Xander
Xander assembles targeted protein-coding genes from metagenomic sequencing reads by integrating de Bruijn graphs with protein profile hidden Markov models (HMMs) to improve assembly accuracy and gene annotation.
Key Features:
- Integrated Graph Structure: Merges de Bruijn graph representations of sequence k-mers with protein profile HMM information to form a combined weighted assembly graph that guides targeted assembly.
- Concomitant Assembly and Annotation: Performs assembly and annotation simultaneously by incorporating HMM information into the assembly graph.
- Enhanced Gene Quality: Targets specific protein-coding genes to produce longer and higher-quality full-length or near-full-length gene sequences compared to bulk metagenome assembly methods and other gene-targeted assemblers, demonstrated on Human Microbiome Project (HMP) Illumina data and rhizosphere soil metagenomic datasets from diverse crops.
- Flexibility in Gene Targeting: Accepts customized protein profile HMMs tailored to specific target genes to improve precision of gene recovery and annotation.
Scientific Applications:
- Targeted gene recovery in metagenomes: Enables recovery of full-length or near-full-length functional protein-coding genes from complex metagenomic samples.
- Microbial functional profiling across environments: Facilitates characterization of microbial functional genes in datasets such as Human Microbiome Project (HMP) Illumina sequences and rhizosphere soil metagenomes from various crops.
Methodology:
Integrates de Bruijn graphs and protein profile HMMs to create a combined weighted assembly graph and performs concomitant assembly and annotation of targeted protein-coding genes.
Topics
Details
- Tool Type:
- command-line tool
- Operating Systems:
- Linux
- Programming Languages:
- Java, Python
- Added:
- 8/3/2017
- Last Updated:
- 11/25/2024
Operations
Publications
Wang Q, Fish JA, Gilman M, Sun Y, Brown CT, Tiedje JM, Cole JR. Xander: employing a novel method for efficient gene-targeted metagenomic assembly. Microbiome. 2015;3(1). doi:10.1186/s40168-015-0093-6. PMID:26246894. PMCID:PMC4526283.
PMID: 26246894
PMCID: PMC4526283
Funding: - U.S. Department of Energy: BER DE-FC02-07ER64494, DE-FG02-99ER62848, DE-SC0010715
- National Institute of Environmental Health Sciences: 5P42ES004911-23, DBI-1356380