Gene Structure Prediction Research Articles

Evaluating the Accuracy of Splice Site Prediction based on Integrating Jensen-Shannon Divergence and a Polynomial Equation of Order 2

Advances in DNA sequencing technology have caused generation of the vast amount of new sequence data. It is essential to understand the functions, features, and structures of every newly sequenced data. Analyzing sequence data by different methods could provide important information about the sequence data. One of the essential tasks for genome annotation is gene prediction that can help to understand the features and determine functions of the genes. One of the key steps towards correct gene structure prediction is accurate splice site detection. There are vast numbers of splice site prediction methods, however, a few of them can be incorporated in gene prediction modules because of their complexity. In this paper, a novel model is presented to recognize unknown splice sites in a new genome without using any prior knowledge. Our model is defined based on integrating Jensen-Shannon divergence and a polynomial equation of order 2. Finally, the proposed model is evaluated on Yeast’s genome to predict splice sites. The experimental results suggest that the proposed method is an effective approach for splice site prediction. General Terms Bioinformatics, Gene structure

A computational approach for prediction of donor splice sites with improved accuracy

Identification of splice sites is important due to their key role in predicting the exon-intron structure of protein coding genes. Though several approaches have been developed for the prediction of splice sites, further improvement in the prediction accuracy will help predict gene structure more accurately. This paper presents a computational approach for prediction of donor splice sites with higher accuracy. In this approach, true and false splice sites were first encoded into numeric vectors and then used as input in artificial neural network (ANN), support vector machine (SVM) and random forest (RF) for prediction. ANN and SVM were found to perform equally and better than RF, while tested on HS3D and NN269 datasets. Further, the performance of ANN, SVM and RF were analyzed by using an independent test set of 50 genes and found that the prediction accuracy of ANN was higher than that of SVM and RF. All the predictors achieved higher accuracy while compared with the existing methods like NNsplice, MEM, MDD, WMM, MM1, FSPLICE, GeneID and ASSP, using the independent test set. We have also developed an online prediction server (PreDOSS) available at http://cabgrid.res.in:8080/predoss, for prediction of donor splice sites using the proposed approach.

Are orphan genes protein-coding, prediction artifacts, or non-coding RNAs?

BackgroundCurrent genome sequencing projects reveal substantial numbers of taxonomically restricted, so called orphan genes that lack homology with genes from other evolutionary lineages. However, it is not clear to what extent orphan genes are real, genomic artifacts, or represent non-coding RNAs.ResultsHere, we use a simple set of assumptions to test the nature of orphan genes. First, a sequence that is transcribed is considered a real biological entity. Second, every sequence that is supported by proteome data or shows a depletion of non-synonymous substitutions is a protein-coding gene. Using genomic, transcriptomic and proteomic data for the nematode Pristionchus pacificus, we show that between 4129–7997 (42–81 %) of predicted orphan genes are expressed and 3818–7545 (39–76 %) of orphan genes are under negative selection. In three cases that exhibited strong evolutionary constraint but lacked expression evidence in 14 RNA-seq samples, we could experimentally validate the predicted gene structures. Comparing different data sets to infer selection on orphan gene clusters, we find that the presence of a closely related genome provides the most powerful resource to robustly identify evidence of negative selection. However, even in the absence of other genomic data, the availability of paralogous sequences was enough to show negative selection in 8–10 % of orphan genes.ConclusionsOur study shows that the great majority of previously identified orphan genes in P. pacificus are indeed protein-coding genes. Even though this work represents a case study on a single species, our approach can be transferred to genomic data of other non-model organisms in order to ascertain the protein-coding nature of orphan genes.

English

Mechanosensitive (MS) ion channels are transmembrane proteins that open and close in response to mechanical forces produced by osmotic pressure, sound, touch and gravity. In plants, MS have an important role in different biological processes like gravity detection, maintenance of plastid shape and size, lateral root emergence, growth of pollen tube, and plant-pathogen interactions. In this study, homologous mechanosensitive channel of small conductance (MscS)-like gene family in common bean was identified. Nine Phaseolus vulgaris MscS-like (PvMSL) genes were found to be distributed on five chromosomes. A complete overview of PvMSL genes in common bean is presented, including gene structures, chromosome locations, phylogeny, protein motifs and expression pattern. Subcellular localization predictions of PvMSL family revealed their location to plasma and chloroplast membrane. Phylogenetic analysis of nine PvMSL proteins resulted in two main classes. The predicted gene structure, conserved motif, domain and presence of transmembrane regions in each PvMSL strongly supported their identity as members of MscS-like gene family. Four duplicate events of PvMSL genes were discovered in P. vulgaris chromosomes, and tandem and segmental duplication may cause the expansion of PvMSL genes. Furthermore, PvMSL genes displayed differential expression patterns in tissues and organs. This is the first step towards genome-wide analyses of MSL genes in common bean. Thus, the data obtained in this study provide resources to select candidate genes for future functional analyses that will help understand plant growth, development, and function of MSL gene family in P. vulgaris . Key words : Mechanosensitive, phylogenetic analysis, gene duplication, plant, in silico.

Genome wide identification of scavenger receptors class A in common carp (Cyprinus carpio) and their expression following Aeromonas hydrophila infection.

Scavenger receptors class A (SCARAs) is a subgroup of diverse families of pattern recognition receptors that bind a range of ligands, and play important roles in innate immune processes through pathogens detection, adhesion, endocytosis, and phagocytosis. However, most studies of SCARAs have focused on mammals, and much less is known of SCARAs in fish species. In this study, we identified 7 SCARAs across the common carp genome, which were classified into four subclasses according to comparative genomic analysis including sequence similarities analysis, gene structure and functional domain prediction. Further phylogenetic and syntenic analysis supported their annotation and orthologies. Through examining gene copy number of SCARA genes across several vertebrates, SCARA2, SCARA3 and SCARA4 were found have undergone gene duplication. The expression patterns of SCARAs in common carp were examined during early developmental stages, in healthy tissues, and after Aeromonas hydrophila infection. Most SCARA genes were ubiquitously expressed during common carp early developmental stages, and presented diverse patterns in various healthy tissues, with relatively high expression levels in spleen, liver, intestine, gill and brain, indicating their critical roles likely in maintaining homeostasis and host immune response activities. After A. hydrophila infection, most SCARA genes were up-regulated at 4h post infection in mucosal tissue intestine, while generally up-regulated at 12h post infection in spleen, suggesting a tissue-specific pattern of regulation. Taken together, all these results suggested that SCARA genes played important roles in host immune response to A. hydrophila infection in common carp, and provided important genomic resources for future studies on fish disease management.

XGDBvm: A Web GUI-Driven Workflow for Annotating Eukaryotic Genomes in the Cloud.

Genome-wide annotation of gene structure requires the integration of numerous computational steps. Currently, annotation is arguably best accomplished through collaboration of bioinformatics and domain experts, with broad community involvement. However, such a collaborative approach is not scalable at today's pace of sequence generation. To address this problem, we developed the xGDBvm software, which uses an intuitive graphical user interface to access a number of common genome analysis and gene structure tools, preconfigured in a self-contained virtual machine image. Once their virtual machine instance is deployed through iPlant's Atmosphere cloud services, users access the xGDBvm workflow via a unified Web interface to manage inputs, set program parameters, configure links to high-performance computing (HPC) resources, view and manage output, apply analysis and editing tools, or access contextual help. The xGDBvm workflow will mask the genome, compute spliced alignments from transcript and/or protein inputs (locally or on a remote HPC cluster), predict gene structures and gene structure quality, and display output in a public or private genome browser complete with accessory tools. Problematic gene predictions are flagged and can be reannotated using the integrated yrGATE annotation tool. xGDBvm can also be configured to append or replace existing data or load precomputed data. Multiple genomes can be annotated and displayed, and outputs can be archived for sharing or backup. xGDBvm can be adapted to a variety of use cases including de novo genome annotation, reannotation, comparison of different annotations, and training or teaching.

Map-based cloning, identification and characterization of the w gene controlling white immature fruit color in cucumber (Cucumis sativus L.).

A single-nucleotide insertion resulted in a premature stop codon that is responsible for white immature fruit color in cucumber. Despite our previous progress in the mapping of the gene controlling white color in immature cucumber fruit and the identification of candidate genes, the specific gene that governs chlorophyll metabolism and its regulatory mechanism remains unknown. Here, we generated a mapping population consisting of 9497 F2 plants to delimit the controlling gene to an 8.2-kb physical interval that defines a sole candidate gene, APRR2. Sequencing the full-length DNA and cDNA of APRR2 allowed for identification of an allele, aprr2, encoding a truncated 101-amino acid protein due to a frameshift mutation and a premature stop codon. Gene structure prediction indicated that these 101 residues are located in a domain necessary for the function of the protein. The expression patterns of APRR2 were entirely consistent with the visual changes in green color intensity during fruit development. A microscopic observation of the fruit pericarp revealed fewer chloroplasts and a lower chloroplast chlorophyll storage capacity in Q24 (white) than in Q30 (green). A single-base insertion in the white color gene w, which leads to a premature stop codon, is hypothesized to have disabled the function of this gene in chlorophyll accumulation and chloroplast development. These findings contribute to basic research and the genetic improvement of fruit color.

Signalign: An Ontology of DNA as Signal for Comparative Gene Structure Prediction Using Information-Coding-and-Processing Techniques.

Conventional character-analysis-based techniques in genome analysis manifest three main shortcomings-inefficiency, inflexibility, and incompatibility. In our previous research, a general framework, called DNA As X was proposed for character-analysis-free techniques to overcome these shortcomings, where X is the intermediates, such as digit, code, signal, vector, tree, graph network, and so on. In this paper, we further implement an ontology of DNA As Signal, by designing a tool named Signalign for comparative gene structure analysis, in which DNA sequences are converted into signal series, processed by modified method of dynamic time warping and measured by signal-to-noise ratio (SNR). The ontology of DNA As Signal integrates the principles and concepts of other disciplines including information coding theory and signal processing into sequence analysis and processing. Comparing with conventional character-analysis-based methods, Signalign can not only have the equivalent or superior performance, but also enrich the tools and the knowledge library of computational biology by extending the domain from character/string to diverse areas. The evaluation results validate the success of the character-analysis-free technique for improved performances in comparative gene structure prediction.

Identification of a Novel Calcium (Ca^(2+))-Activated Chloride Channel Accessory Gene in Xenopus laevis

Calcium (Ca^(2+))-activated chloride channel accessories (CLCAs) are putative anion channel-related proteins with diverse physiological functions. Exploring CLCA diversity is important for prediction of gene structure and function. In an effort to identify novel CLCA genes in Xenopus laevis, we successfully cloned and characterized a Xenopus laevis cDNA predicted to encode the xCLCA3 gene. Cloning of xCLCA3 was achieved by computational analysis, rapid amplification of cDNA ends (RACE), and a tissue distribution analysis by semi-quantitative reverse transcription (RT) PCR or real-time PCR. We obtained a 2958 bp xCLCA3 cDNA sequence with an open reading frame encoding 943 amino acids. According to the primary structure analysis, xCLCA3 contains a predicted signal sequence, multiple sites of N-linked (N-) glycosylation, N-myristoylation, PKA, PKC, and casein kinase II phosphorylation sites, five putative hydrophobic segments, and the HExxH metalloprotease motif. Additionally, the transmembrane prediction server yielded a preserved N-terminal CLCA domain and a von Willebrand factor type A domain with one transmembrane domain in the C-terminal region. Expression analysis showed that xCLCA3 is expressed in a number of tissues, with strong expression in the brain, colon, small intestine, lung, kidney, and spleen, and poor expression in the heart and liver. These results suggest that xCLCA3 may be a candidate CLCA family member as well as a metalloprotease, rather than just an ion channel accessory protein.

Well-characterized sequence features of eukaryote genomes and implications for ab initio gene prediction

In silico analysis of DNA sequences is an important area of computational biology in the post-genomic era. Over the past two decades, computational approaches for ab initio prediction of gene structure from genome sequence alone have largely facilitated our understanding on a variety of biological questions. Although the computational prediction of protein-coding genes has already been well-established, we are also facing challenges to robustly find the non-coding RNA genes, such as miRNA and lncRNA. Two main aspects of ab initio gene prediction include the computed values for describing sequence features and used algorithm for training the discriminant function, and by which different combinations are employed into various bioinformatic tools. Herein, we briefly review these well-characterized sequence features in eukaryote genomes and applications to ab initio gene prediction. The main purpose of this article is to provide an overview to beginners who aim to develop the related bioinformatic tools.

Predicting Gene Structure with the Use of Mixtures of Probability Distributions

The authors consider the problem of reconstruction of hidden state sequences for mixture distributions with constituents described by the generalization of high-order Markov chains and hidden Markov models. A new algorithm to solve the problem using dynamic programming is proposed, as well as its modifications to eliminate recursion and reduce search. The results are applied to the problem of gene fragment recognition in plants.

In silico identification and expression analysis of MscS like gene family in rice

Mechanosensitive channels are membrane proteins that open and shut in response to mechanical forces produced by osmotic pressure, sound, touch and gravity. These channels are involved in multiple physiological functions including hypoosmotic pressure, pain, hearing, blood pressure and cell volume regulation. In plants, these channels play a major role in proprioception, gravity sensing and maintenance of plastid shape and size. In the present study, we identified the mechanosensitive channel of small conductance like (MscS) homologue gene family in rice and analyzed their structure, phylogenetic relationship, localization and expression pattern. Five MscS like genes of rice (OsMSL) were found to be distributed on four chromosomes and clustered into two major groups. Subcellular localization predictions of the OsMSL family revealed their localization to plasma membrane, plastid envelope and mitochondria. The predicted gene structure, bonafide conserved signature motif, domain and the presence of transmembrane regions in each OsMSL strongly supported their identity as members of MscS-like gene family. Furthermore, in silico expression analysis of OsMSL genes revealed differential regulation patterns in tissue specific and abiotic stress libraries. These findings indicate that the in silico approach used here successfully identified in a genome-wide context MscS like gene family in rice, and further suggest the functional importance of MscS-like genes in rice.

An Improved Method for Splice Site Prediction in DNA Sequences Using Support Vector Machines

A vast amount of sequence data has been generated due to advancements in DNA sequencing technology. This exponential increase requires new and efficient methods for the analysis of DNA sequence data. Predicting genes in this newly sequenced data is an important and essential step towards genome annotation. Genome annotation helps in determining function of these genes. Accurate splice site prediction in DNA sequences leads to correct gene structure prediction in eukaryotes and it requires effective modelling of regions surrounding these sites. A large number of methods for splice site prediction are available in literature but very few of them are suitable to be incorporated as gene prediction module because of their complexity. In this paper, a splice site prediction method based on second order markov model and support vector machine is developed. This method shows improvement over most of the existing splice site predictors in use today. The experimental results suggest that second order markov model is an effective pre-processing approach. This approach when combined with support vector machine provides better classification accuracy in predicting splice sites.

Genome update of the dimorphic human pathogenic fungi causing paracoccidioidomycosis.

Paracoccidiodomycosis (PCM) is a clinically important fungal disease that can acquire serious systemic forms and is caused by the thermodimorphic fungal Paracoccidioides spp. PCM is a tropical disease that is endemic in Latin America, where up to ten million people are infected; 80% of reported cases occur in Brazil, followed by Colombia and Venezuela. To enable genomic studies and to better characterize the pathogenesis of this dimorphic fungus, two reference strains of P. brasiliensis (Pb03, Pb18) and one strain of P. lutzii (Pb01) were sequenced [1]. While the initial draft assemblies were accurate in large scale structure and had high overall base quality, the sequences had frequent small scale defects such as poor quality stretches, unknown bases (N's), and artifactual deletions or nucleotide duplications, all of which caused larger scale errors in predicted gene structures. Since assembly consensus errors can now be addressed using next generation sequencing (NGS) in combination with recent methods allowing systematic assembly improvement, we re-sequenced the three reference strains of Paracoccidioides spp. using Illumina technology. We utilized the high sequencing depth to re-evaluate and improve the original assemblies generated from Sanger sequence reads, and obtained more complete and accurate reference assemblies. The new assemblies led to improved transcript predictions for the vast majority of genes of these reference strains, and often substantially corrected gene structures. These include several genes that are central to virulence or expressed during the pathogenic yeast stage in Paracoccidioides and other fungi, such as HSP90, RYP1-3, BAD1, catalase B, alpha-1,3-glucan synthase and the beta glucan synthase target gene FKS1. The improvement and validation of these reference sequences will now allow more accurate genome-based analyses. To our knowledge, this is one of the first reports of a fully automated and quality-assessed upgrade of a genome assembly and annotation for a non-model fungus.

Determination of window size and identification of suitable method for prediction of donor splice sites in rice (Oryza sativa) genome

Accurate prediction of the gene structure depends upon the accurate prediction of splice sites. The conserved feature in splicing junction has been successfully used for the prediction of eukaryotic splice sites. In eukaryotes, though the di-nucleotide GT is conserved at 5′ splice sites, the pattern surrounding the conserved di-nucleotide varies from species to species. Most of the work related to splice site analysis has been extensively done in Homo sapiens and Arabidopsis thaliana. However, such works are yet to be fully explored in Oryza sativa and other species of grass family. In this study, statistical techniques have been applied to discriminate the real splice sites from pseudo splice sites in rice, maize and barley genomes and based on this a suitable window size is determined for the prediction of donor splice sites. Depending upon the determined window size, appropriate methods for predicting donor splice sites in rice have been considered and compared in terms of prediction accuracy. The results revealed that a window size of 9 base pair (3 bp at the exon end and 6 bp at the intron start including the conserved di-nucleotide GT at the beginning of intron) is an effective window size in all the three species of grass family for the prediction of donor splice sites. Further, the Maximum Entropy Model based method is found as best among the short sequence based prediction methods for donor splice sites with the 9 base pair window size.

RiceQTLPro: an integrated database for quantitative trait loci marker mapping in rice plant.

The National Agricultural Biotechnology Information Center (NABIC) in South Korea reconstructed a RiceQTLPro database for gene positional analysis and structure prediction of the chromosomes. This database is an integrated web-based system providing information about quantitative trait loci (QTL) markers in rice plant. The RiceQTLPro has the three main features namely, (1) QTL markers list, (2) searching of markers using keyword, and (3) searching of marker position on the rice chromosomes. This updated database provides 112 QTL markers information with 817 polymorphic markers on each of the 12 chromosomes in rice.AvailabilityThe database is available for free at http://nabic.rda.go.kr/gere/rice/geneticMap/

Cellular Automata in Splice Site Prediction

Splice site prediction is one of the important problems in Bioinformatics. Splicing is the way in which introns are removed from pre-mRNA transcript and exons are joined before translation. The position where the introns are spliced out is called as splice site. Identifying the splice junction plays vital role in understanding the genes. For an efficient study on eukaryotic genes the first step is to predict the splice site accurately. Accurate prediction of splice site will lead to accurate prediction of gene structure. There are three categories of splice site exist; they are acceptor site (AS), donor site (DS) and neither of these. The proposed classifier AIS-SSMACA has to take DNA sequence as input and predict the category (AS/DS/Neither).

Assessment and refinement of eukaryotic gene structure prediction with gene-structure-aware multiple protein sequence alignment.

BackgroundAccurate computational identification of eukaryotic gene organization is a long-standing problem. Despite the fundamental importance of precise annotation of genes encoded in newly sequenced genomes, the accuracy of predicted gene structures has not been critically evaluated, mostly due to the scarcity of proper assessment methods.ResultsWe present a gene-structure-aware multiple sequence alignment method for gene prediction using amino acid sequences translated from homologous genes from many genomes. The approach provides rich information concerning the reliability of each predicted gene structure. We have also devised an iterative method that attempts to improve the structures of suspiciously predicted genes based on a spliced alignment algorithm using consensus sequences or reliable homologs as templates. Application of our methods to cytochrome P450 and ribosomal proteins from 47 plant genomes indicated that 50 ~ 60 % of the annotated gene structures are likely to contain some defects. Whereas more than half of the defect-containing genes may be intrinsically broken, i.e. they are pseudogenes or gene fragments, located in unfinished sequencing areas, or corresponding to non-productive isoforms, the defects found in a majority of the remaining gene candidates can be remedied by our iterative refinement method.ConclusionsRefinement of eukaryotic gene structures mediated by gene-structure-aware multiple protein sequence alignment is a useful strategy to dramatically improve the overall prediction quality of a set of homologous genes. Our method will be applicable to various families of protein-coding genes if their domain structures are evolutionarily stable. It is also feasible to apply our method to gene families from all kingdoms of life, not just plants.

Identification of Protein Coding Regions in the Eukaryotic DNA Sequences Based on Marple Algorithm and Wavelet Packets Transform

The identification of protein coding regions (exons) plays a critical role in eukaryotic gene structure prediction. Many techniques have been introduced for discriminating between the exons and the introns in the eukaryotic DNA sequences, such as the discrete Fourier transform (DFT) based techniques, but these DFT-based methods rapidly lose their effectiveness in the case of short DNA sequences. In this paper, a novel integrated algorithm based on autoregressive spectrum analysis and wavelet packets transform is presented to improve the efficiency and accuracy of the coding regions identification. The experimental results show that the new algorithm outperforms the conventional DFT-based approaches in improving the prediction accuracy of protein coding regions distinctly by testing GENSCAN65, HMR195, and BG570 benchmark datasets.

Expression and accumulation of the two-domain odorant-binding protein AaegOBP45 in the ovaries of blood-fed Aedes aegypti

BackgroundAedes aegypti mosquitoes are the main vectors of dengue viruses. Despite global efforts to reduce the prevalence of dengue using integrated vector management strategies, innovative alternatives are necessary to help prevent virus transmission. Detailed characterizations of Ae. aegypti genes and their products provide information about the biology of mosquitoes and may serve as foundations for the design of new vector control methods.FindingsWe studied the Ae. aegypti gene, AAEL010714, that encodes a two-domain odorant-binding protein, AaegOBP45. The predicted gene structure and sequence were validated, although single nucleotide polymorphisms were observed. Transcriptional and translational products accumulate in the ovaries of blood fed females and are not detected or are at low abundance in other tissues.ConclusionsWe validated the Ae. aegypti AAEL010714 gene sequence and characterized the expression profile of a two-domain OBP expressed in ovaries. We propose that AaegOBP45 function as a component of the mosquito eggshell.