Promoter Sequence Analysis through No Gap Multiple Sequence Alignment of Motif Pairs

Kouser,; Rangarajan, Lalitha

doi:10.1016/j.procs.2015.08.031

Cited by 5 publications

(3 citation statements)

References 8 publications

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“…The construction of the PSMM for a promoter has been described earlier [7,21]. Using the PSMMs of all promoters, feature matrix is identified.…”

Section: Methodsmentioning

confidence: 99%

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Effective Feature Selection for Classification of Promoter Sequences

Kouser¹,

Lavanya²,

Rangarajan³

et al. 2016

PLoS ONE

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Exploring novel computational methods in making sense of biological data has not only been a necessity, but also productive. A part of this trend is the search for more efficient in silico methods/tools for analysis of promoters, which are parts of DNA sequences that are involved in regulation of expression of genes into other functional molecules. Promoter regions vary greatly in their function based on the sequence of nucleotides and the arrangement of protein-binding short-regions called motifs. In fact, the regulatory nature of the promoters seems to be largely driven by the selective presence and/or the arrangement of these motifs. Here, we explore computational classification of promoter sequences based on the pattern of motif distributions, as such classification can pave a new way of functional analysis of promoters and to discover the functionally crucial motifs. We make use of Position Specific Motif Matrix (PSMM) features for exploring the possibility of accurately classifying promoter sequences using some of the popular classification techniques. The classification results on the complete feature set are low, perhaps due to the huge number of features. We propose two ways of reducing features. Our test results show improvement in the classification output after the reduction of features. The results also show that decision trees outperform SVM (Support Vector Machine), KNN (K Nearest Neighbor) and ensemble classifier LibD3C, particularly with reduced features. The proposed feature selection methods outperform some of the popular feature transformation methods such as PCA and SVD. Also, the methods proposed are as accurate as MRMR (feature selection method) but much faster than MRMR. Such methods could be useful to categorize new promoters and explore regulatory mechanisms of gene expressions in complex eukaryotic species.

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“…The construction of the PSMM for a promoter has been described earlier [7,21]. Using the PSMMs of all promoters, feature matrix is identified.…”

Section: Methodsmentioning

confidence: 99%

“…Many researchers have earlier tried to use such features of promoters to predict and/or analyze them [4,5]. We have earlier attempted to analyze promoters using motif-frequency and alignments [6,7]. In this work, we have devised novel computational methods to analyze promoter sequences.…”

Section: Introductionmentioning

confidence: 99%

Effective Feature Selection for Classification of Promoter Sequences

Kouser¹,

Lavanya²,

Rangarajan³

et al. 2016

PLoS ONE

Self Cite

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show abstract

“…This method extracts the motif pair based features from the binarized PSMM [21]. Later, we compare the count of motif pairs between the promoter sequences to find the similarity.…”

Section: Alignment Based Methodsmentioning

confidence: 99%

Statistical and Alignment Based Methods for Comparison of Non-Coding DNA Sequences

Kouser¹,

Rangarajan²

2015

RCS

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Increase in the amount of biological sequence data being generated is due to the advancement in the sequencing technologies. This has led to an upsurge in the need for methods/techniques that analyze these sequences to make biological inferences. Promoter sequences are one such biological sequences that play an important role in the process of gene regulation. These promoter sequences are made up of motifs arranged/configured in a particular order which is very important for its functionality. In this work, we propose a few techniques that capture the information regarding the arrangement of these motifs. Further this captured information help in the analysis of the promoter sequences. The methods use the motif features obtained from the Position Specific Motif Matrices (PSMMs) of the promoter sequences and then give a dissimilarity measure. Alignment-free as well as alignment based methods are proposed. The experimental results show that these methods are quite successful in bringing out the similarity/dissimilarity existing between given promoters/promoter sets. The alignment based method also helps in the process of inferring homology and in the phylogenetic analysis. In general, all the methods might help biologists in applications like drug target identification, biological pathway analysis and gene expression analysis.

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Feature Reduced Weighted Fuzzy Binarization for Histogram Comparison of Promoter Sequences

Kouser

Rangarajan

2017

Communications in Computer and Information Science

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Promoter Sequence Analysis through No Gap Multiple Sequence Alignment of Motif Pairs

Cited by 5 publications

References 8 publications

Effective Feature Selection for Classification of Promoter Sequences

Effective Feature Selection for Classification of Promoter Sequences

Statistical and Alignment Based Methods for Comparison of Non-Coding DNA Sequences

Feature Reduced Weighted Fuzzy Binarization for Histogram Comparison of Promoter Sequences

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