MOTIVATION: While Python has become very popular in bioinformatics, a limited number of libraries exist for fast manipulation of gene coordinates in Ensembl GTF format. RESULTS: We have developed the GTF toolkit Python package (pygtftk), which aims at providing easy and powerful manipulation of gene coordinates in GTF format. For optimal performances, the core engine of pygtftk is a C dynamic library (libgtftk) while the Python API provides usability and readability for developing scripts. Based on this Python package, we have developed the gtftk command line interface that contains 57 sub-commands (v0.9.10) to ease handling of GTF files. These commands may be used to (i) perform basic tasks (e.g. selections, insertions, updates or deletions of features/keys), (ii) select genes/transcripts based on various criteria (e.g. size, exon number, transcription start site location, intron length, GO terms) or (iii) carry out more advanced operations such as coverage analyses of genomic features using bigWig files to create faceted read-coverage diagrams. In conclusion, the pygtftk package greatly simplifies the annotation of GTF files with external information while providing advance tools to perform gene analyses. AVAILABILITY AND IMPLEMENTATION: pygtftk and gtftk have been tested on Linux and MacOSX and are available from https://github.com/dputhier/pygtftk under the MIT license. The libgtftk dynamic library written in C is available from https://github.com/dputhier/libgtftk.

UNLABELLED: Supervised classification based on support vector machines (SVMs) has successfully been used for the prediction of cis-regulatory modules (CRMs). However, no integrated tool using such heterogeneous data as position-specific scoring matrices, ChIP-seq data or conservation scores is currently available. Here, we present LedPred, a flexible SVM workflow that predicts new regulatory sequences based on the annotation of known CRMs, which are associated to a large variety of feature types. LedPred is provided as an R/Bioconductor package connected to an online server to avoid installation of non-R software. Due to the heterogeneous CRM feature integration, LedPred excels at the prediction of regulatory sequences in Drosophila and mouse datasets compared with similar SVM-based software. AVAILABILITY AND IMPLEMENTATION: LedPred is available on GitHub: https://github.com/aitgon/LedPred and Bioconductor: http://bioconductor.org/packages/release/bioc/html/LedPred.html under the MIT license. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Given the complexity of developmental networks, it is often difficult to predict the effect of genetic perturbations, even within coding genes. Regulatory factors generally have pleiotropic effects, exhibit partially redundant roles, and regulate highly interconnected pathways with ample cross-talk. Here, we delineate a logical model encompassing 48 components and 82 regulatory interactions involved in mesoderm specification during Drosophila development, thereby providing a formal integration of all available genetic information from the literature. The four main tissues derived from mesoderm correspond to alternative stable states. We demonstrate that the model can predict known mutant phenotypes and use it to systematically predict the effects of over 300 new, often non-intuitive, loss- and gain-of-function mutations, and combinations thereof. We further validated several novel predictions experimentally, thereby demonstrating the robustness of model. Logical modelling can thus contribute to formally explain and predict regulatory outcomes underlying cell fate decisions.