Benefits of applying molecular barcoding systems are not uniform across different genomic applications.

BACKGROUND: Despite the wide variety of Next Generation Sequencing (NGS)-based methods, it remains challenging to detect mutations present at very low frequencies. This problem is particularly relevant in oncology, where the limiting amount of input material, and its low quality, often limit the performance of the assays. Unique Molecular Identifiers (UMIs) are a molecular barcoding system often coupled with computational methods of noise suppression to improve the reliability of detection of rare variants. Although widely adopted, UMI inclusion imposes additional technical complexity and sequencing cost. Currently, there are no guidelines on UMI usage nor a comprehensive evaluation of their advantage across different applications. METHODS: We used DNA sequencing data generated by molecular barcoding and hybridization-based enrichment, from various types and quantities of input material (fresh frozen, formaldehyde-treated and cell-free DNA), to evaluate the performance of variant calling in different clinically relevant contexts. RESULTS: Noise suppression achieved by read grouping based on fragment mapping positions ensures reliable variant calling for many experimental designs even without exogenous UMIs. Exogenous barcodes significantly improve performance only when mapping position collisions occur, which is common in cell-free DNA. CONCLUSIONS: We demonstrate that UMI usage is not universally beneficial across experimental designs and that it is worthwhile to critically consider the comparative advantage of UMI usage for a given NGS application prior to experimental design.

Robust synchronization of coupled circadian and cell cycle oscillators in single mammalian cells.

Circadian cycles and cell cycles are two fundamental periodic processes with a period in the range of 1 day. Consequently, coupling between such cycles can lead to synchronization. Here, we estimated the mutual interactions between the two oscillators by time-lapse imaging of single mammalian NIH3T3 fibroblasts during several days. The analysis of thousands of circadian cycles in dividing cells clearly indicated that both oscillators tick in a 1:1 mode-locked state, with cell divisions occurring tightly 5 h before the peak in circadian Rev-Erbα-YFP reporter expression. In principle, such synchrony may be caused by either unidirectional or bidirectional coupling. While gating of cell division by the circadian cycle has been most studied, our data combined with stochastic modeling unambiguously show that the reverse coupling is predominant in NIH3T3 cells. Moreover, temperature, genetic, and pharmacological perturbations showed that the two interacting cellular oscillators adopt a synchronized state that is highly robust over a wide range of parameters. These findings have implications for circadian function in proliferative tissues, including epidermis, immune cells, and cancer.

GIInger predicts homologous recombination deficiency and patient response to PARPi treatment from shallow genomic profiles.

Homologous recombination deficiency (HRD) is a predictive biomarker for poly(ADP-ribose) polymerase 1 inhibitor (PARPi) sensitivity. Routine HRD testing relies on identifying BRCA mutations, but additional HRD-positive patients can be identified by measuring genomic instability (GI), a consequence of HRD. However, the cost and complexity of available solutions hamper GI testing. We introduce a deep learning framework, GIInger, that identifies GI from HRD-induced scarring observed in low-pass whole-genome sequencing data. GIInger seamlessly integrates into standard BRCA testing workflows and yields reproducible results concordant with a reference method in a multisite study of 327 ovarian cancer samples. Applied to a BRCA wild-type enriched subgroup of 195 PAOLA-1 clinical trial patients, GIInger identified HRD-positive patients who experienced significantly extended progression-free survival when treated with PARPi. GIInger is, therefore, a cost-effective and easy-to-implement method for accurately stratifying patients with ovarian cancer for first-line PARPi treatment.

Whole-embryo modeling of early segmentation in Drosophila identifies robust and fragile expression domains.

Segmentation of the Drosophila melanogaster embryo results from the dynamic establishment of spatial mRNA and protein patterns. Here, we exploit recent temporal mRNA and protein expression measurements on the full surface of the blastoderm to calibrate a dynamical model of the gap gene network on the entire embryo cortex. We model the early mRNA and protein dynamics of the gap genes hunchback, Kruppel, giant, and knirps, taking as regulatory inputs the maternal Bicoid and Caudal gradients, plus the zygotic Tailless and Huckebein proteins. The model captures the expression patterns faithfully, and its predictions are assessed from gap gene mutants. The inferred network shows an architecture based on reciprocal repression between gap genes that can stably pattern the embryo on a realistic geometry but requires complex regulations such as those involving the Hunchback monomer and dimers. Sensitivity analysis identifies the posterior domain of giant as among the most fragile features of an otherwise robust network, and hints at redundant regulations by Bicoid and Hunchback, possibly reflecting recent evolutionary changes in the gap-gene network in insects.

High-Throughput Nucleotide Resolution Predictions of Assay Limitations Increase the Reliability and Concordance of Clinical Tests.

PURPOSE: The ability of next-generation sequencing (NGS) assays to interrogate thousands of genomic loci has revolutionized genetic testing. However, translation to the clinic is impeded by false-negative results that pose a risk to patients. In response, regulatory bodies are calling for reliability measures to be reported alongside NGS results. Existing methods to estimate reliability do not account for sample- and position-specific variability, which can be significant. Here, we report an approach that computes reliability metrics for every genomic position and sample interrogated by an NGS assay. METHODS: Our approach predicts the limit of detection (LOD), the lowest reliably detectable variant fraction, by taking technical factors into account. We initially explored how LOD is affected by input material amount, library conversion rate, sequencing coverage, and sequencing error rate. This revealed that LOD depends heavily on genomic context and sample properties. Using these insights, we developed a computational approach to predict LOD on the basis of a biophysical model of the NGS workflow. We focused on targeted assays for cell-free DNA, but, in principle, this approach applies to any NGS assay. RESULTS: We validated our approach by showing that it accurately predicts LOD and distinguishes reliable from unreliable results when screening 580 lung cancer samples for actionable mutations. Compared with a standard variant calling workflow, our approach avoided most false negatives and improved interassay concordance from 94% to 99%. CONCLUSION: Our approach, which we name LAVA (LOD-aware variant analysis), reports the LOD for every position and sample interrogated by an NGS assay. This enables reliable results to be identified and improves the transparency and safety of genetic tests.