Next-generation sequencing (NGS) serves is the latest and most efficient way for researchers to solve the genetic problem so far. Instead of seeking the specific mutations for disorders, the whole genomic sequencing allows clinical researchers to inspect full information from genes of each individual to find possible mutations for disorders. As the cost of DNA sequencing continues to drop, the current emphasis is on how to quickly identify valuable variants from the millions of base pairs.
AnnSEQ is a genomics analysis platform with the user-friendly interface, flexible analysis parameters, and reliable information sources. The uploaded sample will be automatically annotated with more than 50 genomic databases, and all analysis result will be shown by the well-design statistical chart. Through these features, you can easily identify the genetic markers valuable for your research.
AnnoSEQ provides multiple analysis modes, including single, case-control, and trio family for different experimental design. The Trio analysis is also divided into four types according to inheritance patterns (autosomal dominant, autosomal & compound recessive, X-linked dominant, and X-linked recessive). You can apply different analysis patterns on one sample and view from different perspectives.
DNArails Genomics Platform provides two versions (GRCh37/hg19 and GRCh38) databases for investigators to annotate genetic variation data. More than 50 databases (including Taiwan Biobank) will regularly update and give you the latest genomics associated research information.
The distribution of genetic variations and analysis results presented in clear charts on AnnoSEQ. Analysis results include cancer-related, disease-related and functional prediction, and you can find the variants which you are interested in by applying your own filter parameters into flexible filter function.
Not only provides different function predict algorithm, DNArails also uses machine learning algorithm to optimize the self-developing protein loss-of-function prediction model called Dr. Score. Dr. Score provides an exceptional performance about evaluating the pathogenic degree of nonsynonymous variants. Used two independent test datasets (PMC4375422) to evaluate the accuracy of this model, and the results show that Dr. Score outperforms (ACC = 90.8%) existing methods in predicting pathogenic.