@comment{{This file has been generated by bib2bib 1.97}}
@comment{{Command line: bib2bib ../bibli.bib -c 'subject:"rnaseq" or keywords:"rnaseq"' -ob tmp.bib}}
@article{Bohnert2009Transcript,
author = {Bohnert, R. and Behr, J. and R\"atsch, G.},
title = {Transcript quantification with {RNA-Seq} data},
journal = {BMC Bioinformatics},
year = {2009},
volume = {10 (Suppl 13)},
pages = {P5},
doi = {10.1186/1471-2105-10-S13-P5},
pdf = {../local/Bohnert2009Transcript.pdf},
file = {Bohnert2009Transcript.pdf:Bohnert2009Transcript.pdf:PDF},
keywords = {ngs, rnaseq},
owner = {jp},
timestamp = {2012.03.06},
url = {http://dx.doi.org/10.1186/1471-2105-10-S13-P5}
}
@article{Jiang2009Statistical,
author = {Jiang, H. and Wong, W. H.},
title = {Statistical inferences for isoform expression in {RNA-Seq}.},
journal = {Bioinformatics},
year = {2009},
volume = {25},
pages = {1026--1032},
number = {8},
month = {Apr},
abstract = {SUMMARY: The development of RNA sequencing (RNA-Seq) makes it possible
for us to measure transcription at an unprecedented precision and
throughput. However, challenges remain in understanding the source
and distribution of the reads, modeling the transcript abundance
and developing efficient computational methods. In this article,
we develop a method to deal with the isoform expression estimation
problem. The count of reads falling into a locus on the genome annotated
with multiple isoforms is modeled as a Poisson variable. The expression
of each individual isoform is estimated by solving a convex optimization
problem and statistical inferences about the parameters are obtained
from the posterior distribution by importance sampling. Our results
show that isoform expression inference in RNA-Seq is possible by
employing appropriate statistical methods.},
doi = {10.1093/bioinformatics/btp113},
pdf = {../local/Jiang2009Statistical.pdf},
file = {Jiang2009Statistical.pdf:Jiang2009Statistical.pdf:PDF},
institution = {Institute for Computational and Mathematical Engineering and Department
of Statistics, Stanford University, Stanford, CA 94305, USA.},
keywords = {ngs, rnaseq},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {btp113},
pmid = {19244387},
timestamp = {2012.03.06},
url = {http://dx.doi.org/10.1093/bioinformatics/btp113}
}
@article{Li2011Sparse,
author = {Li, J. J. and Jiang, C.-R. and Brown, J. B. and Huang, H. and Bickel,
P. J.},
title = {Sparse linear modeling of next-generation {mRNA} sequencing ({RNA-Seq})
data for isoform discovery and abundance estimation},
journal = {Proc. Natl. Acad. Sci. USA},
year = {2011},
volume = {108},
pages = {19867--19872},
number = {50},
month = dec,
abstract = {{Since the inception of next-generation mRNA sequencing (RNA-Seq)
technology, various attempts have been made to utilize RNA-Seq data
in assembling full-length mRNA isoforms de novo and estimating abundance
of isoforms. However, for genes with more than a few exons, the problem
tends to be challenging and often involves identifiability issues
in statistical modeling. We have developed a statistical method called
â sparse linear modeling of RNA-Seq data for isoform discovery
and abundance estimationâ (SLIDE) that takes exon boundaries and
RNA-Seq data as input to discern the set of mRNA isoforms that are
most likely to present in an RNA-Seq sample. SLIDE is based on a
linear model with a design matrix that models the sampling probability
of RNA-Seq reads from different mRNA isoforms. To tackle the model
unidentifiability issue, SLIDE uses a modified Lasso procedure for
parameter estimation. Compared with deterministic isoform assembly
algorithms (e.g., Cufflinks), SLIDE considers the stochastic aspects
of RNA-Seq reads in exons from different isoforms and thus has increased
power in detecting more novel isoforms. Another advantage of SLIDE
is its flexibility of incorporating other transcriptomic data such
as RACE, CAGE, and EST into its model to further increase isoform
discovery accuracy. SLIDE can also work downstream of other RNA-Seq
assembly algorithms to integrate newly discovered genes and exons.
Besides isoform discovery, SLIDE sequentially uses the same linear
model to estimate the abundance of discovered isoforms. Simulation
and real data studies show that SLIDE performs as well as or better
than major competitors in both isoform discovery and abundance estimation.
The SLIDE software package is available at https://sites.google.com/site/jingyijli/SLIDE.zip.}},
citeulike-article-id = {10102447},
citeulike-linkout-0 = {http://dx.doi.org/10.1073/pnas.1113972108},
citeulike-linkout-1 = {http://www.pnas.org/content/early/2011/11/23/1113972108.abstract},
citeulike-linkout-2 = {http://www.pnas.org/content/early/2011/11/23/1113972108.full.pdf},
citeulike-linkout-3 = {http://www.pnas.org/cgi/content/abstract/108/50/19867},
citeulike-linkout-4 = {http://view.ncbi.nlm.nih.gov/pubmed/22135461},
citeulike-linkout-5 = {http://www.hubmed.org/display.cgi?uids=22135461},
day = {13},
doi = {10.1073/pnas.1113972108},
pdf = {../local/Li2011Sparse.pdf},
file = {Li2011Sparse.pdf:Li2011Sparse.pdf:PDF},
issn = {1091-6490},
keywords = {ngs, rnaseq},
pmid = {22135461},
posted-at = {2011-12-16 22:07:32},
priority = {2},
publisher = {National Academy of Sciences},
url = {http://dx.doi.org/10.1073/pnas.1113972108}
}
@article{Li2011IsoLasso,
author = {Li, W. and Feng, J. and Jiang, T.},
title = {IsoLasso: a {LASSO} regression approach to {RNA-Seq} based transcriptome
assembly.},
journal = {J Comput Biol},
year = {2011},
volume = {18},
pages = {1693--1707},
number = {11},
month = {Nov},
__markedentry = {[jp:6]},
abstract = {The new second generation sequencing technology revolutionizes many
biology-related research fields and poses various computational biology
challenges. One of them is transcriptome assembly based on RNA-Seq
data, which aims at reconstructing all full-length mRNA transcripts
simultaneously from millions of short reads. In this article, we
consider three objectives in transcriptome assembly: the maximization
of prediction accuracy, minimization of interpretation, and maximization
of completeness. The first objective, the maximization of prediction
accuracy, requires that the estimated expression levels based on
assembled transcripts should be as close as possible to the observed
ones for every expressed region of the genome. The minimization of
interpretation follows the parsimony principle to seek as few transcripts
in the prediction as possible. The third objective, the maximization
of completeness, requires that the maximum number of mapped reads
(or ?expressed segments? in gene models) be explained by (i.e., contained
in) the predicted transcripts in the solution. Based on the above
three objectives, we present IsoLasso, a new RNA-Seq based transcriptome
assembly tool. IsoLasso is based on the well-known LASSO algorithm,
a multivariate regression method designated to seek a balance between
the maximization of prediction accuracy and the minimization of interpretation.
By including some additional constraints in the quadratic program
involved in LASSO, IsoLasso is able to make the set of assembled
transcripts as complete as possible. Experiments on simulated and
real RNA-Seq datasets show that IsoLasso achieves, simultaneously,
higher sensitivity and precision than the state-of-art transcript
assembly tools.},
doi = {10.1089/cmb.2011.0171},
pdf = {../local/Li2011IsoLasso.pdf},
file = {Li2011IsoLasso.pdf:Li2011IsoLasso.pdf:PDF},
institution = {Department of Computer Science and Engineering, University of California,
Riverside, Riverside, CA 92507, USA. liw@cs.ucr.edu},
keywords = {ngs, rnaseq},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pmid = {21951053},
timestamp = {2013.03.29},
url = {http://dx.doi.org/10.1089/cmb.2011.0171}
}
@article{Mezlini2013iReckon,
author = {Mezlini, A. M. and Smith, E. J. M. and Fiume, M. and Buske, O. and
Savich, G. L. and Shah, S. and Aparicio, S. and Chiang, D. Y. and
Goldenberg, A. and Brudno, M.},
title = {{iReckon}: Simultaneous isoform discovery and abundance estimation
from {RNA}-seq data.},
journal = {Genome Res},
year = {2013},
volume = {23},
pages = {519--529},
number = {3},
month = {Mar},
abstract = {High-throughput RNA sequencing (RNA-seq) promises to revolutionize
our understanding of genes and their role in human disease by characterizing
the RNA content of tissues and cells. The realization of this promise,
however, is conditional on the development of effective computational
methods for the identification and quantification of transcripts
from incomplete and noisy data. In this article, we introduce iReckon,
a method for simultaneous determination of the isoforms and estimation
of their abundances. Our probabilistic approach incorporates multiple
biological and technical phenomena, including novel isoforms, intron
retention, unspliced pre-mRNA, PCR amplification biases, and multimapped
reads. iReckon utilizes regularized expectation-maximization to accurately
estimate the abundances of known and novel isoforms. Our results
on simulated and real data demonstrate a superior ability to discover
novel isoforms with a significantly reduced number of false-positive
predictions, and our abundance accuracy prediction outmatches that
of other state-of-the-art tools. Furthermore, we have applied iReckon
to two cancer transcriptome data sets, a triple-negative breast cancer
patient sample and the MCF7 breast cancer cell line, and show that
iReckon is able to reconstruct the complex splicing changes that
were not previously identified. QT-PCR validations of the isoforms
detected in the MCF7 cell line confirmed all of iReckon's predictions
and also showed strong agreement (r = 0.94) with the predicted abundances.},
doi = {10.1101/gr.142232.112},
pdf = {../local/Mezlini2013iReckon.pdf},
file = {Mezlini2013iReckon.pdf:Mezlini2013iReckon.pdf:PDF},
institution = {Department of Computer Science, University of Toronto, Ontario M5S
2E4, Canada;},
keywords = {ngs, rnaseq},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {gr.142232.112},
pmid = {23204306},
timestamp = {2013.03.29},
url = {http://dx.doi.org/10.1101/gr.142232.112}
}
@article{Roberts2011Identification,
author = {Roberts, A. and Pimentel, H. and Trapnell, C. and Pachter, L.},
title = {Identification of novel transcripts in annotated genomes using {RNA-Seq}.},
journal = {Bioinformatics},
year = {2011},
volume = {27},
pages = {2325--2329},
number = {17},
month = {Sep},
abstract = {We describe a new 'reference annotation based transcript assembly'
problem for RNA-Seq data that involves assembling novel transcripts
in the context of an existing annotation. This problem arises in
the analysis of expression in model organisms, where it is desirable
to leverage existing annotations for discovering novel transcripts.
We present an algorithm for reference annotation-based transcript
assembly and show how it can be used to rapidly investigate novel
transcripts revealed by RNA-Seq in comparison with a reference annotation.The
methods described in this article are implemented in the Cufflinks
suite of software for RNA-Seq, freely available from http://bio.math.berkeley.edu/cufflinks.
The software is released under the BOOST license.cole@broadinstitute.org;
lpachter@math.berkeley.eduSupplementary data are available at Bioinformatics
online.},
doi = {10.1093/bioinformatics/btr355},
pdf = {../local/Roberts2011Identification.pdf},
file = {Roberts2011Identification.pdf:Roberts2011Identification.pdf:PDF},
institution = {Department of Computer Science, UC Berkeley, Berkeley, CA, USA.},
keywords = {ngs, rnaseq},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {btr355},
pmid = {21697122},
timestamp = {2013.03.29},
url = {http://dx.doi.org/10.1093/bioinformatics/btr355}
}
@article{Roberts2011Improving,
author = {Roberts, A. and Trapnell, C. and Donaghey, J. and Rinn, J. L. and
Pachter, L.},
title = {Improving {RNA-Seq} expression estimates by correcting for fragment
bias.},
journal = {Genome Biol},
year = {2011},
volume = {12},
pages = {R22},
number = {3},
abstract = {The biochemistry of RNA-Seq library preparation results in cDNA fragments
that are not uniformly distributed within the transcripts they represent.
This non-uniformity must be accounted for when estimating expression
levels, and we show how to perform the needed corrections using a
likelihood based approach. We find improvements in expression estimates
as measured by correlation with independently performed qRT-PCR and
show that correction of bias leads to improved replicability of results
across libraries and sequencing technologies.},
doi = {10.1186/gb-2011-12-3-r22},
pdf = {../local/Roberts2011Improving.pdf},
file = {Roberts2011Improving.pdf:Roberts2011Improving.pdf:PDF},
institution = {Department of Computer Science, 387 Soda Hall, UC Berkeley, Berkeley,
CA 94720, USA.},
keywords = {ngs, rnaseq},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {gb-2011-12-3-r22},
pmid = {21410973},
timestamp = {2013.03.29},
url = {http://dx.doi.org/10.1186/gb-2011-12-3-r22}
}
@article{Trapnell2013Differential,
author = {Trapnell, C. and Hendrickson, D. G. and Sauvageau, M. and Goff, L.
and Rinn, J. L. and Pachter, L.},
title = {Differential analysis of gene regulation at transcript resolution
with {RNA-seq}.},
journal = {Nat Biotechnol},
year = {2013},
volume = {31},
pages = {46--53},
number = {1},
month = {Jan},
abstract = {Differential analysis of gene and transcript expression using high-throughput
RNA sequencing (RNA-seq) is complicated by several sources of measurement
variability and poses numerous statistical challenges. We present
Cuffdiff 2, an algorithm that estimates expression at transcript-level
resolution and controls for variability evident across replicate
libraries. Cuffdiff 2 robustly identifies differentially expressed
transcripts and genes and reveals differential splicing and promoter-preference
changes. We demonstrate the accuracy of our approach through differential
analysis of lung fibroblasts in response to loss of the developmental
transcription factor HOXA1, which we show is required for lung fibroblast
and HeLa cell cycle progression. Loss of HOXA1 results in significant
expression level changes in thousands of individual transcripts,
along with isoform switching events in key regulators of the cell
cycle. Cuffdiff 2 performs robust differential analysis in RNA-seq
experiments at transcript resolution, revealing a layer of regulation
not readily observable with other high-throughput technologies.},
doi = {10.1038/nbt.2450},
pdf = {../local/Trapnell2013Differential.pdf},
file = {Trapnell2013Differential.pdf:Trapnell2013Differential.pdf:PDF},
institution = {Department of Stem Cell and Regenerative Biology, Harvard University,
Cambridge, Massachusetts, USA.},
keywords = {ngs, rnaseq},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {nbt.2450},
pmid = {23222703},
timestamp = {2013.03.29},
url = {http://dx.doi.org/10.1038/nbt.2450}
}
@article{Trapnell2009TopHat,
author = {Trapnell, C. and Pachter, L. and Salzberg, S. L.},
title = {{TopHat}: discovering splice junctions with {RNA-Seq}.},
journal = {Bioinformatics},
year = {2009},
volume = {25},
pages = {1105--1111},
number = {9},
month = {May},
abstract = {A new protocol for sequencing the messenger RNA in a cell, known as
RNA-Seq, generates millions of short sequence fragments in a single
run. These fragments, or 'reads', can be used to measure levels of
gene expression and to identify novel splice variants of genes. However,
current software for aligning RNA-Seq data to a genome relies on
known splice junctions and cannot identify novel ones. TopHat is
an efficient read-mapping algorithm designed to align reads from
an RNA-Seq experiment to a reference genome without relying on known
splice sites.We mapped the RNA-Seq reads from a recent mammalian
RNA-Seq experiment and recovered more than 72\% of the splice junctions
reported by the annotation-based software from that study, along
with nearly 20,000 previously unreported junctions. The TopHat pipeline
is much faster than previous systems, mapping nearly 2.2 million
reads per CPU hour, which is sufficient to process an entire RNA-Seq
experiment in less than a day on a standard desktop computer. We
describe several challenges unique to ab initio splice site discovery
from RNA-Seq reads that will require further algorithm development.TopHat
is free, open-source software available from http://tophat.cbcb.umd.edu.Supplementary
data are available at Bioinformatics online.},
doi = {10.1093/bioinformatics/btp120},
pdf = {../local/Trapnell2009TopHat.pdf},
file = {Trapnell2009TopHat.pdf:Trapnell2009TopHat.pdf:PDF},
institution = {Center for Bioinformatics and Computational Biology, University of
Maryland, College Park, MD 20742, USA. cole@cs.umd.edu},
keywords = {ngs, rnaseq},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {btp120},
pmid = {19289445},
timestamp = {2013.03.29},
url = {http://dx.doi.org/10.1093/bioinformatics/btp120}
}
@article{Trapnell2012Differential,
author = {Trapnell, C. and Roberts, A. and Goff, L. and Pertea, G. and Kim,
D. and Kelley, D. R. and Pimentel, H. and Salzberg, S. L. and Rinn,
J. L. and Pachter, L.},
title = {Differential gene and transcript expression analysis of {RNA-seq}
experiments with {TopHat} and {Cufflinks}.},
journal = {Nat Protoc},
year = {2012},
volume = {7},
pages = {562--578},
number = {3},
month = {Mar},
abstract = {Recent advances in high-throughput cDNA sequencing (RNA-seq) can reveal
new genes and splice variants and quantify expression genome-wide
in a single assay. The volume and complexity of data from RNA-seq
experiments necessitate scalable, fast and mathematically principled
analysis software. TopHat and Cufflinks are free, open-source software
tools for gene discovery and comprehensive expression analysis of
high-throughput mRNA sequencing (RNA-seq) data. Together, they allow
biologists to identify new genes and new splice variants of known
ones, as well as compare gene and transcript expression under two
or more conditions. This protocol describes in detail how to use
TopHat and Cufflinks to perform such analyses. It also covers several
accessory tools and utilities that aid in managing data, including
CummeRbund, a tool for visualizing RNA-seq analysis results. Although
the procedure assumes basic informatics skills, these tools assume
little to no background with RNA-seq analysis and are meant for novices
and experts alike. The protocol begins with raw sequencing reads
and produces a transcriptome assembly, lists of differentially expressed
and regulated genes and transcripts, and publication-quality visualizations
of analysis results. The protocol's execution time depends on the
volume of transcriptome sequencing data and available computing resources
but takes less than 1 d of computer time for typical experiments
and ∼1 h of hands-on time.},
doi = {10.1038/nprot.2012.016},
pdf = {../local/Trapnell2012Differential.pdf},
file = {Trapnell2012Differential.pdf:Trapnell2012Differential.pdf:PDF},
institution = {Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
cole@broadinstitute.org},
keywords = {ngs, rnaseq},
owner = {laurent},
pii = {nprot.2012.016},
pmid = {22383036},
timestamp = {2012.04.11},
url = {http://dx.doi.org/10.1038/nprot.2012.016}
}
@article{Trapnell2010Transcript,
author = {Trapnell, C. and Williams, B. A. and Pertea, G. and Mortazavi, A.
and Kwan, G. and {van Baren}, M. J. and Salzberg, S. L. and Wold,
B. J. and Pachter, L.},
title = {Transcript assembly and quantification by RNA-Seq reveals unannotated
transcripts and isoform switching during cell differentiation.},
journal = {Nat Biotechnol},
year = {2010},
volume = {28},
pages = {511--515},
number = {5},
month = {May},
abstract = {High-throughput mRNA sequencing (RNA-Seq) promises simultaneous transcript
discovery and abundance estimation. However, this would require algorithms
that are not restricted by prior gene annotations and that account
for alternative transcription and splicing. Here we introduce such
algorithms in an open-source software program called Cufflinks. To
test Cufflinks, we sequenced and analyzed >430 million paired 75-bp
RNA-Seq reads from a mouse myoblast cell line over a differentiation
time series. We detected 13,692 known transcripts and 3,724 previously
unannotated ones, 62\% of which are supported by independent expression
data or by homologous genes in other species. Over the time series,
330 genes showed complete switches in the dominant transcription
start site (TSS) or splice isoform, and we observed more subtle shifts
in 1,304 other genes. These results suggest that Cufflinks can illuminate
the substantial regulatory flexibility and complexity in even this
well-studied model of muscle development and that it can improve
transcriptome-based genome annotation.},
doi = {10.1038/nbt.1621},
pdf = {../local/Trapnell2010Transcript.pdf},
file = {Trapnell2010Transcript.pdf:Trapnell2010Transcript.pdf:PDF},
institution = {Department of Computer Science, University of Maryland, College Park,
Maryland, USA.},
keywords = {ngs, rnaseq},
language = {eng},
medline-pst = {ppublish},
owner = {jp},
pii = {nbt.1621},
pmid = {20436464},
timestamp = {2012.03.06},
url = {http://dx.doi.org/10.1038/nbt.1621}
}
@article{Wang2009RNA,
author = {Wang, Z. and Gerstein, M. and Snyder, M.},
title = {{RNA-Seq}: a revolutionary tool for transcriptomics.},
journal = {Nat. Rev. Genet.},
year = {2009},
volume = {10},
pages = {57--63},
number = {1},
month = {Jan},
abstract = {RNA-Seq is a recently developed approach to transcriptome profiling
that uses deep-sequencing technologies. Studies using this method
have already altered our view of the extent and complexity of eukaryotic
transcriptomes. RNA-Seq also provides a far more precise measurement
of levels of transcripts and their isoforms than other methods. This
article describes the RNA-Seq approach, the challenges associated
with its application, and the advances made so far in characterizing
several eukaryote transcriptomes.},
doi = {10.1038/nrg2484},
pdf = {../local/Wang2009RNA.pdf},
file = {Wang2009RNA.pdf:Wang2009RNA.pdf:PDF},
institution = {Department of Molecular, Cellular and Developmental Biology, Yale
University, 219 Prospect Street, New Haven, Connecticut 06520, USA.},
keywords = {ngs, rnaseq},
owner = {ljacob},
pii = {nrg2484},
pmid = {19015660},
timestamp = {2009.09.14},
url = {http://dx.doi.org/10.1038/nrg2484}
}
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ochim. Biophys. Acta;Bioinformatics;Biometrika;BMC Bioinformatics;Br.
J. Pharmacol.;Breast Cancer Res.;Cell;Cell. Signal.;Chem. Res. Toxicol
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;Comput. Chem.;Comput. Comm. Rev.;Comput. Stat. Data An.;Curr. Genom.;
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Ser. B;Journal of Statistical Planning and Inference;Mach. Learn.;Math
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