Prediction Of Protein Coding Regions Research Articles

Improved Hybrid Approach for Enhancing Protein Coding Regions Identification in DNA Sequences

Introduction: Identifying and predicting protein-coding regions within DNA sequences play a pivotal role in genomic research. This paper introduces an approach for identifying proteincoding regions in DNA sequences, employing a hybrid methodology that combines a digital bandpass filter with wavelet transforms and various spectral estimation techniques to enhance exon prediction. Specifically, the Haar and Daubechies wavelet transforms are applied to improve the accuracy of protein-coding region (exon) prediction, enabling the extraction of intricate details that may be obscured in the original DNA sequences. background: The identification and prediction of protein-coding regions within DNA sequences play a pivotal role in genomic research. Methods: This research showcases the utility of Haar and Daubechies wavelet transforms, both nonparametric and parametric spectral estimation methods, and the deployment of a digital band pass filter for detecting peaks in exon regions. Additionally, the application of the Electron-Ion Interaction Potential (EIIP) method for converting symbolic DNA sequences into numerical values and the utilization of sum-of-sinusoids (SoS) mathematical models with optimized parameters further enrich the toolbox for DNA sequence analysis, ensuring the success of this proposed method in modeling DNA sequences optimally and accurately identifying genes. objective: Enhanced Protein-Coding Region Identification in DNA Sequences Using Wavelet Transforms Results: The outcomes of this approach showcase a substantial enhancement in identification accuracy for protein-coding regions. In terms of peak location detection, the application of Haar and Daubechies wavelet transforms enhances the accuracy of peak localization by approximately (0.01, 3-5 dB). When employing non-parametric and parametric spectral estimation techniques, there is an improvement in peak location by approximately (0.01, 4 dB) compared to the original signal. The proposed approach also achieves higher accuracy when compared with existing methods. method: hybrid methodology that combines a digital band-pass filter with wavelet transforms and various spectral estimation techniques to enhance exon prediction. Conclusion: These findings not only bridge gaps in DNA sequence analysis but also offer a promising pathway for advancing exonic region prediction and gene identification in genomics research. The hybrid methodology presented stands as a robust contribution to the evolving landscape of genomic analysis techniques. result: The results obtained through this proposed method demonstrate significantly improved identification accuracy. These findings offer a promising avenue for DNA sequence analysis, exonic region prediction, and gene identification.

Protein coding regions prediction by fusing DNA shape features

Exons crucial for coding are often hidden within introns, and the two tend to vary greatly in length, which results in deep learning-based protein coding region prediction methods often performing poorly when applied to more structurally complex biological genomes. DNA shape information also plays a role in revealing the underlying logic of gene expression, yet current methods ignore the influence of DNA shape features when distinguishing coding and non-coding regions. We propose a method to predict protein-coding regions using the CNNS-BRNN model, which incorporates DNA shape features and improves the model's ability to distinguish between intronic and exonic features. We use a fusion coding technique that combines DNA shape features and traditional sequence features. Experiments show that this method outperforms the baseline method in metrics such as AUC and F1 by 2.3% and 5.3%, respectively, and the fusion coding method that introduces DNA shape features has a significant improvement in model performance.

NGS Analysis to Detect Mutation in Brain Tumor Diagnostic

Abstract: This study presents an integrated computational approach for analyzing protein sequences and their 3D structures. By leveraging the MMDB Macromolecular database, homologs of a protein sequence of interest are identified, and interactive visualization of their structural properties is provided. The computational alignment method, using BLASTP, allows for efficient determination of sequence similarities and identification of conserved regions among multiple protein sequences. COBALT is employed to refine sequence alignments and facilitate graphical analysis of sequence relationships. RASMOL, a computational analysis program, generates 2-D representations of protein-ligand complexes, enabling visual exploration of their interactions. ORF finder is used to identify coding regions in mRNA sequences, aiding in the prediction of protein-coding regions. The approach is applied to brain tumor diagnostics using human biological samples, exploring the structural properties of brain tumor-related proteins with the help of the 2RHU protein structure and PYMOL visualization software. Overall, this integrated computational framework offers a comprehensive toolkit for protein sequence analysis, structure visualization, and homology modeling, with potential applications in drug discovery, molecular biology, and medical diagnostics

MgCod: Gene Prediction in Phage Genomes with Multiple Genetic Codes

Massive sequencing of microbiomes has led to the discovery of a large number of phage genomes with intermittent stop codon recoding. We have developed a computational tool, MgCod, that identifies genomic regions (blocks) with distinct stop codon recoding simultaneously with the prediction of protein-coding regions. When MgCod was used to scan a large volume of human metagenomic contigs hundreds of viral contigs with intermittent stop codon recoding were revealed. Many of these contigs originated from genomes of known crAssphages. Further analyses had shown that intermittent recoding was associated with subtle patterns in the organization of protein-coding genes, such as ‘single-coding’ and ‘dual-coding’. The dual-coding genes, clustered into blocks, could be translated by two alternative codes producing nearly identical proteins. It was observed that the dual-coded blocks were enriched with the early-stage phage genes, while the late-stage genes were residing in the single-coded blocks. MgCod can identify types of stop codon recoding in novel genomic sequences in parallel with gene prediction. It is available for download from https://github.com/gatech-genemark/MgCod.

Enhancing the prediction of protein coding regions in biological sequence via a deep learning framework with hybrid encoding

Protein coding regions prediction is a very important but overlooked subtask for tasks such as prediction of complete gene structure, coding/noncoding RNA. Many machine learning methods have been proposed for this problem, they first encode a biological sequence into numerical values and then feed them into a classifier for final prediction. However, encoding schemes directly influence the classifier's capability to capture coding features and how to choose a proper encoding scheme remains uncertain. Recently, we proposed a protein coding region prediction method in transcript sequences based on a bidirectional recurrent neural network with non-overlapping 3-mer feature, and achieved considerable improvement over existing methods, but there is still much room to improve the performance. First, 3-mer feature that counts the occurrence frequency of trinucleotides in a biological sequence only reflects local sequence order information between the most contiguous nucleotides, which loses almost all the global sequence order information. Second, kmer features of length k larger than three (e.g., hexamer) may also contain useful information. Based on the two points, we here present a deep learning framework with hybrid encoding for protein coding regions prediction in biological sequences, which effectively exploit global sequence order information, non-overlapping gapped kmer (gkm) features and statistical dependencies among coding labels. 3-fold cross-validation tests on human and mouse biological sequence demonstrate that our proposed method significantly outperforms existing state-of-the-art methods.

A Novel Approach For Identification Of Exon Locations In DNA Sequences Using GLC Window

The application of signal processing techniques for identification of exons in Deoxyribonucleic acid (DNA) sequence is a challenging task. The objective of this paper is to introduce a combinational window approach for locating exons in DNA sequence. In contrast to the traditional single window function for evaluation of short time Fourier transform (STFT), this work proposes a novel method for evaluating STFT coefficients using a combinational window function comprising of Gaussian, Lanczos and Chebyshev (GLC) windows. The chosen combinational window GLC has the highest relative side lobe attenuation values compared to other window functions introduced by various researchers. The proposed algorithm incorporates GLC window function for evaluating STFT coefficients and in the design of FIR bandpass filter. Simulation results revealed its effectiveness in improving the evaluation parameters like Sensitivity, Specificity, Accuracy, Area under curve (AUC), Discrimination Measure (DM). Furthermore, the proposed algorithm has been applied successfully to some universal benchmark datasets like C. elegans, Homosapiens, etc., The proposed method has shown to be an efficient approach for the prediction of protein coding regions compared to other existing methods. All the simulations are done using the MATLAB 2016a.

USING DIT-FFT ALGORITHM FOR IDENTIFICATION OF PROTEIN CODING REGION IN EUKARYOTIC GENE

The new research platform on biomedical engineering by Digital Signal Processing (DSP) is playing a vital role in the prediction of protein coding regions (Exons) from genomic sequences with great accuracy. We can determine the protein coding area in DNA sequences with the help of period-3 property. It has been seen that in order to find out the period-3 property, the DFT algorithm is mostly used but in this paper, we have tested FFT algorithm instead of DFT algorithm. DSP is basically concerned with processing numerical sequences. When digital signal processing used in DNA sequences analysis, it requires conversion of base characters sequence to the numerical version. The numerical representation of DNA sequences strongly impacts the biological properties mirrored through the numerical genre. In this work, the proposed technique based on DIT-FFT algorithm has been used to identify the exonic area with the help of integer value representation for transforming the DNA sequences. Digital filters are used to read out period 3 components from the output spectrum and to eliminate the unwanted high frequency noise from DNA sequences. To overcome background noise means to suppress the non-coding regions, i.e., Introns. Proposed algorithm is tested on four nucleotide sequences having single or multiple numbers of exons.

Transcriptomics and identification of candidate chemosensory genes in antennae of Conogethes punctiferalis (Lepidoptera: Crambidae)

To investigate chemoreception genes in Conogethes punctiferalis, antennal transcriptome of male and female samples were sequenced and analyzed. A total number of 54,442,128 and 51,132,998 clean reads from male and female samples were obtained respectively. By de novo assembly, a total of 51,240 unigenes for both sexes was generated. Among all unigenes, 26,509 showed similarity to known proteins after alignment to Nr protein database by blastx. Go analysis, KEGG pathway analysis and Protein coding region prediction were also performed subsequently to annotate gene functions. A total of 29 candidate odorant binding proteins (OBPs) including 2 general odorant binding proteins (GOBPs) and 5 pheromone binding protein (PBP), as well as 59 odorant receptors (ORs) were identified. Additionally, 10 chemosensory proteins (CSPs) and 19 ionotropic receptors (IRs) were annotated. With gene expression comparison, 920 up-regulated and 684 down-regulated genes were found in female antennae transcriptome, compared with that of the male. Differently expression levels of 9 chemosensory genes including PBPs up-regulated in the male were detected. Results of this study provide basic information about C. punctiferalis chemoreception genes, which helps to understand pheromone communication in this species and may benefit control of this pest with pheromonal techniques.

Polyphase filtering with variable mapping rule in protein coding region prediction

Genomic researches are concerned with the study of genomes of organisms. It has become a challenge to the researchers to identify the segments within the DNA sequence that involved in protein synthesis and called coding region of gene. The methods are generally used to identify the segment that relies on period-3 property of genes. This period-3 property easily can be identified by digital signal processing with great accuracy. Prior to DSP application in gene prediction a conversion rule is required which converts symbolic DNA (ATCGTC…) sequence into numerical representations. Accuracy of gene prediction depends on mapping rule. The effectiveness of mapping rule depends on the application area of genomics. Some mapping rule works well in gene prediction may not performed good in genetic disease prediction. Most of the available conversion rules are fixed mapping technique. In this paper a new conversion rule is proposed prior to DSP application and a polyphase filter is used to suppress the noise in the DNA spectrum. The performance of the proposed mapping is compared with existing mapping and also the performance of the polyphase filtering method is compared with existing filtering methods in terms of signal to noise ratio (SNR) and location accuracy.

Nucleotide distribution variance-based dynamic representation scheme for novel gene prediction

Predicting novel genes is an important topic in bioinformatics. De novo protein coding region prediction techniques are more powerful than homology-based techniques in the analysis of novel DNA sequences for annotating protein regions. In this article, a new gene finding technique is proposed to predict protein coding regions in DNA sequences. The technique is based on the spectral analysis of DNA sequences and calculating the period-3 spectrum. An analysis of the effectiveness of the nucleotides is performed to study the variance in the strength of the nucleotides in the period-3 spectrum in protein coding and non-coding regions. The proposed technique uses a dynamic representation scheme to map DNA sequences into a numerical form. The dynamic representation scheme provides better differentiation between coding and non-coding regions as it enhances the participation of nucleotides that are effective in the period-3 spectrum. The technique also uses post-processing to detect the period-3 spectrum peaks instead of thresholding. The proposed technique is compared with other spectrum-based techniques by plotting the receiver operating characteristic (ROC) curves and calculating the area under the curve (AUC). The results show that the proposed technique outperforms other spectrum-based techniques. In addition, we analyze the false positive peaks that result in from the prediction by scanning the availability of stop-codon-like triplet combinations in the three possible reading frames. This analysis provides an insight for future work to improve the performance of the spectrum-based gene finding techniques.

Prediction of Protein Coding Regions Using a Wide-Range Wavelet Window Method.

Prediction of protein coding regions is an important topic in the field of genomic sequence analysis. Several spectrum-based techniques for the prediction of protein coding regions have been proposed. However, the outstanding issue in most of the proposed techniques is that these techniques depend on an experimentally-selected, predefined value of the window length. In this paper, we propose a new Wide-Range Wavelet Window (WRWW) method for the prediction of protein coding regions. The analysis of the proposed wavelet window shows that its frequency response can adapt its width to accommodate the change in the window length so that it can allow or prevent frequencies other than the basic frequency in the analysis of DNA sequences. This feature makes the proposed window capable of analyzing DNA sequences with a wide range of the window lengths without degradation in the performance. The experimental analysis of applying the WRWW method and other spectrum-based methods to five benchmark datasets has shown that the proposed method outperforms other methods along a wide range of the window lengths. In addition, the experimental analysis has shown that the proposed method is dominant in the prediction of both short and long exons.

A dynamic representation-based, de novo method for protein-coding region prediction and biological information detection

In this paper, we propose a new method for the prediction of protein coding regions that is designed to detect novel genes that do not have known, close homologs. The proposed method uses a dynamic representation scheme to convert DNA sequences into a numerical form, and then it uses the nucleotide distribution variance to calculate the period-3 spectrum. The dynamic representation scheme assigns numerical pairs to the nucleotides to emphasize the effect of the nucleotides that have a stronger participation in the period-3 spectrum. The proposed method also uses the nucleotide distribution variance which has less computational cost than the Fourier transform to extract the period-3 spectrum. A post-processing of the period-3 spectrum signal is performed to smooth the signal, detect the period-3 spectrum peaks, and locate the boundaries of the protein-coding regions.The analysis of the receiver operating characteristic (ROC) curves shows that the proposed method outperforms other Digital Signal Processing (DSP)-based methods. The analysis of the false positive peaks shows that these regions have a similarity with regions that have functional patterns in other DNA sequences. The method also highlights and explores the capabilities of techniques that perform better than homology-based techniques for de novo protein prediction. We believe that this is an area of research that has been underemphasized and deserves additional attention.

De novo assembly and characterization of skin transcriptome using RNAseq in sheep (Ovis aries)

Wool is produced via synthetic processes of wool follicles, which are embedded in the skin of sheep. The development of new-generation sequencing and RNA sequencing provides new approaches that may elucidate the molecular regulation mechanism of wool follicle development and facilitate enhanced selection for wool traits through gene-assisted selection or targeted gene manipulation. We performed de novo transcriptome sequencing of skin using the Illumina Hiseq 2000 sequencing system in sheep (Ovis aries). Transcriptome de novo assembly was carried out via short-read assembly programs, including SOAPdenovo and ESTScan. The protein function, clusters of orthologous group function, gene ontology function, metabolic pathway analysis, and protein coding region prediction of unigenes were annotated by BLASTx, BLAST2GO, and ESTScan. More than 26,266,670 clean reads were collected and assembled into 79,741 unigene sequences, with a final assembly length of 35,447,962 nucleotides. A total of 22,164 unigenes were annotated, accounting for 36.27% of the total number of unigenes, which were divided into 25 classes belonging to 218 signaling pathways. Among them, there were 17 signal paths related to hair follicle development. Based on mass sequencing data of sheepskin obtained by RNA-Seq, many unigenes were identified and annotated, which provides an excellent platform for future sheep genetic and functional genomic research. The data could be used for improving wool quality and as a model for human hair follicle development or disease prevention.

IN-MACA-MCC: Integrated Multiple Attractor Cellular Automata with Modified Clonal Classifier for Human Protein Coding and Promoter Prediction.

Protein coding and promoter region predictions are very important challenges of bioinformatics (Attwood and Teresa, 2000). The identification of these regions plays a crucial role in understanding the genes. Many novel computational and mathematical methods are introduced as well as existing methods that are getting refined for predicting both of the regions separately; still there is a scope for improvement. We propose a classifier that is built with MACA (multiple attractor cellular automata) and MCC (modified clonal classifier) to predict both regions with a single classifier. The proposed classifier is trained and tested with Fickett and Tung (1992) datasets for protein coding region prediction for DNA sequences of lengths 54, 108, and 162. This classifier is trained and tested with MMCRI datasets for protein coding region prediction for DNA sequences of lengths 252 and 354. The proposed classifier is trained and tested with promoter sequences from DBTSS (Yamashita et al., 2006) dataset and nonpromoters from EID (Saxonov et al., 2000) and UTRdb (Pesole et al., 2002) datasets. The proposed model can predict both regions with an average accuracy of 90.5% for promoter and 89.6% for protein coding region predictions. The specificity and sensitivity values of promoter and protein coding region predictions are 0.89 and 0.92, respectively.

AIX-MACA-Y Multiple Attractor Cellular Automata Based Clonal Classifier for Promoter and Protein Coding Region Prediction

AIX-MACA-Y Multiple Attractor Cellular Automata Based Clonal Classifier for Promoter and Protein Coding Region Prediction

Improved Comb Filter based Approach for Effective Prediction of Protein Coding Regions in DNA Sequences

The prediction of protein coding regions in DNA sequences is an important problem in computational biology. It is observed that nucleotides in the protein coding regions or exons of a DNA sequence show period-3 property. Hence identification of the period-3 regions helps in predicting the gene locations within the billions long DNA sequence of eukaryotic cells. The period-3 property exhibited in exons of eukaryotic gene sequences enables signal processing based time-domain and frequency domain methods to predict these regions efficiently. Several approaches based on signal processing tools have, therefore, been applied to this problem, to predict these regions effectively. This paper describes novel and efficient comb filter-based techniques for the prediction of protein coding region based on the period-3 behavior of codon sequences. The proposed method is then validated on Burset/Guigo1996, HMR195 and KEGG standard datasets using various prediction measures. It is shown that cascaded differentiator comb (CDC) filter can be used for prediction of protein coding region with better prediction efficiency, and involves less computational complexity compared with the other signal processing techniques based on period-3 property.

Prediction of protein coding regions by the 3-base periodicity analysis of a DNA sequence

With the exponential growth of genomic sequences, there is an increasing demand to accurately identify protein coding regions (exons) from genomic sequences. Despite many progresses being made in the identification of protein coding regions by computational methods during the last two decades, the performances and efficiencies of the prediction methods still need to be improved. In addition, it is indispensable to develop different prediction methods since combining different methods may greatly improve the prediction accuracy. A new method to predict protein coding regions is developed in this paper based on the fact that most of exon sequences have a 3-base periodicity, while intron sequences do not have this unique feature. The method computes the 3-base periodicity and the background noise of the stepwise DNA segments of the target DNA sequences using nucleotide distributions in the three codon positions of the DNA sequences. Exon and intron sequences can be identified from trends of the ratio of the 3-base periodicity to the background noise in the DNA sequences. Case studies on genes from different organisms show that this method is an effective approach for exon prediction.

3PA128 アミノ酸レベルの特徴の利用による原核生物ゲノム塩基列の蛋白質コード領域推定の高精度化

3PA128 アミノ酸レベルの特徴の利用による原核生物ゲノム塩基列の蛋白質コード領域推定の高精度化