Hanjoo Kim

RNA design rules from a massive open laboratory

Significance Self-assembling RNA molecules play critical roles throughout biology and bioengineering. To accelerate progress in RNA design, we present EteRNA, the first internet-scale citizen science “game” scored by high-throughput experiments. A community of 37,000 nonexperts leveraged continuous remote laboratory feedback to learn new design rules that substantially improve the experimental accuracy of RNA structure designs. These rules, distilled by machine learning into a new automated algorithm EteRNABot, also significantly outperform prior algorithms in a gauntlet of independent tests. These results show that an online community can carry out large-scale experiments, hypothesis generation, and algorithm design to create practical advances in empirical science. Self-assembling RNA molecules present compelling substrates for the rational interrogation and control of living systems. However, imperfect in silico models—even at the secondary structure level—hinder the design of new RNAs that function properly when synthesized. Here, we present a unique and potentially general approach to such empirical problems: the Massive Open Laboratory. The EteRNA project connects 37,000 enthusiasts to RNA design puzzles through an online interface. Uniquely, EteRNA participants not only manipulate simulated molecules but also control a remote experimental pipeline for high-throughput RNA synthesis and structure mapping. We show herein that the EteRNA community leveraged dozens of cycles of continuous wet laboratory feedback to learn strategies for solving in vitro RNA design problems on which automated methods fail. The top strategies—including several previously unrecognized negative design rules—were distilled by machine learning into an algorithm, EteRNABot. Over a rigorous 1-y testing phase, both the EteRNA community and EteRNABot significantly outperformed prior algorithms in a dozen RNA secondary structure design tests, including the creation of dendrimer-like structures and scaffolds for small molecule sensors. These results show that an online community can carry out large-scale experiments, hypothesis generation, and algorithm design to create practical advances in empirical science.

Concurrent simulation platform for energy-aware smart metering systems

We propose a simulation framework that can model a house equipped with various home appliances and next-generation smart metering devices. This simulator can predict the power dissipation profiles of individual appliances as well as the cumulative energy consumption of the house in a realistic manner. We utilize SystemC, a concurrent system-modeling methodology originally developed and populated in the design automation community. According to our experiments with various consumer electronics devices, the simulated and measured power profiles match very closely, producing the average correlation of 0.973. The deviation of simulated energy consumption from the measurement was also negligible. Using the proposed simulation platform, any electricity consumer interested in energy saving as well as the designer of a new smart metering system will be able to simulate and test their system from energy perspectives. As a case study, we show how the size of the accumulative power peak of a house can be reduced significantly by using the information provided by the proposed simulator.

HiTRACE: High-throughput robust analysis for capillary electrophoresis

MOTIVATION Capillary electrophoresis (CE) of nucleic acids is a workhorse technology underlying high-throughput genome analysis and large-scale chemical mapping for nucleic acid structural inference. Despite the wide availability of CE-based instruments, there remain challenges in leveraging their full power for quantitative analysis of RNA and DNA structure, thermodynamics and kinetics. In particular, the slow rate and poor automation of available analysis tools have bottlenecked a new generation of studies involving hundreds of CE profiles per experiment. RESULTS We propose a computational method called high-throughput robust analysis for capillary electrophoresis (HiTRACE) to automate the key tasks in large-scale nucleic acid CE analysis, including the profile alignment that has heretofore been a rate-limiting step in the highest throughput experiments. We illustrate the application of HiTRACE on 13 datasets representing 4 different RNAs, 3 chemical modification strategies and up to 480 single mutant variants; the largest datasets each include 87 360 bands. By applying a series of robust dynamic programming algorithms, HiTRACE outperforms prior tools in terms of alignment and fitting quality, as assessed by measures including the correlation between quantified band intensities between replicate datasets. Furthermore, while the smallest of these datasets required 7-10 h of manual intervention using prior approaches, HiTRACE quantitation of even the largest datasets herein was achieved in 3-12 min. The HiTRACE method, therefore, resolves a critical barrier to the efficient and accurate analysis of nucleic acid structure in experiments involving tens of thousands of electrophoretic bands.

Standardization of RNA Chemical Mapping Experiments

Chemical mapping experiments offer powerful information about RNA structure but currently involve ad hoc assumptions in data processing. We show that simple dilutions, referencing standards (GAGUA hairpins), and HiTRACE/MAPseeker analysis allow rigorous overmodification correction, background subtraction, and normalization for electrophoretic data and a ligation bias correction needed for accurate deep sequencing data. Comparisons across six noncoding RNAs stringently test the proposed standardization of dimethyl sulfate (DMS), 2′-OH acylation (SHAPE), and carbodiimide measurements. Identification of new signatures for extrahelical bulges and DMS “hot spot” pockets (including tRNA A58, methylated in vivo) illustrates the utility and necessity of standardization for quantitative RNA mapping.

HiTRACE-Web: an online tool for robust analysis of high-throughput capillary electrophoresis

To facilitate the analysis of large-scale high-throughput capillary electrophoresis data, we previously proposed a suite of efficient analysis software named HiTRACE (High Throughput Robust Analysis of Capillary Electrophoresis). HiTRACE has been used extensively for quantitating data from RNA and DNA structure mapping experiments, including mutate-and-map contact inference, chromatin footprinting, the Eterna RNA design project and other high-throughput applications. However, HiTRACE is based on a suite of command-line MATLAB scripts that requires nontrivial efforts to learn, use and extend. Here, we present HiTRACE-Web, an online version of HiTRACE that includes standard features previously available in the command-line version and additional features such as automated band annotation and flexible adjustment of annotations, all via a user-friendly environment. By making use of parallelization, the on-line workflow is also faster than software implementations available to most users on their local computers. Free access: http://hitrace.org.

A robust peak detection method for RNA structure inference by high-throughput contact mapping

MOTIVATION For high-throughput prediction of the helical arrangements of large RNA molecules, an innovative method termed multiplexed hydroxyl radical (*OH) cleavage analysis (MOHCA) has been proposed. A key step in this promising technique is to detect peaks accurately from noisy radioactivity profiles. Since manual peak finding is laborious and prone to error, an automated peak detection method to improve the accuracy and throughput of MOHCA is required. Existing methods were not applicable to MOHCA due to their high false positive rates. RESULTS We developed a two-step computational method that can detect peaks from MOHCA profiles in a robust manner. The first step exploits an ensemble of linear and non-linear signal processing techniques to find true peak candidates. In the second step, a binary classifier trained with the characteristics of true and false peaks is used to eliminate false peaks out of the peak candidates. We tested the proposed approach with 2002 MOHCA cleavage profiles and obtained the median recall, precision and F-measure values of 0.917, 0.750 and 0.830, respectively. Compared with the alternatives considered, the proposed method was able to handle false peaks substantially better, thus resulting in 51.0-71.8% higher median values of precision and F-measure. AVAILABILITY The software and supplementary data are available at http://dna.korea.ac.kr/pub/mohca.

vHoT: a database for predicting interspecies interactions between viral microRNA and host genomes

Some viruses have been reported to transcribe microRNAs, implying complex relationships between the host and the pathogen at the post-transcriptional level through microRNAs in virus-infected cells. Although many computational algorithms have been developed for microRNA target prediction, few have been designed exclusively to find cellular or viral mRNA targets of viral microRNAs in a user-friendly manner. To address this, we introduce the viral microRNA host target (vHoT) database for predicting interspecies interactions between viral microRNA and host genomes. vHoT supports target prediction of 271 viral microRNAs from human, mouse, rat, rhesus monkey, cow, and virus genomes. vHoT is freely available at http://dna.korea.ac.kr/vhot.

Constructing Accurate Contact Maps for Hydroxyl-Radical-Cleavage-Based High-Throughput RNA Structure Inference

For rapid ribonucleic acid (RNA) tertiary structure prediction, innovative methods have been proposed that exploit hydroxyl radical cleavage agents in a high-throughput manner. In such techniques, it is critical to determine accurately which residue a specific cleavage agent interacts with, since this information directly reveals the residue-residue interaction points needed for structure inference. Due to lack of effective automated methods, the process of locating contact points has been mostly done manually, becoming a bottleneck of the whole procedure. To address this problem, we propose a novel computational method to determine residue-residue interaction points from 2-D electrophoresis profiles. This method combines the deconvolution method for signal detection and statistical learning techniques for filtering noise, thus boosting specificity and sensitivity in harmony. According to our experiments with over 2000 actual gel profiles, the proposed technique exhibited 56.44%-90.50% higher performance than traditional methods in terms of the accuracy of reproducing manual contact maps measured by the F-measure, a widely used evaluation metric. We expect that adopting the proposed technique will significantly accelerate RNA tertiary structure inference, allowing researchers to explore more structures in given time.

CloudSocket: Smart grid platform for datacenters

Todays datacenters are equipped with diverse computing and storage devices for handling a myriad of data and normally consume a significant amount of electrical energy. This paper proposes a smart grid inspired methodology to monitor and profile the energy consumption of a datacenter, with the aim of providing information useful for reducing the peak power consumption of the datacenter. Our energy measurement platform is named CloudSocket, and each CloudSocket unit can measure the power consumption of an individual computing node and periodically transmit the measurement information wirelessly to the coordinator unit that can manage many Cloud-Sockets simultaneously. We tested our methodology with a 32-node grid system that runs Apache Spark for large-scale data analytics. Analyzing our experimental results reveals how and where the peak power of each node in the grid overlaps, providing opportunities for informative coordination of the computing components for overall power reduction.

CloudSocket: Fine-Grained Power Sensing and Analysis System for Datacenters

Today’s data centers have various computing and storage devices for processing a myriad of data, and they generally consume a considerable amount of electrical energy. This paper proposes a smart grid-inspired methodology to observe and profile the power consumption of a data center. Based on this technique, our paper provides information that is useful for moderating the peak power consumption of the data centers. Our power measurement platform consists of several devices named CloudSockets, and each CloudSocket unit can measure the power consumption of multiple computing nodes and periodically transmit measurement data wirelessly to the coordinator unit. This data can be used to analyze the relationship between the workload and the power consumption of the data center. We tested our methodology through the application of various algorithms with a 32-node distributed system that runs Apache Spark for large-scale data analytics. An analysis of our experimental results reveals how and where the peak power of each node in the grid overlaps, providing opportunities for informed coordination of the computing components for peak power reduction.