A graphical, interactive and GPU-enabled workflow to process long-read sequencing data

Complete genome sequence of Oenococcus oeni strain K19-3 isolated from grape must

The lactic acid bacteria Oenococcus oeni spp. are of significant interest in winemaking due to their ability to carry out malolactic fermentation, thereby improving the organoleptic properties of wine. Here we report the complete circular genome sequence of the Oenococcus oeni strain К19-3, isolated from red grape must at Crimean wineries.

Oxford Nanopore Technology and its Application in Liquid Biopsies

Advanced medical technologies are transforming the future of healthcare, in particular, the screening and detection of molecular-genetic changes in patients suspected of having a neoplasm. They are based on the assumption that neoplasms release small amounts  of  various  neoplasm-specific molecules, such as tumor DNA, called circulating DNA (cirDNA), into the extracellular space and subsequently into the blood. The detection of tumor-specific molecules and specific molecular changes in body fluids in a noninvasive or minimally invasive approach is known as "liquid biopsy." The aim of this review is to summarize the current knowledge of the application of ONT for analyzing circulating DNA in the field of liquid biopsies among cancer patients. Databases were searched using the keywords "nanopore" and "liquid biopsy" and by applying strict inclusion criteria. This technique can be used for the detection of neoplastic disease, including metastases, guiding precision therapy, and monitoring its effects. There are many challenges, however, for the successful implementation of this technology into the clinical practice. The first one is the low amount of tumor-specific molecules in the body fluids. Secondly, a tumor molecular signature should be discriminated from benign conditions like clonal hematopoiesis of unknown significance. Oxford Nanopore Technology (ONT) is a third-generation sequencing technology that seems particularly promising to complete these tasks. It offers rapid sequencing thanks to its ability to detect changes in the density of the electric current passing through nanopores. Even though ONT still needs validation technology, it is a promising approach for early diagnosis, therapy guidance, and monitoring of different neoplasms based on analyzing the cirDNA.

NanoDeep: a deep learning framework for nanopore adaptive sampling on microbial sequencing

Nanopore sequencers can enrich or deplete the targeted DNA molecules in a library by reversing the voltage across individual nanopores. However, it requires substantial computational resources to achieve rapid operations in parallel at read-time sequencing. We present a deep learning framework, NanoDeep, to overcome these limitations by incorporating convolutional neural network and squeeze and excitation. We first showed that the raw squiggle derived from native DNA sequences determines the origin of microbial and human genomes. Then, we demonstrated that NanoDeep successfully classified bacterial reads from the pooled library with human sequence and showed enrichment for bacterial sequence compared with routine nanopore sequencing setting. Further, we showed that NanoDeep improves the sequencing efficiency and preserves the fidelity of bacterial genomes in the mock sample. In addition, NanoDeep performs well in the enrichment of metagenome sequences of gut samples, showing its potential applications in the enrichment of unknown microbiota. Our toolkit is available at https://github.com/lysovosyl/NanoDeep.

Rapid detection of myeloid neoplasm fusions using single-molecule long-read sequencing

Recurrent gene fusions are common drivers of disease pathophysiology in leukemias. Identifying these structural variants helps stratify disease by risk and assists with therapy choice. Precise molecular diagnosis in low-and-middle-income countries (LMIC) is challenging given the complexity of assays, trained technical support, and the availability of reliable electricity. Current fusion detection methods require a long turnaround time (7-10 days) or advance knowledge of the genes involved in the fusions. Recent technology developments have made sequencing possible without a sophisticated molecular laboratory, potentially making molecular diagnosis accessible to remote areas and low-income settings. We describe a long-read sequencing DNA assay designed with CRISPR guides to select and enrich for recurrent leukemia fusion genes, that does not need a priori knowledge of the abnormality present. By applying rapid sequencing technology based on nanopores, we sequenced long pieces of genomic DNA and successfully detected fusion genes in cell lines and primary specimens (e.g., BCR::ABL1, PML::RARA, CBFB::MYH11, KMT2A::AFF1) using cloud-based bioinformatics workflows with novel custom fusion finder software. We detected fusion genes in 100% of cell lines with the expected breakpoints and confirmed the presence or absence of a recurrent fusion gene in 12 of 14 patient cases. With our optimized assay and cloud-based bioinformatics workflow, these assays and analyses could be performed in under 8 hours. The platform's portability, potential for adaptation to lower-cost devices, and integrated cloud analysis make this assay a candidate to be placed in settings like LMIC to bridge the need of bedside rapid molecular diagnostics.

Cloud-enabled Biodepot workflow builder integrates image processing using Fiji with reproducible data analysis using Jupyter notebooks

Modern biomedical image analyses workflows contain multiple computational processing tasks giving rise to problems in reproducibility. In addition, image datasets can span both spatial and temporal dimensions, with additional channels for fluorescence and other data, resulting in datasets that are too large to be processed locally on a laptop. For omics analyses, software containers have been shown to enhance reproducibility, facilitate installation and provide access to scalable computational resources on the cloud. However, most image analyses contain steps that are graphical and interactive, features that are not supported by most omics execution engines. We present the containerized and cloud-enabled Biodepot-workflow-builder platform that supports graphics from software containers and has been extended for image analyses. We demonstrate the potential of our modular approach with multi-step workflows that incorporate the popular and open-source Fiji suite for image processing. One of our examples integrates fully interactive ImageJ macros with Jupyter notebooks. Our second example illustrates how the complicated cloud setup of an computationally intensive process such as stitching 3D digital pathology datasets using BigStitcher can be automated and simplified. In both examples, users can leverage a form-based graphical interface to execute multi-step workflows with a single click, using the provided sample data and preset input parameters. Alternatively, users can interactively modify the image processing steps in the workflow, apply the workflows to their own data, change the input parameters and macros. By providing interactive graphics support to software containers, our modular platform supports reproducible image analysis workflows, simplified access to cloud resources for analysis of large datasets, and integration across different applications such as Jupyter.