Benchmarking methods for extracting microbial signal from host-dominated metatranscriptomes
Abstract
Human RNA sequencing (RNA-seq) data originally generated for human transcriptome profiling are overwhelmingly dominated by host sequences, yet they often contain a small fraction of non-human reads that can be exploited for microbial detection. When such datasets are repurposed for secondary microbiome-oriented analyses, extracting and accurately classifying this weak microbial signal becomes technically challenging, and no ready-to-use pipeline currently exists. In this study, we evaluate computational strategies for filtering host reads and classifying microbial transcripts in host-dominated RNA sequencing data. We compare assembly-based approaches similar to those used in a previous study focusing on microbial translocation with state-of-the-art assembly-free methods, and assess their respective strengths and limitations using simulated datasets reflecting low microbial abundance. Our results show that assembly-based methods yield accurate taxonomic predictions but struggle at low read depth, whereas assembly-free methods are more robust in sparse settings at the cost of reduced precision. To leverage the complementarity of both approaches, we propose a hybrid pipeline that integrates assembly-based and assembly-free classification. On simulated data, this hybrid strategy improves microbial classification performance compared with either approach alone. Application to a real human metatranscriptomic dataset analyzed in a microbial translocation context illustrates the broader microbial signal captured by the hybrid approach, despite intrinsic challenges related to the absence of reliable ground truth and the risk of host read misclassification. Our work provides a framework for extracting microbial signals from host-dominated human metatranscriptomes, enabling the reuse of existing transcriptomic datasets for microbiome-related analyses, including but not limited to microbial translocation studies.
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BibTeX
@article{colajanni2026benchmarking,
title = {{Benchmarking methods for extracting microbial signal from host-dominated metatranscriptomes}},
author = {Antonin Colajanni and Raluca Uricaru and Samuel Darko and Rahul Subramanian and Daniel C Douek and Rodolphe Thiébaut and Patricia Thebault},
journal = {Briefings in Bioinformatics},
year = {2026},
month = sep,
doi = {10.1093/bib/bbag454},
url = {https://inria.hal.science/hal-05762286v1},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Benchmarking methods for extracting microbial signal from host-dominated metatranscriptomes
AU - Antonin Colajanni
AU - Raluca Uricaru
AU - Samuel Darko
AU - Rahul Subramanian
AU - Daniel C Douek
AU - Rodolphe Thiébaut
AU - Patricia Thebault
PY - 2026
DA - 2026/09/01
JO - Briefings in Bioinformatics
DO - 10.1093/bib/bbag454
UR - https://inria.hal.science/hal-05762286v1
AB - Human RNA sequencing (RNA-seq) data originally generated for human transcriptome profiling are overwhelmingly dominated by host sequences, yet they often contain a small fraction of non-human reads that can be exploited for microbial detection. When such datasets are repurposed for secondary microbiome-oriented analyses, extracting and accurately classifying this weak microbial signal becomes technically challenging, and no ready-to-use pipeline currently exists. In this study, we evaluate computational strategies for filtering host reads and classifying microbial transcripts in host-dominated RNA sequencing data. We compare assembly-based approaches similar to those used in a previous study focusing on microbial translocation with state-of-the-art assembly-free methods, and assess their respective strengths and limitations using simulated datasets reflecting low microbial abundance. Our results show that assembly-based methods yield accurate taxonomic predictions but struggle at low read depth, whereas assembly-free methods are more robust in sparse settings at the cost of reduced precision. To leverage the complementarity of both approaches, we propose a hybrid pipeline that integrates assembly-based and assembly-free classification. On simulated data, this hybrid strategy improves microbial classification performance compared with either approach alone. Application to a real human metatranscriptomic dataset analyzed in a microbial translocation context illustrates the broader microbial signal captured by the hybrid approach, despite intrinsic challenges related to the absence of reliable ground truth and the risk of host read misclassification. Our work provides a framework for extracting microbial signals from host-dominated human metatranscriptomes, enabling the reuse of existing transcriptomic datasets for microbiome-related analyses, including but not limited to microbial translocation studies.
SN - 1467-5463
SN - 1477-4054
ER -