Evidence map›Paper›PMID 39075388›Full record

ArticleBMC genomics2024

Comparison between 16S rRNA and shotgun sequencing in colorectal cancer, advanced colorectal lesions, and healthy human gut microbiota.

David Bars-Cortina, Elies Ramon, Blanca Rius-Sansalvador, Elisabet Guinó, Ainhoa Garcia-Serrano, Núria Mach, Olfat Khannous-Lleiffe, Ester Saus, Toni Gabaldón, Gemma Ibáñez-Sanz and 5 more

Abstract readComparative Study
In one paragraph

Article in BMC genomics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers.

0numbers the graph read from it
0cells of the map it votes in
54citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

54 citing papers in PubMed.

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  19. Metagenomic next-generation sequencing: new horizons in microbiology.Frontiers in cellular and infection microbiology · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

David Bars-Cortina *Unit of Biomarkers and Susceptibility (UBS), Oncology Data Analytics Program (ODAP), Catalan Institute of Oncology (ICO), L'Hospitalet del Llobregat, Barcelona, 08908, Spain.
Elies Ramon *Unit of Biomarkers and Susceptibility (UBS), Oncology Data Analytics Program (ODAP), Catalan Institute of Oncology (ICO), L'Hospitalet del Llobregat, Barcelona, 08908, Spain.
Blanca Rius-Sansalvador *Unit of Biomarkers and Susceptibility (UBS), Oncology Data Analytics Program (ODAP), Catalan Institute of Oncology (ICO), L'Hospitalet del Llobregat, Barcelona, 08908, Spain.
Elisabet GuinóUnit of Biomarkers and Susceptibility (UBS), Oncology Data Analytics Program (ODAP), Catalan Institute of Oncology (ICO), L'Hospitalet del Llobregat, Barcelona, 08908, Spain.
Ainhoa Garcia-SerranoDepartment of Clinical Science, Intervention and Technology, Karolinska Institutet, Stockholm, 14186, Sweden.
Núria MachIHAP, Université de Toulouse, INRAE, ENVT, Toulouse, France.
Olfat Khannous-LleiffeBarcelona Supercomputing Centre (BSC-CNS), Barcelona, 08034, Spain.
Ester SausBarcelona Supercomputing Centre (BSC-CNS), Barcelona, 08034, Spain.
Toni GabaldónBarcelona Supercomputing Centre (BSC-CNS), Barcelona, 08034, Spain.
Gemma Ibáñez-SanzUnit of Biomarkers and Susceptibility (UBS), Oncology Data Analytics Program (ODAP), Catalan Institute of Oncology (ICO), L'Hospitalet del Llobregat, Barcelona, 08908, Spain.
Lorena Rodríguez-AlonsoGastroenterology Department, Bellvitge University Hospital, L'Hospitalet de Llobregat, Barcelona, 08907, Spain.
Alfredo MataDigestive System Service, Moisés Broggi Hospital, Sant Joan Despí, 08970, Spain.
Ana García-RodríguezEndoscopy Unit, Digestive System Service, Viladecans Hospital-IDIBELL, Viladecans, 08840, Spain.
Mireia Obón-SantacanaUnit of Biomarkers and Susceptibility (UBS), Oncology Data Analytics Program (ODAP), Catalan Institute of Oncology (ICO), L'Hospitalet del Llobregat, Barcelona, 08908, Spain. mireiaobon@iconcologia.net.
Victor MorenoUnit of Biomarkers and Susceptibility (UBS), Oncology Data Analytics Program (ODAP), Catalan Institute of Oncology (ICO), L'Hospitalet del Llobregat, Barcelona, 08908, Spain. v.moreno@iconcologia.net.

Funding

Catalan Research Agency SGR01551Gordon and Betty Moore Foundation GBMF9742Horizon 2020 874662Horizon 2020 ERC-2016-724173Instituto de Salud Carlos III CD21/00094Instituto de Salud Carlos III FI19/00221Instituto de Salud Carlos III FI21/00056Instituto de Salud Carlos III IMPACT Grant IMP/00019"La Caixa" foundation LCF/PR/HR21/00737Spanish Ministerio de Universidades FPU2020-02907Spanish Ministry of Science and Innovation PDC2022-133266-I00Spanish Ministry of Science and Innovation for grants PID2021-126067NB-I00
6 · The paper itself

Abstract

backgroundGut dysbiosis has been associated with colorectal cancer (CRC), the third most prevalent cancer in the world. This study compares microbiota taxonomic and abundance results obtained by 16S rRNA gene sequencing (16S) and whole shotgun metagenomic sequencing to investigate their reliability for bacteria profiling. The experimental design included 156 human stool samples from healthy controls, advanced (high-risk) colorectal lesion patients (HRL), and CRC cases, with each sample sequenced using both 16S and shotgun methods. We thoroughly compared both sequencing technologies at the species, genus, and family annotation levels, the abundance differences in these taxa, sparsity, alpha and beta diversities, ability to train prediction models, and the similarity of the microbial signature derived from these models.

resultsAs expected, the results showed that 16S detects only part of the gut microbiota community revealed by shotgun, although some genera were only profiled by 16S. The 16S abundance data was sparser and exhibited lower alpha diversity. In lower taxonomic ranks, shotgun and 16S highly differed, partially due to a disagreement in reference databases. When considering only shared taxa, the abundance was positively correlated between the two strategies. We also found a moderate correlation between the shotgun and 16S alpha-diversity measures, as well as their PCoAs. Regarding the machine learning models, only some of the shotgun models showed some degree of predictive power in an independent test set, but we could not demonstrate a clear superiority of one technology over the other. Microbial signatures from both sequencing techniques revealed taxa previously associated with CRC development, e.g., Parvimonas micra.

conclusionsShotgun and 16S sequencing provide two different lenses to examine microbial communities. While we have demonstrated that they can unravel common patterns (including microbial signatures), shotgun often gives a more detailed snapshot than 16S, both in depth and breadth. Instead, 16S will tend to show only part of the picture, giving greater weight to dominant bacteria in a sample. Therefore, we recommend choosing one or another sequencing technique before launching a study. Specifically, shotgun sequencing is preferred for stool microbiome samples and in-depth analyses, while 16S is more suitable for tissue samples and studies with targeted aims.

Indexed as

Colorectal NeoplasmsGastrointestinal MicrobiomeRNA, Ribosomal, 16SBacteriaFecesFemaleHumansMaleMetagenomeMetagenomicsSequence Analysis, DNARNA, Ribosomal, 16S16SColorectal cancerComparisonMicrobiomeShotgun

Identifiers

PMID39075388
PMCPMC11285316

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.