Xander - The Manual
  • Introduction
  • Installing RDP Tools
  • Running Xander
    • Test a Local Xander Installation
    • Interactive Xander Example on MSU's HPCC
    • Submitting Xander Jobs to MSU's HPCC
  • Choosing Xander Parameters
  • Xander Results Explained
  • Adding Gene Models to Xander
  • Xander Results for Multiple Samples
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  • References

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Introduction

NextInstalling RDP Tools

Last updated 5 years ago

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Introduction

Xander, part of the package (Cole et. al., 2014), is a novel method for targeted assembly of specific protein-coding genes from metagenomic data (Wang et. al., 2015). It does this using a graph structure combining both de Bruijn graphs and protein Hidden Markoff Models (HMMs). The inclusion of HMM information guides and speeds the assembly, with concomitant gene annotation.

Xander includes post-assembly steps to quality filter the sequences and find closest matches in a reference data base using (Wang et. al., 2013). Xander comes preconfigured with HMMs and reference databases for the genes amoA AOA, AmoA AOB, nifH, nirK, nirS, norB cNor, norB qNor, nosZ,, nosZ_a2 and rplB. Users may add capability for other genes using information conveniently collected in the (Fish et. al., 2013).

This document provides instructions and exercises on how to use Xander, how to add new gene resource files, and how to collect results from multiple samples into files that can be used to fully populate an experiment-level phyloseq object (McMurdie and Holmes, 2013) for downstream community analysis using R (R Core Team, 2017).

References

Cole, J. R., Wang, Q., Fish, J. A., Chai, B. L., McGarrell, D. M., Sun, Y. N., Brown, C. T., Porras-Alfaro, A., Kuske, C.R., and Tiedje, J. M. (2014). Ribosomal Database Project: data and tools for high throughput rRNA analysis. Nucleic Acids Research, 42(D1), D633-D642. URL: doi:10.1093/nar/gkt1244

Fish, J. A., Chai, B., Wang, Q., Sun, Y., Brown, C. T., Tiedje, J. M., and Cole, J. R. (2013). FunGene: the functional gene pipeline and repository. Frontiers in Microbiology, 4. URL: doi:10.3389/fmicb.2013.00291.

McMurdie, P. J., and Holmes, S. (2013). Phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. Plos One, 8(4). URL: doi:10.1371/journal.pone.0061217.

R Core Team (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL .

Wang, Q., J. A . Fish, M . Gilman, Y . Sun, C. T. B rown, J. M . Tiedje and J. R . Cole. (2015). Xander: Employing a Novel Method for Efficient Gene-Targeted Metagenomic Assembly. Microbiome 3:32. DOI: 10.1186/ s40168-015-0093-6. URL: content/3/1/32.

Wang, Q., Quensen, J. F., Fish, J. A., Lee, T. K., Sun, Y. N., Tiedje, J. M., and Cole, J. R. (2013). Ecological patterns of nifH genes in four terrestrial climatic zones explored with targeted metagenomics using FrameBot, a new informatics tool. MBio, 4(5), e00592-00513. URL: doi:10.1128/mBio.00592-13.

RDPTools
FrameBot
FunGene repository
https://academic.oup.com/nar/article/42/D1/D633/1063201
https://www.frontiersin.org/articles/10.3389/fmicb.2013.00291/full
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0061217
https://www.R-project.org/
https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-015-0093-6
http://mbio.asm.org/content/4/5/e00592-13.full.pdf