Ancient DNA and the rewriting of human history: be sparing with Occam’s razor

Although RNA-Seq data provide unprecedented isoform-level expression information, detection of alternative isoform regulation (AIR) remains difficult, particularly when working with an incomplete transcript annotation. We introduce JunctionSeq, a new method that builds on the statistical techniques used by the well-established DEXSeq package to detect differential usage of both exonic regions and splice junctions. In particular, JunctionSeq is capable of detecting differential usage of novel splice junctions without the need for an additional isoform assembly step, greatly improving performance when the available transcript annotation is flawed or incomplete. JunctionSeq also provides a powerful and streamlined visualization toolset that allows bioinformaticians to quickly and intuitively interpret their results. We tested our method on publicly available data from several experiments performed on the rat pineal gland and Toxoplasma gondii, successfully detecting known and previously validated AIR genes in 19 out of 19 gene-level hypothesis tests. Due to its ability to query novel splice sites, JunctionSeq is still able to detect these differences even when all alternative isoforms for these genes were not included in the transcript annotation. JunctionSeq thus provides a powerful method for detecting alternative isoform regulation even with low-quality annotations. An implementation of JunctionSeq is available as an R/Bioconductor package.

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“…Equivalently, it can be defined as a difference in the isoform fractions between different biological conditions (29,30). …”

Section: Methodsmentioning

confidence: 99%

Detection and visualization of differential splicing in RNA-Seq data with JunctionSeq

2016

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“…For cyst nematodes, we have been unable to identify a similar or equivalent motif in the protein-coding region of effectors (Eves-van den Akker et al., 2016). This apparent distinction can be reconciled by the presence of the highly specialized gland cells as a physical compartment that, during infection, is essentially dedicated to producing and trafficking cargo for in planta delivery.…”

Section: The Dog Box: Finding the Tools For The Jobmentioning

confidence: 99%

“…This 6-bp DOG box, of the canonical motif ATGCCA, is highly enriched ∼150 bp upstream of the coding start site in the promoter region of known dorsal gland effectors. More than 77% of known dorsal gland effectors contain at least one DOG box (average ∼2.5/500 bp promoter), with representatives from 26 of the 28 experimentally validated dorsal gland cell effector families (Eves-van den Akker et al., 2016). Importantly, genes with more DOG boxes in their promoter regions were more likely to encode proteins with a signal peptide for secretion, a required feature of an effector.…”

Section: The Dog Box: Finding the Tools For The Jobmentioning

confidence: 99%

Opening the Effector Protein Toolbox for Plant–Parasitic Cyst Nematode Interactions

Akker

Birch

2016

Molecular Plant

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“…Over the past decade, next-generation RNA sequencing (RNA-seq) technologies have generated numerous data sets with unprecedented nucleotide-level information on transcriptomes, providing new opportunities to study the dynamic expression of known and novel mRNAs in a high-throughput manner (Wang et al, 2009; Conesa et al, 2016; Trapnell et al, 2009). The ideal data would include the sequences of full-length mRNA transcripts; however, most widely used next-generation Illumina sequencers generate millions of short sequences called reads (typically shorter than 400 base pairs) from the two ends of mRNA transcript fragments (Wang et al, 2009), while other third-generation sequencing technologies (e.g., Ion Torrent and Pacific Biosciences) produce longer but more erroneous reads (Quail et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

MSIQ: Joint modeling of multiple RNA-seq samples for accurate isoform quantification

Zhao

Zhang

2018

Ann. Appl. Stat.

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Next-generation RNA sequencing (RNA-seq) technology has been widely used to assess full-length RNA isoform abundance in a high-throughput manner. RNA-seq data offer insight into gene expression levels and transcriptome structures, enabling us to better understand the regulation of gene expression and fundamental biological processes. Accurate isoform quantification from RNA-seq data is challenging due to the information loss in sequencing experiments. A recent accumulation of multiple RNA-seq data sets from the same tissue or cell type provides new opportunities to improve the accuracy of isoform quantification. However, existing statistical or computational methods for multiple RNA-seq samples either pool the samples into one sample or assign equal weights to the samples when estimating isoform abundance. These methods ignore the possible heterogeneity in the quality of different samples and could result in biased and unrobust estimates. In this article, we develop a method, which we call “joint modeling of multiple RNA-seq samples for accurate isoform quantification” (MSIQ), for more accurate and robust isoform quantification by integrating multiple RNA-seq samples under a Bayesian framework. Our method aims to (1) identify a consistent group of samples with homogeneous quality and (2) improve isoform quantification accuracy by jointly modeling multiple RNA-seq samples by allowing for higher weights on the consistent group. We show that MSIQ provides a consistent estimator of isoform abundance, and we demonstrate the accuracy and effectiveness of MSIQ compared with alternative methods through simulation studies on D. melanogaster genes. We justify MSIQ’s advantages over existing approaches via application studies on real RNA-seq data from human embryonic stem cells, brain tissues, and the HepG2 immortalized cell line. We also perform a comprehensive analysis of how the isoform quantification accuracy would be affected by RNA-seq sample heterogeneity and different experimental protocols.

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Ancient DNA and the rewriting of human history: be sparing with Occam’s razor

Abstract: Ancient DNA research is revealing a human history far more complex than that inferred from parsimonious models based on modern DNA. Here, we review some of the key events in the peopling of the world in the light of the findings of work on ancient DNA.

Cited by 1,524 publications

References 50 publications

Detection and visualization of differential splicing in RNA-Seq data with JunctionSeq

Detection and visualization of differential splicing in RNA-Seq data with JunctionSeq

Opening the Effector Protein Toolbox for Plant–Parasitic Cyst Nematode Interactions

MSIQ: Joint modeling of multiple RNA-seq samples for accurate isoform quantification

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