23,675 research outputs found
Quantifying alternative splicing from paired-end RNA-sequencing data
RNA-sequencing has revolutionized biomedical research and, in particular, our
ability to study gene alternative splicing. The problem has important
implications for human health, as alternative splicing may be involved in
malfunctions at the cellular level and multiple diseases. However, the
high-dimensional nature of the data and the existence of experimental biases
pose serious data analysis challenges. We find that the standard data summaries
used to study alternative splicing are severely limited, as they ignore a
substantial amount of valuable information. Current data analysis methods are
based on such summaries and are hence suboptimal. Further, they have limited
flexibility in accounting for technical biases. We propose novel data summaries
and a Bayesian modeling framework that overcome these limitations and determine
biases in a nonparametric, highly flexible manner. These summaries adapt
naturally to the rapid improvements in sequencing technology. We provide
efficient point estimates and uncertainty assessments. The approach allows to
study alternative splicing patterns for individual samples and can also be the
basis for downstream analyses. We found a severalfold improvement in estimation
mean square error compared popular approaches in simulations, and substantially
higher consistency between replicates in experimental data. Our findings
indicate the need for adjusting the routine summarization and analysis of
alternative splicing RNA-seq studies. We provide a software implementation in
the R package casper.Comment: Published in at http://dx.doi.org/10.1214/13-AOAS687 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org). With correction
A polynomial delay algorithm for the enumeration of bubbles with length constraints in directed graphs and its application to the detection of alternative splicing in RNA-seq data
We present a new algorithm for enumerating bubbles with length constraints in
directed graphs. This problem arises in transcriptomics, where the question is
to identify all alternative splicing events present in a sample of mRNAs
sequenced by RNA-seq. This is the first polynomial-delay algorithm for this
problem and we show that in practice, it is faster than previous approaches.
This enables us to deal with larger instances and therefore to discover novel
alternative splicing events, especially long ones, that were previously
overseen using existing methods.Comment: Peer-reviewed and presented as part of the 13th Workshop on
Algorithms in Bioinformatics (WABI2013
PIntron: a Fast Method for Gene Structure Prediction via Maximal Pairings of a Pattern and a Text
Current computational methods for exon-intron structure prediction from a
cluster of transcript (EST, mRNA) data do not exhibit the time and space
efficiency necessary to process large clusters of over than 20,000 ESTs and
genes longer than 1Mb. Guaranteeing both accuracy and efficiency seems to be a
computational goal quite far to be achieved, since accuracy is strictly related
to exploiting the inherent redundancy of information present in a large
cluster. We propose a fast method for the problem that combines two ideas: a
novel algorithm of proved small time complexity for computing spliced
alignments of a transcript against a genome, and an efficient algorithm that
exploits the inherent redundancy of information in a cluster of transcripts to
select, among all possible factorizations of EST sequences, those allowing to
infer splice site junctions that are highly confirmed by the input data. The
EST alignment procedure is based on the construction of maximal embeddings that
are sequences obtained from paths of a graph structure, called Embedding Graph,
whose vertices are the maximal pairings of a genomic sequence T and an EST P.
The procedure runs in time linear in the size of P, T and of the output.
PIntron, the software tool implementing our methodology, is able to process in
a few seconds some critical genes that are not manageable by other gene
structure prediction tools. At the same time, PIntron exhibits high accuracy
(sensitivity and specificity) when compared with ENCODE data. Detailed
experimental data, additional results and PIntron software are available at
http://www.algolab.eu/PIntron
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