  Motivation: The rapid growth of metagenomics sequencing data makes
metagenomics increasingly dependent on computational and statistical methods
for fast and efficient analysis. Consequently, novel analysis tools for
big-data metagenomics are constantly emerging. One of the biggest challenges
for researchers occurs in the analysis planning stage: selecting the most
suitable metagenomics software tool to gain valuable insights from sequencing
data. The building process of data analysis pipelines is often laborious and
time-consuming since it requires a deep and critical understanding of how to
apply a particular tool to complete a specified metagenomics task.
  Results: We have addressed this challenge by using machine learning methods
to develop a classification system of metagenomics software tools into 13
classes (11 semantic annotations of EDAM and two virus-specific classes) based
on the descriptions of the tools. We trained three classifiers (Naive Bayes,
Logistic Regression, and Random Forest) using 15 text feature extraction
techniques (TF-IDF, GloVe, BERT-based models, and others). The manually curated
dataset includes 224 software tools and contains text from the abstract and the
methods section of the tools' publications. The best classification
performance, with an Area Under the Precision-Recall Curve score of 0.85, is
achieved using Logistic regression, BioBERT for text embedding, and text from
abstracts only. The proposed system provides accurate and unified
identification of metagenomics data analysis tools and tasks, which is a
crucial step in the construction of metagenomics data analysis pipelines.
