Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Design and selection of genetic targets for sequence resolved organism detection and identification

A sequence and biological technology, applied in the field of resequencing microarray design, can solve problems such as increasing computational requirements

Inactive Publication Date: 2009-09-16
THE UNITED STATES OF AMERICA AS REPRESENTED BY THE SECRETARY OF THE NAVY
View PDF5 Cites 3 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, this increased detail in the model comes at the cost of an equally increased computational requirement

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Design and selection of genetic targets for sequence resolved organism detection and identification
  • Design and selection of genetic targets for sequence resolved organism detection and identification
  • Design and selection of genetic targets for sequence resolved organism detection and identification

Examples

Experimental program
Comparison scheme
Effect test

example 1

[0035] Example 1: Predicting Primer Interference - The first experimental use of the model algorithm was to understand the base calls that occurred in 42 microarray experiments with blank samples (no nucleic acid added) using the new primer set, where the The new primers try to minimize the interaction of the primers with the prototype sequence. Since the individual primers are still present, they are processed as a set of sample sequences and tested against each prototype sequence located on the chip using the model. The model accurately predicted base calls from primers that occurred in experiments that were still located on the prototype sequence. Additional binding to the central position of the prototype sequence was also observed and is in agreement with the experimental results. Primers designed to prototype sequences from closely related organisms elicit these base calls. For example, the prototype sequence of the adenovirus 4E1A gene allows 19 predicted bases out of...

example 2

[0036] Example 2: Model predictions on long sequences - After successfully demonstrating the accuracy of the model on shorter fragments, predictions on full prototype sequences were tested. The results of using routinely sequenced samples in the model are reported in Table 1, where the results are compared with experimental microarray results for 4 data sets; / H3N2 type California lineage, influenza virus B mountain county / 16 / 88 (Yamagata / 16 / 88) line and influenza virus B Victoria / 2 / 87 (Victoria / 2 / 87). The results report, for example, the general level of samples with great similarity to the Influenza A / H3N2 class Fujian samples, with an average base call rate of 85% for the assay, compared with an average of 97% for the model prediction. The average number of SNPs between the prototype and conventional sequences was 9.8 (1%). Although the model predicted that 9.2 SNPs would be resolved, only 6.3 SNPs were observed in the experiments. The model predicted 8.8 N calls for whic...

Embodiment 1

[0053]Hypothetical example with short sequences - The disclosed methods are illustrated below using artificial short sequences that would not correspond to any particular true species. There is a need to fabricate resequencing microarrays for detection of species A, B, C, D and E. As used herein, "species" may refer to taxonomic species as well as different types or strains of a single species and combinations thereof. Known nominal target 1 ( Figure 5 ) present in the genome of at least one of these species. Similarity sequence searches are performed using databases such as BLAST to generate target lists. A minimum similarity percentage (eg, 70%) can be used to filter out the results. If too many targets or targets from too many species (eg, genetically distant species) are reported, the minimum similarity percentage can be increased to reduce the size of the list. Additionally, the list can be manually checked to remove specific unfavorable targets.

[0054] Figure 5...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

A computer-implemented method as follows. Providing a list of target sequences associated with one or more organisms in a list of organisms. Providing a list of candidate prototype sequences suspected of hybridizing to one or more of the target sequences. Generating a collection of probes corresponding to each candidate prototype sequence, each collection of probes having a set of probes for every subsequence having a predetermined, fixed subsequence length of the corresponding candidate prototype sequence. The sets consist of the corresponding subsequence and every variation of the corresponding subsequence formed by varying a center nucleotide of the corresponding subsequence. Generating a set of fragments corresponding to each target sequence, each set of fragments having every fragment having a predetermined, fixed fragment length of the corresponding target sequence. Calculating the binding free energy of each fragment with a perfect complimentary sequence of the fragment. If any binding free energy is above a predetermined, fixed threshold, the fragment is extended one nucleotide at a time until the binding free energy is below the threshold or the fragment is the same length as the probe, generating a set of extended fragments. Determining which extended fragments are perfect matches to any of the probes. Assembling a base call sequence corresponding to each candidate prototype sequence. The base call sequence has a base call corresponding to the center nucleotide of each probe of the corresponding prototype sequence that is a perfect match to any extended fragment, but for which the other members of the set of probes containing the perfect match probe are not perfect matches to any extended fragment and a non-base call in all other circumstances.

Description

[0001] This application claims the benefit of US Provisional Patent Application No. 60 / 823,101, filed August 22, 2006, and US Provisional Patent Application No. 60 / 823,510, filed August 25, 2006. This application is a continuation-in-part of U.S. Patent Application No. 11 / 177,646 filed July 22, 2005, U.S. Patent Application No. 11 / 177,647 filed July 2, 2005, and November 7, 2005 U.S. Patent Application No. 11 / 268,373 filed June 6, 2006, U.S. Patent Application No. 11 / 422,431 filed June 6, 2006, and U.S. Patent Application No. 11 / 422,431 filed November 14, 2006 Patent Application No. 11 / 559,513. These applications claim priority to the following provisional patent applications: U.S. Provisional Patent Application No. 60 / 590,931, filed July 2, 2004; U.S. Provisional Patent Application No. 60 / 609,918, filed September 15, 2004; U.S. Provisional Patent Application No. 60 / 626,500 filed Nov. 5, U.S. Provisional Patent Application No. 60 / 631,437 filed Nov. 29, 2004, U.S. Provisional Pa...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
IPC IPC(8): G01N33/48C12Q1/68G16B30/00G16B25/20G16B25/30
CPCG06F19/22G06F19/20C12Q1/701C12Q2600/112C12Q1/6834C12Q2600/158G16B25/00G16B30/00G16B25/20G16B25/30G16B30/10
Inventor 安东尼·P.·马拉诺斯基王峥林宝川大卫·A.·斯滕格乔尔·M.·施努尔
Owner THE UNITED STATES OF AMERICA AS REPRESENTED BY THE SECRETARY OF THE NAVY
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com