A comprehensive examination of Nanopore native RNA sequencing for characterization of complex transcriptomes, bioRxiv, 2019-03-12

AbstractA platform for highly parallel direct sequencing of native RNA strands was recently described by Oxford Nanopore Technologies (ONT); in order to assess overall performance in transcript-level investigations, the technology was applied for sequencing sets of synthetic transcripts as well as a yeast transcriptome. However, despite initial efforts it remains crucial to further investigate characteristics of ONT native RNA sequencing when applied to much more complex transcriptomes. Here we thus undertook extensive native RNA sequencing of polyA+ RNA from two human cell lines, and thereby analysed ~5.2 million aligned native RNA reads which consisted of a total of ~4.6 billion bases. To enable informative comparisons, we also performed relevant ONT direct cDNA- and Illumina-sequencing. We find that while native RNA sequencing does enable some of the anticipated advantages, key unexpected aspects 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.

biorxiv bioinformatics 100-200-users 2019

Clustering co-abundant genes identifies components of the gut microbiome that are reproducibly associated with colorectal cancer and inflammatory bowel disease, bioRxiv, 2019-03-06

AbstractBackgroundWhole-genome “shotgun” (WGS) metagenomic sequencing is an increasingly widely used tool for analyzing the metagenomic content of microbiome samples. While WGS data contains gene-level information, it can be challenging to analyze the millions of microbial genes which are typically found in microbiome experiments. To mitigate the ultrahigh dimensionality challenge of gene-level metagenomics, it has been proposed to cluster genes by co-abundance to form Co-Abundant Gene groups (CAGs). However, exhaustive co-abundance clustering of millions of microbial genes across thousands of biological samples has previously been intractable purely due to the computational challenge of performing trillions of pairwise comparisons.ResultsHere we present a novel computational approach to the analysis of WGS datasets in which microbial gene groups are the fundamental unit of analysis. We use the Approximate Nearest Neighbor heuristic for near-exhaustive average linkage clustering to group millions of genes by co-abundance. This results in thousands of high-quality CAGs representing complete and partial microbial genomes. We applied this method to publicly available WGS microbiome surveys and found that the resulting microbial CAGs associated with inflammatory bowel disease (IBD) and colorectal cancer (CRC) were highly reproducible and could be validated independently using multiple independent cohorts.ConclusionsThis powerful approach to gene-level metagenomics provides a powerful path forward for identifying the biological links between the microbiome and human health. By proposing a new computational approach for handling high dimensional metagenomics data, we identified specific microbial gene groups that are associated with disease that can be used to identify strains of interest for further preclinical and mechanistic experimentation.

biorxiv bioinformatics 100-200-users 2019

Compositional Data Analysis is necessary for simulating and analyzing RNA-Seq data, bioRxiv, 2019-03-02

Seq techniques (e.g. RNA-Seq) generate compositional datasets, i.e. the number of fragments sequenced is not proportional to the total RNA present. Thus, datasets carry only relative information, even though absolute RNA copy numbers are often of interest. Current normalization methods assume most features are not changing, which can lead to misleading conclusions when there are large shifts. However, there are few real datasets and no simulation protocols currently available that can directly benchmark methods when such large shifts occur.We present absSimSeq, an R package that simulates compositional data in the form of RNA-Seq reads. We tested several tools used for RNA-Seq differential analysis sleuth, DESeq2, edgeR, limma, sleuth and ALDEx2 (which explicitly takes a compositional approach). For these tools, we compared their standard normalization to either “compositional normalization”, which uses log-ratios to anchor the data on a set of negative control features, or RUVSeq, another tool that directly uses negative control features.We show that common normalizations result in reduced performance with current methods when there is a large change in the total RNA per cell. Performance improves when spike-ins are included and used by a compositional approach, even if the spike-ins have substantial variation. In contrast, RUVSeq, which normalizes count data rather than compositional data, has poor performance. Further, we show that previous criticisms of spike-ins did not take into account the compositional nature of the data. We conclude that absSimSeq can generate more representative datasets for testing performance, and that spike-ins should be more broadly used in a compositional manner to minimize misleading conclusions from differential analyses.

biorxiv bioinformatics 0-100-users 2019

 

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