Journal of Computer Science & Systems Biology

The chromosome is a carrier of genetic information. The number of somatic chromosomes in normal people is 23 pairs, and there have shapes and structures. It has been found that there are more than 100 kinds of chromosomal diseases caused by chromosomal abnormalities. Chromosome diseases can often cause miscarriage, congenital, congenital multiple malformations, cancer, etc. At present the research on chromosomal sequences has carried out. People have been looking for a suitable visualization model. In this type of visualization models, there is no problem of information degradation and data loss, and a complete chromosomal sequence distribution feature can be mapped. There are multiple sets of chromosomal sequences in species, and a comparative analysis is needed to find out some of the relationships between chromosomes in humans during evolution. In this paper, variant maps are used to illustrate the segmentation probability of the chromosome sequences of Homo sapiens and non-human primate species, distributions of different chromosomal sequence features are compared and analyzed by multiple two-dimension statistical probability maps.

Soil microorganisms affect global climate change, food security, soil ecosystem change, pollutant transformation, and the healthy development of human society. In this paper, the 16S rRNA gene sequence of soil microorganisms is visualized on the variation model. Using soil microorganisms as a two-dimensional statistical map list provides some clues for the study of soil microbial diversity.

Objective: Clinical exome sequencing produces between 90,000-100,00 variants per individual. Bottlenecks are manifested due to manual (operator based) interpretation of data. Given an increasing demand for genomic screening, automated computational methodologies are urgently required to meet both throughput and interpretation. Objective: determine if algorithms can be developed to identify and report specific pathogenic variants

Methods: Clinical exome sequencing was performed on 961 individuals presented for diagnostic analysis to King Fahad Medical City (KFMC). Variant Call Format (VCF 4.2) files from each patient were used for algorithm development. Perl (v5.28.1) was used as the construct language. 137 known pathogenic variants were used as a search test bed. A 10-step procedural workflow was implemented to automate the process of searching for targets. Where a positive identification was elicited, variants were annotated, merged with clinical data and output as a pdf report. Negative findings were output as a pdf report with clinical data onl .

Results: 961 VCF files were screened for 137 pathogenic variants of interest to KFMC. Target variants were compared against each variant within a patient’s VCF using logic operators. A total processing time including report production for 961 individuals was completed in 11.38 hours. 177 patients (18.4%) were positive for at least one variant and 15 patients had two variants (1.6%). All positive cases were verified manually in the originating VCF. The 137-target list of variants were “spiked” into a negative control patient VCF to act as a positive control (sensitivity). All variants were detected by the algorithm. 10 negative finding patients were chosen at random and manually checked for the absence (specificity) of the 137 variants. No variants were detected

Conclusion: Automated searching and production of reports for specific pathogenic variants using computational searching is feasible for diagnostic laboratories undertaking clinical exome sequencing.