An approach to comparing tiling array and high throughput sequencing technologies for genomic transcript mapping

<p>Abstract</p> <p>Background</p> <p>There are two main technologies for transcriptome profiling, namely, tiling microarrays and high-throughput sequencing. Recently there has been a tremendous amount of excitement about the latter because of the advent of next-generation sequencing technologies and its promises. Consequently, the question of the moment is how these two technologies compare. Here we attempt to develop an approach to do a fair comparison of transcripts identified from tiling microarray and MPSS sequencing data.</p> <p>Findings</p> <p>This comparison is a challenging task because the sequencing data is discrete while the tiling array data is continuous. We use the published rice and <it>Arabidopsis </it>datasets which provide currently best matched sets of arrays and sequencing experiments using a slightly earlier generation of sequencing, the MPSS tag sequencing technology. After scoring the arrays consistently in both the organisms, a first pass comparison reveals a surprisingly small overlap in transcripts of 22% and 66% respectively, in rice and <it>Arabidopsis</it>. However, when we do the analysis in detail, we find that this is an underestimate. In particular, when we map the probe intensities onto the sequencing tags and then look at their intensity distribution, we see that they are very similar to exons. Furthermore, restricting our comparison to only protein-coding gene loci revealed a very good overlap between the two technologies.</p> <p>Conclusion</p> <p>Our approach to compare genome tiling microarray and MPSS sequencing data suggests that there is actually a reasonable overlap in transcripts identified by the two technologies. This overlap is distorted by the scoring and thresholding in the tiling array scoring procedure.</p

An approach to compare genome tiling microarray and MPSS sequencing data for transcript mapping

We are correcting the abstract of our published article ([1]). The sentence that starts "We observe that 4.5% of MPSS tags...." was not scientifically complete in the original abstract, having only two of the four numbers required to describe a comparison of two technologies in two different organisms. The abstract below more accurately describes our findings, as documented in Figure 1 of the manuscript

Informed Cannabis Policies on Canadian Campuses: Toward the Protection of Youth and Young Adults

Introduction: The legalization of cannabis across Canada in October 2018 introduced issues including regulation at different levels, public and individual education, and discussions about cannabis product safety. We aimed to discuss ineffective and effective cannabis use policy on campuses and associated public areas, given the known short-term and long-term effects pertaining to its neurologic, pulmonary, and purported medicinal effects. Cannabis interferes with many of the body’s basic and executive (higher-level) functions. It is also associated with long-term harmful effects when chronically used. The purpose of this paper is to review and further discuss the responsibility local governments and educational institutions have for creating policies and regulations around cannabis use, particularly within post-secondary institutions, and for implementing educational strategies to promote public knowledge of cannabis. Methods: Peer-reviewed articles published in the last 10 years were searched for through the MEDLINE database. In addition, national and local health-related websites discussing cannabis policies were reviewed and collated. Expert opinions were also sought out to provide further information and resources. Results: 31 peer-reviewed articles and 12 professional websites were retrieved and reviewed. Correspondences with individual experts aware of and involved with campus cannabis policies also provided relevant resources and data used in this document. Conclusion: Smoke-free campus policies create the best health outcomes for the campus population. As well, creating effective and properly regulated policies and prioritizing public education is pertinent especially on universities where the population demographic is relatively young

Failure of Administrative Data to Guide Asthma Care

Rationale: Asthma is a chronic inflammatory disease of the airways that is very common (7.9% ofCanadians over the age of 12). Despite numerous clinical guidelines, education events and administrativedata reviews, there has been little change to the way asthma is managed in the Canadian health caresystem for nearly 30 years. We evaluated, through the Physician Learning Program (PLP) in Alberta,possible reasons why administrative datasets have not been able to provide meaningful information toadjust health policy. Methods: Provincial data was attained through Alberta Health Service and Alberta Health on pulmonaryfunction testing from 2005-2011 (through the PLP). The number of asthma diagnosis made during the sametime frame were then compared. Results: The preliminary results of the PLP found that spirometry was billed for roughly half as often asthe asthma diagnostic codes were utilized during the same time frame. However, the review also revealedinconsistencies in how administrative data are captured, making it difficult to determine whetherspirometry is being underutilized by physicians in making asthma diagnoses. Conclusions: Inconsistencies in how administrative data are captured in Alberta may be contributingto an incomplete picture of the rates of asthma diagnosis and physiological testing, and may explain, inpart, the limited influence of administrative datasets on guiding meaningful change within the healthcaresystem

The Role of Primary Care in Asthma Control and Severity: Asthma and Primary Care in Alberta

Background: Asthma is a common chronic inflammatory disease of the airways affecting 3 millionCanadians. Primary Care Providers (PCPs) are integral to care coordination, enhanced through thedevelopment of a strong patient-PCP relationship with Continuity of Care (COC). A recent CIHI studynoted that 40% of Albertans do not have a COC model for primary care.Objectives: We aim to evaluate how primary care for adults with asthma impacts different measures ofcontrol.Methods: Prospective population-based recruitment of adults through various community venuesacross Alberta. Those who had self-reported asthma and were willing to participate completed a surveywhich included demographics, comorbidities, medication use (including biologics, allergy medications,steroids), Asthma Control Questionnaire (ACQ-5), Asthma Control Test (ACT), Quality of Life (QoL)measured through the mini-Asthma Quality of Life Questionnaire (mini-AQLQ) and health care utilization(including Emergency Department (ED) visits, hospitalizations and ICU stays for asthma).Results: Of the 1685 individuals approached, 61 (3.6%) reported having asthma, of which 47 lived inAlberta. Most (41, 87%) had a PCP, with 30 (64%) visiting their PCP at least twice a year. Uncontrolledasthma was noted in 21 (45%) with either the ACQ-5 or ACT. The mini-AQLQ indicated 5 (11%) withreduced QoL. Mean lifetime hospitalizations, lifetime Emergency Department (ED) visits, and ICU staysrelated to asthma were 1.52, 4.55 and 0.25 respectively. Further, mean hospitalizations and ED visits inthe past 12 months related to asthma were 0.05 and 0.30 respectively.Conclusions: Asthma control was poor in 21 (45%) surveyed individuals, suggesting sub-optimal asthmamanagement in Alberta. Knowledge of Primary Care Networks (PCNs) was low, while ED and hospitalusage was high

Fair Inputs and Fair Outputs: The Incompatibility of Fairness in Privacy and Accuracy

Fairness concerns about algorithmic decision-making systems have been mainly focused on the outputs (e.g., the accuracy of a classifier across individuals or groups). However, one may additionally be concerned with fairness in the inputs. In this paper, we propose and formulate two properties regarding the inputs of (features used by) a classifier. In particular, we claim that fair privacy (whether individuals are all asked to reveal the same information) and need-to-know (whether users are only asked for the minimal information required for the task at hand) are desirable properties of a decision system. We explore the interaction between these properties and fairness in the outputs (fair prediction accuracy). We show that for an optimal classifier these three properties are in general incompatible, and we explain what common properties of data make them incompatible. Finally we provide an algorithm to verify if the trade-off between the three properties exists in a given dataset, and use the algorithm to show that this trade-off is common in real data

Comparison and calibration of transcriptome data from RNA-Seq and tiling arrays

<p>Abstract</p> <p>Background</p> <p>Tiling arrays have been the tool of choice for probing an organism's transcriptome without prior assumptions about the transcribed regions, but RNA-Seq is becoming a viable alternative as the costs of sequencing continue to decrease. Understanding the relative merits of these technologies will help researchers select the appropriate technology for their needs.</p> <p>Results</p> <p>Here, we compare these two platforms using a matched sample of poly(A)-enriched RNA isolated from the second larval stage of <it>C. elegans</it>. We find that the raw signals from these two technologies are reasonably well correlated but that RNA-Seq outperforms tiling arrays in several respects, notably in exon boundary detection and dynamic range of expression. By exploring the accuracy of sequencing as a function of depth of coverage, we found that about 4 million reads are required to match the sensitivity of two tiling array replicates. The effects of cross-hybridization were analyzed using a "nearest neighbor" classifier applied to array probes; we describe a method for determining potential "black list" regions whose signals are unreliable. Finally, we propose a strategy for using RNA-Seq data as a gold standard set to calibrate tiling array data. All tiling array and RNA-Seq data sets have been submitted to the modENCODE Data Coordinating Center.</p> <p>Conclusions</p> <p>Tiling arrays effectively detect transcript expression levels at a low cost for many species while RNA-Seq provides greater accuracy in several regards. Researchers will need to carefully select the technology appropriate to the biological investigations they are undertaking. It will also be important to reconsider a comparison such as ours as sequencing technologies continue to evolve.</p