BACKGROUND: Metagenomics is a rapidly emerging field aimed to analyze microbial 
diversity and dynamics by studying the genomic content of the microbiota. 
Metataxonomics tools analyze high-throughput sequencing data, primarily from 16S 
rRNA gene sequencing and DNAseq, to identify microorganisms and viruses within a 
complex mixture. With the growing demand for analysis of the functional 
microbiome, metatranscriptome studies attract more interest. To make 
metatranscriptomic data sufficient for metataxonomics, new analytical workflows 
are needed to deal with sparse and taxonomically less informative sequencing 
data.
RESULTS: We present a new protocol, IMSA+A, for accurate taxonomy classification 
based on metatranscriptome data of any read length that can efficiently and 
robustly identify bacteria, fungi, and viruses in the same sample. The new 
protocol improves accuracy by using a conservative reference database, employing 
a new counting scheme, and by assembling shotgun reads. Assembly also reduces 
analysis runtime. Simulated data were utilized to evaluate the protocol by 
permuting common experimental variables. When applied to the real 
metatranscriptome data for mouse intestines colonized by ASF, the protocol 
showed superior performance in detection of the microorganisms compared to the 
existing metataxonomics tools. IMSA+A is available at 
https://github.com/JeremyCoxBMI/IMSA-A .
CONCLUSIONS: The developed protocol addresses the need for taxonomy 
classification from RNAseq data. Previously not utilized, i.e., unmapped to a 
reference genome, RNAseq reads can now be used to gather taxonomic information 
about the microbiota present in a biological sample without conducting 
additional sequencing. Any metatranscriptome pipeline that includes assembly of 
reads can add this analysis with minimal additional cost of compute time. The 
new protocol also creates an opportunity to revisit old metatranscriptome data, 
where taxonomic content may be important but was not analyzed.