Evidence map›Paper›PMID 22189864›Full record

ReviewApplied microbiology and biotechnology2012

The great screen anomaly--a new frontier in product discovery through functional metagenomics.

David Matthias Ekkers, Mariana Silvia Cretoiu, Anna Maria Kielak, Jan Dirk van Elsas

Abstract readReview
In one paragraph

Review in Applied microbiology and biotechnology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.

0numbers the graph read from it
0cells of the map it votes in
41citing 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

41 citing papers in PubMed.

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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

4 authors.

David Matthias EkkersDepartment of Microbial Ecology, Centre for Ecological and Evolutionary Studies, University of Groningen, Nijenborgh 7, 9747 AG Groningen, The Netherlands.
Mariana Silvia Cretoiu
Anna Maria Kielak
Jan Dirk van Elsas

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Functional metagenomics, the study of the collective genome of a microbial community by expressing it in a foreign host, is an emerging field in biotechnology. Over the past years, the possibility of novel product discovery through metagenomics has developed rapidly. Thus, metagenomics has been heralded as a promising mining strategy of resources for the biotechnological and pharmaceutical industry. However, in spite of innovative work in the field of functional genomics in recent years, yields from function-based metagenomics studies still fall short of producing significant amounts of new products that are valuable for biotechnological processes. Thus, a new set of strategies is required with respect to fostering gene expression in comparison to the traditional work. These new strategies should address a major issue, that is, how to successfully express a set of unknown genes of unknown origin in a foreign host in high throughput. This article is an opinionating review of functional metagenomic screening of natural microbial communities, with a focus on the optimization of new product discovery. It first summarizes current major bottlenecks in functional metagenomics and then provides an overview of the general metagenomic assessment strategies, with a focus on the challenges that are met in the screening for, and selection of, target genes in metagenomic libraries. To identify possible screening limitations, strategies to achieve optimal gene expression are reviewed, examining the molecular events all the way from the transcription level through to the secretion of the target gene product.

Indexed as

Environmental MicrobiologyGenesBiological FactorsBiotechnologyComputational BiologyEcosystemGene LibraryMetagenomicsBiological Factors

Identifiers

PMID22189864
PMCPMC3264863

What OpenQuestion holds

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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.