, 4) a two-step process of selection first for pseudoabsences from plots surveyed without detection of species presence, followed by selection for pseudoabsences from the areas with predicted probability <25%. We used Random Forests as our statistical method and sixteen predictor variables to model tree species with at least 150 records from Forest Inventory and Analysis surveys in the Laurentian Mixed Forest province of Minnesota.

Conclusions

Pseudoabsence generation strategy completely affected the area predicted as present for species distribution models and may be one of the most influential determinants of models. All the pseudoabsence strategies produced mean AUC values of at least 0.87. More importantly than accuracy metrics, the two-step strategies over-predicted species presence, due to too much environmental distance between the pseudoabsences and recorded presences, whereas models based on random pseudoabsences under-predicted species presence, due to too little environmental distance between the pseudoabsences and recorded presences. Models using pseudoabsences from surveyed plots produced a balance between areas with high and low predicted probabilities and the strongest relationship between density and area with predicted probabilities ≥75%. Because of imperfect accuracy assessment, the best assessment currently may be evaluation of whether the species has been sufficiently but not excessively predicted to occur.     

The origins of eukaryotic gene structure

Most of the phenotypic diversity that we perceive in the natural world is directly attributable to the peculiar structure of the eukaryotic gene, which harbors numerous embellishments relative to the situation in prokaryotes. The most profound changes include introns that must be spliced out of precursor mRNAs, transcribed but untranslated leader and trailer sequences (untranslated regions), modular regulatory elements that drive patterns of gene expression, and expansive intergenic regions that harbor additional diffuse control mechanisms. Explaining the origins of these features is difficult because they each impose an intrinsic disadvantage by increasing the genic mutation rate to defective alleles. To address these issues, a general hypothesis for the emergence of eukaryotic gene structure is provided here. Extensive information on absolute population sizes, recombination rates, and mutation rates strongly supports the view that eukaryotes have reduced genetic effective population sizes relative to prokaryotes, with especially extreme reductions being the rule in multicellular lineages. The resultant increase in the power of random genetic drift appears to be sufficient to overwhelm the weak mutational disadvantages associated with most novel aspects of the eukaryotic gene, supporting the idea that most such changes are simple outcomes of semi-neutral processes rather than direct products of natural selection. However, by establishing an essentially permanent change in the population-genetic environment permissive to the genome-wide repatterning of gene structure, the eukaryotic condition also promoted a reliable resource from which natural selection could secondarily build novel forms of organismal complexity. Under this hypothesis, arguments based on molecular, cellular, and/or physiological constraints are insufficient to explain the disparities in gene, genomic, and phenotypic complexity between prokaryotes and eukaryotes.     

Automatic generation of user material subroutines for biomechanical growth analysis

The analysis of the biomechanics of growth and remodeling in soft tissues requires the formulation of specialized pseudoelastic constitutive relations. The nonlinear finite element analysis package ABAQUS allows the user to implement such specialized material responses through the coding of a user material subroutine called UMAT. However, hand coding UMAT subroutines is a challenge even for simple pseudoelastic materials and requires substantial time to debug and test the code. To resolve this issue, we develop an automatic UMAT code generation procedure for pseudoelastic materials using the symbolic mathematics package MATHEMATICA and extend the UMAT generator to include continuum growth. The performance of the automatically coded UMAT is tested by simulating the stress-stretch response of a material defined by a Fung-orthotropic strain energy function, subject to uniaxial stretching, equibiaxial stretching, and simple shear in ABAQUS. The MATHEMATICA UMAT generator is then extended to include continuum growth by adding a growth subroutine to the automatically generated UMAT. The MATHEMATICA UMAT generator correctly derives the variables required in the UMAT code, quickly providing a ready-to-use UMAT. In turn, the UMAT accurately simulates the pseudoelastic response. In order to test the growth UMAT, we simulate the growth-based bending of a bilayered bar with differing fiber directions in a nongrowing passive layer. The anisotropic passive layer, being topologically tied to the growing isotropic layer, causes the bending bar to twist laterally. The results of simulations demonstrate the validity of the automatically coded UMAT, used in both standardized tests of hyperelastic materials and for a biomechanical growth analysis.