Developing Predictive Molecular Maps of Human Disease through Community-based Modeling

The failure of biology to identify the molecular causes of disease has led to disappointment in the rate of development of new medicines. By combining the power of community-based modeling with broad access to large datasets on a platform that promotes reproducible analyses we can work towards more predictive molecular maps that can deliver better therapeutics

Raising the visibility of protected data: A pilot data catalog project

Sharing research data that is protected for legal, regulatory, or contractual reasons can be challenging and current mechanisms for doing so may act as barriers to researchers and discourage data sharing. Additionally, the infrastructure commonly used for open data repositories does not easily support responsible sharing of protected data. This chapter presents a case study of an academic university library’s work to configure the existing institutional data repository to function as a data catalog. By engaging in this project, university librarians strive to enhance visibility and access to protected datasets produced at the institution and cultivate a data sharing culture

XGAP: a uniform and extensible data model and software platform for genotype and phenotype experiments.

We present an extensible software model for the genotype and phenotype community, XGAP. Readers can download a standard XGAP (http://www.xgap.org) or auto-generate a custom version using MOLGENIS with programming interfaces to R-software and web-services or user interfaces for biologists. XGAP has simple load formats for any type of genotype, epigenotype, transcript, protein, metabolite or other phenotype data. Current functionality includes tools ranging from eQTL analysis in mouse to genome-wide association studies in humans.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

NEK1 variants confer susceptibility to amyotrophic lateral sclerosis

To identify genetic factors contributing to amyotrophic lateral sclerosis (ALS), we conducted whole-exome analyses of 1,022 index familial ALS (FALS) cases and 7,315 controls. In a new screening strategy, we performed gene-burden analyses trained with established ALS genes and identified a significant association between loss-of-function (LOF) NEK1 variants and FALS risk. Independently, autozygosity mapping for an isolated community in the Netherlands identified a NEK1 p.Arg261His variant as a candidate risk factor. Replication analyses of sporadic ALS (SALS) cases and independent control cohorts confirmed significant disease association for both p.Arg261His (10,589 samples analyzed) and NEK1 LOF variants (3,362 samples analyzed). In total, we observed NEK1 risk variants in nearly 3% of ALS cases. NEK1 has been linked to several cellular functions, including cilia formation, DNA-damage response, microtubule stability, neuronal morphology and axonal polarity. Our results provide new and important insights into ALS etiopathogenesis and genetic etiology

Finding Our Way through Phenotypes

Despite a large and multifaceted effort to understand the vast landscape of phenotypic data, their current form inhibits productive data analysis. The lack of a community-wide, consensus-based, human- and machine-interpretable language for describing phenotypes and their genomic and environmental contexts is perhaps the most pressing scientific bottleneck to integration across many key fields in biology, including genomics, systems biology, development, medicine, evolution, ecology, and systematics. Here we survey the current phenomics landscape, including data resources and handling, and the progress that has been made to accurately capture relevant data descriptions for phenotypes. We present an example of the kind of integration across domains that computable phenotypes would enable, and we call upon the broader biology community, publishers, and relevant funding agencies to support efforts to surmount today's data barriers and facilitate analytical reproducibility

Finding Our Way through Phenotypes

Despite a large and multifaceted effort to understand the vast landscape of phenotypic data, their current form inhibits productive data analysis. The lack of a community-wide, consensus-based, human- and machine-interpretable language for describing phenotypes and their genomic and environmental contexts is perhaps the most pressing scientific bottleneck to integration across many key fields in biology, including genomics, systems biology, development, medicine, evolution, ecology, and systematics. Here we survey the current phenomics landscape, including data resources and handling, and the progress that has been made to accurately capture relevant data descriptions for phenotypes. We present an example of the kind of integration across domains that computable phenotypes would enable, and we call upon the broader biology community, publishers, and relevant funding agencies to support efforts to surmount today's data barriers and facilitate analytical reproducibility

Translational bioinformatics in mental health: open access data sources and computational biomarker discovery

Mental illness is increasingly recognized as both a significant cost to society and a significant area of opportunity for biological breakthrough. As -omics and imaging technologies enable researchers to probe molecular and physiological underpinnings of multiple diseases, opportunities arise to explore the biological basis for behavioral health and disease. From individual investigators to large international consortia, researchers have generated rich data sets in the area of mental health, including genomic, transcriptomic, metabolomic, proteomic, clinical and imaging resources. General data repositories such as the Gene Expression Omnibus (GEO) and Database of Genotypes and Phenotypes (dbGaP) and mental health (MH)-specific initiatives, such as the Psychiatric Genomics Consortium, MH Research Network and PsychENCODE represent a wealth of information yet to be gleaned. At the same time, novel approaches to integrate and analyze data sets are enabling important discoveries in the area of mental and behavioral health. This review will discuss and catalog into an organizing framework the increasingly diverse set of MH data resources available, using schizophrenia as a focus area, and will describe novel and integrative approaches to molecular biomarker discovery that make use of mental health data.National Institutes of Health [UL1TR001117, R01LM012095, R01LM012806]Open access articleThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Improving average ranking precision in user searches for biomedical research datasets

Availability of research datasets is keystone for health and life science study reproducibility and scientific progress. Due to the heterogeneity and complexity of these data, a main challenge to be overcome by research data management systems is to provide users with the best answers for their search queries. In the context of the 2016 bioCADDIE Dataset Retrieval Challenge, we investigate a novel ranking pipeline to improve the search of datasets used in biomedical experiments. Our system comprises a query expansion model based on word embeddings, a similarity measure algorithm that takes into consideration the relevance of the query terms, and a dataset categorisation method that boosts the rank of datasets matching query constraints. The system was evaluated using a corpus with 800k datasets and 21 annotated user queries. Our system provides competitive results when compared to the other challenge participants. In the official run, it achieved the highest infAP among the participants, being +22.3% higher than the median infAP of the participant's best submissions. Overall, it is ranked at top 2 if an aggregated metric using the best official measures per participant is considered. The query expansion method showed positive impact on the system's performance increasing our baseline up to +5.0% and +3.4% for the infAP and infNDCG metrics, respectively. Our similarity measure algorithm seems to be robust, in particular compared to Divergence From Randomness framework, having smaller performance variations under different training conditions. Finally, the result categorization did not have significant impact on the system's performance. We believe that our solution could be used to enhance biomedical dataset management systems. In particular, the use of data driven query expansion methods could be an alternative to the complexity of biomedical terminologies

Repeatable and reusable research - Exploring the needs of users for a Data Portal for Disease Phenotyping

Background: Big data research in the field of health sciences is hindered by a lack of agreement on how to identify and define different conditions and their medications. This means that researchers and health professionals often have different phenotype definitions for the same condition. This lack of agreement makes it hard to compare different study findings and hinders the ability to conduct repeatable and reusable research. Objective: This thesis aims to examine the requirements of various users, such as researchers, clinicians, machine learning experts, and managers, for both new and existing data portals for phenotypes (concept libraries). Methods: Exploratory sequential mixed methods were used in this thesis to look at which concept libraries are available, how they are used, what their characteristics are, where there are gaps, and what needs to be done in the future from the point of view of the people who use them. This thesis consists of three phases: 1) two qualitative studies, including one-to-one interviews with researchers, clinicians, machine learning experts, and senior research managers in health data science, as well as focus group discussions with researchers working with the Secured Anonymized Information Linkage databank, 2) the creation of an email survey (i.e., the Concept Library Usability Scale), and 3) a quantitative study with researchers, health professionals, and clinicians. Results: Most of the participants thought that the prototype concept library would be a very helpful resource for conducting repeatable research, but they specified that many requirements are needed before its development. Although all the participants stated that they were aware of some existing concept libraries, most of them expressed negative perceptions about them. The participants mentioned several facilitators that would encourage them to: 1) share their work, such as receiving citations from other researchers; and 2) reuse the work of others, such as saving a lot of time and effort, which they frequently spend on creating new code lists from scratch. They also pointed out several barriers that could inhibit them from: 1) sharing their work, such as concerns about intellectual property (e.g., if they shared their methods before publication, other researchers would use them as their own); and 2) reusing others' work, such as a lack of confidence in the quality and validity of their code lists. Participants suggested some developments that they would like to see happen in order to make research that is done with routine data more reproducible, such as the availability of a drive for more transparency in research methods documentation, such as publishing complete phenotype definitions and clear code lists. Conclusions: The findings of this thesis indicated that most participants valued a concept library for phenotypes. However, only half of the participants felt that they would contribute by providing definitions for the concept library, and they reported many barriers regarding sharing their work on a publicly accessible platform such as the CALIBER research platform. Analysis of interviews, focus group discussions, and qualitative studies revealed that different users have different requirements, facilitators, barriers, and concerns about concept libraries. This work was to investigate if we should develop concept libraries in Kuwait to facilitate the development of improved data sharing. However, at the end of this thesis the recommendation is this would be unlikely to be cost effective or highly valued by users and investment in open access research publications may be of more value to the Kuwait research/academic community

Structural Prediction of Protein–Protein Interactions by Docking: Application to Biomedical Problems

A huge amount of genetic information is available thanks to the recent advances in sequencing technologies and the larger computational capabilities, but the interpretation of such genetic data at phenotypic level remains elusive. One of the reasons is that proteins are not acting alone, but are specifically interacting with other proteins and biomolecules, forming intricate interaction networks that are essential for the majority of cell processes and pathological conditions. Thus, characterizing such interaction networks is an important step in understanding how information flows from gene to phenotype. Indeed, structural characterization of protein–protein interactions at atomic resolution has many applications in biomedicine, from diagnosis and vaccine design, to drug discovery. However, despite the advances of experimental structural determination, the number of interactions for which there is available structural data is still very small. In this context, a complementary approach is computational modeling of protein interactions by docking, which is usually composed of two major phases: (i) sampling of the possible binding modes between the interacting molecules and (ii) scoring for the identification of the correct orientations. In addition, prediction of interface and hot-spot residues is very useful in order to guide and interpret mutagenesis experiments, as well as to understand functional and mechanistic aspects of the interaction. Computational docking is already being applied to specific biomedical problems within the context of personalized medicine, for instance, helping to interpret pathological mutations involved in protein–protein interactions, or providing modeled structural data for drug discovery targeting protein–protein interactions.Spanish Ministry of Economy grant number BIO2016-79960-R; D.B.B. is supported by a predoctoral fellowship from CONACyT; M.R. is supported by an FPI fellowship from the Severo Ochoa program. We are grateful to the Joint BSC-CRG-IRB Programme in Computational Biology.Peer ReviewedPostprint (author's final draft