Today's 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.

MOTIVATION: Capillary electrophoresis (CE) is a powerful approach for structural analysis of nucleic acids, with recent high-throughput variants enabling three-dimensional RNA modeling and the discovery of new rules for RNA structure design. Among the steps composing CE analysis, the process of finding each band in an electrophoretic trace and mapping it to a position in the nucleic acid sequence has required significant manual inspection and remains the most time-consuming and error-prone step. The few available tools seeking to automate this band annotation have achieved limited accuracy and have not taken advantage of information across dozens of profiles routinely acquired in high-throughput measurements. RESULTS: We present a dynamic-programming-based approach to automate band annotation for high-throughput capillary electrophoresis. The approach is uniquely able to define and optimize a robust target function that takes into account multiple CE profiles (sequencing ladders, different chemical probes, different mutants) collected for the RNA. Over a large benchmark of multi-profile datasets for biological RNAs and designed RNAs from the EteRNA project, the method outperforms prior tools (QuSHAPE and FAST) significantly in terms of accuracy compared with gold-standard manual annotations. The amount of computation required is reasonable at a few seconds per dataset. We also introduce an 'E-score' metric to automatically assess the reliability of the band annotation and show it to be practically useful in flagging uncertainties in band annotation for further inspection. AVAILABILITY AND IMPLEMENTATION: The implementation of the proposed algorithm is included in the HiTRACE software, freely available as an online server and for download at http://hitrace.stanford.edu. CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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.

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.