Search bioRxiv⌕ Search

Biology subjects

Ouellet, T.

Publications and source records attributed to Ouellet, T..

2 recordsLinked to original sources

Strategy in wheat-Fusarium dual-genome RNA-seq data processing

In RNA-seq data processing, short read alignments are usually of one species against its own genome; however, in plant-microbe interaction systems, reads from both host and pathogen samples are blended together. In contrast to single-genome, both pathogen and host reference genomes are involved in the alignment process. In such circumstances, the order of alignment to the host, the pathogen, or simultaneously to both genomes results in differences in read counts of certain genes, especially at the advanced infection stage. It is crucial to have an appropriate strategy for aligning the reads to their respective genomes, yet the existing strategies of either sequential or parallel alignment become a problem when mapping mixed reads to their corresponding reference genomes. The challenge lies in the determination of which reads belong to which species, especially when homology exists between the two genomes. This study proposes a combo-genome alignment strategy after comparing three alignment scenarios. Simulation results demonstrated that the degree of discrepancy in the results is correlated with phylogenetic distance of the two species in the mixture which was attributable to the extent of homology between the two genomes involved. This correlation was also found in the analysis using two real RNA-seq datasets of Fusarium-challenged wheat plants. Comparisons of the three RNA-seq processing strategies on three simulation datasets and two real Fusarium-infected wheat datasets showed that an alignment to a combo-genome, consisting of both host and pathogen genomes, improves mapping quality as compared to sequential alignment procedures.

bioinformatics↗

Differential expression feature extraction (DEFE) and its application in RNA-seq data analysis

In differential gene expression data analysis, one objective is to identify groups of co-expressed genes from a large dataset to detect the association between such a group of genes and a phenotypic trait. This is often done through a clustering approach, such as k-means or bipartition hierarchical clustering, based on particular similarity measures in the grouping process. In such a dataset, the gene differential expression itself is an innate attribute that can be used in the feature extraction process. For example, in a dataset consisting of multiple treatments versus their controls, the expression of a gene in each treatment would have three possible behaviors, up-, down- regulated, or unchanged. We propose here a differential expression feature extraction (DEFE) method by using a string consisting of three numerical values at each character to denote such behavior, i.e. 1=up, 2=down, and 0=unchanged, which results in up to 3B differential expression patterns across all B comparisons. This approach has been successfully applied in many datasets, of which we present in this study two sets of RNA-sequencing (RNA-seq) data on wheat challenged with stress related phytohormones or Fusarium graminearum, the causal agent of fusarium head blight (FHB), a devastating wheat disease to illustrate the algorithm. Combinations of multiple schemes of DEFE patterns revealed groups of genes putatively associated with resistance or susceptibility to FHB. DEFE enabled discovery of genes closely associated with defense related signaling molecules such as JAZ10, shikimate and chorismate biosynthesis pathway and groups of wheat genes with differential effects between more or less virulent strains of Fusarium graminearum.

genomics↗