MOTIVATION: Transposon insertion sequencing (Tn-Seq) is a microbial systems-level tool, that can determine on a genome-wide scale and in high-throughput, whether a gene, or a specific genomic region, is important for fitness under a specific experimental condition. RESULTS: Here, we present MAGenTA, a suite of analysis tools which accurately calculate the growth rate for each disrupted gene in the genome to enable the discovery of: (i) new leads for gene function, (ii) non-coding RNAs; (iii) genes, pathways and ncRNAs that are involved in tolerating drugs or induce disease; (iv) higher order genome organization; and (v) host-factors that affect bacterial host susceptibility. MAGenTA is a complete Tn-Seq analysis pipeline making sensitive genome-wide fitness (i.e. growth rate) analysis available for most transposons and Tn-Seq associated approaches (e.g. TraDis, HiTS, IN-Seq) and includes fitness (growth rate) calculations, sliding window analysis, bottleneck calculations and corrections, statistics to compare experiments and strains and genome-wide fitness visualization. AVAILABILITY AND IMPLEMENTATION: MAGenTA is available at the Galaxy public ToolShed repository and all source code can be found and are freely available at https://vanopijnenlab.github.io/MAGenTA/ . CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: A central task of bioinformatics is to develop sensitive and specific means of providing medical prognoses from biomarker patterns. Common methods to predict phenotypes in RNA-Seq datasets utilize machine learning algorithms trained via gene expression. Isoforms, however, generated from alternative splicing, may provide a novel and complementary set of transcripts for phenotype prediction. In contrast to gene expression, the number of isoforms increases significantly due to numerous alternative splicing patterns, resulting in a prioritization problem for many machine learning algorithms. This study identifies the empirically optimal methods of transcript quantification, feature engineering and filtering steps using phenotype prediction accuracy as a metric. At the same time, the complementary nature of gene and isoform data is analyzed and the feasibility of identifying isoforms as biomarker candidates is examined. RESULTS: Isoform features are complementary to gene features, providing non-redundant information and enhanced predictive power when prioritized and filtered. A univariate filtering algorithm, which selects up to the N highest ranking features for phenotype prediction is described and evaluated in this study. An empirical comparison of pipelines for isoform quantification is reported by performing cross-validation prediction tests with datasets from human non-small cell lung cancer (NSCLC) patients, human patients with chronic obstructive pulmonary disease (COPD) and amyotrophic lateral sclerosis (ALS) transgenic mice, each including samples of diseased and non-diseased phenotypes. AVAILABILITY AND IMPLEMENTATION: https://github.com/clabuzze/Phenotype-Prediction-Pipeline.git CONTACT: [email protected], [email protected], [email protected], [email protected].