Additional file 6: Table S2. Polycistronic read statistics for NOD mouse. This table shows the tally of non-overlapping paired-end reads that were assembled into contigs by MetaPro through rnaSPADes and subsequently annotated into discrete genes by MetaGeneMark. This table also shows the prevalence of polycistronic reads that exist within the data. BWA was used to align the assembled paired-end reads against the genes to identify discordant alignments between the forward and reverse-end read of a pair with the same ID. This table has seven columns: 1) the sample ID. 2) the sample description. 3) the total number of alignments is the number of alignments of a read to a gene that BWA reported. 4) The total number of pairs is the number of IDs that BWA aligned, be it forward, reverse, or both paired-end reads. 5) The paired-end disagreements column are the number of times a forward-end and reverse-end read had different alignments for each NOD mouse sample. 6) The paired-end agreements column shows the number of times a forward-read and reverse-end read aligned to the same gene. 7) The percentage of paired-end disagreements, relative to the total number of paired-end reads in the sample. The percentage of disagreements (polycistronic reads) are at-best 0.23%, and at-worst 7.8% of assembled, non-overlapped paired-end reads in the NOD mouse samples.

Additional file 12: Table S8. Computational performance statistics of MetaPro, HUMAnN3, and SAMSA2. This table reports the amount of processing time required for each run of the three pipelines. MetaPro additionally exports the timing data of each stage independently. HUMAnN3’s pre-processing step is a separate stage using a separate tool called KneadData. SAMSA2 cleans the data in the pipeline, but it is integrated and does not export timing data.

Additional file 5: Table S1. Summary of Sequence Read Processing for Three Metatranscriptomic Datasets (NOD Mouse gut; Kimchi and Human Oral Biofilm) Processed by HUMAnN3, HUMAnN2, MetaPro and SAMSA2. This table reports the processing results from the four pipelines on samples from three different datasets. HUMAnN2 and HUMAnN3’s preprocessing tool concatenates paired reads into 1 single file and treats them as 2 separate reads. The NOD mouse samples are paired-end data, while the kimchi and human oral biofilm represent single-end sequence datasets. Unlike MetaPro and SAMSA2, HUMAnN3 and HUMAnN2 do not report transcripts but instead group proteins identified in their pipelines into gene families that are reported in the final column.

MetaFunc is a computational pipeline that can take input reads and pass it through a pipeline that will then analyse host genes from the reads on one side, and microbiome taxonomies and gene ontology annotations on the other, and finally allowing for microbe-host gene correlations. This dataset contains databases used for analysing the microbiome component of the pipeline. Full description of the pipeline can be found at https://metafunc.readthedocs.io/en/latest/#.

Additional file 11: Table S7.Comparisons of Enzyme annotations between MetaPro and HUMAnN2 for NOD mouse, kimchi, and human oral biofilm datasets. This table compares the ECs of MetaPro against HUMAnN2 on the NOD mouse, kimchi, and human oral biofilm datasets. The HUMAnN2 ECs were filtered for EC co-occurrence pairs that were not found in Swiss-Prot, and multiple unique ECs that annotated to the same gene family. The resulting HUMAnN2 ECs were contrasted against MetaPro’s EC, yielding a common set of ECs found in both tools, ECs found only by MetaPro, and ECs found only by HUMAnN2. The same comparison is shown for MetaPro’s high-qaulity EC predictions.

MetaFunc is a computational pipeline that can take input reads and pass it through a pipeline that will then analyse host genes from the reads on one side, and microbiome taxonomies and gene ontology annotations on the other, and finally allowing for microbe-host gene correlations. This dataset contains databases used for analysing the microbiome component of the pipeline. Full description of the pipeline can be found at https://metafunc.readthedocs.io/en/latest/#.

Additional file 8: Table S4.Read annotation statistics for kimchi fermentation datasets from MetaPro, HUMAnN3, HUMAnN2, SAMSA2, compared with the gold standard. This table shows the number of reads in each kimchi sample, annotated to the expected 5 lactic acid bacteria (LAB) from each pipeline: Leuconostic mesenteroides, Lactobacillus sakei, Weissella koreensis, Leuconostoc carnosum, and Leuconostoc gelidum. The expected results were obtained by annotating the kimchi datasets against a database containing only the reference gene sequences for the 5 LAB, using BWA.

Shotgun Metatranscriptomics Clinical Services Market Outlook

According to our latest research, the global shotgun metatranscriptomics clinical services market size reached USD 624.7 million in 2024, with a robust compound annual growth rate (CAGR) of 17.9% observed over the last year. The market is being driven by increased demand for advanced molecular diagnostic tools and the growing adoption of high-throughput sequencing technologies in clinical practice. Based on current growth trajectories, the market is forecasted to reach USD 2,271.9 million by 2033, underscoring the significant expansion underway in this sector as per our latest research findings.

The primary growth factor fueling the shotgun metatranscriptomics clinical services market is the surging need for comprehensive and unbiased profiling of microbial communities in clinical samples. Traditional diagnostic methods often fall short in detecting low-abundance pathogens or capturing the full diversity of microbial populations, particularly in complex diseases such as sepsis, chronic infections, and rare disorders. Shotgun metatranscriptomics enables simultaneous analysis of all RNA transcripts present in a sample, providing a holistic view of active microbial and host gene expression. This capability is especially valuable for infectious disease diagnostics, where rapid and accurate pathogen identification can significantly impact patient outcomes. As healthcare providers increasingly prioritize precision medicine and personalized treatment strategies, the adoption of metatranscriptomic approaches is accelerating, further propelling market growth.

Another significant driver is the technological advancements in next-generation sequencing (NGS) platforms and bioinformatics. The evolution of NGS has reduced the cost and turnaround time for sequencing, making shotgun metatranscriptomics more accessible to clinical laboratories and research institutions worldwide. Enhanced library preparation kits, improved RNA extraction protocols, and automated data analysis pipelines have streamlined workflows, enabling higher throughput and reproducibility. Moreover, the integration of artificial intelligence and machine learning in data interpretation is addressing the challenges of large-scale data analysis, extracting meaningful clinical insights from complex datasets. These technological innovations are not only expanding the range of clinical applications but also improving the sensitivity and specificity of metatranscriptomic assays, thereby driving their adoption across various end-users.

A third pivotal growth factor is the increasing awareness of the role of the microbiome and host-microbe interactions in health and disease. Recent research has highlighted the impact of the human microbiome on conditions ranging from cancer and autoimmune disorders to mental health and metabolic diseases. Shotgun metatranscriptomics uniquely enables the simultaneous analysis of both microbial and host gene expression, providing a comprehensive perspective on disease mechanisms and therapeutic targets. This multidimensional approach is attracting significant interest from pharmaceutical and biotechnology companies seeking to develop novel diagnostics and therapeutics based on microbiome and transcriptomic insights. As regulatory agencies begin to recognize the clinical utility of metatranscriptomic data, the market is poised for further expansion.

From a regional perspective, North America currently dominates the shotgun metatranscriptomics clinical services market, driven by strong investment in genomics research, a well-established healthcare infrastructure, and early adoption of advanced diagnostic technologies. Europe follows closely, benefiting from robust government initiatives and collaborative research projects. Meanwhile, the Asia Pacific region is emerging as a high-growth market, propelled by rising healthcare expenditure, expanding biotechnology sectors, and increasing awareness of precision medicine. Latin America and the Middle East & Africa, while smaller in market share, are witnessing steady growth due to improving healthcare access and growing demand for innovative diagnostic solutions. Regional disparities in regulatory frameworks, reimbursement policies, and technological adoption continue to shape the competitive landscape and market dynamics.

Service Type Analysis

The service type segment within the shotgun metatranscriptomic

Metagenomics and metatranscriptomics are powerful methods to uncover key micro-organisms and processes driving biogeochemical cycling in natural ecosystems. Databases dedicated to depicting biogeochemical pathways (for example, metabolism of dimethylsulfoniopropionate (DMSP), which is an abundant organosulfur compound) from metagenomic/metatranscriptomic data are rarely seen. Additionally, a recognized normalization model to estimate the relative abundance and environmental importance of pathways from metagenomic and metatranscriptomic data has not been organized to date. These limitations impact the ability to accurately relate key microbial-driven biogeochemical processes to differences in environmental conditions. Thus, an easy-to-use, specialized tool that infers and visually compares the potential for biogeochemical processes, including DMSP cycling, is urgently required. To solve these issues, we developed DiTing, a tool wrapper to infer and compare biogeochemical pathways among a set of given metagenomic or metatranscriptomic reads in one step, based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) and a manually created DMSP cycling gene database. Accurate and specific formulae for over 100 pathways were developed to calculate their relative abundance. Output reports detail the relative abundance of biogeochemical pathways in both text and graphical format. DiTing was applied to simulated metagenomic data and resulted in consistent genetic features of simulated benchmark genomic data. Subsequently, when applied to natural metagenomic and metatranscriptomic data from hydrothermal vents and the Tara Ocean project, the functional profiles predicted by DiTing were correlated with environmental condition changes. DiTing can now be confidently applied to wider metagenomic and metatranscriptomic datasets, and it is available at https://github.com/xuechunxu/DiTing.

Additional file 10: Table S6.Comparisons of Enzyme annotations between MetaPro and SAMSA2 for NOD mouse, kimchi, and human oral biofilm datasets. This table compares the ECs of MetaPro against SAMSA2 on the NOD mouse, kimchi, and human oral biofilm datasets. The SAMSA2 ECs were filtered for EC co-occurrence pairs that were not found in Swiss-Prot, and multiple unique ECs that annotated to the same gene family. The resulting SAMSA2 ECs were contrasted against MetaPro’s EC, yielding a common set of ECs found in both tools, ECs found only by MetaPro, and ECs found only by SAMSA2. The same comparison is shown for MetaPro’s high-qaulity EC predictions.

Metatranscriptomic gene annotation of sample ROW1. Identified unique genes in the metatranscriptome of sample ROW1, using transcripts assembled using Trinity and annotating using the Trinotate pipeline. Swissprot was used as reference database. (XLSX 1123Â kb)

Metabolomic analyses of human gut microbiome samples can unveil the metabolic potential of host tissues and the numerous microorganisms they support, concurrently. As such, metabolomic information bears immense potential to improve disease diagnosis and therapeutic drug discovery. Unfortunately, as cohort sizes increase, comprehensive metabolomic profiling becomes costly and logistically difficult to perform at a large scale. To address these difficulties, we tested the feasibility of predicting the metabolites of a microbial community based solely on microbiome sequencing data. Paired microbiome sequencing (16S rRNA gene amplicons, shotgun metagenomics, and metatranscriptomics) and metabolome (mass spectrometry and nuclear magnetic resonance spectroscopy) datasets were collected from six independent studies spanning multiple diseases. We used these datasets to evaluate two reference-based gene-to-metabolite prediction pipelines and a machine-learning (ML) based metabolic profile prediction approach. With the pre-trained model on over 900 microbiome-metabolome paired samples, the ML approach yielded the most accurate predictions (i.e., highest F1 scores) of metabolite occurrences in the human gut and outperformed reference-based pipelines in predicting differential metabolites between case and control subjects. Our findings demonstrate the possibility of predicting metabolites from microbiome sequencing data, while highlighting certain limitations in detecting differential metabolites, and provide a framework to evaluate metabolite prediction pipelines, which will ultimately facilitate future investigations on microbial metabolites and human health.

Metatranscriptomic gene annotation of sample AT3. Identified unique genes in the metatranscriptome of sample AT3, using transcripts assembled using Trinity and annotating using the Trinotate pipeline. Swissprot was used as reference database. (XLSX 1326Â kb)

Host–Microbiome Multiomics Services Market Outlook

According to our latest research, the global Host–Microbiome Multiomics Services market size reached USD 1.36 billion in 2024, propelled by the rapid adoption of advanced omics technologies in both clinical and research settings. The industry is demonstrating robust momentum, registering a CAGR of 18.7% from 2025 to 2033. By the end of 2033, the market is forecasted to achieve a valuation of approximately USD 6.22 billion. This impressive growth trajectory is primarily driven by the increasing integration of multiomics approaches to unravel complex host–microbiome interactions, which are crucial for personalized medicine, drug discovery, and agricultural innovation.

The growth of the Host–Microbiome Multiomics Services market is significantly influenced by the surging demand for personalized medicine and precision therapeutics. Multiomics approaches, which combine metagenomics, metatranscriptomics, metaproteomics, and metabolomics, enable a comprehensive understanding of the intricate relationships between hosts and their associated microbiomes. This holistic insight is vital for identifying novel biomarkers, understanding disease mechanisms, and tailoring interventions to individual patient profiles. As healthcare providers and pharmaceutical companies increasingly recognize the value of multiomics data in enhancing patient outcomes, investment in host–microbiome multiomics services is accelerating. Additionally, the proliferation of high-throughput sequencing technologies and improvements in bioinformatics platforms are making these services more accessible and cost-effective, further fueling market expansion.

Another key driver for the Host–Microbiome Multiomics Services market is the expanding application of microbiome research in drug discovery and development. Pharmaceutical and biotechnology companies are leveraging multiomics analyses to identify new drug targets, optimize therapeutic efficacy, and minimize adverse effects. The integration of host and microbiome data provides a more nuanced understanding of drug–microbiome interactions, which is crucial for developing next-generation therapeutics. Furthermore, regulatory agencies are increasingly acknowledging the importance of microbiome data in clinical trials, prompting companies to adopt multiomics strategies throughout the drug development pipeline. This trend is expected to continue, as the pharmaceutical industry seeks innovative solutions to address unmet medical needs and improve patient care.

The agricultural sector is also contributing to the growth of the Host–Microbiome Multiomics Services market. Multiomics technologies are being employed to study plant and animal microbiomes, with the aim of enhancing crop yield, improving soil health, and promoting sustainable farming practices. By elucidating the complex interactions between hosts and their microbial communities, researchers can develop targeted interventions to boost agricultural productivity and resilience. Government initiatives and funding for agricultural genomics research are further supporting the adoption of multiomics services in this sector. The increasing focus on food security and sustainable agriculture is likely to drive continued demand for host–microbiome multiomics services in the coming years.

Regionally, North America holds the largest share of the Host–Microbiome Multiomics Services market, accounting for over 38% of the global revenue in 2024. This dominance is attributed to the presence of leading biotechnology firms, advanced healthcare infrastructure, and significant investment in research and development. Europe follows closely, driven by strong government support for genomics research and a growing emphasis on personalized medicine. The Asia Pacific region is expected to exhibit the highest CAGR of 21.2% during the forecast period, fueled by increasing healthcare expenditure, expanding biotechnology sectors, and rising awareness of the importance of microbiome research. Latin America and the Middle East & Africa are also witnessing steady growth, supported by ongoing improvements in healthcare infrastructure and research capabilities.

Differentially expressed transcripts when comparing between wild-type and Tyk2-deficient mice in DSS-induced colitis. This is a tab-delimited file, with all entries included, sorted by ascending multiple hypothesis-adjusted p value. (XLS 3680 kb)

Supplementary tables. Tables S1–S12. Detailed table legends available within file. (XLSX 4350 kb)

BackgroundMetatranscriptomic landscapes can provide insights in functional relationships within natural microbial communities. Analysis of complex metatranscriptome datasets of these communities poses a considerable bioinformatic challenge since they are non-restricted with a varying number of participating strains and species. For RNA-Seq data a standard approach is to align the generated reads to a set of closely related reference genomes. This only works well for microbial communities for which a near complete catalogue of reference genomes is available at a small evolutionary distance. In this study, we focus on the design of a validated de novo metatranscriptome assembly pipeline for single-end Illumina RNA-Seq data to obtain functional and taxonomic profiles of murine microbial communities.ResultsThe here developed de novo assembly metatranscriptome pipeline combined rRNA removal, IDBA-UD assembler, functional annotation and taxonomic classification. Different assemblers were tested and validated using RNA-Seq data from an in silico generated mock community and in vivo RNA-Seq data from a restricted microbial community taken from a mouse model colonized with Altered Schaedler Flora (ASF). Precision and recall of resulting gene expression, functional and taxonomic profiles were compared to those obtained with a standard alignment method. The validated pipeline was subsequently used to generate expression profiles from non-restricted cecal communities of four C57BL/6J mice fed on a high-fat high-protein diet spiked with an RNA-Seq data set from a well-characterized human sample. The spike in control was used to estimate precision and recall at assembly, functional and taxonomic level of non-restricted communities.ConclusionsA generic de novo assembly pipeline for metatranscriptome data analysis was designed for microbial ecosystems, which can be applied for microbial metatranscriptome analysis in any chosen niche.

A microbiome meta-transcriptomics pipeline identifies a novel human neutrophil elastase inhibitor that protects the colonic epithelial barrier

Table S14. Relative abundance values of metagenomics and metatranscriptomics reads calculated by using the Metagenomic Intra-Species Diversity Analysis System (MIDAS) pipeline. Bacterial taxa that contributed with 0.5% or more to the total DNA or mRNA read abundance at any time point were included in the analysis. (XLSX 51 kb)

The improvement of sequencing technologies has facilitated generalization of metagenomic sequencing, which has become a standard procedure for analyzing the structure and functionality of microbiomes. Bioinformatic analysis of sequencing results poses a challenge because it involves many different complex steps. SqueezeMeta is a fully automatic pipeline for metagenomics/metatranscriptomics, covering all steps of the analysis. SqueezeMeta includes multi-metagenome support that enables co-assembly of related metagenomes and retrieval of individual genomes via binning procedures. SqueezeMeta features several unique characteristics: co-assembly procedure or co-assembly of unlimited number of metagenomes via merging of individual assembled metagenomes, both with read mapping for estimation of the abundances of genes in each metagenome. It also includes binning and bin checking for retrieving individual genomes. Internal checks for the assembly and binning steps provide information about the consistency of contigs and bins. Moreover, results are stored in a MySQL database, where they can be easily exported and shared, and can be inspected anywhere using a flexible web interface that allows simple creation of complex queries. We illustrate the potential of SqueezeMeta by analyzing 32 gut metagenomes in a fully automatic way, enabling retrieval of several million genes and several hundreds of genomic bins. One of the motivations in the development of SqueezeMeta was producing a software capable of running in small desktop computers and thus amenable to all users and settings. We were also able to co-assemble two of these metagenomes and complete the full analysis in less than one day using a simple laptop computer. This reveals the capacity of SqueezeMeta to run without high-performance computing infrastructure and in absence of any network connectivity. It is therefore adequate for in situ, real time analysis of metagenomes produced by nanopore sequencing. SqueezeMeta can be downloaded from https://github.com/jtamames/SqueezeMeta.