D3GB: An Interactive Genome Browser for R, Python, and WordPress

Deep-learning-assisted Sort-Seq enables high-throughput profiling of gene expression characteristics with high precision

Owing to the nondeterministic and nonlinear nature of gene expression, the steady-state intracellular protein abundance of a clonal population forms a distribution. The characteristics of this distribution, including expression strength and noise, are closely related to cellular behavior. However, quantitative description of these characteristics has so far relied on arrayed methods, which are time-consuming and labor-intensive. To address this issue, we propose a deep-learning-assisted Sort-Seq approach (dSort-Seq) in this work, enabling high-throughput profiling of expression properties with high precision. We demonstrated the validity of dSort-Seq for large-scale assaying of the dose-response relationships of biosensors. In addition, we comprehensively investigated the contribution of transcription and translation to noise production in Escherichia coli, from which we found that the expression noise is strongly coupled with the mean expression level. We also found that the transcriptional interference caused by overlapping RpoD-binding sites contributes to noise production, which suggested the existence of a simple and feasible noise control strategy in E. coli.

Gos: a declarative library for interactive genomics visualization in Python

SUMMARY

Gos is a declarative Python library designed to create interactive multiscale visualizations of genomics and epigenomics data. It provides a consistent and simple interface to the flexible Gosling visualization grammar. Gos hides technical complexities involved with configuring web-based genome browsers and integrates seamlessly within computational notebooks environments to enable new interactive analysis workflows.

AVAILABILITY AND IMPLEMENTATION

Gos is released under the MIT License and available on the Python Package Index (PyPI). The source code is publicly available on GitHub (https://github.com/gosling-lang/gos), and documentation with examples can be found at https://gosling-lang.github.io/gos.

Marker-free characterization of full-length transcriptomes of single live circulating tumor cells

The identification and characterization of circulating tumor cells (CTCs) are important for gaining insights into the biology of metastatic cancers, monitoring disease progression, and medical management of the disease. The limiting factor in the enrichment of purified CTC populations is their sparse availability, heterogeneity, and altered phenotypes relative to the primary tumor. Intensive research both at the technical and molecular fronts led to the development of assays that ease CTC detection and identification from peripheral blood. Most CTC detection methods based on single-cell RNA sequencing (scRNA-seq) use a mix of size selection, marker-based white blood cell (WBC) depletion, and antibodies targeting tumor-associated antigens. However, the majority of these methods either miss out on atypical CTCs or suffer from WBC contamination. We present unCTC, an R package for unbiased identification and characterization of CTCs from single-cell transcriptomic data. unCTC features many standard and novel computational and statistical modules for various analyses. These include a novel method of scRNA-seq clustering, named deep dictionary learning using k-means clustering cost (DDLK), expression-based copy number variation (CNV) inference, and combinatorial, marker-based verification of the malignant phenotypes. DDLK enables robust segregation of CTCs and WBCs in the pathway space, as opposed to the gene expression space. We validated the utility of unCTC on scRNA-seq profiles of breast CTCs from six patients, captured and profiled using an integrated ClearCell FX and Polaris workflow that works by the principles of size-based separation of CTCs and marker-based WBC depletion.

JBrowse Jupyter: a Python interface to JBrowse 2

MOTIVATION

JBrowse Jupyter is a package that aims to close the gap between Python programming and genomic visualization. Web-based genome browsers are routinely used for publishing and inspecting genome annotations. Historically they have been deployed at the end of bioinformatics pipelines, typically decoupled from the analysis itself. However, emerging technologies such as Jupyter notebooks enable a more rapid iterative cycle of development, analysis and visualization.

RESULTS

We have developed a package that provides a Python interface to JBrowse 2's suite of embeddable components, including the primary Linear Genome View. The package enables users to quickly set up, launch and customize JBrowse views from Jupyter notebooks. In addition, users can share their data via Google's Colab notebooks, providing reproducible interactive views.

AVAILABILITY AND IMPLEMENTATION

JBrowse Jupyter is released under the Apache License and is available for download on PyPI. Source code and demos are available on GitHub at https://github.com/GMOD/jbrowse-jupyter.

SeMPI 2.0-A Web Server for PKS and NRPS Predictions Combined with Metabolite Screening in Natural Product Databases

Microorganisms produce secondary metabolites with a remarkable range of bioactive properties. The constantly increasing amount of published genomic data provides the opportunity for efficient identification of biosynthetic gene clusters by genome mining. On the other hand, for many natural products with resolved structures, the encoding biosynthetic gene clusters have not been identified yet. Of those secondary metabolites, the scaffolds of nonribosomal peptides and polyketides (type I modular) can be predicted due to their building block-like assembly. SeMPI v2 provides a comprehensive prediction pipeline, which includes the screening of the scaffold in publicly available natural compound databases. The screening algorithm was designed to detect homologous structures even for partial, incomplete clusters. The pipeline allows linking of gene clusters to known natural products and therefore also provides a metric to estimate the novelty of the cluster if a matching scaffold cannot be found. Whereas currently available tools attempt to provide comprehensive information about a wide range of gene clusters, SeMPI v2 aims to focus on precise predictions. Therefore, the cluster detection algorithm, including building block generation and domain substrate prediction, was thoroughly refined and benchmarked, to provide high-quality scaffold predictions. In a benchmark based on 559 gene clusters, SeMPI v2 achieved comparable or better results than antiSMASH v5. Additionally, the SeMPI v2 web server provides features that can help to further investigate a submitted gene cluster, such as the incorporation of a genome browser, and the possibility to modify a predicted scaffold in a workbench before the database screening.

GIVE: portable genome browsers for personal websites

Growing popularity and diversity of genomic data demand portable and versatile genome browsers. Here, we present an open source programming library called GIVE that facilitates the creation of personalized genome browsers without requiring a system administrator. By inserting HTML tags, one can add to a personal webpage interactive visualization of multiple types of genomics data, including genome annotation, "linear" quantitative data, and genome interaction data. GIVE includes a graphical interface called HUG (HTML Universal Generator) that automatically generates HTML code for displaying user chosen data, which can be copy-pasted into user's personal website or saved and shared with collaborators. GIVE is available at: https://www.givengine.org/ .