The field of cancer genomics has demonstrated the power of massively parallel sequencing techniques to inform on the genes and specific alterations that drive tumor onset and progression. Although large comprehensive sequence data sets continue to be made increasingly available, data analysis remains an ongoing challenge, particularly for laboratories lacking dedicated resources and bioinformatics expertise. To address this, we have produced a collection of Galaxy tools that represent many popular algorithms for detecting somatic genetic alterations from cancer genome and exome data. We developed new methods for parallelization of these tools within Galaxy to accelerate runtime and have demonstrated their usability and summarized their runtimes on multiple cloud service providers. Some tools represent extensions or refinement of existing toolkits to yield visualizations suited to cohort-wide cancer genomic analysis. For example, we present Oncocircos and Oncoprintplus, which generate data-rich summaries of exome-derived somatic mutation. Workflows that integrate these to achieve data integration and visualizations are demonstrated on a cohort of 96 diffuse large B-cell lymphomas and enabled the discovery of multiple candidate lymphoma-related genes. Our toolkit is available from our GitHub repository as Galaxy tool and dependency definitions and has been deployed using virtualization on multiple platforms including Docker.

Background

The field of next-generation sequencing informatics has matured to a point where algorithmic advances in sequence alignment and individual feature detection methods have stabilized. Practical and robust implementation of complex analytical workflows (where such tools are structured into "best practices" for automated analysis of next-generation sequencing datasets) still requires significant programming investment and expertise.

Results

We present Kronos, a software platform for facilitating the development and execution of modular, auditable, and distributable bioinformatics workflows. Kronos obviates the need for explicit coding of workflows by compiling a text configuration file into executable Python applications. Making analysis modules would still require programming. The framework of each workflow includes a run manager to execute the encoded workflows locally (or on a cluster or cloud), parallelize tasks, and log all runtime events. The resulting workflows are highly modular and configurable by construction, facilitating flexible and extensible meta-applications that can be modified easily through configuration file editing. The workflows are fully encoded for ease of distribution and can be instantiated on external systems, a step toward reproducible research and comparative analyses. We introduce a framework for building Kronos components that function as shareable, modular nodes in Kronos workflows.

Conclusions

The Kronos platform provides a standard framework for developers to implement custom tools, reuse existing tools, and contribute to the community at large. Kronos is shipped with both Docker and Amazon Web Services Machine Images. It is free, open source, and available through the Python Package Index and at https://github.com/jtaghiyar/kronos.

Genomic profiling has identified a subtype of high-risk B-progenitor acute lymphoblastic leukemia (B-ALL) with alteration of IKZF1, a gene expression profile similar to BCR-ABL1-positive ALL and poor outcome (Ph-like ALL). The genetic alterations that activate kinase signaling in Ph-like ALL are poorly understood. We performed transcriptome and whole genome sequencing on 15 cases of Ph-like ALL and identified rearrangements involving ABL1, JAK2, PDGFRB, CRLF2, and EPOR, activating mutations of IL7R and FLT3, and deletion of SH2B3, which encodes the JAK2-negative regulator LNK. Importantly, several of these alterations induce transformation that is attenuated with tyrosine kinase inhibitors, suggesting the treatment outcome of these patients may be improved with targeted therapy.