Per-run Algorithm Selection with Warm-starting using Trajectory-based Features

Per-instance algorithm selection seeks to recommend, for a given problem instance and a given performance criterion, one or several suitable algorithms that are expected to perform well for the particular setting. The selection is classically done offline, using openly available information about the problem instance or features that are extracted from the instance during a dedicated feature extraction step. This ignores valuable information that the algorithms accumulate during the optimization process. In this work, we propose an alternative, online algorithm selection scheme which we coin per-run algorithm selection. In our approach, we start the optimization with a default algorithm, and, after a certain number of iterations, extract instance features from the observed trajectory of this initial optimizer to determine whether to switch to another optimizer. We test this approach using the CMA-ES as the default solver, and a portfolio of six different optimizers as potential algorithms to switch to. In contrast to other recent work on online per-run algorithm selection, we warm-start the second optimizer using information accumulated during the first optimization phase. We show that our approach outperforms static per-instance algorithm selection. We also compare two different feature extraction principles, based on exploratory landscape analysis and time series analysis of the internal state variables of the CMA-ES, respectively. We show that a combination of both feature sets provides the most accurate recommendations for our test cases, taken from the BBOB function suite from the COCO platform and the YABBOB suite from the Nevergrad platform

Utility of Post-Mortem Genetic Testing in Cases of Sudden Arrhythmic Death Syndrome.

BACKGROUND: Sudden arrhythmic death syndrome (SADS) describes a sudden death with negative autopsy and toxicological analysis. Cardiac genetic disease is a likely etiology. OBJECTIVES: This study investigated the clinical utility and combined yield of post-mortem genetic testing (molecular autopsy) in cases of SADS and comprehensive clinical evaluation of surviving relatives. METHODS: We evaluated 302 expertly validated SADS cases with suitable DNA (median age: 24 years; 65% males) who underwent next-generation sequencing using an extended panel of 77 primary electrical disorder and cardiomyopathy genes. Pathogenic and likely pathogenic variants were classified using American College of Medical Genetics (ACMG) consensus guidelines. The yield of combined molecular autopsy and clinical evaluation in 82 surviving families was evaluated. A gene-level rare variant association analysis was conducted in SADS cases versus controls. RESULTS: A clinically actionable pathogenic or likely pathogenic variant was identified in 40 of 302 cases (13%). The main etiologies established were catecholaminergic polymorphic ventricular tachycardia and long QT syndrome (17 [6%] and 11 [4%], respectively). Gene-based rare variants association analysis showed enrichment of rare predicted deleterious variants in RYR2 (p = 5 × 10(-5)). Combining molecular autopsy with clinical evaluation in surviving families increased diagnostic yield from 26% to 39%. CONCLUSIONS: Molecular autopsy for electrical disorder and cardiomyopathy genes, using ACMG guidelines for variant classification, identified a modest but realistic yield in SADS. Our data highlighted the predominant role of catecholaminergic polymorphic ventricular tachycardia and long QT syndrome, especially the RYR2 gene, as well as the minimal yield from other genes. Furthermore, we showed the enhanced utility of combined clinical and genetic evaluation

Multi-messenger observations of a binary neutron star merger

On 2017 August 17 a binary neutron star coalescence candidate (later designated GW170817) with merger time 12:41:04 UTC was observed through gravitational waves by the Advanced LIGO and Advanced Virgo detectors. The Fermi Gamma-ray Burst Monitor independently detected a gamma-ray burst (GRB 170817A) with a time delay of ~1.7 s with respect to the merger time. From the gravitational-wave signal, the source was initially localized to a sky region of 31 deg2 at a luminosity distance of 40+8-8 Mpc and with component masses consistent with neutron stars. The component masses were later measured to be in the range 0.86 to 2.26 Mo. An extensive observing campaign was launched across the electromagnetic spectrum leading to the discovery of a bright optical transient (SSS17a, now with the IAU identification of AT 2017gfo) in NGC 4993 (at ~40 Mpc) less than 11 hours after the merger by the One- Meter, Two Hemisphere (1M2H) team using the 1 m Swope Telescope. The optical transient was independently detected by multiple teams within an hour. Subsequent observations targeted the object and its environment. Early ultraviolet observations revealed a blue transient that faded within 48 hours. Optical and infrared observations showed a redward evolution over ~10 days. Following early non-detections, X-ray and radio emission were discovered at the transient’s position ~9 and ~16 days, respectively, after the merger. Both the X-ray and radio emission likely arise from a physical process that is distinct from the one that generates the UV/optical/near-infrared emission. No ultra-high-energy gamma-rays and no neutrino candidates consistent with the source were found in follow-up searches. These observations support the hypothesis that GW170817 was produced by the merger of two neutron stars in NGC4993 followed by a short gamma-ray burst (GRB 170817A) and a kilonova/macronova powered by the radioactive decay of r-process nuclei synthesized in the ejecta

Functional Studies of Transcription, from RNA-protein Interactions, to Promoter Proximal Pausing, to the Fundamental Units of Transcription Initiation

To understand gene regulation, we need both targeted approaches to probe individual regulatory components, and systems level approaches to understand the functional state of cells. Presented here are several studies at different points on this functional spectrum, with a focus on the crucial regulatory step of promoter proximal pausing. RNA-protein interactions have critical roles in gene regulation. We adapted an Illumina GAIIx sequencer to make several millions of these measurements with a High-Throughput Sequencing – RNA Affinity Profiling (HiTS-RAP) assay. Millions of cDNAs are sequenced, bound by the E. coli replication terminator protein Tus, and transcribed in situ, whereupon Tus halts transcription leaving RNA stably attached to its template DNA. Binding of fluorescently-labeled protein is then quantified in the sequencer. By measuring the affinity of mutagenized libraries of an RNA aptamer to NELF-E, an RNA binding subunit of the pausing factor NELF, we show that this interaction is due to a short RNA motif, but the three dimensional structure of the aptamer is critical for its high affinity. We used this aptamer as an in vivo inhibitor of the interaction between NELF-E and nascent RNA in Drosophila S2 cells. Pausing was globally reduced, but promoters with the transcription factor GAF were unchanged. Thus, the interaction between NELF-E and nascent RNA is not critical for pausing when GAF aids NELF recruitment, but is a strong component of NELF recruitment elsewhere. In higher eukaryotes, the timing and level of transcription at gene promoters by RNA Polymerase II (Pol II) is specified largely by the sum of information from the promoter itself and from distal enhancers .Transcription widely occurs at enhancers, suggesting Pol II may be a ubiquitous nexus of regulatory signaling. To explore this, we sequenced nascent RNAs at single-molecule resolution to identify Pol II initiation, capping, and pause sites. Our analyses reveal distinct sequence-specified pause classes associated with differences in RNA capping dynamics. Initiation typically occurs within large clusters, especially at gene promoters. Integrated analysis of nearby chromatin and transcription factors suggests a model of gene regulation in which Pol II initiation provides a biophysical scaffold to create and maintain regulatory domains

Trajectory-based Algorithm Selection with Warm-starting

International audienceLandscape-aware algorithm selection approaches have so far mostly been relying on landscape feature extraction as a preprocessing step, independent of the execution of optimization algorithms in the portfolio. This introduces a significant overhead in computational cost for many practical applications, as features are extracted and computed via sampling and evaluating the problem instance at hand, similarly to what the optimization algorithm would perform anyway within its search trajectory. As suggested in [Jankovic et al., EvoAPP 2021], trajectory-based algorithm selection circumvents the problem of costly feature extraction by computing landscape features from points that a solver sampled and evaluated during the optimization process. Features computed in this manner are used to train algorithm performance regression models, upon which a per-run algorithm selector is then built. In this work, we apply the trajectory-based approach onto a portfolio of five algorithms. We study the quality and accuracy of performance regression and algorithm selection models in the scenario of predicting different algorithm performances after a fixed budget of function evaluations. We rely on landscape features of the problem instance computed using one portion of the aforementioned budget of the same function evaluations. Moreover, we consider the possibility of switching between the solvers once, which requires them to be warm-started, i.e. when we switch, the second solver continues the optimization process already being initialized appropriately by making use of the information collected by the first solver. In this new context, we show promising performance of the trajectory-based per-run algorithm selection with warm-starting

Trajectory-based Algorithm Selection with Warm-starting

Landscape-aware algorithm selection approaches have so far mostly been relying on landscape feature extraction as a preprocessing step, independent of the execution of optimization algorithms in the portfolio. This introduces a significant overhead in computational cost for many practical applications, as features are extracted and computed via sampling and evaluating the problem instance at hand, similarly to what the optimization algorithm would perform anyway within its search trajectory. As suggested in Jankovic et al. (EvoAPPs 2021), trajectory-based algorithm selection circumvents the problem of costly feature extraction by computing landscape features from points that a solver sampled and evaluated during the optimization process. Features computed in this manner are used to train algorithm performance regression models, upon which a per-run algorithm selector is then built. In this work, we apply the trajectory-based approach onto a portfolio of five algorithms. We study the quality and accuracy of performance regression and algorithm selection models in the scenario of predicting different algorithm performances after a fixed budget of function evaluations. We rely on landscape features of the problem instance computed using one portion of the aforementioned budget of the same function evaluations. Moreover, we consider the possibility of switching between the solvers once, which requires them to be warm-started, i.e. when we switch, the second solver continues the optimization process already being initialized appropriately by making use of the information collected by the first solver. In this new context, we show promising performance of the trajectory-based per-run algorithm selection with warm-starting

The Drosophila BEAF insulator protein interacts with the polybromo subunit of the PBAP chromatin remodeling complex

The Drosophila Boundary Element-Associated Factor of 32 kDa (BEAF) binds in promoter regions of a few thousand mostly housekeeping genes. BEAF is implicated in both chromatin domain boundary activity and promoter function, although molecular mechanisms remain elusive. Here, we show that BEAF physically interacts with the polybromo subunit (Pbro) of PBAP, a SWI/SNF-class chromatin remodeling complex. BEAF also shows genetic interactions with Pbro and other PBAP subunits. We examine the effect of this interaction on gene expression and chromatin structure using precision run-on sequencing and micrococcal nuclease sequencing after RNAi-mediated knockdown in cultured S2 cells. Our results are consistent with the interaction playing a subtle role in gene activation. Fewer than 5% of BEAF-associated genes were significantly affected after BEAF knockdown. Most were downregulated, accompanied by fill-in of the promoter nucleosome-depleted region and a slight upstream shift of the +1 nucleosome. Pbro knockdown caused downregulation of several hundred genes and showed a correlation with BEAF knockdown but a better correlation with promoter-proximal GAGA factor binding. Micrococcal nuclease sequencing supports that BEAF binds near housekeeping gene promoters while Pbro is more important at regulated genes. Yet there is a similar general but slight reduction of promoter-proximal pausing by RNA polymerase II and increase in nucleosome-depleted region nucleosome occupancy after knockdown of either protein. We discuss the possibility of redundant factors keeping BEAF-associated promoters active and masking the role of interactions between BEAF and the Pbro subunit of PBAP in S2 cells. We identify Facilitates Chromatin Transcription (FACT) and Nucleosome Remodeling Factor (NURF) as candidate redundant factors

Rapid cost-effective viral genome sequencing by V-seq

ABSTRACT Conventional methods for viral genome sequencing largely use metatranscriptomic approaches or, alternatively, enrich for viral genomes by amplicon sequencing with virus-specific PCR or hybridization-based capture. These existing methods are costly, require extensive sample handling time, and have limited throughput. Here, we describe V-seq, an inexpensive, fast, and scalable method that performs targeted viral genome sequencing by multiplexing virus-specific primers at the cDNA synthesis step. We designed densely tiled reverse transcription (RT) primers across the SARS-CoV-2 genome, with a subset of hexamers at the 3’ end to minimize mis-priming from the abundant human rRNA repeats and human RNA PolII transcriptome. We found that overlapping RT primers do not interfere, but rather act in concert to improve viral genome coverage in samples with low viral load. We provide a path to optimize V-seq with SARS-CoV-2 as an example. We anticipate that V-seq can be applied to investigate genome evolution and track outbreaks of RNA viruses in a cost-effective manner. More broadly, the multiplexed RT approach by V-seq can be generalized to other applications of targeted RNA sequencing