Identification and correction of systematic error in high-throughput sequence data

A feature common to all DNA sequencing technologies is the presence of base-call errors in the sequenced reads. The implications of such errors are application specific, ranging from minor informatics nuisances to major problems affecting biological inferences. Recently developed “next-gen” sequencing technologies have greatly reduced the cost of sequencing, but have been shown to be more error prone than previous technologies. Both position specific (depending on the location in the read) and sequence specific (depending on the sequence in the read) errors have been identified in Illumina and Life Technology sequencing platforms. We describe a new type of _systematic_ error that manifests as statistically unlikely accumulations of errors at specific genome (or transcriptome) locations. We characterize and describe systematic errors using overlapping paired reads form high-coverage data. We show that such errors occur in approximately 1 in 1000 base pairs, and that quality scores at systematic error sites do not account for the extent of errors. We identify motifs that are frequent at systematic error sites, and describe a classifier that distinguishes heterozygous sites from systematic error. Our classifier is designed to accommodate data from experiments in which the allele frequencies at heterozygous sites are not necessarily 0.5 (such as in the case of RNA-Seq). Systematic errors can easily be mistaken for heterozygous sites in individuals, or for SNPs in population analyses. Systematic errors are particularly problematic in low coverage experiments, or in estimates of allele-specific expression from RNA-Seq data. Our characterization of systematic error has allowed us to develop a program, called SysCall, for identifying and correcting such errors. We conclude that correction of systematic errors is important to consider in the design and interpretation of high-throughput sequencing experiments

Automatically Discovering, Reporting and Reproducing Android Application Crashes

Mobile developers face unique challenges when detecting and reporting crashes in apps due to their prevailing GUI event-driven nature and additional sources of inputs (e.g., sensor readings). To support developers in these tasks, we introduce a novel, automated approach called CRASHSCOPE. This tool explores a given Android app using systematic input generation, according to several strategies informed by static and dynamic analyses, with the intrinsic goal of triggering crashes. When a crash is detected, CRASHSCOPE generates an augmented crash report containing screenshots, detailed crash reproduction steps, the captured exception stack trace, and a fully replayable script that automatically reproduces the crash on a target device(s). We evaluated CRASHSCOPE's effectiveness in discovering crashes as compared to five state-of-the-art Android input generation tools on 61 applications. The results demonstrate that CRASHSCOPE performs about as well as current tools for detecting crashes and provides more detailed fault information. Additionally, in a study analyzing eight real-world Android app crashes, we found that CRASHSCOPE's reports are easily readable and allow for reliable reproduction of crashes by presenting more explicit information than human written reports.Comment: 12 pages, in Proceedings of 9th IEEE International Conference on Software Testing, Verification and Validation (ICST'16), Chicago, IL, April 10-15, 2016, pp. 33-4

A Systematic Aspect-Oriented Refactoring and Testing Strategy, and its Application to JHotDraw

Aspect oriented programming aims at achieving better modularization for a system's crosscutting concerns in order to improve its key quality attributes, such as evolvability and reusability. Consequently, the adoption of aspect-oriented techniques in existing (legacy) software systems is of interest to remediate software aging. The refactoring of existing systems to employ aspect-orientation will be considerably eased by a systematic approach that will ensure a safe and consistent migration. In this paper, we propose a refactoring and testing strategy that supports such an approach and consider issues of behavior conservation and (incremental) integration of the aspect-oriented solution with the original system. The strategy is applied to the JHotDraw open source project and illustrated on a group of selected concerns. Finally, we abstract from the case study and present a number of generic refactorings which contribute to an incremental aspect-oriented refactoring process and associate particular types of crosscutting concerns to the model and features of the employed aspect language. The contributions of this paper are both in the area of supporting migration towards aspect-oriented solutions and supporting the development of aspect languages that are better suited for such migrations.Comment: 25 page

StoryDroid: Automated Generation of Storyboard for Android Apps

Mobile apps are now ubiquitous. Before developing a new app, the development team usually endeavors painstaking efforts to review many existing apps with similar purposes. The review process is crucial in the sense that it reduces market risks and provides inspiration for app development. However, manual exploration of hundreds of existing apps by different roles (e.g., product manager, UI/UX designer, developer) in a development team can be ineffective. For example, it is difficult to completely explore all the functionalities of the app in a short period of time. Inspired by the conception of storyboard in movie production, we propose a system, StoryDroid, to automatically generate the storyboard for Android apps, and assist different roles to review apps efficiently. Specifically, StoryDroid extracts the activity transition graph and leverages static analysis techniques to render UI pages to visualize the storyboard with the rendered pages. The mapping relations between UI pages and the corresponding implementation code (e.g., layout code, activity code, and method hierarchy) are also provided to users. Our comprehensive experiments unveil that StoryDroid is effective and indeed useful to assist app development. The outputs of StoryDroid enable several potential applications, such as the recommendation of UI design and layout code

Imprint of DESI fiber assignment on the anisotropic power spectrum of emission line galaxies

The Dark Energy Spectroscopic Instrument (DESI), a multiplexed fiber-fed spectrograph, is a Stage-IV ground-based dark energy experiment aiming to measure redshifts for 29 million Emission-Line Galaxies (ELG), 4 million Luminous Red Galaxies (LRG), and 2 million Quasi-Stellar Objects (QSO). The survey design includes a pattern of tiling on the sky and the locations of the fiber positioners in the focal plane of the telescope, with the observation strategy determined by a fiber assignment algorithm that optimizes the allocation of fibers to targets. This strategy allows a given region to be covered on average five times for a five-year survey, but with coverage varying between zero and twelve, which imprints a spatially-dependent pattern on the galaxy clustering. We investigate the systematic effects of the fiber assignment coverage on the anisotropic galaxy clustering of ELGs and show that, in the absence of any corrections, it leads to discrepancies of order ten percent on large scales for the power spectrum multipoles. We introduce a method where objects in a random catalog are assigned a coverage, and the mean density is separately computed for each coverage factor. We show that this method reduces, but does not eliminate the effect. We next investigate the angular dependence of the contaminated signal, arguing that it is mostly localized to purely transverse modes. We demonstrate that the cleanest way to remove the contaminating signal is to perform an analysis of the anisotropic power spectrum $P(k,\mu)$ and remove the lowest $\mu$ bin, leaving $\mu>0$ modes accurate at the few-percent level. Here, $\mu$ is the cosine of the angle between the line-of-sight and the direction of $\vec{k}$. We also investigate two alternative definitions of the random catalog and show they are comparable but less effective than the coverage randoms method.Comment: Submitted to JCA

Task Specific Uncertainty in Coordinate Measurement

Task specific uncertainty is the measurement uncertainty associated with the measurement of a specific feature using a specific measurement plan. This paper surveys techniques developed to model and estimate task specific uncertainty for coordinate measuring systems, primarily coordinate measuring machines using contacting probes. Sources of uncertainty are also reviewed