Abstract: A method of compressive read mapping. A high-resolution homology table is created for the reference genomic sequence, preferably by mapping the reference to itself. Once the homology table is created, the reads are compressed to eliminate full or partial redundancies across reads in the dataset. Preferably, compression is achieved through self-mapping of the read dataset. Next, a coarse mapping from the compressed read data to the reference is performed. Each read link generated represents a cluster of substrings from one or more reads in the dataset and stores their differences from a locus in the reference. Preferably, read links are further expanded to obtain final mapping results through traversal of the homology table, and final mapping results are reported. As compared to prior techniques, substantial speed-up gains are achieved through the compressive read mapping technique due to efficient utilization of redundancy within read sequences as well as the reference.

Abstract: Computationally-efficient techniques facilitate secure pharmacological collaboration with respect to private drug target interaction (DTI) data. In one embodiment, a method begins by receiving, via a secret sharing protocol, observed DTI data from individual participating entities. A secure computation then is executed against the secretly-shared data to generate a pooled DTI dataset. For increased computational efficiency, at least a part of the computation is executed over dimensionality-reduced data. The resulting pooled DTI dataset is then used to train a neural network model. The model is then used to provide one or more DTI predictions that are then returned to the participating entities (or other interested parties).

Abstract: This disclosure provides for a highly-efficient and scalable compression tool that compresses quality scores, preferably by capitalizing on sequence redundancy. In one embodiment, compression is achieved by smoothing a large fraction of quality score values based on k-mer neighborhood of their corresponding positions in read sequences. The approach exploits the intuition that any divergent base in a k-mer likely corresponds to either a single-nucleotide polymorphism (SNP) or sequencing error; thus, a preferred approach is to only preserve quality scores for probable variant locations and compress quality scores of concordant bases, preferably by resetting them to a default value. By viewing individual read datasets through the lens of k-mer frequencies in a corpus of reads, the approach herein ensures that compression “lossiness” does not affect accuracy in a deleterious way.

Abstract: A method of compressive read mapping. A high-resolution homology table is created for the reference genomic sequence, preferably by mapping the reference to itself. Once the homology table is created, the reads are compressed to eliminate full or partial redundancies across reads in the dataset. Preferably, compression is achieved through self-mapping of the read dataset. Next, a coarse mapping from the compressed read data to the reference is performed. Each read link generated represents a cluster of substrings from one or more reads in the dataset and stores their differences from a locus in the reference. Preferably, read links are further expanded to obtain final mapping results through traversal of the homology table, and final mapping results are reported. As compared to prior techniques, substantial speed-up gains are achieved through the compressive read mapping technique due to efficient utilization of redundancy within read sequences as well as the reference.

Abstract: This disclosure provides for a highly-efficient and scalable compression tool that compresses quality scores, preferably by capitalizing on sequence redundancy. In one embodiment, compression is achieved by smoothing a large fraction of quality score values based on k-mer neighborhood of their corresponding positions in read sequences. The approach exploits the intuition that any divergent base in a k-mer likely corresponds to either a single-nucleotide polymorphism (SNP) or sequencing error; thus, a preferred approach is to only preserve quality scores for probable variant locations and compress quality scores of concordant bases, preferably by resetting them to a default value. By viewing individual read datasets through the lens of k-mer frequencies in a corpus of reads, the approach herein ensures that compression “lossiness” does not affect accuracy in a deleterious way.

Abstract: Computationally-efficient techniques facilitate secure crowdsourcing of genomic and phenotypic data, e.g., for large-scale association studies. In one embodiment, a method begins by receiving, via a secret sharing protocol, genomic and phenotypic data of individual study participants. Another data set, comprising results of pre-computation over random number data, e.g., mutually independent and uniformly-distributed random numbers and results of calculations over those random numbers, is also received via secret sharing. A secure computation then is executed against the secretly-shared genomic and phenotypic data, using the secretly-shared results of the pre-computation over random number data, to generate a set of genome-wide association study (GWAS) statistics. For increased computational efficiency, at least a part of the computation is executed over dimensionality-reduced genomic data.

Abstract: The redundancy in genomic sequence data is exploited by compressing sequence data in such a way as to allow direct computation on the compressed data using methods that are referred to herein as “compressive” algorithms. This approach reduces the task of computing on many similar genomes to only slightly more than that of operating on just one. In this approach, the redundancy among genomes is translated into computational acceleration by storing genomes in a compressed format that respects the structure of similarities and differences important to analysis. Specifically, these differences are the nucleotide substitutions, insertions, deletions, and rearrangements introduced by evolution. Once such a compressed library has been created, analysis is performed on it in time proportional to its compressed size, rather than having to reconstruct the full data set every time one wishes to query it.

Abstract: The redundancy in genomic sequence data is exploited by compressing sequence data in such a way as to allow direct computation on the compressed data using methods that are referred to herein as “compressive” algorithms. This approach reduces the task of computing on many similar genomes to only slightly more than that of operating on just one. In this approach, the redundancy among genomes is translated into computational acceleration by storing genomes in a compressed format that respects the structure of similarities and differences important to analysis. Specifically, these differences are the nucleotide substitutions, insertions, deletions, and rearrangements introduced by evolution. Once such a compressed library has been created, analysis is performed on it in time proportional to its compressed size, rather than having to reconstruct the full data set every time one wishes to query it.

Abstract: This disclosure provides for a highly-efficient and scalable compression tool that compresses quality scores, preferably by capitalizing on sequence redundancy. In one embodiment, compression is achieved by smoothing a large fraction of quality score values based on k-mer neighborhood of their corresponding positions in read sequences. The approach exploits the intuition that any divergent base in a k-mer likely corresponds to either a single-nucleotide polymorphism (SNP) or sequencing error; thus, a preferred approach is to only preserve quality scores for probable variant locations and compress quality scores of concordant bases, preferably by resetting them to a default value. By viewing individual read datasets through the lens of k-mer frequencies in a corpus of reads, the approach herein ensures that compression “lossiness” does not affect accuracy in a deleterious way.

Abstract: A method of compressive read mapping. A high-resolution homology table is created for the reference genomic sequence, preferably by mapping the reference to itself. Once the homology table is created, the reads are compressed to eliminate full or partial redundancies across reads in the dataset. Preferably, compression is achieved through self-mapping of the read dataset. Next, a coarse mapping from the compressed read data to the reference is performed. Each read link generated represents a cluster of substrings from one or more reads in the dataset and stores their differences from a locus in the reference. Preferably, read links are further expanded to obtain final mapping results through traversal of the homology table, and final mapping results are reported. As compared to prior techniques, substantial speed-up gains are achieved through the compressive read mapping technique due to efficient utilization of redundancy within read sequences as well as the reference.

Abstract: A similarity measure is computed between nodes of first and second networks. Sets of pairwise scores are computed to find nodes in the individual networks that are good matches to one another. A pairwise score is computed for a node i in the first network and a node j in the second network. Similar pairwise scores are computed for each of the nodes in each network. The process identifies node pairs that exhibit high pairwise values. Preferably, nodes i and j are a good match if their neighbors are a good match. This technique produces a measure of network similarity. If node feature data is available, nodes i and j are considered a good match if their neighbors are a good match (network similarity) and their node features are a good match (node similarity). Using the similarity scores, a common subgraph between the first and second networks is computed.

Abstract: A scalable infrastructure for searching multiple disparate textual databases by mapping their contents onto a structured ontology, e.g., of medical concepts. This framework can be leveraged against any database where free-text attributes are used to describe the constituent records.

Abstract: A similarity measure is computed between nodes of first and second networks. Sets of pairwise scores are computed to find nodes in the individual networks that are good matches to one another. A pairwise score is computed for a node i in the first network and a node j in the second network. Similar pairwise scores are computed for each of the nodes in each network. The process identifies node pairs that exhibit high pairwise values. Preferably, nodes i and j are a good match if their neighbors are a good match. This technique produces a measure of network similarity. If node feature data is available, nodes i and j are considered a good match if their neighbors are a good match (network similarity) and their node features are a good match (node similarity). Using the similarity scores, a common subgraph between the first and second networks is computed.

Abstract: A scalable infrastructure for searching multiple disparate textual databases by mapping their contents onto a structured ontology, e.g., of medical concepts. This framework can be leveraged against any database where free-text attributes are used to describe the constituent records.

Abstract: A genomic read dataset is mapped from multiple individuals to a reference genome in a time- and storage-efficient manner. The approach begins by building a set of data structures that collectively represents a knowledge base of similarity information. The knowledge base comprises a set of data structures that, when combined, intrinsically represent all reads to whole-reference match (similarity) information for a reference genome. After this knowledge base is generated, it is then accessed and used in a mapping decision layer. The mapping layer taps into the similarity knowledge within the set of data structures to decide on the mappings and report them, thereby avoiding redundant and unnecessary computations that would otherwise be necessary to find matches and report mappings for each read individually. The approach exploits the redundancy in the read datasets to enable significant speed-up of the sequence matching layer, which preferably is performed collectively for all reads.

Abstract: A method of computing a measure of similarity between nodes of first and second networks is described. In particular, sets of pairwise scores are computed to find nodes in the individual networks that are good matches to one another. Thus, a pairwise score, referred to as Rij, is computed for a node i in the first network and a node j in the second network. Similar pairwise scores are computed for each of the nodes in each network. The goal of this process is to identify node pairs that exhibit high Rij values. According to the technique described herein, the intuition is that nodes i and j are a good match if their neighbors are a good match. This technique produces a measure of “network similarity.” If node feature data also is available, the intuition may be expanded such that nodes i and j are considered a good match if their neighbors are a good match (network similarity) and their node features are a good match (node similarity). Node feature data typically is domain-specific.

Abstract: The redundancy in genomic sequence data is exploited by compressing sequence data in such a way as to allow direct computation on the compressed data using methods that are referred to herein as “compressive” algorithms. This approach reduces the task of computing on many similar genomes to only slightly more than that of operating on just one. In this approach, the redundancy among genomes is translated into computational acceleration by storing genomes in a compressed format that respects the structure of similarities and differences important to analysis. Specifically, these differences are the nucleotide substitutions, insertions, deletions, and rearrangements introduced by evolution. Once such a compressed library has been created, analysis is performed on it in time proportional to its compressed size, rather than having to reconstruct the full data set every time one wishes to query it.

Abstract: The present invention generally relates to engineered bacteriophages which express amyloid peptides for the modulation (e.g. increase or decrease) of protein aggregates and amyloid formation. In some embodiments, the engineered bacteriophages express anti-amyloid peptides for inhibiting protein aggregation and amyloid formation, which can be useful in the treatment and prevention of and bacterial infections and biofilms. In some embodiments, the engineered bacteriophages express amyloid peptides for promoting amyloid formation, which are useful for increasing amyloid formation such as promoting bacterial biofilms. Other aspects relate to methods to inhibit bacteria biofilms, and methods for the treatment of amyloid related disorders, e.g., Alzheimer's disease using an anti-amyloid peptide engineered bacteriophages. Other aspects of the invention relate to engineered bacteriophages to express the amyloid peptides on the bacteriophage surface and/or secrete the amyloid peptides, e.g.

Abstract: A method of computing a measure of similarity between nodes of first and second networks is described. In particular, sets of pairwise scores are computed to find nodes in the individual networks that are good matches to one another. Thus, a pairwise score, referred to as Rij, is computed for a node i in the first network and a node j in the second network. Similar pairwise scores are computed for each of the nodes in each network. The goal of this process is to identify node pairs that exhibit high Rij values. According to the technique described herein, the intuition is that nodes i and j are a good match if their neighbors are a good match. This technique produces a measure of “network similarity.” If node feature data also is available, the intuition may be expanded such that nodes i and j are considered a good match if their neighbors are a good match (network similarity) and their node features are a good match (node similarity). Node feature data typically is domain-specific.

Abstract: A method of computing a measure of similarity between nodes of first and second networks is described. In particular, sets of pairwise scores are computed to find nodes in the individual networks that are good matches to one another. Thus, a pairwise score, referred to as Rij, is computed for a node i in the first network and a node j in the second network. Similar pairwise scores are computed for each of the nodes in each network. The goal of this process is to identify node pairs that exhibit high Rij values. According to the technique described herein, the intuition is that nodes i and j are a good match if their neighbors are a good match. This technique produces a measure of “network similarity.” If node feature data also is available, the intuition may be expanded such that nodes i and j are considered a good match if their neighbors are a good match (network similarity) and their node features are a good match (node similarity). Node feature data typically is domain-specific.