Identification Of Exons Research Articles

In-silico structural and phylogenetic analysis of human beta globin gene family

There are 5 members of the human beta globin gene family. They have blood transportation function. There are some diseases related to the mutation in these gene. the common disease related to mutation these genes are sickle cell anemia, thalassemia etc. The structural and phylogenetic analysis is performed by using some bioinformatics tools. The gene sequence is retrieved and run under some tools to perform the specific analysis. The analysis performed include are similarity with other organism, determination of conserved domains and motifs, multiple sequence alignment, generation of phylogenetic tree, identification of exons and introns and determination of restriction sites. And the tools used for these analyses are BLASTP, pfam, clustal omega, MEGA7, gene structure display tool and serial cloner respectively. This study came out with a result that the genes diverged due to duplication and then mutation. The gene contain mostly non coding portion but there are 3 exons and 2 introns. The length of 2 introns is far more than that of 3 exons. In future the techniques like CRISPR/cas gene editing tool will be used to treat the disease related the beta globin gene family. Key words:human beta globin gene, bioinformatics study of HBB, genome wise study of HBB, Beta subunit, hemoglobin beta subunit.

Characterization of alternatively spliced transcript variants of glycophorin A and glycophorin B genes in Chinese blood donors.

The molecular basis of MNS blood group variants is not fully clear yet. In this study, we have characterized mRNA variants of GYPA and GYPB genes to reveal whether alternative RNA splicing may cause antigenic diversity of the MNS system. Total RNA was extracted from peripheral blood of Chinese blood donors and full-length cDNA products were generated. A nested polymerase chain reaction (PCR)-based method was established for fragment amplification and Sanger sequencing. Resulted full-length mRNA sequences were aligned with GYPA or GYPB genomic sequences respectively for exon identification. Amino acid (AA) sequences of GPA and GPB proteins were extrapolated and GYPA-EGFP, GYPB-EGFP fusion genes were generated to monitor subcellular distribution of the encoded glycophorin (GP) proteins. Totally 10 blood samples were analysed. GYPB mRNAs of all the subjects demonstrated frequent exon insertion or deletion whereas this kind of variation was only observed in 3 of 10 GYPA mRNA samples. None of the reported Miltenberger hybrids was detected in any of the mRNA samples. The alternative splicing resulted in changes of AA sequences in N-terminal domains where the MNS antigenic motifs resided; however, subcellular localizations of GP-EGFP fusion proteins showed that the above-mentioned AA changes did not affect cell surface distribution of the encoded GP proteins. Alternative RNA splicing may influence the antigenic features of GP proteins but not their cell surface distribution. Therefore, GYPA and GYPB mRNA characterization might be an invaluable supplement to serological phenotyping and DNA-based genotyping in MNS blood grouping.

Study of effectiveness of FIR and IIR filters in Exon identification: A comparative approach

In recent years, DSP has been widely used in the area of DNA sequence analysis, detection of protein coding and non-coding regions and also in finding abnormalities present in coding and non-coding regions. Using DSP tool, the detection of protein coding and non-coding regions has been executed with great accuracy and less complexity. In this paper, DNA nucleotide sequence is converted into corresponding EIIP sequence in the first step. In order to make a comparative study between different FIR and IIR filters, the sequence is passed through overall six FIR and IIR filters separately to extract the period three frequency components. Finally, Gaussian filter is used to suppress the high frequency noise present in the power spectrum. This study aims to find out most efficient FIR and IIR filters in predicting exons (coding regions) and introns (non-coding regions) based on different evaluating parameters as follows: (i) specificity-sensitivity values (ii) Matthews correlation coefficient (iii) Miss rate and wrong rate (iv) Discriminating factor (v) ROC.

Empirical mode decomposition based adaptive noise canceller for improved identification of exons in eukaryotes

Empirical mode decomposition based adaptive noise canceller for improved identification of exons in eukaryotes

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.

Prediction of Eukaryotic Exons using Bidirectional LSTM-RNN based Deep Learning Model

Exon prediction has always been a challenge for computational biologist. Although there have been many advances in identification and prediction of exons by computational methods. The efficacy and efficiency of prediction methods need to be further improved using new parameters and algorithms. Moreover, it is essential to develop new prediction methods by combining already existing approaches that can greatly improve prediction accuracy. A eukaryotic gene contains several exons and introns that are separated by splice site junction. It is important to accurately identify splice sites in a gene. Splice sites regions are known, but computational signal prediction is still challenging due to numerous false positives and other problems. In this paper, a novel combination of Support Vector Machine and bidirectional LSTM-RNN based Deep Learning approaches has been applied to improve the efficiency and accuracy of exon prediction. The proposed method takes into account the conventional machine learning as well as the deep learning approach on predictive accuracy of eukaryotic exons.

Exon size and sequence conservation improves identification of splice-altering nucleotides.

Pre-mRNA splicing is regulated through multiple trans-acting splicing factors. These regulators interact with the pre-mRNA at intronic and exonic positions. Given that most exons are protein coding, the evolution of exons must be modulated by a combination of selective coding and splicing pressures. It has previously been demonstrated that selective splicing pressures are more easily deconvoluted when phylogenetic comparisons are made for exons of identical size, suggesting that exon size–filtered sequence alignments may improve identification of nucleotides evolved to mediate efficient exon ligation. To test this hypothesis, an exon size database was created, filtering 76 vertebrate sequence alignments based on exon size conservation. In addition to other genomic parameters, such as splice-site strength, gene position, or flanking intron length, this database permits the identification of exons that are size- and/or sequence-conserved. Highly size-conserved exons are always sequence-conserved. However, sequence conservation does not necessitate exon size conservation. Our analysis identified evolutionarily young exons and demonstrated that length conservation is a strong predictor of alternative splicing. A published data set of approximately 5000 exonic SNPs associated with disease was analyzed to test the hypothesis that exon size–filtered sequence comparisons increase detection of splice-altering nucleotides. Improved splice predictions could be achieved when mutations occur at the third codon position, especially when a mutation decreases exon inclusion efficiency. The results demonstrate that coding pressures dominate nucleotide composition at invariable codon positions and that exon size–filtered sequence alignments permit identification of splice-altering nucleotides at wobble positions.

Identification of Short Exons Disunited by a Short Intron in Eukaryotic DNA Regions.

Weak codon bias in short exons and separation by a short intron induces difficulty in extracting period-3 component that marks the presence of exonic regions. The annotation task of such short exons has been addressed in the proposed model independent signal processing based method with following features: (a) DNA sequences have been mapped using multiple mapping schemes, (b) period-3 spectrums corresponding to multiple mappings have been optimized to enhance short exon-short intron discrimination, and (c) spectrums corresponding to multiple mapping schemes have been subjected to Principal Component Analysis (PCA) for identifying greater number of such short exons. A comparative study with other methods indicates improved detection of contiguous short exons disunited by a short intron. Apart from the annotation of exonic and intronic regions, the proposed algorithm can also complement the methods for the detection of alternative splicing by intron retention, as one of the characteristic feature for intron retention is the presence of two short exons flanking a short intron.

Exploration of germline variants responsible for adverse events of crizotinib in anaplastic lymphoma kinase-positive non-small cell lung cancer by target-gene panel sequencing

ObjectivesCrizotinib is a standard treatment for advanced anaplastic lymphoma kinase (ALK)- or ROS1-fusion-gene-positive non-small cell lung cancer; however, serious adverse events (AEs), including elevated alanine aminotransferase (ALT)/aspartate aminotransferase (AST) and interstitial lung disease (ILD), develop occasionally. Here, we evaluated relationships between clinically significant crizotinib-associated AEs and germline variations. Materials and MethodsDNA obtained from 75 patients allowed selection of 147 genes according to function, exon identification and sequencing, and determination of germline single nucleotide variants (SNVs). Correlations between clinically significant AEs and presence of germline variants were estimated by Fisher’s exact test. ResultsWe defined clinically significant AEs as grade 4 hematological toxicity, grade ≥3 non-hematological toxicity, and any grade of ILD. These AEs were observed in 26 patients (35%), with elevated AST/ALT (15%) the most common, followed by neutropenia (5%), ILD (4%), and thromboembolic events (4%). Nonsynonymous SNVs in epoxide hydrolase 1 (EPHX1) [odds ratio (OR): 3.86; p = 0.0009) and transcription factor 7-like 2 (TCF7L2) (OR: 2.51; p = 0.025) were associated with the presence of clinically significant AEs. ConclusionNonsynonymous EPHX1 and TCF7L2 SNVs might be associated with clinically significant crizotinib-associated AEs. These data indicated that target-gene sequencing could be feasible for predicting anticancer-agent toxicity, and that germline multi-gene information might be useful for predicting patient-specific AEs to promote precision medicine.

Efficient Adaptive Exon Prediction for DNA study using Proportionate LMS Variants

In the field of Bio-informatics, locating the exon fragments in a deoxyribonucleic acid (DNA) sequence is an important and vital work. Study of protein coding regions is a wide phenomenon in identification of diseases and design of drugs. The regions of DNA that have the protein coding information are termed as exons. Hence identifying the exon segments in a genomic sequence is a crucial job in bio-informatics. Three base periodicity (TBP) has been observed in the regions of DNA sequences can be easily determined by applying signal processing methods. Adaptive signal processing techniques found to be useful than other available methods. This is due to their unique capability to alter weight coefficients based on genomic sequence. We propose efficient adaptive exon predictors (AEPs) based on these considerations using Proportionate Normalized LMS (PNLMS) algorithm and Maximum Proportionate Normalized LMS (MPNLMS) algorithm to improve exon locating ability and better convergence. To ease the complexity of computations in the denominator during filtering process, proposed AEPs using PNLMS and its maximum variants are combined with signature algorithms. Hybrid variants of proposed AEPs include PNLMS, DCPNLMS, ECPNLMS, SSPNLMS, MPNLMS, MDCPNLMS, MECPNLMS and MSSPNLMS algorithms. It was shown that the AEP based on MDCPNLMS is superior in applications of exon identification depending on performance measures with Sensitivity 0.7346, Specificity 0.7483 and precision 0.7325 for a genomic sequence with accession AF009962 at a threshold of 0.8. Finally the capability of several AEPs in predicting exon locations is verified using different DNA sequences found in National Center for Biotechnology Information (NCBI) gene database.

Coding and functional defect region prediction of placental protein in an embryo cell of first trimester using ANN approach

The protein coding and functional regions in DNA sequences has become an exciting task in bioinformatics. In particular, the coding region has a 3-base periodicity, which helps for exon identification. Many signal processing tools and techniques have been successfully applied to identify tasks, but still need to be improved in this direction. In our work, we employ ANN classifier to predict coding and functional region of proteinin human embryo cell protein in first trimester, and evaluate their performances according to the comparison energy levels of coding region. The obtained from the threshold energy level, results show that in a box plot finally predict the mutation.

Cloud-based adaptive exon prediction for DNA analysis.

Cloud computing offers significant research and economic benefits to healthcare organisations. Cloud services provide a safe place for storing and managing large amounts of such sensitive data. Under conventional flow of gene information, gene sequence laboratories send out raw and inferred information via Internet to several sequence libraries. DNA sequencing storage costs will be minimised by use of cloud service. In this study, the authors put forward a novel genomic informatics system using Amazon Cloud Services, where genomic sequence information is stored and accessed for processing. True identification of exon regions in a DNA sequence is a key task in bioinformatics, which helps in disease identification and design drugs. Three base periodicity property of exons forms the basis of all exon identification techniques. Adaptive signal processing techniques found to be promising in comparison with several other methods. Several adaptive exon predictors (AEPs) are developed using variable normalised least mean square and its maximum normalised variants to reduce computational complexity. Finally, performance evaluation of various AEPs is done based on measures such as sensitivity, specificity and precision using various standard genomic datasets taken from National Center for Biotechnology Information genomic sequence database.

EGFR Mutations Status For Non-Small-Cell Lung Cancer Patients

With the advent of the molecular-targeted therapy, rapid progress has been made in the treatment of advanced or recurrent non-small-cell lung cancer (NSCLC). EGFR mutations detection in tumor is important to determine an appropriate treatment of EGFR TK inhibitors. We investigated EGFR mutation status for patients with non-small cells lung carcinoma (NSCLC) patients’ tumorous cells from FFPE material in Klaipeda University Hospital, Lithuania (mutation test for the qualitative detection and identification of mutations in exons 18, 19, 20 and 21 of the EGFR gene) and T790M mutation in cfDNA (Liquid biopsy). Mutations of EGFR from FFPE were detected in 24 of the 119 patients (20%). EGFR mutations were more frequently found in women (13 of 36, 36%). cfDNA (Liquid biopsy) testing results show one of the patients had EGFR 20 exon T790M mutation detected which is resistance determining factor for the first and second generation EGFR TK Inhibitors treatment and a predicting marker for the third generation EGFR TK Inhibitors treatment.

ExSurv: A Web Resource for Prognostic Analyses of Exons Across Human Cancers Using Clinical Transcriptomes.

Survival analysis in biomedical sciences is generally performed by correlating the levels of cellular components with patients’ clinical features as a common practice in prognostic biomarker discovery. While the common and primary focus of such analysis in cancer genomics so far has been to identify the potential prognostic genes, alternative splicing – a posttranscriptional regulatory mechanism that affects the functional form of a protein due to inclusion or exclusion of individual exons giving rise to alternative protein products, has increasingly gained attention due to the prevalence of splicing aberrations in cancer transcriptomes. Hence, uncovering the potential prognostic exons can not only help in rationally designing exon-specific therapeutics but also increase specificity toward more personalized treatment options. To address this gap and to provide a platform for rational identification of prognostic exons from cancer transcriptomes, we developed ExSurv (https://exsurv.soic.iupui.edu), a web-based platform for predicting the survival contribution of all annotated exons in the human genome using RNA sequencing-based expression profiles for cancer samples from four cancer types available from The Cancer Genome Atlas. ExSurv enables users to search for a gene of interest and shows survival probabilities for all the exons associated with a gene and found to be significant at the chosen threshold. ExSurv also includes raw expression values across the cancer cohort as well as the survival plots for prognostic exons. Our analysis of the resulting prognostic exons across four cancer types revealed that most of the survival-associated exons are unique to a cancer type with few processes such as cell adhesion, carboxylic, fatty acid metabolism, and regulation of T-cell signaling common across cancer types, possibly suggesting significant differences in the posttranscriptional regulatory pathways contributing to prognosis.

Identification of tissue-enriched novel transcripts and novel exons in mice.

BackgroundRNA sequencing (RNA-seq) has revolutionized the detection of transcriptomic signatures due to its high-throughput sequencing ability. Therefore, genomic annotations on different animal species have been rapidly updated using information from tissue-enriched novel transcripts and novel exons.Results34 putative novel transcripts and 236 putative tissue-enriched exons were identified using RNA-Seq datasets representing six tissues available in mouse databases. RT-PCR results indicated that expression of 21 and 2 novel transcripts were enriched in testes and liver, respectively, while 31 of the 39 selected novel exons were detected in the testes or heart. The novel isoforms containing the identified novel exons exhibited more dominant expression than the known isoforms in heart and testes. We also identified an example of pathology-associated exclusion of heart-enriched novel exons such as Sorbs1 and Cluh during pressure-overload cardiac hypertrophy.ConclusionThe present study depicted tissue-enriched novel transcripts, a tissue-specific isoform switch, and pathology-associated alternative splicing in a mouse model, suggesting tissue-specific genomic diversity and plasticity.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-592) contains supplementary material, which is available to authorized users.

Detection of Mutation-Hotspots and Exon-Deletions using Digital Signal Processing in Duchenne Muscular Dystrophy (DMD) Gene

Duchenne muscular dystrophy (DMD) is a life threatening disease which mostly occurs due to deletions in the dystrophin gene. The deletions are reported to be clustered in two main regions in the gene involving nearly one fourth of exons. These regions also represent major meiotic recombination hotspots. The detection of deletions/mutation-hotspots in the coding sequences (exons) is not much possible till date due to the gene specific-potential variations or the complex organization of introns and exons in the gene. A Digital signal processing (DSP) method based on antinotch filter that exploits the period-3 property of a DNA sequence is applied for the identification of exons in DNA sequences by providing concerned peaks in magnitude-plot. A two digit numerical representation has been proposed for generating indicator sequence. In our approach to detect deletion in DMD gene, all-exon-sequence was taken and analyzed through digital filter to obtain normal peaks in the plot, which was than compared with the plots obtained after deletion of hotspot exons one by one. It was found that most of the hotspot exons share the peaks and deletions are marked by significant modifications in peaks. Thus, it was known that the peaks in filter-plot of DMD gene are associated with the location of the hotspots and their modifications/absence can be utilized to detect the deletions of exons.

Assessment of transcript reconstruction methods for RNA-seq.

We evaluated 25 protocol variants of 14 independent computational methods for exon identification, transcript reconstruction and expression-level quantification from RNA-seq data. Our results show that most algorithms are able to identify discrete transcript components with high success rates but that assembly of complete isoform structures poses a major challenge even when all constituent elements are identified. Expression-level estimates also varied widely across methods, even when based on similar transcript models. Consequently, the complexity of higher eukaryotic genomes imposes severe limitations on transcript recall and splice product discrimination that are likely to remain limiting factors for the analysis of current-generation RNA-seq data.

Systematic screening for PRKAR1A gene rearrangement in Carney complex: identification and functional characterization of a new in-frame deletion

Point mutations of the PRKAR1A gene are a genetic cause of Carney complex (CNC) and primary pigmented nodular adrenocortical disease (PPNAD), but in 30% of the patients no mutation is detected. Set up a routine-based technique for systematic detection of large deletions or duplications of this gene and functionally characterize these mutations. Multiplex ligation-dependent probe amplification (MLPA) of the 12 exons of the PRKAR1A gene was validated and used to detect large rearrangements in 13 typical CNC and 39 confirmed or putative PPNAD without any mutations of the gene. An in-frame deletion was characterized by western blot and bioluminescence resonant energy transfer technique for its interaction with the catalytic subunit. MLPA allowed identification of exons 3-6 deletion in three patients of a family with typical CNC. The truncated protein is expressed, but rapidly degraded, and does not interact with the protein kinase A catalytic subunit. MLPA is a powerful technique that may be used following the lack of mutations detected by direct sequencing in patients with bona fide CNC or PPNAD. We report here one such new deletion, as an example. However, these gene defects are not a frequent cause of CNC or PPNAD.

Identification of novel exons and transcripts by comprehensive RNA-Seq of horn cancer transcriptome in Bos indicus

Horn cancer (HC) is a widely prevalent cancer amongst Bos indicus cattle and frequently associated with economic losses due to reduction in draught and milk production capacity. To identify novel transcripts and characterize HC transcriptome, we employed high throughput RNA-Seq analysis of HC and horn normal (HN) tissue. Total of 584,450 and 920,450 high quality reads were analyzed for HN and HC tissue, respectively, resulting in the identification of 16,803 unique genes comprising of 14,754 annotated genes and 2049 novel transcripts. We found 251 significant differentially expressed genes in HC transcriptome, which includes 52 putative novel transcripts. We validated presence of 23 out of 28 novel transcripts by RT-PCR and confirmed differential expression of 12 novel transcripts through RT-qPCR on 4 samples each of HC and HN. Functional analysis revealed that HC is characterized by deregulation of genes involved in epithelial cell differentiation and complement cascade. Our study will serve as a step further in detailed characterization of HC transcriptome and provide firm base to explore and mitigate HC at finer resolution.

New encoded single-indicator sequences based on physico-chemical parameters for efficient exon identification

The first step in gene identification problem based on genomic signal processing is to convert character strings into numerical sequences. These numerical sequences are then analysed spectrally or using digital filtering techniques for the period-3 peaks, which are present in exons (coding areas) and absent in introns (non-coding areas). In this paper, we have shown that single-indicator sequences can be generated by encoding schemes based on physico-chemical properties. Two new methods are proposed for generating single-indicator sequences based on hydration energy and dipole moments. The proposed methods produce high peak at exon locations and effectively suppress false exons (intron regions having greater peak than exon regions) resulting in high discriminating factor, sensitivity and specificity.