Evidence map›Paper›PMID 25225611›Full record

ArticleMicrobiome2014

PathoScope 2.0: a complete computational framework for strain identification in environmental or clinical sequencing samples.

Changjin Hong, Solaiappan Manimaran, Ying Shen, Joseph F Perez-Rogers, Allyson L Byrd, Eduardo Castro-Nallar, Keith A Crandall, William Evan Johnson

Open access · goldAbstract read
In one paragraph

Article in Microbiome, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 155 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
155citing papers in PubMed, 1 pooled it
6.1field-weighted citation impact, top 3% of its field
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

155 citing papers in PubMed, 1 synthesis or guideline pooled it, 270 citations in OpenAlex.

  1. Pooled it
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  5. 16S rRNA sequence captures microbial functional potential.bioRxiv : the preprint server for biology · 2026
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  14. International journal of microbiology · 2026
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95 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

8 authors at 3 institutions in 1 country.

Changjin HongComputational Biomedicine, Boston University School of Medicine, 72 E Concord St. E645, Boston, MA 02118, USA.
Solaiappan ManimaranComputational Biomedicine, Boston University School of Medicine, 72 E Concord St. E645, Boston, MA 02118, USA.
Ying ShenComputational Biomedicine, Boston University School of Medicine, 72 E Concord St. E645, Boston, MA 02118, USA.
Joseph F Perez-RogersComputational Biomedicine, Boston University School of Medicine, 72 E Concord St. E645, Boston, MA 02118, USA ; Bioinformatics Program, Boston University, Boston, MA 02125, USA.
Allyson L ByrdBioinformatics Program, Boston University, Boston, MA 02125, USA.
Eduardo Castro-NallarComputational Biology Institute, George Washington University, Ashburn, VA 20147, USA.
Keith A CrandallComputational Biology Institute, George Washington University, Ashburn, VA 20147, USA.
William Evan JohnsonComputational Biomedicine, Boston University School of Medicine, 72 E Concord St. E645, Boston, MA 02118, USA ; Bioinformatics Program, Boston University, Boston, MA 02125, USA.
Boston University · USGeorge Washington University Virginia Campus · USUniversity of Massachusetts Boston · US

Funding

BIOLOGY OF THE LUNG--MULTIDISCIPLINARY PROGRAMT32HL007035 · NHLBI · BOSTON UNIVERSITY MEDICAL CAMPUS · PI Darrell N. Kotton, JOSEPH P MIZGERD · 1985 to 2026
$24.3M
NHLBI NIH HHS T32 HL007035
6 · The paper itself

Abstract

backgroundRecent innovations in sequencing technologies have provided researchers with the ability to rapidly characterize the microbial content of an environmental or clinical sample with unprecedented resolution. These approaches are producing a wealth of information that is providing novel insights into the microbial ecology of the environment and human health. However, these sequencing-based approaches produce large and complex datasets that require efficient and sensitive computational analysis workflows. Many recent tools for analyzing metagenomic-sequencing data have emerged, however, these approaches often suffer from issues of specificity, efficiency, and typically do not include a complete metagenomic analysis framework.

resultsWe present PathoScope 2.0, a complete bioinformatics framework for rapidly and accurately quantifying the proportions of reads from individual microbial strains present in metagenomic sequencing data from environmental or clinical samples. The pipeline performs all necessary computational analysis steps; including reference genome library extraction and indexing, read quality control and alignment, strain identification, and summarization and annotation of results. We rigorously evaluated PathoScope 2.0 using simulated data and data from the 2011 outbreak of Shiga-toxigenic Escherichia coli O104:H4.

conclusionsThe results show that PathoScope 2.0 is a complete, highly sensitive, and efficient approach for metagenomic analysis that outperforms alternative approaches in scope, speed, and accuracy. The PathoScope 2.0 pipeline software is freely available for download at: http://sourceforge.net/projects/pathoscope/.

Identifiers

PMID25225611
PMCPMC4164323
OpenAlexW2106533426

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.