Dataset Information

Improved assemblies using a source-agnostic pipeline for MetaGenomic Assembly by Merging (MeGAMerge) of contigs.

ABSTRACT: Assembly of metagenomic samples is a very complex process, with algorithms designed to address sequencing platform-specific issues, (read length, data volume, and/or community complexity), while also faced with genomes that differ greatly in nucleotide compositional biases and in abundance. To address these issues, we have developed a post-assembly process: MetaGenomic Assembly by Merging (MeGAMerge). We compare this process to the performance of several assemblers, using both real, and in-silico generated samples of different community composition and complexity. MeGAMerge consistently outperforms individual assembly methods, producing larger contigs with an increased number of predicted genes, without replication of data. MeGAMerge contigs are supported by read mapping and contig alignment data, when using synthetically-derived and real metagenomic data, as well as by gene prediction analyses and similarity searches. MeGAMerge is a flexible method that generates improved metagenome assemblies, with the ability to accommodate upcoming sequencing platforms, as well as present and future assembly algorithms.

SUBMITTER: Scholz M

PROVIDER: S-EPMC4180827 | biostudies-other | 2014

REPOSITORIES: biostudies-other

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Improved assemblies using a source-agnostic pipeline for MetaGenomic Assembly by Merging (MeGAMerge) of contigs.

Scholz Matthew M Lo Chien-Chi CC Chain Patrick S G PS

Scientific reports 20141001

Assembly of metagenomic samples is a very complex process, with algorithms designed to address sequencing platform-specific issues, (read length, data volume, and/or community complexity), while also faced with genomes that differ greatly in nucleotide compositional biases and in abundance. To address these issues, we have developed a post-assembly process: MetaGenomic Assembly by Merging (MeGAMerge). We compare this process to the performance of several assemblers, using both real, and in-silic ...[more]

PMID: 25270300

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Project description: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.

Project description:BACKGROUND:Biochemical and regulatory pathways have until recently been thought and modelled within one cell type, one organism and one species. This vision is being dramatically changed by the advent of whole microbiome sequencing studies, revealing the role of symbiotic microbial populations in fundamental biochemical functions. The new landscape we face requires the reconstruction of biochemical and regulatory pathways at the community level in a given environment. In order to understand how environmental factors affect the genetic material and the dynamics of the expression from one environment to another, we want to evaluate the quantity of gene protein sequences or transcripts associated to a given pathway by precisely estimating the abundance of protein domains, their weak presence or absence in environmental samples. RESULTS:MetaCLADE is a novel profile-based domain annotation pipeline based on a multi-source domain annotation strategy. It applies directly to reads and improves identification of the catalog of functions in microbiomes. MetaCLADE is applied to simulated data and to more than ten metagenomic and metatranscriptomic datasets from different environments where it outperforms InterProScan in the number of annotated domains. It is compared to the state-of-the-art non-profile-based and profile-based methods, UProC and HMM-GRASPx, showing complementary predictions to UProC. A combination of MetaCLADE and UProC improves even further the functional annotation of environmental samples. CONCLUSIONS:Learning about the functional activity of environmental microbial communities is a crucial step to understand microbial interactions and large-scale environmental impact. MetaCLADE has been explicitly designed for metagenomic and metatranscriptomic data and allows for the discovery of patterns in divergent sequences, thanks to its multi-source strategy. MetaCLADE highly improves current domain annotation methods and reaches a fine degree of accuracy in annotation of very different environments such as soil and marine ecosystems, ancient metagenomes and human tissues.

Dataset Information

Improved assemblies using a source-agnostic pipeline for MetaGenomic Assembly by Merging (MeGAMerge) of contigs.

Publications

Improved assemblies using a source-agnostic pipeline for MetaGenomic Assembly by Merging (MeGAMerge) of contigs.

Similar Datasets

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