Dr. Weimer and PhD student, DJ Darwin Bandoy join “The Conversation.”

 Dr. Weimer and DJ Darwin Bandoy join The Conversation with their latest article:
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Every article you read here is written by university scholars and researchers with deep expertise in their subjects, sharing their knowledge in their own words. We don’t oversimplify complicated issues, but we do explain and clarify. We believe bringing the voices of experts into the public discourse is good for democracy.
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Tools for Hunting Antimicrobial Resistance (AMR) Genes

Contributed by Cory Schlesener, B.S.
Predicting bacterial isolate susceptibility for disease treatment and surveying resistance prevalence broadly is of great interest, especially as antibiotic resistance is increasing globally among clinical isolates. Screening sequence data for genes and alleles that confer antibiotic resistance has increased in availability but also in complexity over the past decade. There is now a myriad of bioinformatics tools available that utilize different interfaces and reference databases, as described (~50 listed) in the review by Hendriksen et al. (2019). Among the many options available, the database can be paired with the co-developed bioinformatic tool or independently developed tool, allowing for mix and match options. Additional complexity is added by the other programs used in setting up a bioinformatic pipeline to get raw sequence data pre-process to prepare for AMR search. Many AMR tools rely on annotating and searching genome assemblies, but more recent tools can operate off of local read alignments from sequencing reads, even from metagenomes. The differing AMR tools and pipelines lead to different outcomes in real-world usage, with further exacerbation with lower sequence quality, and outlined in comparisons by Doyle et al. (2020). It is important to do the proper pre-processing and input quality assessment for chosen pipeline and AMR tools. A helpful recent review, on the more common tools used, describes the differences among the latest version of tools and how they can be used. Additionally, the review provides an overview of other tools used to compile a pipeline (Lee et al. 2021).

References:
Hendriksen, Rene S., Valeria Bortolaia, Heather Tate, Gregory H. Tyson, Frank M. Aarestrup, and Patrick F. McDermott. “Using genomics to track global antimicrobial resistance.” Frontiers in public health 7 (2019): 242.
Doyle, Ronan M., Denise M. O’Sullivan, Sean D. Aller, Sebastian Bruchmann, Taane Clark, Andreu Coello Pelegrin, Martin Cormican et al. “Discordant bioinformatic predictions of antimicrobial resistance from whole-genome sequencing data of bacterial isolates: an inter-laboratory study.” Microbial genomics 6, no. 2 (2020).
Lee, Kihyun, Dae-Wi Kim, and Chang-Jun Cha. “Overview of bioinformatic methods for analysis of antibiotic resistome from genome and metagenome data.” Journal of Microbiology 59, no. 3 (2021): 270-280.
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Dr. Weimer & graduate student DJ Darwin Bandoy’s manuscript Analysis of SARS-CoV-2 genomic epidemiology reveals disease transmission coupled to variant emergence and allelic variation published in Nature

This past week UC Davis did a press release for the new paper that Dr. Weimer and graduate student DJ Darwin Bandoy have being published in Nature. You can read the article Analysis of SARS-CoV-2 genomic epidemiology reveals disease transmission coupled to variant emergence and allelic variation, here.  You can also access the UC Davis press release, Genome Variation Gives Insight Into Coronavirus Spread, here. Congratulations Dr. Weimer and Darwin Bandoy on a very informative article!

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Vaccines work!

Contributed by Darwin Bandoy, PhD Candidate

One year into the pandemic and I already managed to get my first dose of vaccine. The pandemic will end with the achievement of herd immunity and the best path towards herd immunity is via vaccination. Vaccines are generally safe. Several variants are causing trouble by reducing the efficacy of certain vaccines, but it is still a much better option than not having any protection at all. UC Davis is providing free COVID-19 to all the students, faculty and staff and this resulted to low infection levels.  We have exciting data about variants and disease transmission which we will preprint in the coming weeks.

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Cell Line Makes the Phenotyp

Contributed by Cory Schlesener, B.S.
When conducting biological assays using mammalian cell tissue culture, the cell line used is crucial. It is a given to choose cells derived from the tissue of interest to study. However, as immortalized cell lines are not entirely normal, lines derived from the same tissue types can ultimately have differences in their regulatory networks. These changes can lead to differences in the way different lines behave in an assay. This can be particularly true if studying regulatory genes involved in cancer formation, as immortalized cells are already caner-like.
A somewhat recent example of this is in a publication (reference below) on the effect a bacterial pathogen has on mammalian cells when injecting a cytotoxin. Specifically, they observed the effect on a particular regulatory gene’s expression, which can potentially lead to oncogenesis. The study showed multiple assays using different mammalian cell lines. Interestingly, the baseline expression of the protein of interest and its partner protein were at very different levels between the cell lines. Further, the ratios of expression between the proteins were also variable between cell lines. Choosing a subset of the lines from their working set, they characterized the cellular morphology and the changes in abundance of their protein of interest after the pathogenic bacteria were introduced. In the assay the three lines showed different increases in expression from ~1.4-2.3x baseline, which can significantly impact the statistical power. Additionally, the cell morphology drastically changes, measured at 24 hours. There was some variation in the proportion of cells that changed morphology, however one cell line reached this percentage morphology after 5 hrs (~25%), while the other lines remain below 15% of cells changed. These observed variations show how cell lines can behave differently, and their sensitivities (underlying regulatory and gene expression patterns) determine the phenotype observed for assay.
Reference:
Tiffon et al. “TAZ Controls Helicobacter pylori-Induced Epithelial–Mesenchymal Transition and Cancer Stem Cell-Like Invasive and Tumorigenic Properties.” Cells 9, no. 6 (2020): 1462.
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Tune in for a live Q&A session with UC Davis experts on Coronavirus Mutations & Variants

Click here to watch as Dr. Weimer joins UC Davis live to discuss coronavirus. 

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2021

Contributed by Darwin Bandoy, PhD candidate

It is the start of another year and we are still in the middle of the winter wave of COVID-19 with a record number of mortalities and new infections. What is different is the renewed interest in tracking mutational variants and emerging lineages, particularly B.1.1.7. While we expect new mutations to arise, what is notable is that a large-scale genome sequencing effort in the UK enabled the identification of the variant. We cannot say the same level of genomic tracking is occurring in other countries. We need to sequence more in order to understand the virus. This is perfectly aligned with what we have been saying with the 100K Pathogen Genome Project.

 

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Parsing large data files to working chunks

Contributed by Cory Schlesener, B.S.

When analyzing large data files in the realm of gigabytes, system memory and program/function set memory limits becomes an issue. Creating a smaller data set for analysis can bring the working data down to a manageable size. This downsizing would preferably be removing redundancy or features not relevant to analysis, over fracturing data into chunks to analyze separately. There is however the problem of manipulating data in the first place if the file is too big to load into memory. For tabular data, the best work around is to load the specific row(s) to manipulate. This can be achieved through python coded manipulations using pandas tool library (built on numpy). With pandas, the files (e.g. comma separated values (CSV) files) can be read in chunks of rows, specifying which row(s) in the file to read/load, and can then be formed into a list for manipulations. With further python code, the list can be manipulated in a standardized format, removing columns/data or replacing multiple-columns/long-data-strings with condensed representative values. The manipulated list can then be written out to a new file. This process, repeated to iterate over the original file’s rows, will generate a new file of curated reduced data that is more manageable for manipulation or analysis as a whole.

Reference:
Documentation on pandas.read_csv() function [look for chunksize and skiprows options]
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html

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AutoML, easier use of machine learning mode

Contributed by Cory Schlesener, B.S.
Machine Learning (ML) enables powerful analysis of data to formulate models. These models can be utilized in applications or help dive into the data for insight on relationships between features.  There are many varieties of models and the algorithms that create them. Different varieties of core learning algorithms are better suited to generating models for some types of datasets and questions over others. The complexity of decisions extend beyond algorithm/model-type selection and fans out into many possible sub-choices. The additional choices, in selecting parameter values, determines how an algorithm carries out learning, affecting the end resulting model. Trail and error testing, combined with insights to how ML is being carried out, can be used to assess and fine tune the process. However, acquiring detail expertise in various ML subcategories is not required with the aid of further automation. Automated machine learning
(autoML) can run multiple programs to test out under a variety of parameters, and repeat in an iterative process. Running an autoML algorithm will gradually find the best ML algorithms, under the right parameters, to produce the best model(s) to fit the dataset and address the objective tasked. Automating this model selection and tuning process expands usability to a wider population, and this goal is being pursued by a large variety of teams and platforms. One of the more useful/popular autoML tools is auto-sklearn, built on the popular, open-source machine learning toolkit SciKit-Learn. Auto-sklearn utilizes a wide variety of algorithms, allowing for broader use, and is entering version 2.0 with improvements on efficiency.
Feurer, Matthias, Katharina Eggensperger, Stefan Falkner, Marius Lindauer, and Frank Hutter. “Auto-sklearn 2.0: The next generation.” arXiv preprint arXiv:2007.04074 (2020).
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USDA Food Safety Fellow Day 1

Contributed by Darwin Bandoy, PhD Candidate

We formally started the USDA Food Safety fellowship with a kick-off meeting. Our proposal is to utilize machine learning to parse the basis for virulence and antimicrobial resistance in Salmonella Dublin. This particular strain of Salmonella is notorious for causing systemic infections and uniquely adapted to cattle. The question is why, and we will seek use the state of the art machine learning techniques applied to biological big data.

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