MetaMLP: A fast word embedding based classifier to profile target gene databases in metagenomic samples

The functional profile of metagenomic samples allows the understanding of the role of the microbes in their environment. Such analysis consists of assigning short sequencing reads to a particular functional category. Normally, manually curated databases are used for functional assignment where genes are arranged into different classes. Sequence alignment has been widely used to profile metagenomic samples against curated databases. However, this method is time consuming and requires high computing resources. Although several alignment free methods based on k-mer composition have been developed in the recent years, they still require a large amount of memory. In this paper, MetaMLP (Metagenomics Machine Learning Profiler) a machine learning method that represents sequences into numerical vectors (embeddings) and uses a simple one hidden layer neural network to profile functional categories is proposed. Unlike other methods, MetaMLP enables partial matching by using a reduced alphabet to build sequence embeddings from full and partial kmers. MetaMLP is able to identify a slightly larger number of reads compared to Diamond (one of the fastest sequence alignment method) as well as to perform accurate predictions with 0.99 precision and 0.99 recall. MetaMLP can process 100M reads in around 10 minutes in a laptop computer which is 50x faster than Diamond. MetaMLP is free for use, available at https://bitbucket.org/gaarangoa/metamlp/src/master/.