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We presented vipR, a tool for variant detection in DNA pools that uses sequence information from multiple sequenced DNA pools in parallel for improving the sensitivity of variant detection.
The Pfam database, utilizing multiple sequence alignments and hidden Markov models (HMMs), uses sequence similarity to formulate protein family classifications [16] and serves as a useful tool for exploiting these relationships.
AODV uses sequence numbers as freshness indicator and loop-free guarantee [8].
AODV uses sequence numbers to determine the timeliness of each packet and to prevent loops.
Molecular phylogenetics uses sequence data to infer these relationships for both organisms and the genes they maintain.
Also for scientific workflows, [33] proposes a recommendation service that uses sequence mining, a data mining technique for finding frequent sequential events in a dataset.
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In our real data analysis, we used PolyPhen [19] which uses sequence-, structure-, and annotation-based information as input.
It has been described that TANGO is a good predictor of aggregation in solution, as it uses sequence-derived structural parameters to forecast aggregation [26].
At similar run-time, it aligns low sequence identity instances substantially more accurate than RAF, which uses sequence-based heuristics.
This construct uses sequence-specific hybridization and directs the dimerization of two S1 hosts for the near-infrared cluster.
PolyPhen-2 uses sequence-based predictive features in light of tertiary structures of proteins to predict the functional significance of nonsynonymous amino acid substitutions.
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