Bioinformatics Lab lecture 2022

1. Hypothesis tes+ng
MICROBIO 590B Bioinformatics Lab: Bacterial Genomics
Professor Kristen DeAngelis
UMass Amherst
Fall 2022
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2. Lecture Learning Goals
• Explain the types of scientific knowledge, and how the hypothesis
testing is one way of many to do science.
• Describe the multiple ways of knowing.
• Define bias, list a few different kinds of bias, and explain how bias can
affect scientific questions and results.
• Describe the hallmarks of a good hypothesis, and explain the
rationale behind testing a hypothesis.
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3. Scientific method
• Not a single method or set of
rules, but more of a cycle of
asking and answering questions
• Hypotheses and questions in
science are usually an
interchangeable format
• Hypothesis: my novel isolate has
flagellar motility.
• Question: does my novel isolate
have flagellar motility?
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4. Science is a system for creating knowledge
• Hun$ng
• Hypothesis tes>ng, looking to confirm a specific idea
• Gathering
• Making observa>ons, finding paBerns, genera>ng hypotheses
• “Science” is not a single en$ty, free of cultural influences and
value-neutral in principle.
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5. Multiple ways of
knowing
• “Tradi'onal ecological knowledge
refers to the knowledge, prac'ce, and
belief concerning the rela'onship of
living beings to one another and to
the physical environment, which is
held by peoples in rela'vely
nontechnological socie'es with a
direct dependence upon local
resources. ”
• Robin Wall Kimmerer
• Observa'ons are important
• Personal narra'ves are data
• People are part of ecosystems
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A great example of TEK is the Australian government giving back
land to the Aboriginal people to practice their tradition of
controlled fires. This made the areas more biologically diverse
and decreased the threat of wildfires and their severity.

6. “everything is related, that is, connected in dynamic,
interac4ve, and mutually reciprocal rela4onships.”
– Tewa scholar Gregory Cajete
• Indigenous sciences build
knowledge about the world through
a distinct set of orienting values,
concepts, and questions. These
include: What is worthy of
attention? What needs explanation?
Who is related? How? Why does it
matter?
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Bang, M., Marin, A., & Medin (2018). If Indigenous Peoples Stand with
the Sciences, Will Scientists Stand with Us? Daedalus, 147(2), 148-159.
For many years, wildlife biologists who observed
coyotes and badgers hunting in the same area
hypothesized that they were competing. Indigenous
people knew that they hunt cooperatively.

7. Biases
What makes good decisions so hard?
From Katie Kennedy, Nature Conservancy guest lecturer

8. Survivorship bias
• During WWII, the damaged
portions of returning planes
show locations where they
can sustain damage and still
return home.
• The Statistical Research Group
at Columbia University
examined the damage done to
aircraft that had returned
from missions and
(mistakenly) recommended
adding armor to the areas that
showed the least damage.
• Planes hit in other places
presumedly did not survive.
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9. From: article by Buster Benson of Slack; https://www.visualcapitalist.com/18-cognitive-bias-examples-mental-mistakes/

10. Beliefs and bias
• Ques>ons and hypotheses are shaped by scien>sts’ beliefs
and biases.
• This can introduce bias into science, because science is done
by scien>sts.
• What can you do about it?
• Get to know your biases: implicit.harvard.edu has a bunch of
Implicit AssociaVon Tests (IAT) that you can take to diagnose your
own implicit (subconscious, hidden) biases.
• If you know your biases, you can make beWer decisions.
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11. Asking a scientific question
• Read science papers.
• Write down your ques>ons as you read papers.
• ABempt to answer ques>ons by doing literature research.
• As you read these new papers, con>nue to write down your
ques>ons.
• Eventually, you will come up with a ques>on that no one else has ever
asked! This is where you begin the scien>fic method.
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12. Hypothesis
• Your hypothesis is related to the question that you come up with.
• A hypothesis is a suggested explanation of a phenomenon, or
alternately a reasoned proposal suggesting a possible correlation
between or among a set of phenomena.
• It must
• be a specific statement
• be defeatable
• not include non-directional words like “change,” “alter,” “be different”
• You should try to disprove your hypothesis
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13. Example hypothesis
• Question: What is the structure of DNA?
• Hypothesis: The structure of DNA is a triple
helix.
• Alternative hypothesis: The structure of DNA
is a double helix.
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Pencil sketch of the DNA double
helix by Francis Crick in 1953
Photo 51, Gosling &
Franklin, 1952

14. CharacterisCcs of a good hypothesis
• Simple
• Specific
• Stated in writing, in advance
• Should be accompanied by a plan for testing
• Types of hypotheses
• Null and alternative hypotheses
• One- and two-tailed hypotheses
• Some examples …
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Banerjee et al., Ind Psychiatry J. 2009

15. Complete Genome Sequence of the Nonmotile
Myxococcus xanthus Strain NM
• Most myxobacteria exhibit swarming motility,
which involves social and adventurous motility
• M. xanthus strain NM lost both motility
systems, but the genomic basis of nonmotility
was not understood
• Hypothesis: there was a mutation responsible
for loss of motility.
• Alternative Hypothesis: there was gene loss
responsible for loss of motility.
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https://doi.org/10.1128/MRA.00989-21

16. Complete Genome Sequences of Two Shiga Toxin-
Producing Escherichia coli O146:H10 Strains
Recovered from a Foodborne Outbreak in China
• Escherichia coli O157:H7 (aka O157) is the Shiga
toxin-producing E. coli (STEC) serotype most
frequently isolated and most often associated
with hemolytic uremic syndrome (HUS) in the US.
• Non-O157 STECs are not commonly observed
• Hypothesis: there is a genomic signature of toxin
production different from O157
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hNps://doi.org/10.1128/MRA.00825-21

17. Draft Genome Sequence of Cellulomonas sp. PS-
H5, Isolated from Sekinchan Beach in Selangor,
Malaysia
• Cellulomonas are alkali-
tolerant bacteria and
industrial enzyme producers
• Hypothesis: like it’s relatives,
this Cellulomonas strain
possesses many CAZymes as
well as some potential new
enzymes
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https://doi.org/10.1128/MRA.00956-21; https://doi.org/10.1007/s00203-020-01816-z

18. How can bias have unintended effects in science?
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https://sitn.hms.harvard.edu/flash/2020/racial-discrimination-in-face-recognition-technology/
• Racial Discrimina>on in Face
Recogni>on Technology
• Racism, Toxic Stress, and
Educa>on Policy
• Racial Dispari>es in COVID-19
• Racism and Exploita>on in
Phase I Clinical Trials

19. Bias can lead to unintended consequences in how
artificial intelligence interprets people’s data
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hNps://sitn.hms.harvard.edu/flash/2020/racial-discriminaVon-in-face-recogniVon-technology/

20. Bias can lead to unequal application of new
technology and surveilance
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https://sitn.hms.harvard.edu/flash/2020/racial-discrimination-in-face-recognition-technology/

21. Bias leads to differences in medical care and
health outcomes
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https://www.apmresearchlab.org/covid/deaths-by-race

22. Lecture Learning Goals
• Explain the types of scien>fic knowledge, and how the hypothesis
tes>ng is one way of many to do science.
• Describe the mul>ple ways of knowing.
• Define bias, list a few different kinds of bias, and explain how bias can
affect scien>fic ques>ons and results.
• Describe the hallmarks of a good hypothesis, and explain the
ra>onale behind tes>ng a hypothesis.
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