Evidence map›Paper›PMID 31344945›Full record

ReviewBiomolecules2019

Small Genomes and Big Data: Adaptation of Plastid Genomics to the High-Throughput Era.

Christen M Klinger, Elisabeth Richardson

Abstract readReview
In one paragraph

Review in Biomolecules, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

2 authors.

Christen M KlingerDivision of Infectious Diseases, Department of Medicine, University of Alberta, Edmonton, AB T6G 2R3, Canada.
Elisabeth RichardsonDepartment of Biological Sciences, University of Alberta, Edmonton, AB T6G 2R3, Canada. ehrichar@ualberta.ca.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Plastid genome sequences are becoming more readily available with the increase in high-throughput sequencing, and whole-organelle genetic data is available for algae and plants from across the diversity of photosynthetic eukaryotes. This has provided incredible opportunities for studying species which may not be amenable to in vivo study or genetic manipulation or may not yet have been cultured. Research into plastid genomes has pushed the limits of what can be deduced from genomic information, and in particular genomic information obtained from public databases. In this Review, we discuss how research into plastid genomes has benefitted enormously from the explosion of publicly available genome sequence. We describe two case studies in how using publicly available gene data has supported previously held hypotheses about plastid traits from lineage-restricted experiments across algal and plant diversity. We propose how this approach could be used across disciplines for inferring functional and biological characteristics from genomic approaches, including integration of new computational and bioinformatic approaches such as machine learning. We argue that the techniques developed to gain the maximum possible insight from plastid genomes can be applied across the eukaryotic tree of life.

Indexed as

Big DataComputational BiologyEvolution, MolecularGenome, PlastidGenome SizeGenomicsHigh-Throughput Nucleotide SequencingMachine LearningPhylogenyPlantsPlastidsbioinformaticsbiotechnologynext-generation sequencingplastid biology

Identifiers

PMID31344945
PMCPMC6723049

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.