Thirty‐four sorghum (S. bicolor (L.) Moench) landraces consisting of 1020 individual plants (30 plants of each landrace) collected from five agroecological sites (ecosites) in North Shewa and South Welo regions of Ethiopia (Bati 8, Fontenina 5, Haik 2, Layignaw ataye 17 and Merewa Adere 2) were classified on the basis of 4 classifying variables: administrational zones; Woredas (smallest administrative unit), ecosite of origin and altitude. Morphological variation for the fourteen qualitative characters that showed two or more phenotypic classes were estimated using the Shanon–Weaver diversity index (H′). Phenotypic variation was found between and within each classifying variables. The value of H′ for all landraces varied from 0.32 to 0.98 with an overall mean of 0.77±0.04. One‐way analysis of variance (ANOVA) showed significant differences between characters within all classifying variables and this contributed to the largest portion of the total variance. Cluster analysis based on ordinal variables grouped the landraces into five clusters. A higher proportion of landraces sharing similar altitude classes and similar ecosites were grouped together. Panicle compactness and shape as well as stalk juiciness were the predominant characters in grouping the landraces into their respective clusters. Panicle compactness and shape also contributed relatively more to altitudinal and ecological differentiation. This differential distribution of landraces with panicle types with respect to compactness and shape revealed the adaptive significance of panicle compactness and shape that reflected the patterns of distribution of different races in north Shewa and south Welo. χ2 test of all characters did not show significant differences between the frequencies of observed and expected characters.

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Bioinformatics analysis tools can enhance drug target identification and drug candidate screening and refinement and identify and condoling the mutant gene for antibiotic resistance. Identification of mutant genes for antibiotics resistance using bioinformatics tools is vital for understanding mechanisms and monitoring of mutant genes for antibiotics resistance. Antibiotic resistance has contributed immensely to the continuously growing concerns about the ineffective treatment against microbial infections. The bioinformatics tools play a key role in identifying and monitoring mutant genes for antibiotic resistance. Whole-genome sequencing is also useful to identify trends in antibiotic resistance, targeting the bacteria that are phenotypically sensitive but genotypically positive for a mutant gene for antibiotics resistance. The development and implementation of certain technologies such as whole-genome sequencing and, therefore the creation of national and international databases allowed bioinformatics to study the mutant gene for antibiotics resistance that allows fast, simplified, and accurate identification of the mutant gene for antimicrobial resistance. A web-based method, ResFinder uses BLAST for the identification of the mutant gene for antimicrobial resistance genes in whole-genome data. As input, the method can use both pre-assembled, complete, and partial genomes, and short sequence reads from four different sequencing platforms. A web server providing a convenient way of identifying mutant genes of antibiotic resistance in completely sequenced isolates was assigned. Therefore, Whole-genome sequencing-based bioinformatics tools (ResFinder (read-based), and Typewriter (BLAST-based) in terms of identification of the presence or absence of the mutant gene for antibiotics' resistance can be accessed at NCBI.