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IFIM specifications


Unique identifier OMICS_08726
Alternative name Integrates quantitative Fitness Information for Microbial genes
Restrictions to use None
Maintained No


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Publication for Integrates quantitative Fitness Information for Microbial genes

IFIM citations


Flow Management to Control Excessive Growth of Macrophytes – An Assessment Based on Habitat Suitability Modeling

Front Plant Sci
PMCID: 5868135
PMID: 29616067
DOI: 10.3389/fpls.2018.00356
call_split See protocol

[…] le of the wetted area classifies as usable area for a certain species or species group.We used two different methods to calculate the habitat suitability. The classical, deterministic approach of the IFIM calculates a CSI as the geometric mean of the separate suitability indices for depth, velocity, and substrate size. It is directly integrated into the River2D Model on the basis of the HSC for ea […]


Selection for energy efficiency drives strand biased gene distribution in prokaryotes

Sci Rep
PMCID: 5585166
PMID: 28874819
DOI: 10.1038/s41598-017-11159-3

[…] wo groups according to the strands (leading versus lagging) they are located on.Fitness scores (i.e. quantitative measurements of gene essentiality) for 2,074 prokaryotic genomes were downloaded from IFIM, a database of Integrated Fitness Information for Microbial genes. Fitness scores in IFIM were predicted using Geptop based on orthology and phylogeny; the scores range from 0 to 1, with lower sc […]


Essentiality drives the orientation bias of bacterial genes in a continuous manner

Sci Rep
PMCID: 4642330
PMID: 26560889
DOI: 10.1038/srep16431

[…] y sequenced. The essential genes predicted by the Geptop have many features, such as higher codon bias, higher distribution bias and abundant protein–protein interactions, indicating its reliability. IFIM, which allows easy access to fitness data of microbial genes, contains data from 16 experiments and 2186 theoretical predictions. The highly significant correlation between the experimentally-der […]


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IFIM institution(s)
Center of Bioinformatics and Key Laboratory for NeuroInformation of the Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
IFIM funding source(s)
Program for New Century Excellent Talents in University (NCET-11-0059), the Fundamental Research Funds for the Central Universities of China (ZYGX2013J101) and the China Postdoctoral Science Foundation (201104687, 2013M540705)

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