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Assessment of transcript reconstruction methods for RNA-seq | Nature Methods
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The RGASP consortium compared 25 RNA-seq analysis programs in their ability to identify exons, reconstruct transcripts and quantify expression levels. Assembly of isoforms and their expression levels in higher eukaryotes remains a challenge. We evaluated 25 protocol variants of 14 independent computational methods for exon identification, transcript reconstruction and expression-level quantification from RNA-seq data. Our results show that most algorithms are able to identify discrete transcript components with high success rates but that assembly of complete isoform structures poses a major challenge even when all constituent elements are identified. Expression-level estimates also varied widely across methods, even when based on similar transcript models. Consequently, the complexity of higher eukaryotic genomes imposes severe limitations on transcript recall and splice product discrimination that are likely to remain limiting factors for the analysis of current-generation RNA-seq data.
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transcript, data, rnaseq, exons, methods, pubmed, supplementary, fig, transcripts, article, gene, annotated, google, scholar, reported, genome, cas, annotation, exon, read, genes, central, detection, nature, reference, isoforms, sequencing, analysis, identified, augustus, mgene, expression, transomics, precision, reconstruction, assembly, ireckon, performance, elegans, melanogaster, sensitivity, source, predicted, rpkm, isoform, expressed, sapiens, nanostring, coverage, human,
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nature portfolio cancer research privacy policy accession e-mtab-1730 open reading frames library construction requirements advertising current-generation rna-seq data software tools long-read sequencing reveals nature 489 nature 471 nature social media accession numbers srr023546 rna-seq-based transcript quantitation access submissions libraries cold spring harbor 0/ reprints sequence read archive development short-read sequencing lead accession srr065719 paul bertone gene-based expression profiling rna-seq rpkms ranged full-length transcript sequencing rna-seq data sets low-quality genome builds independent rna-seq aligner 76-nt paired-end format rna-seq experiments based health/nhgri grants u54hg004555 rna-seq data relative human rna-seq data high-quality gene annotation rna-seq read alignments daniel zerbino & michael methods rna-seq data exon-level metrics measure nucleotide-level metrics measure cell long poly full-length transcriptome assembly adequate rna-seq alignments rna-seq rpkm values spliced protein-coding transcripts rna-seq reads mapped rna-seq tamara steijger parameter -min-intron-length
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headline:Assessment of transcript reconstruction methods for RNA-seq
description:The RGASP consortium compared 25 RNA-seq analysis programs in their ability to identify exons, reconstruct transcripts and quantify expression levels. Assembly of isoforms and their expression levels in higher eukaryotes remains a challenge. We evaluated 25 protocol variants of 14 independent computational methods for exon identification, transcript reconstruction and expression-level quantification from RNA-seq data. Our results show that most algorithms are able to identify discrete transcript components with high success rates but that assembly of complete isoform structures poses a major challenge even when all constituent elements are identified. Expression-level estimates also varied widely across methods, even when based on similar transcript models. Consequently, the complexity of higher eukaryotic genomes imposes severe limitations on transcript recall and splice product discrimination that are likely to remain limiting factors for the analysis of current-generation RNA-seq data.
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headline:Assessment of transcript reconstruction methods for RNA-seq
description:The RGASP consortium compared 25 RNA-seq analysis programs in their ability to identify exons, reconstruct transcripts and quantify expression levels. Assembly of isoforms and their expression levels in higher eukaryotes remains a challenge. We evaluated 25 protocol variants of 14 independent computational methods for exon identification, transcript reconstruction and expression-level quantification from RNA-seq data. Our results show that most algorithms are able to identify discrete transcript components with high success rates but that assembly of complete isoform structures poses a major challenge even when all constituent elements are identified. Expression-level estimates also varied widely across methods, even when based on similar transcript models. Consequently, the complexity of higher eukaryotic genomes imposes severe limitations on transcript recall and splice product discrimination that are likely to remain limiting factors for the analysis of current-generation RNA-seq data.
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