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Motivation: The rapid growth of metagenomics sequencing data makesmetagenomics increasingly dependent on computational and statistical methodsfor fast and efficient analysis. Consequently, novel analysis tools forbig-data metagenomics are constantly emerging. One of the biggest challengesfor researchers occurs in the analysis planning stage: selecting the mostsuitable metagenomics software tool to gain valuable insights from sequencingdata. The building process of data analysis pipelines is often laborious andtime-consuming since it requires a deep and critical understanding of how toapply a particular tool to complete a specified metagenomics task.  Results: We have addressed this challenge by using machine learning methodsto develop a classification system of metagenomics software tools into 13classes (11 semantic annotations of EDAM and two virus-specific classes) basedon the descriptions of the tools. We trained three classifiers (Naive Bayes,Logistic Regression, and Random Forest) using 15 text feature extractiontechniques (TF-IDF, GloVe, BERT-based models, and others). The manually curateddataset includes 224 software tools and contains text from the abstract and themethods section of the tools' publications. The best classificationperformance, with an Area Under the Precision-Recall Curve score of 0.85, isachieved using Logistic regression, BioBERT for text embedding, and text fromabstracts only. The proposed system provides accurate and unifiedidentification of metagenomics data analysis tools and tasks, which is acrucial step in the construction of metagenomics data analysis pipelines.

NLP-based classification of software tools for metagenomics sequencing data analysis into EDAM semantic annotation