Mitochondrial mRNA is a stable and convenient reference for the normalization of RNA-seq data

The normalization of high-throughput RNA sequencing (RNA-seq) data is needed to accurately analyze gene expression levels. Traditional normalization methods can either correct the differences in sequencing depth, or correct both the sequencing depth and other unwanted variations introduced during sequencing library preparation through exogenous spike-ins1-4. However, the exogenous spike-ins are prone to variation5,6. Therefore, a better normalization approach with a more appropriate reference is an ongoing demand. In this study, we demonstrated that mitochondrial mRNA (mRNA encoded by mitochondria genome) can serve as a steady endogenous reference for RNA-seq data analysis, and performs better than exogenous spike-ins. We also found that using mitochondrial mRNA as a reference can reduce batch effects for RNA-seq data. These results provide a simple and practical normalization strategy for RNA-seq data, which will serve as a valuable tool widely applicable to transcriptomic studies.

Methyltransferase-directed orthogonal tagging and sequencing of miRNAs and bacterial small RNAs

Background Targeted installation of designer chemical moieties on biopolymers provides an orthogonal means for their visualisation, manipulation and sequence analysis. Although high-throughput RNA sequencing is a widely used method for transcriptome analysis, certain steps, such as 3′ adapter ligation in strand-specific RNA sequencing, remain challenging due to structure- and sequence-related biases introduced by RNA ligases, leading to misrepresentation of particular RNA species. Here, we remedy this limitation by adapting two RNA 2′-O-methyltransferases from the Hen1 family for orthogonal chemo-enzymatic click tethering of a 3′ sequencing adapter that supports cDNA production by reverse transcription of the tagged RNA. Results We showed that the ssRNA-specific DmHen1 and dsRNA-specific AtHEN1 can be used to efficiently append an oligonucleotide adapter to the 3′ end of target RNA for sequencing library preparation. Using this new chemo-enzymatic approach, we identified miRNAs and prokaryotic small non-coding sRNAs in probiotic Lactobacillus casei BL23. We found that compared to a reference conventional RNA library preparation, methyltransferase-Directed Orthogonal Tagging and RNA sequencing, mDOT-seq, avoids misdetection of unspecific highly-structured RNA species, thus providing better accuracy in identifying the groups of transcripts analysed. Our results suggest that mDOT-seq has the potential to advance analysis of eukaryotic and prokaryotic ssRNAs. Conclusions Our findings provide a valuable resource for studies of the RNA-centred regulatory networks in Lactobacilli and pave the way to developing novel transcriptome and epitranscriptome profiling approaches in vitro and inside living cells. As RNA methyltransferases share the structure of the AdoMet-binding domain and several specific cofactor binding features, the basic principles of our approach could be easily translated to other AdoMet-dependent enzymes for the development of modification-specific RNA-seq techniques.

Evaluation of whole-genome DNA methylation sequencing library preparation protocols

Background With rapidly dropping sequencing cost, the popularity of whole-genome DNA methylation sequencing has been on the rise. Multiple library preparation protocols currently exist. We have performed 22 whole-genome DNA methylation sequencing experiments on snap frozen human samples, and extensively benchmarked common library preparation protocols for whole-genome DNA methylation sequencing, including three traditional bisulfite-based protocols and a new enzyme-based protocol. In addition, different input DNA quantities were compared for two kits compatible with a reduced starting quantity. In addition, we also present bioinformatic analysis pipelines for sequencing data from each of these library types. Results An assortment of metrics were collected for each kit, including raw read statistics, library quality and uniformity metrics, cytosine retention, and CpG beta value consistency between technical replicates. Overall, the NEBNext Enzymatic Methyl-seq and Swift Accel-NGS Methyl-Seq kits performed quantitatively better than the other two protocols. In addition, the NEB and Swift kits performed well at low-input amounts, validating their utility in applications where DNA is the limiting factor. Results The NEBNext Enzymatic Methyl-seq kit appeared to be the best option for whole-genome DNA methylation sequencing of high-quality DNA, closely followed by the Swift kit, which potentially works better for degraded samples. Further, a general bioinformatic pipeline is applicable across the four protocols, with the exception of extra trimming needed for the Swift Biosciences’s Accel-NGS Methyl-Seq protocol to remove the Adaptase sequence.

Evaluation of Whole-Genome DNA Methylation Sequencing Library Preparation Protocols

Background: With rapidly dropping sequencing cost, the popularity of whole-genome DNA methylation sequencing has been on the rise. Multiple library preparation protocols exist, but a systematic evaluation and benchmarking of their performance against each other is currently lacking. We have performed 22 whole-genome DNA methylation sequencing experiments on fresh frozen human samples, and extensively benchmarked common library preparation protocols for whole-genome DNA methylation sequencing, including three traditional bisulfite-based protocols and a new enzyme-based protocol. Additionally, different input DNA quantities were compared for two kits compatible with a reduced starting quantity. In addition, we also present bioinformatic analysis pipelines for sequencing data from each of these library types. Results: An assortment of metrics were collected for each kit, including raw read statistics, library quality and uniformity metrics, cytosine retention, and CpG beta value consistency between technical replicates. Overall, the NEBNext Enzymatic Methyl-seq kit performed quantitatively better than the other three protocols at two different DNA input amounts. Additionally, the results for the different input amounts were generally consistent across all metrics. Conclusions: Based on these results, we recommend use of the NEBNext Enzymatic Methyl-seq kit for whole-genome DNA methylation sequencing. Further, a general bioinformatic pipeline is applicable across the four protocols, with the exception of extra trimming needed for the Swift Bioscience's Accel-NGS Methyl-Seq protocol to remove the Adaptase sequence.

Obtaining deeper insights into microbiome diversity using a simple method to block host and non-targets in amplicon sequencing

Microbiome profiling is revolutionizing our understanding of biological mechanisms such as metaorganismal (host+microbiome) assembly, functions and adaptation. Amplicon sequencing of multiple conserved, phylogenetically informative loci is an instrumental tool for characterization of the highly diverse microbiomes of natural systems. Investigations in many study systems are hindered by loss of essential sequencing depth due to amplification of non-target DNA from hosts or overabundant microorganisms. This issue requires urgent attention to address ecologically relevant problems using high throughput, high resolution microbial profiling. Here, we introduce a simple, low cost and highly flexible method using standard oligonucleotides (“blocking oligos”) to block amplification of non-targets and an R package to aid in their design. They can be dropped into practically any two-step amplicon sequencing library preparation pipeline. We apply them in leaves, a system presenting exceptional challenges with host and non-target microbial amplification. Blocking oligos designed for use in eight target loci reduce undesirable amplification of host and non-target microbial DNA by up to 90%. In addition, 16S and 18S “universal” plant blocking oligos efficiently block most plant hosts, leading to increased microbial alpha diversity discovery without biasing beta diversity measurements. By blocking only chloroplast 16S amplification, we show that blocking oligos do not compromise quantitative microbial load information inherent to plant-associated amplicon sequencing data. Using these tools, we generated a near-complete survey of the Arabidopsis thaliana leaf microbiome based on diversity data from eight loci and discuss complementarity of commonly used amplicon sequencing regions for describing leaf microbiota. The blocking oligo approach has potential to make new questions in a variety of study systems more tractable by making amplicon sequencing more targeted, leading to deeper, systems-based insights into microbial discovery.