BSPAT

[…] uencing. Newer tools such as Bismark [] and BS-Seeker [] have been utilized more efficiently and can handle larger datasets generated by NGS technologies. In addition, a web application service named bisulfite sequencing pattern analysis tool (BSPAT) uses Bismark’s read alignments and methylation calling functionalities to provide further quality control, co-occurrence pattern analysis, simple all […]

[…] er out sites with coverage, calculate the average methylation level and generate informative plots. Table  lists 5 post-alignment analysis tools, and each of these tools has specific functions, e.g., BSPAT can detect allele-specific methylation [], SAAP-RRBS can extract the annotation of each C [] and MethGo can convert context methylation levels into average and genome-wide plots, as well as extr […]

[…] f our dataset.Convolution prior was adopted for φ[i] and ν[j,i] [], denoting they both may be decomposed into the sum of unstructured (e.g. ush, ssh) and spatial structured random effects (e.g. bind, bspat):7φi=ushi+sshi8υji=bindji+bspatjiThen ush and bind were all assigned Normal prior with their mean zero and α0[j] respectively, while that of ssh, bspat and β[i] were Conditional Autoregressive ( […]

[…] y: ε jm = 2ε j,m-1+ε j,m-2+u j.m where m = 3,4,…,d and uj.m~N(0,τj2) are random errors. The spatial effect was modelled by the tensor product of two dimensional p-spline defined as fspat(x1,x2)=∑ik∑jkBspat, ijB1i(x1)B2j(x2)(4) where (x 1,x 2) refers to the coordinates of the location of the data point, latitude and longitude, or location centroids based on the map. Note that f spat(x 1,x 2) repres […]

[…] εj,m = 2εj,m − 1 + εj,m − 2 + uj,m where m = 3, 4, …, d and uj.m~N0,τj2 are random errors. The spatial effect was modelled by the tensor product of two dimensional spline defined as2fspatx1,x2=∑ik∑jkBspat,ijB1ix1B2jx2where (x1, x2) refers to the coordinates of the location of the data point, latitude and longitude, or location centroids based on the map. The prior for Bspat, ij = (Bspat,11, Bspat […]

[…] graphic (spatial) variability known as the sill and ρ^=maxij‖si-sj‖/0.001 is the empirically estimated rate of spatial correlation decay (). Multivariate Gaussian random field (GRF) priors fspat(s)=p(βspat|σspat2)∝|∑|-12exp-1σspat2(βspat)′∑-1βspat, where βspat is the vector of household specific structured spatial coefficients, whereas the non-structured utilize the independent and identically dis […]