tantale: Transcription Activator-Like Effectors (TALEs) tools

Tools to find the TALE genes of Xanthomonas in DNA sequences, compare and group them, align their repeat arrays and predict the plant promoter sites they bind. The package website has worked examples of each step.

Details

Note

tantale runs on Linux and on Intel macOS, the platforms the pinned conda tools exist for; it does not run on Windows. The Java tools need Java on the PATH, which tantale_setup() checks.

Finding TALEs

tell_tales() finds TALE genes with HMMER profiles of the TALE domains, much as AnnoTALE does, and is written for noisy sequences such as draft assemblies or long reads: it can correct frameshifts against reference TALEs before AnnoTALE reads out the RVDs. run_annotale_predict() runs AnnoTALE itself, and correct_tales() runs TALEcorrection. tales_from_telltales() and tales_from_annotale() load either result as a tales object.

Working with TALEs in R

A tales object holds one row per part (N-terminus, repeat or C-terminus) of each TALE, and tales_anomalies() reports the TALEs whose structure is not standard. A tales_msa adds the alignment of the arrays. tale_distances and domain_distances objects hold distances between TALEs or between their domains.

Comparing and grouping TALEs

tales_compare_distal() and tales_compare_functal() reimplement the DisTAL and FuncTAL comparisons of QueTAL. tales_group_kmedoids() and tales_group_hclust() group TALEs from these distances, and talomes_heatmap() compares the groups across strains. AnnoTALE's own classes come from run_annotale_build(), and its published catalogue from run_annotale_load_classes() and run_annotale_assign(). The tale_annotations dataset gives 128 curated TALEs of ten X. oryzae genomes to compare against.

Aligning TALEs

tales_align() aligns the RVD or domain sequences of the arrays with MAFFT; tales_consensus() and the plot() method summarise the alignment.

Predicting targets

tales_predict_targets() runs Talvez (talvez()) or PrediTALE (preditale()) on DNA sequences such as promoters, and plot_target_preds() draws a predicted site with the RVDs facing their bases.

Setting up

MAFFT, HMMER, mmseqs2 and the Perl that Talvez runs on come from a conda environment the package builds for itself, so conda (or mamba, or micromamba) is needed for most of the package. tantale_setup() called bare reports what is present and changes nothing; tantale_setup(install = TRUE) builds or repairs the environment and downloads the Java tools (AnnoTALE, PrediTALE, TALEcorrection) and the example genomes. It checks tool versions as well as presence: MAFFT and HMMER are pinned because later MAFFT versions align TALE repeat strings differently.

Without conda, reticulate::install_miniconda() installs one from inside R. An existing conda, mamba or micromamba is found through reticulate::conda_binary().

See also

Useful links:

• https://scunnac.github.io/tantale/

• https://github.com/scunnac/tantale

• Report bugs at https://github.com/scunnac/tantale/issues

Author

Maintainer: Sebastien Cunnac sebastien.cunnac@ird.fr (ORCID)

Authors:

• Sebastien Cunnac sebastien.cunnac@ird.fr (ORCID)

• Bao Tram Vi vbt576@gmail.com (ORCID)

Other contributors:

• Alvaro L. Pérez-Quintero (Author of the bundled TALVEZ 3.2 and of the FuncTAL table behind rvd_dna_specificity) [copyright holder]

• Molly Megraw (Author of the PlantTFBS Java classes bundled with TALVEZ (simplescancode/)) [copyright holder]

• Artemis G. Hatzigeorgiou (Author of the PlantTFBS Java classes bundled with TALVEZ (simplescancode/)) [copyright holder]