MOTIVATION: There are many tools for variant calling and effect prediction, but little to tie together large sample groups. Aggregating, sorting and summarizing variants and effects across a cohort is often done with ad hoc scripts that must be re-written for every new project. In response, we have written MuCor, a tool to gather variants from a variety of input formats (including multiple files per sample), perform database lookups and frequency calculations, and write many types of reports. In addition to use in large studies with numerous samples, MuCor can also be employed to directly compare variant calls from the same sample across two or more platforms, parameters or pipelines. A companion utility, DepthGauge, measures coverage at regions of interest to increase confidence in calls. AVAILABILITY AND IMPLEMENTATION: Source code is freely available at https://github.com/blachlylab/mucor and a Docker image is available at https://hub.docker.com/r/blachlylab/mucor/ CONTACT: [email protected] data: Supplementary data are available at Bioinformatics online.

The gold standard in follicular lymphoma (FL) diagnosis and prognosis is histopathological examination of tumor tissue samples. However, the qualitative manual evaluation is tedious and subject to considerable inter- and intra-reader variations. In this study, we propose an image analysis system for quantitative evaluation of digitized FL tissue slides. The developed system uses a robust feature space analysis method, namely the mean shift algorithm followed by a hierarchical grouping to segment a given tissue image into basic cytological components. We then apply further morphological operations to achieve the segmentation of individual cells. Finally, we generate a likelihood measure to detect candidate cancer cells using a set of clinically driven features. The proposed approach has been evaluated on a dataset consisting of 100 region of interest (ROI) images and achieves a promising 89% average accuracy in detecting target malignant cells.

In this paper, a novel color texture classification approach is introduced and applied to computer-assisted grading of follicular lymphoma from whole-slide tissue samples. The digitized tissue samples of follicular lymphoma were classified into histological grades under a statistical framework. The proposed method classifies the image either into low or high grades based on the amount of cytological components. To further discriminate the lower grades into low and mid grades, we proposed a novel color texture analysis approach. This approach modifies the gray level co-occurrence matrix method by using a non-linear color quantization with self-organizing feature maps (SOFMs). This is particularly useful for the analysis of H&E stained pathological images whose dynamic color range is considerably limited. Experimental results on real follicular lymphoma images demonstrate that the proposed approach outperforms the gray level based texture analysis.