Statistical algorithms improve accuracy of gene fusion detection

Dual Protein Corona-Mediated Target Recognition System for Visual Detection and Single-Molecule Counting of Nucleic Acids

Rapid, highly sensitive, and specific nucleic acid detection plays a crucial role in advancing point-of-care (POC) diagnostics for pathogens and viruses, cancer monitoring, and optimizing clinical treatments. Herein, leveraging the precise recognition ability of CRISPR/dCas9 and the powerful localized surface plasmon resonance (LSPR) of gold nanoparticles (AuNPs), we report the design of a dual protein corona-mediated detection platform to simultaneously fulfill rapid POC testing and single-molecule counting of nucleic acids in a one-pot and one-step manner. This system uses guide RNA as a molecular bridge to anchor dCas9 protein onto AuNPs, forming artificial protein coronas. Upon recognizing a target, the interaction between the two protein coronas on the same nucleic acid molecule triggers cross-linked aggregation of AuNPs. Then, a target as low as 100 aM can be visually detected within 30 min, making the platform particularly well-suited for rapid POC application and the screening of emerging epidemics. Additionally, the superior LSPR properties of AuNPs increase the light-scattering signal generated during target-induced aggregation, enabling the visualization of the aggregated AuNPs as diffraction-limited spots under confocal microscopy. By counting these spots, the platform achieves unprecedented detection sensitivity, identifying a target as low as 1 aM, which is equivalent to just 6 molecules in a 10 μL system, demonstrating single-molecule detection capability. This dual protein corona-mediated detection system offers exceptional promise for large-scale screening of pathogenic viruses and the early detection of cancer, particularly in applications requiring ultrahigh sensitivity at the single-molecule level.

A convergent malignant phenotype in B-cell acute lymphoblastic leukemia involving the splicing factor SRRM1

A significant proportion of infant B-cell acute lymphoblastic leukemia (B-ALL) patients remains with a dismal prognosis due to yet undetermined mechanisms. We performed a comprehensive multicohort analysis of gene expression, gene fusions, and RNA splicing alterations to uncover molecular signatures potentially linked to the observed poor outcome. We identified 87 fusions with significant allele frequency across patients and shared functional impacts, suggesting common mechanisms across fusions. We further identified a gene expression signature that predicts high risk independently of the gene fusion background and includes the upregulation of the splicing factor SRRM1. Experiments in B-ALL cell lines provided further evidence for the role of SRRM1 on cell survival, proliferation, and invasion. Supplementary analysis revealed that SRRM1 potentially modulates splicing events associated with poor outcomes through protein-protein interactions with other splicing factors. Our findings reveal a potential convergent mechanism of aberrant RNA processing that sustains a malignant phenotype independently of the underlying gene fusion and that could potentially complement current clinical strategies in infant B-ALL.

Specific splice junction detection in single cells with SICILIAN

Precise splice junction calls are currently unavailable in scRNA-seq pipelines such as the 10x Chromium platform but are critical for understanding single-cell biology. Here, we introduce SICILIAN, a new method that assigns statistical confidence to splice junctions from a spliced aligner to improve precision. SICILIAN is a general method that can be applied to bulk or single-cell data, but has particular utility for single-cell analysis due to that data's unique challenges and opportunities for discovery. SICILIAN's precise splice detection achieves high accuracy on simulated data, improves concordance between matched single-cell and bulk datasets, and increases agreement between biological replicates. SICILIAN detects unannotated splicing in single cells, enabling the discovery of novel splicing regulation through single-cell analysis workflows.

Assembling and Validating Bioinformatic Pipelines for Next-Generation Sequencing Clinical Assays

CONTEXT.—

Clinical next-generation sequencing (NGS) is being rapidly adopted, but analysis and interpretation of large data sets prompt new challenges for a clinical laboratory setting. Clinical NGS results rely heavily on the bioinformatics pipeline for identifying genetic variation in complex samples. The choice of bioinformatics algorithms, genome assembly, and genetic annotation databases are important for determining genetic alterations associated with disease. The analysis methods are often tuned to the assay to maximize accuracy. Once a pipeline has been developed, it must be validated to determine accuracy and reproducibility for samples similar to real-world cases. In silico proficiency testing or institutional data exchange will ensure consistency among clinical laboratories.

OBJECTIVE.—

To provide molecular pathologists a step-by-step guide to bioinformatics analysis and validation design in order to navigate the regulatory and validation standards of implementing a bioinformatic pipeline as a part of a new clinical NGS assay.

DATA SOURCES.—

This guide uses published studies on genomic analysis, bioinformatics methods, and methods comparison studies to inform the reader on what resources, including open source software tools and databases, are available for genetic variant detection and interpretation.

CONCLUSIONS.—

This review covers 4 key concepts: (1) bioinformatic analysis design for detecting genetic variation, (2) the resources for assessing genetic effects, (3) analysis validation assessment experiments and data sets, including a diverse set of samples to mimic real-world challenges that assess accuracy and reproducibility, and (4) if concordance between clinical laboratories will be improved by proficiency testing designed to test bioinformatic pipelines.

Visual Detection of Fusion Genes by Ligation-Triggered Isothermal Exponential Amplification: A Point-of-Care Testing Method for Highly Specific and Sensitive Quantitation of Fusion Genes with a Smartphone

Fusion genes, playing a causal role in human tumorigenesis and developments, are deemed as gold standard molecular biomarkers in cancer diagnosis, therapy, and prognosis. A rapid, robust, and sensitive method of detection of fusion genes for point-of-care (POC) diagnosis is urgently needed. Here, taking the advantages of the superior specificity of the ligation reaction and the highly amplified efficiency of isothermal exponential amplification with a pH indicator, we developed a colorimetric method for visual detection of fusion genes with high sensitivity and specificity by the naked eye. More importantly, we first found that fusion genes can be accurately quantified in a wide dynamic range (2 zmol to 2 fmol) by an open-source app with a smartphone-assisted RGB (red, green, and blue value) reading mode. The proposed method for Visual detection of Fusion genes by Ligation-triggered Isothermal Exponential Amplification is termed Vis-Fusion LIEXA. We have demonstrated that the Vis-Fusion LIEXA is a practical and reliable method for accurate quantitative detection of the fusion gene in a complex biological sample at zmol level in 40 min only with a smartphone, thereby providing a user-friendly and point-of-care testing (POCT) tool for molecular diagnostics.

Improved detection of gene fusions by applying statistical methods reveals oncogenic RNA cancer drivers

The extent to which gene fusions function as drivers of cancer remains a critical open question. Current algorithms do not sufficiently identify false-positive fusions arising during library preparation, sequencing, and alignment. Here, we introduce Data-Enriched Efficient PrEcise STatistical fusion detection (DEEPEST), an algorithm that uses statistical modeling to minimize false-positives while increasing the sensitivity of fusion detection. In 9,946 tumor RNA-sequencing datasets from The Cancer Genome Atlas (TCGA) across 33 tumor types, DEEPEST identifies 31,007 fusions, 30% more than identified by other methods, while calling 10-fold fewer false-positive fusions in nontransformed human tissues. We leverage the increased precision of DEEPEST to discover fundamental cancer biology. Namely, 888 candidate oncogenes are identified based on overrepresentation in DEEPEST calls, and 1,078 previously unreported fusions involving long intergenic noncoding RNAs, demonstrating a previously unappreciated prevalence and potential for function. DEEPEST also reveals a high enrichment for fusions involving oncogenes in cancers, including ovarian cancer, which has had minimal treatment advances in recent decades, finding that more than 50% of tumors harbor gene fusions predicted to be oncogenic. Specific protein domains are enriched in DEEPEST calls, indicating a global selection for fusion functionality: kinase domains are nearly 2-fold more enriched in DEEPEST calls than expected by chance, as are domains involved in (anaerobic) metabolism and DNA binding. The statistical algorithms, population-level analytic framework, and the biological conclusions of DEEPEST call for increased attention to gene fusions as drivers of cancer and for future research into using fusions for targeted therapy.

The poplar pangenome provides insights into the evolutionary history of the genus

The genus Populus comprises a complex amalgam of ancient and modern species that has become a prime model for evolutionary and taxonomic studies. Here we sequenced the genomes of 10 species from five sections of the genus Populus, identified 71 million genomic variations, and observed new correlations between the single-nucleotide polymorphism-structural variation (SNP-SV) density and indel-SV density to complement the SNP-indel density correlation reported in mammals. Disease resistance genes (R genes) with heterozygous loss-of-function (LOF) were significantly enriched in the 10 species, which increased the diversity of poplar R genes during evolution. Heterozygous LOF mutations in the self-incompatibility genes were closely related to the self-fertilization of poplar, suggestive of genomic control of self-fertilization in dioecious plants. The phylogenetic genome-wide SNPs tree also showed possible ancient hybridization among species in sections Tacamahaca, Aigeiros, and Leucoides. The pangenome resource also provided information for poplar genetics and breeding.

A Comprehensive Infrastructure for Big Data in Cancer Research: Accelerating Cancer Research and Precision Medicine

Advancements in next-generation sequencing and other -omics technologies are accelerating the detailed molecular characterization of individual patient tumors, and driving the evolution of precision medicine. Cancer is no longer considered a single disease, but rather, a diverse array of diseases wherein each patient has a unique collection of germline variants and somatic mutations. Molecular profiling of patient-derived samples has led to a data explosion that could help us understand the contributions of environment and germline to risk, therapeutic response, and outcome. To maximize the value of these data, an interdisciplinary approach is paramount. The National Cancer Institute (NCI) has initiated multiple projects to characterize tumor samples using multi-omic approaches. These projects harness the expertise of clinicians, biologists, computer scientists, and software engineers to investigate cancer biology and therapeutic response in multidisciplinary teams. Petabytes of cancer genomic, transcriptomic, epigenomic, proteomic, and imaging data have been generated by these projects. To address the data analysis challenges associated with these large datasets, the NCI has sponsored the development of the Genomic Data Commons (GDC) and three Cloud Resources. The GDC ensures data and metadata quality, ingests and harmonizes genomic data, and securely redistributes the data. During its pilot phase, the Cloud Resources tested multiple cloud-based approaches for enhancing data access, collaboration, computational scalability, resource democratization, and reproducibility. These NCI-led efforts are continuously being refined to better support open data practices and precision oncology, and to serve as building blocks of the NCI Cancer Research Data Commons.