Bioinformatics combines the elements of biology, computer science, and statistics to work with genome sequencing. My project utilizes a sequence analysis technique, k-mer minimizers, to identify bacterium from a shotgun genomic DNA sample. We used the algorithm Bevel to compare DNA sequences against standardized reference genomes in the PATRIC whole genome bacterial database. Bevel is a sequence similarity tool that uses a minimizer database. Minimizers are representative k-mers, subsequences of length k observed to have the minimum hash value across a genomic region and are therefore unique and comparable to that genomic region. The two databases are queried against each other, resulting in a list of positions where two or more sequences match. I am developing two Python applications that first, process the results of the algorithm and secondly, return a score that enable the ranking of bacterium matches. The higher the score, the better the match between the unknown bacteria and the standardized reference genome. The goal of this experiment is to show that minimizers are a fast mean of characterizing bacterial shotgun assembly contigs.

1. The Use of K-mer Minimizers to Identify Bacterium Genomes in High Throughput DNA
Sequence Data
Mackenna Galicia - UC Davis Genome Center, Bioinformatics Core
B.S. Biotechnology, UC Davis 2019
Supervisor: Matthew Settles
Abstract
Background Information
Methods
Discussion/Conclusion
I would like to thank my supervisor and mentor, Matthew
Settles, for proposing and guiding me throughout this research
project; Zev Kronenberg for the support and providing me
with the similarity search tool, Bevel.
My project utilizes a sequence analysis technique, k-mer
minimizers, to identify bacterium from a shotgun genomic
DNA sample. We used the algorithm, Bevel, to compare DNA
sequences against standardized referenced genomes in the
PATRIC whole genome bacterial database. Bevel is a sequence
similarity tool that uses a minimizer database. Minimizers are
representative k-mers, subsequences of length k observed to
have the minimum hash value across a genomic region and are
therefore unique and comparable to that genomic region. The
two databases are queried against each other, resulting in a list
of positions where two or more sequences match. I am
developing two Python applications that first, process the
results of the algorithm and secondly, return a score that
enable the ranking of bacterium matches. The higher the
score, the better the match between the unknown bacteria and
the standardized reference genome.
Sample “Seqmatch” Output
What is Bioinformatics?
● Combines the elements of biology, computer science, and
statistics to work with genome sequencing
● Large genomes are difficult to sequence due to their size
and complex structure, so bioinformatics is an efficient way
to sequence the genomes
What is Whole Shotgun Genome Sequencing?
● A quick, efficient, and more accurate way to sequence large
genomes
● Cuts genome into small fragments of DNA that are then
reassembled by computer programs
Reads are the small fragments of DNA produced from Whole
Shotgun Sequencing. The sequence reads are assembled and form
contiguous genomic sequences called contigs. Scaffolds consist of
one or more contigs, typically joined with NNN’s which represent
sequencing gaps. The scaffolds are then properly ordered,
oriented, and assembled to form complete assemblies.
What are k-mer minimizers?
● A hash-based counting method that reduces redundancy
from neighboring k-mers, who differ from each other in
only one nucleotide position
Future work to build upon this project would include:
1. Continue collection of query minimizer scores of
query-target sequences pairs remaining to be processed
2. Correlate the results of my project with the previous
findings acquired in a laboratory
● The goal of this experiment is to show that minimizers
are a fast mean of characterizing bacterial shotgun
assembly contigs
● Given assembled contigs we can compare those to a
database of whole genome sequences
● The Query Sequence and the Target Sequence with the
most matches is likely the same organism
● This minimizer approach is used to identify unknown
samples, or to check for contamination, samples with
multiple organisms in it
Sample “Bevel” Output
Whole Shotgun Sequencing
Dot Plot Results
K-mer Minimizers
Acknowledgements
● Running Bevel
○ Store every other match (-w 2)
○ K-mer/word size of 15 (-k 15)
○ Filter matches occurring > 10 times (-n
10)
● Tally the hits/matches and assign a score.
● Target sequences with a higher score
suggest a likely match with the querying
organism.
Why use a Dot Plot?
● Useful to easily identify long regions of strong similarity between two
sequences
● Clearly reveals the presence of insertions, deletions, and mutations that are
usually hard to identify with other methods
● Plot of target sequence accn|CP005975 and query
sequence NODE_2_length_654753_cov_26.8031_ID_3779
● The diagonal line of dots shows the regions of local
similarity between the two sequences
● The gaps in the diagonal lines represent mutations or
distinctions between the sequences
● Isolated dots outside of the diagonal line represent random
matches
● The Bevel output provides a “raw” listing of all target
sequences (and query sequences) with more than one match
with an organism sequence
● Seqmatch is a Python application I created that takes the
Bevel output for each query/target sequence pair and
calculates and assigns a “minimizer score”
● The higher the score, the greater number of “hits” or
matches between the two sequences
● The score is the sum of all query minimizers for each
unique target/query sequence ID pair
https://www.ncbi.nlm.nih.gov/nuccore/CP005975.1
https://en.wikipedia.org/wiki/Shotgun_sequencing
Sample GenBank Result
● Using the highest query minimizer scores (“best matches”), we
can search the NCBI GenBank for unidentified bacteria using
their accession number
Future Work
(A) The two sequences are broken down
into its constituent k-mers.
(B) All k-mers are converted into hash
values. In this example, the window
size is four (r1...r4).
(C) The lowest hash scores
(minimizers/min-mers) for each k-mer
is extracted and listed.
(D) The fragments are assembled
according to the four lowest
minimizers to find overlapped regionshttp://dx/doi.org/10.1101/008003