Comparison of Bacteriophage Annotation Methods

The rise of antibiotic-resistant bacteria has increased interest in bacteriophages (viruses that kill bacteria) in recent years. Due to the decreasing cost of genome sequencing, the number of sequenced phage genomes is growing at a geometric rate. Sequencing is followed by annotation, in which genes, start codons, and putative protein functions are identified. Most phage genomes are auto-annotated with programs designed for prokaryotes. Accuracy metrics for these programs with regard to phage genomes are not available. The genome of Escherichia coli phage Lambda was used to benchmark the accuracy of several genome annotation methods and programs. Discovered in 1951, Lambda is the most well studied phage, with nearly all gene functions and start sites demonstrated experimentally. Eight programs were used to annotate the Lambda genome: Glimmer, BASys, RAST, GeneMark, GeneMark.hmm, GeneMarkS, GeneMarkS2, and GeneMark with Heuristic models. Calls were compared to the reference genome from the literature in order to determine the accuracy of the eight selected programs in regard to bacteriophage genome annotation. Manual curation and compilation of auto-annotation results obtained from several programs is expected to yield more accurate gene feature and start codon prediction than auto-annotation alone

A Method for Improving the Accuracy and Efficiency of Bacteriophage Genome Annotation

Bacteriophages are the most numerous entities on Earth. The number of sequenced phage genomes is approximately 8000 and increasing rapidly. Sequencing of a genome is followed by annotation, where genes, start codons, and functions are putatively identified. The mainstays of phage genome annotation are auto-annotation programs such as Glimmer and GeneMark. Due to the relatively small size of phage genomes, many groups choose to manually curate auto-annotation results to increase accuracy. An additional benefit of manual curation of auto-annotated phage genomes is that the process is amenable to be performed by students, and has been shown to improve student recruitment to the sciences. However, despite its greater accuracy and pedagogical value, manual curation suffers from high labor cost, lack of standardization and a degree of subjectivity in decision making, and susceptibility to mistakes. Here, we present a method developed in our lab that is designed to produce accurate annotations while reducing subjectivity and providing a degree of standardization in decision-making. We show that our method produces genome annotations more accurate than auto-annotation programs while retaining the pedagogical benefits of manual genome curation

Effectiveness and cost-effectiveness of a telehealth intervention to support the management of long-term conditions:study protocol for two linked randomized controlled trials

As the population ages, more people are suffering from long-term health conditions (LTCs). Health services around the world are exploring new ways of supporting people with LTCs and there is great interest in the use of telehealth: technologies such as the Internet, telephone and home self-monitoring

Complete Genome Sequences of Mycobacterium smegmatis Phages NihilNomen and Carlyle, Isolated in Las Vegas, Nevada

We present the complete genomes of the Mycobacterium smegmatis phages Carlyle and NihilNomen, isolated from soil in Las Vegas, Nevada. The phages were isolated and annotated by undergraduate students enrolled in the Phage Discovery course offered by the School of Life Sciences at the University of Nevada Las Vegas

Complete Genome Sequences of Mycobacterium smegmatis Phages Chewbacca, Reptar3000, and Riparian, Isolated in Las Vegas, Nevada

Here, we present the complete genome sequences of Mycobacterium smegmatis phages Chewbacca, Reptar3000, and Riparian, isolated from soil in Las Vegas, NV. The phages were isolated and annotated by undergraduate students enrolled in the Phage Discovery course offered by the School of Life Sciences at the University of Nevada, Las Vega

Who does not participate in telehealth trials and why? A cross-sectional survey

BackgroundTelehealth interventions use information and communication technology to provide clinical support. Some randomised controlled trials of telehealth report high patient decline rates. A large study was undertaken to determine which patients decline to participate in telehealth trials and their reasons for doing so.MethodsTwo linked randomised controlled trials were undertaken, one for patients with depression and one for patients with raised cardiovascular disease risk (the Healthlines Study). The trials compared usual care with additional support delivered by the telephone and internet. Patients were recruited via their general practice and could return a form about why they were not participating.ResultsOf the patients invited, 82.9 % (20,021/24,152) did not accept the study invite, either by returning a decline form (n = 7134) or by not responding (n = 12,887). In both trials patients registered at deprived general practices were less likely to accept the study invite. Decline forms were received from 29.5 % (7134/24,152) of patients invited. There were four frequently reported types of reasons for declining. The most common was telehealth-related: 54.7 % (3889/,7115) of decliners said they did not have access or the skills to use the internet and/or computers. This was more prevalent amongst older patients and patients registered at deprived general practices. The second was health need-related: 40.1 % (n = 2852) of decliners reported that they did not need additional support for their health condition. The third was related to life circumstances: 27.2 % (n = 1932) of decliners reported being too busy. The fourth was research-related: 15.3 % (n = 1092) of decliners were not interested in the research.ConclusionsA large proportion of patients declining participation in these telehealth trials did so because they were unable to engage with telehealth or did not perceive a need for it. This has implications for engagement with telehealth in routine practice, as well as for trials, with a need to offer technological support to increase patients’ engagement with telehealth. More generally, triallists should assess why people decline to participate in their studies

Complete Genome Sequences of Paenibacillus Larvae Phages BN12, Dragolir, Kiel007, Leyra, Likha, Pagassa, PBL1c, and Tadhana

We present here the complete genomes of eight phages that infect Paenibacillus larvae, the causative agent of American foulbrood in honeybees. Phage PBL1c was originally isolated in 1984 from a P. larvae lysogen, while the remaining phages were isolated in 2014 from bee debris, honeycomb, and lysogens from three states in the USA

Are people with chronic diseases interested in using telehealth?: a cross-sectional postal survey

Background There is growing interest in telehealth—the use of technology to support the remote delivery of health care and promote self-management—as a potential alternative to face-to-face care for patients with chronic diseases. However, little is known about what precipitates interest in the use of telehealth among these patients. Objective This survey forms part of a research program to develop and evaluate a telehealth intervention for patients with two exemplar chronic diseases: depression and raised cardiovascular disease (CVD) risk. The survey was designed to explore the key factors that influence interest in using telehealth in these patient groups. Methods Thirty-four general practices were recruited from two different regions within England. Practice records were searched for patients with (1) depression (aged 18+ years) or (2) 10-year risk of CVD ≥20% and at least one modifiable risk factor (aged 40-74 years). Within each general practice, 54 patients in each chronic disease group were randomly selected to receive a postal questionnaire. Questions assessed five key constructs: sociodemographics, health needs, difficulties accessing health care, technology-related factors (availability, confidence using technology, perceived benefits and drawbacks of telehealth), and satisfaction with prior use of telehealth. Respondents also rated their interest in using different technologies for telehealth (phone, email and Internet, or social media). Relationships between the key constructs and interest in using the three mediums of telehealth were examined using multivariable regression models. Results Of the 3329 patients who were sent a study questionnaire, 44.40% completed it (872/1740, 50.11% CVD risk; 606/1589, 38.14% depression). Overall, there was moderate interest in using phone-based (854/1423, 60.01%) and email/Internet-based (816/1425, 57.26%) telehealth, but very little interest in social media (243/1430, 16.99%). After adjusting for health needs, access difficulties, technology-related factors, and prior use of telehealth, interest in telehealth had largely no association with sociodemographic variables. For both patient groups and for each of the three technology mediums, the most important constructs related to interest in telehealth were having the confidence to use the associated technology, as well as perceiving greater advantages and fewer disadvantages from using telehealth. To illustrate, greater confidence using phone technologies (b=.16, 95% CI 0.002-0.33), while also perceiving more benefits (b=.31, 95% CI 0.21-0.40) and fewer drawbacks (b=-.23, 95% CI -0.28 to -0.17) to using telehealth were associated with more interest in using phone-based telehealth technologies for patients with depression. Conclusions There is widespread interest in using phone-based and email/Internet-based telehealth among patients with chronic diseases, regardless of their health status, access difficulties, age, or many other sociodemographic factors. This interest could be increased by helping patients gain confidence using technologies and through highlighting benefits and addressing concerns about telehealth. While the same pattern exists for social media telehealth, interest in using these technologies is minimal

Telehealth for patients at high risk of cardiovascular disease: pragmatic randomised controlled trial

Objective: To assess whether non-clinical staff can effectively manage people at high risk of cardiovascular disease using digital health technologies. Design: Pragmatic, multicentre, randomised controlled trial. Setting: 42 general practices in three areas of England. Participants: Between 3 December 2012 and 23 July 2013 we recruited 641 adults aged 40 to 74 years with a 10 year cardiovascular disease risk of 20% or more, no previous cardiovascular event, at least one modifiable risk factor (systolic blood pressure ≥140 mm Hg, body mass index ≥30, current smoker), and access to a telephone, the internet, and email. Participants were individually allocated to intervention (n=325) or control (n=316) groups using automated randomisation stratified by site, minimised by practice and baseline risk score. Interventions: Intervention was the Healthlines service (alongside usual care), comprising regular telephone calls from trained lay health advisors following scripts generated by interactive software. Advisors facilitated self-management by supporting participants to use online resources to reduce risk factors, and sought to optimise drug use, improve treatment adherence, and encourage healthier lifestyles. The control group comprised usual care alone. Main outcome measures: The primary outcome was the proportion of participants responding to treatment, defined as maintaining or reducing their cardiovascular risk after 12 months. Outcomes were collected six and 12 months after randomisation and analysed masked. Participants were not masked. Results: 50% (148/295) of participants in the intervention group responded to treatment compared with 43% (124/291) in the control group (adjusted odds ratio 1.3, 95% confidence interval 1.0 to 1.9; number needed to treat=13); a difference possibly due to chance (P=0.08). The intervention was associated with reductions in blood pressure (difference in mean systolic −2.7 mm Hg (95% confidence interval −4.7 to −0.6 mm Hg), mean diastolic −2.8 (−4.0 to −1.6 mm Hg); weight −1.0 kg (−1.8 to −0.3 kg), and body mass index −0.4 (−0.6 to −0.1) but not cholesterol −0.1 (−0.2 to 0.0), smoking status (adjusted odds ratio 0.4, 0.2 to 1.0), or overall cardiovascular risk as a continuous measure (−0.4, −1.2 to 0.3)). The intervention was associated with improvements in diet, physical activity, drug adherence, and satisfaction with access to care, treatment received, and care coordination. One serious related adverse event occurred, when a participant was admitted to hospital with low blood pressure. Conclusions: This evidence based telehealth approach was associated with small clinical benefits for a minority of people with high cardiovascular risk, and there was no overall improvement in average risk. The Healthlines service was, however, associated with improvements in some risk behaviours, and in perceptions of support and access to care