Machine Learning and Genome Annotation: A Match Meant to Be?

By its very nature, genomics produces large, high-dimensional datasets that are well suited to analysis by machine learning approaches. Here, we explain some key aspects of machine learning that make it useful for genome annotation, with illustrative examples from ENCODE

Forest Sign Maker

Executive Summary: The Inyo National Forest is arguably one of the most beautiful locations in California, containing natural masterpieces such as Mount Whitney and the Ancient Bristlecone Pine Forest. Despite its magnificence, the Inyo National Forest can be a treacherous region. The Friends of the Inyo take pride in being able to facilitate the viewing experience for all outdoorsmen by maintaining the mountain trails, which includes providing adequate trail signage. Unfortunately, there is a fundamental issue with the recent state of trail signage in the Inyo National Forest: the rate at which signs are being vandalized or naturally destroyed is greater than the rate at which signs can be produced. More specifically, the problem is that the current sign production process is completely manual; the process of routing the necessary letters and symbols consumes the majority of the production time, since it takes approximately two days to complete. Without adequate signage on the mountain trails, hikers and explorers are at a heightened risk for injury. We, the Cal Poly Forest Friends, have been commissioned by the Friends of the Inyo to resolve the issue of manufacturing trail signs. We plan on designing, building, and testing a prototype CNC machine for Paul McFarland, an employee of the Friends of the Inyo whom is responsible for replacing signs. This CNC machine can automatically produce a trail sign from a wooden blank so as to expedite the sign replacement process. By comparing different industry methods of etching letters into a wood substrate, researching all applicable signage guidelines for compliance, and optimizing the prototype design for the intended use cases, we have developed a low cost, high capacity CNC router that can be installed directly in Paul McFarland’s workshop. There has been much work done in the field of CNC machinery, so we believe it is feasible to design a functioning prototype that has been optimized for this purpose. The positional accuracy range of the machine will be broadened from the industry standard of ±0.0005 in to our requirement of ±0.063 inches. This optimized accuracy will allow for emphasis on increased workpiece capacity at a lower total cost. Additionally, by building the prototype CNC router as part of the Cal Poly Multidisciplinary Senior Project class, we will be able to adhere to the revised $3,500 budget. With a successful prototype in hand by June 2015, the sign production rate for the Friends of the Inyo will potentially increase tenfold, and provide the Friends of the Inyo with the ability to replace illegible trail signs within the Inyo National Forest

Genome-wide analysis of chromatin features identifies histone modification sensitive and insensitive yeast transcription factors

We propose a method to predict yeast transcription factor targets by integrating histone modification profiles with transcription factor binding motif information. It shows improved predictive power compared to a binding motif-only method. We find that transcription factors cluster into histone-sensitive and -insensitive classes. The target genes of histone-sensitive transcription factors have stronger histone modification signals than those of histone-insensitive ones. The two classes also differ in tendency to interact with histone modifiers, degree of connectivity in protein-protein interaction networks, position in the transcriptional regulation hierarchy, and in a number of additional features, indicating possible differences in their transcriptional regulation mechanisms

The role of linked building data (LBD) in aligning augmented reality (AR) with sustainable construction

Over the years, the construction industry has been evolving to embrace the delicate balance between buildings and a sustainable environment by optimizing resource use to create greener and more energy efficient constructions. Sustainable building design and optimization is a highly iterative and complicated process. This is mainly attributed to the complex interaction between the different heterogenous but heuristic construction processes, building systems and workflows involved in achieving this goal. Augmented Reality (AR) has rapidly emerged as a revolutionary technology that could play a key role towards improving coordination of sustainable design processes. AR makes possible the real-time visualization of a three-dimensional (3D) building prototype with linked design information in a real-world environment based on a two-dimensional drawing. From past research, it is evident that this technology relies heavily on a common data environment (CDE) that syncs all construction processes with their related building information in one central model. However, due to the fragmented nature of the construction industry, different domain experts generate and exchange vast amounts of heterogenous information using different software tools outside a CDE. This paper therefore investigates the performance gap that exists within Malaysia’s construction industry towards using linked building data (LBD) with AR to improve the lifecycle sustainability of buildings. The results of this study clearly delineate how current construction practices in Malaysia do not favor the use of AR however, stakeholder perception is positive towards adoption of workflows that link heterogenous building data to streamline AR with sustainable building design and construction

Association of Medicaid Expansion and Health Insurance with Receipt of Smoking Cessation Services and Smoking Behaviors in Substance Use Disorder Treatment.

This study examined whether living in a Medicaid-expanded state or having health insurance was associated with receipt of smoking cessation services or smoking behaviors among substance use disorder (SUD) treatment clients. In 2015 and 2016, 1702 SUD clients in 14 states were surveyed for health insurance status, smoking cessation services received in their treatment program, and smoking behaviors. Services and behaviors were then compared by state Medicaid expansion and health insurance status independently. Clients in Medicaid-expanded states were more likely to be insured (89.9% vs. 54.4%, p < 0.001) and to have quit smoking during treatment (AOR = 3.77, 95% CI = 2.47, 5.76). Insured clients had higher odds of being screened for smoking status in their treatment program and making quit attempts in the past year. Medicaid expansion supports greater health insurance coverage of individuals in SUD treatment and may enhance smoking cessation

A scoping umbrella review to identify anti-racist interventions to reduce ethnic disparities in health and care

Objectives: To identify anti-racist interventions which aim to reduce ethnic disparities in health and care. / Eligibility criteria for selecting studies: Only studies reporting systematic reviews of anti-racist interventions were included. Studies were excluded if no interventions were reported, no comparators reported, or the paper was primarily descriptive. / The following databases were searched: Embase, Medline, Social Policy and Practice, Social care online and Web of Science. Quality appraisal (including risk of bias) was assessed using the AMSTAR-2 tool. Due to the nature of the selected reviews, the lack of meta-analyses and heterogeneity of included studies, a narrative synthesis using an inductive thematic analysis approach was conducted to integrate and categorise the evidence on anti-racist interventions for health and care. / Results: A total of 18 systematic reviews are included in the final review. 15 are from the healthcare sector and three are from education and criminal justice. 17 reviews are focused on interventions and one focused on implementation. All 18 reviews described interventions which targeted individuals and their communities, and 11 reviews described interventions targeting both individuals and communities, and healthcare organisations. On an individual level, the most promising interventions reviewed include group-based health education led by professional staff and providing culturally tailored or adapted interventions. On a community level, participation in all aspects of care pathway development that empowers ethnic minority groups may provide an effective approach to reducing ethnic health disparities. Targeted interventions to improve clinician patient interactions and quality of care for conditions with disproportionately worse outcomes in ethnic minority groups show promise. / Discussion: Many of the included studies were low or critically low quality due to methodological or reporting limitations. The heterogeneity of intervention approaches, study designs, and limited reporting of cultural adaptation, implementation and lack of comparison with White ethnic groups limited our understanding of the impact on ethnic health inequalities. In summary, for programme delivery, different types of pathway integration and providing a more person-centred approach with fewer steps for patients to navigate can contribute to reducing disparities. For organisations, there is an overemphasis on patient education and individual behaviour change rather than organisational change, and recommendations should include a shift in focus and resources to policies and practices that seek to dismantle institutional and systemic racism through a multi-level approach

Improved Reconstruction of In Silico Gene Regulatory Networks by Integrating Knockout and Perturbation Data

We performed computational reconstruction of the in silico gene regulatory networks in the DREAM3 Challenges. Our task was to learn the networks from two types of data, namely gene expression profiles in deletion strains (the ‘deletion data’) and time series trajectories of gene expression after some initial perturbation (the ‘perturbation data’). In the course of developing the prediction method, we observed that the two types of data contained different and complementary information about the underlying network. In particular, deletion data allow for the detection of direct regulatory activities with strong responses upon the deletion of the regulator while perturbation data provide richer information for the identification of weaker and more complex types of regulation. We applied different techniques to learn the regulation from the two types of data. For deletion data, we learned a noise model to distinguish real signals from random fluctuations using an iterative method. For perturbation data, we used differential equations to model the change of expression levels of a gene along the trajectories due to the regulation of other genes. We tried different models, and combined their predictions. The final predictions were obtained by merging the results from the two types of data. A comparison with the actual regulatory networks suggests that our approach is effective for networks with a range of different sizes. The success of the approach demonstrates the importance of integrating heterogeneous data in network reconstruction

OMMA enables population-scale analysis of complex genomic features and phylogenomic relationships from nanochannel-based optical maps.

BackgroundOptical mapping is an emerging technology that complements sequencing-based methods in genome analysis. It is widely used in improving genome assemblies and detecting structural variations by providing information over much longer (up to 1 Mb) reads. Current standards in optical mapping analysis involve assembling optical maps into contigs and aligning them to a reference, which is limited to pairwise comparison and becomes bias-prone when analyzing multiple samples.FindingsWe present a new method, OMMA, that extends optical mapping to the study of complex genomic features by simultaneously interrogating optical maps across many samples in a reference-independent manner. OMMA captures and characterizes complex genomic features, e.g., multiple haplotypes, copy number variations, and subtelomeric structures when applied to 154 human samples across the 26 populations sequenced in the 1000 Genomes Project. For small genomes such as pathogenic bacteria, OMMA accurately reconstructs the phylogenomic relationships and identifies functional elements across 21 Acinetobacter baumannii strains.ConclusionsWith the increasing data throughput of optical mapping system, the use of this technology in comparative genome analysis across many samples will become feasible. OMMA is a timely solution that can address such computational need. The OMMA software is available at https://github.com/TF-Chan-Lab/OMTools