Abstract: Embodiments provide for prediction and mitigation of network faults. Information associated network nodes may be compiled and used to generate network analytical records (NARs). A first model may be executed against the NARs to predict faults associated with one or more nodes of the network. Tickets are generated for predicted faults and stored in a ticket database. The tickets may be analyzed to predict executable actions to mitigate the faults associated with each ticket. To analyze the tickets, ticket data may be compiled and used to generate ticket analytical records (TARs). A second model may be executed against the TARs predict actions to resolve the predicted faults. The predicted actions may be executed to mitigate the impact that the faults have on the network, which may include preventing the faults entirely (e.g., via preventative maintenance) or minimizing the impact of the faults via use of the predicted actions.

Abstract: A targeted panel with low sample input requirements from a tumor only sample may be processed to estimate mutation load in a tumor sample. The method may include detecting variants in nucleic acid sequence reads corresponding to targeted locations in the tumor sample genome; annotating detected variants with an annotation information from a population database; filtering the detected variants, wherein the filtering rule set retains the somatic variants and removes germ-line variants; counting the identified somatic variants to give a number of somatic variants; determining a number of bases in covered regions of the targeted locations in the tumor sample genome; and calculating a number of somatic variants per megabase, provides an estimate of the mutation load per megabase in the tumor sample genome.

Abstract: A targeted panel with low sample input requirements from a tumor only sample may be processed to estimate mutation load in a tumor sample. The method may include: detecting variants in nucleic acid sequence reads corresponding to targeted locations in the tumor sample genome; annotating detected variants with an annotation information from a population database; filtering the detected variants, wherein the filtering rule set retains the somatic variants and removes germ-line variants; counting the identified somatic variants to give a number of somatic variants; determining a number of bases in covered regions of the targeted locations in the tumor sample genome; and calculating a number of somatic variants per megabase, provides an estimate of the mutation load per megabase in the tumor sample genome.

Abstract: A targeted panel with low sample input requirements from a tumor only sample may be processed to estimate mutation load in a tumor sample. The method may include: detecting variants in nucleic acid sequence reads corresponding to targeted locations in the tumor sample genome; annotating detected variants with an annotation information from a population database; filtering the detected variants, wherein the filtering retains the somatic variants and removes germline variants; calculating an initial TMB; and applying a calibration to the initial TMB level to produce a final TMB level for the mutation load of the tumor sample genome. The filtering may also include retaining nonsynonymous SNVs and indels for the analysis.

Abstract: A targeted panel with low sample input requirements from a tumor only sample may be processed to estimate mutation load in a tumor sample. The method, including detecting variants in nucleic acid sequence reads corresponding to targeted locations in the tumor sample genome; annotating detected variants with an annotation information from a population database; filtering the detected variants, wherein the filtering rule set retains the somatic variants and removes germ-line variants; counting the identified somatic variants to give a number of somatic variants; determining a number of bases in covered regions of the targeted locations in the tumor sample genome; and calculating a number of somatic variants per megabase, provides an estimate of the mutation load per megabase in the tumor sample genome.