Yeo, K. | Search bioRxiv

bioRxiv2024

A comparison between full-length 16S rRNA Oxford Nanopore sequencing and Illumina V3-V4 16S rRNA sequencing in head and neck cancer tissues

IntroductionDescribing the microbial community within the tumour has been a key aspect in understanding the pathophysiology of the tumour microenvironment. In head and neck cancer (HNC), most studies on tissue samples have only performed 16S ribosomal RNA (rRNA) short-read sequencing (SRS) on V3-V5 region. SRS is mostly limited to genus level identification. In this study, we compared full-length 16S rRNA long-read sequencing (FL-ONT) from Oxford Nanopore Technology (ONT) to V3-V4 Illumina SRS (V3V4-Illumina). To date, this is the largest study using HNC tissues samples to perform FL-ONT of the 16S rRNA using ONT. MethodsSequencing of the full-length and the V3-V4 16S rRNA region was conducted on tumour samples from 26 HNC patients, using ONT and Illumina technologies respectively. Paired sample analysis was applied to compare differences in diversities and abundance of microbial communities. Further validation was also performed using culture-based methods in 16 bacterial isolates obtained from 4 patients using MALDI-TOF MS. ResultsWe observed similar alpha diversity indexes between FL-ONT and V3V4-Illumina technologies. However, beta-diversity was significantly different between techniques (PERMANOVA - R2 = 0.083, p < 0.0001). At higher taxonomic levels (Phylum to Family), all metrics were more similar among sequencing techniques, while lower taxonomy displayed more discrepancies. At higher taxonomic levels, correlation in microbial abundance from FL-ONT and V3V4-Illumina were higher, while this correlation decreased at lower levels. Finally, FL-ONT was able to identify more isolates at the species level that were identified using MALDI-TOF MS (81.3% v.s. 62.5%). ConclusionsFL-ONT was able to identify lower taxonomic levels at a better resolution as compared to V3V4-Illumina 16S rRNA sequencing. Depending on application purposes, both methods are suitable for identification of microbial communities, with FL-ONT being more superior at species level.

microbiology↗

bioRxiv2022

Context-Aware Transcript Quantification from Long Read RNA-Seq data with Bambu

Most approaches to transcript quantification rely on fixed reference annotations. However, the transcriptome is dynamic, and depending on the context, such static annotations contain inactive isoforms for some genes while they are incomplete for others. To address this, we have developed Bambu, a method that performs machine-learning based transcript discovery to enable quantification specific to the context of interest using long-read RNA-Seq data. To identify novel transcripts, Bambu employs a precision-focused threshold referred to as the novel discovery rate (NDR), which replaces arbitrary per-sample thresholds with a single interpretable parameter. Bambu retains the full-length and unique read counts, enabling accurate quantification in presence of inactive isoforms. Compared to existing methods for transcript discovery, Bambu achieves greater precision without sacrificing sensitivity. We show that context-aware annotations improve abundance estimates for both novel and known transcripts. We apply Bambu to human embryonic stem cells to quantify isoforms from repetitive HERVH-LTR7 retrotransposons, demonstrating the ability to estimate transcript expression specific to the context of interest.

bioinformatics↗