Dataset Information

16Stimator: statistical estimation of ribosomal gene copy numbers from draft genome assemblies.

ABSTRACT: The 16S rRNA gene (16S) is an accepted marker of bacterial taxonomic diversity, even though differences in copy number obscure the relationship between amplicon and organismal abundances. Ancestral state reconstruction methods can predict 16S copy numbers through comparisons with closely related reference genomes; however, the database of closed genomes is limited. Here, we extend the reference database of 16S copy numbers to de novo assembled draft genomes by developing 16Stimator, a method to estimate 16S copy numbers when these repetitive regions collapse during assembly. Using a read depth approach, we estimate 16S copy numbers for 12 endophytic isolates from Arabidopsis thaliana and confirm estimates by qPCR. We further apply this approach to draft genomes deposited in NCBI and demonstrate accurate copy number estimation regardless of sequencing platform, with an overall median deviation of 14%. The expanded database of isolates with 16S copy number estimates increases the power of phylogenetic correction methods for determining organismal abundances from 16S amplicon surveys.

SUBMITTER: Perisin M

PROVIDER: S-EPMC4796925 | biostudies-literature | 2016 Apr

REPOSITORIES: biostudies-literature

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16Stimator: statistical estimation of ribosomal gene copy numbers from draft genome assemblies.

Perisin Matthew M Vetter Madlen M Gilbert Jack A JA Bergelson Joy J

The ISME journal 20150911 4

The 16S rRNA gene (16S) is an accepted marker of bacterial taxonomic diversity, even though differences in copy number obscure the relationship between amplicon and organismal abundances. Ancestral state reconstruction methods can predict 16S copy numbers through comparisons with closely related reference genomes; however, the database of closed genomes is limited. Here, we extend the reference database of 16S copy numbers to de novo assembled draft genomes by developing 16Stimator, a method to ...[more]

PMID: 26359911

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Project description:BackgroundAs compared with traditional transgene copy number detection technologies such as Southern blot analysis, real-time PCR provides a fast, inexpensive and high-throughput alternative. However, the real-time PCR based transgene copy number estimation tends to be ambiguous and subjective stemming from the lack of proper statistical analysis and data quality control to render a reliable estimation of copy number with a prediction value. Despite the recent progresses in statistical analysis of real-time PCR, few publications have integrated these advancements in real-time PCR based transgene copy number determination.ResultsThree experimental designs and four data quality control integrated statistical models are presented. For the first method, external calibration curves are established for the transgene based on serially-diluted templates. The Ct number from a control transgenic event and putative transgenic event are compared to derive the transgene copy number or zygosity estimation. Simple linear regression and two group T-test procedures were combined to model the data from this design. For the second experimental design, standard curves were generated for both an internal reference gene and the transgene, and the copy number of transgene was compared with that of internal reference gene. Multiple regression models and ANOVA models can be employed to analyze the data and perform quality control for this approach. In the third experimental design, transgene copy number is compared with reference gene without a standard curve, but rather, is based directly on fluorescence data. Two different multiple regression models were proposed to analyze the data based on two different approaches of amplification efficiency integration. Our results highlight the importance of proper statistical treatment and quality control integration in real-time PCR-based transgene copy number determination.ConclusionThese statistical methods allow the real-time PCR-based transgene copy number estimation to be more reliable and precise with a proper statistical estimation. Proper confidence intervals are necessary for unambiguous prediction of trangene copy number. The four different statistical methods are compared for their advantages and disadvantages. Moreover, the statistical methods can also be applied for other real-time PCR-based quantification assays including transfection efficiency analysis and pathogen quantification.

Project description:Genome assembly of short reads from large plant genomes remains a challenge in computational biology despite major developments in next generation sequencing. Of late several draft assemblies have been reported in sequenced plant genomes. The reported draft genome assemblies of Cajanus cajan have different levels of genome completeness, a large number of repeats, gaps, and segmental duplications. Draft assemblies with portions of genome missing are shorter than the referenced original genome. These assemblies come with low map accuracy affecting further functional annotation and the prediction of gene components as desired by crop researchers. Genome coverage, i.e., the number of sequenced raw reads mapped onto a certain location of the genome is an important quality indicator of completeness and assembly quality in draft assemblies. The present work aimed to improve the coverage in reported de novo sequenced draft genomes (GCA_000340665.1 and GCA_000230855.2) of pigeonpea, a legume widely cultivated in India. The two recently sequenced assemblies, A1 and A2 comprised 72% and 75% of the estimated coverage of the genome, respectively. We employed an assembly reconciliation approach to compare the draft assemblies and merge them, filling the gaps by employing an algorithm size sorting mate-pair library to generate a high quality and near complete assembly with enhanced contiguity. The majority of gaps present within scaffolds were filled with right-sized mate-pair reads. The improved assembly reduced the number of gaps than those reported in draft assemblies resulting in an improved genome coverage of 82.4%. Map accuracy of the improved assembly was evaluated using various quality metrics and for the presence of specific trait-related functional genes. Employed pair-end and mate-pair local libraries helped us to reduce gaps, repeats, and other sequence errors resulting in lengthier scaffolds compared to the two draft assemblies. We reported the prediction of putative host resistance genes against Fusarium wilt disease by their performance and evaluated them both in wet laboratory and field phenotypic conditions.

Dataset Information

16Stimator: statistical estimation of ribosomal gene copy numbers from draft genome assemblies.

Publications

16Stimator: statistical estimation of ribosomal gene copy numbers from draft genome assemblies.

Similar Datasets

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