April 21, 2020  |  

A comprehensive evaluation of long read error correction methods

Motivation: Third-generation sequencing technologies can sequence long reads, which is advancing the frontiers of genomics research. However, their high error rates prohibit accurate and efficient downstream analysis. This difficulty has motivated the development of many long read error correction tools, which tackle this problem through sampling redundancy and/or leveraging accurate short reads of the same biological samples. Existing studies to asses these tools use simulated data sets, and are not sufficiently comprehensive in the range of software covered or diversity of evaluation measures used. Results: In this paper, we present a categorization and review of long read error correction methods, and provide a comprehensive evaluation of the corresponding long read error correction tools. Leveraging recent real sequencing data, we establish benchmark data sets and set up evaluation criteria for a comparative assessment which includes quality of error correction as well as run-time and memory usage. We study how trimming and long read sequencing depth affect error correction in terms of length distribution and genome coverage post-correction, and the impact of error correction performance on an important application of long reads, genome assembly. We provide guidelines for practitioners for choosing among the available error correction tools and identify directions for future research.


April 21, 2020  |  

Single-Molecule Sequencing: Towards Clinical Applications.

In the past several years, single-molecule sequencing platforms, such as those by Pacific Biosciences and Oxford Nanopore Technologies, have become available to researchers and are currently being tested for clinical applications. They offer exceptionally long reads that permit direct sequencing through regions of the genome inaccessible or difficult to analyze by short-read platforms. This includes disease-causing long repetitive elements, extreme GC content regions, and complex gene loci. Similarly, these platforms enable structural variation characterization at previously unparalleled resolution and direct detection of epigenetic marks in native DNA. Here, we review how these technologies are opening up new clinical avenues that are being applied to pathogenic microorganisms and viruses, constitutional disorders, pharmacogenomics, cancer, and more.Copyright © 2018 Elsevier Ltd. All rights reserved.


April 21, 2020  |  

Computational aspects underlying genome to phenome analysis in plants.

Recent advances in genomics technologies have greatly accelerated the progress in both fundamental plant science and applied breeding research. Concurrently, high-throughput plant phenotyping is becoming widely adopted in the plant community, promising to alleviate the phenotypic bottleneck. While these technological breakthroughs are significantly accelerating quantitative trait locus (QTL) and causal gene identification, challenges to enable even more sophisticated analyses remain. In particular, care needs to be taken to standardize, describe and conduct experiments robustly while relying on plant physiology expertise. In this article, we review the state of the art regarding genome assembly and the future potential of pangenomics in plant research. We also describe the necessity of standardizing and describing phenotypic studies using the Minimum Information About a Plant Phenotyping Experiment (MIAPPE) standard to enable the reuse and integration of phenotypic data. In addition, we show how deep phenotypic data might yield novel trait-trait correlations and review how to link phenotypic data to genomic data. Finally, we provide perspectives on the golden future of machine learning and their potential in linking phenotypes to genomic features. © 2018 The Authors The Plant Journal published by John Wiley & Sons Ltd and Society for Experimental Biology.


April 21, 2020  |  

Full-length transcriptome sequences obtained by a combination of sequencing platforms applied to heat shock proteins and polyunsaturated fatty acids biosynthesis in Pyropia haitanensis

Pyropia haitanensis is a high-yield commercial seaweed in China. Pyropia haitanensis farms often suffer from problems such as severe germplasm degeneration, while the mechanisms underlying resistance to abiotic stresses remain unknown because of lacking genomic information. Although many previous studies focused on using next-generation sequencing (NGS) technologies, the short-read sequences generated by NGS generally prevent the assembly of full-length transcripts, and then limit screening functional genes. In the present study, which was based on hybrid sequencing (NGS and single-molecular real-time sequencing) of the P. haitanensis thallus transcriptome, we obtained high-quality full-length transcripts with a mean length of 2998 bp and an N50 value of 3366 bp. A total of 14,773 unigenes (93.52%) were annotated in at least one database, while approximately 60% of all unigenes were assembled by short Illumina reads. Moreover, we herein suggested that the genes involved in the biosynthesis of polyunsaturated fatty acids and heat shock proteins play an important role in the process of development and resistance to abiotic stresses in P. haitanensis. The present study, together with previously published ones, may facilitate seaweed transcriptome research.


April 21, 2020  |  

Assignment of virus and antimicrobial resistance genes to microbial hosts in a complex microbial community by combined long-read assembly and proximity ligation.

We describe a method that adds long-read sequencing to a mix of technologies used to assemble a highly complex cattle rumen microbial community, and provide a comparison to short read-based methods. Long-read alignments and Hi-C linkage between contigs support the identification of 188 novel virus-host associations and the determination of phage life cycle states in the rumen microbial community. The long-read assembly also identifies 94 antimicrobial resistance genes, compared to only seven alleles in the short-read assembly. We demonstrate novel techniques that work synergistically to improve characterization of biological features in a highly complex rumen microbial community.


April 21, 2020  |  

A comparative evaluation of hybrid error correction methods for error-prone long reads.

Third-generation sequencing technologies have advanced the progress of the biological research by generating reads that are substantially longer than second-generation sequencing technologies. However, their notorious high error rate impedes straightforward data analysis and limits their application. A handful of error correction methods for these error-prone long reads have been developed to date. The output data quality is very important for downstream analysis, whereas computing resources could limit the utility of some computing-intense tools. There is a lack of standardized assessments for these long-read error-correction methods.Here, we present a comparative performance assessment of ten state-of-the-art error-correction methods for long reads. We established a common set of benchmarks for performance assessment, including sensitivity, accuracy, output rate, alignment rate, output read length, run time, and memory usage, as well as the effects of error correction on two downstream applications of long reads: de novo assembly and resolving haplotype sequences.Taking into account all of these metrics, we provide a suggestive guideline for method choice based on available data size, computing resources, and individual research goals.


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