Biological classification with RNA-seq data: Can alternatively spliced transcript expression enhance machine learning classifiers?
نویسندگان
چکیده
منابع مشابه
Biological classification with RNA-Seq data: Can alternative splicing enhance machine learning classifier?
The extent to which the genes are expressed in the cell can be simplistically defined as a function of one or more factors of the environment, lifestyle, and genetics. RNA sequencing (RNA-Seq) is becoming a prevalent approach to quantify gene expression, and is expected to gain better insights to a number of biological and biomedical questions, compared to the DNA microarrays. Most importantly,...
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RNA-Seq technology allows for studying the transcriptional state of the cell at an unprecedented level of detail. Beyond quantification of whole-gene expression, it is now possible to disentangle the abundance of individual alternatively spliced transcript isoforms of a gene. A central question is to understand the regulatory processes that lead to differences in relative abundance variation du...
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Motivation Recent advances in high-throughput RNA sequencing (RNA-seq) technologies have made it possible to reconstruct the full transcriptome of various types of cells. It is important to accurately assemble transcripts or identify isoforms for an improved understanding of molecular mechanisms in biological systems. Results We have developed a novel Bayesian method, SparseIso, to reliably i...
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Integrating large-scale functional genomic data has significantly accelerated our understanding of gene functions. However, no algorithm has been developed to differentiate functions for isoforms of the same gene using high-throughput genomic data. This is because standard supervised learning requires 'ground-truth' functional annotations, which are lacking at the isoform level. To address this...
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MOTIVATION Statistical methods development for differential expression analysis of RNA sequencing (RNA-seq) requires software tools to assess accuracy and error rate control. Since true differential expression status is often unknown in experimental datasets, artificially constructed datasets must be utilized, either by generating costly spike-in experiments or by simulating RNA-seq data. RES...
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ژورنال
عنوان ژورنال: RNA
سال: 2018
ISSN: 1355-8382,1469-9001
DOI: 10.1261/rna.062802.117