RNA-seq read alignment evaluation
Ontology highlight
ABSTRACT:
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| ERR310201_1.fastq.gz | Fastqsanger.gz | |||
| ERR310201_2.fastq.gz | Fastqsanger.gz | |||
| ERR310202_1.fastq.gz | Fastqsanger.gz | |||
| ERR310202_2.fastq.gz | Fastqsanger.gz | |||
| ERR310203_1.fastq.gz | Fastqsanger.gz |
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Project description:The source of most errors in RNA sequencing (RNA-seq) read alignment is in the repetitive structure of the genome and not with the alignment algorithm. Genetic variation away from the reference sequence exacerbates this problem causing reads to be assigned to the wrong location. We developed a method, implemented as the software package Seqnature, to construct the imputed genomes of individuals (individualized genomes) of experimental model organisms including inbred mouse strains and genetically unique outbred animals. Alignment to individualized genomes increases read mapping accuracy and improves transcript abundance estimates. In an application to expression QTL mapping, this approach corrected erroneous linkages and unmasked thousands of hidden associations. Individualized genomes accounting for genetic variation will be useful for human short-read sequencing and other sequencing applications including ChIP-seq. Illumina 100bp single-end liver RNA-seq from 277 male and female Diversity Outbred 26-week old mice raised on standard chow or high fat diet. In addition, Illumina 100bp single-end liver RNA-seq from 128 male 26-week old male mice (20 weeks for NZO strain) from each of the DO founder strains raised on standard chow or high fat diet (8 males per strain by diet group). Each sample was sequenced in 2-4x technical replicates across multiple flowcells. Samples were randomly assigned lanes and multiplexed at 12-24x.
2014-06-10 | E-GEOD-45684 | biostudies-arrayexpress
Project description:The source of most errors in RNA sequencing (RNA-seq) read alignment is in the repetitive structure of the genome and not with the alignment algorithm. Genetic variation away from the reference sequence exacerbates this problem causing reads to be assigned to the wrong location. We developed a method, implemented as the software package Seqnature, to construct the imputed genomes of individuals (individualized genomes) of experimental model organisms including inbred mouse strains and genetically unique outbred animals. Alignment to individualized genomes increases read mapping accuracy and improves transcript abundance estimates. In an application to expression QTL mapping, this approach corrected erroneous linkages and unmasked thousands of hidden associations. Individualized genomes accounting for genetic variation will be useful for human short-read sequencing and other sequencing applications including ChIP-seq.
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Project description:MotivationHigh-throughput sequencing has transformed the study of gene expression levels through RNA-seq, a technique that is now routinely used by various fields, such as genetic research or diagnostics. The advent of third generation sequencing technologies providing significantly longer reads opens up new possibilities. However, the high error rates common to these technologies set new bioinformatics challenges for the gapped alignment of reads to their genomic origin. In this study, we have explored how currently available RNA-seq splice-aware alignment tools cope with increased read lengths and error rates. All tested tools were initially developed for short NGS reads, but some have claimed support for long Pacific Biosciences (PacBio) or even Oxford Nanopore Technologies (ONT) MinION reads.ResultsThe tools were tested on synthetic and real datasets from two technologies (PacBio and ONT MinION). Alignment quality and resource usage were compared across different aligners. The effect of error correction of long reads was explored, both using self-correction and correction with an external short reads dataset. A tool was developed for evaluating RNA-seq alignment results. This tool can be used to compare the alignment of simulated reads to their genomic origin, or to compare the alignment of real reads to a set of annotated transcripts. Our tests show that while some RNA-seq aligners were unable to cope with long error-prone reads, others produced overall good results. We further show that alignment accuracy can be improved using error-corrected reads.Availability and implementationhttps://github.com/kkrizanovic/RNAseqEval, https://figshare.com/projects/RNAseq_benchmark/24391.Contactmile.sikic@fer.hr.Supplementary informationSupplementary data are available at Bioinformatics online.
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