Linking big biomedical datasets to modular analysis with Portable Encapsulated Projects

BGCFlow: systematic pangenome workflow for the analysis of biosynthetic gene clusters across large genomic datasets

Genome mining is revolutionizing natural products discovery efforts. The rapid increase in available genomes demands comprehensive computational platforms to effectively extract biosynthetic knowledge encoded across bacterial pangenomes. Here, we present BGCFlow, a novel systematic workflow integrating analytics for large-scale genome mining of bacterial pangenomes. BGCFlow incorporates several genome analytics and mining tools grouped into five common stages of analysis such as: (i) data selection, (ii) functional annotation, (iii) phylogenetic analysis, (iv) genome mining, and (v) comparative analysis. Furthermore, BGCFlow provides easy configuration of different projects, parallel distribution, scheduled job monitoring, an interactive database to visualize tables, exploratory Jupyter Notebooks, and customized reports. Here, we demonstrate the application of BGCFlow by investigating the phylogenetic distribution of various biosynthetic gene clusters detected across 42 genomes of the Saccharopolyspora genus, known to produce industrially important secondary/specialized metabolites. The BGCFlow-guided analysis predicted more accurate dereplication of BGCs and guided the targeted comparative analysis of selected RiPPs. The scalable, interoperable, adaptable, re-entrant, and reproducible nature of the BGCFlow will provide an effective novel way to extract the biosynthetic knowledge from the ever-growing genomic datasets of biotechnologically relevant bacterial species.

Buffering of transcription rate by mRNA half-life is a conserved feature of Rett syndrome models

Transcriptional changes in Rett syndrome (RTT) are assumed to directly correlate with steady-state mRNA levels, but limited evidence in mice suggests that changes in transcription can be compensated by post-transcriptional regulation. We measure transcription rate and mRNA half-life changes in RTT patient neurons using RATEseq, and re-interpret nuclear and whole-cell RNAseq from Mecp2 mice. Genes are dysregulated by changing transcription rate or half-life and are buffered when both change. We utilized classifier models to predict the direction of transcription rate changes and find that combined frequencies of three dinucleotides are better predictors than CA and CG. MicroRNA and RNA-binding Protein (RBP) motifs are enriched in 3'UTRs of genes with half-life changes. Nuclear RBP motifs are enriched on buffered genes with increased transcription rate. We identify post-transcriptional mechanisms in humans and mice that alter half-life or buffer transcription rate changes when a transcriptional modulator gene is mutated in a neurodevelopmental disorder.

Transcriptional buffering and 3'UTR lengthening are shaped during human neurodevelopment by shifts in mRNA stability and microRNA load

The contribution of mRNA half-life is commonly overlooked when examining changes in mRNA abundance during development. mRNA levels of some genes are regulated by transcription rate only, but others may be regulated by mRNA half-life only shifts. Furthermore, transcriptional buffering is predicted when changes in transcription rates have compensating shifts in mRNA half-life resulting in no change to steady-state levels. Likewise, transcriptional boosting should result when changes in transcription rate are accompanied by amplifying half-life shifts. During neurodevelopment there is widespread 3'UTR lengthening that could be shaped by differential shifts in the stability of existing short or long 3'UTR transcript isoforms. We measured transcription rate and mRNA half-life changes during induced human Pluripotent Stem Cell (iPSC)-derived neuronal development using RATE-seq. During transitions to progenitor and neuron stages, transcriptional buffering occurred in up to 50%, and transcriptional boosting in up to 15%, of genes with changed transcription rates. The remaining changes occurred by transcription rate only or mRNA half-life only shifts. Average mRNA half-life decreased two-fold in neurons relative to iPSCs. Short gene isoforms were more destabilized in neurons and thereby increased the average 3'UTR length. Small RNA sequencing captured an increase in microRNA copy number per cell during neurodevelopment. We propose that mRNA destabilization and 3'UTR lengthening are driven in part by an increase in microRNA load in neurons. Our findings identify mRNA stability mechanisms in human neurodevelopment that regulate gene and isoform level abundance and provide a precedent for similar post-transcriptional regulatory events as other tissues develop.

SODAR: managing multiomics study data and metadata

Scientists employing omics in life science studies face challenges such as the modeling of multiassay studies, recording of all relevant parameters, and managing many samples with their metadata. They must manage many large files that are the results of the assays or subsequent computation. Users with diverse backgrounds, ranging from computational scientists to wet-lab scientists, have dissimilar needs when it comes to data access, with programmatic interfaces being favored by the former and graphical ones by the latter. We introduce SODAR, the system for omics data access and retrieval. SODAR is a software package that addresses these challenges by providing a web-based graphical user interface for managing multiassay studies and describing them using the ISA (Investigation, Study, Assay) data model and the ISA-Tab file format. Data storage is handled using the iRODS data management system, which handles large quantities of files and substantial amounts of data. SODAR also offers programmable APIs and command-line access for metadata and file storage. SODAR supports complex omics integration studies and can be easily installed. The software is written in Python 3 and freely available at https://github.com/bihealth/sodar-server under the MIT license.

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/.