Year: 2026
Type: preprint
Abstract: 2. Abstract Metagenomes facilitate Escherichia coli strain-level profiling in human gut microbiomes, informing our understanding of colonisation diversity and the genetic features distinguishing infection from carriage. Among numerous reference-based tools for short-read metagenomic strain-level profiling, the best approach remains unclear. Here, we benchmarked six publicly available tools—PanTax, PathoScope 2.0, StrainGE, Strainify, StrainR2 and StrainScan—for their ability to detect co-existing strains of E. coli and estimate their relative abundance across real and simulated metagenomes of increasing complexity with varying reference database composition. We show that the tools varied substantially in both the number and relative abundance of strains predicted, indicating that despite being designed for the same broad purpose, they produce markedly different representations of strain composition from the same metagenomic samples. StrainR2 achieved the highest F1 score across our experiments (0.995) and successfully distinguished all strains within a complex, 10-strain spike-in mixture containing strains sharing up to 99.9% average nucleotide identity and included in a 304-strain E. coli reference database. However, when ground-truth strains were removed from the reference database to mimic the profiling of samples of unknown strain content PathoScope 2.0 and StrainGE performed strongest: both estimated phylogroup abundances accurately and frequently correctly assigned strain identities to the closest available genomic neighbours to the ground truth. Computational requirements also varied substantially (median runtime: 0.11– 68.45 h; peak memory: 2.08–351.53 GiB). Collectively, these results demonstrate that tool selection should reflect the intended application, and we provide practical guidance on the relative strengths and use cases of each approach. These findings will facilitate characterisation of E. coli strain-level diversity within short-read gut metagenomes with greater accuracy than previously possible. 3. Impact statement Strain-level diversity within the human gut microbiome can be important for human health, with species such as Escherichia coli existing as both commensal and pathogenic strains. Most gut microbiome datasets are from short-read i.e. , Illumina, sequencing, and numerous bioinformatic tools have been developed to profile strain-level variation from these data. However, available tools have been benchmarked in various ways and are subject to author bias. This is, to our knowledge, the first independent benchmarking of six publicly available tools for E. coli strain profiling in short-read metagenomes. Using both real and simulated datasets of increasing complexity, we demonstrate tool performance varies in terms of strain detection and relative abundance estimation, highlighting that tool choice should be guided by the specific research question, as no single method performs optimally across all scenarios. This work provides an unbiased framework for tool selection, supporting more accurate and reproducible E. coli strain-level analyses in gut microbiome research from short-metagenomic data. 4. Data summary The authors confirm all supporting data, code and protocols have been provided. Supplementary methods, tables and figures are available in the online Supplementary Material. Code for simulating metagenomes and generating E. coli spike-ins using InSilicoSeq, running tools on the simulated gut metagenomes and real-world spike-in datasets, inclusive of computational benchmarking, and R visualization and statistical analysis are available within a dedicated GitHub repository ( https://github.com/mattgal11/benchmarking_short_read_strain_profilers ). The following supplementary data files required for reproducible analyses are available on FigShare ( https://doi.org/10.6084/m9.figshare.32125474 ): ZymoBIOMICS® D6331 gut microbiome standard dataset predicted relative abundances (Zymobiomics_D6331_raw_predicted_abundance.csv) SRR13355226 mock community paired-end reads with human reads depleted (SRR13355226_depleted_R1.fastq.gz & SRR13355226_depleted_R2.fastq.gz) SRR13355226 mock community dataset predicted abundance data, with and without human read removal (SRR13355226_raw_predicted_abundance_with_and_without_human_read_r emoval.csv) Normalised per-contig relative abundances for 98 species used to construct the baseline gut microbiome profile for InSilicoSeq metagenome simulation (Normalised_relative_abundance_for_InSilicoSeq_simulated_metagenomes_ gut_microbiome_profile.csv) Simulated metagenomes dataset call types and detection metric across detection thresholds (Simulated_metagenomes_raw_call_type_assingments_and_detection_thres holds.csv) Simulated metagenomes dataset (all references) predicted relative abundances (Simulated_metagenomes_all_references_raw_predicted_abundances.csv) Simulated metagenomes dataset (all references) mapped reads for PathoScope and Strainify (all_refs_pathoscope_reads_mapped.csv & all_refs_strainify_reads_mapped.csv) Simulated metagenomes dataset (reduced reference database) predicted relative abundances (Simulated_metagenomes_K12_and_Sakai_removed_from_reference_datab ase_raw_predicted_abundance.csv) E. coli reference genomes used in real-world spike ins dataset (304_ecoli_strains_reference_genomes.csv) NCBI datasets assembly stats data frame for 304 E. coli reference genomes (abram_et_al_assembly_stats.xlsx) FastANI all-vs-all whole-genome ANI for 304 E. coli reference database (fastANI_dRep_genomes_all_vs_all_real_world_spike_ins.tsv) Real world spike-ins dataset call types and detection metric across detection thresholds (real_world_spike_ins_detection_metrics_all_thresholds.csv) Real world spike-ins dataset predicted relative abundances (real_world_spike_ins_benchmarking_results.csv) Real world spike-ins dataset (reduced reference database) predicted relative abundances (real_world_spike_ins_benchmarking_no_ground_truth_results.csv) Real world spike-ins dataset computational benchmarking results (real_world_spike_ins_comp_benchmark_DB_step.csv & real_world_spike_ins_comp_benchmark_profiling.csv & strainR2_memory_comparisons.csv) Real world spike-ins dataset paired metagenome file sizes (real_world_s
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7161784641