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De novo assembly of highly polymorphic metagenomic data using in situ generated reference sequences and a novel BLAST-based assembly pipeline.

ABSTRACT: The 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.In 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.Both 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.

SUBMITTER: Lin YY

PROVIDER: S-EPMC5406902 | biostudies-other | 2017 Apr

REPOSITORIES: biostudies-other

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De novo assembly of highly polymorphic metagenomic data using in situ generated reference sequences and a novel BLAST-based assembly pipeline.

Lin You-Yu YY Hsieh Chia-Hung CH Chen Jiun-Hong JH Lu Xuemei X Kao Jia-Horng JH Chen Pei-Jer PJ Chen Ding-Shinn DS Wang Hurng-Yi HY

BMC bioinformatics 20170426 1

<h4>Background</h4>The 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 ...[more]

PMID: 28446139

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Project description:The de novo assembly of transcriptomes from short shotgun sequences raises challenges due to random and non-random sequencing biases and inherent transcript complexity. We sought to define a pipeline for de novo transcriptome assembly to aid researchers working with emerging model systems where well annotated genome assemblies are not available as a reference. To detail this experimental and computational method, we used early embryos of the sea anemone, Nematostella vectensis, an emerging model system for studies of animal body plan evolution. We performed RNA-seq on embryos up to 24 h of development using Illumina HiSeq technology and evaluated independent de novo assembly methods. The resulting reads were assembled using either the Trinity assembler on all quality controlled reads or both the Velvet and Oases assemblers on reads passing a stringent digital normalization filter. A control set of mRNA standards from the National Institute of Standards and Technology (NIST) was included in our experimental pipeline to invest our transcriptome with quantitative information on absolute transcript levels and to provide additional quality control.We generated >200 million paired-end reads from directional cDNA libraries representing well over 20 Gb of sequence. The Trinity assembler pipeline, including preliminary quality control steps, resulted in more than 86% of reads aligning with the reference transcriptome thus generated. Nevertheless, digital normalization combined with assembly by Velvet and Oases required far less computing power and decreased processing time while still mapping 82% of reads. We have made the raw sequencing reads and assembled transcriptome publically available.Nematostella vectensis was chosen for its strategic position in the tree of life for studies into the origins of the animal body plan, however, the challenge of reference-free transcriptome assembly is relevant to all systems for which well annotated gene models and independently verified genome assembly may not be available. To navigate this new territory, we have constructed a pipeline for library preparation and computational analysis for de novo transcriptome assembly. The gene models defined by this reference transcriptome define the set of genes transcribed in early Nematostella development and will provide a valuable dataset for further gene regulatory network investigations.

Dataset Information

De novo assembly of highly polymorphic metagenomic data using in situ generated reference sequences and a novel BLAST-based assembly pipeline.

Publications

De novo assembly of highly polymorphic metagenomic data using in situ generated reference sequences and a novel BLAST-based assembly pipeline.

Similar Datasets

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