Dataset Information

CADBURE: A generic tool to evaluate the performance of spliced aligners on RNA-Seq data.

ABSTRACT: The fundamental task in RNA-Seq-based transcriptome analysis is alignment of millions of short reads to the reference genome or transcriptome. Choosing the right tool for the dataset in hand from many existent RNA-Seq alignment packages remains a critical challenge for downstream analysis. To facilitate this choice, we designed a novel tool for comparing alignment results of user data based on the relative reliability of uniquely aligned reads (CADBURE). CADBURE can easily evaluate different aligners, or different parameter sets using the same aligner, and selects the best alignment result for any RNA-Seq dataset. Strengths of CADBURE include the ability to compare alignment results without the need for synthetic data such as simulated genomes, alignment regeneration and randomly subsampled datasets. The benefit of a CADBURE selected alignment result was supported by differentially expressed gene (DEG) analysis. We demonstrated that the use of CADBURE to select the best alignment from a number of different alignment results could change the number of DEGs by as much as 10%. In particular, the CADBURE selected alignment result favors fewer false positives in the DEG analysis. We also verified differential expression of eighteen genes with RT-qPCR validation experiments. CADBURE is an open source tool (http://cadbure.sourceforge.net/).

SUBMITTER: Kumar PK

PROVIDER: S-EPMC4548254 | biostudies-literature | 2015 Aug

REPOSITORIES: biostudies-literature

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CADBURE: A generic tool to evaluate the performance of spliced aligners on RNA-Seq data.

Kumar Praveen Kumar Raj PK Hoang Thanh V TV Robinson Michael L ML Tsonis Panagiotis A PA Liang Chun C

Scientific reports 20150825

The fundamental task in RNA-Seq-based transcriptome analysis is alignment of millions of short reads to the reference genome or transcriptome. Choosing the right tool for the dataset in hand from many existent RNA-Seq alignment packages remains a critical challenge for downstream analysis. To facilitate this choice, we designed a novel tool for comparing alignment results of user data based on the relative reliability of uniquely aligned reads (CADBURE). CADBURE can easily evaluate different ali ...[more]

PMID: 26304587

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Project description:Transcript quantification is a long-standing problem in genomics and estimating the relative abundance of alternatively-spliced isoforms from the same transcript is an important special case. Both problems have recently been illuminated by high-throughput RNA sequencing experiments which are quickly generating large amounts of data. However, much of the signal present in this data is corrupted or obscured by biases resulting in non-uniform and non-proportional representation of sequences from different transcripts. Many existing analyses attempt to deal with these and other biases with various task-specific approaches, which makes direct comparison between them difficult. However, two popular tools for isoform quantification, MISO and Cufflinks, have adopted a general probabilistic framework to model and mitigate these biases in a more general fashion. These advances motivate the need to investigate the effects of RNA-seq biases on the accuracy of different approaches for isoform quantification. We conduct the investigation by building models of increasing sophistication to account for noise introduced by the biases and compare their accuracy to the established approaches. We focus on methods that estimate the expression of alternatively-spliced isoforms with the percent-spliced-in (PSI) metric for each exon skipping event. To improve their estimates, many methods use evidence from RNA-seq reads that align to exon bodies. However, the methods we propose focus on reads that span only exon-exon junctions. As a result, our approaches are simpler and less sensitive to exon definitions than existing methods, which enables us to distinguish their strengths and weaknesses more easily. We present several probabilistic models of of position-specific read counts with increasing complexity and compare them to each other and to the current state-of-the-art methods in isoform quantification, MISO and Cufflinks. On a validation set with RT-PCR measurements for 26 cassette events, some of our methods are more accurate and some are significantly more consistent than these two popular tools. This comparison demonstrates the challenges in estimating the percent inclusion of alternatively spliced junctions and illuminates the tradeoffs between different approaches.

Project description:BACKGROUND:RNA sequencing (RNA-seq) has become the standard means of analyzing gene and transcript expression in high-throughput. While previously sequence alignment was a time demanding step, fast alignment methods and even more so transcript counting methods which avoid mapping and quantify gene and transcript expression by evaluating whether a read is compatible with a transcript, have led to significant speed-ups in data analysis. Now, the most time demanding step in the analysis of RNA-seq data is preprocessing the raw sequence data, such as running quality control and adapter, contamination and quality filtering before transcript or gene quantification. To do so, many researchers chain different tools, but a comprehensive, flexible and fast software that covers all preprocessing steps is currently missing. RESULTS:We here present FastqPuri, a light-weight and highly efficient preprocessing tool for fastq data. FastqPuri provides sequence quality reports on the sample and dataset level with new plots which facilitate decision making for subsequent quality filtering. Moreover, FastqPuri efficiently removes adapter sequences and sequences from biological contamination from the data. It accepts both single- and paired-end data in uncompressed or compressed fastq files. FastqPuri can be run stand-alone and is suitable to be run within pipelines. We benchmarked FastqPuri against existing tools and found that FastqPuri is superior in terms of speed, memory usage, versatility and comprehensiveness. CONCLUSIONS:FastqPuri is a new tool which covers all aspects of short read sequence data preprocessing. It was designed for RNA-seq data to meet the needs for fast preprocessing of fastq data to allow transcript and gene counting, but it is suitable to process any short read sequencing data of which high sequence quality is needed, such as for genome assembly or SNV (single nucleotide variant) detection. FastqPuri is most flexible in filtering undesired biological sequences by offering two approaches to optimize speed and memory usage dependent on the total size of the potential contaminating sequences. FastqPuri is available at https://github.com/jengelmann/FastqPuri . It is implemented in C and R and licensed under GPL v3.

Dataset Information

CADBURE: A generic tool to evaluate the performance of spliced aligners on RNA-Seq data.

Publications

CADBURE: A generic tool to evaluate the performance of spliced aligners on RNA-Seq data.

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