WGS和WES测序和分析会产生大量的variant数据。 显然直接分析全部的variant是非常不靠谱的。 做疾病的话,有一些常用的过滤套路。 variant作用于基因表达主要分两大类: 1. coding,可以直接影响RNA的形成,以及后面蛋白的折叠组装; 2. non-coding,现在最流行的就是enhancer这个媒介,已经有比较好的
Table 1. Summary data of noncoding variants with modifying effect in neurodegenerative diseases discussed in this reviewa Empty Cell Empty Cell Empty CellGenetic association Empty Cell Empty Cell SNP identifierLocationNBDOROriginFunctional interpretationEffect rs405509 APOE promoter AD 2.4 [1.1–5.8]b ...
The majority of studies aiming to investigate the effect of rare variants share the common approach of using a test that collapses the effects of multiple genetic variants. It is also common that a cutoff for minor allele frequency (MAF)12,16is used, sometimes limiting the analysis to only ve...
The overall role of non-coding variants in tumorigenesis is currently likely underestimated as only a handful of genome-wide studies of tumours have analysed them. However, current and future efforts involving large-scale whole-genome sequencing of tumours are likely to shed more light on the impor...
This is in part because SNPs have considerable effects on protein function and gene expression when they occur in coding regions and regulatory sequences, respectively. Therefore, a tool that can help users to obtain the allele frequency for a corresponding transcript is the need of the day. ...
Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat. Protoc. 4, 1073–1081 (2009). Article CAS Google Scholar Adzhubei, I. & Jordan, D. M. Predicting functional effect of human missense mutations using PolyPhen-2. Curr. Protoc. Hum. ...
Unraveling functional noncoding variants associated with complex diseases is still a great challenge. We present a novel algorithm, Prioritization And Functional Assessment (PAFA), that prioritizes and assesses the functionality of genetic variants by in
Thus, determining the effect of these multiple variant SNPs on target transcript levels has been a major challenge. Here, we provide evidence that for six common autoimmune disorders (rheumatoid arthritis, Crohn's disease, celiac disease, multiple sclerosis, lupus, and ulcerative colitis), the GWAS...
The two main categories of such tools include those that predict whether a missense change is damaging to the resultant protein function or structure and those that predict whether there is an effect on splicing (Table 2). Newer tools are beginning to address additional noncoding sequences.13 ...
objectvariant=variants[0]# groupby_gene_name returns a dictionary whose keys are gene names# and whose values are themselves VariantCollectionsgene_groups=variants.groupby_gene_name()# get variants which affect the TP53 geneTP53_variants=gene_groups["TP53"]# predict protein coding effect of every...