Dataset Information

ASElux: an ultra-fast and accurate allelic reads counter.

ABSTRACT: Motivation:Mapping bias causes preferential alignment to the reference allele, forming a major obstacle in allele-specific expression (ASE) analysis. The existing methods, such as simulation and SNP-aware alignment, are either inaccurate or relatively slow. To fast and accurately count allelic reads for ASE analysis, we developed a novel approach, ASElux, which utilizes the personal SNP information and counts allelic reads directly from unmapped RNA-sequence (RNA-seq) data. ASElux significantly reduces runtime by disregarding reads outside single nucleotide polymorphisms (SNPs) during the alignment. Results:When compared to other tools on simulated and experimental data, ASElux achieves a higher accuracy on ASE estimation than non-SNP-aware aligners and requires a much shorter time than the benchmark SNP-aware aligner, GSNAP with just a slight loss in performance. ASElux can process 40 million read-pairs from an RNA-sequence (RNA-seq) sample and count allelic reads within 10?min, which is comparable to directly counting the allelic reads from alignments based on other tools. Furthermore, processing an RNA-seq sample using ASElux in conjunction with a general aligner, such as STAR, is more accurate and still ?4× faster than STAR?+?WASP, and ?33× faster than the lead SNP-aware aligner, GSNAP, making ASElux ideal for ASE analysis of large-scale transcriptomic studies. We applied ASElux to 273 lung RNA-seq samples from GTEx and identified a splice-QTL rs11078928 in lung which explains the mechanism underlying an asthma GWAS SNP rs11078927. Thus, our analysis demonstrated ASE as a highly powerful complementary tool to cis-expression quantitative trait locus (eQTL) analysis. Availability and implementation:The software can be downloaded from https://github.com/abl0719/ASElux. Contact:zmiao@ucla.edu or a5ko@ucla.edu. Supplementary information:Supplementary data are available at Bioinformatics online.

SUBMITTER: Miao Z

PROVIDER: S-EPMC5905663 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

ASElux: an ultra-fast and accurate allelic reads counter.

Miao Zong Z Alvarez Marcus M Pajukanta Päivi P Ko Arthur A

Bioinformatics (Oxford, England) 20180401 8

<h4>Motivation</h4>Mapping bias causes preferential alignment to the reference allele, forming a major obstacle in allele-specific expression (ASE) analysis. The existing methods, such as simulation and SNP-aware alignment, are either inaccurate or relatively slow. To fast and accurately count allelic reads for ASE analysis, we developed a novel approach, ASElux, which utilizes the personal SNP information and counts allelic reads directly from unmapped RNA-sequence (RNA-seq) data. ASElux signif ...[more]

PMID: 29186329

Similar Datasets

Project description:BACKGROUND: Adapter trimming is a prerequisite step for analyzing next-generation sequencing (NGS) data when the reads are longer than the target DNA/RNA fragments. Although typically used in small RNA sequencing, adapter trimming is also used widely in other applications, such as genome DNA sequencing and transcriptome RNA/cDNA sequencing, where fragments shorter than a read are sometimes obtained because of the limitations of NGS protocols. For the newly emerged Nextera long mate-pair (LMP) protocol, junction adapters are located in the middle of all properly constructed fragments; hence, adapter trimming is essential to gain the correct paired reads. However, our investigations have shown that few adapter trimming tools meet both efficiency and accuracy requirements simultaneously. The performances of these tools can be even worse for paired-end and/or mate-pair sequencing. RESULTS: To improve the efficiency of adapter trimming, we devised a novel algorithm, the bit-masked k-difference matching algorithm, which has O(kn) expected time with O(m) space, where k is the maximum number of differences allowed, n is the read length, and m is the adapter length. This algorithm makes it possible to fully enumerate all candidates that meet a specified threshold, e.g. error ratio, within a short period of time. To improve the accuracy of this algorithm, we designed a simple and easy-to-explain statistical scoring scheme to evaluate candidates in the pattern matching step. We also devised scoring schemes to fully exploit the paired-end/mate-pair information when it is applicable. All these features have been implemented in an industry-standard tool named Skewer (https://sourceforge.net/projects/skewer). Experiments on simulated data, real data of small RNA sequencing, paired-end RNA sequencing, and Nextera LMP sequencing showed that Skewer outperforms all other similar tools that have the same utility. Further, Skewer is considerably faster than other tools that have comparative accuracies; namely, one times faster for single-end sequencing, more than 12 times faster for paired-end sequencing, and 49% faster for LMP sequencing. CONCLUSIONS: Skewer achieved as yet unmatched accuracies for adapter trimming with low time bound.

Dataset Information

ASElux: an ultra-fast and accurate allelic reads counter.

Publications

ASElux: an ultra-fast and accurate allelic reads counter.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure