Evidence map›Paper›PMID 25988396›Full record

ArticleScientific reports2015

MICCA: a complete and accurate software for taxonomic profiling of metagenomic data.

Davide Albanese, Paolo Fontana, Carlotta De Filippo, Duccio Cavalieri, Claudio Donati

Abstract read
In one paragraph

Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 147 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
147citing papers in PubMed, 1 pooled it
–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

147 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. VSL#3British journal of biomedical science · 2026
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  13. Assessment of bacterial diversity in bovine bulk tank milk samples from Brazil.Brazilian journal of microbiology : [publication of the Brazilian Society for Microbiology] · 2025
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  20. Bacterial dynamics and exchange in plant-insect interactions.Current research in insect science · 2025
    Article

87 more citing papers are in PubMed but not listed here.

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

5 authors.

Davide AlbaneseFondazione Edmund Mach, Research and Innovation Centre, Computational Biology Department, Via E. Mach 1, 38010 - S. Michele all'Adige (TN), Italy.
Paolo FontanaFondazione Edmund Mach, Research and Innovation Centre, Computational Biology Department, Via E. Mach 1, 38010 - S. Michele all'Adige (TN), Italy.
Carlotta De FilippoFondazione Edmund Mach, Research and Innovation Centre, Food Quality Nutrition &Health Department, Via E. Mach 1, 38010 - S. Michele all'Adige (TN), Italy.
Duccio CavalieriFondazione Edmund Mach, Research and Innovation Centre, Computational Biology Department, Via E. Mach 1, 38010 - S. Michele all'Adige (TN), Italy.
Claudio DonatiFondazione Edmund Mach, Research and Innovation Centre, Computational Biology Department, Via E. Mach 1, 38010 - S. Michele all'Adige (TN), Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The introduction of high throughput sequencing technologies has triggered an increase of the number of studies in which the microbiota of environmental and human samples is characterized through the sequencing of selected marker genes. While experimental protocols have undergone a process of standardization that makes them accessible to a large community of scientist, standard and robust data analysis pipelines are still lacking. Here we introduce MICCA, a software pipeline for the processing of amplicon metagenomic datasets that efficiently combines quality filtering, clustering of Operational Taxonomic Units (OTUs), taxonomy assignment and phylogenetic tree inference. MICCA provides accurate results reaching a good compromise among modularity and usability. Moreover, we introduce a de-novo clustering algorithm specifically designed for the inference of Operational Taxonomic Units (OTUs). Tests on real and synthetic datasets shows that thanks to the optimized reads filtering process and to the new clustering algorithm, MICCA provides estimates of the number of OTUs and of other common ecological indices that are more accurate and robust than currently available pipelines. Analysis of public metagenomic datasets shows that the higher consistency of results improves our understanding of the structure of environmental and human associated microbial communities. MICCA is an open source project.

Indexed as

SoftwareAlgorithmsBiodiversityComputational BiologyDatasets as TopicDNA Barcoding, TaxonomicMetagenomicsMicrobiota

Identifiers

PMID25988396
PMCPMC4649890

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.