Community development, implementation, and assessment of a NIBLSE bioinformatics sequence similarity learning resource.
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ABSTRACT:
SUBMITTER: Kleinschmit AJ
PROVIDER: S-EPMC8432852 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The use of deep learning models in computational biology has increased massively in recent years, and it is expected to continue with the current advances in the fields such as Natural Language Processing. These models, although able to draw complex relations between input and target, are also inclined to learn noisy deviations from the pool of data used during their development. In order to assess their performance on unseen data (their capacity to generalize), it is common to split the available data randomly into development (train/validation) and test sets. This procedure, although standard, has been shown to produce dubious assessments of generalization due to the existing similarity between samples in the databases used. In this work, we present SpanSeq, a database partition method for machine learning that can scale to most biological sequences (genes, proteins and genomes) in order to avoid data leakage between sets. We also explore the effect of not restraining similarity between sets by reproducing the development of two state-of-the-art models on bioinformatics, not only confirming the consequences of randomly splitting databases on the model assessment, but expanding those repercussions to the model development. SpanSeq is available at https://github.com/genomicepidemiology/SpanSeq.
| S-EPMC11327874 | biostudies-literature
Project description:BackgroundComparative sequence analysis is considered as the first step towards annotating new proteins in genome annotation. However, sequence comparison may lead to creation and propagation of function assignment errors. Thus, it is important to perform a thorough analysis for the quality of sequence-based function assignment using large-scale data in a systematic way.ResultsWe present an analysis of the relationship between sequence similarity and function similarity for the proteins in four model organisms, i.e., Arabidopsis thaliana, Saccharomyces cerevisiae, Caenorrhabditis elegans, and Drosophila melanogaster. Using a measure of functional similarity based on the three categories of Gene Ontology (GO) classifications (biological process, molecular function, and cellular component), we quantified the correlation between functional similarity and sequence similarity measured by sequence identity or statistical significance of the alignment and compared such a correlation against randomly chosen protein pairs.ConclusionVarious sequence-function relationships were identified from BLAST versus PSI-BLAST, sequence identity versus Expectation Value, GO indices versus semantic similarity approaches, and within genome versus between genome comparisons, for the three GO categories. Our study provides a benchmark to estimate the confidence in assignment of functions purely based on sequence similarity.
| S-EPMC1949826 | biostudies-literature
Project description:BACKGROUND:Standardized measurement of physical and mental health is useful for identification of health problems. Personalized feedback of the results can influence health behavior, and treatment outcomes can be improved by monitoring feedback over time. However, few resources are available that are free for users, provide feedback from validated measurement instruments, and measure a wide range of health domains. OBJECTIVE:This study aimed to develop an internet self-assessment resource that fills the identified gap and collects data to generate and test hypotheses about health, to test its feasibility, and to describe the characteristics of its users. METHODS:The Self-Assessment Kiosk was built using validated health measurement instruments and implemented on a commercial internet survey platform. Data regarding usage and the characteristics of users were collected over 54 weeks. The rate of accrual of new users, popularity of measurement domains, frequency with which multiple domains were selected for measurement, and characteristics of users who chose particular questionnaires were assessed. RESULTS:Of the 1435 visits, 441 (30.73%) were visiting for the first time, completed at least 1 measure, indicated that their responses were truthful, and consented to research. Growth in the number of users over time was approximately linear. Users were skewed toward old age and higher income and education. Most (53.9%, 234/434) reported at least 1 medical condition. The median number of questionnaires completed was 5. Internal reliability of most measures was good (Cronbach alpha>.70), with lower reliability for some subscales of coping (self-distraction alpha=.35, venting alpha=.50, acceptance alpha=.51) and personality (agreeableness alpha=.46, openness alpha=.45). The popular questionnaires measured depression (61.0%, 269/441), anxiety (60.5%, 267/441), attachment insecurity (54.2%, 239/441), and coping (46.0%, 203/441). Demographic characteristics somewhat influenced choice of instruments, accounting for <9% of the variance in this choice. Mean depression and anxiety scores were intermediate between previously studied populations with and without mental illness. Modeling to estimate the sample size required to study relationships between variables suggested that the accrual of users required to study the relationship between 3 variables was 2 to 3 times greater than that required to study a single variable. CONCLUSIONS:The value of the Self-Assessment Kiosk to users and the feasibility of providing this resource are supported by the steady accumulation of new users over time. The Self-Assessment Kiosk database can be interrogated to understand the relationships between health variables. Users who select particular instruments tend to have scores that are higher than those found in the general population, indicating that instruments are more likely to be selected when they are salient. Self-selection bias limits generalizability and needs to be taken into account when using the Self-Assessment Kiosk database for research. Ethical issues that were considered in developing and implementing the Self-Assessment Kiosk are discussed.
| S-EPMC5981055 | biostudies-literature
Project description:Networks are powerful tools for the presentation and analysis of interactions in multi-component systems. A commonly studied mesoscopic feature of networks is their community structure, which arises from grouping together similar nodes into one community and dissimilar nodes into separate communities. Here, the community structure of protein sequence similarity networks is determined with a new method: Attribute Clustering Dependent Communities (ACDC). Sequence similarity has hitherto typically been quantified by the alignment score or its expectation value. However, pair alignments with the same score or expectation value cannot thus be differentiated. To overcome this deficiency, the method constructs, for pair alignments, an extended alignment metric, the link attribute vector, which includes the score and other alignment characteristics. Rescaling components of the attribute vectors qualitatively identifies a systematic variation of sequence similarity within protein superfamilies. The problem of community detection is then mapped to clustering the link attribute vectors, selection of an optimal subset of links and community structure refinement based on the partition density of the network. ACDC-predicted communities are found to be in good agreement with gold standard sequence databases for which the "ground truth" community structures (or families) are known. ACDC is therefore a community detection method for sequence similarity networks based entirely on pair similarity information. A serial implementation of ACDC is available from https://cmb.ornl.gov/resources/developments.
| S-EPMC5524321 | biostudies-literature
Project description:PurposeDescribe primary care practices' implementation of CommunityRx-H3, a community resource referral intervention that utilized practice facilitators to support cardiovascular disease (CVD) prevention quality improvement.MethodsQualitative focus groups were conducted with practice facilitators to elicit perceptions of practices' experiences with CommunityRx-H3, practice-level factors affecting, and practice facilitator strategies to promote implementation. Qualitative data were analyzed using directed content analysis. The Consolidated Framework for Implementation Research was applied deductively to organize and interpret findings.ResultsFourteen of all 19 practice facilitators participated. Practice facilitators perceived that staff attitudes about connecting patients to community resources for CVD were largely positive. Practices were already using a range of non-systematic strategies to refer to community resources. Practice-level factors that facilitated CommunityRx-H3 implementation included clinician "champions," engaged practice managers, and a practice culture that valued community resources. Implementation barriers included a practice's unwillingness to integrate the intervention into existing workflows, limited staff capacity to complete the resource inventory, and unavailability or cost of materials needed to print the resource referral list ("HealtheRx-H3"). Practice facilitator strategies to promote implementation included supporting ongoing customization of the HealtheRx-H3 and material support. Practice facilitators felt implementation would be improved by integration of CommunityRx-H3 with electronic medical record workflows and alternative methods for engaging practices in the implementation process.ConclusionsPractice facilitators are increasingly being utilized by primary care practices to support quality improvement interventions and, as shown here, can also play an important role in implementation science. This study yields insights to improve implementation of community resource referral solutions to support primary care CVD prevention efforts.
| S-EPMC7708286 | biostudies-literature
Project description:BackgroundImplementation science comprises a large set of theories suggesting interacting factors at different organisational levels. Development of literature syntheses and frameworks for implementation have contributed to comprehensive descriptions of implementation. However, corresponding instruments for measuring these comprehensive descriptions are currently lacking. The present study aimed to develop an instrument measuring care providers' perceptions of an implementation effort, and to explore the instrument's psychometric properties.MethodsBased on existing implementation literature, a questionnaire was designed with items on individual and team factors and on stages of change in an implementation process. The instrument was tested in a Norwegian study on implementation of evidence based practices for psychosis. Item analysis, factor structure, and internal consistency at baseline were examined.ResultsThe 27-item Implementation Process Assessment Tool (IPAT) revealed large variation between mean score of the items. The total scale scores were widely dispersed across respondents. Internal consistency for the total scale was high (Cronbach's alpha: .962), and all but one item contributed positively to the construct. The results indicated four underlying constructs: individual stages for behavioural change, individual activities and perceived support, collective readiness and support, and individual perceptions of the intervention.ConclusionsThe IPAT appears to be a feasible instrument for investigating the implementation process from the perspective of those making the change. It can enable examination of the relative importance of factors thought to be essential for implementation outcomes. It may also provide ongoing feedback for leaders tailoring support for teams to improve implementation. However, further research is needed to detect the instrument's properties later in the implementation process and in different contexts.Trial registrationClinicalTrials.gov code NCT03271242 (retrospective registered September 5, 2017).
| S-EPMC6805659 | biostudies-literature
Project description:Several viruses within the Coronaviridae family have been categorized as either emerging or re-emerging human pathogens, with Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV) being the most well known. The NIAID-sponsored Virus Pathogen Database and Analysis Resource (ViPR, www.viprbrc.org) supports bioinformatics workflows for a broad range of human virus pathogens and other related viruses, including the entire Coronaviridae family. ViPR provides access to sequence records, gene and protein annotations, immune epitopes, 3D structures, host factor data, and other data types through an intuitive web-based search interface. Records returned from these queries can then be subjected to web-based analyses including: multiple sequence alignment, phylogenetic inference, sequence variation determination, BLAST comparison, and metadata-driven comparative genomics statistical analysis. Additional tools exist to display multiple sequence alignments, view phylogenetic trees, visualize 3D protein structures, transfer existing reference genome annotations to new genomes, and store or share results from any search or analysis within personal private 'Workbench' spaces for future access. All of the data and integrated analysis and visualization tools in ViPR are made available without charge as a service to the Coronaviridae research community to facilitate the research and development of diagnostics, prophylactics, vaccines and therapeutics against these human pathogens.
| S-EPMC3509690 | biostudies-literature
Project description:Mock communities are an important tool for validating, optimizing, and comparing bioinformatics methods for microbial community analysis. We present mockrobiota, a public resource for sharing, validating, and documenting mock community data resources, available at http://caporaso-lab.github.io/mockrobiota/. The materials contained in mockrobiota include data set and sample metadata, expected composition data (taxonomy or gene annotations or reference sequences for mock community members), and links to raw data (e.g., raw sequence data) for each mock community data set. mockrobiota does not supply physical sample materials directly, but the data set metadata included for each mock community indicate whether physical sample materials are available. At the time of this writing, mockrobiota contains 11 mock community data sets with known species compositions, including bacterial, archaeal, and eukaryotic mock communities, analyzed by high-throughput marker gene sequencing. IMPORTANCE The availability of standard and public mock community data will facilitate ongoing method optimizations, comparisons across studies that share source data, and greater transparency and access and eliminate redundancy. These are also valuable resources for bioinformatics teaching and training. This dynamic resource is intended to expand and evolve to meet the changing needs of the omics community.
| S-EPMC5080401 | biostudies-other
Project description:BackgroundThe inference of homology between proteins is a key problem in molecular biology The current best approaches only identify approximately 50% of homologies (with a false positive rate set at 1/1000).ResultsWe present Homology Induction (HI), a new approach to inferring homology. HI uses machine learning to bootstrap from standard sequence similarity search methods. First a standard method is run, then HI learns rules which are true for sequences of high similarity to the target (assumed homologues) and not true for general sequences, these rules are then used to discriminate sequences in the twilight zone. To learn the rules HI describes the sequences in a novel way based on a bioinformatic knowledge base, and the machine learning method of inductive logic programming. To evaluate HI we used the PDB40D benchmark which lists sequences of known homology but low sequence similarity. We compared the HI methodology with PSI-BLAST alone and found HI performed significantly better. In addition, Receiver Operating Characteristic (ROC) curve analysis showed that these improvements were robust for all reasonable error costs. The predictive homology rules learnt by HI by can be interpreted biologically to provide insight into conserved features of homologous protein families.ConclusionsHI is a new technique for the detection of remote protein homology--a central bioinformatic problem. HI with PSI-BLAST is shown to outperform PSI-BLAST for all error costs. It is expect that similar improvements would be obtained using HI with any sequence similarity method.
| S-EPMC107726 | biostudies-literature
Project description:The increasing availability and decreasing cost of high-throughput sequencing has transformed academic medical microbiology, delivering an explosion in available genomes while also driving advances in bioinformatics. However, many microbiologists are unable to exploit the resulting large genomics datasets because they do not have access to relevant computational resources and to an appropriate bioinformatics infrastructure. Here, we present the Cloud Infrastructure for Microbial Bioinformatics (CLIMB) facility, a shared computing infrastructure that has been designed from the ground up to provide an environment where microbiologists can share and reuse methods and data.
| S-EPMC5537631 | biostudies-literature