Evaluation of tools for long read RNA-seq splice-aware alignment.
High-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.
The 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.
https://github.com/kkrizanovic/RNAseqEval, https://figshare.com/projects/RNAseq_benchmark/24391.
mile.sikic@fer.hr.
Supplementary data are available at Bioinformatics online.
Križanovic K
，Echchiki A
，Roux J
，Šikic M
... -
《-》
LRCstats, a tool for evaluating long reads correction methods.
Third-generation sequencing (TGS) platforms that generate long reads, such as PacBio and Oxford Nanopore technologies, have had a dramatic impact on genomics research. However, despite recent improvements, TGS reads suffer from high-error rates and the development of read correction methods is an active field of research. This motivates the need to develop tools that can evaluate the accuracy of noisy long reads correction tools.
We introduce LRCstats, a tool that measures the accuracy of long reads correction tools. LRCstats takes advantage of long reads simulators that provide each simulated read with an alignment to the reference genome segment they originate from, and does not rely on a step of mapping corrected reads onto the reference genome. This allows for the measurement of the accuracy of the correction while being consistent with the actual errors introduced in the simulation process used to generate noisy reads. We illustrate the usefulness of LRCstats by analyzing the accuracy of four hybrid correction methods for PacBio long reads over three datasets.
https://github.com/cchauve/lrcstats.
laseanl@sfu.ca or cedric.chauve@sfu.ca.
Supplementary data are available at Bioinformatics online.
La S
，Haghshenas E
，Chauve C
《-》
Evaluating long-read de novo assembly tools for eukaryotic genomes: insights and considerations.
Assembly algorithm choice should be a deliberate, well-justified decision when researchers create genome assemblies for eukaryotic organisms from third-generation sequencing technologies. While third-generation sequencing by Oxford Nanopore Technologies (ONT) and Pacific Biosciences (PacBio) has overcome the disadvantages of short read lengths specific to next-generation sequencing (NGS), third-generation sequencers are known to produce more error-prone reads, thereby generating a new set of challenges for assembly algorithms and pipelines. However, the introduction of HiFi reads, which offer substantially reduced error rates, has provided a promising solution for more accurate assembly outcomes. Since the introduction of third-generation sequencing technologies, many tools have been developed that aim to take advantage of the longer reads, and researchers need to choose the correct assembler for their projects.
We benchmarked state-of-the-art long-read de novo assemblers to help readers make a balanced choice for the assembly of eukaryotes. To this end, we used 12 real and 64 simulated datasets from different eukaryotic genomes, with different read length distributions, imitating PacBio continuous long-read (CLR), PacBio high-fidelity (HiFi), and ONT sequencing to evaluate the assemblers. We include 5 commonly used long-read assemblers in our benchmark: Canu, Flye, Miniasm, Raven, and wtdbg2 for ONT and PacBio CLR reads. For PacBio HiFi reads , we include 5 state-of-the-art HiFi assemblers: HiCanu, Flye, Hifiasm, LJA, and MBG. Evaluation categories address the following metrics: reference-based metrics, assembly statistics, misassembly count, BUSCO completeness, runtime, and RAM usage. Additionally, we investigated the effect of increased read length on the quality of the assemblies and report that read length can, but does not always, positively impact assembly quality.
Our benchmark concludes that there is no assembler that performs the best in all the evaluation categories. However, our results show that overall Flye is the best-performing assembler for PacBio CLR and ONT reads, both on real and simulated data. Meanwhile, best-performing PacBio HiFi assemblers are Hifiasm and LJA. Next, the benchmarking using longer reads shows that the increased read length improves assembly quality, but the extent to which that can be achieved depends on the size and complexity of the reference genome.
Cosma BM
，Shirali Hossein Zade R
，Jordan EN
，van Lent P
，Peng C
，Pillay S
，Abeel T
... -
《GigaScience》
ResearchGate
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钛学术
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