Dataset Information

A De Novo-Assembly Based Data Analysis Pipeline for Plant Obligate Parasite Metatranscriptomic Studies.

ABSTRACT: Current and emerging plant diseases caused by obligate parasitic microbes such as rusts, downy mildews, and powdery mildews threaten worldwide crop production and food safety. These obligate parasites are typically unculturable in the laboratory, posing technical challenges to characterize them at the genetic and genomic level. Here we have developed a data analysis pipeline integrating several bioinformatic software programs. This pipeline facilitates rapid gene discovery and expression analysis of a plant host and its obligate parasite simultaneously by next generation sequencing of mixed host and pathogen RNA (i.e., metatranscriptomics). We applied this pipeline to metatranscriptomic sequencing data of sweet basil (Ocimum basilicum) and its obligate downy mildew parasite Peronospora belbahrii, both lacking a sequenced genome. Even with a single data point, we were able to identify both candidate host defense genes and pathogen virulence genes that are highly expressed during infection. This demonstrates the power of this pipeline for identifying genes important in host-pathogen interactions without prior genomic information for either the plant host or the obligate biotrophic pathogen. The simplicity of this pipeline makes it accessible to researchers with limited computational skills and applicable to metatranscriptomic data analysis in a wide range of plant-obligate-parasite systems.

SUBMITTER: Guo L

PROVIDER: S-EPMC4939292 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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A De Novo-Assembly Based Data Analysis Pipeline for Plant Obligate Parasite Metatranscriptomic Studies.

Guo Li L Allen Kelly S KS Deiulio Greg G Zhang Yong Y Madeiras Angela M AM Wick Robert L RL Ma Li-Jun LJ

Frontiers in plant science 20160711

Current and emerging plant diseases caused by obligate parasitic microbes such as rusts, downy mildews, and powdery mildews threaten worldwide crop production and food safety. These obligate parasites are typically unculturable in the laboratory, posing technical challenges to characterize them at the genetic and genomic level. Here we have developed a data analysis pipeline integrating several bioinformatic software programs. This pipeline facilitates rapid gene discovery and expression analysi ...[more]

PMID: 27462318

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Project description:BackgroundThe accuracy of metagenomic assembly is usually compromised by high levels of polymorphism due to divergent reads from the same genomic region recognized as different loci when sequenced and assembled together. A viral quasispecies is a group of abundant and diversified genetically related viruses found in a single carrier. Current mainstream assembly methods, such as Velvet and SOAPdenovo, were not originally intended for the assembly of such metagenomics data, and therefore demands for new methods to provide accurate and informative assembly results for metagenomic data.ResultsIn this study, we present a hybrid method for assembling highly polymorphic data combining the partial de novo-reference assembly (PDR) strategy and the BLAST-based assembly pipeline (BBAP). The PDR strategy generates in situ reference sequences through de novo assembly of a randomly extracted partial data set which is subsequently used for the reference assembly for the full data set. BBAP employs a greedy algorithm to assemble polymorphic reads. We used 12 hepatitis B virus quasispecies NGS data sets from a previous study to assess and compare the performance of both PDR and BBAP. Analyses suggest the high polymorphism of a full metagenomic data set leads to fragmentized de novo assembly results, whereas the biased or limited representation of external reference sequences included fewer reads into the assembly with lower assembly accuracy and variation sensitivity. In comparison, the PDR generated in situ reference sequence incorporated more reads into the final PDR assembly of the full metagenomics data set along with greater accuracy and higher variation sensitivity. BBAP assembly results also suggest higher assembly efficiency and accuracy compared to other assembly methods. Additionally, BBAP assembly recovered HBV structural variants that were not observed amongst assembly results of other methods. Together, PDR/BBAP assembly results were significantly better than other compared methods.ConclusionsBoth PDR and BBAP independently increased the assembly efficiency and accuracy of highly polymorphic data, and assembly performances were further improved when used together. BBAP also provides nucleotide frequency information. Together, PDR and BBAP provide powerful tools for metagenomic data studies.

Project description:BACKGROUND:De novo assembly of RNA-seq data allows the study of transcriptome in absence of a reference genome either if data is obtained from a single organism or from a mixed sample as in metatranscriptomics studies. Given the high number of sequences obtained from NGS approaches, a critical step in any analysis workflow is the assembly of reads to reconstruct transcripts thus reducing the complexity of the analysis. Despite many available tools show a good sensitivity, there is a high percentage of false positives due to the high number of assemblies considered and it is likely that the high frequency of false positive is underestimated by currently used benchmarks. The reconstruction of not existing transcripts may false the biological interpretation of results as - for example - may overestimate the identification of "novel" transcripts. Moreover, benchmarks performed are usually based on RNA-seq data from annotated genomes and assembled transcripts are compared to annotations and genomes to identify putative good and wrong reconstructions, but these tests alone may lead to accept a particular type of false positive as true, as better described below. RESULTS:Here we present a novel methodology of de novo assembly, implemented in a software named STAble (Short-reads Transcriptome Assembler). The novel concept of this assembler is that the whole reads are used to determine possible alignments instead of using smaller k-mers, with the aim of reducing the number of chimeras produced. Furthermore, we applied a new set of benchmarks based on simulated data to better define the performance of assembly method and carefully identifying true reconstructions. STAble was also used to build a prototype workflow to analyse metatranscriptomics data in connection to a steady state metabolic modelling algorithm. This algorithm was used to produce high quality metabolic interpretations of small gene expression sets obtained from already published RNA-seq data that we assembled with STAble. CONCLUSIONS:The presented results, albeit preliminary, clearly suggest that with this approach is possible to identify informative reactions not directly revealed by raw transcriptomic data.

Dataset Information

A De Novo-Assembly Based Data Analysis Pipeline for Plant Obligate Parasite Metatranscriptomic Studies.

Publications

A De Novo-Assembly Based Data Analysis Pipeline for Plant Obligate Parasite Metatranscriptomic Studies.

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