Progress in Gene Prediction: Principles and Challenges

Maji, Srabanti; Garg, Deepak

doi:10.2174/1574893611308020011

Cited by 24 publications

(10 citation statements)

References 97 publications

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“…83 At present the success of correct gene prediction is measured in terms of degree to which they predict correct amino acid sequence for the given gene segment. 84 Given the paradigm that the HCC plays a role with onset of infection and changes in immune response, the challenge here is to prioritize SNPs and variants associated with the disease across the given population. There are wide number of Genome Wide Association Studies (GWAS) across cancers, but the ones specific to Gene X Environment (GXE) would be of primordial interest to see if there are genes expressed in coherent way.…”

Section: Genomic Variants Associated With Hcc: Systems Biology Challementioning

confidence: 99%

Systems Challenges of Hepatic Carcinomas: A Review

Madduru

Ijaq

Dhar³

et al. 2019

Journal of Clinical and Experimental Hepatology

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Hepatocellular Carcinoma (HCC) is ubiquitous in its prevalence in most of the developing countries. In the era of systems biology, multi-omics has evinced an extensive approach to define the underlying mechanism of disease progression. HCC is a multifactorial disease and the investigation of progression of liver cirrhosis becomes much extensive with cultivating omics approaches. We have performed a comprehensive review about such challenges in multi-omics approaches that are concerned to identify the immunological, genetics and epidemiological factors associated with HCC. ( J CLIN EXP HEPATOL 2019;9:233-244)

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Section: Genomic Variants Associated With Hcc: Systems Biology Challementioning

confidence: 99%

Systems Challenges of Hepatic Carcinomas: A Review

Madduru

Ijaq

Dhar³

et al. 2019

Journal of Clinical and Experimental Hepatology

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“…A division is frmed by crucial gene sequences in organisms that are required for the fertility, growth, or persistence [2]. As a result, identifying the protein coding sections is not only interesting, but also has realistic value to find out human diseases [3] and determine drug targets in new pathogens [4] - [6]. The intragenic and genic sections are available in a DNA sequence.…”

Section: Introductionmentioning

confidence: 99%

Efficient Adaptive Exon Prediction for DNA study using Proportionate LMS Variants

Putluri¹,

Rahman²

2018

IJET

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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.

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“…These coding segments are termed as exons and noncoding segments as introns ( Figure 1). In case of human eukaryotes only 3% of DNA sequence is coding [5,6], so it is a challenging task to identify the protein coding regions (exons).…”

Section: Introductionmentioning

confidence: 99%

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

Liu

Luan

2014

Abstract and Applied Analysis

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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.

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Progress in Gene Prediction: Principles and Challenges

Cited by 24 publications

References 97 publications

Systems Challenges of Hepatic Carcinomas: A Review

Systems Challenges of Hepatic Carcinomas: A Review

Efficient Adaptive Exon Prediction for DNA study using Proportionate LMS Variants

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

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