A Fast Algorithm for Detecting Frame Shifts in DNA sequences

Masoom, Hassan; Datta, Samir; Asif, Amir; Cunningham, Lesley A.; Wu, Gillian E.

doi:10.1109/cibcb.2006.330971

Cited by 7 publications

(4 citation statements)

References 17 publications

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“…( 23 ) developed a measure, known as Spectral Content Measure (SCM) to investigate the intensity of TP and further construct a gene predictor. A family of methods have grown from the original SCM, since researchers extended and improved Tiwari’s original method in many ways ( 27–38 ). Among these methods, Anastassiou’s ( 27 , 28 ) Optimized Spectral Content Measure (OSCM) and Kotlar and Lavner’s ( 29 ) Spectral Rotation Measure (SRM) are said to be distinctive.…”

Section: Introductionmentioning

confidence: 99%

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

Chen

2010

Nucleic Acids Research

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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).

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Section: Introductionmentioning

confidence: 99%

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

Chen

2010

Nucleic Acids Research

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“…All retroviruses have three (gag, pol, env) genes in this order, but it is difficult to identify the genes from the sequence data alone. Therefore, we use our earlier work on frameshift prediction [10].…”

Section: Our Algorithmmentioning

confidence: 99%

Fast algorithms for recognizing retroviruses

Ashlock

Datta

2010

2010 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS)

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Retroviruses have important roles to play in medicine, evolution, and biology. A key step towards understanding the effect of retroviruses on hosts is identifying them in the host genome. Detecting retroviruses using sequence alignment is difficult because are very diverse and have high mutation rates. We propose a fast, accurate algorithm for detecting retroviruses that uses supervised machine learning and three sets of features. One set of novel features identify the characteristic reading frame structure of retroviruses. The other two sets include features that have been used by other researchers for exon finding. Our algorithm distinguishes retroviral genomes from non-coding sequences and endogenous retroviruses from non-coding sequences and from genes with high accuracy. It also distinguishes endogenous retroviruses from intact retroviral genomes, lentiviruses from other retroviruses, all with high accuracy.

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“…For the determination of TP phase shifts, mathematical methods such as the Fourier transform, 27 wavelet transform, 28 dynamic programming 29 or methods based on comparison of periodicity matrices 30 have been employed. The Fourier transform method produced good results on artificial sequences and genes with a high level of TP.…”

Section: Introductionmentioning

confidence: 99%

“…The Fourier transform method produced good results on artificial sequences and genes with a high level of TP. However, the method requires a window of rather long length (the authors recommended a window size of about 750 nt) 27 . Methods based on dynamic programming or matrices comparison are characterized by enhanced sensitivity, but are incapable of detecting frameshifts in sequences with a low level of TP 18 …”

Section: Introductionmentioning

confidence: 99%

Search for potential reading frameshifts in cds from Arabidopsis thaliana and other genomes

et al. 2019

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A new mathematical method for potential reading frameshift detection in protein-coding sequences (cds) was developed. The algorithm is adjusted to the triplet periodicity of each analysed sequence using dynamic programming and a genetic algorithm. This does not require any preliminary training. Using the developed method, cds from the Arabidopsis thaliana genome were analysed. In total, the algorithm found 9,930 sequences containing one or more potential reading frameshift(s). This is ∼21% of all analysed sequences of the genome. The Type I and Type II error rates were estimated as 11% and 30%, respectively. Similar results were obtained for the genomes of Caenorhabditis elegans, Drosophila melanogaster, Homo sapiens, Rattus norvegicus and Xenopus tropicalis . Also, the developed algorithm was tested on 17 bacterial genomes. We compared our results with the previously obtained data on the search for potential reading frameshifts in these genomes. This study discussed the possibility that the reading frameshift seems like a relatively frequently encountered mutation; and this mutation could participate in the creation of new genes and proteins.

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A Fast Algorithm for Detecting Frame Shifts in DNA sequences

Cited by 7 publications

References 17 publications

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

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

Fast algorithms for recognizing retroviruses

Search for potential reading frameshifts in cds from Arabidopsis thaliana and other genomes

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