SigmoID is all about transcription factor binding sites (TFBS) in bacterial genomes: find known ones, infer unknown and view them all in genomic context. It is a GUI application written in Xojo with some additions in python.
Functions:
- de novo inference of transcription factor binding sites (new in v.2);
- 3D structure (DNA-protein contact) based application of known TFBS profiles to new genomes (new in v.2);
- visualise binding site alignments with sequence logo;
- integrated access to RegPrecise, CollecTF and RegulonDB;
- extend, shorten and mask TFBS alignments;
- create calibrated hmm profiles from TFBS alignments;
- search bacterial genome sequences with calibrated (and uncalibrated) hmm profiles;
- annotate promoters and transcription factor binding sites in GenBank-formatted genomes;
- view regulatory sites in genomic context with the integrated genome browser;
- view RNA-seq coverage data;
- verify and edit genome annotation via the integrated database search.
Sigmoid v.2 (currently in development) is strongly recommended for routine use. It is available as 64-bit application for OS X and Linux on the Releases page. Supported OS versions include OS X Yosemite 10.10 and up and several Linux distributions: Linux Mint 16 or later CentOS 7.0 or later Ubuntu 14.04 LTS or later Debian 6.0 or later OpenSUSE 11.3 or later Fedora 13 Desktop or later. Other Linux distributions may work provided required libraries are installed. Windows and 32-bit versions are available for SigmoID v.1 only.
Adding annotations to GenBank files and some other functions are implemented as python scripts, hence BioPython (version 1.64 and up) is required as well as Python 2.7.x. de novo TFBS search requires full installation of the MEME Suite (meme-suite.org).
Additional info and usage instructions are in the help file accessible from SigmoID.
A paper describing SigmoID v.1 is available: Nikolaichik Y, Damienikan AU. (2016) SigmoID: a user-friendly tool for improving bacterial genome annotation through analysis of transcription control signals. PeerJ 4:e2056 https://doi.org/10.7717/peerj.2056