Despite the growing variety of sequencing and variant-calling tools, no workflow performs equally well across the entire human genome. Understanding context-dependent performance is critical for enabling researchers, clinicians, and developers to make informed tradeoffs when selecting sequencing hardware and software. Here we describe a set of “stratifications,” which are BED files that define distinct contexts throughout the genome. We define these for GRCh37/38 as well as the new T2T-CHM13 reference, adding many new hard-to-sequence regions which are critical for understanding performance as the field progresses. Specifically, we highlight the increase in hard-to-map and GC-rich stratifications in CHM13 relative to the previous references. We then compare the benchmarking performance with each reference and show the performance penalty brought about by these additional difficult regions in CHM13. Additionally, we demonstrate how the stratifications can track context-specific improvements over different platform iterations, using Oxford Nanopore Technologies as an example. The means to generate these stratifications are available as a snakemake pipeline at https://github.com/usnistgov/giab-stratifications. We anticipate this being useful in enabling precise risk-reward calculations when building sequencing pipelines for any of the commonly-used reference genomes.

Summary: Facultative pathogens have extremely dynamic pan‐genomes, to a large extent derived from bacteriophages and other mobile elements. We developed a simple approach to identify phage‐derived genomic islands and apply it to show that pathogens from diverse bacterial genera are significantly enriched in clustered phage‐derived genes compared with related benign strains. These findings show that genome expansion by integration of prophages containing virulence factors is a major route of evolution of facultative bacterial pathogens.

Purpose: Clonal hematopoiesis (CH) is thought to be the origin of myeloid neoplasms (MN). Yet, our understanding of the mechanisms driving CH progression to MN and clinical risk prediction of MN remains limited. The human proteome reflects complex interactions between genetic and epigenetic regulation of biological systems. We hypothesized that the plasma proteome might predict MN risk and inform our understanding of the mechanisms promoting MN development. Experimental Design: We jointly characterized CH and plasma proteomic profiles of 46,237 individuals in the UK Biobank at baseline study entry. During 500,036 person-years of follow-up, 115 individuals developed MN. Cox proportional hazard regression was used to test for an association between plasma protein levels and MN risk. Results: We identified 115 proteins associated with MN risk, of which 30% (N = 34) were also associated with CH. These were enriched for known regulators of the innate and adaptive immune system. Plasma proteomics improved the prediction of MN risk (AUC = 0.85; P = 5×10–9) beyond clinical factors and CH (AUC = 0.80). In an independent group (N = 381,485), we used inherited polygenic risk scores (PRS) for plasma protein levels to validate the relevance of these proteins toMNdevelopment. PRS analyses suggest that most MN-associated proteins we identified are not directly causally linked toMN risk, but rather represent downstream markers of pathways regulating the progression of CH to MN. Conclusions: These data highlight the role of immune cell regulation in the progression of CH to MN and the promise of leveraging multi-omic characterization of CH to improveMN risk stratification. See related commentary by Bhalgat and Taylor, p. 3095

Intrinsic and acquired resistance to drug therapies remains a challenge for malignant melanoma patients. Intratumoral heterogeneities within the tumor microenvironment contribute additional complexity to the determinants of drug efficacy and acquired resistance. We use 3D biomimetic platforms to understand dynamics in extracellular matrix (ECM) biogenesis following pharmaceutical intervention against mitogen-activated protein kinases (MAPK) signaling. We further determined temporal evolution of secreted ECM components by isogenic melanoma cell clones. We found that the cell clones differentially secrete and assemble a myriad of ECM molecules into dense fibrillar and globular networks. We show that cells can modulate their ECM biosynthesis in response to external insults. Fibronectin (FN) is one of the key architectural components, modulating the efficacy of a broad spectrum of drug therapies. Stable cell lines engineered to secrete minimal levels of FN showed a concomitant increase in secretion of Tenascin-C and became sensitive to BRAFV600E and ERK inhibition as clonally- derived 3D tumor aggregates. These cells failed to assemble exogenous FN despite maintaining the integrin machinery to facilitate cell- ECM cross-talk. We determined that only clones that increased FN production via p38 MAPK and β1 integrin survived drug treatment. These data suggest that tumor cells engineer drug resistance by altering their ECM biosynthesis. Therefore, drug treatment may induce ECM biosynthesis, contributing to de novo resistance.

RNA-Seq Viewer is a web application that enables users to visualize genome-wide expression data from NCBI’s Sequence Read Archive (SRA) and Gene Expression Omnibus (GEO) databases. The application prototype was created by a small team during a three-day hackathon facilitated by NCBI at Brandeis University. The backend data pipeline was developed and deployed on a shared AWS EC2 instance. Source code is available at https://github.com/NCBI-Hackathons/rnaseqview.

Next-generation sequencing technologies can produce tens of millions of reads, often paired-end, from transcripts or genomes. But few programs can align RNA on the genome and accurately discover introns, especially with long reads. We introduce Magic-BLAST, a new aligner based on ideas from the Magic pipeline. It uses innovative techniques that include the optimization of a spliced alignment score and selective masking during seed selection. We evaluate the performance of Magic-BLAST to accurately map short or long sequences and its ability to discover introns on real RNA-seq data sets from PacBio, Roche and Illumina runs, and on six benchmarks, and compare it to other popular aligners. Additionally, we look at alignments of human idealized RefSeq mRNA sequences perfectly matching the genome. We show that Magic-BLAST is the best at intron discovery over a wide range of conditions and the best at mapping reads longer than 250 bases, from any platform. It is versatile and robust to high levels of mismatches or extreme base composition, and reasonably fast. It can align reads to a BLAST database or a FASTA file. It can accept a FASTQ file as input or automatically retrieve an accession from the SRA repository at the NCBI.