ArticleFrontiers in genetics2018
A New Machine Learning-Based Framework for Mapping Uncertainty Analysis in RNA-Seq Read Alignment and Gene Expression Estimation.
Article in Frontiers in genetics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Using transfer learning approaches to predict RNA-Seq gene expression data for cancer classification.Frontiers in artificial intelligence · 2026Article
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- RNA-seq analysis reveals genes associated with Macrophomina phaseolina-induced host senescence in soybean.BMC genomics · 2024Article
- DifferentialRegulation: a Bayesian hierarchical approach to identify differentially regulated genes.Biostatistics (Oxford, England) · 2024Article
- De novo assembly of transcriptomes and differential gene expression analysis using short-read data from emerging model organisms - a brief guide.Frontiers in zoology · 2024Review
- DifferentialRegulation: a Bayesian hierarchical approach to identify differentially regulated genes.bioRxiv : the preprint server for biology · 2024Article
- Disregarding multimappers leads to biases in the functional assessment of NGS data.BMC genomics · 2024Article
- N-of-one differential gene expression without control samples using a deep generative model.Genome biology · 2023Article
- MGcount: a total RNA-seq quantification tool to address multi-mapping and multi-overlapping alignments ambiguity in non-coding transcripts.BMC bioinformatics · 2022Article
- A method for simultaneous detection of small and long RNA biotypes by ribodepleted RNA-Seq.Scientific reports · 2022Article
- Unsupervised Machine Learning-Based Analysis of Clinical Features, Bone Mineral Density Features and Medical Care Costs of Rotator Cuff Tears.Risk management and healthcare policy · 2021Article
- Integrative Methods and Practical Challenges for Single-Cell Multi-omics.Trends in biotechnology · 2020Review
- Handling multi-mapped reads in RNA-seq.Computational and structural biotechnology journal · 2020Review
- NCResNet: Noncoding Ribonucleic Acid Prediction Based on a Deep Resident Network of Ribonucleic Acid Sequences.Frontiers in genetics · 2020Article
- RNA sequencing: the teenage years.Nature reviews. Genetics · 2019Review
- Single-Cell RNA Sequencing of Plant-Associated Bacterial Communities.Frontiers in microbiology · 2019Review
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Authors and funding
7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
One of the main benefits of using modern RNA-Sequencing (RNA-Seq) technology is the more accurate gene expression estimations compared with previous generations of expression data, such as the microarray. However, numerous issues can result in the possibility that an RNA-Seq read can be mapped to multiple locations on the reference genome with the same alignment scores, which occurs in plant, animal, and metagenome samples. Such a read is so-called a multiple-mapping read (MMR). The impact of these MMRs is reflected in gene expression estimation and all downstream analyses, including differential gene expression, functional enrichment, etc. Current analysis pipelines lack the tools to effectively test the reliability of gene expression estimations, thus are incapable of ensuring the validity of all downstream analyses. Our investigation into 95 RNA-Seq datasets from seven plant and animal species (totaling 1,951 GB) indicates an average of roughly 22% of all reads are MMRs. Here we present a machine learning-based tool called
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