The blooming of long-read sequencing reforms biomedical research

Au, Kin Fai

doi:10.1186/s13059-022-02604-2

Cited by 11 publications

(12 citation statements)

References 18 publications

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“…Furthermore, long-read RNA-Seq allows for the straightforward identification of variants that are inherited together as haplotypes. 7, 8 The identified loci of these haplotypes can then be overlaid with short-read RNA-Seq reads. This integration provides the read counts of nucleotides existing at each locus.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Allele Specific Expression Resource for the Equine Transcriptome

Heath,

Peng,

Szmatola

et al. 2024

Preprint

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BackgroundAllele-specific expression (ASE) analysis provides a nuanced view of cis-regulatory mechanisms affecting gene expression.ResultsIn this work, we introduce and highlight the significance of an equine ASE analysis, containing integrated long- and short-read RNA sequencing data, along with insight from histone modification data, from four healthy Thoroughbreds (2 mares and 2 stallions) across 9 tissues.ConclusionsThis valuable publicly accessible resource is poised to facilitate investigations into regulatory variation in equine tissues and foster a deeper understanding of the impact of allelic imbalance in equine health and disease at the molecular level.

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Section: Introductionmentioning

confidence: 99%

A Comprehensive Allele Specific Expression Resource for the Equine Transcriptome

Heath,

Peng,

Szmatola

et al. 2024

Preprint

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“…Long-read sequencing technology has come of age, and was recently deemed the 2022 method of the year. Indeed, it is rapidly becoming the preferred choice for genomic and metagenomic analyses over short-read sequencing technologies (Au 2022;Amarasinghe et al 2020). In contrast to shorter sequence fragments, longer reads offer improved resolution and accuracy across several tasks such as de novo assembly (De Maio et al 2019;Marx 2021), structural variant identification (Liu et al 2022b;Mahmoud et al 2019), metagenomic assembly (Kolmogorov et al 2020), and microbiome analyses (Curry et al 2022;Dilthey et al 2019).…”

Section: Introductionmentioning

confidence: 99%

SeqScreen-Nano: a computational platform for rapid, in-field characterization of previously unseen pathogens

Balaji

Liu

Nute

et al. 2023

Preprint

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The COVID-19 pandemic forever underscored the need for biosurveillance platforms capable of rapid detection of previously unseen pathogens. Oxford Nanopore Technology (ONT) couples long-read sequencing with in-field capability, opening the door to real-time, in-field biosurveillance. Though a promising technology, streaming assignment of accurate functional and taxonomic labels with nanopore reads remains challenging given: (i) individual reads can span multiple genes, (ii) individual reads may contain truncated genes, and pseudogenes, (iii) the error rate of the ONT platform that may introduce frameshifts and missense errors, and (iv) the computational costs of read-by-read analysis may exceed that of in-field computational equipment. Altogether, these challenges highlight a need for novel computational approaches. To this end, we describe SeqSeqscreen-Nano, a novel and portable computational platform for the characterization of novel pathogens. Based on results from simulated and synthetic microbial communities, SeqScreen-Nano can identify Open Reading Frames (ORFs) across the length of raw ONT reads and then use the predicted ORFs for accurate functional characterization and taxonomic classification. SeqScreen-Nano can run efficiently in a memory-constrained environment (less than 32GB of RAM), allowing it to be utilized in resource-limited settings. SeqScreen-Nano can also process reads directly from the ONT MinION sequencing device, enabling rapid, in-field characterization of previously unseen pathogens. SeqScreen-Nano (v4.0) is available on GitLab at:https://gitlab.com/treangenlab/seqscreen

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“…PacBio and ONT sequencing have been successfully used for resolving and refining genome assemblies and expanding transcriptome characterization in a variety of species [ 12 , 13 , 14 , 15 , 16 , 17 ]. Long-read sequencing technologies have also been applied in diagnostics, and epigenetic and epitranscriptomic studies [ 18 , 19 , 20 ]. Therefore, the irruption of third-generation sequencers, with the possibility to obtain long, theoretically full-length reads is improving the quality of genome assemblies and transcriptomes.…”

Section: Introductionmentioning

confidence: 99%

The Integration of Data from Different Long-Read Sequencing Platforms Enhances Proteoform Characterization in Arabidopsis

García-Campa

Valledor

Pascual

2023

Plants

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The increasing availability of massive omics data requires improving the quality of reference databases and their annotations. The combination of full-length isoform sequencing (Iso-Seq) with short-read transcriptomics and proteomics has been successfully used for increasing proteoform characterization, which is a main ongoing goal in biology. However, the potential of including Oxford Nanopore Technologies Direct RNA Sequencing (ONT-DRS) data has not been explored. In this paper, we analyzed the impact of combining Iso-Seq- and ONT-DRS-derived data on the identification of proteoforms in Arabidopsis MS proteomics data. To this end, we selected a proteomics dataset corresponding to senescent leaves and we performed protein searches using three different protein databases: AtRTD2 and AtRTD3, built from the homonymous transcriptomes, regarded as the most complete and up-to-date available for the species; and a custom hybrid database combining AtRTD3 with publicly available ONT-DRS transcriptomics data generated from Arabidopsis leaves. Our results show that the inclusion and combination of long-read sequencing data from Iso-Seq and ONT-DRS into a proteogenomic workflow enhances proteoform characterization and discovery in bottom-up proteomics studies. This represents a great opportunity to further investigate biological systems at an unprecedented scale, although it brings challenges to current protein searching algorithms.

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The blooming of long-read sequencing reforms biomedical research

Cited by 11 publications

References 18 publications

A Comprehensive Allele Specific Expression Resource for the Equine Transcriptome

A Comprehensive Allele Specific Expression Resource for the Equine Transcriptome

SeqScreen-Nano: a computational platform for rapid, in-field characterization of previously unseen pathogens

The Integration of Data from Different Long-Read Sequencing Platforms Enhances Proteoform Characterization in Arabidopsis

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