Evidence map›Paper›PMID 25979264›Full record

ArticleNucleic acids research2015

SIFTER search: a web server for accurate phylogeny-based protein function prediction.

Sayed M Sahraeian, Kevin R Luo, Steven E Brenner

Abstract read
In one paragraph

Article in Nucleic acids research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

25 citing papers in PubMed.

  1. ACS omega · 2025
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  2. Article
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  4. Clustering protein functional families at large scale with hierarchical approaches.Protein science : a publication of the Protein Society · 2024
    Article
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  6. Article
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  14. Functional Prediction and Assignment ofFrontiers in genetics · 2020
    Article
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  16. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Sayed M SahraeianDepartment of Plant and Microbial Biology, University of California, Berkeley, CA 94720, USA.
Kevin R LuoDepartment of Molecular and Cell Biology, University of California, Berkeley, CA 94720, USA.
Steven E BrennerDepartment of Plant and Microbial Biology, University of California, Berkeley, CA 94720, USA Department of Molecular and Cell Biology, University of California, Berkeley, CA 94720, USA Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA brenner@compbio.berkeley.edu.

Funding

Protein function prediction by statistical phylogenomicsR01GM071749 · NIGMS · UNIVERSITY OF CALIFORNIA BERKELEY · PI BRENNER, STEVEN E · 2007 to 2010
$1.4M
NIGMS NIH HHS R01 GM071749
6 · The paper itself

Abstract

We are awash in proteins discovered through high-throughput sequencing projects. As only a minuscule fraction of these have been experimentally characterized, computational methods are widely used for automated annotation. Here, we introduce a user-friendly web interface for accurate protein function prediction using the SIFTER algorithm. SIFTER is a state-of-the-art sequence-based gene molecular function prediction algorithm that uses a statistical model of function evolution to incorporate annotations throughout the phylogenetic tree. Due to the resources needed by the SIFTER algorithm, running SIFTER locally is not trivial for most users, especially for large-scale problems. The SIFTER web server thus provides access to precomputed predictions on 16 863 537 proteins from 232 403 species. Users can explore SIFTER predictions with queries for proteins, species, functions, and homologs of sequences not in the precomputed prediction set. The SIFTER web server is accessible at http://sifter.berkeley.edu/ and the source code can be downloaded.

Indexed as

PhylogenySoftwareAlgorithmsInternetModels, StatisticalMolecular Sequence AnnotationProteinsProtein Structure, TertiarySequence Homology, Amino AcidProteins

Identifiers

PMID25979264
PMCPMC4489292

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.