We are analyzing https://link.springer.com/article/10.1186/1471-2105-12-323.

Title:
RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome | BMC Bioinformatics
Description:
Background RNA-Seq is revolutionizing the way transcript abundances are measured. A key challenge in transcript quantification from RNA-Seq data is the handling of reads that map to multiple genes or isoforms. This issue is particularly important for quantification with de novo transcriptome assemblies in the absence of sequenced genomes, as it is difficult to determine which transcripts are isoforms of the same gene. A second significant issue is the design of RNA-Seq experiments, in terms of the number of reads, read length, and whether reads come from one or both ends of cDNA fragments. Results We present RSEM, an user-friendly software package for quantifying gene and isoform abundances from single-end or paired-end RNA-Seq data. RSEM outputs abundance estimates, 95% credibility intervals, and visualization files and can also simulate RNA-Seq data. In contrast to other existing tools, the software does not require a reference genome. Thus, in combination with a de novo transcriptome assembler, RSEM enables accurate transcript quantification for species without sequenced genomes. On simulated and real data sets, RSEM has superior or comparable performance to quantification methods that rely on a reference genome. Taking advantage of RSEM
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Keywords {🔍}

reads, data, rnaseq, rsem, transcript, gene, read, pubmed, quantification, methods, set, model, estimates, article, abundance, genome, number, length, google, scholar, quality, isoform, reference, genes, file, transcripts, sequencing, fragment, accuracy, alignments, distribution, abundances, error, values, refseq, central, additional, ensembl, results, sequences, parameters, cas, simulated, sets, expression, cufflinks, accurate, experiments, alignment, estimation,

Topics {✒️}

user-friendly scripts rsem-prepare-reference rna-seq-based transcript quantitation rsem-calculate-expression script handles paired-end rna-seq data conserved multi-exonic structure nih grant 1r01hg005232-01a1 taqman qrt-pcr measurements including taqman qrt-pcr rna-seq data generation full-length transcriptome assembly full size image rna-seq libraries generated simulate rna-seq data allocating ambiguous short-reads maqc rna-seq data raw rna-seq reads se rna-seq analysis rna-seq data sets real rna-seq data article download pdf se rna-seq protocols position-specific bias correction rna-seq abundance predictions sequenced rna-seq fragment simulated rna-seq data user-friendly software package rna-seq data set generated rna-seq data maqc rna-seq samples color-space reads generated user-friendly software tool modeling rna-seq data ucsc wig-formatted file global isoform-level estimates reference preparation script cell type-specific transcriptomes kent wj probabilistically-weighted read alignments rna-seq quantification methods gene-level abundance estimates article li gtf-formatted annotation file privacy choices/manage cookies gene isoform-level estimates position-dependent error model accurate gene-level estimates rsem-simulate-reads program full access short single-end reads springer berlin/heidelberg

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         description:RNA-Seq is revolutionizing the way transcript abundances are measured. A key challenge in transcript quantification from RNA-Seq data is the handling of reads that map to multiple genes or isoforms. This issue is particularly important for quantification with de novo transcriptome assemblies in the absence of sequenced genomes, as it is difficult to determine which transcripts are isoforms of the same gene. A second significant issue is the design of RNA-Seq experiments, in terms of the number of reads, read length, and whether reads come from one or both ends of cDNA fragments. We present RSEM, an user-friendly software package for quantifying gene and isoform abundances from single-end or paired-end RNA-Seq data. RSEM outputs abundance estimates, 95% credibility intervals, and visualization files and can also simulate RNA-Seq data. In contrast to other existing tools, the software does not require a reference genome. Thus, in combination with a de novo transcriptome assembler, RSEM enables accurate transcript quantification for species without sequenced genomes. On simulated and real data sets, RSEM has superior or comparable performance to quantification methods that rely on a reference genome. Taking advantage of RSEM's ability to effectively use ambiguously-mapping reads, we show that accurate gene-level abundance estimates are best obtained with large numbers of short single-end reads. On the other hand, estimates of the relative frequencies of isoforms within single genes may be improved through the use of paired-end reads, depending on the number of possible splice forms for each gene. RSEM is an accurate and user-friendly software tool for quantifying transcript abundances from RNA-Seq data. As it does not rely on the existence of a reference genome, it is particularly useful for quantification with de novo transcriptome assemblies. In addition, RSEM has enabled valuable guidance for cost-efficient design of quantification experiments with RNA-Seq, which is currently relatively expensive.
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         Microarrays
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         Algorithms
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