MagicLamp

A software package for annotation of genomic datasets using discreet HMM sets.

Citing MagicLamp

There is no official publication for MagicLamp. If it was useful for your work, please cite as follows:

Garber, AI., Ramirez, GA., Merino, N., Pavia MJ., McAllister, SM. (2020) MagicLamp: toolkit for annotation of 'omics datasets using curated HMM sets. 2021: MagicLamp, GitHub repository: https://github.com/Arkadiy-Garber/MagicLamp.

Installation

Quick install (if you have Conda installed)

git clone https://github.com/Arkadiy-Garber/MagicLamp.git
cd MagicLamp
bash setup.sh
source activate magiclamp

(if "source activate magiclamp" does not work, you can use "conda activate magiclamp")

Installation if you don't have Conda

Put MagicLamp.py script $PATH into your bash profile

export PATH=$PATH:$(pwd)

Dependencies (only if you did not use the setup.sh script to configure a Conda environment)

Required

• Python (version 3.6 or higher)
• BLAST (version 2.7.1+)
• Prodigal (version 2.6.3)
• HMMER (version 3.2.1)

Optional

• Diamond (version 0.9.22.123) -- if you performing cross-validation against a reference database
• R (version 3.5.1) -- if generating plots
• Rscript -- if generating plots

Tutorials

There are two tutorials available for this software, available in this repository's wiki: https://github.com/Arkadiy-Garber/MagicLamp/wiki#welcome-to-the-magiclamp-wiki

Using HmmGenie

An improtant feature of MagicLamp is the ability of users to use their own HMM sets with HmmGenie, like so:

MagicLamp.py HmmGenie -bin_dir bins/ -bin_ext fna -hmm_dir HMMs/ -hmm_ext hmm -rules rules-template.csv

In the above example, the HMMs/ directory contains the user-compiled or created HMMs, where each HMM file ends with a .hmm filename extension. The rules-template.csv file is provided in the main MagicLamp repository (should be in the same folder as this readme). Users are to fill out this file, providing information on each HMM that they wish to use with this program.

Submitting custom HMM sets

We encourage users to submit their custom-built or compiled HMM sets for specific pathways or processes. Please email the following material to agarber4@asu.edu: 1) zipped folder containing the HMM files, 2) filled-out rules-template.csv file. Optionally, users can also provide 1) genome(s) known to encode the genetic pathway(s), and 2) genome(s) known not to encode the genetic pathway.

Usage

(for a list of all available genies)

MagicLamp.py help

(for a detailed help menu for each genie)

  MagicLamp.py FeGenie -h
  MagicLamp.py LithoGenie -h
  MagicLamp.py WspGenie -h
  MagicLamp.py GasGenie -h
  MagicLamp.py MagnetoGenie -h
  MagicLamp.py RosGenie -h
  MagicLamp.py Lucifer -h
  MagicLamp.py HmmGenie -h

Quick-start

simplest form of the command

MagicLamp.py FeGenie -bin_dir genomes/ -bin_ext fna -out fegenie_output

if you are providing gene-calls add the --orfs flag

MagicLamp.py FeGenie -bin_dir genomes/ -bin_ext fna -out fegenie_output --orfs

you can also provide GenBank files using the --gbk flag

MagicLamp.py FeGenie -bin_dir genomes/ -bin_ext fna -out fegenie_output --gbk

if you want to normalize the number of identified genes to the total number of ORFs in the dataset, use the --norm flag

MagicLamp.py FeGenie -bin_dir genomes/ -bin_ext fna -out fegenie_output --norm

with LithoGenie, you can also specficy a specific category for heatmap analysis using the -cat flag

MagicLamp.py LithoGenie -bin_dir genomes/ -bin_ext fna -out lithogenie_output -cat sulfur

if you already ran LithoGenie once, but want to re-do the heatmap file with a different category, use the --skip flag

MagicLamp.py LithoGenie -bin_dir genomes/ -bin_ext fna -out lithogenie_output --cat iron --skip

you can also include prediction of signal peptides and transmembrane domains with Phobius, using the --phobius flag

MagicLamp.py LithoGenie -bin_dir genomes/ -bin_ext fna -out lithogenie_output --cat iron --skip

full list of LithoGenie categories (default action of the program is to create a broad-category heatmap)

• iron
• sulfur
• nitrogen
• oxygen
• methane
• hydrogen
• arsenic
• nitriles
• manganese
• carbon-monoxide
• halogenetated-compounds
• C1compounds
• urea
• selenium

many of the HMMs used by LithoGenie were developed and compiled by Karthik Anantharaman: https://github.com/kanantharaman/metabolic-hmms

Phobius is not available through Anaconda, so the executables (phobius.pl, phobius.options, phobius.model, decodeanhmm, and decodeanhmm.64bit) are included in this repository. Standalone copy of Phobius was obtained from the following website: https://phobius.sbc.su.se/data.html, and users should cite the following article:

Käll, L., Krogh, A., & Sonnhammer, E. L. L. (2004). A combined transmembrane topology and signal peptide prediction method. Journal of Molecular Biology, 338(5), 1027–1036.