In this paper we focus on the task of transcript quantification, which is the estimation of relative abundances, at both the gene and isoform levels. The reads produced can be used for a number of transcriptome analyses, including transcript quantification, differential expression testing, reference-based gene annotation, and de novo transcript assembly. Leveraging recent advances in sequencing technology, RNA-Seq experiments produce millions of relatively short reads from the ends of cDNAs derived from fragments of sample RNA. RNA-Seq is a powerful technology for analyzing transcriptomes that is predicted to replace microarrays. In addition, RSEM has enabled valuable guidance for cost-efficient design of quantification experiments with RNA-Seq, which is currently relatively expensive. As it does not rely on the existence of a reference genome, it is particularly useful for quantification with de novo transcriptome assemblies.

RSEM is an accurate and user-friendly software tool for quantifying transcript abundances from RNA-Seq data.

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.

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 simulated and real data sets, RSEM has superior or comparable performance to quantification methods that rely on a reference genome. Thus, in combination with a de novo transcriptome assembler, RSEM enables accurate transcript quantification for species without sequenced genomes. In contrast to other existing tools, the software does not require a reference genome. RSEM outputs abundance estimates, 95% credibility intervals, and visualization files and can also simulate RNA-Seq data. We present RSEM, an user-friendly software package for quantifying gene and isoform abundances from single-end or paired-end RNA-Seq data. 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. 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 key challenge in transcript quantification from RNA-Seq data is the handling of reads that map to multiple genes or isoforms. RNA-Seq is revolutionizing the way transcript abundances are measured.

How to obtain verizon text transcripts