An automated quality control pipeline for eQTL analysis with RNAseq data Expression quantitative trait loci (eQTL) analysis is of critical importance to understand the mechanism underlying trait associated variants. Evaluating and controlling the data quality of transcripts and genotypes, which are basis of eQTL analysis, remains challenging for researchers with limited computational backgrounds. There is a strong need for a userfriendly and comprehensive tool to preprocess those data sets automatically. Here we propose such a solution, eQTLQC, an automated quality control pipeline for preprocessing both RNAseq and genotype data. The eQTLQC pipeline provides multiple informative quality control measurements and data normalization approaches. And it provides a easytouse configuration file for users to flexibly set up the parameters and control the pipeline. We demonstrate its utility by performing RNAseq and genotype preprocessing on real data sets. eQTLQC is open source and freely available at https:github.comruanjunpengeQTLQC.

Exposing and Characterizing Subpopulations of Distinctly Regulated Genes by KPlane Regression Understanding the roles and interplays of histone marks and transcription factors in the regulation of gene expression is of great interest in the development of noninvasive and personalized therapies. Computational studies at genomewide scale represent a powerful explorative framework, allowing to draw general conclusions. However, a genomewide approach only identifies generic regulative motifs, and possible multifunctional or coregulative interactions may remain concealed. In this work, we hypothesize the presence of a number of distinct subpopulations of transcriptional regulative patterns within the set of protein coding genes that explain the statistical redundancy observed at a genomewide level. We propose the application of a KPlane Regression algorithm to partition the set of protein coding genes into clusters with specific shared regulative mechanisms. Our approach is completely datadriven and computes clusters of genes significantly better fitted by specific linear models, in contrast to single regressions. These clusters are characterized by distinct and sharper histonic input patterns, and different mean expression values.