Goth migration induced changes in the matrilineal genetic structure of the central-east European population.

For years, the issues related to the origin of the Goths and their early migrations in the Iron Age have been a matter of hot debate among archaeologists. Unfortunately, the lack of new independent data has precluded the evaluation of the existing hypothesis. To overcome this problem, we initiated systematic studies of the populations inhabiting the contemporary territory of Poland during the Iron Age. Here, we present an analysis of mitochondrial DNA isolated from 27 individuals (collectively called the Mas-VBIA group) excavated from an Iron Age cemetery (dated to the 2nd-4th century A.D.) attributed to Goths and located near Maslomecz, eastern Poland. We found that Mas-VBIA has similar genetic diversity to present-day Asian populations and higher diversity than that of contemporary Europeans. Our studies revealed close genetic links between the Mas-VBIA and two other Iron Age populations from the Jutland peninsula and from Kowalewko, located in western Poland. We disclosed the genetic connection between the Mas-VBIA and ancient Pontic-Caspian steppe groups. Similar connections were absent in the chronologically earlier Kowalewko and Jutland peninsula populations. The collected results seem to be consistent with the historical narrative that assumed that the Goths originated in southern Scandinavia; then, at least part of the Goth population moved south through the territory of contemporary Poland towards the Black Sea region, where they mixed with local populations and formed the Chernyakhov culture. Finally, a fraction of the Chernyakhov population returned to the southeast region of present-day Poland and established the archaeological formation called the "Maslomecz group".

The Impact of Normalization Methods on RNA-Seq Data Analysis.

High-throughput sequencing technologies, such as the Illumina Hi-seq, are powerful new tools for investigating a wide range of biological and medical problems. Massive and complex data sets produced by the sequencers create a need for development of statistical and computational methods that can tackle the analysis and management of data. The data normalization is one of the most crucial steps of data processing and this process must be carefully considered as it has a profound effect on the results of the analysis. In this work, we focus on a comprehensive comparison of five normalization methods related to sequencing depth, widely used for transcriptome sequencing (RNA-seq) data, and their impact on the results of gene expression analysis. Based on this study, we suggest a universal workflow that can be applied for the selection of the optimal normalization procedure for any particular data set. The described workflow includes calculation of the bias and variance values for the control genes, sensitivity and specificity of the methods, and classification errors as well as generation of the diagnostic plots. Combining the above information facilitates the selection of the most appropriate normalization method for the studied data sets and determines which methods can be used interchangeably.