GenoThreat / GenoGUARD -- open source biosecurity solution for the gene synthesis industry and the synthetic biology community.

GenoTHREAT
A biosecurity software to screen DNA
synthesis orders against pathogens
GBCB seminar
Laura Adam
10/07/2014

7/10/2014 GenoTHREAT 3
(2005) Science, 310(5745), 77. AAAS..

http://www.washingtonpost.com/wp-srv/nation/daily/graphics/wmdbio_123004.html

CURRENT REGULATIONS

The Gene Synthesis Industry
7/10/2014 7GenoTHREAT

Industry Response to Dual Use
• 5 members (all based in
Germany)
• Undersigned by:
► 6 German or
German/American
► 2 Chinese
• “Code of Conduct for Best
Practices in Gene Synthesis”
• 5 companies (American)
• 80% of worldwide synthesis
capacity
• “Harmonized Screening
Protocol”

Major Sections:
Customer screening
Sequence screening
Record retention
Government contact

Our Primary Objectives
1. Interpret the (draft) guidance as an algorithm
2. Implement as a software: GenoTHREAT
3. Characterize screening efficacy

Road Map
I. Current regulations
II. Sequence screening algorithm: interpreting the
guidance
III. GenoTHREAT: implementation and
characterization
IV. Conclusions

[Guidance] : Purpose
“[…] to minimize the risk that unauthorized individuals
or individuals with malicious intent will obtain “toxins
and agents of concern” through the use of nucleic
acid synthesis technologies, and to simultaneously
minimize any negative impacts on the conduct of
research and business operations.”

[Guidance] : Goals of sequence screening
• Agent of concern?
• Select Agents and Toxins
• Sequences of concern?
• “dsDNA sequences derived from or encoding Select Agents and Toxins”
• Sequence unique to select agent
• No house-keeping genes
• Both DNA strands and the six-frames translation
• Detect any “sequence of concern”
• Embedded : as small as 200bps
 Use Best match approach (at least)

[Guidance] : Major Points
1. Perform Six Frame Translation
2. Divide the query sequences into
subsequences of 200bp or 66aa
3. For each subsequence
i. BLAST
ii. Best Matches
iii. Flag if SAT
4. Automatic decision

Road Map
II. Sequence screening algorithm: interpreting the guidance
2. Divide the query sequences into subsequences of 200bp or 66aa
i. BLAST
ii. Best Matches
iii. Flag if SAT
III. GenoTHREAT: implementation and characterization
IV. Conclusions

[Algorithm] : Input a query DNA sequence to
screen

[Algorithm] : Six Frame translation

Road Map
i. BLAST
ii. Best Matches
iii. Flag if SAT
IV. Conclusions

[Algorithm] : Division

Road Map
i. BLAST
ii. Best Matches
iii. Flag if SAT
IV. Conclusions

[Algorithm] : What should we do with
subsequences?

Road Map
i. BLAST
ii. Best Matches
iii. Flag if SAT
IV. Conclusions

[Algorithm] : BLAST subsequences
against entire Genbank database

Basic Local Alignment Search Tool (BLAST)
• Developed at the U.S. National Center for
Biotechnology Information
• One of the most widely used bioinformatics tools
• Aligns query sequences against sequences in the
GenBank sequence database
• Algorithm emphasizes speed over sensitivity

BLAST
Query Sequence
Database of sequences
Local alignment

BLAST Output
Percent Identity
► The percentage of identical nucleotides (or amino acid) in
the sequence aligned
Query Coverage
► The length of sequence aligned

[Algorithm] : What should we do with all
those results of BLAST?

Road Map
i. BLAST
ii. Best Matches
iii. Flag if SAT
IV. Conclusions

[Guidance] : The Best match approach
• Use local sequence alignment tool
• suggest Blast
• Best matches = greatest percent identity over
the entire fragment
• 66AA or 200bps fragments

[Algorithm] : Identify Best Matches

Best matches
Mus musculus
Mus musculus
BLAST results PI QC (%)
Mus musculus 100 100
Mus musculus 100 100
Danio rerio 97 100
Danio rerio 43 80
BLAST
[Example]

Road Map
i. BLAST
ii. Best Matches
iii. Flag if SAT
IV. Conclusions

[Algorithm]: Determine nature of Best
Matches

[Algorithm] : How can we know if a Best
Match is to a Select Agent or Toxin?
Problem: no suggestion in guidance
Solution:
keyword
and anti-keyword list

BLAST
[Example] : Is this subsequence a
hit?
BLAST results PI QC (%)
Bacillus anthracis 100 100
Bacillus anthracis str. Sterne 100 100
Danio rerio 97 100
Danio rerio 43 80
Best matches
Bacillus anthracis
Bacillus anthracis str. Sterne

[Example] : Keyword vs. Anti-keyword
If a GenBank entry contains a keyword, then the
sequence is flagged
SA

[Example] : Keyword vs. Anti-keyword
If a GenBank entry contains both a keyword and anti-
keyword, the order is not flagged
NSA

[Algorithm] : When to flag the subsequence?

QC (%)
100
100
100
80
Best matches
Mus musculus
Mus musculus
BLAST results Score
Mus musculus 100
Mus musculus 100
Danio rerio 97
Danio rerio 43
BLAST
hit?

QC (%)
100
100
100
80
Best matches
Lumpy skin disease virus
Sheeppox virus
BLAST results Score
Lumpy skin disease virus 100
Sheeppox virus 100
Goatpox virus 98
Dearpox virus 44
BLAST
hit?

QC (%)
100
100
100
80
Best matches
Bacillus anthracis
Bacillus cereus
BLAST results Score
Bacillus anthracis 100
Bacillus cereus 100
Plasmodium falciparum 63
Clostridium ljungdahlii 44
BLAST
hit?
[Guidance] :
« unique to
Select Agent »
!!!

[Algorithm] : No Best Matches…

[Algorithm] : Points of the Guidance left to
interpretation
How do you identify sequences of concern of 200bp or
greater which partially span two adjacent
subsequences?
Problem: no suggestion in guidance
Solution: extension method

[Algorithm] : Extension Method

Extend to
meet possible
alignments
120bp 80bp
120bp80bp
New subsequence

Road Map
i. BLAST
ii. Best Matches
iii. Flag if SAT
IV. Conclusions

[Algorithm] : Recap

Road Map
1. Software implementation
2. Software Characterization
IV. Conclusions

Using BLAST
Online BLAST
Performs BLAST via NCBI
website interface
► Faster per BLAST
► Computationally less
expensive
► Only sequential, due to NCBI
restrictions
► Lack of privacy
Local BLAST
Performs BLAST in parallel on
local machine
► User privacy
► Faster per sequence due to
parallelization
► Computational expensive
(Memory + CPU intensive )

Screening time & hardware
Online Desktop Business Class Server
Sequence length (bp) Screening time (min)*
2,000 2
10,000 12.5
*Screening performed using business class server

Road Map
i. Database of test sequences
ii. Keyword list variation
iii. Detection of Potentially dangerous sequences
iv. BLAST parameters
v. Real world gene orders simulation
IV. Conclusions

Database of Test Sequences
• Implementations must be compared to assess quality
• Standardized set of test sequences is needed
• Test Set contains 184 sequences:
• Select Agents
o Genes associated with toxins or pathogenicity
o Genes associated with normal function
• Model Organisms
64

Database of Test Sequences
Contribute to the development of a standard test set
of sequences
65

Road Map
IV. Conclusions

Keyword and Anti-Keyword list
• Test with the unmodified sequences (184 sequences)
• Two lists of keywords
• Limited
• extensive
• Plus
• anti-keyword list
• or not

Keyword List Content Variation
0
20
40
60
80
100
120
Limited keywords Extensivekeywords
Correct SAT Correct NSAT
Keyword list method not mentioned in guidance
Limited keyword
list:
uniquely composed
of words in SAT List
Extensive keyword
list:
extension of limited
keyword list
containing words
uniquely related to
SAT.

Anti-Keywords
Anti

Road Map
iii. Detection of potentially dangerous sequences
IV. Conclusions

Modified Test Sequences
Modification performed on the initial unmodified
sequences
► Intervening sequences
► Degenerate sequences
► Mutated sequences (BLAST parameters)

Degenerate Sequences
Potential Danger: Codon optimized nucleotide sequences
GATTTGGACACTCATTTCACC
DLDTHFT
Unmodified Nucleotide
Degenerate
NucleotideGATACGTCAACCTTTTAA
GC
Amino Acid
Sequence
Result: all codon optimized sequences detected due to screening of amino acid
sequences

Intervening sequences
Potential Danger: SAT sequences hidden within larger, benign sequences
300bps
NSAT
200bps
SAT
300bps
NSAT
300bps
NSAT
300bps
NSAT
250bps
SAT
Result: All hidden sequences were detected

Road Map
IV. Conclusions

Mutated sequences
Potential Danger: mutated, but still active, SAT sequences which do not align to
GenBank entries

Nucleotides subsequences
Result: BLAST parameter settings affect screening capability

Amino-Acid subsequences
Result: BLAST parameters do not clearly change the efficiency of the screening

Nucleotides subsequences
Result: Direct relationship between screening time and ability to identify mutated
sequences

Amino-Acid subsequences

Road Map
IV. Conclusions

Real world gene orders simulation
Gene Synthesis company: low number of false hit
needed
1. iGEM registry
• Registry completed by iGEM teams each year
• Contains 10,000 sequences
2. GenoCAD database
• 1,258 sequences longer than 200 bp

iGEM Registry
First step: screen registry sequences 1-->1724
Hit rate: 6.5%
Major causes of hits:
• 100% query coverage for Best Match too restrictive
• Some results have 100% query coverage but very low
Percent Identity
• Keyword list issues
95%
60%
solved
2.9%

iGEM Registry

GenoCAD database
• 1,258 sequences
• 32 hits: 2.54%
• Manual review:
• YopH: protein from Y.pestis (gi|14488772)

Real world gene orders simulation
Hits left are due to:
• Very often: 1 subsequence of 1 Protein frame leads
to a correct hit
 Is it worth flagging the entire sequence?
• Sometimes: many subsequences leads to correct hits
 Probably worth flagging

Road Map
IV. Conclusions

GenoTHREAT
• “Best Match”
• Hardware and software parameters
• Keyword list
• BLAST parameters
• Certain types of sequence modifications
• High-resolution screen

Guidance conclusion
Government Guidance potentially usable by
companies:
• Reasonable time
• Good detection of sequences of concern
• Number of false hits potentially low (manual review)

http://www.dagorret.net/2009/12/18/new-technology-
developed-by-microsoft-for-photography-dna-image/
http://www.wadsworth.org/testing/biodefense/education.shtml

A T A A C T C C C T G G G T C G T T A A A C C G G
C G G C T G C G G C A G T C T T A G C A T A A T A
A T C G G A T A G C A C T T T A T G A C C T G T C
G T C G G G G C A C T A A A T G A A C T A G T G G
C A G T A A C T G T C A G G C A G C A T A T A C A
A C G T T C A A A T A A C T G C A T A G A A C C C
A G A A T A A C T A C C A C C A C C G A A T C T T
T A T C C A G A C G A C T G C A T G A C T C G C T
T C T A C G A C G G T G A A T G A C G T T G G G T
T G C G T C G C A T G G T A C C T A C T T A A C T
T C G G T C G C T C A A T G A T C T G C A A A A G
A A T C G G C T A T T G G A C T C C T A G G C G C
G T C T T A T A T A T G C G G C G C T T T T A C G
A T C C G G A C A T A A T C T A A G G T A T C G T
A C G C G C G G G A A C A C G A G G T T G T A A C
A C C G T A G C T A T C T C A T G C A T T C C G A
C C A G C G G T T A T A T A A T A C T C G T T T T
T T C C G C G T G C C A T C A T A C G A C G C T G
G C C G C C G C G T T A G T G T C G T G T G T A C
A C A C C G A G T T A C C C T C C T T C G T T C G
C A C C A G C G T T A C T G C G T G T A G A G G A
A A T T G G C T T G A G A G C T T T G C C C C A C
C G C A C G A G G T A A C T A T T G A G A T C A G
T C T A C A G A G T G C A A T A C A C C A A C G C
http://sourceforge.net/projects/genothreat/

Acknowledgeme
nts
Dr. Jean Peccoud
Mandy L. Wilson
The VT-ENSIMAG iGEM team (2010):
Michael Kozar
Gaelle Letort
Olivier Mirat
Arunima Srivastava
Tyler Stewart
My PhD committee:
Dr. Bevan
Dr. Garner
Dr. Peccoud
Dr. Ramakrishnan
Dr. Setubal

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