Genome-wide cell-free DNA mutational integration enables ultra-sensitive cancer monitoring.

In many areas of oncology, we lack sensitive tools to track low-burden disease. Although cell-free DNA (cfDNA) shows promise in detecting cancer mutations, we found that the combination of low tumor fraction (TF) and limited number of DNA fragments restricts low-disease-burden monitoring through the prevailing deep targeted sequencing paradigm. We reasoned that breadth may supplant depth of sequencing to overcome the barrier of cfDNA abundance. Whole-genome sequencing (WGS) of cfDNA allowed ultra-sensitive detection, capitalizing on the cumulative signal of thousands of somatic mutations observed in solid malignancies, with TF detection sensitivity as low as 10-5. The WGS approach enabled dynamic tumor burden tracking and postoperative residual disease detection, associated with adverse outcome. Thus, we present an orthogonal framework for cfDNA cancer monitoring via genome-wide mutational integration, enabling ultra-sensitive detection, overcoming the limitation of cfDNA abundance and empowering treatment optimization in low-disease-burden oncology care.

A deep recurrent neural network discovers complex biological rules to decipher RNA protein-coding potential.

The current deluge of newly identified RNA transcripts presents a singular opportunity for improved assessment of coding potential, a cornerstone of genome annotation, and for machine-driven discovery of biological knowledge. While traditional, feature-based methods for RNA classification are limited by current scientific knowledge, deep learning methods can independently discover complex biological rules in the data de novo. We trained a gated recurrent neural network (RNN) on human messenger RNA (mRNA) and long noncoding RNA (lncRNA) sequences. Our model, mRNA RNN (mRNN), surpasses state-of-the-art methods at predicting protein-coding potential despite being trained with less data and with no prior concept of what features define mRNAs. To understand what mRNN learned, we probed the network and uncovered several context-sensitive codons highly predictive of coding potential. Our results suggest that gated RNNs can learn complex and long-range patterns in full-length human transcripts, making them ideal for performing a wide range of difficult classification tasks and, most importantly, for harvesting new biological insights from the rising flood of sequencing data.

HopBase: a unified resource for Humulus genomics.

Hop (Humulus lupulus L. var lupulus) is a dioecious plant of worldwide significance, used primarily for bittering and flavoring in brewing beer. Studies on the medicinal properties of several unique compounds produced by hop have led to additional interest from pharmacy and healthcare industries as well as livestock production as a natural antibiotic. Genomic research in hop has resulted a published draft genome and transcriptome assemblies. As research into the genomics of hop has gained interest, there is a critical need for centralized online genomic resources. To support the growing research community, we report the development of an online resource "HopBase.org." In addition to providing a gene annotation to the existing Shinsuwase draft genome, HopBase makes available genome assemblies and annotations for both the cultivar "Teamaker" and male hop accession number USDA 21422M. These genome assemblies, gene annotations, along with other common data, coupled with a genome browser and BLAST database enable the hop community to enter the genomic age. The HopBase genomic resource is accessible at http://hopbase.org and http://hopbase.cgrb.oregonstate.edu.

P-SAMS: a web site for plant artificial microRNA and synthetic trans-acting small interfering RNA design.

SUMMARY: The Plant Small RNA Maker Site (P-SAMS) is a web tool for the simple and automated design of artificial miRNAs (amiRNAs) and synthetic trans-acting small interfering RNAs (syn-tasiRNAs) for efficient and specific targeted gene silencing in plants. P-SAMS includes two applications, P-SAMS amiRNA Designer and P-SAMS syn-tasiRNA Designer. The navigation through both applications is wizard-assisted, and the job runtime is relatively short. Both applications output the sequence of designed small RNA(s), and the sequence of the two oligonucleotides required for cloning into 'B/c' compatible vectors. AVAILABILITY AND IMPLEMENTATION: The P-SAMS website is available at http://p-sams.carringtonlab.org. CONTACT: acarbonell@ibmcp.upv.es or nfahlgren@danforthcenter.org.

A Versatile Phenotyping System and Analytics Platform Reveals Diverse Temporal Responses to Water Availability in Setaria.

Phenotyping has become the rate-limiting step in using large-scale genomic data to understand and improve agricultural crops. Here, the Bellwether Phenotyping Platform for controlled-environment plant growth and automated multimodal phenotyping is described. The system has capacity for 1140 plants, which pass daily through stations to record fluorescence, near-infrared, and visible images. Plant Computer Vision (PlantCV) was developed as open-source, hardware platform-independent software for quantitative image analysis. In a 4-week experiment, wild Setaria viridis and domesticated Setaria italica had fundamentally different temporal responses to water availability. While both lines produced similar levels of biomass under limited water conditions, Setaria viridis maintained the same water-use efficiency under water replete conditions, while Setaria italica shifted to less efficient growth. Overall, the Bellwether Phenotyping Platform and PlantCV software detected significant effects of genotype and environment on height, biomass, water-use efficiency, color, plant architecture, and tissue water status traits. All ∼ 79,000 images acquired during the course of the experiment are publicly available.