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

Hamp, Tobias; Kaßner, Rebecca; Seemayer, Stefan; Vicedo, Esmeralda; Schaefer, Christian; Achten, Dominik; Auer, Florian; Boehm, Ariane C; Braun, Tatjana; Hecht, Maximilian; Heron, Mark; Hönigschmid, Peter; Hopf, Thomas A.; Kaufmann, Stefanie; Kiening, Michael; Krompaß, Denis; Landerer, Cedric; Mahlich, Yannick; Roos, Manfred; Rost, Burkhard

doi:10.1186/1471-2105-14-s3-s7

Cited by 46 publications

(42 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The method metastudent (29) predicts GO (42) terms through homology inference. It first BLASTs queries against proteins with experimental GO annotations taken from Swiss-Prot (12), i.e.…”

Section: Methodsmentioning

confidence: 99%

“…A meta-classifier combines the three through linear regression. metastudent achieves a maximum F1 score of 0.36 in the biological process ontology and of 0.48 in the molecular function ontology (29). Although this is slightly worse (within the error estimates (43)) than the best method for predicting GO terms (44), the advantage is that metastudent predictions can easily be traced back to the experimental annotations upon which they are based.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

PredictProtein—an open resource for online prediction of protein structural and functional features

Yachdav

Kloppmann

Kaján

et al. 2014

Nucleic Acids Research

Self Cite

557

438

View full text Add to dashboard Cite

PredictProtein is a meta-service for sequence analysis that has been predicting structural and functional features of proteins since 1992. Queried with a protein sequence it returns: multiple sequence alignments, predicted aspects of structure (secondary structure, solvent accessibility, transmembrane helices (TMSEG) and strands, coiled-coil regions, disulfide bonds and disordered regions) and function. The service incorporates analysis methods for the identification of functional regions (ConSurf), homology-based inference of Gene Ontology terms (metastudent), comprehensive subcellular localization prediction (LocTree3), protein–protein binding sites (ISIS2), protein–polynucleotide binding sites (SomeNA) and predictions of the effect of point mutations (non-synonymous SNPs) on protein function (SNAP2). Our goal has always been to develop a system optimized to meet the demands of experimentalists not highly experienced in bioinformatics. To this end, the PredictProtein results are presented as both text and a series of intuitive, interactive and visually appealing figures. The web server and sources are available at http://ppopen.rostlab.org.

show abstract

“…The method metastudent (29) predicts GO (42) terms through homology inference. It first BLASTs queries against proteins with experimental GO annotations taken from Swiss-Prot (12), i.e.…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

PredictProtein—an open resource for online prediction of protein structural and functional features

Yachdav

Kloppmann

Kaján

et al. 2014

Nucleic Acids Research

Self Cite

557

438

View full text Add to dashboard Cite

show abstract

“…The methods that transfer known biological roles to homologous genes via sequence similarity searches are well-established and appear quite successful in community evaluations (Hamp et al, 2013). Thus, they present a baseline that future methods must build and improve upon, aiming to provide predictions complementary to the commonly employed methods such as PSI-Blast or Pfam searches.…”

Section: Discussionmentioning

confidence: 99%

Extensive complementarity between gene function prediction methods

2016

View full text Add to dashboard Cite

Motivation: The number of sequenced genomes rises steadily, but we still lack the knowledge about the biological roles of many genes. Automatic function prediction (AFP) is thus a necessity. We hypothesize that AFP approaches which draw on distinct genome features may be useful for predicting different types of gene functions, motivating a systematic analysis of the benefits gained by obtaining and integrating such predictions. Results: Our pipeline amalgamates 5,133,543 genes from 2,071 genomes in a single massive analysis that evaluates five established genomic AFP methodologies. While 1,227 Gene Ontology terms yielded reliable predictions, the majority of these functions were accessible to only one or two of the methods. Moreover, different methods tend to assign a GO term to non-overlapping sets of genes. Thus, inferences made by diverse AFP methods display a striking complementary, both gene-wise and function-wise. Because of this, a viable integration strategy is to simply rely on a single mostconfident prediction per gene/function, instead of enforcing agreement across multiple AFP methods. Using an information-theoretic approach, we estimate that current databases contain 29.2 bits/gene of known E. coli gene functions. This can be increased by up to 5.5 bits/gene using individual AFP methods, or by 11 additional bits/gene upon integration, thereby providing a highly-ranking predictor on the CAFA2 community benchmark. Availability of more sequenced genomes boosts the predictive accuracy of AFP approaches and also the benefit from integrating them.

show abstract

“…Even if orthology is an evolutionary concept, rather than a functional one, it can be used to link functionally equivalent genes across genomes and enables the functional inference of unknown proteins using one or more functionally characterized orthologs in other species [6,7]. …”

Section: Introductionmentioning

confidence: 99%

Think globally and solve locally: secondary memory-based network learning for automated multi-species function prediction

Mesiti

Ré

Valentini

2014

GigaSci

View full text Add to dashboard Cite

BackgroundNetwork-based learning algorithms for automated function prediction (AFP) are negatively affected by the limited coverage of experimental data and limited a priori known functional annotations. As a consequence their application to model organisms is often restricted to well characterized biological processes and pathways, and their effectiveness with poorly annotated species is relatively limited. A possible solution to this problem might consist in the construction of big networks including multiple species, but this in turn poses challenging computational problems, due to the scalability limitations of existing algorithms and the main memory requirements induced by the construction of big networks. Distributed computation or the usage of big computers could in principle respond to these issues, but raises further algorithmic problems and require resources not satisfiable with simple off-the-shelf computers.ResultsWe propose a novel framework for scalable network-based learning of multi-species protein functions based on both a local implementation of existing algorithms and the adoption of innovative technologies: we solve “locally” the AFP problem, by designing “vertex-centric” implementations of network-based algorithms, but we do not give up thinking “globally” by exploiting the overall topology of the network. This is made possible by the adoption of secondary memory-based technologies that allow the efficient use of the large memory available on disks, thus overcoming the main memory limitations of modern off-the-shelf computers. This approach has been applied to the analysis of a large multi-species network including more than 300 species of bacteria and to a network with more than 200,000 proteins belonging to 13 Eukaryotic species. To our knowledge this is the first work where secondary-memory based network analysis has been applied to multi-species function prediction using biological networks with hundreds of thousands of proteins.ConclusionsThe combination of these algorithmic and technological approaches makes feasible the analysis of large multi-species networks using ordinary computers with limited speed and primary memory, and in perspective could enable the analysis of huge networks (e.g. the whole proteomes available in SwissProt), using well-equipped stand-alone machines.

show abstract

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

Cited by 46 publications

References 16 publications

PredictProtein—an open resource for online prediction of protein structural and functional features

PredictProtein—an open resource for online prediction of protein structural and functional features

Extensive complementarity between gene function prediction methods

Think globally and solve locally: secondary memory-based network learning for automated multi-species function prediction

Contact Info

Product

Resources

About