Abstract: An example computer-implemented genotyping method includes identifying a locus of interest from a reference sequence; and obtaining sequencing data collected from a target subject. The sequencing data collected from the target subject includes a plurality of sequencing reads collected from the target subject using a hardware sequencing machine. The method further includes: aligning each sequencing read in the plurality of sequencing reads collected from the target subject based on the locus of interest identified from the reference sequence to produce a plurality of aligned sequencing reads; obtaining genotype data from a plurality of baseline subjects, wherein the plurality of baseline subjects are subjects different from the target subject; and identifying a genotype for the target subject at the locus of interest identified from the reference sequence based on (1) the plurality of aligned sequencing reads and (2) the genotype data obtained from a plurality of baseline subjects.

Abstract: Base calls for a target sequence may be identified relative to a reference sequence by using values from sequencing reads at locations satisfying a high-confidence condition to identify base calls at a given location not satisfying the high-confidence condition. The high-confidence condition may relate to the level of coverage by the sequencing reads at a location of the reference sequence. The quality of measurements of the sequencing reads may be incorporated into the base-call process.

Abstract: Base calls for a target sequence may be identified relative to a reference sequence by using values from sequencing reads at locations satisfying a high-confidence condition to identify base calls at a given location not satisfying the high-confidence condition. The high-confidence condition may relate to the level of coverage by the sequencing reads at a location of the reference sequence. The quality of measurements of the sequencing reads may be incorporated into the base-call process.

Abstract: Base calls for a target sequence may be identified relative to a reference sequence by using values from sequencing reads at locations satisfying a high-confidence condition to identify base calls at a given location not satisfying the high-confidence condition. The high-confidence condition may relate to the level of coverage by the sequencing reads at a location of the reference sequence. The quality of measurements of the sequencing reads may be incorporated into the base-call process.

Abstract: Sequencing reads from a measurement system may be classified based on quality scores associated with the measurement system, and corresponding error characteristics may be provided. The sequencing reads may correspond to at least one of deoxyribonucleic acid (DNA), complementary DNA (cDNA), or ribonucleic acid (RNA).

Abstract: Base calls for a target sequence may be identified relative to a reference sequence by using values from sequencing reads at locations satisfying a high-confidence condition to identify base calls at a given location not satisfying the high-confidence condition. The high-confidence condition may relate to the level of coverage by the sequencing reads at a location of the reference sequence. The quality of measurements of the sequencing reads may be incorporated into the base-call process.

Abstract: Our research conducted with the genome sequences of more than 250 species of organisms (including viral, microbial, and multi-cellular organisms, and human) results in the discovery that the occurrence of a particular subsequence (the so-called “motifs” or “n-mers,” (n being the length of the subsequences), which can be up to 25 and higher) in the genome of a particular species can be considered as a nearly random event; and that the occurrences of a particular subsequence in the genome sequences of different species can be considered as nearly independent events (with the exception of the cases where extremely closely related species are compared). The set of subsequences that occur in a particular species' genome can therefore be used as a genomic “fingerprint” of this species. This discovery leads to the concept of utilizing a set of pseudo-randomly designed subsequences for species identification or discrimination.