Abstract: Portable biosurveillance kits for sequencing and sample identification are provided, and techniques for configuring said kits are provided. A system for configuring a kit may receive data representing a first and second set of nucleic acid sequences, and may generate and store first and second indexes representing the respective sets. The system may then use the indexes to identify conserved-signature sequences that satisfy abundance criteria with respect to the first set and sparsity criteria with respect to the second set. The identified conserved-signature sequences may be stored on (or represented in storage on) a portable sequencing and sample-identification kit, which may compare the conserved-signature sequence to a sample sequence in order to identify the sample sequence.

Abstract: A portable kit for nucleic acid sequencing is disclosed, the kit including a DNA extraction system configured to perform a DNA extraction protocol, a DNA sequencing preparation system configured to perform a DNA sequencing preparation protocol, a sequencer system, and a portable enclosure configured to house the DNA extraction system, the sequencer preparation system, and the sequencer system.

Abstract: Techniques for identifying and/or classifying genomic information are provided. In some embodiments, genomic information may be identified by computing systems without access to a database of reference genomic information, instead relying on locally stored probabilistic data structures representing reference genomic information. Query genomic data, such as data taken from a read-set, may be divided into sub-strings, and each of the locally-stored probabilistic data structures may be queried by each of the extracted sub-strings, generating probabilistic outputs indicating either that (a) the sub-string is probably included in the set of data represented by the probabilistic data structure or (b) the sub-string is definitely not included in the set of data. Based on the number and/or proportion of sub-strings from a read-set that are indicated as being likely represented by a probabilistic data structure, a likely identity or classification for the genomic information in the read-set may be determined.

Abstract: Techniques for identifying and/or classifying genomic information are provided. In some embodiments, genomic information may be identified by computing systems without access to a database of reference genomic information, instead relying on locally stored probabilistic data structures representing reference genomic information. Query genomic data, such as data taken from a read-set, may be divided into sub-strings, and each of the locally-stored probabilistic data structures may be queried by each of the extracted sub-strings, generating probabilistic outputs indicating either that (a) the sub-string is probably included in the set of data represented by the probabilistic data structure or (b) the sub-string is definitely not included in the set of data. Based on the number and/or proportion of sub-strings from a read-set that are indicated as being likely represented by a probabilistic data structure, a likely identity or classification for the genomic information in the read-set may be determined.