Hierarchical analysis of RNA-seq reads improves the accuracy of allele-specific expression.

Motivation: Allele-specific expression (ASE) refers to the differential abundance of the allelic copies of a transcript. RNA sequencing (RNA-seq) can provide quantitative estimates of ASE for genes with transcribed polymorphisms. When short-read sequences are aligned to a diploid transcriptome, read-mapping ambiguities confound our ability to directly count reads. Multi-mapping reads aligning equally well to multiple genomic locations, isoforms or alleles can comprise the majority (>85%) of reads. Discarding them can result in biases and substantial loss of information. Methods have been developed that use weighted allocation of read counts but these methods treat the different types of multi-reads equivalently. We propose a hierarchical approach to allocation of read counts that first resolves ambiguities among genes, then among isoforms, and lastly between alleles. We have implemented our model in EMASE software (Expectation-Maximization for Allele Specific Expression) to estimate total gene expression, isoform usage and ASE based on this hierarchical allocation. Results: Methods that align RNA-seq reads to a diploid transcriptome incorporating known genetic variants improve estimates of ASE and total gene expression compared to methods that use reference genome alignments. Weighted allocation methods outperform methods that discard multi-reads. Hierarchical allocation of reads improves estimation of ASE even when data are simulated from a non-hierarchical model. Analysis of RNA-seq data from F1 hybrid mice using EMASE reveals widespread ASE associated with cis-acting polymorphisms and a small number of parent-of-origin effects. Availability and implementation: EMASE software is available at https://github.com/churchill-lab/emase. Supplementary information: Supplementary data are available at Bioinformatics online.

Mouse Tumor Biology Database (MTB): status update and future directions.

The Mouse Tumor Biology (MTB) database provides access to data about endogenously arising tumors (both spontaneous and induced) in genetically defined mice (inbred, hybrid, mutant and genetically engineered mice). Data include information on the frequency and latency of mouse tumors, pathology reports and images, genomic changes occurring in the tumors, genetic (strain) background and literature or contributor citations. Data are curated from the primary literature or submitted directly from researchers. MTB is accessed via the Mouse Genome Informatics web site (http://www.informatics.jax.org). Integrated searches of MTB are enabled through use of multiple controlled vocabularies and by adherence to standardized nomenclature, when available. Recently MTB has been redesigned and its database infrastructure replaced with a robust relational database management system (RDMS). Web interface improvements include a new advanced query form and enhancements to already existing search capabilities. The Tumor Frequency Grid has been revised to enhance interactivity, providing an overview of reported tumor incidence across mouse strains and an entrée into the database. A new pathology data submission tool allows users to submit, edit and release data to the MTB system.