title: A fuzzy method for RNA-Seq differential expression analysis in presence of multireads.
abstract: BACKGROUND
  When 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.
  
  
  RESULTS
  We 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.
  
  
  CONCLUSIONS
  The 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.
authors.affiliation: Institute for Biomedical Technologies of Bari - ITB, National Research Council, Bari, 70126, Italy. arianna.consiglio@ba.itb.cnr.it.
authors.forename: Arianna
authors.initials: A
authors.lastname: Consiglio
authors.affiliation: Department of Informatics, University of Bari Aldo Moro, Bari, 70121, Italy.
authors.forename: Corrado
authors.initials: C
authors.lastname: Mencar
authors.affiliation: Institute for Biomedical Technologies of Bari - ITB, National Research Council, Bari, 70126, Italy.
authors.forename: Giorgio
authors.initials: G
authors.lastname: Grillo
authors.affiliation: Institute for Biomedical Technologies of Bari - ITB, National Research Council, Bari, 70126, Italy.
authors.forename: Flaviana
authors.initials: F
authors.lastname: Marzano
authors.affiliation: Institute for Biomedical Technologies of Bari - ITB, National Research Council, Bari, 70126, Italy.
authors.forename: Mariano Francesco
authors.initials: MF
authors.lastname: Caratozzolo
authors.affiliation: Institute for Biomedical Technologies of Bari - ITB, National Research Council, Bari, 70126, Italy.
authors.forename: Sabino
authors.initials: S
authors.lastname: Liuni
mesh_terms: D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods*; D059014:High-Throughput Nucleotide Sequencing; D012313:RNA / Q000235:genetics*; D015203:Reproducibility of Results; D017423:Sequence Analysis, RNA / Q000379:methods; D012984:Software
chemical_list: D012313:RNA
keywords: Differential expression; Fuzzy sets; Multireads; Possibilistic modeling; RNA-Seq