De-biasing microbiome sequencing data: bacterial morphology-based correction of extraction bias and correlates of chimera formation
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ABSTRACT:
PROVIDER: PRJEB67827 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| ERR12157629_1.fastq.gz | Fastqsanger.gz | |||
| ERR12157629_2.fastq.gz | Fastqsanger.gz | |||
| ERR12157630_1.fastq.gz | Fastqsanger.gz | |||
| ERR12157630_2.fastq.gz | Fastqsanger.gz | |||
| ERR12157631_1.fastq.gz | Fastqsanger.gz |
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Project description:IntroductionMicrobiome amplicon sequencing data are distorted by multiple protocol-dependent biases from bacterial DNA extraction, contamination, sequence errors, and chimeras, hindering clinical microbiome applications. In particular, extraction bias is a major confounder in sequencing-based microbiome analyses, with no correction method available to date. Here, we suggest using mock community controls to computationally correct extraction bias based on bacterial morphological properties.MethodsWe compared dilution series of 3 cell mock communities with an even or staggered composition. DNA of these mock, and additional skin microbiome samples, was extracted with 8 different extraction protocols (2 buffers, 2 extraction kits, 2 lysis conditions). Extracted DNA was sequenced (V1-V3 16S rRNA gene) together with corresponding DNA mocks.ResultsMicrobiome composition was significantly different between extraction kits and lysis conditions, but not between buffers. Independent of the extraction protocol, chimera formation increased with higher input cell numbers. Contaminants originated mostly from buffers, and considerable cross-contamination was observed in low-input samples. Comparing the microbiome composition of the cell mocks to corresponding DNA mocks revealed taxon-specific protocol-dependent extraction bias. Strikingly, this extraction bias per species was predictable by bacterial cell morphology. Morphology-based computational correction of extraction bias significantly improved resulting microbial compositions when applied to different mock samples, even with different taxa. Equivalent correction of the skin samples showed a substantial impact on microbiome compositions.ConclusionsOur results indicate that higher DNA density increases chimera formation during PCR amplification. Furthermore, we show that computational correction of extraction bias based on bacterial cell morphology would be feasible using appropriate positive controls, thus constituting an important step toward overcoming protocol biases in microbiome analysis. Video Abstract.
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Project description:Background: During the analysis of ripening related gene expression in tomato fruit, we observed a bias towards certain classes of messenger RNA based upon the type of buffer used in the initial step of the extraction procedure. We postulated that there was a functional association of the separated transcripts. To test this hypothesis, we carried out extractions from the same tissue sample where only the buffer varied. Transcripts were hybridised to Affymetrix oligo arrays and the data was analysed according to the predicted cellular component of the encoded proteins. Results: The use of an extraction buffer that lacked high levels of caeotropic agents resulted in a reduction of mRNAs encoding proteins that were either secreted or were predicted to be routed through the endomembrane system and hence could have been bound to the endoplasmic reticulum in polyribosomes. Extraction of the cell debris immediately following the initial buffer based extraction and subsequent micro array analysis revealed that the expected transcripts could be recovered. This demonstrated that the buffer was separating the mRNAs based on the cellular component of the encoded proteins. Analysis of selected transcripts by northern hybridisation again supported this theory. Conclusions: Some traditional buffers used for fruit RNA extraction selectively deplete for transcripts encoding proteins that are membrane-associated or secreted. This can be explained if polyribosomes that are bound to the endoplasmic reticulum (ER) are not effectively disrupted when the extraction buffer lacks either detergent or organic solvents. These findings have important implications with respect to experimental bias, as well as opportunities for message enrichment and protein characterisation.
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Project description:Thermostable group II intron reverse transcriptases (TGIRTs) with high fidelity and processivity have been used for a variety of RNA sequencing (RNA-seq) applications, including comprehensive profiling of whole-cell, exosomal, and human plasma RNAs; quantitative tRNA-seq based on the ability of TGIRT enzymes to give full-length reads of tRNAs and other structured small ncRNAs; high-throughput mapping of post-transcriptional modifications; and RNA structure mapping. Here, we improved TGIRT-seq methods for comprehensive transcriptome profiling by rationally designing RNA-seq adapters that minimize adapter dimer formation. Additionally, we developed biochemical and computational methods for remediating 5'- and 3'-end biases, the latter based on a random forest regression model that provides insight into the contribution of different factors to these biases. These improvements, some of which may be applicable to other RNA-seq methods, increase the efficiency of TGIRT-seq library construction and improve coverage of very small RNAs, such as miRNAs. Our findings provide insight into the biochemical basis of 5'- and 3'-end biases in RNA-seq and suggest general approaches for remediating biases and decreasing adapter dimer formation.
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Project description:mRNA abundance microarrays are the most widely used high-throughput technology for functional genomics. But despite this extensive characterization, and despite their wide-spread adoption in discovery research, the uptake of microarrays as clinical diagnostic tools remains relatively limited due to sample degradation. Recently, the NuGEN WT-Ovation Pico kit was developed to resolve this problem. Here, we comprehensively evaluate its utility on two separate biological problems. First, we evaluate its effect on 12 malignant and 2 normal cervix samples. Second, we evaluate its effect on 2 normal kidney samples. In each case, we directly compare aliquots of the same RNA sample that are prepared by standard and low-input/low-RIN protocols to address three key questions. First, does the use of a low-input/degraded-sample protocol alter transcriptional profiles either globally or of specific genes? Second, do these changes alter biological conclusions drawn from the dataset? Third, are these changes biased towards specific classes of genes, either in terms of their sequence characteristics (e.g. length, GC-content) or their biological function?
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