Rolf Lindén

Quantitative maps of genetic interactions in yeast - Comparative evaluation and integrative analysis

BackgroundHigh-throughput genetic screening approaches have enabled systematic means to study how interactions among gene mutations contribute to quantitative fitness phenotypes, with the aim of providing insights into the functional wiring diagrams of genetic interaction networks on a global scale. However, it is poorly known how well these quantitative interaction measurements agree across the screening approaches, which hinders their integrated use toward improving the coverage and quality of the genetic interaction maps in yeast and other organisms.ResultsUsing large-scale data matrices from epistatic miniarray profiling (E-MAP), genetic interaction mapping (GIM), and synthetic genetic array (SGA) approaches, we carried out here a systematic comparative evaluation among these quantitative maps of genetic interactions in yeast. The relatively low association between the original interaction measurements or their customized scores could be improved using a matrix-based modelling framework, which enables the use of single- and double-mutant fitness estimates and measurements, respectively, when scoring genetic interactions. Toward an integrative analysis, we show how the detections from the different screening approaches can be combined to suggest novel positive and negative interactions which are complementary to those obtained using any single screening approach alone. The matrix approximation procedure has been made available to support the design and analysis of the future screening studies.ConclusionsWe have shown here that even if the correlation between the currently available quantitative genetic interaction maps in yeast is relatively low, their comparability can be improved by means of our computational matrix approximation procedure, which will enable integrative analysis and detection of a wider spectrum of genetic interactions using data from the complementary screening approaches.

On Study Habits on an Introductory Course on Programming.

Computer aided assessment systems enable the collection of exact time and date information on students’ activity on a course. These activity patterns reflect students’ study habits and these study habits further predict students’ likelihood to pass or fail a course. By identifying such patterns, those who design the courses can enforce positive study habits and to prevent or minimize habits that lead to poor student performance. Hypothetically, by identifying and adjusting the short-term patterns, the teachers might be able to do the same during the course. This publication examines students’ short-term study habits on an introductory level programming course and presents multiple statistically significant connections between students’ assignment submission patterns and their respective final grades. Students who receive the highest grade start and finish their work early, do not work on weekends, and do not work at night, whereas those who fail the course do not show similar behavior but exhibit significant enrichment among those who work large amounts during the night. Course’s mandatory tutorial sessions that act both as assignment release events and as collaborative assignment solving sessions strongly increase assignment submission counts regardless of the students’ final grades and ensure an early start to solving the assignments, possibly preventing those who would otherwise fail the course from starting their work near deadlines.

Genome-wide scoring of positive and negative epistasis through decomposition of quantitative genetic interaction fitness matrices.

Recent technological developments in genetic screening approaches have offered the means to start exploring quantitative genotype-phenotype relationships on a large-scale. What remains unclear is the extent to which the quantitative genetic interaction datasets can distinguish the broad spectrum of interaction classes, as compared to existing information on mutation pairs associated with both positive and negative interactions, and whether the scoring of varying degrees of such epistatic effects could be improved by computational means. To address these questions, we introduce here a computational approach for improving the quantitative discrimination power encoded in the genetic interaction screening data. Our matrix approximation model decomposes the original double-mutant fitness matrix into separate components, representing variability across the array and query mutants, which can be utilized for estimating and correcting the single-mutant fitness effects, respectively. When applied to three large-scale quantitative interaction datasets in yeast, we could improve the accuracy of scoring various interaction classes beyond that obtained with the original fitness data, especially in synthetic genetic array (SGA) and in genetic interaction mapping (GIM) datasets. In addition to the known pairs of interactions used in the evaluation of the computational approach, a number of novel interaction pairs were also predicted, along with underlying biological mechanisms, which remained undetected by the original datasets. It was shown that the optimal choice of the scoring function depends heavily on the screening approach and on the interaction class under analysis. Moreover, a simple preprocessing of the fitness matrix could further enhance the discrimination power of the epistatic miniarray profiling (E-MAP) dataset. These systematic evaluation results provide in-depth information on the optimal analysis of the future, large-scale screening experiments. In general, the modeling framework, enabling accurate identification and classification of genetic interactions, provides a solid basis for completing and mining the genetic interaction networks in yeast and other organisms.

Interactive Exercises for Teaching Logic Circuits

Logic circuits are a central concept in understanding how computers and electronics work underneath. While there are educational, web-based systems for visualizing and building circuits, few systems exist which are capable of providing automatically assessed exercises with visual feedback on the topic. In this paper, we describe the lechef library for providing exercises on logic circuits. The library supports two types of exercises: 1) giving students a logic circuit and the input to the circuit asking them to give the input and output values for each gate in the circuit, and 2) giving students a truth table of the wanted circuit and requiring them to construct a circuit fulfilling the requirements. Furthermore, we explain how we integrated it into the ViLLE learning environment and used it on our introductory computer science course. We also report on our initial findings on student experiences on using the exercises as well as their results. We believe the system and our experiences provide valuable feedback to other members of the community on how and why to use such exercises.

The Impact of Prior Programming Knowledge on Lecture Attendance and Final Exam

In this article, we report the results of the impact of prior programming knowledge (PPK) on lecture attendance (LA) and on subsequent final programming exam performance in a university level introductory programming course. This study used Spearman’s rank correlation coefficient, multiple regression, Kruskal–Wallis, and Bonferroni correction statistical techniques via SPSS software to analyze the student data for academic years 2012, 2013, and 2014 to test the hypotheses. Only LA, PPK, and final exam (FE) scores were considered for this analysis. Research suggests that PPK influences student LA and FE performance. Similar analysis was conducted on the impact of LA on FE results regardless of students’ PPK levels. The results delivered mixed conclusions. Furthermore, the correlation coefficient results indicated that LA and FE were negatively correlated. However, the coefficient value was not sufficiently statistically significant to conclude that LA does not have an impact on FE results. On the other hand, the results of average LA on student FE results, with linear regression results, revealed that nonattendance of lectures had no effect on student performance in the FE. The multiple regression results of our study identified that, PPK in a regression model, is a good fit of the data, but LA in a regression model is not a good fit of the data.

Refactoring a CS0 course for engineering students to use active learning

The purpose of this paper was to determine whether applying e-learning material to a course leads to consistently improved student performance.,This paper analyzes grade data from seven instances of the course. The first three instances were performed traditionally. After an intervention, in the form of applying e-learning methodologies, more data were collected from four course instances. These data are then analyzed and compared.,The main finding of this paper is that the application of e-learning improved the overall grades and decreased the fail rates of students who took this course.,This paper demonstrates the efficacy of applying e-learning methods to an undergraduate course. This paper is of special interest to educators, who wish to improve and enhance their teaching.