A Novel Approach for Predicting Disordered Regions in A Protein Sequence

Li, Meijing; Cho, Seongbeom; Ryu, Keun Ho

doi:10.1016/j.phrp.2014.06.006

Cited by 5 publications

(3 citation statements)

References 43 publications

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“…Bioinformatics studies over the past few decades have shed light on topics such as sequence analysis, structural analysis, and network and systems biology [ 1 , 2 ]. In recent years, rapid improvements in technology and decreased sequencing costs have made next-generation sequencing possible, facilitating millions of short sequence reads that have broad genomic research applications [ 3 – 5 ].…”

Section: Introductionmentioning

confidence: 99%

Comparing the normalization methods for the differential analysis of Illumina high-throughput RNA-Seq data

et al. 2015

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BackgroundRecently, rapid improvements in technology and decrease in sequencing costs have made RNA-Seq a widely used technique to quantify gene expression levels. Various normalization approaches have been proposed, owing to the importance of normalization in the analysis of RNA-Seq data. A comparison of recently proposed normalization methods is required to generate suitable guidelines for the selection of the most appropriate approach for future experiments.ResultsIn this paper, we compared eight non-abundance (RC, UQ, Med, TMM, DESeq, Q, RPKM, and ERPKM) and two abundance estimation normalization methods (RSEM and Sailfish). The experiments were based on real Illumina high-throughput RNA-Seq of 35- and 76-nucleotide sequences produced in the MAQC project and simulation reads. Reads were mapped with human genome obtained from UCSC Genome Browser Database. For precise evaluation, we investigated Spearman correlation between the normalization results from RNA-Seq and MAQC qRT-PCR values for 996 genes. Based on this work, we showed that out of the eight non-abundance estimation normalization methods, RC, UQ, Med, TMM, DESeq, and Q gave similar normalization results for all data sets. For RNA-Seq of a 35-nucleotide sequence, RPKM showed the highest correlation results, but for RNA-Seq of a 76-nucleotide sequence, least correlation was observed than the other methods. ERPKM did not improve results than RPKM. Between two abundance estimation normalization methods, for RNA-Seq of a 35-nucleotide sequence, higher correlation was obtained with Sailfish than that with RSEM, which was better than without using abundance estimation methods. However, for RNA-Seq of a 76-nucleotide sequence, the results achieved by RSEM were similar to without applying abundance estimation methods, and were much better than with Sailfish. Furthermore, we found that adding a poly-A tail increased alignment numbers, but did not improve normalization results.ConclusionSpearman correlation analysis revealed that RC, UQ, Med, TMM, DESeq, and Q did not noticeably improve gene expression normalization, regardless of read length. Other normalization methods were more efficient when alignment accuracy was low; Sailfish with RPKM gave the best normalization results. When alignment accuracy was high, RC was sufficient for gene expression calculation. And we suggest ignoring poly-A tail during differential gene expression analysis.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0778-7) contains supplementary material, which is available to authorized users.

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Section: Introductionmentioning

confidence: 99%

Comparing the normalization methods for the differential analysis of Illumina high-throughput RNA-Seq data

et al. 2015

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“…Convolutional Neural Network (CNN) is widely used as an image recognition classifier because of its high performance for image data [17]. Recently, scientists also use CNN on protein prediction such as DNA-protein binding [18]. Haoyang Zeng et al [19] identified the best CNN architectures by varying CNN parameters, depth, and pooling designs.…”

Section: Introductionmentioning

confidence: 99%

A High Accurate Machine Learning Meta-Strategy for the Prediction of Intrinsically Disorder Proteins

Qin

et al. 2021

Preprint

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Background: Many proteins or partial regions of proteins do not have stable and well-defined three-dimensional structures in vitro. Understanding Intrinsically Disorder Proteins (IDPs) is significant for interpreting biological function as well as studying many diseases. Although more than 70 disorder predictors have been invented, many existing predictors are limited on the characteristics of proteins and do not have very high accuracy. Therefore, it is critical to formulate new strategies on disorder protein prediction. Results: Here, we propose a machine learning meta-strategy to improve the accuracy of disordered proteins and disordered regions prediction. We first use logistic forward parameter selection to select eight most significant predictors from the current available IDP predictors. Then we design a novel meta-strategy using several machine learning models, including Decision-tree based algorithm, Naive Bayes, Random forest, and Convolutional Neural Network (CNN). By applying different strategies, the results suggest Random forest can improve the predicted single amino acid accuracy significantly to 93.35%. Using the combination vector data of eight most significant predictors as input, the Convolution Neural Network can improve the whole protein prediction to 95.62%. Conclusion: According to the performance of our machine learning meta-strategy, the Random forest and CNN models can improve the accuracy to predict IDPs.

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“…Convolutional Neural Network (CNN) is widely used as image recognition classifier because of its high performance for image data [17]. Recently, scientist also use CNN on protein prediction such as DNA-protein binding [18]. Haoyang Zeng et al [19] identified the best CNN architectures by varying CNN parameters, depth and pooling designs.…”

Section: Introductionmentioning

confidence: 99%

A High Accurate Machine Learning Meta-Strategy for the Prediction of Intrinsically Disorder Proteins

Qin

et al. 2020

Preprint

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Background: Many proteins or partial regions of proteins do not have stable and well-defined three-dimensional structures in vitro. Understanding intrinsically disorder proteins (IDPs) is significant for interpreting biological function as well as studying many diseases. Although more than 70 disorder predictors have been invented, many existing predictors are limited on the characteristics of proteins and do not have very high accuracy. Therefore, it is critical to formulate new strategies on disorder protein prediction.Results: Here, we propose a machine learning meta-strategy to improve the accuracy of disordered proteins and disordered regions prediction. We first use logistic forward parameter selection to select eight most significant predictors from the current available IDP predictors. Then we design a novel meta-strategy using several machine learning models, including Decision-tree based algorithm, Naive Bayes, Random forest, and Convolutional Neural Network (CNN). By applying different strategies, the results suggest Random forest can improve the predicted single amino acid accuracy significantly to 93.35%. Using the combination vector data of eight most significant predictors as input, the Convolution Neural Network can improve the whole protein prediction to 95.62%.Conclusion: According to the performance of our machine learning meta-strategy, the Random forest and CNN models can improve the accuracy to predict intrinsically disorder proteins.

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A Novel Approach for Predicting Disordered Regions in A Protein Sequence

Cited by 5 publications

References 43 publications

Comparing the normalization methods for the differential analysis of Illumina high-throughput RNA-Seq data

Comparing the normalization methods for the differential analysis of Illumina high-throughput RNA-Seq data

A High Accurate Machine Learning Meta-Strategy for the Prediction of Intrinsically Disorder Proteins

A High Accurate Machine Learning Meta-Strategy for the Prediction of Intrinsically Disorder Proteins

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