MetaAssemblyEval

Assessment of metagenomic assemblers based on hybrid reads of real and simulated metagenomic sequences. This is an evaluation strategy for metagenomic assemblers based on hybrid reads of real and simulated metagenomic sequences. The statistics of the contigs are based on metagenomic assembly evaluation tool, metaQUAST[1].

Getting Started

We recommend using Anaconda to run MetaAssemblyEval. Note: Mason[2], MEGAHIT[3], SPAdes[4], Faucet[6] and ntcard[7] should be installed to run MetaAssemblyEval. Users need to set the directories of dependency software in environmental variables.

After installing Anaconda, fisrt obtain MetaAssemblyEval:

git clone https://github.com/ziyewang/MetaAssemblyEval

Create a Anaconda environment and activate it:

conda create -n metaAssemblyeval_env python=2
source activate metaAssemblyeval_env

Install the MetaAssemblyEval dependencies into this environment:

conda install quast 
Note: Bioconda is needed to install quast.

Usage

Usage: [--r1 forward read file of real metagenome] [--r2 reverse read file of real metagenome] [--sequencing_depth SEQUENCING_DEPTH] [--read_length READ_LENGTH] [--genome_sequence_dir GENOME_SEQUENCE_DIR] [--output OUTPUT]

arguments

--r1 R1: 
          File with forward reads of real metagenome.

--r2 R2: 
          File with reverse reads of real metagenome.
     
--sequencing_depth SEQUENCING_DEPTH:
          The sequencing depth of simulated genomes.
          
--genome_sequence_dir GENOME_SEQUENCE_DIR:
          The input dir where the sequence of the genomes to be added in the real metagenome locates，ending with '/'.
          
--output OUTPUT:       
          The output dir, storing the statistics results of the assembly.

optional

--read_length READ_LENGTH:
          The read length.

References

[1] Mikheenko A, Saveliev V, and Gurevich A. MetaQUAST: Evaluation of metagenome assemblies. Bioinformatics, 32:1088-1090, 2016.

[2] Holtgrewe M. Mason-A read simulator for second generation sequencing data. Technical Report, FU Berlin, 2010.

[3] Li D, Luo R, Liu CM, et al. MEGAHIT v1.0: A fast and scalable metagenome assembler driven by advanced methodologies and community practices. Methods, 102:3-11, 2016.

[4] Nurk S, Meleshko D, Korobeynikov A, et al. MetaSPAdes: A new versatile metagenomic assembler. Genome Res, 27:824-834, 2017.

[5] Peng Y, Leung HC, Yiu SM, et al. IDBA-UD: A de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth. Bioinformatics, 28:1420-1428, 2012.

[6] Rozov R, Goldshlager G, Halperin E, et al. Faucet: streaming de novo assembly graph con- struction. Bioinformatics, 34:147-154, 2018.

[7] Mohamadi H, Khan H, and Birol I. ntCard: a streaming algorithm for cardinality estimation in genomics data. Bioinformatics, 33:1324-1330, 2017.