TY - JOUR AU - Bzhalava, Zurab AU - Tampuu, Ardi AU - Bała, Piotr AU - Vicente, Raul AU - Dillner, Joakim PY - 2018 DA - 2018/09/24 TI - Machine Learning for detection of viral sequences in human metagenomic datasets JO - BMC Bioinformatics SP - 336 VL - 19 IS - 1 AB - Detection of highly divergent or yet unknown viruses from metagenomics sequencing datasets is a major bioinformatics challenge. When human samples are sequenced, a large proportion of assembled contigs are classified as “unknown”, as conventional methods find no similarity to known sequences. We wished to explore whether machine learning algorithms using Relative Synonymous Codon Usage frequency (RSCU) could improve the detection of viral sequences in metagenomic sequencing data. SN - 1471-2105 UR - https://doi.org/10.1186/s12859-018-2340-x DO - 10.1186/s12859-018-2340-x ID - Bzhalava2018 ER -