{"title":"与智力残疾相关的基因编码区新型错义变异的计算鉴定","authors":"Anna Liu, Junmeng Yang, Tina Yuan, Yongsheng Bai","doi":"10.17706/IJBBB.2021.11.2.22-33","DOIUrl":null,"url":null,"abstract":"Single nucleotide variants (SNVs) are mutations in the DNA sequence of a gene that are responsible for a variety of genetic disorders. Exonic variants located in the coding region of a gene can be especially harmful to an organism when they alter the biological functions of proteins. In this study, we performed multiple functional annotations and sequence alignments to assess the impact of SNVs in coding regions of genes associated with intellectual disability (ID). Through bioinformatics analysis, we identified three significant motifs in the coding region of ACTB, PTPN11, and GNAO1 prioritized by our pipeline. Detailed examination of these three genes revealed that eight variant coordinates were located in the three motifs, two of which from ACTB are reported to be pathogenic by MISCAST and three from GNAO1 are reported likely pathogenic according to clinical significance from National Center for Biotechnology Information (NCBI). In particular, we discovered two novel SNVs located at chr16:56370698 and chr16:56370711 in GNAO1 that are highly likely to be pathogenic and possibly associated with ID. Overall, our results revealed the pathogenic roles of SNVs within coding regions of ID candidate genes. Future work is to automate this process through developing a bioinformatics software that can identify novel SNVs in coding regions responsible for various human diseases.","PeriodicalId":13816,"journal":{"name":"International Journal of Bioscience, Biochemistry and Bioinformatics","volume":"37 1","pages":"22-33"},"PeriodicalIF":0.0000,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Computational Identification of Novel Missense Variants in Coding Regions of Genes Associated with Intellectual Disability\",\"authors\":\"Anna Liu, Junmeng Yang, Tina Yuan, Yongsheng Bai\",\"doi\":\"10.17706/IJBBB.2021.11.2.22-33\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Single nucleotide variants (SNVs) are mutations in the DNA sequence of a gene that are responsible for a variety of genetic disorders. Exonic variants located in the coding region of a gene can be especially harmful to an organism when they alter the biological functions of proteins. In this study, we performed multiple functional annotations and sequence alignments to assess the impact of SNVs in coding regions of genes associated with intellectual disability (ID). Through bioinformatics analysis, we identified three significant motifs in the coding region of ACTB, PTPN11, and GNAO1 prioritized by our pipeline. Detailed examination of these three genes revealed that eight variant coordinates were located in the three motifs, two of which from ACTB are reported to be pathogenic by MISCAST and three from GNAO1 are reported likely pathogenic according to clinical significance from National Center for Biotechnology Information (NCBI). In particular, we discovered two novel SNVs located at chr16:56370698 and chr16:56370711 in GNAO1 that are highly likely to be pathogenic and possibly associated with ID. Overall, our results revealed the pathogenic roles of SNVs within coding regions of ID candidate genes. Future work is to automate this process through developing a bioinformatics software that can identify novel SNVs in coding regions responsible for various human diseases.\",\"PeriodicalId\":13816,\"journal\":{\"name\":\"International Journal of Bioscience, Biochemistry and Bioinformatics\",\"volume\":\"37 1\",\"pages\":\"22-33\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Bioscience, Biochemistry and Bioinformatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.17706/IJBBB.2021.11.2.22-33\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Bioscience, Biochemistry and Bioinformatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.17706/IJBBB.2021.11.2.22-33","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Computational Identification of Novel Missense Variants in Coding Regions of Genes Associated with Intellectual Disability
Single nucleotide variants (SNVs) are mutations in the DNA sequence of a gene that are responsible for a variety of genetic disorders. Exonic variants located in the coding region of a gene can be especially harmful to an organism when they alter the biological functions of proteins. In this study, we performed multiple functional annotations and sequence alignments to assess the impact of SNVs in coding regions of genes associated with intellectual disability (ID). Through bioinformatics analysis, we identified three significant motifs in the coding region of ACTB, PTPN11, and GNAO1 prioritized by our pipeline. Detailed examination of these three genes revealed that eight variant coordinates were located in the three motifs, two of which from ACTB are reported to be pathogenic by MISCAST and three from GNAO1 are reported likely pathogenic according to clinical significance from National Center for Biotechnology Information (NCBI). In particular, we discovered two novel SNVs located at chr16:56370698 and chr16:56370711 in GNAO1 that are highly likely to be pathogenic and possibly associated with ID. Overall, our results revealed the pathogenic roles of SNVs within coding regions of ID candidate genes. Future work is to automate this process through developing a bioinformatics software that can identify novel SNVs in coding regions responsible for various human diseases.