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Targeted transcript profiling by sequencing.

ABSTRACT: In this work we present a targeted gene expression strategy employing trinucleotide threading (TnT) amplification and massive parallel sequencing. We have previously shown that TnT combined with array readout accurately monitors expression levels. However, with this detection strategy spurious products go undetected. Accordingly, we adapted the TnT protocol to massive parallel sequencing to acquire an unbiased view of the entire TnT-generated product population. In this manner we investigated the identity of undesired products, their extent at different oligonucleotide:RNA ratios and their effect on the expression levels. We demonstrate that TnT gene expression profiling with massive sequencing readout renders reliable expression data from as low as 3.5 ng of total RNA. Moreover, using 350 ng of total RNA results in only 0.7% to 1.1% undesired products. When lowering the amount of input material, the undesired product fraction increases but this does not influence the expression profiles.

SUBMITTER: Zajac P

PROVIDER: S-EPMC3492873 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Targeted transcript profiling by sequencing.

Zajac Pawel P Ahmadian Afshin A

Scientific reports 20121108

In this work we present a targeted gene expression strategy employing trinucleotide threading (TnT) amplification and massive parallel sequencing. We have previously shown that TnT combined with array readout accurately monitors expression levels. However, with this detection strategy spurious products go undetected. Accordingly, we adapted the TnT protocol to massive parallel sequencing to acquire an unbiased view of the entire TnT-generated product population. In this manner we investigated th ...[more]

PMID: 23139866

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Project description:Current sequencing-based methods for profiling microbial communities rely on marker gene (e.g., 16S rRNA) or metagenome shotgun sequencing (mWGS) analysis. We present an approach based on a single-primer extension reaction using a highly multiplexed oligonucleotide probe pool. This approach, termed MA-GenTA (microbial abundances from genome tagged analysis), enables quantitative, straightforward, cost-effective microbiome profiling that combines desirable features of both 16S rRNA and mWGS strategies. The use of multiple probes per target genome and rigorous probe design criteria enabled robust determination of relative abundance. To test the utility of the MA-GenTA assay, probes were designed for 830 genome sequences representing bacteria present in mouse stool specimens. Comparison of the MA-GenTA data with mWGS data demonstrated excellent correlation down to 0.01% relative abundance and a similar number of organisms detected per sample. Despite the incompleteness of the reference database, nonmetric multidimensional scaling (NMDS) clustering based on the Bray-Curtis dissimilarity metric of sample groups was consistent between MA-GenTA, mWGS, and 16S rRNA data sets. MA-GenTA represents a potentially useful new method for microbiome community profiling based on reference genomes.IMPORTANCE New methods for profiling the microbial communities can create new approaches to understanding the composition and function of those communities. In this study, we combined bacterial genome-specific probe design with a highly multiplexed single primer extension reaction as a new method to profile microbial communities, using stool from various mouse strains as a test case. This method, termed MA-GenTA, was benchmarked against 16S rRNA gene sequencing and metagenome sequencing methods and delivered similar relative abundance and clustering data. Since the probes were generated from reference genomes, MA-GenTA was also able to provide functional pathway data for the stool microbiome in the assayed samples. The method is more informative than 16S rRNA analysis while being less costly than metagenome shotgun sequencing.

Dataset Information

Targeted transcript profiling by sequencing.

Publications

Targeted transcript profiling by sequencing.

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