Evidence map›Paper›PMID 27911946›Full record

ArticlePloS one2016

K-shuff: A Novel Algorithm for Characterizing Structural and Compositional Diversity in Gene Libraries.

Kamlesh Jangid, Ming-Hung Kao, Aishwarya Lahamge, Mark A Williams, Stephen L Rathbun, William B Whitman

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.7field-weighted citation impact, top 25% 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

3 citing papers in PubMed, 8 citations in OpenAlex.

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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

6 authors at 4 institutions in 2 countries.

Kamlesh JangidDepartment of Microbiology, University of Georgia, Athens, Georgia, United States of America.
Ming-Hung KaoSchool of Mathematical & Statistical Sciences, Arizona State University, Tempe, Arizona, United States of America.
Aishwarya LahamgeInstitute of Biotechnology and Bioinformatics, Savitribai Phule Pune University, Pune, Maharashtra, India.
Mark A WilliamsCollege of Agriculture and Life Sciences, Virginia Polytechnic and State University, Blacksburg, Virginia, United States of America.
Stephen L RathbunDepartment of Epidemiology and Biostatistics, University of Georgia, Athens, Georgia, United States of America.
William B WhitmanDepartment of Microbiology, University of Georgia, Athens, Georgia, United States of America.
University of Georgia · USArizona State University · USSavitribai Phule Pune University · INVirginia Tech · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

K-shuff is a new algorithm for comparing the similarity of gene sequence libraries, providing measures of the structural and compositional diversity as well as the significance of the differences between these measures. Inspired by Ripley's K-function for spatial point pattern analysis, the Intra K-function or IKF measures the structural diversity, including both the richness and overall similarity of the sequences, within a library. The Cross K-function or CKF measures the compositional diversity between gene libraries, reflecting both the number of OTUs shared as well as the overall similarity in OTUs. A Monte Carlo testing procedure then enables statistical evaluation of both the structural and compositional diversity between gene libraries. For 16S rRNA gene libraries from complex bacterial communities such as those found in seawater, salt marsh sediments, and soils, K-shuff yields reproducible estimates of structural and compositional diversity with libraries greater than 50 sequences. Similarly, for pyrosequencing libraries generated from a glacial retreat chronosequence and Illumina® libraries generated from US homes, K-shuff required >300 and 100 sequences per sample, respectively. Power analyses demonstrated that K-shuff is sensitive to small differences in Sanger or Illumina® libraries. This extra sensitivity of K-shuff enabled examination of compositional differences at much deeper taxonomic levels, such as within abundant OTUs. This is especially useful when comparing communities that are compositionally very similar but functionally different. K-shuff will therefore prove beneficial for conventional microbiome analysis as well as specific hypothesis testing.

Indexed as

AlgorithmsGenomic LibraryBacteriaRNA, BacterialRNA, Ribosomal, 16SSeawaterWater MicrobiologyRNA, BacterialRNA, Ribosomal, 16S

Identifiers

PMID27911946
PMCPMC5135132
OpenAlexW2557774468

What OpenQuestion holds

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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.