Zhao Lai | Search bioRxiv

bioRxiv2016

Most Compositae (Asteraceae) are descendants of a paleohexaploid and all share a paleotetraploid ancestor with the Calyceraceae

Premise of the studyLike many other flowering plants, members of the Compositae (Asteraceae) have a polyploid ancestry. Previous analyses have found evidence for an ancient duplication or possibly triplication in the early evolutionary history of the family. We sought to better place this paleopolyploidy in the phylogeny and assess its nature.\n\nMethodsWe sequenced new transcriptomes for Barnadesia, the lineage sister to all other Compositae, and four representatives of closely related families. Using a recently developed algorithm, MAPS, we analyzed nuclear gene family phylogenies for evidence of paleopolyploidy.\n\nKey resultsWe found that the previously recognized Compositae paleopolyploidy is also in the ancestry of the Calyceraceae. Our phylogenomic analyses uncovered evidence for a successive second round of genome duplication among all sampled Compositae except Barnadesia.\n\nConclusionsOur analyses of new samples with new tools provide a revised view of paleopolyploidy in the Compositae. Together with results from recent linkage maps, our results suggest that the Compositae and Calyceraceae have a common paleotetraploid ancestor and most Compositae are descendants of a paleohexaploid. Although paleohexaploids have been previously identified, this is the first example where the paleotetraploid and paleohexaploid lineages have survived over tens of millions of years. The complex polyploidy in the ancestry of the Compositae and Calyceraceae represents a unique opportunity to study the long-term evolutionary fates and consequences of different ploidal levels.

Evolutionary Biology↗

bioRxiv2015

Differential expression analysis of RNA sequencing data by incorporating non-exonic mapped reads

BackgroundRNA sequencing (RNA-seq) is a powerful tool for genome-wide expression profiling of biological samples with the advantage of high-throughput and high resolution. There are many existing algorithms nowadays for quantifying expression levels and detecting differential gene expression, but none of them takes the misaligned reads that are mapped to non-exonic regions into account. We developed a novel algorithm, XBSeq, where a statistical model was established based on the assumption that observed signals are the convolution of true expression signals and sequencing noises. The mapped reads in non-exonic regions are considered as sequencing noises, which follows a Poisson distribution. Given measureable observed and noise signals from RNA-seq data, true expression signals, assuming governed by the negative binomial distribution, can be delineated and thus the accurate detection of differential expressed genes.\n\nResultsWe implemented our novel XBSeq algorithm and evaluated it by using a set of simulated expression datasets under different conditions, using a combination of negative binomial and Poisson distributions with parameters derived from real RNA-seq data. We compared the performance of our method with other commonly used differential expression analysis algorithms. We also evaluated the changes in true and false positive rates with variations in biological replicates, differential fold changes, and expression levels in non-exonic regions. We also tested the algorithm on a set of real RNA-seq data where the common and different detection results from different algorithms were reported.\n\nConclusionsIn this paper, we proposed a novel XBSeq, a differential expression analysis algorithm for RNA-seq data that takes non-exonic mapped reads into consideration. When background noise is at baseline level, the performance of XBSeq and DESeq are mostly equivalent. However, our method surpasses DESeq and other algorithms with the increase of non-exonic mapped reads. Only in very low read count condition XBSeq had a slightly higher false discovery rate, which may be improved by adjusting the background noise effect in this situation. Taken together, by considering non-exonic mapped reads, XBSeq can provide accurate expression measurement and thus detect differential expressed genes even in noisy conditions.

Bioinformatics↗