GeneSeqer@PlantGDB: Gene structure prediction in plant genomes.
Ontology highlight
ABSTRACT:
SUBMITTER: Schlueter SD
PROVIDER: S-EPMC168940 | biostudies-literature | 2003 Jul
REPOSITORIES: biostudies-literature
Publications
GeneSeqer@PlantGDB: Gene structure prediction in plant genomes.
Schlueter Shannon D SD Dong Qunfeng Q Brendel Volker V
Nucleic acids research 20030701 13
The GeneSeqer@PlantGDB Web server (http://www.plantgdb.org/cgi-bin/GeneSeqer.cgi) provides a gene structure prediction tool tailored for applications to plant genomic sequences. Predictions are based on spliced alignment with source-native ESTs and full-length cDNAs or non-native probes derived from putative homologous genes. The tool is illustrated with applications to refinement of current gene structure annotation and de novo annotation of draft genomic sequences. The service should facilitat ...[more]
Similar Datasets
Project description:BackgroundGene prediction is one of the most important steps in the genome annotation process. A large number of software tools and pipelines developed by various computing techniques are available for gene prediction. However, these systems have yet to accurately predict all or even most of the protein-coding regions. Furthermore, none of the currently available gene-finders has a universal Hidden Markov Model (HMM) that can perform gene prediction for all organisms equally well in an automatic fashion.ResultsWe present an automated gene prediction pipeline, Seqping that uses self-training HMM models and transcriptomic data. The pipeline processes the genome and transcriptome sequences of the target species using GlimmerHMM, SNAP, and AUGUSTUS pipelines, followed by MAKER2 program to combine predictions from the three tools in association with the transcriptomic evidence. Seqping generates species-specific HMMs that are able to offer unbiased gene predictions. The pipeline was evaluated using the Oryza sativa and Arabidopsis thaliana genomes. Benchmarking Universal Single-Copy Orthologs (BUSCO) analysis showed that the pipeline was able to identify at least 95% of BUSCO's plantae dataset. Our evaluation shows that Seqping was able to generate better gene predictions compared to three HMM-based programs (MAKER2, GlimmerHMM and AUGUSTUS) using their respective available HMMs. Seqping had the highest accuracy in rice (0.5648 for CDS, 0.4468 for exon, and 0.6695 nucleotide structure) and A. thaliana (0.5808 for CDS, 0.5955 for exon, and 0.8839 nucleotide structure).ConclusionsSeqping provides researchers a seamless pipeline to train species-specific HMMs and predict genes in newly sequenced or less-studied genomes. We conclude that the Seqping pipeline predictions are more accurate than gene predictions using the other three approaches with the default or available HMMs.
| S-EPMC5333190 | biostudies-literature
Project description:Chromatin accessibility is a highly informative structural feature for understanding gene transcription regulation, because it indicates the degree to which nuclear macromolecules such as proteins and RNAs can access chromosomal DNA. Studies have shown that chromatin accessibility is highly dynamic during stress response, stimulus response, and developmental transition. Moreover, physical access to chromosomal DNA in eukaryotes is highly cell-specific. Therefore, current technologies such as DNase-seq, ATAC-seq, and FAIRE-seq reveal only a portion of the open chromatin regions (OCRs) present in a given species. Thus, the genome-wide distribution of OCRs remains unknown. In this study, we developed a bioinformatics tool called CharPlant for the de novo prediction of OCRs in plant genomes. To develop this tool, we constructed a three-layer convolutional neural network (CNN) and subsequently trained the CNN using DNase-seq and ATAC-seq datasets of four plant species. The model simultaneously learns the sequence motifs and regulatory logics, which are jointly used to determine DNA accessibility. All of these steps are integrated into CharPlant, which can be run using a simple command line. The results of data analysis using CharPlant in this study demonstrate its prediction power and computational efficiency. To our knowledge, CharPlant is the first de novo prediction tool that can identify potential OCRs in the whole genome. The source code of CharPlant and supporting files are freely available from https://github.com/Yin-Shen/CharPlant.
| S-EPMC9170768 | biostudies-literature
Project description:BackgroundPlants have played a special role in inositol polyphosphate (IP) research since in plant seeds was discovered the first IP, the fully phosphorylated inositol ring of phytic acid (IP6). It is now known that phytic acid is further metabolized by the IP6 Kinases (IP6Ks) to generate IP containing pyro-phosphate moiety. The IP6K are evolutionary conserved enzymes identified in several mammalian, fungi and amoebae species. Although IP6K has not yet been identified in plant chromosomes, there are many clues suggesting its presences in vegetal cells.ResultsIn this paper we propose a new approach to search for the plant IP6K gene, that lead to the identification in plant genome of a nucleotide sequence corresponding to a specific tag of the IP6K family. Such a tag has been found in all IP6K genes identified up to now, as well as in all genes belonging to the Inositol Polyphosphate Kinases superfamily (IPK). The tag sequence corresponds to the inositol-binding site of the enzyme, and it can be considered as characterizing all IPK genes. To this aim we applied a technique based on motif discovery. We exploited DLSME, a software recently proposed, which allows for the motif structure to be only partially specified by the user. First we applied the new method on mitochondrial DNA (mtDNA) of plants, where such a gene could have been nested, possibly encrypted and hidden by virtue of the editing and/or trans-splicing processes. Then we looked for the gene in nuclear genome of two model plants, Arabidopsis thaliana and Oryza sativa.ConclusionsThe analysis we conducted in plant mitochondria provided the negative, though we argue relevant, result that IP6K does not actually occur in vegetable mtDNA. Very interestingly, the tag search in nuclear genomes lead us to identify a promising sequence in chromosome 5 of Oryza sativa. Further analyses are in course to confirm that this sequence actually corresponds to IP6K mammalian gene.
| S-EPMC3090757 | biostudies-literature
Project description:A simple method for understanding how gene duplication has contributed to genomic structure was applied to the complete genomes of Caenorhabditis elegans, Drosophila melanogaster, and yeast Saccharomyces cerevisiae. By this method, the genes belonging to gene families (the paranome) were identified, and the extent of sharing of two or more families between genomic windows was compared with that expected under a null model. The results showed significant evidence of duplication of genomic blocks in both C. elegans and yeast. In C. elegans, the five block duplications identified all occurred intra-chromosomally, and all but one occurred quite recently. In yeast, by contrast, 39 duplicated blocks were identified, and all but one of these was inter-chromosomal. Of these 39 blocks, 28 showed evidence of ancient duplication, possibly as a result of an ancient polyploidization event. By contrast, three blocks showed evidence of very recent duplication, while seven others showed a mixture of ancient and recent duplication events. Thus, duplication of genomic blocks has been an ongoing feature of yeast evolution over the past 200--300 million years.
| S-EPMC311031 | biostudies-literature
Project description:BackgroundRecurrent gene duplication and retention played an important role in angiosperm genome evolution. It has been hypothesized that these processes contribute significantly to plant adaptation but so far this hypothesis has not been tested at the genome scale.ResultsWe studied available sequenced angiosperm genomes to assess the frequency of positive selection footprints in lineage specific expanded (LSE) gene families compared to single-copy genes using a dN/dS-based test in a phylogenetic framework. We found 5.38% of alignments in LSE genes with codons under positive selection. In contrast, we found no evidence for codons under positive selection in the single-copy reference set. An analysis at the branch level shows that purifying selection acted more strongly on single-copy genes than on LSE gene clusters. Moreover we detect significantly more branches indicating evolution under positive selection and/or relaxed constraint in LSE genes than in single-copy genes.ConclusionsIn this - to our knowledge -first genome-scale study we provide strong empirical support for the hypothesis that LSE genes fuel adaptation in angiosperms. Our conservative approach for detecting selection footprints as well as our results can be of interest for further studies on (plant) gene family evolution.
| S-EPMC4049390 | biostudies-literature
Project description:BACKGROUND: Despite a remarkable success in the computational prediction of genes in Bacteria and Archaea, a lack of comprehensive understanding of prokaryotic gene structures prevents from further elucidation of differences among genomes. It continues to be interesting to develop new ab initio algorithms which not only accurately predict genes, but also facilitate comparative studies of prokaryotic genomes. RESULTS: This paper describes a new prokaryotic genefinding algorithm based on a comprehensive statistical model of protein coding Open Reading Frames (ORFs) and Translation Initiation Sites (TISs). The former is based on a linguistic "Entropy Density Profile" (EDP) model of coding DNA sequence and the latter comprises several relevant features related to the translation initiation. They are combined to form a so-called Multivariate Entropy Distance (MED) algorithm, MED 2.0, that incorporates several strategies in the iterative program. The iterations enable us to develop a non-supervised learning process and to obtain a set of genome-specific parameters for the gene structure, before making the prediction of genes. CONCLUSION: Results of extensive tests show that MED 2.0 achieves a competitive high performance in the gene prediction for both 5' and 3' end matches, compared to the current best prokaryotic gene finders. The advantage of the MED 2.0 is particularly evident for GC-rich genomes and archaeal genomes. Furthermore, the genome-specific parameters given by MED 2.0 match with the current understanding of prokaryotic genomes and may serve as tools for comparative genomic studies. In particular, MED 2.0 is shown to reveal divergent translation initiation mechanisms in archaeal genomes while making a more accurate prediction of TISs compared to the existing gene finders and the current GenBank annotation.
| S-EPMC1847833 | biostudies-other
Project description:The existence of Metabolic Gene Clusters (MGCs) in plant genomes has recently raised increased interest. Thus far, MGCs were commonly identified for pathways of specialized metabolism, mostly those associated with terpene type products. For efficient identification of novel MGCs, computational approaches are essential. Here, we present PhytoClust; a tool for the detection of candidate MGCs in plant genomes. The algorithm employs a collection of enzyme families related to plant specialized metabolism, translated into hidden Markov models, to mine given genome sequences for physically co-localized metabolic enzymes. Our tool accurately identifies previously characterized plant MGCs. An exhaustive search of 31 plant genomes detected 1232 and 5531 putative gene cluster types and candidates, respectively. Clustering analysis of putative MGCs types by species reflected plant taxonomy. Furthermore, enrichment analysis revealed taxa- and species-specific enrichment of certain enzyme families in MGCs. When operating through our web-interface, PhytoClust users can mine a genome either based on a list of known cluster types or by defining new cluster rules. Moreover, for selected plant species, the output can be complemented by co-expression analysis. Altogether, we envisage PhytoClust to enhance novel MGCs discovery which will in turn impact the exploration of plant metabolism.
| S-EPMC5499548 | biostudies-literature
Project description:Plants are a tremendous source of diverse chemicals, including many natural product-derived drugs. It has recently become apparent that the genes for the biosynthesis of numerous different types of plant natural products are organized as metabolic gene clusters, thereby unveiling a highly unusual form of plant genome architecture and offering novel avenues for discovery and exploitation of plant specialized metabolism. Here we show that these clustered pathways are characterized by distinct chromatin signatures of histone 3 lysine trimethylation (H3K27me3) and histone 2 variant H2A.Z, associated with cluster repression and activation, respectively, and represent discrete windows of co-regulation in the genome. We further demonstrate that knowledge of these chromatin signatures along with chromatin mutants can be used to mine genomes for cluster discovery. The roles of H3K27me3 and H2A.Z in repression and activation of single genes in plants are well known. However, our discovery of highly localized operon-like co-regulated regions of chromatin modification is unprecedented in plants. Our findings raise intriguing parallels with groups of physically linked multi-gene complexes in animals and with clustered pathways for specialized metabolism in filamentous fungi.
| S-EPMC4797310 | biostudies-literature
Project description:Comparative algal genomics often relies on predicted genes from de novo assembled genomes. However, the artifacts introduced by different gene-prediction approaches, and their impact on comparative genomic analysis remain poorly understood. Here, using available genome data from six dinoflagellate species in the Symbiodiniaceae, we identified methodological biases in the published genes that were predicted using different approaches and putative contaminant sequences in the published genome assemblies. We developed and applied a comprehensive customized workflow to predict genes from these genomes. The observed variation among predicted genes resulting from our workflow agreed with current understanding of phylogenetic relationships among these taxa, whereas the variation among the previously published genes was largely biased by the distinct approaches used in each instance. Importantly, these biases affect the inference of homologous gene families and synteny among genomes, thus impacting biological interpretation of these data. Our results demonstrate that a consistent gene-prediction approach is critical for comparative analysis of dinoflagellate genomes.
| S-EPMC7065002 | biostudies-literature
Project description:Stenodactylin is one of the most potent type 2 ribosome-inactivating proteins (RIPs); its high toxicity has been demonstrated in several models both in vitro and in vivo. Due to its peculiarities, stenodactylin could have several medical and biotechnological applications in neuroscience and cancer treatment. In this work, we report the complete amino acid sequence of stenodactylin and 3D structure prediction. The comparison between the primary sequence of stenodactylin and other RIPs allowed us to identify homologies/differences and the amino acids involved in RIP toxic activity. Stenodactylin RNA was isolated from plant caudex, reverse transcribed through PCR and the cDNA was amplificated and cloned into a plasmid vector and further analyzed by sequencing. Nucleotide sequence analysis showed that stenodactylin A and B chains contain 251 and 258 amino acids, respectively. The key amino acids of the active site described for ricin and most other RIPs are also conserved in the stenodactylin A chain. Stenodactylin amino acid sequence shows a high identity degree with volkensin (81.7% for A chain, 90.3% for B chain), whilst when compared with other type 2 RIPs the identity degree ranges from 27.7 to 33.0% for the A chain and from 42.1 to 47.7% for the B chain.
| S-EPMC7551084 | biostudies-literature