Evidence map›Paper›PMID 28158396›Full record

ArticleBioinformatics (Oxford, England)2017

FIT: statistical modeling tool for transcriptome dynamics under fluctuating field conditions.

Koji Iwayama, Yuri Aisaka, Natsumaro Kutsuna, Atsushi J Nagano

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 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.

Koji IwayamaResearch Institute for Food and Agriculture, Ryukoku University, Otsu, Shiga, Japan.
Yuri AisakaLPixel Inc, Hongo, Bunkyo-ku, Tokyo, Japan.
Natsumaro KutsunaLPixel Inc, Hongo, Bunkyo-ku, Tokyo, Japan.
Atsushi J NaganoDepartment of Plant Life Science, Faculty of Agriculture, Ryukoku University, Otsu, Shiga, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

motivationConsiderable attention has been given to the quantification of environmental effects on organisms. In natural conditions, environmental factors are continuously changing in a complex manner. To reveal the effects of such environmental variations on organisms, transcriptome data in field environments have been collected and analyzed. Nagano et al. proposed a model that describes the relationship between transcriptomic variation and environmental conditions and demonstrated the capability to predict transcriptome variation in rice plants. However, the computational cost of parameter optimization has prevented its wide application.

results: We propose a new statistical model and efficient parameter optimization based on the previous study. We developed and released FIT, an R package that offers functions for parameter optimization and transcriptome prediction. The proposed method achieves comparable or better prediction performance within a shorter computational time than the previous method. The package will facilitate the study of the environmental effects on transcriptomic variation in field conditions. AVAILABILITY AND IMPLEMENTATION: Freely available from CRAN ( https://cran.r-project.org/web/packages/FIT/ ). CONTACT: : anagano@agr.ryukoku.ac.jp. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Indexed as

Models, GeneticModels, StatisticalSoftwareTranscriptomeComputational BiologyGene Expression Regulation, PlantOryza

Identifiers

PMID28158396
PMCPMC5447243

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LicenceCC BY
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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.