Yonathan Tilahun Editor at IgMin Research
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Biography
In the past, provided research-based, informal educational programs in the area of biotechnology, Elements of Crop Science and Elements of Soil Science. Focus on agriculture, family, and community development, environment, 4-H youth development, energy conservation and management, especially in the area of aquaponic resources.
Conduct bioinformatic driven data analysis, utilizing open source Linux/Unix operating systems in combination with a pipeline of suites such as Tuxedo (tophat, bowtie, cufflinks, etc.,), BLAST2GO, Qlik, and Golden Helix. Past data analysis has comprised of genome-wide association studies (GWAS), genomics and functional genomic data. Current areas of study include metagenomics, and meta-transcriptomics.
The completions of studies have resulted in publications in peer-reviewed journals. Performance includes design and implementation of hands-on laboratory instruction, aiding in recruitment and retention of students, and assisted in serving as a mentor. Research in biotechnology continues through the utilization of bioinformatic tools and also as an academic and professional advisor for students.
Research Interest
Genomics, transcriptomics, metagenomics
Editor
Work Details
Assistant Professor
Langston University
Sherman Lewis School of Agriculture and Applied Science
United States
Contribution by Topic Area
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Research Articles
- Lunar-Derived Propellants for Fueling Mars-Bound Spacecraft in Cis-Lunar Space
- Effect of Rainfall on Water Parameters in Recreational Lakes in Heidelberg, Germany
- Examining the Causal Connection between Lipid-lowering Medications and Malignant Meningiomas through Drug-target Mendelian Randomization Analysis
- The Influence of Dynamical Downscaling and Boundary Layer Selection on Egypt’s Potential Evapotranspiration using a Calibrated Version of the Hargreaves-samani Equation: RegCM4 Approach
- Prevalence of Non-specific Low Back Pain Among Chinese Healthcare Workers (Surgeons and Surgical Nurses): A Multi-Center Survey Study
- Deep Learning-based Multi-class Three-dimensional (3-D) Object Classification using Phase-only Digital Holographic Information
