ArticleBMC bioinformatics2016
A fuzzy method for RNA-Seq differential expression analysis in presence of multireads.
Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed.
- Longitudinal Transcriptomic Analysis Reveals Systemic Effects of Risdiplam in Adults with Spinal Muscular Atrophy.Brain sciences · 2026Article
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- Accurate allocation of multimapped reads enables regulatory element analysis at repeats.Genome research · 2024Article
- Disregarding multimappers leads to biases in the functional assessment of NGS data.BMC genomics · 2024Article
- Exogenous and endogenous dsRNAs perceived by plant Dicer-like 4 protein in the RNAi-depleted cellular context.Cellular & molecular biology letters · 2023Article
- Whole-Exome and Transcriptome Sequencing Expands the Genotype of Majewski Osteodysplastic Primordial Dwarfism Type II.International journal of molecular sciences · 2023Article
- Lost in HELLS: Disentangling the mystery of SALNR existence in senescence cellular models.PloS one · 2023Article
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- Sequence deeper without sequencing more: Bayesian resolution of ambiguously mapped reads.PLoS computational biology · 2021Article
- Plant miRNAs Reduce Cancer Cell Proliferation by Targeting MALAT1 and NEAT1: A Beneficial Cross-Kingdom Interaction.Frontiers in genetics · 2020Article
- Handling multi-mapped reads in RNA-seq.Computational and structural biotechnology journal · 2020Review
- Integrated Analysis of microRNA and mRNA Expression Profiles: An Attempt to Disentangle the Complex Interaction Network in Attention Deficit Hyperactivity Disorder.Brain sciences · 2019Article
- Dysregulation of MicroRNAs and Target Genes Networks in Peripheral Blood of Patients With Sporadic Amyotrophic Lateral Sclerosis.Frontiers in molecular neuroscience · 2018Article
- BITS 2015: the annual meeting of the Italian Society of Bioinformatics.BMC bioinformatics · 2016Article
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6 authors.
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Abstract
backgroundWhen the reads obtained from high-throughput RNA sequencing are mapped against a reference database, a significant proportion of them - known as multireads - can map to more than one reference sequence. These multireads originate from gene duplications, repetitive regions or overlapping genes. Removing the multireads from the mapping results, in RNA-Seq analyses, causes an underestimation of the read counts, while estimating the real read count can lead to false positives during the detection of differentially expressed sequences.
resultsWe present an innovative approach to deal with multireads and evaluate differential expression events, entirely based on fuzzy set theory. Since multireads cause uncertainty in the estimation of read counts during gene expression computation, they can also influence the reliability of differential expression analysis results, by producing false positives. Our method manages the uncertainty in gene expression estimation by defining the fuzzy read counts and evaluates the possibility of a gene to be differentially expressed with three fuzzy concepts: over-expression, same-expression and under-expression. The output of the method is a list of differentially expressed genes enriched with information about the uncertainty of the results due to the multiread presence. We have tested the method on RNA-Seq data designed for case-control studies and we have compared the obtained results with other existing tools for read count estimation and differential expression analysis.
conclusionsThe management of multireads with the use of fuzzy sets allows to obtain a list of differential expression events which takes in account the uncertainty in the results caused by the presence of multireads. Such additional information can be used by the biologists when they have to select the most relevant differential expression events to validate with laboratory assays. Our method can be used to compute reliable differential expression events and to highlight possible false positives in the lists of differentially expressed genes computed with other tools.
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