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Homology-based inference sets the bar high for protein function prediction.

Hamp T, Kassner R, Seemayer S, Vicedo E, Schaefer C, Achten D, Auer F, Boehm A, Braun T, Hecht M, Heron M, Hönigschmid P, Hopf TA, Kaufmann S, Kiening M, Krompass D, Landerer C, Mahlich Y, Roos M, Rost B - BMC Bioinformatics (2013)

Bottom Line: Firstly, our most successful implementation for the baseline ranked very high at CAFA1.It puts more emphasis on the details of protein function than the other measures employed by CAFA and may best reflect the expectations of users.Clearly, the definition of proper goals remains one major objective for CAFA.

View Article: PubMed Central - HTML - PubMed

Affiliation: TUM, Department of Informatics, Bioinformatics & Computational Biology - I12 Boltzmannstr, 3, 85748 Garching/Munich, Germany.

ABSTRACT

Background: Any method that de novo predicts protein function should do better than random. More challenging, it also ought to outperform simple homology-based inference.

Methods: Here, we describe a few methods that predict protein function exclusively through homology. Together, they set the bar or lower limit for future improvements.

Results and conclusions: During the development of these methods, we faced two surprises. Firstly, our most successful implementation for the baseline ranked very high at CAFA1. In fact, our best combination of homology-based methods fared only slightly worse than the top-of-the-line prediction method from the Jones group. Secondly, although the concept of homology-based inference is simple, this work revealed that the precise details of the implementation are crucial: not only did the methods span from top to bottom performers at CAFA, but also the reasons for these differences were unexpected. In this work, we also propose a new rigorous measure to compare predicted and experimental annotations. It puts more emphasis on the details of protein function than the other measures employed by CAFA and may best reflect the expectations of users. Clearly, the definition of proper goals remains one major objective for CAFA.

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Flow chart of StudentC. StudentC first counts how often each GO term appeared in the BLAST hits and performs a cumulative propagation: for each inner node, all counts of its child terms are summed up and added to its own count (depth-first traversal). Dividing each count by that of the root term, we obtain a score between 0 and 1 for each term. In parallel, we calculate a second score for each term by assigning it the maximum percentage of positives of all associated hits (see text). Finally, we multiply the two scores, determine the highest scoring leaf term and output only its propagation to the user.
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Figure 4: Flow chart of StudentC. StudentC first counts how often each GO term appeared in the BLAST hits and performs a cumulative propagation: for each inner node, all counts of its child terms are summed up and added to its own count (depth-first traversal). Dividing each count by that of the root term, we obtain a score between 0 and 1 for each term. In parallel, we calculate a second score for each term by assigning it the maximum percentage of positives of all associated hits (see text). Finally, we multiply the two scores, determine the highest scoring leaf term and output only its propagation to the user.

Mentions: Finally, we multiply template quality and combined leaf score for each leaf, combine all the leaf-score pairs in one set and output its propagation to the user.


Homology-based inference sets the bar high for protein function prediction.

Hamp T, Kassner R, Seemayer S, Vicedo E, Schaefer C, Achten D, Auer F, Boehm A, Braun T, Hecht M, Heron M, Hönigschmid P, Hopf TA, Kaufmann S, Kiening M, Krompass D, Landerer C, Mahlich Y, Roos M, Rost B - BMC Bioinformatics (2013)

Flow chart of StudentC. StudentC first counts how often each GO term appeared in the BLAST hits and performs a cumulative propagation: for each inner node, all counts of its child terms are summed up and added to its own count (depth-first traversal). Dividing each count by that of the root term, we obtain a score between 0 and 1 for each term. In parallel, we calculate a second score for each term by assigning it the maximum percentage of positives of all associated hits (see text). Finally, we multiply the two scores, determine the highest scoring leaf term and output only its propagation to the user.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3584931&req=5

Figure 4: Flow chart of StudentC. StudentC first counts how often each GO term appeared in the BLAST hits and performs a cumulative propagation: for each inner node, all counts of its child terms are summed up and added to its own count (depth-first traversal). Dividing each count by that of the root term, we obtain a score between 0 and 1 for each term. In parallel, we calculate a second score for each term by assigning it the maximum percentage of positives of all associated hits (see text). Finally, we multiply the two scores, determine the highest scoring leaf term and output only its propagation to the user.
Mentions: Finally, we multiply template quality and combined leaf score for each leaf, combine all the leaf-score pairs in one set and output its propagation to the user.

Bottom Line: Firstly, our most successful implementation for the baseline ranked very high at CAFA1.It puts more emphasis on the details of protein function than the other measures employed by CAFA and may best reflect the expectations of users.Clearly, the definition of proper goals remains one major objective for CAFA.

View Article: PubMed Central - HTML - PubMed

Affiliation: TUM, Department of Informatics, Bioinformatics & Computational Biology - I12 Boltzmannstr, 3, 85748 Garching/Munich, Germany.

ABSTRACT

Background: Any method that de novo predicts protein function should do better than random. More challenging, it also ought to outperform simple homology-based inference.

Methods: Here, we describe a few methods that predict protein function exclusively through homology. Together, they set the bar or lower limit for future improvements.

Results and conclusions: During the development of these methods, we faced two surprises. Firstly, our most successful implementation for the baseline ranked very high at CAFA1. In fact, our best combination of homology-based methods fared only slightly worse than the top-of-the-line prediction method from the Jones group. Secondly, although the concept of homology-based inference is simple, this work revealed that the precise details of the implementation are crucial: not only did the methods span from top to bottom performers at CAFA, but also the reasons for these differences were unexpected. In this work, we also propose a new rigorous measure to compare predicted and experimental annotations. It puts more emphasis on the details of protein function than the other measures employed by CAFA and may best reflect the expectations of users. Clearly, the definition of proper goals remains one major objective for CAFA.

Show MeSH
Related in: MedlinePlus