The Experts below are selected from a list of 3258 Experts worldwide ranked by ideXlab platform
Trac D. Tran - One of the best experts on this subject based on the ideXlab platform.
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Abundance Estimation for Bilinear Mixture Models via Joint Sparse and Low-Rank Representation
IEEE Transactions on Geoscience and Remote Sensing, 2014Co-Authors: Qing Qu, Nasser M. Nasrabadi, Trac D. TranAbstract:Sparsity-based unmixing algorithms, exploiting the sparseness property of the Abundances, have recently been proposed with promising performances. However, these algorithms are developed for the linear mixture model (LMM), which cannot effectively handle the nonlinear effects. In this paper, we extend the current sparse regression methods for the LMM to bilinear mixture models (BMMs), where the BMMs introduce additional bilinear terms in the LMM in order to model second-order photon scattering effects. To solve the Abundance Estimation problem for the BMMs, we propose to perform a sparsity-based Abundance Estimation by using two dictionaries: a linear dictionary containing all the pure endmembers and a bilinear dictionary consisting of all the possible second-order endmember interaction components. Then, the Abundance values can be estimated from the sparse codes associated with the linear dictionary. Moreover, to exploit the spatial data structure where the adjacent pixels are usually homogeneous and are often mixtures of the same materials, we first employ the joint-sparsity (row-sparsity) model to enforce structured sparsity on the Abundance coefficients. However, the joint-sparsity model is often a strict assumption, which might cause some aliasing artifacts for the pixels that lie on the boundaries of different materials. To deal with this problem, the low-rank-representation model, which seeks the lowest rank representation of the data, is further introduced to better capture the spatial data structure. Our simulation results demonstrate that the proposed algorithms provide much enhanced performance compared with state-of-the-art algorithms.
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Low rank representation for bilinear Abundance Estimation problem
2013 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2013Co-Authors: Qing Qu, Nasser M. Nasrabadi, Trac D. TranAbstract:In the hyperspectral Abundance Estimation problem, how to effectively utilize spatial information in the data remains as a challenging task: the mixed pixels within a small neighbourhood are usually correlated and have similar pure endmembers. Recently, the total variation and the joint sparsity priors have been incorporated to exploit this structure for linear mixture model (LMM) and bilinear mixture model (BMM), respectively. However, these structured priors are either too complicated or do not fit well for the spatial data structure. In this paper, we propose a low rank representation model which can better capture the spatial data structure for the BMM. The experimental results demonstrate the effectiveness of the proposed algorithms.
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WHISPERS - Low rank representation for bilinear Abundance Estimation problem
2013 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2013Co-Authors: Qing Qu, Nasser M. Nasrabadi, Trac D. TranAbstract:In the hyperspectral Abundance Estimation problem, how to effectively utilize spatial information in the data remains as a challenging task: the mixed pixels within a small neighbourhood are usually correlated and have similar pure endmembers. Recently, the total variation and the joint sparsity priors have been incorporated to exploit this structure for linear mixture model (LMM) and bilinear mixture model (BMM), respectively. However, these structured priors are either too complicated or do not fit well for the spatial data structure. In this paper, we propose a low rank representation model which can better capture the spatial data structure for the BMM. The experimental results demonstrate the effectiveness of the proposed algorithms.
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Hyperspectral Abundance Estimation for the generalized bilinear model with joint sparsity constraint
2013 IEEE International Conference on Acoustics Speech and Signal Processing, 2013Co-Authors: Qing Qu, Nasser M. Nasrabadi, Trac D. TranAbstract:In this paper, we present a novel Abundance Estimation method for the generalized bilinear model (GBM) via sparse representation for hyperspectral imagery. Because the GBM generalizes the linear mixture model (LMM) by introducing an additional bilinear term, our sparsity-based Abundance Estimation is performed by utilizing two dictionaries-a linear dictionary containing all the pure endmembers and a bilinear dictionary consisting of all the possible bilinear interaction components. Because the components within the bilinear term are also linearly combined, by employing a composite dictionary made up by the concatenation of the linear and bilinear dictionaries we can reformulate the bilinear problem in a linear sparse regression framework. In this way, the Abundance values are estimated from the sparse codes only associated with the linear dictionary. To further improve the Estimation performance, we incorporate the joint-sparsity model to exploit the spatial information in the data. The experiments demonstrate the effectiveness of the proposed algorithms on both synthetic and real data.
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ICASSP - Hyperspectral Abundance Estimation for the generalized bilinear model with joint sparsity constraint
2013 IEEE International Conference on Acoustics Speech and Signal Processing, 2013Co-Authors: Qing Qu, Nasser M. Nasrabadi, Trac D. TranAbstract:In this paper, we present a novel Abundance Estimation method for the generalized bilinear model (GBM) via sparse representation for hyperspectral imagery. Because the GBM generalizes the linear mixture model (LMM) by introducing an additional bilinear term, our sparsity-based Abundance Estimation is performed by utilizing two dictionaries-a linear dictionary containing all the pure endmembers and a bilinear dictionary consisting of all the possible bilinear interaction components. Because the components within the bilinear term are also linearly combined, by employing a composite dictionary made up by the concatenation of the linear and bilinear dictionaries we can reformulate the bilinear problem in a linear sparse regression framework. In this way, the Abundance values are estimated from the sparse codes only associated with the linear dictionary. To further improve the Estimation performance, we incorporate the joint-sparsity model to exploit the spatial information in the data. The experiments demonstrate the effectiveness of the proposed algorithms on both synthetic and real data.
Qing Qu - One of the best experts on this subject based on the ideXlab platform.
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Abundance Estimation for Bilinear Mixture Models via Joint Sparse and Low-Rank Representation
IEEE Transactions on Geoscience and Remote Sensing, 2014Co-Authors: Qing Qu, Nasser M. Nasrabadi, Trac D. TranAbstract:Sparsity-based unmixing algorithms, exploiting the sparseness property of the Abundances, have recently been proposed with promising performances. However, these algorithms are developed for the linear mixture model (LMM), which cannot effectively handle the nonlinear effects. In this paper, we extend the current sparse regression methods for the LMM to bilinear mixture models (BMMs), where the BMMs introduce additional bilinear terms in the LMM in order to model second-order photon scattering effects. To solve the Abundance Estimation problem for the BMMs, we propose to perform a sparsity-based Abundance Estimation by using two dictionaries: a linear dictionary containing all the pure endmembers and a bilinear dictionary consisting of all the possible second-order endmember interaction components. Then, the Abundance values can be estimated from the sparse codes associated with the linear dictionary. Moreover, to exploit the spatial data structure where the adjacent pixels are usually homogeneous and are often mixtures of the same materials, we first employ the joint-sparsity (row-sparsity) model to enforce structured sparsity on the Abundance coefficients. However, the joint-sparsity model is often a strict assumption, which might cause some aliasing artifacts for the pixels that lie on the boundaries of different materials. To deal with this problem, the low-rank-representation model, which seeks the lowest rank representation of the data, is further introduced to better capture the spatial data structure. Our simulation results demonstrate that the proposed algorithms provide much enhanced performance compared with state-of-the-art algorithms.
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Low rank representation for bilinear Abundance Estimation problem
2013 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2013Co-Authors: Qing Qu, Nasser M. Nasrabadi, Trac D. TranAbstract:In the hyperspectral Abundance Estimation problem, how to effectively utilize spatial information in the data remains as a challenging task: the mixed pixels within a small neighbourhood are usually correlated and have similar pure endmembers. Recently, the total variation and the joint sparsity priors have been incorporated to exploit this structure for linear mixture model (LMM) and bilinear mixture model (BMM), respectively. However, these structured priors are either too complicated or do not fit well for the spatial data structure. In this paper, we propose a low rank representation model which can better capture the spatial data structure for the BMM. The experimental results demonstrate the effectiveness of the proposed algorithms.
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WHISPERS - Low rank representation for bilinear Abundance Estimation problem
2013 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2013Co-Authors: Qing Qu, Nasser M. Nasrabadi, Trac D. TranAbstract:In the hyperspectral Abundance Estimation problem, how to effectively utilize spatial information in the data remains as a challenging task: the mixed pixels within a small neighbourhood are usually correlated and have similar pure endmembers. Recently, the total variation and the joint sparsity priors have been incorporated to exploit this structure for linear mixture model (LMM) and bilinear mixture model (BMM), respectively. However, these structured priors are either too complicated or do not fit well for the spatial data structure. In this paper, we propose a low rank representation model which can better capture the spatial data structure for the BMM. The experimental results demonstrate the effectiveness of the proposed algorithms.
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Hyperspectral Abundance Estimation for the generalized bilinear model with joint sparsity constraint
2013 IEEE International Conference on Acoustics Speech and Signal Processing, 2013Co-Authors: Qing Qu, Nasser M. Nasrabadi, Trac D. TranAbstract:In this paper, we present a novel Abundance Estimation method for the generalized bilinear model (GBM) via sparse representation for hyperspectral imagery. Because the GBM generalizes the linear mixture model (LMM) by introducing an additional bilinear term, our sparsity-based Abundance Estimation is performed by utilizing two dictionaries-a linear dictionary containing all the pure endmembers and a bilinear dictionary consisting of all the possible bilinear interaction components. Because the components within the bilinear term are also linearly combined, by employing a composite dictionary made up by the concatenation of the linear and bilinear dictionaries we can reformulate the bilinear problem in a linear sparse regression framework. In this way, the Abundance values are estimated from the sparse codes only associated with the linear dictionary. To further improve the Estimation performance, we incorporate the joint-sparsity model to exploit the spatial information in the data. The experiments demonstrate the effectiveness of the proposed algorithms on both synthetic and real data.
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ICASSP - Hyperspectral Abundance Estimation for the generalized bilinear model with joint sparsity constraint
2013 IEEE International Conference on Acoustics Speech and Signal Processing, 2013Co-Authors: Qing Qu, Nasser M. Nasrabadi, Trac D. TranAbstract:In this paper, we present a novel Abundance Estimation method for the generalized bilinear model (GBM) via sparse representation for hyperspectral imagery. Because the GBM generalizes the linear mixture model (LMM) by introducing an additional bilinear term, our sparsity-based Abundance Estimation is performed by utilizing two dictionaries-a linear dictionary containing all the pure endmembers and a bilinear dictionary consisting of all the possible bilinear interaction components. Because the components within the bilinear term are also linearly combined, by employing a composite dictionary made up by the concatenation of the linear and bilinear dictionaries we can reformulate the bilinear problem in a linear sparse regression framework. In this way, the Abundance values are estimated from the sparse codes only associated with the linear dictionary. To further improve the Estimation performance, we incorporate the joint-sparsity model to exploit the spatial information in the data. The experiments demonstrate the effectiveness of the proposed algorithms on both synthetic and real data.
Nasser M. Nasrabadi - One of the best experts on this subject based on the ideXlab platform.
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Abundance Estimation for Bilinear Mixture Models via Joint Sparse and Low-Rank Representation
IEEE Transactions on Geoscience and Remote Sensing, 2014Co-Authors: Qing Qu, Nasser M. Nasrabadi, Trac D. TranAbstract:Sparsity-based unmixing algorithms, exploiting the sparseness property of the Abundances, have recently been proposed with promising performances. However, these algorithms are developed for the linear mixture model (LMM), which cannot effectively handle the nonlinear effects. In this paper, we extend the current sparse regression methods for the LMM to bilinear mixture models (BMMs), where the BMMs introduce additional bilinear terms in the LMM in order to model second-order photon scattering effects. To solve the Abundance Estimation problem for the BMMs, we propose to perform a sparsity-based Abundance Estimation by using two dictionaries: a linear dictionary containing all the pure endmembers and a bilinear dictionary consisting of all the possible second-order endmember interaction components. Then, the Abundance values can be estimated from the sparse codes associated with the linear dictionary. Moreover, to exploit the spatial data structure where the adjacent pixels are usually homogeneous and are often mixtures of the same materials, we first employ the joint-sparsity (row-sparsity) model to enforce structured sparsity on the Abundance coefficients. However, the joint-sparsity model is often a strict assumption, which might cause some aliasing artifacts for the pixels that lie on the boundaries of different materials. To deal with this problem, the low-rank-representation model, which seeks the lowest rank representation of the data, is further introduced to better capture the spatial data structure. Our simulation results demonstrate that the proposed algorithms provide much enhanced performance compared with state-of-the-art algorithms.
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Low rank representation for bilinear Abundance Estimation problem
2013 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2013Co-Authors: Qing Qu, Nasser M. Nasrabadi, Trac D. TranAbstract:In the hyperspectral Abundance Estimation problem, how to effectively utilize spatial information in the data remains as a challenging task: the mixed pixels within a small neighbourhood are usually correlated and have similar pure endmembers. Recently, the total variation and the joint sparsity priors have been incorporated to exploit this structure for linear mixture model (LMM) and bilinear mixture model (BMM), respectively. However, these structured priors are either too complicated or do not fit well for the spatial data structure. In this paper, we propose a low rank representation model which can better capture the spatial data structure for the BMM. The experimental results demonstrate the effectiveness of the proposed algorithms.
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WHISPERS - Low rank representation for bilinear Abundance Estimation problem
2013 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2013Co-Authors: Qing Qu, Nasser M. Nasrabadi, Trac D. TranAbstract:In the hyperspectral Abundance Estimation problem, how to effectively utilize spatial information in the data remains as a challenging task: the mixed pixels within a small neighbourhood are usually correlated and have similar pure endmembers. Recently, the total variation and the joint sparsity priors have been incorporated to exploit this structure for linear mixture model (LMM) and bilinear mixture model (BMM), respectively. However, these structured priors are either too complicated or do not fit well for the spatial data structure. In this paper, we propose a low rank representation model which can better capture the spatial data structure for the BMM. The experimental results demonstrate the effectiveness of the proposed algorithms.
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Hyperspectral Abundance Estimation for the generalized bilinear model with joint sparsity constraint
2013 IEEE International Conference on Acoustics Speech and Signal Processing, 2013Co-Authors: Qing Qu, Nasser M. Nasrabadi, Trac D. TranAbstract:In this paper, we present a novel Abundance Estimation method for the generalized bilinear model (GBM) via sparse representation for hyperspectral imagery. Because the GBM generalizes the linear mixture model (LMM) by introducing an additional bilinear term, our sparsity-based Abundance Estimation is performed by utilizing two dictionaries-a linear dictionary containing all the pure endmembers and a bilinear dictionary consisting of all the possible bilinear interaction components. Because the components within the bilinear term are also linearly combined, by employing a composite dictionary made up by the concatenation of the linear and bilinear dictionaries we can reformulate the bilinear problem in a linear sparse regression framework. In this way, the Abundance values are estimated from the sparse codes only associated with the linear dictionary. To further improve the Estimation performance, we incorporate the joint-sparsity model to exploit the spatial information in the data. The experiments demonstrate the effectiveness of the proposed algorithms on both synthetic and real data.
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ICASSP - Hyperspectral Abundance Estimation for the generalized bilinear model with joint sparsity constraint
2013 IEEE International Conference on Acoustics Speech and Signal Processing, 2013Co-Authors: Qing Qu, Nasser M. Nasrabadi, Trac D. TranAbstract:In this paper, we present a novel Abundance Estimation method for the generalized bilinear model (GBM) via sparse representation for hyperspectral imagery. Because the GBM generalizes the linear mixture model (LMM) by introducing an additional bilinear term, our sparsity-based Abundance Estimation is performed by utilizing two dictionaries-a linear dictionary containing all the pure endmembers and a bilinear dictionary consisting of all the possible bilinear interaction components. Because the components within the bilinear term are also linearly combined, by employing a composite dictionary made up by the concatenation of the linear and bilinear dictionaries we can reformulate the bilinear problem in a linear sparse regression framework. In this way, the Abundance values are estimated from the sparse codes only associated with the linear dictionary. To further improve the Estimation performance, we incorporate the joint-sparsity model to exploit the spatial information in the data. The experiments demonstrate the effectiveness of the proposed algorithms on both synthetic and real data.
Stefan Canzar - One of the best experts on this subject based on the ideXlab platform.
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RECOMB - CIDANE: Comprehensive Isoform Discovery and Abundance Estimation
Lecture Notes in Computer Science, 2020Co-Authors: Stefan Canzar, Sandro Andreotti, David Weese, Knut Reinert, Gunnar W KlauAbstract:High-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.
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cidane comprehensive isoform discovery and Abundance Estimation
Genome Biology, 2016Co-Authors: Stefan Canzar, Sandro Andreotti, David Weese, Knut Reinert, Gunnar W KlauAbstract:We present CIDANE, a novel framework for genome-based transcript reconstruction and quantification from RNA-seq reads. CIDANE assembles transcripts efficiently with significantly higher sensitivity and precision than existing tools. Its algorithmic core not only reconstructs transcripts ab initio, but also allows the use of the growing annotation of known splice sites, transcription start and end sites, or full-length transcripts, which are available for most model organisms. CIDANE supports the integrated analysis of RNA-seq and additional gene-boundary data and recovers splice junctions that are invisible to other methods. CIDANE is available at
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cidane comprehensive isoform discovery and Abundance Estimation
bioRxiv, 2015Co-Authors: Stefan Canzar, Sandro Andreotti, David Weese, Knut Reinert, Gunnar W KlauAbstract:We present CIDANE, a novel framework for genome-based transcript reconstruction and quantification from RNA-seq reads. CIDANE assembles transcripts with significantly higher sensitivity and precision than existing tools, while competing in speed with the fastest methods. In addition to reconstructing transcripts ab initio, the algorithm also allows to make use of the growing annotation of known splice sites, transcription start and end sites, or full-length transcripts, which are available for most model organisms. CIDANE supports the integrated analysis of RNA-seq and additional gene-boundary data and recovers splice junctions that are invisible to other methods. CIDANE is available at http://ccb.jhu.edu/software/cidane/.
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cidane comprehensive isoform discovery and Abundance Estimation
Research in Computational Molecular Biology, 2015Co-Authors: Stefan Canzar, Sandro Andreotti, David Weese, Knut Reinert, Gunnar W KlauAbstract:High-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.
Jingyi Jessica Li - One of the best experts on this subject based on the ideXlab platform.
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Joint Sparse Modeling of Multiple RNA-Seq Samples for mRNA Isoform Discovery and Abundance Estimation
2020Co-Authors: Jingyi Jessica LiAbstract: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 and estimating the isoform Abundance. The problem is challenging and often involves identifiability issues in statistical modeling. We have developed a statistical method called "Sparse Linear modeling of RNA-Seq data for Isoform Discovery and Abundance Estimation" (SLIDE) that takes exon boundaries and RNA-Seq data as input to discern the set of mRNA isoforms most likely presenting in an RNA-Seq sample (Li et al. (2011) PNAS 108(50), pp.19867–72). The published version of SLIDE takes one RNA-Seq dataset as input. As the cost of RNA-Seq decreases, biologists tend to produce more biological or technical replicates, in order to reduce possible noises in a single RNA-Seq experiment. In this talk, we propose a new method called JSLIDE, which is based on Bayesian hierarchical modeling and extends our SLIDE work to effectively merge publicly available RNA-Seq replicates. From the replicates, JSLIDE combines their common information and discards their inconsistent noise, so that the discovery and quantification of mRNA isoforms can achieve better accuracy than using a single RNA-Seq dataset.
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aide annotation assisted isoform discovery and Abundance Estimation with high precision
bioRxiv, 2019Co-Authors: Wei Vivian Li, Shan Li, Xin Tong, Ling Deng, Jingyi Jessica LiAbstract:Abstract Genome-wide accurate identification and quantification of full-length mRNA isoforms is crucial for investigating transcriptional and post-transcriptional regulatory mechanisms of biological phenomena. Despite continuing efforts in developing effective computational tools to identify or assemble full-length mRNA isoforms from next-generation RNA-seq data, it remains a challenge to accurately identify mRNA isoforms from short sequence reads due to the substantial information loss in RNA-seq experiments. Here we introduce a novel statistical method, AIDE (Annotation-assisted Isoform Discovery and Abundance Estimation), the first approach that directly controls false isoform discoveries by implementing the statistical model selection principle. Solving the isoform discovery problem in a stepwise and conservative manner, AIDE prioritizes the annotated isoforms and precisely identifies novel isoforms whose addition significantly improves the explanation of observed RNA-seq reads. We evaluate the performance of AIDE based on multiple simulated and real RNA-seq datasets followed by a PCR-Sanger sequencing validation. Our results show that AIDE effectively leverages the annotation information to compensate the information loss due to short read lengths. AIDE achieves the highest precision in isoform discovery and the lowest error rates in isoform Abundance Estimation, compared with three state-of-the-art methods Cufflinks, SLIDE, and StringTie. As a robust bioinformatics tool for transcriptome analysis, AIDE will enable researchers to discover novel transcripts with high confidence.
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aide annotation assisted isoform discovery and Abundance Estimation from rna seq data
bioRxiv, 2018Co-Authors: Wei Vivian Li, Shan Li, Xin Tong, Ling Deng, Jingyi Jessica LiAbstract:Genome-wide accurate identification and quantification of full-length mRNA isoforms is crucial for investigating transcriptional and post-transcriptional regulatory mechanisms of biological phenomena. Despite continuing efforts in developing effective computational tools to identify or assemble full-length mRNA isoforms from next-generation RNA-seq data, it remains a challenge to accurately identify mRNA isoforms from short sequence reads due to the substantial information loss in RNA-seq experiments. Here we introduce a novel statistical method, AIDE (Annotation-assisted Isoform Discovery and Abundance Estimation), the first approach that directly controls false isoform discoveries by implementing the statistical model selection principle. Solving the isoform discovery problem in a stepwise and conservative manner, AIDE prioritizes the annotated isoforms and precisely identifies novel isoforms whose addition significantly improves the explanation of observed RNA-seq reads. We evaluate the performance of AIDE based on multiple simulated and real RNA-seq datasets followed by a PCR-Sanger sequencing validation. Our results show that AIDE effectively leverages the annotation information to compensate the information loss due to short read lengths. AIDE achieves the highest precision in isoform discovery and the lowest error rates in isoform Abundance Estimation, compared with three state-of-the-art methods Cufflinks, SLIDE, and StringTie. As a robust bioinformatics tool for transcriptome analysis, AIDE will enable researchers to discover novel transcripts with high confidence.
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sparse linear modeling of next generation mrna sequencing rna seq data for isoform discovery and Abundance Estimation
Proceedings of the National Academy of Sciences of the United States of America, 2011Co-Authors: Jingyi Jessica Li, Ciren Jiang, J Brown, Haiyan Huang, Peter J BickelAbstract: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 “sparse linear modeling of RNA-Seq data for isoform discovery and Abundance Estimation” (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.