186 research outputs found
MSIQ: Joint Modeling of Multiple RNA-seq Samples for Accurate Isoform Quantification
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
CIDANE: Comprehensive Isoform Discovery and Abundance Estimation
International audienceHigh-throughput sequencing of cellular RNA (RNA-seq) allows to assess the set of all RNA molecules, the transcriptome, produced by a cell at a high resolution, under various conditions. The assembly of short sequencing reads to full-length transcripts, however, poses profound challenges to bioinformatics tools
Methods to study splicing from high-throughput RNA Sequencing data
The development of novel high-throughput sequencing (HTS) methods for RNA
(RNA-Seq) has provided a very powerful mean to study splicing under multiple
conditions at unprecedented depth. However, the complexity of the information
to be analyzed has turned this into a challenging task. In the last few years,
a plethora of tools have been developed, allowing researchers to process
RNA-Seq data to study the expression of isoforms and splicing events, and their
relative changes under different conditions. We provide an overview of the
methods available to study splicing from short RNA-Seq data. We group the
methods according to the different questions they address: 1) Assignment of the
sequencing reads to their likely gene of origin. This is addressed by methods
that map reads to the genome and/or to the available gene annotations. 2)
Recovering the sequence of splicing events and isoforms. This is addressed by
transcript reconstruction and de novo assembly methods. 3) Quantification of
events and isoforms. Either after reconstructing transcripts or using an
annotation, many methods estimate the expression level or the relative usage of
isoforms and/or events. 4) Providing an isoform or event view of differential
splicing or expression. These include methods that compare relative
event/isoform abundance or isoform expression across two or more conditions. 5)
Visualizing splicing regulation. Various tools facilitate the visualization of
the RNA-Seq data in the context of alternative splicing. In this review, we do
not describe the specific mathematical models behind each method. Our aim is
rather to provide an overview that could serve as an entry point for users who
need to decide on a suitable tool for a specific analysis. We also attempt to
propose a classification of the tools according to the operations they do, to
facilitate the comparison and choice of methods.Comment: 31 pages, 1 figure, 9 tables. Small corrections adde
Characterization of the human ESC transcriptome by hybrid sequencing
Although transcriptional and posttranscriptional events are detected
in RNA-Seq data from second-generation sequencing, fulllength
mRNA isoforms are not captured. On the other hand, thirdgeneration
sequencing, which yields much longer reads, has
current limitations of lower raw accuracy and throughput. Here,
we combine second-generation sequencing and third-generation
sequencing with a custom-designed method for isoform identification
and quantification to generate a high-confidence isoform
dataset for human embryonic stem cells (hESCs). We report 8,084
RefSeq-annotated isoforms detected as full-length and an additional
5,459 isoforms predicted through statistical inference. Over
one-third of these are novel isoforms, including 273 RNAs from
gene loci that have not previously been identified. Further characterization
of the novel loci indicates that a subset is expressed in
pluripotent cells but not in diverse fetal and adult tissues; moreover,
their reduced expression perturbs the network of pluripotency-
associated genes. Results suggest that gene identification,
even in well-characterized human cell lines and tissues, is likely far
from complete
Efficient RNA Isoform Identification and Quantification from RNA-Seq Data with Network Flows
International audienceSeveral state-of-the-art methods for isoform identification and quantification are based on l1- regularized regression, such as the Lasso. However, explicitly listing the--possibly exponentially-- large set of candidate transcripts is intractable for genes with many exons. For this reason, existing approaches using the l1-penalty are either restricted to genes with few exons, or only run the regression algorithm on a small set of pre-selected isoforms. We introduce a new technique called FlipFlop which can efficiently tackle the sparse estimation problem on the full set of candidate isoforms by using network flow optimization. Our technique removes the need of a preselection step, leading to better isoform identification while keeping a low computational cost. Experiments with synthetic and real RNA-Seq data confirm that our approach is more accurate than alternative methods and one of the fastest available. Source code is freely available as an R package from the Bioconductor web site (http://www.bioconductor.org/) and more information is available at http://cbio.ensmp.fr/flipflop
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