Dataset Information

RNA-eXpress annotates novel transcript features in RNA-seq data.

ABSTRACT: Next-generation sequencing is rapidly becoming the approach of choice for transcriptional analysis experiments. Substantial advances have been achieved in computational approaches to support these technologies. These approaches typically rely on existing transcript annotations, introducing a bias towards known genes, require specific experimental design and computational resources, or focus only on identification of splice variants (ignoring other biologically relevant transcribed features contained within the data that may be important for downstream analysis). Biologically relevant transcribed features also include large and small non-coding RNA, new transcription start sites, alternative promoters, RNA editing and processing of coding transcripts. Also, many existing solutions lack accessible interfaces required for wide scale adoption. We present a user-friendly, rapid and computation-efficient feature annotation framework (RNA-eXpress) that enables identification of transcripts and other genomic and transcriptional features independently of current annotations. RNA-eXpress accepts mapped reads in the standard binary alignment (BAM) format and produces a study-specific feature annotation in GTF format, comparison statistics, sequence extraction and feature counts. The framework is designed to be easily accessible while allowing advanced users to integrate new feature-identification algorithms through simple class extension, thus facilitating expansion to novel feature types or identification of study-specific feature types.

SUBMITTER: Forster SC

PROVIDER: S-EPMC3597146 | biostudies-literature | 2013 Mar

REPOSITORIES: biostudies-literature

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RNA-eXpress annotates novel transcript features in RNA-seq data.

Forster Samuel C SC Finkel Alexander M AM Gould Jodee A JA Hertzog Paul J PJ

Bioinformatics (Oxford, England) 20130208 6

Next-generation sequencing is rapidly becoming the approach of choice for transcriptional analysis experiments. Substantial advances have been achieved in computational approaches to support these technologies. These approaches typically rely on existing transcript annotations, introducing a bias towards known genes, require specific experimental design and computational resources, or focus only on identification of splice variants (ignoring other biologically relevant transcribed features conta ...[more]

PMID: 23396121

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Project description:Alternative splicing leverages genomic content by allowing the synthesis of multiple transcripts and, by implication, protein isoforms, from a single gene. However, estimating the abundance of transcripts produced in a given tissue from short sequencing reads is difficult and can result in both the construction of transcripts that do not exist, and the failure to identify true transcripts. An alternative approach is to catalog the events that make up isoforms (splice junctions and exons). We present here the Event Analysis (EA) approach, where we project transcripts onto the genome and identify overlapping/unique regions and junctions. In addition, all possible logical junctions are assembled into a catalog. Transcripts are filtered before quantitation based on simple measures: the proportion of the events detected, and the coverage. We find that mapping to a junction catalog is more efficient at detecting novel junctions than mapping in a splice aware manner. We identify 99.8% of true transcripts while iReckon identifies 82% of the true transcripts and creates more transcripts not included in the simulation than were initially used in the simulation. Using PacBio Iso-seq data from a mouse neural progenitor cell model, EA detects 60% of the novel junctions that are combinations of existing exons while only 43% are detected by STAR. EA further detects ?5,000 annotated junctions missed by STAR. Filtering transcripts based on the proportion of the transcript detected and the number of reads on average supporting that transcript captures 95% of the PacBio transcriptome. Filtering the reference transcriptome before quantitation, results in is a more stable estimate of isoform abundance, with improved correlation between replicates. This was particularly evident when EA is applied to an RNA-seq study of type 1 diabetes (T1D), where the coefficient of variation among subjects (n = 81) in the transcript abundance estimates was substantially reduced compared to the estimation using the full reference. EA focuses on individual transcriptional events. These events can be quantitate and analyzed directly or used to identify the probable set of expressed transcripts. Simple rules based on detected events and coverage used in filtering result in a dramatic improvement in isoform estimation without the use of ancillary data (e.g., ChIP, long reads) that may not be available for many studies.

Project 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.

Dataset Information

RNA-eXpress annotates novel transcript features in RNA-seq data.

Publications

RNA-eXpress annotates novel transcript features in RNA-seq data.

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