(searched for: doi:10.36462/h.biosci.20193)
Journal of Applied Genetics pp 1-15; https://doi.org/10.1007/s13353-021-00626-3
The goal of this research was to develop a new genetic database of simple sequence repetition (SSR) primers for faba and classify them according to their target genes and respective biological processes. Approximately 75,605 and 148,196 previously published genomic and transcriptomic faba sequences, respectively, have been used to detect possible SSRs. The numbers of identified SSRs from each dataset were 25,502 and 12,319, respectively. The distribution of different repeat classes indicated that trinucleotides represent the largest number of repeat counts, followed by dinucleotides. The extracted genic SSR sequences were used to design 1091 polymerase chain reaction (PCR) primers, of which only 238 (21.8%) primers target genomic sequences and the other 853 PCR primers targeted transcriptomic sequences. The annotation of gene-targeted SSRs showed that approximately 897 genes were targeted by our SSR primers. Approximately 1890 gene ontology (GO) identification codes have been obtained. The GO keywords were distributed among distinct molecular cell features. The highest redundancies involved 554 technical words, 196 domains, and 160 molecular feature phrases. These GO codes belonged to the general level of GO and included molecular function, cellular component, and biological process (544, 670, and 676 GOs, respectively). Twenty-seven SSR PCR primers were synthesized to 12 Egyptian faba bean genotypes. Approximately 11 SSR provided one to two PCR bands, whereas other SSRs provided only one sharp band with polymorphic band size. There were 13 polymorphic primers. The polymorphism information content was 0.3, which implied moderate informativeness.
Highlights in BioScience; https://doi.org/10.36462/h.biosci.20195
Extracting gene data from the human genome is a tricky task. Gene name is the key information for harvesting its sequence, annotation, and other related data. Unfortunately, most human genes have different and multiple names, depending on the database and the resource in which they have been published. Such an issue is delaying the ability of researchers to gather the necessary knowledge and to build their opinion on the function of genes. Here we introduce GeneSyno, a simple, versatile, and reliable tool that can be used to extract gene information from human genome data even though it is synonymous gene names. GeneSyno was written using C and Python programming languages and could easily be integrated into another pipeline