Computational platforms and environments

Traditional bulk sequencing data lack information about cell-type-specific gene expression. Here, the authors develop a Tissue-AdaPtive autoEncoder (TAPE), a deep learning method connecting bulk RNA-seq and single-cell RNA-seq, and apply it to analyze the cell type fractions and cell-type-specific gene expression in clinical data.

Biomedical data is accumulating at a fast pace and integrating it into a unified framework is a major challenge. Here, the authors present a resource that contains pre-calculated biomedical descriptors derived from a very large knowledge graph.

Automated design and build processes can rapidly accelerate work in synthetic biology and metabolic engineering. Here the authors present Galaxy-SynBioCAD, a toolshed for synthetic biology, metabolic engineering, and industrial biotechnology that they use to build and execute Galaxy scientific workflows from pathway design to strain engineering through the automated generation of scripts driving robotic workstations.

There are only a few platforms that integrate multiple omics data types, bioinformatics tools, and interfaces for integrative analyses and visualization that do not require programming skills. Here the authors present an integrative web-based platform for analysis of omics data and signatures of cellular perturbations.

Cellular contexts such as disease state, organismal life stage and tissue microenvironment, shape intercellular communication, and ultimately affect an organism’s phenotypes. Here, the authors present Tensor-cell2cell, an unsupervised method for deciphering context-driven intercellular communication.

Characterization of cell-free genetic networks is inherently difficult. Here the authors use optimal experimental design and microfluidics to improve characterization, demonstrating modularity and predictability of parts in applied test cases.

Small-scale bioreactors are increasingly used in quantitative biology. Here, the authors report ReacSight, a software solution to connect reactor arrays with sensitive measurement devices using low-cost pipetting robots and provide applications leveraging optogenetic control in yeast.

Studies of circular RNAs have often been limited to the tissue or organism level. Here, authors investigate the comprehensive expression landscape of circRNAs in human and mouse at single-cell resolution, revealing highly specific and dynamic changes of circRNAs during multiple biological processes.

Multiple methods to infer cell-cell communication (CCC) from single cell data are currently available. Here, the authors systematically compare 16 CCC inference resources and 7 methods, and develop the LIANA framework as an interface to use and compare all these approaches.

Immunopeptidomics benefits from highly sensitive mass spectrometry (MS). Here, the authors present a computational platform for integrating data-dependent and -independent acquisition MS approaches, and demonstrate its utility for deeper immunopeptidome profiling.

This paper describes the ‘4DN Data Portal’ that hosts data generated by the 4D Nucleome network, including Hi-C and other chromatin conformation capture assays, as well as various sequencing-based and imaging-based assays. Raw data have been uniformly processed to increase comparability and the portal is implemented with visualization tools to browse the data without download.

In microscopy, applications in which reactiveness is needed are multifarious. Here the authors report MicroMator, a Python software package for reactive experiments, which they use for applications requiring real-time tracking and light-targeting at the single-cell level.

Here the authors introduce Cellar, an interactive webserver for analyzing single-cell omics data. They show that Cellar supports all aspects of the analysis and modeling process and can be used to integrate different types of single cell omics and spatial data.

“Mapping the dark matter of metabolism remains an open challenge that can be addressed globally and systematically by existing computational solutions. Here the authors present ATLASx, a repository of known and predicted enzymatic reaction, connecting millions of compounds to help synthetic biologists and metabolic engineers to design and explore metabolic pathways.”

Cell types are typically identified in single cell transcriptomic data by manual annotation of cell clusters using established marker genes. Here the authors present a fully-automated computational platform that can quickly and accurately distinguish between cell types.

The interpretation of somatic variants in cancer is challenging due to the scale and complexity of sequencing data. Here, the authors present PORI, an open-source framework for interpreting somatic variants in cancer using graph knowledge base tools, automated reporting, and manual curation.

Individual-based models have become important tools in the global battle against infectious diseases, yet model complexity can make calibration challenging. Here, the authors propose a Bayesian optimization framework to calibrate a complex malaria transmission simulator.

There is an unmet need for adaptable tools allowing biomedical researchers to employ network-based drug repurposing approaches for their individual use cases. Here, the authors close this gap with NeDRex, an integrative and interactive platform.

It is no secret that a significant part of scientific research is difficult to reproduce. Here, the authors present a cloud-computing platform called ORCESTRA that facilitates reproducible processing of multimodal biomedical data using customizable pipelines and well-documented data objects.

The increasing scale and scope of biomedical data is generating tremendous opportunities for improving health outcomes, but also raises new challenges ranging from data acquisition and storage to data analysis and utilization. To meet these challenges, the authors develop the Personal Health Dashboard, which provides an end-to-end solution for deep biomedical data analytics.

The authors present flDPnn, a computational tool for disorder and disorder function predictions from protein sequences. flDPnn was assessed with the data from the “Critical Assessment of Protein Intrinsic Disorder Prediction” experiment and on an independent and low-similarity test dataset, which show that flDPnn offers accurate predictions of disorder, fully disordered proteins and four common disorder functions.

Deep learning algorithms trained on data streamed temporally from different clinical sites and from a multitude of physiological sensors are generally affected by a degradation in performance. To mitigate this, the authors propose a continual learning strategy that employs a replay buffer.

Molecular networking connects molecules based on their fragment ion mass spectra (MS²), but may leave adduct species from the same molecular family separate. To address this issue, the authors develop a networking approach that fuses MS¹- and MS²-based networks and integrate it into the GNPS environment.

Whole genome sequencing data are increasingly becoming routinely available but generating actionable insights is challenging. Here, the authors describe Pathogenwatch, a web tool for genomic surveillance of S. Typhi, and demonstrate its use for antimicrobial resistance assignment and strain risk assessment.

Data-rich networks can be difficult to interpret beyond a certain size. Here, the authors introduce a platform that uses virtual reality to allow the visual exploration of large networks, while interfacing with data repositories and other analytical methods to improve the interpretation of big data.

The Danish health system has been collecting health-related data on the entire Danish population for years. Here the authors present the Danish Disease Trajectory Browser (DTB), which allows users to explore population-wide disease progression patterns from data collected between 1994 and 2018.

The computational prediction of protein allostery can guide experimental studies of protein function and cellular activity. Here, the authors develop a network-based method to detect allosteric coupling within proteins solely based on their structures, and set up a webserver for allostery prediction.

Deep learning is becoming a popular approach for understanding biological processes but can be hard to adapt to new questions. Here, the authors develop Janggu, a python library that aims to ease data acquisition and model evaluation and facilitate deep learning applications in genomics.

Many approaches exist to process data from individual imaging modalities, but integrating them is challenging. The authors develop an automated resource that enables co-registered network- and tract-level analysis of macroscopic in-vivo imaging and microscopic imaging of cleared tissue.

Taxonomy classification of amplicon sequences is an important step in investigating microbial communities in microbiome analysis. Here, the authors show incorporating environment-specific taxonomic abundance information can lead to improved species-level classification accuracy across common sample types.

The Scalable Precision Medicine Oriented Knowledge Engine (SPOKE) is a heterogeneous knowledge network that integrates information from 29 public databases. Here, Nelson et al. extend SPOKE to embed clinical data from electronic health records to create medically meaningful barcodes for each medical variable.

Most morphological visualization platforms are not designed to share research data, or are limited to data visualization. Here the authors present MorphoNet, an open-source, web-based tool for interactive visualization and sharing of complex morphodynamic datasets, onto which users can project their own data.

Adaptive immunity from both B and T cells critically controls the rejection or survival of transplanted organs. Here the authors show, by analyzing human B cell receptor repertoire in longitudinal studies of patients receiving kidney transplants, that repertoire diversity is positively associated with the incidence of kidney rejection.

Imputation can effectively augment marker density in existing genetic datasets and enable integration across germplasm resources. Here Wang et al. present a public imputation server for rice using a diverse reference panel to facilitate imputation in the rice genetics community.

Recombination contributes to HIV evolution in patients, but its identification can be difficult. Here, the authors develop a computational tool called RAPR to track recombination in patients, identify recombination hot spots, and show contribution of recombination to antibody escape.

Individual bacteria interact with each other and their environment to produce population-level patterns of gene expression. Here the authors use an automated platform combined with optogenetic feedback to manipulate population behaviors through dynamic control of individual cells.

Availability of computing power can limit computational analysis of large genetic and genomic datasets. Here, Canela-Xandri, et al. describe a software called DISSECT that is capable of analyzing large-scale genetic data by distributing the work across thousands of networked computers.

Identifying which genes and pathways explain genetic associations is challenging. Here, the authors present DEPICT, a tool for gene prioritization, pathway analysis and tissue/cell-type enrichment analysis that can be used to generate testable hypotheses from genetic association studies.