A simple, cost-effective, and robust method for rRNA depletion in RNA-sequencing studies
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ABSTRACT:
ORGANISM(S): Escherichia coli Bacillus subtilis Caulobacter vibrioides
PROVIDER: GSE142656 | GEO | 2019/12/28
REPOSITORIES: GEO
Similar Datasets
Project description:A simple, cost-effective, and robust method for rRNA depletion in RNA-sequencing studies
| PRJNA597883 | ENA
Project description:RNA sequencing (RNA-seq) has become a standard method for quantifying gene expression transcriptome-wide. Due to the extremely high proportion of ribosomal RNA (rRNA) in total RNA, sequencing libraries usually incorporate messenger RNA (mRNA) enrichment. Although polyadenylate (poly(A)) tail selection is widely used, many applications require alternate approaches such as rRNA depletion. Recently, selective rRNA digestion, using RNaseH and antisense DNA oligomers that tile the length of target RNAs, has emerged as an easy, cost-effective alternative to commercial rRNA depletion kits. Here, we present a streamlined RNaseH-mediated rRNA depletion method that uses shorter antisense oligos that only sparsely tile the target RNA, in a digestion reaction of only 5 minutes. We wrote a Web tool, Oligo-ASST, that simplifies oligo design to favor target regions with optimal thermodynamic properties, and additionally allows users to design common oligo pools that can simultaneously target divergent RNAs in their regions of higher sequence similarity. We demonstrate the efficacy of these oligos by building rRNA-depleted sequencing libraries for Xenopus laevis as well as zebrafish, which expresses two distinct versions of the 28S, 18S, 5.8S, and 5S rRNAs during embryogenesis. These libraries efficiently deplete rRNA to <5% of total reads, on par with poly(A) selection, and also reveal expression of many non-adenylated RNA species. Oligo-ASST is freely available at https://mtleelab.pitt.edu/oligo to design antisense oligos for any taxon or to target any abundant RNA for depletion.
2020-06-28 | GSE152902 | GEO
Project description:The measurement of RNA abundance derived from massively parallel sequencing experiments is an essential technique. Methods that reduce ribosomal RNA levels are usually required prior to sequencing library construction because ribosomal RNA typically comprises >90% of the total RNA molecules in a sample. For some experiments, ribosomal RNA depletion is favored over poly(A) selection because it offers a more inclusive representation of the transcriptome. However, methods to deplete ribosomal RNA are generally proprietary, complex, inefficient, applicable to only specific species, or compatible with only a narrow range of RNA input levels. Here, we describe Ribo-Pop (ribosomal RNA depletion for popular use), a simple workflow and antisense oligo design strategy that we demonstrate works over a wide input range and can be easily adapted to any organism with a sequenced genome. We provide a computational pipeline for probe selection, a streamlined 20-minute protocol, and ready-to-use oligo sequences for several organisms. We anticipate that our simple and generalizable “open source” design strategy would enable virtually any lab to pursue full transcriptome sequencing in their organism of interest with minimal time and resources.
2020-08-12 | GSE150332 | GEO
Project description:Here, we compile valuable insights gathered over years of generating Ribo-seq datasets from different plant species and experimental setups. We tested the effects of variable ribonuclease treatments for the generation of ribosome protected fragments (RPFs). We tested rRNA depletion strategies designed specifically for Arabidopsis and Tobacco. We also compare ligation-free to ligation-based library preparation strategies for generating Ribo-seq libraries.
2024-07-05 | GSE226508 | GEO
Project description:This experiment was designed to improve the proportion of miRNA mapping reads from sRNA library sequencing, by reducing the amount of particularly abundant rRNA fragments.
2018-02-25 | GSE98832 | GEO
Project description:This experiment was designed to improve the percentage of miRNA mapping reads from sRNA library sequencing, by reducing the amount of one particularly abundant rRNA 30mer sequence.
2018-02-25 | GSE86313 | GEO
Project description:Ribo-Pop: Simple, cost-effective, and widely applicable ribosomal RNA depletion
| PRJNA631775 | ENA
Project description:The profiling of gene expression by RNA sequencing (RNA-seq) has enabled powerful studies of global transcriptional patterns in all organisms, including bacteria. Because the vast majority of RNA in bacteria is rRNA, it is standard practice to deplete the rRNA from a total RNA sample such that the reads in an RNA-seq experiment derive predominantly from mRNA. One of the most commonly used commercial kits for rRNA depletion, the Ribo-Zero kit from Illumina, was recently discontinued abruptly and for an extended period of time. Here, we report the development of a simple, cost-effective, and robust method for depleting rRNA that can be easily implemented by any lab or facility. We first developed an algorithm for designing biotinylated oligonucleotides that will hybridize tightly and specifically to the 23S, 16S, and 5S rRNAs from any species of interest. Precipitation of these oligonucleotides bound to rRNA by magnetic streptavidin-coated beads then depletes rRNA from a complex, total RNA sample such that ?75 to 80% of reads in a typical RNA-seq experiment derive from mRNA. Importantly, we demonstrate a high correlation of RNA abundance or fold change measurements in RNA-seq experiments between our method and the Ribo-Zero kit. Complete details on the methodology are provided, including open-source software for designing oligonucleotides optimized for any bacterial species or community of interest.IMPORTANCE The ability to examine global patterns of gene expression in microbes through RNA sequencing has fundamentally transformed microbiology. However, RNA-seq depends critically on the removal of rRNA from total RNA samples. Otherwise, rRNA would comprise upward of 90% of the reads in a typical RNA-seq experiment, limiting the reads coming from mRNA or requiring high total read depth. A commonly used kit for rRNA subtraction from Illumina was recently unavailable for an extended period of time, disrupting routine rRNA depletion. Here, we report the development of a "do-it-yourself" kit for rapid, cost-effective, and robust depletion of rRNA from total RNA. We present an algorithm for designing biotinylated oligonucleotides that will hybridize to the rRNAs from a target set of species. We then demonstrate that the designed oligonucleotides enable sufficient rRNA depletion to produce RNA-seq data with 75 to 80% of reads coming from mRNA. The methodology presented should enable RNA-seq studies on any species or metagenomic sample of interest.
| S-EPMC7175087 | biostudies-literature
Project description:A decade since its invention, single-cell RNA sequencing (scRNA-seq) has become a mainstay technology for profiling transcriptional heterogeneity in individual cells. Yet, most existing scRNA-seq methods capture only polyadenylated mRNA to avoid expending resources on profiling the types of transcripts that are usually not-of-interest, such as ribosomal RNA (rRNA). Hence, protocols that enable analysis of the whole transcriptome remain scarce. We adapted a method called DASH (Depletion of Abundant Sequences by Hybridisation) for rRNA depletion from single-cell total RNA-seq libraries. Our analyses show that with our single-cell DASH (scDASH), rRNAs are effectively depleted with minimal non-specificity. Importantly, the rest of the transcriptome is significantly enriched for detection as a result of liberated sequencing quota from depleted rRNA.
2020-10-31 | GSE158880 | GEO
Project description:Background: Genetic and genomic selection programs require large numbers of phenotypes observed for animals in shared environments. Direct measurements of phenotypes like meat quality, methane emission, and disease susceptibility are difficult and expensive to measure at scale but are critically important to livestock production. Our work leans on our understanding of the “Central Dogma” of molecular genetics to leverage molecular intermediates as cheaply-measured proxies of organism-level phenotypes. The rapidly declining cost of next-generation sequencing presents opportunities for population-level molecular phenotyping. While the cost of whole transcriptome sequencing has declined recently, its required sequencing depth still makes it an expensive choice for wide-scale molecular phenotyping. We aim to optimize 3′ mRNA sequencing (3′ mRNA-Seq) approaches for collecting cost-effective proxy molecular phenotypes for cattle from easy-to-collect tissue samples (i.e., whole blood). We used matched 3′ mRNA-Seq samples for 15 Holstein male calves in a heat stress trail to identify the 1) best library preparation kit (Takara SMART-Seq v4 3′ DE and Lexogen QuantSeq) and 2) optimal sequencing depth (0.5 to 20 million reads/sample) to capture gene expression phenotypes most cost-effectively. Results: Takara SMART-Seq v4 3′ DE outperformed Lexogen QuantSeq libraries across all metrics: number of quality reads, expressed genes, informative genes, differentially expressed genes, and 3′ biased intragenic variants. Serial downsampling analyses identified that as few as 8.0 million reads per sample could effectively capture most of the between-sample variation in gene expression. However, progressively more reads did provide marginal increases in recall across metrics. These 3′ mRNA-Seq reads can also capture animal genotypes that could be used as the basis for downstream imputation. The 10 million read downsampled groups called an average of 104,386 SNPs and 20,131 INDELs, many of which segregate at moderate minor allele frequencies in the population. Conclusion: This work demonstrates that 3′ mRNA-Seq with Takara SMART-Seq v4 3′ DE can provide an incredibly cost-effective (<$25/sample) approach to quantifying molecular phenotypes (gene expression) while discovering sufficient variation for use in genotype imputation. Ongoing work is evaluating the accuracy of imputation and the ability of much larger datasets to predict individual animal phenotypes.
2024-07-22 | GSE272596 | GEO