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Pileup format is a text-based format for summarizing the base calls of aligned reads to a reference sequence. This format facilitates visual display of SNP/indel calling and alignment.
SNPs are currently the marker of choice due to their large numbers in virtually all populations of individuals. The location of these biomarkers can be tremendously important in terms of predicting functional significance, genetic mapping and population genetics. [3] Each SNP represents a nucleotide change between two individuals at a defined ...
The next step is to identify SNPs from aligned tags and score all discovered SNPs for various coverage, depth and genotypic statistics. Once a large-scale, species-wide SNP production has been run, it is possible to quickly call known SNPs in newly sequenced samples. [8]
The calculation of prior probabilities depends on available data from the genome being studied, and the type of analysis being performed. For studies where good reference data containing frequencies of known mutations is available (for example, in studying human genome data), these known frequencies of genotypes in the population can be used to estimate priors.
The SNP sites that partition the haplotypes into the same group are called redundant sites. The SNP sites which contain distinct information within a block are called non-redundant sites (NRS). In order to further compress the haplotype matrix, the algorithm needs to find the tag SNPs such that all haplotypes of the matrix can be distinguished.
The upper DNA molecule differs from the lower DNA molecule at a single base-pair location (a G/A polymorphism) In genetics and bioinformatics, a single-nucleotide polymorphism (SNP / s n ɪ p /; plural SNPs / s n ɪ p s /) is a germline substitution of a single nucleotide at a specific position in the genome.
Nucleotide sequence analyses identify functional elements like protein binding sites, uncover genetic variations like SNPs, study gene expression patterns, and understand the genetic basis of traits. It helps to understand mechanisms that contribute to processes like replication and transcription. Some of the tasks involved are outlined below.
For example, the algorithm QualComp [9] performs lossy compression with a rate (number of bits per quality value) specified by the user. Based on rate-distortion theory results, it allocates the number of bits so as to minimize the MSE (mean squared error) between the original (uncompressed) and the reconstructed (after compression) quality values.