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Allows integrative investigation of next generation sequencing (NGS) microbiology data. Orione supports the whole life cycle of microbiology research data from production and annotation to publication and sharing. It can be used for a variety of microbiological projects including bacteria resequencing, de novo assembling and microbiome investigations. This tool is implemented on the Galaxy web platform.
Allows scaffolding of polymorphic genomes and metagenomes. Bambus implements scaffolding algorithms optimized for non-clonal assembly. When applied to metagenomic datasets, the software generates large scaffolds while avoiding false joins between distantly related organisms. It can automatically identify genomic regions of variation that correspond to previously characterized polymorphic loci. Bambus can be applied to virtually all existing sequencing technologies, and it is sufficient to start with an assembler that is best suited for that type of data.
Subtractive Assembly
A de novo assembly approach for comparative metagenomics that directly assembles only the differential reads that distinguish between two groups of metagenomes. Using both simulated and real metagenomes, we have shown that subtractive assembly improves the assembly of the differential genome between two metagenomes and facilitates downstream analysis. If the short reads from many genomes are directly assembled and annotated, it takes a tremendous amount of computational resources, as well as degrading the quality of the assembly.
A modification of QUAST, the state-of-the-art tool for genome assembly evaluation based on alignment of contigs to a reference. MetaQUAST addresses such metagenome datasets features as (1) unknown species content by detecting and downloading reference sequences, (2) huge diversity by giving comprehensive reports for multiple genomes, (3) presence of highly relative species by detecting chimeric contigs. We demonstrate MetaQUAST performance by comparing several leading assemblers on one simulated and two real datasets.
Builds an assembly graph incrementally as each read is processed. Faucet is a 2-pass streaming algorithm for assembly graph construction. This algorithm is composed of an online phase and an offline phase. During the online phase, two passes are made on the reads without storing them locally to first load their k-mers into a Bloom filter, and then identify and record structural characteristics of the graph and associated metadata essential for achieving high contiguity in assembly. The offline phase uses all of this information together to iteratively clean and refine the graph structure.
A strain aware gene assembler for reconstruction of gene domains of interest from shotgun metagenomic data of microbial communities. Snowball performs gene assembly of individual gene domains based on read overlaps and error-correction using read quality scores at the same time, which result in very low per-base error rates. Snowball uses profile Hidden Markov Models (HMMs) to guide the assembly. Nonetheless, it does not require closely related reference genomes of the studied species to be available.
An extension of the single-genome assembler Velvet. It has been proved to generate assemblies with higher N50 scores and higher quality than single-genome assemblers such as Velvet and SOAPdenovo when applied to metagenomic sequence reads and is frequently used in this research community. One important open problem for MetaVelvet is its low accuracy and sensitivity in detecting chimeric nodes in the assembly (de Bruijn) graph, which prevents the generation of longer contigs and scaffolds.
A-GAME / A GAlaxy suite for functional MEtagenomics
Incorporates tools and workflows for the analysis of environmental DNA (eDNA) sequence data. A-GAME is a general bioinformatics workflow management system implemented within Galaxy. The software contains pre-designed workflows that utilize standard tools for data pre-processing, sequence assembly and annotation; as well as custom utilities dedicated to the analysis of functional metagenomics data. It allows the incorporation of most widely used bioinformatics tools. A-GAME can be used to build and customize bioinformatics workflows.
InteMAP / Integrated Metagenomic Assembly Pipeline
Integrates three assemblers, ABySS, IDBA-UD and CABOG, which were found to complement each other in assembling metagenomic sequences. Making a decision of which assembling approaches to use according to the sequencing coverage estimation algorithm for each short read, InteMAP presents an automatic platform suitable to assemble real metagenomic NGS data with uneven coverage distribution of sequencing depth. The pipeline outperforms previous assembly methods on metagenomic assembly by providing a longer total contig length, a higher contiguity and covering more genes.
Previous approaches have focused on pre-processing reads and optimizing assemblers. BIGMAC takes an alternative perspective to focus on the post-processing step. Using both the assembled contigs and original long reads as input, BIGMAC first breaks the contigs at potentially mis-assembled locations and subsequently scaffolds contigs. Our experiments on metagenomes assembled from long reads show that BIGMAC can improve assembly quality by reducing the number of mis-assemblies while maintaining/increasing N50 and N75. Moreover, BIGMAC is also the only tool that can handle abundance information, which makes it an attractive candidate for improving metagenomic assembly.
BBAP / BLAST-based assembly pipeline
Implements a unique BLAST-based greedy algorithm to assemble data set reads. BBAP is a BLAST-based assembly pipeline that provides multiple intuitive parameters, depending on the nature of the data set, the sequencing platform, and information demands, to adjust the threshold for read alignment, variant retention, and error removal during assembly. BBAP assembly results of both real and simulated next generation sequencing (NGS) data sets were of higher quality than assembly results of other methods compared.
Allows to compare, align, and assemble large sets of DNA sequences. PHRAP uses a banded version of the Smith-Waterman-Gotoh algorithm to do pairwise comparisons of the sequences. It compares sequences by searching for pairs of perfectly matching “words” or sequence regions that meet criteria, tries to extend the alignment if a match of the designated word size is found and then scores it. The software uses quality values produced by the PHRED basecaller. Cross match/Swat is included in the PHRAP package.
Utilizes the clustering algorithm affinity propagation to cluster assemblies using coverage alone, removing potential composition based biases in clustering contigs. BinSanity uses composition data (tetranucleotide frequency and % of G+C content) to refine bins containing multiple source organisms. It was developed and tested on artificial metagenomes varying in size and complexity. The tool had a higher success at consistently generating accurate genomes from strain and species-level diversity. It generates high quality genomes across varying community structures indicates that it is a strong alternative to the compositional based clustering of metagenomic data.
Proposes an iterative approach to assembling metagenomic datasets. Spherical has been designed to produce a more complete assembly from deep sequenced metagenomic data. Utilization of multiple iterations of assembly allows for regions which otherwise would be missed to be assembled without a reduction in contig accuracy. Another use for Spherical is its ability to produce metagenomic assemblies using a subset of the initial input file, allowing for assembly of a metagenome whilst using a fraction of the RAM that would otherwise be required.
VALET / VALidating mETagenomic assemblies
Allows to perform de novo validation of metagenomic assemblies. VALET verifies a number of properties that should hold true for a correct assembly. If invariants are incorrect, they are reported one to pinpoint areas that were potentially mis-assembled, or to compare the quality of different assemblies. The tool can compare multiple assemblies of the same data-sets by reporting an overall estimate of the likelihood a particular assembly is correct.
MetaCAA / Metagenomic assembly using a Clustering-Aided Approach
Employs a clustering-aided assembly procedure in order to achieve improved efficiency of contig assembly. MetaCAA initially groups sequences constituting a given metagenome into smaller clusters. Subsequently, sequences in each cluster are independently assembled using CAP3, an existing single genome assembly program. Contigs formed in each of the clusters along with the unassembled reads are then subjected to another round of assembly for generating the final set of contigs. Validation using simulated and real-world metagenomic datasets indicates that MetaCAA aids in improving the overall quality of assembly.
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