ライフサイエンスコーパス: RAST

1 RAST incorporated with an empirical four-parameter equat

2 (p = 0.0004) and house dust mite (p = 0.04), RAST to grass (p = 0.002) and bronchial reactivity to me

3 e dust mite and timothy grass, measured as a RAST Index, ad a h(2)(N) of 33.8% (SE = 7.3%).

4 ls were then built and compared with AST and RAST, based on which a new modeling strategy coupling FN

5 interconnectedness of the SEED database and RAST, the RAST annotation pipeline and updates to both r

6 Commercial rapid AST (RAST) is an emerging and quickly expanding area.

7 This time period was called rapid AST (RAST).

8 AST (0.164) and slightly higher than that by RAST (0.069) or the best FN model (0.068).

9 RMSE = 0.10), which is comparable to that by RAST (0.10) and much lower than those by AST (0.26) and

10 ) and IgE (sera from peanut-allergic donors, RAST>3).

11 Therefore, the FN-RAST enjoys both satisfactory prediction accuracy and so

12 square error (RMSE) of predictions by the FN-RAST is 0.082 log units for 50 bisolute adsorption on MN

13 The FN-RAST model further provided satisfactory predictions for

14 For RAST, a technologist on the evening shift verified the d

15 r RAST and 44.4 h for NAST (5.2 h faster for RAST [P = 0.001]).

16 the reporting of AST results was 39.2 h for RAST and 44.4 h for NAST (5.2 h faster for RAST [P = 0.0

17 for NAST, a difference of 2.0 days less for RAST (P = 0.006).

18 a difference of $1,750 less per patient for RAST (P = 0.001).

19 ge variable cost was $4, 927 per patient for RAST and $6,677 for NAST, a difference of $1,750 less pe

20 length of stay was 10.7 days per patient for RAST and 12.6 days for NAST, a difference of 2.0 days le

21 h sera from CM-allergic patients (n = 26) in RAST-based assays and with rat basophils transfected wit

22 Recently, in order to make RAST a more useful research tool and to keep pace with a

23 s, which runs in conjunction with Metagenome-RAST (MG-RAST) servers.

24 The open-source metagenomics RAST service provides a new paradigm for the annotation

25 houses abstract data types, the metagenomics RAST is stable, extensible, and freely available to all

26 In the metagenomics RAST, all users retain full control of their data, and e

27 ent through our experience with metagenomics-RAST (MG-RAST), the most popular metagenomics analysis p

28 MG-RAST is an open-submission data portal for processing, a

29 For example, as of summer 2013, MG-RAST has been used to annotate over 110,000 data sets to

30 were analyzed through PathoScope (PS) and MG-RAST (MR), and mapped to 380 bacterial, 56 archaeal, and

31 using the Ribosomal Database Project and MG-RAST analysis tools.

32 n popular metagenomics resources, such as MG-RAST, EBI Metagenomics, and probeBASE, as well as a newl

33 ce of various bioinformatic tools (BLAST, MG-RAST, NBC, VMGAP, MetaVir, VIROME) for analysing the vir

34 phylogenetic analysis of these contigs by MG-RAST revealed predominance of Bacteroidetes, followed by

35 This API complements the existing MG-RAST web interface and constitutes the basis of KBase's

36 129 assembled metagenomes retrieved from MG-RAST for environmental GHs and identified 160,790 additi

37 amples integrating data and analyses from MG-RAST into popular third-party analysis tools or sequence

38 To show possible uses for the data from MG-RAST, we present several examples integrating data and a

39 We created the graphical MG-RAST API explorer to help researchers more easily build

40 e present an API that exposes the data in MG-RAST for consumption by our users, greatly enhancing the

41 ased analysis tools and infrastructure in MG-RAST provide limited capability for data retrieval and a

42 ata abstractions and indices available in MG-RAST; and use the results presented in-browser for explo

43 By opening MG-RAST up via a web services API (application programmers

44 runs in conjunction with Metagenome-RAST (MG-RAST) servers.

45 gh our experience with metagenomics-RAST (MG-RAST), the most popular metagenomics analysis pipeline.

46 Working with the MG-RAST annotation server, we constructed the Simple Annota

47 The MG-RAST API provides search capabilities and delivers organ

48 agenomic datasets were analyzed using the MG-RAST metagenomic analysis server and STAMP v2.1.3 (Stati

49 all data and data objects created by the MG-RAST pipeline accessible as JSON objects.

50 ers, greatly enhancing the utility of the MG-RAST service.

51 ier to entry for occasional or first-time MG-RAST API users.

52 rface) we have greatly expanded access to MG-RAST data, as well as provided a mechanism for the use o

53 es were sequenced and characterised using MG-RAST.

54 he use of third-party analysis tools with MG-RAST data.

55 s in sequenced metagenomes processed with MG-RAST.

56 Implementation of RAST has the potential to expedite antimicrobial therape

57 atients whose samples were tested as part of RAST (P = 0.006).

58 atients whose samples were tested as part of RAST and NAST, respectively (P = 0.45).

59 is the first major software restructuring of RAST since its inception.

60 RAST tool kit (RASTtk), a modular version of RAST that enables researchers to build custom annotation

61 t has become desirable to build a version of RAST that is both customizable and extensible.

62 and underwent prick skin tests (n = 254) or RASTs (n = 16) for environmental and LA allergens.

63 However, on the basis of prick skin tests or RASTs, only half are sensitized to LA.

64 s the current state of commercial phenotypic RAST including tests designed to report antimicrobial su

65 These phenotypic RAST systems use various novel methods to monitor bacter

66 in reactions were associated with a positive RAST (14/23) as well as with specific IgE (13/23) and Ig

67 tides thereof were studied with quantitative RAST-based methods, in ELISA, basophil activation and Ig

68 ied by competitive radioallergosorbent test (RAST) and antibody-nanoarchitecture complexes by transmi

69 era is within +/-1 radioallergosorbent test (RAST) class.

70 odified monoclonal radioallergosorbent test (RAST).

71 positive serum radioallergosorbent testing (RAST), nasal eosinophilia, or elevated nasal ECP (odds r

72 e activity coefficients, gammai, better than RAST combined with the Wilson equation or the Nonrandom

73 erimental bisolute adsorption isotherms than RAST.

74 The RAST (Rapid Annotation using Subsystem Technology) annot

75 In this paper, we describe the RAST tool kit (RASTtk), a modular version of RAST that e

76 notype and genomic feature prediction in the RAST and PATRIC annotation services.

77 roteins, some of which bound IgE well in the RAST, but lost IgE reactivity upon immunoblotting.

78 n families), which represent the core of the RAST annotation engine.

79 ectedness of the SEED database and RAST, the RAST annotation pipeline and updates to both resources.

80 he genome annotation was performed using the RAST service.

81 ory (AST) and real adsorbed solution theory (RAST) either frequently show high prediction deviations

82 T (SIAST) and Real Adsorbed Solution Theory (RAST), to bisolute adsorption of organic compounds onto

83 which a new modeling strategy coupling FN to RAST and requiring only single-solute data was proposed.

84 When a new genome is submitted to RAST, genes are called and their annotations are made by

85 han 10,000, are consistently annotated using RAST, the Rapid Annotations using Subsystems Technology.

86 ave annotated >60 000 distinct genomes using RAST.

87 peptides respectively, were identified using RAST.