GEOfetch: a command-line tool for downloading data and standardized metadata from GEO and SRA.

MOTIVATION: The Gene Expression Omnibus has become an important source of biological data for secondary analysis. However, there is no simple, programmatic way to download data and metadata from Gene Expression Omnibus (GEO) in a standardized annotation format. RESULTS: To address this, we present GEOfetch-a command-line tool that downloads and organizes data and metadata from GEO and SRA. GEOfetch formats the downloaded metadata as a Portable Encapsulated Project, providing universal format for the reanalysis of public data. AVAILABILITY AND IMPLEMENTATION: GEOfetch is available on Bioconda and the Python Package Index (PyPI).

TCR signal strength defines distinct mechanisms of T cell dysfunction and cancer evasion.

T cell receptor (TCR) signal strength is a key determinant of T cell responses. We developed a cancer mouse model in which tumor-specific CD8 T cells (TST cells) encounter tumor antigens with varying TCR signal strength. High-signal-strength interactions caused TST cells to up-regulate inhibitory receptors (IRs), lose effector function, and establish a dysfunction-associated molecular program. TST cells undergoing low-signal-strength interactions also up-regulated IRs, including PD1, but retained a cell-intrinsic functional state. Surprisingly, neither high- nor low-signal-strength interactions led to tumor control in vivo, revealing two distinct mechanisms by which PD1hi TST cells permit tumor escape; high signal strength drives dysfunction, while low signal strength results in functional inertness, where the signal strength is too low to mediate effective cancer cell killing by functional TST cells. CRISPR-Cas9-mediated fine-tuning of signal strength to an intermediate range improved anti-tumor activity in vivo. Our study defines the role of TCR signal strength in TST cell function, with important implications for T cell-based cancer immunotherapies.

Linking big biomedical datasets to modular analysis with Portable Encapsulated Projects.

BACKGROUND: Organizing and annotating biological sample data is critical in data-intensive bioinformatics. Unfortunately, metadata formats from a data provider are often incompatible with requirements of a processing tool. There is no broadly accepted standard to organize metadata across biological projects and bioinformatics tools, restricting the portability and reusability of both annotated datasets and analysis software. RESULTS: To address this, we present the Portable Encapsulated Project (PEP) specification, a formal specification for biological sample metadata structure. The PEP specification accommodates typical features of data-intensive bioinformatics projects with many biological samples. In addition to standardization, the PEP specification provides descriptors and modifiers for project-level and sample-level metadata, which improve portability across both computing environments and data processing tools. PEPs include a schema validator framework, allowing formal definition of required metadata attributes for data analysis broadly. We have implemented packages for reading PEPs in both Python and R to provide a language-agnostic interface for organizing project metadata. CONCLUSIONS: The PEP specification is an important step toward unifying data annotation and processing tools in data-intensive biological research projects. Links to tools and documentation are available at http://pep.databio.org/.

PEPATAC: an optimized pipeline for ATAC-seq data analysis with serial alignments.

As chromatin accessibility data from ATAC-seq experiments continues to expand, there is continuing need for standardized analysis pipelines. Here, we present PEPATAC, an ATAC-seq pipeline that is easily applied to ATAC-seq projects of any size, from one-off experiments to large-scale sequencing projects. PEPATAC leverages unique features of ATAC-seq data to optimize for speed and accuracy, and it provides several unique analytical approaches. Output includes convenient quality control plots, summary statistics, and a variety of generally useful data formats to set the groundwork for subsequent project-specific data analysis. Downstream analysis is simplified by a standard definition format, modularity of components, and metadata APIs in R and Python. It is restartable, fault-tolerant, and can be run on local hardware, using any cluster resource manager, or in provided Linux containers. We also demonstrate the advantage of aligning to the mitochondrial genome serially, which improves the accuracy of alignment statistics and quality control metrics. PEPATAC is a robust and portable first step for any ATAC-seq project. BSD2-licensed code and documentation are available at https://pepatac.databio.org.

L1CAM defines the regenerative origin of metastasis-initiating cells in colorectal cancer.

Metastasis-initiating cells with stem-like properties drive cancer lethality, yet their origins and relationship to primary-tumor-initiating stem cells are not known. We show that L1CAM+ cells in human colorectal cancer (CRC) have metastasis-initiating capacity, and we define their relationship to tissue regeneration. L1CAM is not expressed in the homeostatic intestinal epithelium, but is induced and required for epithelial regeneration following colitis and in CRC organoid growth. By using human tissues and mouse models, we show that L1CAM is dispensable for adenoma initiation but required for orthotopic carcinoma propagation, liver metastatic colonization and chemoresistance. L1CAMhigh cells partially overlap with LGR5high stem-like cells in human CRC organoids. Disruption of intercellular epithelial contacts causes E-cadherin-REST transcriptional derepression of L1CAM, switching chemoresistant CRC progenitors from an L1CAMlow to an L1CAMhigh state. Thus, L1CAM dependency emerges in regenerative intestinal cells when epithelial integrity is lost, a phenotype of wound healing deployed in metastasis-initiating cells.

Refgenie: a reference genome resource manager.

BACKGROUND: Reference genome assemblies are essential for high-throughput sequencing analysis projects. Typically, genome assemblies are stored on disk alongside related resources; e.g., many sequence aligners require the assembly to be indexed. The resulting indexes are broadly applicable for downstream analysis, so it makes sense to share them. However, there is no simple tool to do this. RESULTS: Here, we introduce refgenie, a reference genome assembly asset manager. Refgenie makes it easier to organize, retrieve, and share genome analysis resources. In addition to genome indexes, refgenie can manage any files related to reference genomes, including sequences and annotation files. Refgenie includes a command line interface and a server application that provides a RESTful API, so it is useful for both tool development and analysis. CONCLUSIONS: Refgenie streamlines sharing genome analysis resources among groups and across computing environments. Refgenie is available at https://refgenie.databio.org.

2-hydroxyglutarate inhibits MyoD-mediated differentiation by preventing H3K9 demethylation.

Oncogenic IDH1/2 mutations produce 2-hydroxyglutarate (2HG), resulting in competitive inhibition of DNA and protein demethylation. IDH-mutant cancer cells show an inability to differentiate but whether 2HG accumulation is sufficient to perturb differentiation directed by lineage-specifying transcription factors is unknown. A MyoD-driven model was used to study the role of IDH mutations in the differentiation of mesenchymal cells. The presence of 2HG produced by oncogenic IDH2 blocks the ability of MyoD to drive differentiation into myotubes. DNA 5mC hypermethylation is dispensable while H3K9 hypermethylation is required for this differentiation block. IDH2-R172K mutation results in H3K9 hypermethylation and impaired accessibility at myogenic chromatin regions but does not result in genome-wide decrease in accessibility. The results demonstrate the ability of the oncometabolite 2HG to block transcription factor-mediated differentiation in a molecularly defined system.