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May
21
RNA-seq analysis of allele-specific expression, hybrid effects, and regulatory divergence in hybrids
Filed Under Publications | Leave a Comment
Hybridization is a prominent process among natural plant populations that can result in phenotypic novelty, heterosis, and changes in gene expression. The effects of intraspecific hybridization on F1 hybrid gene expression were investigated using parents from divergent, natural populations of Cirsium arvense, an invasive Compositae weed.
Using an RNA-seq approach, the expression of 68,746 unigenes was quantified in parents and hybrids. The expression levels of 51% of transcripts differed between parents, a majority of which had <1.25x fold-changes. More unigenes had higher expression in the invasive parent (P1) than the non-invasive parent (P2). Of those that were divergently expressed between parents, 10% showed additive and 81% showed non-additive (transgressive or dominant) modes of gene action in the hybrids. A majority of the dominant cases had P2-like expression patterns in the hybrids. Comparisons of allele-specific expression also enabled a survey of cis- and trans-regulatory effects. Cis- and trans-regulatory divergence was found at 70% and 68% of 62,281 informative SNP sites, respectively. Of the 17% of sites exhibiting both cis- and trans- effects, a majority (70%) had antagonistic regulatory interactions (cis x trans); trans-divergence tended to drive higher expression of the P1 allele whereas cis-divergence tended to increase P2 transcript abundance. Trans-effects correlated more highly than cis- with parental expression divergence and accounted for a greater proportion of the regulatory divergence at sites with additive compared to non-additive inheritance patterns. This study explores the nature of, and types of mechanisms underlying, expression changes that occur in upon intraspecific hybridization in natural populations.
- Bell GD, Kane NC, Rieseberg LH, Adams KL. (2013) RNA-seq analysis of allele-specific expression, hybrid effects, and regulatory divergence in hybrids compared with their parents from natural populations. Genome Biol Evol [Epub ahead of print]. [abstract]
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Dec
7
Alternative strategies for development of a reference transcriptome for quantification of allele specific expression
Filed Under Data Analysis, Publications | Leave a Comment
In recent years RNA-Seq technology has been used not only to quantify differences in gene expression but also to understand the underlying mechanisms that lead to these differences. Nucleotide sequence variation arising through evolution may differentially affect the expression profiles of divergent species. RNA-Seq technology, combined with techniques to differentiate parental alleles and quantify their abundance, have recently become popular methods for allele specific gene expression (ASGE) analyses. However, analysis of gene expression within interspecies hybrids may be difficult when one of the two parental genomes represented in the hybrid does not have robust genomic resources or available transcriptome data. Read more
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Nov
7
FREQ-Seq – to Determine Allele Frequencies Directly from Mixed Populations
Filed Under Expression and Quantification, Other Tools | Leave a Comment
Researchers at Harvard University have developed a rapid, cost-effective method (FREQ-Seq) that leverages Illumina next-generation sequencing for localized, quantitative allele frequency detection. Analogous to RNA-Seq, FREQ-Seq relies upon counts from the >105 reads generated per locus per time-point to determine allele frequencies. Loci of interest are directly amplified from a mixed population via two rounds of PCR using inexpensive, user-designed oligonucleotides and a bar-coded bridging primer system that can be regenerated in-house. The resulting bar-coded PCR products contain the adapters needed for Illumina sequencing, eliminating further library preparation. Read more
Incoming search terms:
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May
16
A new strategy for mapping RNA-Seq reads
Filed Under Splicing and Junction Mapping | Leave a Comment
Accurate estimation of expression levels from RNA-Seq data entails precise mapping of the sequence reads to a reference genome. Because the standard reference genome contains only one allele at any given locus, reads overlapping polymorphic loci that carry a non-reference allele are at least one mismatch away from the reference and, hence, are less likely to be mapped. This bias in read mapping leads to inaccurate estimates of allele-specific expression (ASE).
To address this read-mapping bias, researchers at the DoD Biotechnology Software Applications Institute proposed the construction of an enhanced reference genome that includes the alternative alleles at known polymorphic loci. They show that mapping to this enhanced reference reduced the read-mapping biases, leading to more reliable estimates of ASE.
Experiments on simulated data show that the proposed strategy reduced the number of loci with mapping bias by ≥63% when compared with a previous approach that relies on masking the polymorphic loci and by ≥18% when compared with the standard approach that uses an unaltered reference. When this strategy was applied to actual RNA-Seq data, up to 15% more reads were mapped than the previous approaches and many seemingly incorrect inferences were identified.
The executables to construct the enhanced reference genome and the Perl scripts to analyze the mapped reads are available for download from http://www.bhsai.org/downloads/ase/
- Vijaya Satya R, Zavaljevski N, Reifman J. (2012) A new strategy to reduce allelic bias in RNA-Seq readmapping. Nucleic Acids Res [Epub ahead of print]. [article]
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Aug
29
Allele-specific gene expression by RNA-seq
Filed Under Expression and Quantification | Leave a Comment
Variation in gene expression is thought to make a significant contribution to phenotypic diversity among individuals within populations. Although high-throughput cDNA sequencing offers a unique opportunity to delineate the genome-wide architecture of regulatory variation, new statistical methods need to be developed to capitalize on the wealth of information contained in RNA-seq data sets.
Researchers at the University of Washington have developed a powerful and flexible hierarchical Bayesian model that combines information across loci to allow both global and locus-specific inferences about allele-specific expression (ASE).
They applied the methodology to a large RNA-seq data set obtained in a diploid hybrid of two diverse Saccharomyces cerevisiae strains, as well as to RNA-seq data from an individual human genome. They found that their statistical framework accurately quantifies levels of ASE with specified false-discovery rates, achieving high reproducibility between independent sequencing platforms and they were able to pinpoint loci that show unusual and biologically interesting patterns of ASE, including allele-specific alternative splicing and transcription termination sites.
Availability: R code to implement the statistical model described is available at: http://akeylab.gs.washington.edu/downloads.shtml.
- Skelly DA, Johansson M, Madeoy J, Wakefield J, Akey JM. (2011) A powerful and flexible statistical framework for testing hypotheses of allele-specific gene expression from RNA-seq data. Genome Research [Epub ahead of print]. [abstract]
Incoming search terms:
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- edgeR: very low p-value and very high variance within the group of replicates. What's my problem??I'm using edgeR in order to perform differential expression analysis from RNA-seq experiment. I have 6 samples of tumor cell, same tumor and same treatment: 3 patient with good prognosis and 3 patient with bad prognosis. I want to compare the gene expression among the two groups. I ran the edgeR pakage like follow: x […]
- Normalising tag count to RPKMHi! I was wondering if their is a way to normalise the number of reads in a region and the RPKM of the nearest gene to that region, so that a correlation could be computed. Like the following data shows number of tags in first column and RPKM in second column Tags RPKM 15 0.14619 11 0 203 0.2259 129 10.701 300 7.0772 122 2.3234 346 10.666 77 3.117 201 16.749 […]
- a simple question on RNA-Seq terminologyThis question may be very simple and basic, but I just need to confirm that I understand the differences among those terminologies in the RNA-Seq context. Suppose I have a sample called SLR, and it is sequenced on 5 lanes, so I have (among other output files) BAM files like L1_SLR, L2_SLR, L3_SLR, L5_SLR and L7_SLR.bam. Here, the letter "L" denotes […]
- FInding regions of interest with minimum coverageHi, I have a bam file of all my accepted hits (tophat output) and an gtf file with my genes of interest for which I am trying to find potential antisense transcripts. I would like to create a list - preferably one that can be visualized in a genome browser - that shows all genes that have antisense reads in the accepted hits.bam file provided that there are […]
- How to remove the intronic reads before countingI got RNASeq data in several samples. I checked the FastQC, seems the read quality are good (Hiseq 2000). But the problem is many reads are mapped to intronic region, and the regions have no any reference exons there (Refseq, ensembl, gencode). We don't know what they are. We guess the problem happend in library preparation, the concentration was low. N […]
- Which strand of the mRNA molecule does the sequencer output as a "read"?In Illumina Stranded RNA-Seq (using the dUTP method), do the final reads in the fastq files correspond to the initial molecule (that was transcribed), or to the reverse complement of the molecule? C […]
- edgeR: very low p-value and very high variance within the group of replicates. What's my problem??