RATTLE: reference-free reconstruction and quantification of transcriptomes from Nanopore sequencing.
Ontology highlight
ABSTRACT:
SUBMITTER: de la Rubia I
PROVIDER: S-EPMC9264490 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The genomes of DNA viruses encode deceptively complex transcriptomes evolved to maximize coding potential within the confines of a relatively small genome. Defining the full range of viral RNAs produced during an infection is key to understanding the viral replication cycle and its interactions with the host cell. Traditional short-read (Illumina) sequencing approaches are problematic in this setting due to the difficulty of assigning short reads to individual RNAs in regions of transcript overlap and to the biases introduced by the required recoding and amplification steps. Additionally, different methodologies may be required to analyze the 5' and 3' ends of RNAs, which increases both cost and effort. The advent of long-read nanopore sequencing simplifies this approach by providing a single assay that captures and sequences full length RNAs, either in cDNA or native RNA form. The latter is particularly appealing as it reduces known recoding biases whilst allowing more advanced analyses such as estimation of poly(A) tail length and the detection of RNA modifications including N6 -methyladenosine. Using herpes simplex virus (HSV)-infected primary fibroblasts as a template, we provide a step-by-step guide to the production of direct RNA sequencing libraries suitable for sequencing using Oxford Nanopore Technologies platforms and provide a simple computational approach to deriving a high-quality annotation of the HSV transcriptome from the resulting sequencing data. © 2020 by John Wiley & Sons, Inc. Basic Protocol 1: Productive infection of primary fibroblasts with herpes simplex virus Support Protocol: Cell passage and plating of primary fibroblasts Basic Protocol 2: Preparation and sequencing of dRNA-seq libraries from virus-infected cells Basic Protocol 3: Processing, alignment, and analysis of dRNA-seq datasets.
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Project description:The transcriptomes of Pseudomonas aeruginosa clone C isolates NN2 and SG17M during the mid-exponential and early stationary phases of planktonic growth were evaluated by direct RNA sequencing on the nanopore platform and compared with established short-read cDNA sequencing on the Illumina platform. Fifty to ninety percent of the sense RNAs turned out to be rRNA molecules, followed by similar proportions of mRNA transcripts and noncoding RNAs. The two platforms detected similar proportions of uncharged tRNAs and 29 yet-undescribed antisense tRNAs. For example, the rarest arginine codon was paired with the most abundant tRNAArg, and the tRNAArg gene is missing for the most frequent arginine codon. More than 90% of the antisense RNA molecules were complementary to a coding sequence. The antisense RNAs were evenly distributed in the genomes. Direct RNA sequencing identified more than 4,000 distinct nonoverlapping antisense RNAs during exponential and stationary growth. Besides highly expressed small antisense RNAs less than 200 bases in size, a population of longer antisense RNAs was sequenced that covered a broad range (a few hundred to thousands of bases) and could be complementary to a contig of several genes. In summary, direct RNA sequencing identified yet-undescribed RNA molecules and an unexpected composition of the pools of tRNAs and sense and antisense RNAs. IMPORTANCE Genome-wide gene expression of bacteria is commonly studied by high-throughput sequencing of size-selected cDNA fragment libraries of reverse-transcribed RNA preparations. However, the depletion of rRNAs, enzymatic reverse transcription, and the fragmentation, size selection, and amplification during library preparation lead to inevitable losses of information about the initial composition of the RNA pool. We demonstrate that direct RNA sequencing on the Nanopore platform can overcome these limitations. Nanopore sequencing of total RNA yielded novel insights into the Pseudomonas aeruginosa transcriptome that-if replicated in other species-will change our view of the bacterial RNA world. The discovery of sense-antisense pairs of transfer-messenger RNA (tmRNA), tRNAs, and mRNAs indicates a further and unknown level of gene regulation in bacteria.
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Project description:Despite the rapid advance in single-cell RNA sequencing (scRNA-seq) technologies within the last decade, single-cell transcriptome analysis workflows have primarily used gene expression data while isoform sequence analysis at the single-cell level still remains fairly limited. Detection and discovery of isoforms in single cells is difficult because of the inherent technical shortcomings of scRNA-seq data, and existing transcriptome assembly methods are mainly designed for bulk RNA samples. To address this challenge, we developed RNA-Bloom, an assembly algorithm that leverages the rich information content aggregated from multiple single-cell transcriptomes to reconstruct cell-specific isoforms. Assembly with RNA-Bloom can be either reference-guided or reference-free, thus enabling unbiased discovery of novel isoforms or foreign transcripts. We compared both assembly strategies of RNA-Bloom against five state-of-the-art reference-free and reference-based transcriptome assembly methods. In our benchmarks on a simulated 384-cell data set, reference-free RNA-Bloom reconstructed 37.9%-38.3% more isoforms than the best reference-free assembler, whereas reference-guided RNA-Bloom reconstructed 4.1%-11.6% more isoforms than reference-based assemblers. When applied to a real 3840-cell data set consisting of more than 4 billion reads, RNA-Bloom reconstructed 9.7%-25.0% more isoforms than the best competing reference-based and reference-free approaches evaluated. We expect RNA-Bloom to boost the utility of scRNA-seq data beyond gene expression analysis, expanding what is informatically accessible now.
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Project description:We describe ReorientExpress, a method to perform reference-free orientation of transcriptomic long sequencing reads. ReorientExpress uses deep learning to correctly predict the orientation of the majority of reads, and in particular when trained on a closely related species or in combination with read clustering. ReorientExpress enables long-read transcriptomics in non-model organisms and samples without a genome reference without using additional technologies and is available at https://github.com/comprna/reorientexpress.
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Project description:A platform for highly parallel direct sequencing of native RNA strands was recently described by Oxford Nanopore Technologies, but despite initial efforts it remains crucial to further investigate the technology for quantification of complex transcriptomes. Here we undertake native RNA sequencing of polyA + RNA from two human cell lines, analysing ~5.2 million aligned native RNA reads. To enable informative comparisons, we also perform relevant ONT direct cDNA- and Illumina-sequencing. We find that while native RNA sequencing does enable some of the anticipated advantages, key unexpected aspects currently hamper its performance, most notably the quite frequent inability to obtain full-length transcripts from single reads, as well as difficulties to unambiguously infer their true transcript of origin. While characterising issues that need to be addressed when investigating more complex transcriptomes, our study highlights that with some defined improvements, native RNA sequencing could be an important addition to the mammalian transcriptomics toolbox.
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Project description:Currently, one of the fastest-growing DNA sequencing technologies is nanopore sequencing. One of the key stages involved in processing sequencer data is the basecalling process, where the input sequence of currents measured on the nanopores of the sequencer reproduces the DNA sequences, called DNA reads. Many of the applications dedicated to basecalling, together with the DNA sequence, provide the estimated quality of the reconstruction of a given nucleotide (quality symbols are contained on every fourth line of the FASTQ file; each nucleotide in the FASTQ file corresponds to exactly one estimated nucleotide reconstruction quality symbol). Herein, we compare the estimated nucleotide reconstruction quality symbols (signs from every fourth line of the FASTQ file) reported by other basecallers. The conducted experiments consisted of basecalling the same raw datasets from the nanopore device by other basecallers and comparing the provided quality symbols, denoting the estimated quality of the nucleotide reconstruction. The results show that the estimated quality reported by different basecallers may vary, depending on the tool used, particularly in terms of range and distribution. Moreover, we mapped basecalled DNA reads to reference genomes and calculated matched and mismatched rates for groups of nucleotides with the same quality symbol. Finally, the presented paper shows that the estimated nucleotide reconstruction quality reported in the basecalling process is not used in any investigated tool for processing nanopore DNA reads.
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