Oases: robust de novo RNA-seq assembly across the dynamic range of expression levels

By Marcel H. Schulz, Daniel R. Zerbino, Martin Vingron, Ewan Birney. Bioinform., 2012

1,459 citations recorded by the source corpus.

Motivation: High-throughput sequencing has made the analysis of new model organisms more affordable. Although assembling a new genome can still be costly and difficult, it is possible to use RNA-seq to sequence mRNA. In the absence of a known genome, it is necessary to assemble these sequences de novo, taking into account possible alternative isoforms and the dynamic range of expression values. Results: We present a software package named Oases designed to heuristically assemble RNA-seq reads in the absence of a reference genome, across a broad spectrum of expression values and in presence of alternative isoforms. It achieves this by using an array of hash lengths, a dynamic filtering of noise, a robust resolution of alternative splicing events and the efficient merging of multiple assemblies. It was tested on human and mouse RNA-seq data and is shown to improve significantly on the transABySS and Trinity de novo transcriptome assemblers. Availability and implementation: Oases is freely available under the GPL license at www.ebi.ac.uk/~zerbino/oases/ Contact: dzerbino@ucsc.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/8192d46d1422567c708ee9fb981bf39e89a64e87

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Citations in the corpus

Sequence assembly demystified (2013, background): “Constructing high-quality assemblies using, for example, Trinity 8 or Oases 72 requires substantial computational resources, whereas more efficient assemblers such as Trans-ABySS 9 may generate fragmented transcriptomes.”
rnaSPAdes: a de novo transcriptome assembler and its application to RNA-Seq data (2018, positive): “To address these constraints, de novo transcriptome assemblers [7–11] have emerged as a viable complement to the reference-based tools.”
A Comparative Study of Techniques for Differential Expression Analysis on RNA-Seq Data (2014, background): “Compared to microarray techniques, RNA-Seq can be performed without prior knowledge of reference sequences and enables a wide range of applications including transcriptome de novo assembly [1-4], abundance estimation [5-9], and detection of alternative splicing events [10-12], all of which have revo”
De novo transcript sequence reconstruction from RNA-Seq: reference generation and analysis with Trinity (2013, methodology): “Trans-ABySS 6 , Velvet-Oases 7 and SOAPdenovo-trans (http://soap. genomics.org.cn/SOAPdenovo-Trans.html) are all extensions of earlier developed genome assemblers.”
TransRate: reference-free quality assessment of de novo transcriptome assemblies (2015, methodology): “On average, Oases produced the highest mean contig scores for mouse and rice, while Trinity produced the highest mean contig scores for human and yeast (Figure 5B).”
Applications of next-generation sequencing to the study of biological invasions (2015, methodology): “There is many software available for de novo transcriptome assembly (Zerbino and Birney, 2008; Simpson et al., 2009; Grabherr et al., 2011; Luo et al., 2012; Schulz et al., 2012), which utilize different approaches to assemble the data, often with large differences be-”
SOAPdenovo-Trans: de novo transcriptome assembly with short RNA-Seq reads (2013, background): “Historically, the first assemblers for next generation sequences, like Velvet (Zerbino and Birney, 2008), ABySS(Simpson, et al., 2009) and SOAPdenovo (Li, et al., 2010), were developed for whole genome assembly.”
Identification of protein coding regions in RNA transcripts (2014, methodology): “melanogaster transcripts generated in (3) by Cufflinks (18), Augustus (19), Velvet (20), Oases (21) and Exonerate (22) from co-ordinates of the exons of assembled transcripts as provided by the authors of (3).”
Gaining comprehensive biological insight into the transcriptome by performing a broad-spectrum RNA-seq analysis (2017, methodology): “On the other hand, Oases achieved its peak in the far right of the plot, i.e., after including most of the genes, indicating its effectiveness in capturing low-expression genes.”
A survey of best practices for RNA-seq data analysis (2016, methodology): “2c) into a transcriptome using packages such as SOAPdenovoTrans [30], Oases [31], Trans-ABySS [32] or Trinity [33].”
Accuracy assessment of fusion transcript detection via read-mapping and de novo fusion transcript assembly-based methods (2019, methodology): “JAFFA-Assembly [32] A After removing intronic and intergenic region aligning reads defined by Bowtie genome alignments, the remaining reads are assembled using Oases [33] and the assembled contigs are mapped directly to the transcriptome using BLAT.”