An Efficient Lightweight Environment for Biomedical Computation View Homepage


Ontology type: schema:MonetaryGrant     


Grant Info

YEARS

2009-2012

FUNDING AMOUNT

1325743 USD

ABSTRACT

DESCRIPTION (provided by applicant): The translation from large volumes of experimental data to clinically relevant insights relies on sophisticated computational analysis tools that can handle the enormous high-throughput sequence, polymorphism, and functional datasets. Developing appropriate tools is necessary but not sufficient, because the independent analysis tools in themselves do not solve an increasingly problematic barrier blocking the bench-to-bedside path outlined in the NIH Roadmap for medical research: making powerful new computational tools readily accessible and useful for experimental biologists. Developing usable and consistent user interfaces requires significant effort, and few tool developers can afford to devote time and resources to this goal. Currently many powerful, independent analysis tools exist, but lack integrated, easy-to-use interfaces that would allow experimental biologists to take advantage of them. Thus, developing tools to analyze overwhelming amounts of data is no longer the main challenge in biomedical research. Instead the problem lies in making existing tools usable for bench biologists so that they can take full advantage of existing data. We have developed a system - GALAXY - that makes substantial progress toward solving this problem. For experimental biologists, it provides an intuitive and consistent interface for performing sophisticated analyses with minimal effort, regardless of the scale of data involved. For computational tool developers, it makes it easy to integrate existing tools with a modern user interface by writing a simple, concise interface description. For data providers, it features a simple, elegant data access protocol. Thus, GALAXY bridges a critically important gap between data resources, computational tools and users, by making it easy to modernize the interfaces of any existing tool, freeing developers of new tools from the need to develop interfaces from scratch, and facilitating tool interoperability and complex analyses by seamlessly integrating analysis outputs, applications and external data. Here we propose to develop novel features specifically designed for translational research. First, we will engineer a tool integration framework streamlining delivery of analysis software to experimentalists. Second, we will develop a statistical genetics toolkit allowing clinicians to manipulate and interpret human variation data on any scale. Third, we will implement the first integrated system for analysis of short-read sequencing data. Fourth, we will design utilities for manipulation of the most valuable comparative genomics resource - multi- genome alignments. Finally, we will build a workflow system to enable reproducible and collaborative analysis of genomic data. PUBLIC HEALTH RELEVANCE: Genomic data discovery is no longer a limiting factor for much of the medical research. The NIH Roadmap recognizes that many challenges in biomedical research will only be overcome through appropriate investment to improve integrative access to existing data and tools, so researchers can more effectively and rapidly trans- late their findings into practice. The proposed project addresses this challenge by allowing biomedical re- searchers to take advantage of the enormous sequence, polymorphism, and functional datasets easily and effectively. More... »

URL

http://projectreporter.nih.gov/project_info_description.cfm?aid=8035956

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/2208", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "type": "DefinedTerm"
      }
    ], 
    "amount": {
      "currency": "USD", 
      "type": "MonetaryAmount", 
      "value": "1325743"
    }, 
    "description": "DESCRIPTION (provided by applicant): The translation from large volumes of experimental data to clinically relevant insights relies on sophisticated computational analysis tools that can handle the enormous high-throughput sequence, polymorphism, and functional datasets. Developing appropriate tools is necessary but not sufficient, because the independent analysis tools in themselves do not solve an increasingly problematic barrier blocking the bench-to-bedside path outlined in the NIH Roadmap for medical research: making powerful new computational tools readily accessible and useful for experimental biologists. Developing usable and consistent user interfaces requires significant effort, and few tool developers can afford to devote time and resources to this goal. Currently many powerful, independent analysis tools exist, but lack integrated, easy-to-use interfaces that would allow experimental biologists to take advantage of them. Thus, developing tools to analyze overwhelming amounts of data is no longer the main challenge in biomedical research. Instead the problem lies in making existing tools usable for bench biologists so that they can take full advantage of existing data. We have developed a system - GALAXY - that makes substantial progress toward solving this problem. For experimental biologists, it provides an intuitive and consistent interface for performing sophisticated analyses with minimal effort, regardless of the scale of data involved. For computational tool developers, it makes it easy to integrate existing tools with a modern user interface by writing a simple, concise interface description. For data providers, it features a simple, elegant data access protocol. Thus, GALAXY bridges a critically important gap between data resources, computational tools and users, by making it easy to modernize the interfaces of any existing tool, freeing developers of new tools from the need to develop interfaces from scratch, and facilitating tool interoperability and complex analyses by seamlessly integrating analysis outputs, applications and external data. Here we propose to develop novel features specifically designed for translational research. First, we will engineer a tool integration framework streamlining delivery of analysis software to experimentalists. Second, we will develop a statistical genetics toolkit allowing clinicians to manipulate and interpret human variation data on any scale. Third, we will implement the first integrated system for analysis of short-read sequencing data. Fourth, we will design utilities for manipulation of the most valuable comparative genomics resource - multi- genome alignments. Finally, we will build a workflow system to enable reproducible and collaborative analysis of genomic data. PUBLIC HEALTH RELEVANCE: Genomic data discovery is no longer a limiting factor for much of the medical research. The NIH Roadmap recognizes that many challenges in biomedical research will only be overcome through appropriate investment to improve integrative access to existing data and tools, so researchers can more effectively and rapidly trans- late their findings into practice. The proposed project addresses this challenge by allowing biomedical re- searchers to take advantage of the enormous sequence, polymorphism, and functional datasets easily and effectively.", 
    "endDate": "2012-02-29T00:00:00Z", 
    "funder": {
      "id": "https://www.grid.ac/institutes/grid.280128.1", 
      "type": "Organization"
    }, 
    "id": "sg:grant.2529356", 
    "identifier": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "2529356"
        ]
      }, 
      {
        "name": "nih_id", 
        "type": "PropertyValue", 
        "value": [
          "R01HG004909"
        ]
      }
    ], 
    "inLanguage": [
      "en"
    ], 
    "keywords": [
      "important gap", 
      "translational research", 
      "data", 
      "modern user interfaces", 
      "sophisticated computational analysis tools", 
      "workflow systems", 
      "Biomedical Computation", 
      "factors", 
      "sophisticated analysis", 
      "developers", 
      "enormous high-throughput sequence", 
      "consistent user interface", 
      "data resources", 
      "consistent interface", 
      "external data", 
      "elegant data access protocol", 
      "advantages", 
      "medical research", 
      "large volumes", 
      "genomic data", 
      "experimental biologists", 
      "interface", 
      "bench biologists", 
      "appropriate investment", 
      "bedside path", 
      "tool integration framework", 
      "translation", 
      "application", 
      "data", 
      "manipulation", 
      "computational tools", 
      "experimental data", 
      "collaborative analysis", 
      "experimentalists", 
      "biomedical research", 
      "enormous sequence", 
      "concise interface description", 
      "novel feature", 
      "Efficient Lightweight Environment", 
      "tool interoperability", 
      "goal", 
      "users", 
      "valuable comparative genomics resource - multi- genome alignments", 
      "significant effort", 
      "analysis software", 
      "utility", 
      "problematic barriers", 
      "time", 
      "first integrated system", 
      "problem", 
      "full advantage", 
      "integrative access", 
      "delivery", 
      "analysis", 
      "overwhelming amount", 
      "polymorphism", 
      "bench", 
      "appropriate tool", 
      "description", 
      "scratch", 
      "applicants", 
      "use interface", 
      "functional datasets", 
      "tool", 
      "main challenges", 
      "minimal effort", 
      "challenges", 
      "Genomic data discovery", 
      "resources", 
      "data providers", 
      "scale", 
      "complex analysis", 
      "project", 
      "substantial progress", 
      "statistical genetics toolkit", 
      "short-read sequencing data", 
      "independent analysis tool", 
      "new tool", 
      "clinicians", 
      "NIH Roadmap", 
      "system - GALAXY", 
      "few tool developers", 
      "powerful new computational tools", 
      "human variation data", 
      "need", 
      "relevant insights", 
      "galaxies", 
      "computational tool developers", 
      "biomedical re- searchers", 
      "findings", 
      "practice", 
      "analysis output", 
      "public health relevance", 
      "researchers", 
      "many challenges"
    ], 
    "name": "An Efficient Lightweight Environment for Biomedical Computation", 
    "recipient": [
      {
        "id": "https://www.grid.ac/institutes/grid.29857.31", 
        "type": "Organization"
      }, 
      {
        "affiliation": {
          "id": "https://www.grid.ac/institutes/grid.29857.31", 
          "name": "PENNSYLVANIA STATE UNIVERSITY-UNIV PARK", 
          "type": "Organization"
        }, 
        "familyName": "NEKRUTENKO", 
        "givenName": "ANTON", 
        "id": "sg:person.0665513754.70", 
        "type": "Person"
      }, 
      {
        "member": "sg:person.0665513754.70", 
        "roleName": "PI", 
        "type": "Role"
      }
    ], 
    "sameAs": [
      "https://app.dimensions.ai/details/grant/grant.2529356"
    ], 
    "sdDataset": "grants", 
    "sdDatePublished": "2019-03-07T11:58", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com.uberresearch.data.processor/core_data/20181219_192338/projects/base/nih_projects_10.xml.gz", 
    "startDate": "2009-05-01T00:00:00Z", 
    "type": "MonetaryGrant", 
    "url": "http://projectreporter.nih.gov/project_info_description.cfm?aid=8035956"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/grant.2529356'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/grant.2529356'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/grant.2529356'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/grant.2529356'


 

This table displays all metadata directly associated to this object as RDF triples.

138 TRIPLES      19 PREDICATES      116 URIs      108 LITERALS      5 BLANK NODES

Subject Predicate Object
1 sg:grant.2529356 schema:about anzsrc-for:2208
2 schema:amount N951825bf3b164c85b0b11a8f8d3c83b2
3 schema:description DESCRIPTION (provided by applicant): The translation from large volumes of experimental data to clinically relevant insights relies on sophisticated computational analysis tools that can handle the enormous high-throughput sequence, polymorphism, and functional datasets. Developing appropriate tools is necessary but not sufficient, because the independent analysis tools in themselves do not solve an increasingly problematic barrier blocking the bench-to-bedside path outlined in the NIH Roadmap for medical research: making powerful new computational tools readily accessible and useful for experimental biologists. Developing usable and consistent user interfaces requires significant effort, and few tool developers can afford to devote time and resources to this goal. Currently many powerful, independent analysis tools exist, but lack integrated, easy-to-use interfaces that would allow experimental biologists to take advantage of them. Thus, developing tools to analyze overwhelming amounts of data is no longer the main challenge in biomedical research. Instead the problem lies in making existing tools usable for bench biologists so that they can take full advantage of existing data. We have developed a system - GALAXY - that makes substantial progress toward solving this problem. For experimental biologists, it provides an intuitive and consistent interface for performing sophisticated analyses with minimal effort, regardless of the scale of data involved. For computational tool developers, it makes it easy to integrate existing tools with a modern user interface by writing a simple, concise interface description. For data providers, it features a simple, elegant data access protocol. Thus, GALAXY bridges a critically important gap between data resources, computational tools and users, by making it easy to modernize the interfaces of any existing tool, freeing developers of new tools from the need to develop interfaces from scratch, and facilitating tool interoperability and complex analyses by seamlessly integrating analysis outputs, applications and external data. Here we propose to develop novel features specifically designed for translational research. First, we will engineer a tool integration framework streamlining delivery of analysis software to experimentalists. Second, we will develop a statistical genetics toolkit allowing clinicians to manipulate and interpret human variation data on any scale. Third, we will implement the first integrated system for analysis of short-read sequencing data. Fourth, we will design utilities for manipulation of the most valuable comparative genomics resource - multi- genome alignments. Finally, we will build a workflow system to enable reproducible and collaborative analysis of genomic data. PUBLIC HEALTH RELEVANCE: Genomic data discovery is no longer a limiting factor for much of the medical research. The NIH Roadmap recognizes that many challenges in biomedical research will only be overcome through appropriate investment to improve integrative access to existing data and tools, so researchers can more effectively and rapidly trans- late their findings into practice. The proposed project addresses this challenge by allowing biomedical re- searchers to take advantage of the enormous sequence, polymorphism, and functional datasets easily and effectively.
4 schema:endDate 2012-02-29T00:00:00Z
5 schema:funder https://www.grid.ac/institutes/grid.280128.1
6 schema:identifier N63a128043cf64c6987059b733526f484
7 N7e48e09bb41b48ec84e18e180417e027
8 schema:inLanguage en
9 schema:keywords Biomedical Computation
10 Efficient Lightweight Environment
11 Genomic data discovery
12 NIH Roadmap
13 advantages
14 analysis
15 analysis output
16 analysis software
17 applicants
18 application
19 appropriate investment
20 appropriate tool
21 bedside path
22 bench
23 bench biologists
24 biomedical re- searchers
25 biomedical research
26 challenges
27 clinicians
28 collaborative analysis
29 complex analysis
30 computational tool developers
31 computational tools
32 concise interface description
33 consistent interface
34 consistent user interface
35 data
36 data providers
37 data resources
38 delivery
39 description
40 developers
41 elegant data access protocol
42 enormous high-throughput sequence
43 enormous sequence
44 experimental biologists
45 experimental data
46 experimentalists
47 external data
48 factors
49 few tool developers
50 findings
51 first integrated system
52 full advantage
53 functional datasets
54 galaxies
55 genomic data
56 goal
57 human variation data
58 important gap
59 independent analysis tool
60 integrative access
61 interface
62 large volumes
63 main challenges
64 manipulation
65 many challenges
66 medical research
67 minimal effort
68 modern user interfaces
69 need
70 new tool
71 novel feature
72 overwhelming amount
73 polymorphism
74 powerful new computational tools
75 practice
76 problem
77 problematic barriers
78 project
79 public health relevance
80 relevant insights
81 researchers
82 resources
83 scale
84 scratch
85 short-read sequencing data
86 significant effort
87 sophisticated analysis
88 sophisticated computational analysis tools
89 statistical genetics toolkit
90 substantial progress
91 system - GALAXY
92 time
93 tool
94 tool integration framework
95 tool interoperability
96 translation
97 translational research
98 use interface
99 users
100 utility
101 valuable comparative genomics resource - multi- genome alignments
102 workflow systems
103 schema:name An Efficient Lightweight Environment for Biomedical Computation
104 schema:recipient N55bbaee04b0648b1a4706d48366c13a3
105 sg:person.0665513754.70
106 https://www.grid.ac/institutes/grid.29857.31
107 schema:sameAs https://app.dimensions.ai/details/grant/grant.2529356
108 schema:sdDatePublished 2019-03-07T11:58
109 schema:sdLicense https://scigraph.springernature.com/explorer/license/
110 schema:sdPublisher N477d740e53b44b52b5ce62ce35e8ea4c
111 schema:startDate 2009-05-01T00:00:00Z
112 schema:url http://projectreporter.nih.gov/project_info_description.cfm?aid=8035956
113 sgo:license sg:explorer/license/
114 sgo:sdDataset grants
115 rdf:type schema:MonetaryGrant
116 N477d740e53b44b52b5ce62ce35e8ea4c schema:name Springer Nature - SN SciGraph project
117 rdf:type schema:Organization
118 N55bbaee04b0648b1a4706d48366c13a3 schema:member sg:person.0665513754.70
119 schema:roleName PI
120 rdf:type schema:Role
121 N63a128043cf64c6987059b733526f484 schema:name dimensions_id
122 schema:value 2529356
123 rdf:type schema:PropertyValue
124 N7e48e09bb41b48ec84e18e180417e027 schema:name nih_id
125 schema:value R01HG004909
126 rdf:type schema:PropertyValue
127 N951825bf3b164c85b0b11a8f8d3c83b2 schema:currency USD
128 schema:value 1325743
129 rdf:type schema:MonetaryAmount
130 anzsrc-for:2208 schema:inDefinedTermSet anzsrc-for:
131 rdf:type schema:DefinedTerm
132 sg:person.0665513754.70 schema:affiliation https://www.grid.ac/institutes/grid.29857.31
133 schema:familyName NEKRUTENKO
134 schema:givenName ANTON
135 rdf:type schema:Person
136 https://www.grid.ac/institutes/grid.280128.1 schema:Organization
137 https://www.grid.ac/institutes/grid.29857.31 schema:name PENNSYLVANIA STATE UNIVERSITY-UNIV PARK
138 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...