Stable population structure in Europe since the Iron Age, despite high mobility.

Ancient DNA research in the past decade has revealed that European population structure changed dramatically in the prehistoric period (14,000-3000 years before present, YBP), reflecting the widespread introduction of Neolithic farmer and Bronze Age Steppe ancestries. However, little is known about how population structure changed from the historical period onward (3000 YBP - present). To address this, we collected whole genomes from 204 individuals from Europe and the Mediterranean, many of which are the first historical period genomes from their region (e.g. Armenia and France). We found that most regions show remarkable inter-individual heterogeneity. At least 7% of historical individuals carry ancestry uncommon in the region where they were sampled, some indicating cross-Mediterranean contacts. Despite this high level of mobility, overall population structure across western Eurasia is relatively stable through the historical period up to the present, mirroring geography. We show that, under standard population genetics models with local panmixia, the observed level of dispersal would lead to a collapse of population structure. Persistent population structure thus suggests a lower effective migration rate than indicated by the observed dispersal. We hypothesize that this phenomenon can be explained by extensive transient dispersal arising from drastically improved transportation networks and the Roman Empire's mobilization of people for trade, labor, and military. This work highlights the utility of ancient DNA in elucidating finer scale human population dynamics in recent history.

MAGenTA: a Galaxy implemented tool for complete Tn-Seq analysis and data visualization.

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: vanopijn@bc.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Complementary feature selection from alternative splicing events and gene expression for phenotype prediction.

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: clabuzze@iastate.edu, antoniom@bc.edu, watsondk@musc.edu, andersonpe2@cofc.edu.