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Information


Unique identifier OMICS_14470
Name ACS
Alternative name Annotation Confidence Score
Software type Package/Module
Interface Command line interface
Restrictions to use None
Input data Annotation of genes
Operating system Unix/Linux, Mac OS, Windows
Programming languages R
Computer skills Advanced
Stability No
Maintained No

Versioning


No version available

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This tool is not available anymore.

Publication for Annotation Confidence Score

ACS citations

 (7)
library_books

Network reconstruction and systems analysis of plant cell wall deconstruction by Neurospora crassa

2017
Biotechnol Biofuels
PMCID: 5609067
PMID: 28947916
DOI: 10.1186/s13068-017-0901-2

[…] , followed by published mutant phenotypes (factor of 8), proteomic data (factor of 4), transcriptomic data (factor of 2), and functional genomics-based predictions (factor of 1). To obtain an overall annotation confidence score in the range of 0–1, the weighted sum of evidence support from the five data types was normalized by dividing by the maximal possible score of 31 (Fig. ). Note that the cho […]

library_books

Functional coherence metrics in protein families

2016
J Biomed Semantics
PMCID: 4917928
PMID: 27338101
DOI: 10.1186/s13326-016-0076-y

[…] to obtain results similar to those produced by another literature-based functional coherence assessing method [].Since functional annotation quality is paramount, [] developed a system to provide an annotation confidence score for genome annotations. The system operates on the basis of a genome comparison approach whereby annotations in a target genome are scored in comparison with a reference ge […]

library_books

Robust classification of protein variation using structural modelling and large scale data integration

2016
Nucleic Acids Res
PMCID: 4824117
PMID: 26926108
DOI: 10.1093/nar/gkw120

[…] the confidence score. We count the number of proband and sibling mutations found in each score bin and compare this ratio to the confidence score of that bin. We calculate the correlation between the annotation confidence score and proband enrichment to compare each method's ability to confidently identify disease-associated mutations.The simple BLOSUM62 matrix achieves an impressive enrichment fo […]

library_books

BLAST based structural annotation of protein residues using Protein Data Bank

2016
Biol Direct
PMCID: 4727276
PMID: 26810894
DOI: 10.1186/s13062-016-0106-9

[…] structure/function information of PDB chains is derived from the ccPDB database and non-redundant databases are created using the NCBI toolkit. The number of PDB chains can be increased to boost the annotation confidence score and PDB search space. Since, many PDB chains are similar to each other; the user can select the various non-redundant databases to increase the annotation coverage in PDB. […]

library_books

The Evolution of MicroRNA Pathway Protein Components in Cnidaria

2013
Mol Biol Evol
PMCID: 3840309
PMID: 24030553
DOI: 10.1093/molbev/mst159

[…] this domain at the N-terminus (B). Moreover, when using PFAM or CDD to annotate the domain structure (see Materials and Methods), we could detect a DSRBM domain in Arabidopsis and rice HEN1, but the annotation confidence score was insignificant (data not shown). However, X-ray crystallography demonstrated that Arabidopsis HEN1 has not one, but two DSRBMs, which are probably too divergent to be ac […]

library_books

Re Annotation Is an Essential Step in Systems Biology Modeling of Functional Genomics Data

2010
PLoS One
PMCID: 2871057
PMID: 20498845
DOI: 10.1371/journal.pone.0010642

[…] mean GAQ scores for the differentially expressed mRNA dataset and this increased more than 11.0- and 2.6-fold respectively (P<0.023 and P<5.4e-6, respectively). The total GO depth score and total GO annotation confidence score both increased by more than 10.8- and 9.6-fold respectively. […]

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ACS institution(s)
School of Informatics and Computing, Department of Biology, Indiana University, Bloomington, IN, USA; Department of Biology, Indiana University, Bloomington, IN, USA
ACS funding source(s)
This project was supported by the MetaCyt Microbial Systems Biology from the Lilly Foundation and by the National Science Foundation (grant MCB 0731950).

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