Show them what they can't see! An evaluation of the use of customized 3D printed models in head and neck anatomy

Difficulty in visualizing anatomical structures has been identified as a challenge in anatomy learning and the emergence of three-dimensional printed models (3DPMs) offers a potential solution. This study evaluated the effectiveness of 3DPMs for learning the arterial supply of the head and neck region. One hundred eighty-four undergraduate medical students were randomly assigned to one of four learning modalities including wet specimen, digital model, 3DPM, and textbook image. Posttest scores indicated that all four modalities supported participants' knowledge acquisition, most significantly in the wet specimen group. While the participants rated 3DPMs lower for helping correct identification of structures than wet specimens, they praised 3DPMs for their ability to demonstrate topographical relationships between the arterial supply and adjacent structures. The data further suggested that the biggest limitation of the 3DPMs was their simplicity, thus making it more difficult for users to recognize the equivalent structures on the wet specimens. It was concluded that future designs of 3DPMs will need to consider the balance between the ease of visualization of anatomical structures and the degree of complexity required for successful transfer of learning. Overall, this study presented some conflicting evidence of the favorable outcomes of 3DPMs reported in other similar studies. While effective for anatomy learning as a standalone modality, educators must identify the position 3DPM models hold relative to other modalities in the continuum of undergraduate anatomy education in order to maximize their advantages for students.

3-Dimensional simulations and student learning in orthodontic education

INTRODUCTION

The electronic dental model (e-model) is an example of a digital 3-dimensional technology to support inquiry-based learning in undergraduate dental education. As student perceptions of and engagement with e-models vary, it is uncertain whether these perceptions have implications for their learning processes and outcomes.

MATERIALS AND METHODS

Third-year dental students (N = 40) completed a questionnaire to identify their perceptions of and preferences for model modalities. They were divided into three groups based on their preference: Preferring plaster models (Group 1); Preferring e-models (Group 2); No preference (Group 3). Students from three groups (N = 9) attended a hands-on digital occlusion evaluation workshop, and then completed a case-based diagnostic evaluation test using digital occlusion evaluation software. Camtasia Studio™ recorded real-time and on-screen data of the number of mouse-clicks and time spent.

RESULTS

Students reported positive feedbacks on the use of e-models, and 72.5% of the students preferred combination use of e-models and plaster models. After attending the hands-on digital dental occlusion evaluation workshop, Group 2 scored higher on the diagnostic evaluation test (p < .05) and registered more mouse-clicks than Group 1 when evaluating the arch symmetry (p < .05). Group 2 registered fewer mouse-clicks than Group 3 during tooth size measurement (p < .05). There was no significant difference regarding the time used to answer the knowledge questions amongst the three groups.

CONCLUSION

Undergraduate dental students indicated a generally high acceptance of e-models for their learning in orthodontics, and more prefer a blended approach. Students preferring e-models presented higher performance outcomes, which supports cognitive load theory regarding prior exposure to simulation-based environments.

Enhancing patient-centred communication across barriers: The case of intersubjectivity management in medical interpreting

OBJECTIVE

This study introduces the concept of intersubjectivity management in medical interpreting and identifies relevant interactional strategies employed by the interpreter, also explores their effectiveness in facilitating positive clinician-patient communication.

METHODS

We used conversation analysis (CA) to analyse 27 video recordings of interpreter-mediated dental visits, participants involve English-speaking dentists, Cantonese as the first language (L1) patients and bilingual dental surgery assistants (DSA) who also play the role of ad hoc interpreters.

RESULTS

The DSA-as-interpreter manages intersubjectivity for the dentist and patient through interactional strategies, such as reformulating action types, redesigning contents and information capacity, summarising and concentrating turns, constantly monitoring the situation and eliciting spoken or unspoken expressions that are medically relevant from both sides to validate them. The strategies effectively enabled and enhanced the mutual understanding and interpersonal alignment between the dentist and patient. More importantly, the DSA constantly orients to patient-centred communication.

CONCLUSION

Although not professionally trained for interpreting, the DSA-as-interpreters demonstrated discursive strategies. The strategies evidently facilitated positive dentist-patient communication and relationships.

PRACTICE IMPLICATIONS

The conceptualisation and significant strategies demonstrated by the DSA-as-interpreters could potentially inform the solution of enhancing multilingual health communication in clinical staff training.

A conceptual model for clinical psychomotor skill development in an era of simulated and virtual reality

Psychomotor skill development is central to a beginner practitioner's learning pathway. Curriculum constraints around time, access to facilities and resources in health professions education have prompted the growth in alternative approaches to clinical skill development in both simulation and direct patient care. Among these is the increased incorporation of virtual reality (VR) systems with haptic feedback alongside traditional, solid simulations. Given the rapid growth in the adoption of technological affordances to support skill development, it is cogent to pause and examine whether the underpinning concepts regarding psychomotor skill development that have driven much of the approaches to teach clinical skill acquisition in dentistry remain fit-for-purpose. This conceptual paper proposes a new taxonomy for clinical simulation psychomotor skill development in the era of increasing variety of simulation modalities.

Why is Anatomy Difficult to Learn? The Implications for Undergraduate Medical Curricula

The impact of the medical curricular reform on anatomy education has been inconclusive. A pervasive perception is that graduates do not possess a sufficient level of anatomical knowledge for safe medical practice; however, the reason is less well-studied. This qualitative study investigated the perceived challenges in learning anatomy, possible explanations, and ways to overcome these challenges. Unlike previous work, it explored the perceptions of multiple stakeholders in anatomy learning. Semi-structured interviews were conducted and the transcripts were analyzed by a grounded theory approach. Three main themes emerged from the data: (1) visualization of structures, (2) body of information, and (3) issues with curriculum design. The decreasing time spent in anatomy laboratories forced students to rely on alternative resources to learn anatomy but they lacked the opportunities to apply to human specimens, which impeded the "near" transfer of learning. The lack of clinical integration failed to facilitate the "far" transfer of learning. Learners also struggled to cope with the large amount of surface knowledge, which was pre-requisite to successful deep and transfer of learning. It was theorized that the perceived decline in anatomical knowledge was derived from this combination of insufficient surface knowledge and impeded "near" transfer resulting in impeded deep and "far" transfer of learning. Moving forward, anatomy learning should still be cadaveric-based coupled with complementary technological innovations that demonstrate "hidden" structures. A constant review of anatomical disciplinary knowledge with incremental integration of clinical contexts should also be adopted in medical curricula which could promote deep and far transfer of learning.

Learning environments for interprofessional education: A micro-ethnography of sociomaterial assemblages in team-based learning

BACKGROUND

Teamwork and collaboration are central to interprofessional education but fostering these attributes in large undergraduate cohorts is challenging.

OBJECTIVES

This study aimed to examine the complexities of IPE group learning processes by examining how the material and intersubjective intertwine when newly formed interprofessional groups (Chinese medicine, medicine, nursing, pharmacy, and social work) synchronously engaged with face-to-face and online learning in a blended, team-based learning environment.

METHODS

It was a micro-ethnography study using a sociomaterial theoretical lens. We selected two undergraduate interprofessional healthcare student groups within a large scale programme for contrastive video analysis of synchronous spatial and physical configurations, associated talk, and online activity.

RESULTS

Video analysis of evolving physical configurations indicated that Group B was spatially more evenly grouped, and physically orientated to an identifiable leader, despite their blinded peer evaluations indicating distributed leadership. Group A faced a critical event at a public forum leading to spatial disruption breaking into subgroups and isolates; however, this group identified one member as a defined leader in the peer evaluations.

CONCLUSIONS

Based on online scores, we found that peer identification of leaders may influence learning processes but not learning outcomes in the first IPE team meeting. The design of the physical and virtual learning environments contributed to the developing, sociomaterial processes of group cohesion in interprofessional team-based learning.

How does video case-based learning influence clinical decision-making by midwifery students? An exploratory study

BACKGROUND

Clinical decision-making skills are essential for providing high-quality patient care. To enhance these skills, many institutions worldwide use case-based learning (CBL) as an educational strategy of pre-clinical training. However, to date, the influence of different learning modalities on students' clinical decision-making processes has not been fully explored. This study aims to explore the influence of video and paper case modalities on the clinical decision-making process of midwifery students during CBL.

METHODS

CBL involving a normal pregnant woman was provided for 45 midwifery students. They were divided into 12 groups; six groups received the video modality, and six groups received the paper modality. Group discussions were video-recorded, and focus groups were conducted after the CBL. Transcripts of the group discussions were analysed in terms of their interaction patterns, and focus groups were thematically analysed based on the three-stage model of clinical decision-making, which includes cue acquisition, interpretation, and evaluation/decision-making.

RESULTS

The students in the video groups paid more attention to psychosocial than biomedical aspects and discussed tailored care for the woman and her family members. They refrained from vaginal examinations and electric fetal heart monitoring. Conversely, the students in the paper groups paid more attention to biomedical than psychosocial aspects and discussed when to perform vaginal examinations and electric fetal heart monitoring.

CONCLUSION

This study clarified that video and paper case modalities have different influences on learners' clinical decision-making processes. Video case learning encourages midwifery students to have a woman- and family-centred holistic perspective of labour and birth care, which leads to careful consideration of the psychosocial aspects. Paper case learning encourages midwifery students to have a healthcare provider-centred biomedical perspective of labour and childbirth care, which leads to thorough biomedical assessment.

Chickspress: a resource for chicken gene expression

High-throughput sequencing and proteomics technologies are markedly increasing the amount of RNA and peptide data that are available to researchers, which are typically made publicly available via data repositories such as the NCBI Sequence Read Archive and proteome archives, respectively. These data sets contain valuable information about when and where gene products are expressed, but this information is not readily obtainable from archived data sets. Here we report Chickspress (http://geneatlas.arl.arizona.edu), the first publicly available gene expression resource for chicken tissues. Since there is no single source of chicken gene models, Chickspress incorporates both NCBI and Ensembl gene models and links these gene sets with experimental gene expression data and QTL information. By linking gene models from both NCBI and Ensembl gene prediction pipelines, researchers can, for the first time, easily compare gene models from each of these prediction workflows to available experimental data for these products. We use Chickspress data to show the differences between these gene annotation pipelines. Chickspress also provides rapid search, visualization and download capacity for chicken gene sets based upon tissue type, developmental stage and experiment type. This first Chickspress release contains 161 gene expression data sets, including expression of mRNAs, miRNAs, proteins and peptides. We provide several examples demonstrating how researchers may use this resource.

Getting to the Root of Fine Motor Skill Performance in Dentistry: Brain Activity During Dental Tasks in a Virtual Reality Haptic Simulation

Background

There is little evidence considering the relationship between movement-specific reinvestment (a dimension of personality which refers to the propensity for individuals to consciously monitor and control their movements) and working memory during motor skill performance. Functional near-infrared spectroscopy (fNIRS) measuring oxyhemoglobin demands in the frontal cortex during performance of virtual reality (VR) psychomotor tasks can be used to examine this research gap.

Objective

The aim of this study was to determine the potential relationship between the propensity to reinvest and blood flow to the dorsolateral prefrontal cortices of the brain. A secondary aim was to determine the propensity to reinvest and performance during 2 dental tasks carried out using haptic VR simulators.

Methods

We used fNIRS to assess oxygen demands in 24 undergraduate dental students during 2 dental tasks (clinical, nonclinical) on a VR haptic simulator. We used the Movement-Specific Reinvestment Scale questionnaire to assess the students’ propensity to reinvest.

Results

Students with a high propensity for movement-specific reinvestment displayed significantly greater oxyhemoglobin demands in an area associated with working memory during the nonclinical task (Spearman correlation, r_s=.49, P=.03).

Conclusions

This small-scale study suggests that neurophysiological differences are evident between high and low reinvesters during a dental VR task in terms of oxyhemoglobin demands in an area associated with working memory.

Implementation of an interprofessional team-based learning program involving seven undergraduate health and social care programs from two universities, and students' evaluation of their readiness for interprofessional learning

BACKGROUND

Interprofessional learning is gaining momentum in revolutionizing healthcare education. During the academic year 2015/16, seven undergraduate-entry health and social care programs from two universities in Hong Kong took part in an interprofessional education program. Based on considerations such as the large number of students involved and the need to incorporate adult learning principles, team-based learning was adopted as the pedagogy for the program, which was therefore called the interprofessional team-based learning program (IPTBL). The authors describe the development and implementation of the IPTBL program and evaluate the effectiveness of the program implementation.

METHODS

Eight hundred and one students, who are predominantly Chinese, participated in the IPTBL. The quantitative design (a pretest-posttest experimental design) was utilized to examine the students' gains on their readiness to engage in interprofessional education (IPE).

RESULTS

Three instructional units (IUs) were implemented, each around a clinical area which could engage students from complementary health and social care disciplines. Each IU followed a team-based learning (TBL) process: pre-class study, individual readiness assurance test, team readiness assurance test, appeal, feedback, and application exercise. An electronic platform was developed and was progressively introduced in the three IUs. The students' self-perceived attainment of the IPE learning outcomes was high. Across all four subscales of RIPLS, there was significant improvement in student's readiness to engage in interprofessional learning after the IPTBL. A number of challenges were identified: significant time involvement of the teachers, difficulty in matching students from different programs, difficulty in making IPTBL count towards a summative assessment score, difficulty in developing the LAMS platform, logistics difficulty in managing paper TBL, and inappropriateness of the venue.

CONCLUSIONS

Despite some challenges in developing and implementing the IPTBL program, our experience showed that TBL is a viable pedagogy to be used in interprofessional education involving hundreds of students. The significant improvement in all four subscales of RIPLS showed the effects of the IPTBL program in preparing students for collaborative practice. Factors that contributed to the success of the use of TBL for IPE are discussed.

Development and evaluation of a Dental Patient Feedback on Consultation skills (DPFC) measure to enhance communication

OBJECTIVES

To adapt an existing medical questionnaire on patient-provider communication for use in the dental setting, and to evaluate the performance of the measure in a first dental encounter (validity and reliability).

METHODS

A patient feedback questionnaire on consultation skills was adapted for use in dental settings through content and convergent validity. A survey of dentist consultation skills was conducted among adults attending a teaching hospital. Patients self-completed a 16-item Dental Patient Feedback on Consultation skills (DPFC) questionnaire during their first dental consultations. Repeat assessments were conducted on -10% of the sample. Variations in DPFC responses (scale and item level) were examined in relation to socio-demographics and dental attendance pattern in bivariate and regression analyses. Internal reliability (Cronbach's alpha) and test-retest reliability (Intraclass Correlation Coefficient - ICC) were examined.

RESULTS

A DPFC questionnaire was derived following minor modifications. The clarity of items ranged from 81.1-100% and content validity index ranged from 0.73-1.00. Exploratory item factor analysis showed a one-dimensional construct. The response rate to the survey was 90.5% (389/430). Variations in DPFC scores with respect to global rating of satisfaction were apparent (P < 0.001). Cronbach's alpha value was 0.94 and ICC value was 0.89. Bivariate and regression analyses identified dental attendance pattern as a key factor associated with DPFC (P < 0.05); but no significant differences were observed with respect to socio-demographic factors.

CONCLUSIONS

A DPFC questionnaire was adapted with acceptable validity and reliability. Dental service utilization pattern was associated with dentist-patient clinical communication rather than socio-demographics.

A review of communication models and frameworks in a healthcare context

This paper reviews six key communication models and frameworks in healthcare contexts. Comparison suggests key inter-relationships between the different stages of the clinical consultations. Implications are identified for future study in healthcare provider-patient communication. Clinical Relevance: To understand the healthcare provider-patient interaction through communication models.

Social media in adolescent health literacy education: a pilot study

Background

While health literacy has gained notice on a global stage, the initial focus on seeking associations with medical conditions may have overlooked its impact across generations. Adolescent health literacy, specifically in dentistry, is an underexplored area despite the significance of this formative stage on an individual’s approach to healthy lifestyles and behaviors.

Objective

The aim is to conduct a pilot study to evaluate the efficacy of three major social media outlets - Twitter, Facebook, and YouTube - in supporting adolescents’ oral health literacy (OHL) education.

Methods

A random sample of 22 adolescents (aged 14-16 years) from an English-medium international school in Hong Kong provided informed consent. Sociodemographic information, including English language background, social media usage, and dental experience were collected via a questionnaire. A pre- and post-test of OHL (REALD-30) was administered by two trained, calibrated examiners. Following pre-test, participants were randomly assigned to one of three social media outlets: Twitter, Facebook, or YouTube. Participants received alerts posted daily for 5 consecutive days requiring online accessing of modified and original OHL education materials. One-way ANOVA ( analysis of variance) was used to compare the mean difference between the pre- and the post-test results among the three social media.

Results

No associations were found between the social media allocated and participants’ sociodemographics, including English language background, social media usage, and dental experience. Of the three social media, significant differences in literacy assessment scores were evident for participants who received oral health education messages via Facebook (P=.02) and YouTube (P=.005).

Conclusions

Based on the results of the pilot study, Facebook and YouTube may be more efficient media outlets for OHL promotion and education among adolescent school children when compared to Twitter. Further analyses with a larger study group is warranted.

Efficacy of instruments measuring oral health literacy: a systematic review

PURPOSE

To identify and review the psychometric properties of instruments available for measuring oral health literacy.

MATERIALS AND METHODS

A comprehensive computerised search was carried out using six databases. The final papers were rated for level of evidence and scientific quality.

RESULTS

A total of 621 potentially relevant articles were retrieved in the primary search. Twenty-nine studies using 13 oral health literacy instruments were included in the final analysis. After applying an international standards framework, all included studies were categorised as evidence level '2c'. Qualities of evidence were rated with STROBE guidelines. Psychometric analysis indicated various levels of validity and reliability across the instruments.

CONCLUSIONS

As an emerging field, the number and reliability of oral health literacy instruments is rapidly growing, although many are in preliminary stages of testing. The majority of these focus on functional literacy and were developed in English for North American contexts. Further work is indicated to measure oral health literacy as a wider construct across diverse populations.

Educational technologies in problem-based learning in health sciences education: a systematic review

Background

As a modern pedagogical philosophy, problem-based learning (PBL) is increasingly being recognized as a major research area in student learning and pedagogical innovation in health sciences education. A new area of research interest has been the role of emerging educational technologies in PBL. Although this field is growing, no systematic reviews of studies of the usage and effects of educational technologies in PBL in health sciences education have been conducted to date.

Objective

The aim of this paper is to review new and emerging educational technologies in problem-based curricula, with a specific focus on 3 cognate clinical disciplines: medicine, dentistry, and speech and hearing sciences. Analysis of the studies reviewed focused on the effects of educational technologies in PBL contexts while addressing the particular issue of scaffolding of student learning.

Methods

A comprehensive computerized database search of full-text articles published in English from 1996 to 2014 was carried out using 3 databases: ProQuest, Scopus, and EBSCOhost. Eligibility criteria for selection of studies for review were also determined in light of the population, intervention, comparison, and outcomes (PICO) guidelines. The population was limited to postsecondary education, specifically in dentistry, medicine, and speech and hearing sciences, in which PBL was the key educational pedagogy and curriculum design. Three types of educational technologies were identified as interventions used to support student inquiry: learning software and digital learning objects; interactive whiteboards (IWBs) and plasma screens; and learning management systems (LMSs).

Results

Of 470 studies, 28 were selected for analysis. Most studies examined the effects of learning software and digital learning objects (n=20) with integration of IWB (n=5) and LMS (n=3) for PBL receiving relatively less attention. The educational technologies examined in these studies were seen as potentially fit for problem-based health sciences education. Positive outcomes for student learning included providing rich, authentic problems and/or case contexts for learning; supporting student development of medical expertise through the accessing and structuring of expert knowledge and skills; making disciplinary thinking and strategies explicit; providing a platform to elicit articulation, collaboration, and reflection; and reducing perceived cognitive load. Limitations included cumbersome scenarios, infrastructure requirements, and the need for staff and student support in light of the technological demands of new affordances.

Conclusions

This literature review demonstrates the generally positive effect of educational technologies in PBL. Further research into the various applications of educational technology in PBL curricula is needed to fully realize its potential to enhance problem-based approaches in health sciences education.

The Relation Between Caregivers' Multiliterate Reading Habits and Their Children's Oral Health Status

BACKGROUND

Caregivers' oral health literacy (OHL) assessment results have been found to be related to their children's oral health status. A further aspect of this relationship may be the role of caregivers' reading habits.

OBJECTIVE

Our goal was to describe the relationship between caregivers' multimodal (digital and print) and multilingual (English and Chinese) reading habits, their OHL, and their child's oral health status in Hong Kong.

METHODS

A random sample of 301 child-caregiver dyads was recruited from kindergartens in Hong Kong. Data included sociodemographic information and caregivers' self-reported digital print and reading habits across two languages (Chinese and English). Caregivers' OHL levels were assessed by two locally developed and validated oral health literacy assessment tasks: Hong Kong Rapid Estimate of Adult Literacy in Dentistry-30 (HKREALD-30) and the Hong Kong Oral Health Literacy Assessment Task for Pediatric Dentistry (HKOHLAT-P). Children's oral health status was assessed using two measures: dental caries experience (number of decayed, missing, and filled teeth) and oral hygiene status (Visible Plaque Index).

RESULTS

Bivariate variations revealed significant differences in mean OHL scores between caregivers with different reading habits (P<.01). Correlations revealed significant associations between caregivers' practices of reading multimodal (print/digital) and multilingual (English/Chinese) texts, their literacy levels, and their children's oral health status (P<.01). Adjusting for sociodemographics and all other reading habits in the regression analysis, the caregivers' habit of reading digital and print texts was significantly retained in the final model. Regression analysis revealed significant associations between caregivers' reading habits (digital Chinese) and their OHL word recognition scores: OR 5.00, 95% CI 1.10-3.65, P=.027. Significant associations were also evident for their OHL comprehension scores (digital Chinese: OR 2.30, 95% CI 1.30-4.20, P=.004; print Chinese: OR 2.50, 95% CI 1.40-4.30, P=.001). However, no significant associations were found between caregivers' reading habits and child's oral health status (P>.05).

CONCLUSIONS

Caregivers' habits of reading print and digital Chinese texts are significantly associated with their OHL scores. Their reading habits, however, do not affect their children's oral health status.

Genome comparison of Listeria monocytogenes serotype 4a strain HCC23 with selected lineage I and lineage II L. monocytogenes strains and other Listeria strains

More than 98% of reported human listeriosis cases are caused by specific serotypes within genetic lineages I and II. The genome sequence of Listeria monocytogenes lineage III strain HCC23 (serotype 4a) enables whole genomic comparisons across all three L. monocytogenes lineages. Protein cluster analysis indicated that strain HCC23 has the most unique protein pairs with nonpathogenic species Listeria innocua. Orthology analysis of the genome sequences of representative strains from the three L. monocytogenes genetic lineages and L. innocua (CLIP11262) identified 319 proteins unique to nonpathogenic strains HCC23 and CLIP11262 and 58 proteins unique to pathogenic strains F2365 and EGD-e. BLAST comparison of these proteins with all the sequenced L. monocytogenes and L. innocua revealed 126 proteins unique to serotype 4a and/or L. innocua; 14 proteins were only found in pathogenic serotypes. Some of the 58 proteins unique to pathogenic strains F2365 and EGD-e were previously published and are already known to contribute to listerial virulence.

The relationship between caregiver functional oral health literacy and child oral health status

OBJECTIVE

To describe the relationship between caregivers' oral health literacy (OHL) and the oral health status of their children in an Asian population.

METHODS

A random sample of 301 child/caregiver dyads was recruited from kindergartens in Hong Kong. Two locally-developed and validated OHL assessment tasks were administered to caregivers with Hong Kong Rapid Estimate of Adult Literacy in Dentistry-30 (HKREALD-30) assessing word recognition and Hong Kong Oral Health Literacy Assessment Task for Paediatric Dentistry (HKOHLAT-P) assessing comprehension. Their children's oral health status was assessed [dental caries experience - decayed, missing, filled teeth index - (dmft) and oral hygiene status - Visible Plaque Index (VPI)].

RESULTS

Caregivers' literacy was associated with children's oral health status. The HKOHLAT-P had a stronger association with children's oral health than HKREALD-30. HKOHLAT-P and HKREALD-30 remained associated with dmft in the adjusted negative binomial regression models (accounting for socio-demographics), Incidence Rate Ratio (IRR) 0.97, p=0.02, and 0.96, p=0.03, respectively. In the adjusted model, HKOHLAT-P was associated with VPI (IRR 0.90, p<0.05), but no association between HKREALD-30 and VPI was evident.

CONCLUSION

The main conclusion of this study was that caregiver oral health literacy was associated with their child's oral health status. A comprehension instrument had a more robust association with children's oral status than a word recognition instrument.

PRACTICE IMPLICATIONS

This study has implications for general public health education for designing community-level interventions.

Development of functional oral health literacy assessment instruments: application of literacy and cognitive theories

OBJECTIVES

This paper describes the development of a new literacy assessment instrument, the Hong Kong Oral Health Literacy Assessment Task for Paediatric Dentistry (HKOHLAT-P). Its relationship to literacy theory is analyzed to establish content and face validity. Implications for construct validity are examined by analyzing cognitive demand to determine how "comprehension" is measured.

METHODS

Key influences from literacy assessment were identified to analyze item development. Cognitive demand was analyzed using an established taxonomy.

RESULTS

The HKOHLAT-P focuses on the functional domain of health literacy assessment. Items had strong content and face validity reflecting established principles from modern literacy theory. Inclusion of new text types signified relevant developments in the area of new literacies. Analysis of cognitive demand indicated that this instrument assesses the "comprehension" domain, specifically the areas of factual and procedural knowledge, with some assessment of conceptual knowledge. Metacognitive knowledge was not assessed.

CONCLUSIONS

Comprehension tasks assessing patient health literacy predominantly examine functional health literacy at the lower levels of comprehension. Item development is influenced by the fields of situated and authentic literacy. Inclusion of content regarding multiliteracies is suggested for further research. Development of functional health literacy assessment instruments requires careful consideration of the clinical context in determining construct validity.

Validation of the Hong Kong Oral Health Literacy Assessment Task for paediatric dentistry (HKOHLAT-P)

BACKGROUND

Oral health literacy is a newly emerging field with considerable research potential.

AIM

To validate an original instrument, the Hong Kong Oral Health Literacy Assessment Task (HKOHLAT-P) for paediatric dentistry.

DESIGN

A convenient sample of 200 child/parent dyads attending a dental hospital in Hong Kong was selected. Convergent validity was tested by examining the association of HKOHLAT-P scores with those derived from the Test of Functional Health Literacy in Dentistry (TOFHLiD) and Hong Kong Rapid Estimate of Adult Literacy in Dentistry (HKREALD-30). The predictive validity of HKOHLAT-P was determined by testing the association between HKOHLAT-P and children's caries experience (dmft) and the Chinese Early Childhood Oral Health Impact Scale (ECOHIS). The test-retest reliability and internal consistency of HKOHLAT-P were also evaluated.

RESULTS

HKOHLAT-P was positively correlated with TOFHLiD and HKREALD-30 (P < 0.01), and was negatively correlated with children's dmft and ECOHIS. In the regression model, HKOHLAT-P was associated with TOFHLiD, HKEALD-30, children's dmft, and ECOHIS (P < 0.05) after controlling for participants' demographic characteristics. The intra-class correlation coefficient of HKOHLAT-P was 0.63 and the Cronbach's α was 0.71.

CONCLUSION

Initial testing of HKOHLAT-P suggested that it is a valid and reliable instrument.

Interestingness measures and strategies for mining multi-ontology multi-level association rules from gene ontology annotations for the discovery of new GO relationships

The Gene Ontology (GO), a set of three sub-ontologies, is one of the most popular bio-ontologies used for describing gene product characteristics. GO annotation data containing terms from multiple sub-ontologies and at different levels in the ontologies is an important source of implicit relationships between terms from the three sub-ontologies. Data mining techniques such as association rule mining that are tailored to mine from multiple ontologies at multiple levels of abstraction are required for effective knowledge discovery from GO annotation data. We present a data mining approach, Multi-ontology data mining at All Levels (MOAL) that uses the structure and relationships of the GO to mine multi-ontology multi-level association rules. We introduce two interestingness measures: Multi-ontology Support (MOSupport) and Multi-ontology Confidence (MOConfidence) customized to evaluate multi-ontology multi-level association rules. We also describe a variety of post-processing strategies for pruning uninteresting rules. We use publicly available GO annotation data to demonstrate our methods with respect to two applications (1) the discovery of co-annotation suggestions and (2) the discovery of new cross-ontology relationships.

Polycomb group gene OsFIE2 regulates rice (Oryza sativa) seed development and grain filling via a mechanism distinct from Arabidopsis

Cereal endosperm represents 60% of the calories consumed by human beings worldwide. In addition, cereals also serve as the primary feedstock for livestock. However, the regulatory mechanism of cereal endosperm and seed development is largely unknown. Polycomb complex has been shown to play a key role in the regulation of endosperm development in Arabidopsis, but its role in cereal endosperm development remains obscure. Additionally, the enzyme activities of the polycomb complexes have not been demonstrated in plants. Here we purified the rice OsFIE2-polycomb complex using tandem affinity purification and demonstrated its specific H3 methyltransferase activity. We found that the OsFIE2 gene product was responsible for H3K27me3 production specifically in vivo. Genetic studies showed that a reduction of OsFIE2 expression led to smaller seeds, partially filled seeds, and partial loss of seed dormancy. Gene expression and proteomics analyses found that the starch synthesis rate limiting step enzyme and multiple storage proteins are down-regulated in OsFIE2 reduction lines. Genome wide ChIP–Seq data analysis shows that H3K27me3 is associated with many genes in the young seeds. The H3K27me3 modification and gene expression in a key helix-loop-helix transcription factor is shown to be regulated by OsFIE2. Our results suggest that OsFIE2-polycomb complex positively regulates rice endosperm development and grain filling via a mechanism highly different from that in Arabidopsis.

Rice is the staple food for over half of the world's population and an important feedstock for livestock. The rice grain is mainly endosperm tissue. The regulatory mechanism of rice endosperm development is still largely unknown thus far. Understanding the underlying mechanism will lead to crop yield and quality improvement in the long term, besides gaining new knowledge. Polycomb complex is a protein complex with a potential role in endosperm development according to prior publications. In this manuscript, we purified the rice OsFIE2-polycomb protein complex and demonstrated the enzyme activity of the complex. Genetic studies showed that a reduction of polycomb group gene OsFIE2 expression led to smaller seeds, partially filled seeds, and seed germination before seed maturation. Gene expression and proteomics analyses found that the starch synthesis rate limiting step enzyme and multiple storage proteins are down-regulated while a key transcription factor is up-regulated in OsFIE2 reduction lines. In addition, we identified many loci in the rice genome whose histone proteins are modified by the polycomb complex enzyme via a method called ChIP–Seq. Our results demonstrate that OsFIE2-polycomb complex positively regulates rice grain development via a mechanism distinct from that in Arabidopsis and provide new insight into the regulation of rice grain development.

Cross-Ontology multi-level association rule mining in the Gene Ontology

The Gene Ontology (GO) has become the internationally accepted standard for representing function, process, and location aspects of gene products. The wealth of GO annotation data provides a valuable source of implicit knowledge of relationships among these aspects. We describe a new method for association rule mining to discover implicit co-occurrence relationships across the GO sub-ontologies at multiple levels of abstraction. Prior work on association rule mining in the GO has concentrated on mining knowledge at a single level of abstraction and/or between terms from the same sub-ontology. We have developed a bottom-up generalization procedure called Cross-Ontology Data Mining-Level by Level (COLL) that takes into account the structure and semantics of the GO, generates generalized transactions from annotation data and mines interesting multi-level cross-ontology association rules. We applied our method on publicly available chicken and mouse GO annotation datasets and mined 5368 and 3959 multi-level cross ontology rules from the two datasets respectively. We show that our approach discovers more and higher quality association rules from the GO as evaluated by biologists in comparison to previously published methods. Biologically interesting rules discovered by our method reveal unknown and surprising knowledge about co-occurring GO terms.

Development and validation of Hong Kong Rapid Estimate of Adult Literacy in Dentistry

AIM

To develop and validate an instrument, the Hong Kong Rapid Estimate of Adult Literacy in Dentistry (HKREALD-30).

METHODS

The Rapid Estimate of Adult Literacy in Dentistry (REALD-99) was translated into Chinese and modified in the pretest. A total of 200 parents of pediatric dental patients were interviewed using this modified scale and administered additional three sets of self-reported questionnaires. The 99 items of the scale were reduced to 30 (HKREALD-30). Concurrent validity was tested by comparing the HKREALD-30 scores with the participants' educational level, pattern of dental visits and reading habits. Convergent validity was tested by examining the association between HKREALD-30 and the Test of Functional Health Literacy in Dentistry (TOFHLiD). The test-retest reliability and internal consistency of HKREALD-30 were also evaluated.

RESULTS

A significant correlation (P < 0.01) was found between HKREALD-30 and participants' reading habits. HKREALD-30 was also highly correlated with TOFHLiD (Spearman's rho = 0.693, P < 0.01). In the regression model, HKREALD-30 was positively associated with TOFHLiD (P < 0.05) after controlling for participants' characteristics. The intra-class correlation coefficient of HKREALD-30 was 0.78 and the Cronbach's alpha was 0.84.

CONCLUSION

Initial testing of HKREALD-30 suggested that it is a valid and reliable instrument for the basic screening of oral health literacy among Chinese people in Hong Kong.

Comparison of four ChIP-Seq analytical algorithms using rice endosperm H3K27 trimethylation profiling data

Chromatin immunoprecipitation coupled with high throughput DNA Sequencing (ChIP-Seq) has emerged as a powerful tool for genome wide profiling of the binding sites of proteins associated with DNA such as histones and transcription factors. However, no peak calling program has gained consensus acceptance by the scientific community as the preferred tool for ChIP-Seq data analysis. Analyzing the large data sets generated by ChIP-Seq studies remains highly challenging for most molecular biology laboratories.Here we profile H3K27me3 enrichment sites in rice young endosperm using the ChIP-Seq approach and analyze the data using four peak calling algorithms (FindPeaks, PeakSeq, USeq, and MACS). Comparison of the four algorithms reveals that these programs produce very different peaks in terms of peak size, number, and position relative to genes. We verify the peak predictions using ChIP-PCR to evaluate the accuracy of peak prediction of the four algorithms. We discuss the approach of each algorithm and compare similarities and differences in the results. Despite their differences in the peaks identified, all of the programs reach similar conclusions about the effect of H3K27me3 on gene expression. Its presence either upstream or downstream of a gene is predominately associated with repression of the gene. Additionally, GO analysis finds that a substantially higher ratio of genes associated with H3K27me3 were involved in multicellular organism development, signal transduction, response to external and endogenous stimuli, and secondary metabolic pathways than the rest of the rice genome.

Implant dentistry in undergraduate dental curricula in South-East Asia: forum workshop at the University of Hong Kong, Prince Philip Dental Hospital, 19-20 November 2010

This paper reports on the discussions arising from a 2-day forum on implant dentistry education in South-East Asia. The 10 institutions present represented undergraduate and postgraduate dental curricula from seven countries, including Hong Kong, Indonesia, Malaysia, Taiwan, Thailand, the Philippines, and Singapore. While not aiming to reach consensus as in other such conferences, the outcome was positive in establishing realistic goals in university education in implant dentistry for curriculum leaders and developers.

Prediction of cell penetrating peptides by support vector machines

Cell penetrating peptides (CPPs) are those peptides that can transverse cell membranes to enter cells. Once inside the cell, different CPPs can localize to different cellular components and perform different roles. Some generate pore-forming complexes resulting in the destruction of cells while others localize to various organelles. Use of machine learning methods to predict potential new CPPs will enable more rapid screening for applications such as drug delivery. We have investigated the influence of the composition of training datasets on the ability to classify peptides as cell penetrating using support vector machines (SVMs). We identified 111 known CPPs and 34 known non-penetrating peptides from the literature and commercial vendors and used several approaches to build training data sets for the classifiers. Features were calculated from the datasets using a set of basic biochemical properties combined with features from the literature determined to be relevant in the prediction of CPPs. Our results using different training datasets confirm the importance of a balanced training set with approximately equal number of positive and negative examples. The SVM based classifiers have greater classification accuracy than previously reported methods for the prediction of CPPs, and because they use primary biochemical properties of the peptides as features, these classifiers provide insight into the properties needed for cell-penetration. To confirm our SVM classifications, a subset of peptides classified as either penetrating or non-penetrating was selected for synthesis and experimental validation. Of the synthesized peptides predicted to be CPPs, 100% of these peptides were shown to be penetrating.

Cell penetrating peptides (CPPs) are peptides that can potentially transport other functional molecules across cellular membranes and therefore serve a role as drug delivery vehicles. The properties of a given peptide that make it cell penetrating are unclear, and the rapid screening of potential CPPs aids researchers by allowing focus on those peptides most likely to be utilized in a therapeutic capacity. This paper shows that basic features representing primary biochemical properties of these peptides can be used to train a classifier that can accurately predict cell penetrating potential of peptides and provide insight into the biochemical properties associated with cell penetration.

The proteogenomic mapping tool

Background

High-throughput mass spectrometry (MS) proteomics data is increasingly being used to complement traditional structural genome annotation methods. To keep pace with the high speed of experimental data generation and to aid in structural genome annotation, experimentally observed peptides need to be mapped back to their source genome location quickly and exactly. Previously, the tools to do this have been limited to custom scripts designed by individual research groups to analyze their own data, are generally not widely available, and do not scale well with large eukaryotic genomes.

Results

The Proteogenomic Mapping Tool includes a Java implementation of the Aho-Corasick string searching algorithm which takes as input standardized file types and rapidly searches experimentally observed peptides against a given genome translated in all 6 reading frames for exact matches. The Java implementation allows the application to scale well with larger eukaryotic genomes while providing cross-platform functionality.

Conclusions

The Proteogenomic Mapping Tool provides a standalone application for mapping peptides back to their source genome on a number of operating system platforms with standard desktop computer hardware and executes very rapidly for a variety of datasets. Allowing the selection of different genetic codes for different organisms allows researchers to easily customize the tool to their own research interests and is recommended for anyone working to structurally annotate genomes using MS derived proteomics data.

Multilingual interactions in clinical dental education: a focus on mediated interpreting

In clinical dental consultations in multilingual contexts, medical interpreting is often performed by the supporting staff as part of routine triadic formulations. As academic dentistry becomes increasingly internationalized, issues of language and culture add to the interactional complexity of clinical communication and education. A multivariate approach was adopted to investigate one case of multilingualism in dentistry in Asia. Collection of both survey (n = 86) and interactional data provided empirical evidence regarding language use and language demands across integrated Polyclinics. Descriptive statistics of Dental Surgery Assistant (DSA) perception data and conversation analysis (CA) of mediated interpretation indicate that, as members of the oral healthcare team, DSAs in Hong Kong are an essential resource in their role of intercultural mediators between patients and clinicians, both staff and students. Discussion of sociolinguistic notions of place-as-location and place-as-meaning supports a wider conceptualization of the role of support staff as interpreters in clinical settings. Implications are drawn for policy, curriculum and staff development.

AgBase: supporting functional modeling in agricultural organisms

AgBase (http://www.agbase.msstate.edu/) provides resources to facilitate modeling of functional genomics data and structural and functional annotation of agriculturally important animal, plant, microbe and parasite genomes. The website is redesigned to improve accessibility and ease of use, including improved search capabilities. Expanded capabilities include new dedicated pages for horse, cat, dog, cotton, rice and soybean. We currently provide 590 240 Gene Ontology (GO) annotations to 105 454 gene products in 64 different species, including GO annotations linked to transcripts represented on agricultural microarrays. For many of these arrays, this provides the only functional annotation available. GO annotations are available for download and we provide comprehensive, species-specific GO annotation files for 18 different organisms. The tools available at AgBase have been expanded and several existing tools improved based upon user feedback. One of seven new tools available at AgBase, GOModeler, supports hypothesis testing from functional genomics data. We host several associated databases and provide genome browsers for three agricultural pathogens. Moreover, we provide comprehensive training resources (including worked examples and tutorials) via links to Educational Resources at the AgBase website.

An atlas of bovine gene expression reveals novel distinctive tissue characteristics and evidence for improving genome annotation

BACKGROUND

A comprehensive transcriptome survey, or gene atlas, provides information essential for a complete understanding of the genomic biology of an organism. We present an atlas of RNA abundance for 92 adult, juvenile and fetal cattle tissues and three cattle cell lines.

RESULTS

The Bovine Gene Atlas was generated from 7.2 million unique digital gene expression tag sequences (300.2 million total raw tag sequences), from which 1.59 million unique tag sequences were identified that mapped to the draft bovine genome accounting for 85% of the total raw tag abundance. Filtering these tags yielded 87,764 unique tag sequences that unambiguously mapped to 16,517 annotated protein-coding loci in the draft genome accounting for 45% of the total raw tag abundance. Clustering of tissues based on tag abundance profiles generally confirmed ontology classification based on anatomy. There were 5,429 constitutively expressed loci and 3,445 constitutively expressed unique tag sequences mapping outside annotated gene boundaries that represent a resource for enhancing current gene models. Physical measures such as inferred transcript length or antisense tag abundance identified tissues with atypical transcriptional tag profiles. We report for the first time the tissue-specific variation in the proportion of mitochondrial transcriptional tag abundance.

CONCLUSIONS

The Bovine Gene Atlas is the deepest and broadest transcriptome survey of any livestock genome to date. Commonalities and variation in sense and antisense transcript tag profiles identified in different tissues facilitate the examination of the relationship between gene expression, tissue, and gene function.

Integrated database for identifying candidate genes for Aspergillus flavus resistance in maize

BACKGROUND

Aspergillus flavus Link:Fr, an opportunistic fungus that produces aflatoxin, is pathogenic to maize and other oilseed crops. Aflatoxin is a potent carcinogen, and its presence markedly reduces the value of grain. Understanding and enhancing host resistance to A. flavus infection and/or subsequent aflatoxin accumulation is generally considered an efficient means of reducing grain losses to aflatoxin. Different proteomic, genomic and genetic studies of maize (Zea mays L.) have generated large data sets with the goal of identifying genes responsible for conferring resistance to A. flavus, or aflatoxin.

RESULTS

In order to maximize the usage of different data sets in new studies, including association mapping, we have constructed a relational database with web interface integrating the results of gene expression, proteomic (both gel-based and shotgun), Quantitative Trait Loci (QTL) genetic mapping studies, and sequence data from the literature to facilitate selection of candidate genes for continued investigation. The Corn Fungal Resistance Associated Sequences Database (CFRAS-DB) (http://agbase.msstate.edu/) was created with the main goal of identifying genes important to aflatoxin resistance. CFRAS-DB is implemented using MySQL as the relational database management system running on a Linux server, using an Apache web server, and Perl CGI scripts as the web interface. The database and the associated web-based interface allow researchers to examine many lines of evidence (e.g. microarray, proteomics, QTL studies, SNP data) to assess the potential role of a gene or group of genes in the response of different maize lines to A. flavus infection and subsequent production of aflatoxin by the fungus.

CONCLUSIONS

CFRAS-DB provides the first opportunity to integrate data pertaining to the problem of A. flavus and aflatoxin resistance in maize in one resource and to support queries across different datasets. The web-based interface gives researchers different query options for mining the database across different types of experiments. The database is publically available at http://agbase.msstate.edu.

GOModeler--a tool for hypothesis-testing of functional genomics datasets

Background

Functional genomics technologies that measure genome expression at a global scale are accelerating biological knowledge discovery. Generating these high throughput datasets is relatively easy compared to the downstream functional modelling necessary for elucidating the molecular mechanisms that govern the biology under investigation. A number of publicly available ‘discovery-based’ computational tools use the computationally amenable Gene Ontology (GO) for hypothesis generation. However, there are few tools that support hypothesis-based testing using the GO and none that support testing with user defined hypothesis terms.

Here, we present GOModeler, a tool that enables researchers to conduct hypothesis-based testing of high throughput datasets using the GO. GOModeler summarizes the overall effect of a user defined gene/protein differential expression dataset on specific GO hypothesis terms selected by the user to describe a biological experiment. The design of the tool allows the user to complement the functional information in the GO with his/her domain specific expertise for comprehensive hypothesis testing.

Results

GOModeler tests the relevance of the hypothesis terms chosen by the user for the input gene dataset by providing the individual effects of the genes on the hypothesis terms and the overall effect of the entire dataset on each of the hypothesis terms. It matches the GO identifiers (ids) of the genes with the GO ids of the hypothesis terms and parses the names of those ids that match to assign effects. We demonstrate the capabilities of GOModeler with a dataset of nine differentially expressed cytokine genes and compare the results to those obtained through manual analysis of the dataset by an immunologist. The direction of overall effects on all hypothesis terms except one was consistent with the results obtained by manual analysis. The tool’s editing capability enables the user to augment the information extracted. GOModeler is available as a part of the AgBase tool suite (http://www.agbase.msstate.edu).

Conclusions

GOModeler allows hypothesis driven analysis of high throughput datasets using the GO. Using this tool, researchers can quickly evaluate the overall effect of quantitative expression changes of gene set on specific biological processes of interest. The results are provided in both tabular and graphical formats.

Complete genome and comparative analysis of the chemolithoautotrophic bacterium Oligotropha carboxidovorans OM5

Background

Oligotropha carboxidovorans OM5 T. (DSM 1227, ATCC 49405) is a chemolithoautotrophic bacterium capable of utilizing CO (carbon monoxide) and fixing CO₂ (carbon dioxide). We previously published the draft genome of this organism and recently submitted the complete genome sequence to GenBank.

Results

The genome sequence of the chemolithoautotrophic bacterium Oligotropha carboxidovorans OM5 consists of a 3.74-Mb chromosome and a 133-kb megaplasmid that contains the genes responsible for utilization of carbon monoxide, carbon dioxide, and hydrogen. To our knowledge, this strain is the first one to be sequenced in the genus Oligotropha, the closest fully sequenced relatives being Bradyrhizobium sp. BTAi and USDA110 and Nitrobacter hamburgiensis X14. Analysis of the O. carboxidovorans genome reveals potential links between plasmid-encoded chemolithoautotrophy and chromosomally-encoded lipid metabolism. Comparative analysis of O. carboxidovorans with closely related species revealed differences in metabolic pathways, particularly in carbohydrate and lipid metabolism, as well as transport pathways.

Conclusion

Oligotropha, Bradyrhizobium sp and Nitrobacter hamburgiensis X14 are phylogenetically proximal. Although there is significant conservation of genome organization between the species, there are major differences in many metabolic pathways that reflect the adaptive strategies unique to each species.

Gene model detection using mass spectrometry

The utility of a genome sequence in biological research depends entirely on the comprehensive description of all of its functional elements. Analysis of genome sequences is still predominantly gene-centric (i.e., identifying gene models/open reading frames). In this article, we describe a proteomics-based method for identifying open reading frames that are missed by computational algorithms. Mass spectrometry-based identification of peptides and proteins from biological samples provide evidence for the expression of the genome sequence at the protein level. This proteogenomic annotation method combines computationally predicted ORFs and the genome sequence with proteomics to identify novel gene models. We also describe our proteogenomic mapping pipeline - a set of computational tools that automate the proteogenomic annotation work flow. This pipeline is available for download at www.agbase.msstate.edu/tools/ .

Comparing gene annotation enrichment tools for functional modeling of agricultural microarray data

The widespread availability of microarray technology has driven functional genomics to the forefront as scientists seek to draw meaningful biological conclusions from their microarray results. Gene annotation enrichment analysis is a functional analysis technique that has gained widespread attention and for which many tools have been developed. Unfortunately, most of these tools have limited support for agricultural species. Here, we evaluate and compare four publicly available computational tools (Onto-Express, EasyGO, GOstat, and DAVID) that support analysis of gene expression datasets in agricultural species. We use AgBase as the functional annotation reference for agricultural species. The selected tools were evaluated based on i) available features, usage and accessibility, ii) implemented statistical computational methods, and iii) annotation and enrichment performance analysis. Annotation was assessed using a randomly selected test gene annotation set and an experimental differentially expressed gene-set – both from chicken. The experimental set was also used to evaluate identification of enriched functional groups.

Comparison of the tools shows that they produce different sets of annotations for the two datasets and different functional groups for the experimental dataset. While DAVID, GOstat and Onto-Express annotate comparable numbers of genes, DAVID provides by far the most annotations per gene. However, many of DAVID's annotations appear to be redundant or are at very high levels in the GO hierarchy. The GOSlim distribution of annotations shows that GOstat, Onto-Express and EasyGO provide similar GO distributions to those found in AgBase while annotations from DAVID show a different GOSlim distribution, again probably due to duplication and many non-specific terms. No consistent trends were found in results of GO term over/under representation analysis applied to the experimental data using different tools. While GOstat, David and Onto-Express could retrieve some significantly enriched terms, EasyGO did not show any significantly enriched terms. There was little agreement about the enriched terms identified by the tools.

Conclusion

Different tools for functionally annotating gene sets and identifying significantly enriched GO categories differ widely in their results when applied to a test annotation gene set and an experimental dataset from chicken. These results emphasize the need for care when interpreting the results of such analysis and the lack of standardization of approaches.

Integrating phenotype and gene expression data for predicting gene function

Background

This paper presents a framework for integrating disparate data sets to predict gene function. The algorithm constructs a graph, called an integrated similarity graph, by computing similarities based upon both gene expression and textual phenotype data. This integrated graph is then used to make predictions about whether individual genes should be assigned a particular annotation from the Gene Ontology.

Results

A combined graph was generated from publicly-available gene expression data and phenotypic information from Saccharomyces cerevisiae. This graph was used to assign annotations to genes, as were graphs constructed from gene expression data and textual phenotype information alone. While the F-measure appeared similar for all three methods, annotations based upon the integrated similarity graph exhibited a better overall precision than gene expression or phenotype information alone can generate. The integrated approach was also able to assign almost as many annotations as the gene expression method alone, and generated significantly more total and correct assignments than the phenotype information could provide.

Conclusion

These results suggest that augmenting standard gene expression data sets with publicly-available textual phenotype data can help generate more precise functional annotation predictions while mitigating the weaknesses of a standard textual phenotype approach.

Exploratory visual analysis of conserved domains on multiple sequence alignments

Background

Multiple alignment of protein sequences can provide insight into sequence conservation across many species and thus allow identification of those sections of the sequence most critical to protein function. This insight can be augmented by joint display of conserved domains along the sequences. By fusing this metadata visually, biologists can analyze sequence conservation and functional motifs simultaneously and efficiently.

Results

We present MSAVis, a new approach combining luminance and hue for simultaneous visualization of conserved motifs and sequence alignment. Input for the algorithm is a multiple sequence alignment in a standard format. The NCBI Conserved Domain Database (CDD) is used for finding conserved domains along the alignment. The visualization quickly identifies conserved domains, and allows both macro (sequence-long) and micro (small amino-acid neighborhood) views.

Conclusion

MSAVis utilizes two visual cues, luminance and hue, to facilitate at-a-glance summary of the conservation of a user-provided protein alignment while enabling multiple comparisons among functional domains. These visual cues are preattentive and separable so that the relationship between conservation strength and domain membership can be understood. The MSAVis software, written in Python and using BioPython and OpenGL, can be found at http://agbase.msstate.edu/tools/MSAVis.html.

Comparative analysis of proteome differential regulation during cell dedifferentiation in Arabidopsis

Cell dedifferentiation is a cell fate switching process in which differentiated cells undergo genome reprogramming to regain the competency of cell division and organ regeneration. The molecular mechanism underlying the cell dedifferentiation process remains obscure. In this report, we investigate the cell dedifferentiation process in Arabidopsis using a shotgun proteomics approach. A total of 758 proteins are identified by two or more matched peptides. Comparative analyses at four time points using two label-free methods reveal that 193 proteins display up-regulation and 183 proteins display down-regulation within 48 h. While the results of the two label-free quantification methods match well with each other, comparison with previously published 2-DE gel results reveal that label-free quantification results differ substantially from those of the 2-DE method for proteins with peptides common to multiple proteins, suggesting a limitation of the label-free methods in quantifying proteins with closely related family members in complex samples. Our results show that the shotgun approach and the traditional 2-DE gel approach complement each other in both protein identification and quantification. An interesting observation is that core histones and histone variants are subjected to extensive down-regulation, indicating that there is a dramatic change in the chromatin during cell differentiation.

Introduction to the Proceedings of the Avian Genomics and Gene Ontology Annotation Workshop

The Avian Genomics Conference and Gene Ontology Annotation Workshop brought together researchers and students from around the world to present their latest research addressing the delivery of value from the billions of base-pairs of Archosaur sequence that have become available in the last few years. This editorial describes the conference itself and introduces the ten peer-reviewed manuscripts accepted for publications in the proceedings. These manuscripts address issues ranging from the poultry industry view of USDA genomics policy to the genomics of a wide variety of Archeosaur species including chicken, duck, alligator, and condors and their pathogens.

Accelerating string set matching in FPGA hardware for bioinformatics research

Background

This paper describes techniques for accelerating the performance of the string set matching problem with particular emphasis on applications in computational proteomics. The process of matching peptide sequences against a genome translated in six reading frames is part of a proteogenomic mapping pipeline that is used as a case-study. The Aho-Corasick algorithm is adapted for execution in field programmable gate array (FPGA) devices in a manner that optimizes space and performance. In this approach, the traditional Aho-Corasick finite state machine (FSM) is split into smaller FSMs, operating in parallel, each of which matches up to 20 peptides in the input translated genome. Each of the smaller FSMs is further divided into five simpler FSMs such that each simple FSM operates on a single bit position in the input (five bits are sufficient for representing all amino acids and special symbols in protein sequences).

Results

This bit-split organization of the Aho-Corasick implementation enables efficient utilization of the limited random access memory (RAM) resources available in typical FPGAs. The use of on-chip RAM as opposed to FPGA logic resources for FSM implementation also enables rapid reconfiguration of the FPGA without the place and routing delays associated with complex digital designs.

Conclusion

Experimental results show storage efficiencies of over 80% for several data sets. Furthermore, the FPGA implementation executing at 100 MHz is nearly 20 times faster than an implementation of the traditional Aho-Corasick algorithm executing on a 2.67 GHz workstation.

Gene Ontology annotation quality analysis in model eukaryotes

Functional analysis using the Gene Ontology (GO) is crucial for array analysis, but it is often difficult for researchers to assess the amount and quality of GO annotations associated with different sets of gene products. In many cases the source of the GO annotations and the date the GO annotations were last updated is not apparent, further complicating a researchers’ ability to assess the quality of the GO data provided. Moreover, GO biocurators need to ensure that the GO quality is maintained and optimal for the functional processes that are most relevant for their research community. We report the GO Annotation Quality (GAQ) score, a quantitative measure of GO quality that includes breadth of GO annotation, the level of detail of annotation and the type of evidence used to make the annotation. As a case study, we apply the GAQ scoring method to a set of diverse eukaryotes and demonstrate how the GAQ score can be used to track changes in GO annotations over time and to assess the quality of GO annotations available for specific biological processes. The GAQ score also allows researchers to quantitatively assess the functional data available for their experimental systems (arrays or databases).

Prediction of peptides observable by mass spectrometry applied at the experimental set level

Background

When proteins are subjected to proteolytic digestion and analyzed by mass spectrometry using a method such as 2D LC MS/MS, only a portion of the proteotypic peptides associated with each protein will be observed. The ability to predict which peptides can and cannot potentially be observed for a particular experimental dataset has several important applications in proteomics research including calculation of peptide coverage in terms of potentially detectable peptides, systems biology analysis of data sets, and protein quantification.

Results

We have developed a methodology for constructing artificial neural networks that can be used to predict which peptides are potentially observable for a given set of experimental, instrumental, and analytical conditions for 2D LC MS/MS (a.k.a Multidimensional Protein Identification Technology [MudPIT]) datasets. Neural network classifiers constructed using this procedure for two MudPIT datasets exhibit 10-fold cross validation accuracy of about 80%. We show that a classifier constructed for one dataset has poor predictive performance with the other dataset, thus demonstrating the need for dataset specific classifiers. Classification results with each dataset are used to compute informative percent amino acid coverage statistics for each protein in terms of the predicted detectable peptides in addition to the percent coverage of the complete sequence. We also demonstrate the utility of predicted peptide observability for systems analysis to help determine if proteins that were expected but not observed generate sufficient peptides for detection.

Conclusion

Classifiers that accurately predict the likelihood of detecting proteotypic peptides by mass spectrometry provide proteomics researchers with powerful new approaches for data analysis. We demonstrate that the procedure we have developed for building a classifier based on an individual experimental data set results in classifiers with accuracy comparable to those reported in the literature based on large training sets collected from multiple experiments. Our approach allows the researcher to construct a classifier that is specific for the experimental, instrument, and analytical conditions of a single experiment and amenable to local, condition-specific, implementation. The resulting classifiers have application in a number of areas such as determination of peptide coverage for protein identification, pathway analysis, and protein quantification.

ProtQuant: a tool for the label-free quantification of MudPIT proteomics data

Background

Effective and economical methods for quantitative analysis of high throughput mass spectrometry data are essential to meet the goals of directly identifying, characterizing, and quantifying proteins from a particular cell state. Multidimensional Protein Identification Technology (MudPIT) is a common approach used in protein identification. Two types of methods are used to detect differential protein expression in MudPIT experiments: those involving stable isotope labelling and the so-called label-free methods. Label-free methods are based on the relationship between protein abundance and sampling statistics such as peptide count, spectral count, probabilistic peptide identification scores, and sum of peptide Sequest XCorr scores (ΣXCorr). Although a number of label-free methods for protein quantification have been described in the literature, there are few publicly available tools that implement these methods. We describe ProtQuant, a Java-based tool for label-free protein quantification that uses the previously published ΣXCorr method for quantification and includes an improved method for handling missing data.

Results

ProtQuant was designed for ease of use and portability for the bench scientist. It implements the ΣXCorr method for label free protein quantification from MudPIT datasets. ProtQuant has a graphical user interface, accepts multiple file formats, is not limited by the size of the input files, and can process any number of replicates and any number of treatments. In addition,ProtQuant implements a new method for dealing with missing values for peptide scores used for quantification. The new algorithm, called ΣXCorr*, uses "below threshold" peptide scores to provide meaningful non-zero values for missing data points. We demonstrate that ΣXCorr* produces an average reduction in false positive identifications of differential expression of 25% compared to ΣXCorr.

Conclusion

ProtQuant is a tool for protein quantification built for multi-platform use with an intuitive user interface. ProtQuant efficiently and uniquely performs label-free quantification of protein datasets produced with Sequest and provides the user with facilities for data management and analysis. Importantly, ProtQuant is available as a self-installing executable for the Windows environment used by many bench scientists.

GOing from functional genomics to biological significance

The chicken genome is sequenced and this, together with microarray and other functional genomics technologies, makes post-genomic research possible in the chicken. At this time, however, such research is hindered by a lack of genomic structural and functional annotations. Bio-ontologies have been developed for different annotation requirements, as well as to facilitate data sharing and computational analysis, but these are not yet optimally utilized in the chicken. Here we discuss genomic annotation and bio-ontologies. We focus specifically on the Gene Ontology (GO), chicken GO annotations and how these can facilitate functional genomics in the chicken. The GO is the most developed and widely used bio-ontology. It is the de facto standard for functional annotation. Despite its critical importance in analyzing microarray and other functional genomics data, relatively few chicken gene products have any GO annotation. When these are available, the average quality of chicken gene products annotations (defined using evidence code weight and annotation depth) is much less than in mouse. Moreover, tools allowing chicken researchers to easily and rapidly use the GO are either lacking or hard to use. To address all of these problems we developed ChickGO and AgBase. Chicken GO annotations are provided by complementary work at MSU-AgBase and EBI-GOA. The GO tools pipeline at AgBase uses GO to derive functional and biological significance from microarray and other functional genomics data. Not only will improved genomic annotation and tools to use these annotations benefit the chicken research community but they will also facilitate research in other avian species and comparative genomics.