Differential Expression Analysis in RNA-Seq by a Naive Bayes Classifier with Local Normalization.

Yongchao Dou, Xiaomei Guo, Lingling Yuan, David R Holding, Chi Zhang

Abstract read

In one paragraph

Article in BioMed research international, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

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3 · Its place in the literature

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2 citing papers in PubMed.

mRBioM: An Algorithm for the Identification of Potential mRNA Biomarkers From Complete Transcriptomic Profiles of Gastric Adenocarcinoma.Frontiers in genetics · 2021
Article
BERT-m7G: A Transformer Architecture Based on BERT and Stacking Ensemble to Identify RNA N7-Methylguanosine Sites from Sequence Information.Computational and mathematical methods in medicine · 2021
Article

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5 · Who and what money

Authors and funding

5 authors.

Yongchao DouSchool of Biological Sciences, University of Nebraska, Lincoln, NE 68588, USA.

Xiaomei GuoDepartment of Agronomy and Horticulture, University of Nebraska, Lincoln, NE 68588, USA.

Lingling YuanDepartment of Agronomy and Horticulture, University of Nebraska, Lincoln, NE 68588, USA.

David R HoldingDepartment of Agronomy and Horticulture, University of Nebraska, Lincoln, NE 68588, USA ; Center for Plant Science and Innovation, University of Nebraska, Lincoln, NE 68588, USA.

Chi ZhangSchool of Biological Sciences, University of Nebraska, Lincoln, NE 68588, USA ; Center for Plant Science and Innovation, University of Nebraska, Lincoln, NE 68588, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To improve the applicability of RNA-seq technology, a large number of RNA-seq data analysis methods and correction algorithms have been developed. Although these new methods and algorithms have steadily improved transcriptome analysis, greater prediction accuracy is needed to better guide experimental designs with computational results. In this study, a new tool for the identification of differentially expressed genes with RNA-seq data, named GExposer, was developed. This tool introduces a local normalization algorithm to reduce the bias of nonrandomly positioned read depth. The naive Bayes classifier is employed to integrate fold change, transcript length, and GC content to identify differentially expressed genes. Results on several independent tests show that GExposer has better performance than other methods. The combination of the local normalization algorithm and naive Bayes classifier with three attributes can achieve better results; both false positive rates and false negative rates are reduced. However, only a small portion of genes is affected by the local normalization and GC content correction.

Indexed as

Bayes TheoremGene ExpressionAlgorithmsBase CompositionHigh-Throughput Nucleotide SequencingSequence Analysis, RNATranscriptome

Identifiers

PMID26339642

DOI10.1155/2015/789516

PMCPMC4538581

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.