Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Prediction of Sphingosine protein-coding regions with a self adaptive spectral rotation method.

ABSTRACT: Identifying protein coding regions in DNA sequences by computational methods is an active research topic. Welan gum produced by Sphingomonas sp. WG has great application potential in oil recovery and concrete construction industry. Predicting the coding regions in the Sphingomonas sp. WG genome and addressing the mechanism underlying the explanation for the synthesis of Welan gum metabolism is an important issue at present. In this study, we apply a self adaptive spectral rotation (SASR, for short) method, which is based on the investigation of the Triplet Periodicity property, to predict the coding regions of the whole-genome data of Sphingomonas sp. WG without any previous training process, and 1115 suspected gene fragments are obtained. Suspected gene fragments are subjected to a similarity search against the non-redundant protein sequences (nr) database of NCBI with blastx, and 762 suspected gene fragments have been labeled as genes in the nr database.

SUBMITTER: Li Z

PROVIDER: S-EPMC6447165 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Prediction of Sphingosine protein-coding regions with a self adaptive spectral rotation method.

Li Zhongwei Z Guan Yanan Y Yuan Xiang X Zheng Pan P Zhu Hu H

PloS one 20190403 4

Identifying protein coding regions in DNA sequences by computational methods is an active research topic. Welan gum produced by Sphingomonas sp. WG has great application potential in oil recovery and concrete construction industry. Predicting the coding regions in the Sphingomonas sp. WG genome and addressing the mechanism underlying the explanation for the synthesis of Welan gum metabolism is an important issue at present. In this study, we apply a self adaptive spectral rotation (SASR, for sho ...[more]

PMID: 30943219

Similar Datasets

Visualization of the protein-coding regions with a self adaptive spectral rotation approach.

Project description:Identifying protein-coding regions in DNA sequences is an active issue in computational biology. In this study, we present a self adaptive spectral rotation (SASR) approach, which visualizes coding regions in DNA sequences, based on investigation of the Triplet Periodicity property, without any preceding training process. It is proposed to help with the rough coding regions prediction when there is no extra information for the training required by other outstanding methods. In this approach, at each position in the DNA sequence, a Fourier spectrum is calculated from the posterior subsequence. Following the spectrums, a random walk in complex plane is generated as the SASR's graphic output. Applications of the SASR on real DNA data show that patterns in the graphic output reveal locations of the coding regions and the frame shifts between them: arcs indicate coding regions, stable points indicate non-coding regions and corners' shapes reveal frame shifts. Tests on genomic data set from Saccharomyces Cerevisiae reveal that the graphic patterns for coding and non-coding regions differ to a great extent, so that the coding regions can be visually distinguished. Meanwhile, a time cost test shows that the SASR can be easily implemented with the computational complexity of O(N).

| S-EPMC3017620 | biostudies-literature

Ab initio gene prediction for protein-coding regions

Project description:Abstract Motivation Ab initio gene prediction in nonmodel organisms is a difficult task. While many ab initio methods have been developed, their average accuracy over long segments of a genome, and especially when assessed over a wide range of species, generally yields results with sensitivity and specificity levels in the low 60% range. A common weakness of most methods is the tendency to learn patterns that are species-specific to varying degrees. The need exists for methods to extract genetic features that can distinguish coding and noncoding regions that are not sensitive to specific organism characteristics. Results A new method based on a neural network (NN) that uses a collection of sensors to create input features is presented. It is shown that accurate predictions are achieved even when trained on organisms that are significantly different phylogenetically than test organisms. A consensus prediction algorithm for a CoDing Sequence (CDS) is subsequently applied to the first nucleotide level of NN predictions that boosts accuracy through a data-driven procedure that optimizes a CDS/non-CDS threshold. An aggregate accuracy benchmark at the nucleotide level shows that this new approach performs better than existing ab initio methods, while requiring significantly less training data. Availability and implementation https://github.com/BioMolecularPhysicsGroup-UNCC/MachineLearning.

| S-EPMC10448985 | biostudies-literature

An Adaptive Mapping Method Using Spectral Envelope Approach for DNA Spectral Analysis

Project description:The digital signal processing approaches were investigated as a preliminary indicator for discriminating between the protein coding and non-coding regions of DNA. This is because a three-base periodicity (TBP) has already been proven to exist in protein-coding regions arising from the length of codons (three nucleic acids). This demonstrates that there is a prominent peak in the energy spectrum of a DNA coding sequence at frequency

| S-EPMC9323741 | biostudies-literature

A modified statistically optimal null filter method for recognizing protein-coding regions.

Project description:Computer-aided protein-coding gene prediction in uncharacterized genomic DNA sequences is one of the most important issues of biological signal processing. A modified filter method based on a statistically optimal null filter (SONF) theory is proposed for recognizing protein-coding regions. The square deviation gain (SDG) between the input and output of the model is used to identify the coding regions. The effective SDG amplification model with Class I and Class II enhancement is designed to suppress the non-coding regions. Also, an evaluation algorithm has been used to compare the modified model with most gene prediction methods currently available in terms of sensitivity, specificity and precision. The performance for identification of protein-coding regions has been evaluated at the nucleotide level using benchmark datasets and 91.4%, 96%, 93.7% were obtained for sensitivity, specificity and precision, respectively. These results suggest that the proposed model is potentially useful in gene finding field, which can help recognize protein-coding regions with higher precision and speed than present algorithms.

| S-EPMC5054498 | biostudies-literature

A comparative method for finding and folding RNA secondary structures within protein-coding regions.

Project description:Existing computational methods for RNA secondary-structure prediction tacitly assume RNA to only encode functional RNA structures. However, experimental studies have revealed that some RNA sequences, e.g. compact viral genomes, can simultaneously encode functional RNA structures as well as proteins, and evidence is accumulating that this phenomenon may also be found in Eukaryotes. We here present the first comparative method, called RNA-DECODER, which explicitly takes the known protein-coding context of an RNA-sequence alignment into account in order to predict evolutionarily conserved secondary-structure elements, which may span both coding and non-coding regions. RNA-DECODER employs a stochastic context-free grammar together with a set of carefully devised phylogenetic substitution-models, which can disentangle and evaluate the different kinds of overlapping evolutionary constraints which arise. We show that RNA-DECODER's parameters can be automatically trained to successfully fold known secondary structures within the HCV genome. We scan the genomes of HCV and polio virus for conserved secondary-structure elements, and analyze performance as a function of available evolutionary information. On known secondary structures, RNA-DECODER shows a sensitivity similar to the programs MFOLD, PFOLD and RNAALIFOLD. When scanning the entire genomes of HCV and polio virus for structure elements, RNA-DECODER's results indicate a markedly higher specificity than MFOLD, PFOLD and RNAALIFOLD.

| S-EPMC519121 | biostudies-literature

A Bipartite Network-based Method for Prediction of Long Non-coding RNA-protein Interactions.

Project description:As one large class of non-coding RNAs (ncRNAs), long ncRNAs (lncRNAs) have gained considerable attention in recent years. Mutations and dysfunction of lncRNAs have been implicated in human disorders. Many lncRNAs exert their effects through interactions with the corresponding RNA-binding proteins. Several computational approaches have been developed, but only few are able to perform the prediction of these interactions from a network-based point of view. Here, we introduce a computational method named lncRNA-protein bipartite network inference (LPBNI). LPBNI aims to identify potential lncRNA-interacting proteins, by making full use of the known lncRNA-protein interactions. Leave-one-out cross validation (LOOCV) test shows that LPBNI significantly outperforms other network-based methods, including random walk (RWR) and protein-based collaborative filtering (ProCF). Furthermore, a case study was performed to demonstrate the performance of LPBNI using real data in predicting potential lncRNA-interacting proteins.

| S-EPMC4792848 | biostudies-literature

Adaptive coding of reward prediction errors is gated by striatal coupling.

Project description:To efficiently represent all of the possible rewards in the world, dopaminergic midbrain neurons dynamically adapt their coding range to the momentarily available rewards. Specifically, these neurons increase their activity for an outcome that is better than expected and decrease it for an outcome worse than expected, independent of the absolute reward magnitude. Although this adaptive coding is well documented, it remains unknown how this rescaling is implemented. To investigate the adaptive coding of prediction errors and its underlying rescaling process, we used human functional magnetic resonance imaging (fMRI) in combination with a reward prediction task that involved different reward magnitudes. We demonstrate that reward prediction errors in the human striatum are expressed according to an adaptive coding scheme. Strikingly, we show that adaptive coding is gated by changes in effective connectivity between the striatum and other reward-sensitive regions, namely the midbrain and the medial prefrontal cortex. Our results provide evidence that striatal prediction errors are normalized by a magnitude-dependent alteration in the interregional connectivity within the brain's reward system.

| S-EPMC3306682 | biostudies-literature

Identification of protein coding regions in RNA transcripts.

Project description:Massive parallel sequencing of RNA transcripts by next-generation technology (RNA-Seq) generates critically important data for eukaryotic gene discovery. Gene finding in transcripts can be done by statistical (alignment-free) as well as by alignment-based methods. We describe a new tool, GeneMarkS-T, for ab initio identification of protein-coding regions in RNA transcripts. The algorithm parameters are estimated by unsupervised training which makes unnecessary manually curated preparation of training sets. We demonstrate that (i) the unsupervised training is robust with respect to the presence of transcripts assembly errors and (ii) the accuracy of GeneMarkS-T in identifying protein-coding regions and, particularly, in predicting translation initiation sites in modelled as well as in assembled transcripts compares favourably to other existing methods.

| S-EPMC4499116 | biostudies-literature

An organism-specific method to rank predicted coding regions in Trypanosoma brucei.

Project description:Genome annotation in differently evolved organisms presents challenges because the lack of sequence-based homology limits the ability to determine the function of putative coding regions. To provide an alternative to annotation by sequence homology, we developed a method that takes advantage of unusual trypanosomatid biology and skews in nucleotide composition between coding regions and upstream regions to rank putative open reading frames based on the likelihood of coding. The method is 93% accurate when tested on known genes. We have applied our method to the full complement of open reading frames on Chromosome I of Trypanosoma brucei, and we can predict with high confidence that 226 putative coding regions are likely to be functional. Methods such as the one described here for discriminating true coding regions are critical for genome annotation when other sources of evidence for function are limited.

| S-EPMC219476 | biostudies-literature

Adaptive Feedforward Compensating Self-Sensing Method for Active Flutter Suppression.

Project description:A single piezoelectric patch can be used as both a sensor and an actuator by means of the self-sensing piezoelectric actuator, and the function of self-sensing shows several advantages in many application fields. However, some problems exist in practical application. First, a capacitance bridge circuit is set up to realize the function of self-sensing, but the precise matching of the capacitance of the bridge circuit is hard to obtain due to the standardization of electric components and variations of environmental conditions. Second, a local strain is induced by the self-sensing actuator that is not related to the global vibration of the structure, which would affect the performance of applications, especially in active vibration control. The above problems can be tackled by the feedforward compensation method that is proposed in this paper. A configured piezoelectric self-sensing circuit is improved by a feedforward compensation tunnel, and a gain of compensation voltage is adjusted by the time domain and frequency domain's steepest descent algorithms to alleviate the capacitance mismatching and local strain problems. The effectiveness of the method is verified in the experiment of the active vibration control in a wind tunnel, and the control performance of compensated self-sensing actuation is compared to the performance with capacitance mismatching and local strain. It is found that the above problems have negative effects on the stability and performance of the vibration control and can be significantly eliminated by the proposed method.

| S-EPMC6210643 | biostudies-other

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data