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 《-》

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 ... -  《-》

CoLoRMap: Correcting Long Reads by Mapping short reads.

Second generation sequencing technologies paved the way to an exceptional increase in the number of sequenced genomes, both prokaryotic and eukaryotic. However, short reads are difficult to assemble and often lead to highly fragmented assemblies. The recent developments in long reads sequencing methods offer a promising way to address this issue. However, so far long reads are characterized by a high error rate, and assembling from long reads require a high depth of coverage. This motivates the development of hybrid approaches that leverage the high quality of short reads to correct errors in long reads. We introduce CoLoRMap, a hybrid method for correcting noisy long reads, such as the ones produced by PacBio sequencing technology, using high-quality Illumina paired-end reads mapped onto the long reads. Our algorithm is based on two novel ideas: using a classical shortest path algorithm to find a sequence of overlapping short reads that minimizes the edit score to a long read and extending corrected regions by local assembly of unmapped mates of mapped short reads. Our results on bacterial, fungal and insect data sets show that CoLoRMap compares well with existing hybrid correction methods. The source code of CoLoRMap is freely available for non-commercial use at https://github.com/sfu-compbio/colormap ehaghshe@sfu.ca or cedric.chauve@sfu.ca Supplementary data are available at Bioinformatics online.

Haghshenas E ，Hach F ，Sahinalp SC ，Chauve C ... -  《-》

A Sequence-Based Novel Approach for Quality Evaluation of Third-Generation Sequencing Reads.

Zhang W ，Huang N ，Zheng J ，Liao X ，Wang J ，Li HD ... -  《Genes》

Lerna: transformer architectures for configuring error correction tools for short- and long-read genome sequencing.

Sequencing technologies are prone to errors, making error correction (EC) necessary for downstream applications. EC tools need to be manually configured for optimal performance. We find that the optimal parameters (e.g., k-mer size) are both tool- and dataset-dependent. Moreover, evaluating the performance (i.e., Alignment-rate or Gain) of a given tool usually relies on a reference genome, but quality reference genomes are not always available. We introduce Lerna for the automated configuration of k-mer-based EC tools. Lerna first creates a language model (LM) of the uncorrected genomic reads, and then, based on this LM, calculates a metric called the perplexity metric to evaluate the corrected reads for different parameter choices. Next, it finds the one that produces the highest alignment rate without using a reference genome. The fundamental intuition of our approach is that the perplexity metric is inversely correlated with the quality of the assembly after error correction. Therefore, Lerna leverages the perplexity metric for automated tuning of k-mer sizes without needing a reference genome. First, we show that the best k-mer value can vary for different datasets, even for the same EC tool. This motivates our design that automates k-mer size selection without using a reference genome. Second, we show the gains of our LM using its component attention-based transformers. We show the model's estimation of the perplexity metric before and after error correction. The lower the perplexity after correction, the better the k-mer size. We also show that the alignment rate and assembly quality computed for the corrected reads are strongly negatively correlated with the perplexity, enabling the automated selection of k-mer values for better error correction, and hence, improved assembly quality. We validate our approach on both short and long reads. Additionally, we show that our attention-based models have significant runtime improvement for the entire pipeline-18[Formula: see text] faster than previous works, due to parallelizing the attention mechanism and the use of JIT compilation for GPU inferencing. Lerna improves de novo genome assembly by optimizing EC tools. Our code is made available in a public repository at: https://github.com/icanforce/lerna-genomics .

Sharma A ，Jain P ，Mahgoub A ，Zhou Z ，Mahadik K ，Chaterji S ... -  《BMC BIOINFORMATICS》