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MOCCA: a flexible suite for modelling DNA sequence motif occurrence combinatorics.

Bjørn André Bredesen1, Marc Rehmsmeier2

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Summary

The MOCCA suite offers advanced machine learning tools for modeling cis-regulatory elements (CREs) by analyzing motif combinations. RF-MOCCA, a new Random Forest method within MOCCA, demonstrates superior performance in predicting PREs and BEs.

Keywords:
Cis-regulatory elementMachine learningMotifRandom forestSupport vector machine

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Cis-regulatory elements (CREs) like promoters and enhancers control gene expression through specific DNA sequence motifs.
  • Previous methods for predicting CREs, such as Support Vector Machines (SVMs), have limitations in modeling motif occurrence landscapes.
  • A hierarchical machine learning approach (SVM-MOCCA) was previously developed for predicting Polycomb Response Elements (PREs) in Drosophila, showing improved performance.

Purpose of the Study:

  • To present MOCCA (Motif Occurrence Combinatorics Classification Algorithms), an expanded suite for modeling CRE sequences using motif occurrence combinatorics.
  • To introduce RF-MOCCA, a novel Random Forest-based method within the MOCCA suite.
  • To evaluate the performance of SVM-MOCCA and RF-MOCCA in predicting Drosophila PREs and Boundary Elements (BEs).

Main Methods:

  • Implementation of MOCCA, a suite containing SVM-MOCCA and the new RF-MOCCA method.
  • Application of hierarchical machine learning approaches (SVM and Random Forest) to model motif occurrence combinatorics in CREs.
  • Cross-validation experiments to assess the generalization capabilities of the models for PREs and BEs.

Main Results:

  • Both SVM-MOCCA and RF-MOCCA significantly improve the prediction of PREs and BEs compared to previous methods.
  • RF-MOCCA, utilizing Random Forests, yielded the best results in predicting both PREs and BEs.
  • This study represents the first application of Random Forests for modeling PREs and the first use of the hierarchical MOCCA approach for BE prediction.

Conclusions:

  • MOCCA is a flexible and powerful suite for motif-based modeling of CRE sequences based on motif composition.
  • The suite supports various motif formats (IUPAC, PWM) and simplifies the modeling process by including negative data generation and a simplified input mode.
  • MOCCA is available under the MIT license on Github, facilitating its use by researchers for new CRE modeling challenges.