• New Version of PhenDB

    New Version of PhenDB We are proud to present our updated version of PhenDB. While the frontend did not change that much, we added more functionality and up to date resources: Faster, more robust trait prediction PICA has been updated ...
  • Job submission interface ready to use


    Besides phenotypic models and trait precalculations for NCBI RefSeq genomes, PhenDB also facilitates processing of user-specific data.

    For this purpose, a job submission form is now available. It accepts archive files (e.g., .zip or .tar.gz), which contain genome sequences ...

  • NCBI RefSeq representative and reference genomes precalculated in PhenDB


    PhenDB now contains in the "Browse" section the trait precalculations for the reference genomes and representative genomes in NCBI RefSeq.

    The trait prediction results can be filtered by user-specific criteria for minimal prediction confidence and minimal balanced accuracy.

    Three figures ...

  • PICA talk on ASM Microbe 2017 conference

    PICA and the concept of phenotypic trait prediction for draft genomes will be presented in a Plenary Lecture on the ASM Microbe Conference 2017 in New Orleans. Don't miss it: June 2, 2017, 8:45 AM. 018. Understanding the Microbiome: Old ...


Prediction of microbial phenotypes based on comparative genomics.

The accessibility of almost complete genome sequences of uncultivable microbial species from metagenomes necessitates computational methods predicting microbial phenotypes solely based on genomic data. Here we investigate how comparative genomics can be utilized for the prediction of microbial phenotypes. The PICA framework facilitates application and comparison of different machine learning techniques for phenotypic trait prediction. We have improved and extended PICA's support vector machine plug-in and suggest its applicability to large-scale genome databases and incomplete genome sequences. We have demonstrated the stability of the predictive power for phenotypic traits, not perturbed by the rapid growth of genome databases. A new software tool facilitates the in-depth analysis of phenotype models, which associate expected and unexpected protein functions with particular traits. Most of the traits can be reliably predicted in only 60-70% complete genomes. We have established a new phenotypic model that predicts intracellular microorganisms. Thereby we could demonstrate that also independently evolved phenotypic traits, characterized by genome reduction, can be reliably predicted based on comparative genomics. Our results suggest that the extended PICA framework can be used to automatically annotate phenotypes in near-complete microbial genome sequences, as generated in large numbers in current metagenomics studies.
Feldbauer R, Schulz F, Horn M, Rattei T
2015 - BMC Bioinformatics, S1