Introduction to Genomic Technologies complete course is currently being offered by Johns Hopkins University through Coursera platform.
Introduction to Genomic Technologies Coursera Week 1 Quiz Answers
Quiz 1 Answers - Overview and Molecular Biology
Q1. The central dogma of molecular biology tells us that
information is passed from
- DNA
to RNA to protein
- DNA
to methylation to RNA to protein
- DNA
to RNA to methylation to protein
- RNA
to DNA to protein
Q2. Which of the following is one of the major drivers of
the sequencing revolution that began after 2008?
- Decreased
cost of sequencing
- Improved
Sanger sequencing
- Decreased
computational analysis time
- Increased
sample collection
Q3. Which of the following is an exclusive characteristic
of genomics compared to traditional biology?
- Studies
considering the entire genome
- Targeted
studies of one or a few genes
- Measurements
of molecules in the Central Dogma
- Clever
experimental design
Q4. Genomic data science involves techniques from which of
these disciplines?
- Computer
Science
- Molecular
Biology
- Statistics
- All
of the these options
Q5. Which of the following is an activity that genomic data
scientists do not perform?
- Pipetting
- Population
genomics
- Preprocessing
and normalization
- Statistics
and machine learning
Q6. Which of these is not one of the DNA nucleic acids?
- Tyrosine
- Thymine
- Adenine
- Guanine
- Alanine
Q7. Transcription is a process that converts DNA to
- genes
- polymerases
- RNA
- ribosomes
Q8. The cost to sequence a human genome today, in U.S.
dollars, is approximately
- $30
million
- None
of these options
- $1000
- $20,000
Q9. DNA encodes instructions for
- Creating
an entire human being from scratch
- Regulating
body temperature
- Helping
us to see objects
- Enveloping
viruses that infect a cell
Q10. One major difference between humans and bacteria is
- Human
cells have a nucleus, and bacterial cells do not.
- The
human genome is made of DNA, while bacteria are made of RNA.
- Human
genes are first transcribed to RNA, while bacterial genes are not.
- Human
proteins are made of combinations of 20 amino acids, while bacterial
proteins use a smaller set of 12 amino acids.
Introduction to Genomic Technologies Coursera Week 2 Quiz Answers
Quiz 1 Answers: Measurement Technology
Q1. Genome assembly refers to
- A
computational method for reconstructing chromosomes from short reads
- A
computational method to identify the genes being expressed in a cell or
tissue
- The
process whereby a cell copies its DNA
- A
method for capturing gene sequences
Q2. Which of the following is not true about DNA?
- It
is a doublestranded molecule
- It
doesn’t matter which direction you write the sequence in
- Each
strand has a direction
- One
strand is complementary to the other
Q3. RNA molecules are translated into
- Modified
RNA molecules
- Introns
- DNA
molecules
- Proteins
Q4. Messenger RNA is
- A
template from which proteins are constructed by ribosomes
- A
special signal that helps a cell communicate with other cells
- A
reverse copy of DNA
- The
genetic material inherited by offspring
Q5. DNA is copied into DNA in order to
- Replicate
a cell
- Create
species diversity
- Encourage
evolutionary changes
- Respond
to an infection
Q6. Evolutionary biology involves the study of
- The
process of natural selection that allows some DNA mutations to survive and
cause others to die out
- How
the cell membrane is formed
- The
process through which RNA is exported from the nucleus
- The
origin of the very first living organisms
Q7. Which of the following can we measure with next
generation sequencing?
- DNA-protein
binding
- Cell
structure
- Protein
levels
- RNA
secondary structure
Q8. What is the first step in ChIP-sequencing to measure
protein-DNA binding?
- Cross-linking
proteins to the DNA
- Sequencing
the bound DNA fragments
- Antibody
pulldown of the linked proteinDNA fragments
- Fragmenting
the DNA
Q9. Which of the following can be measured using bisulfite
conversion and then sequencing?
- DNA
methylation
- DNA
secondary structure
- DNA
variants
- DNA-protein
binding
Q10. What is the primary measurement technology used in most
modern genomics experiments?
- Nanopore
sequencing
- Polymerase
chain reaction
- Next
generation sequencing
- Sanger
sequencing
- Oligonucleotide
arrays
- Western
blotting
Introduction to Genomic Technologies Coursera Week 3 Quiz Answers
Quiz 1 Answers: Computing Technology
Q1. A computer algorithm is
- A
description of the memory organization within a computer
- A
protocol for transmitting data over a network
- A
description of the hardware and software capabilities of a computer
- A
precise specification of all the steps needed to compute a solution to a
problem
Q2. You can make a program more efficient by
- Re-designing
the data structures to require less storage
- Using
a faster computer
- Running
it on the Amazon cloud
- Replacing
your hard drives with faster solid state drives
Q3. DNA sequences can be represented efficiently using
- Two
bits for each of the 4 possible bases
- One
byte for each of the 4 possible bases
- One
codon for each amino acid
- The
SAM alignment format
Q4. A programming language is
- A
formal language used to instruct computers what to do
- The
way we describe high-level algorithms
- Anything
written in Python
- A
method for translating between languages such as English and French
Q5. Software engineering involves
- Debugging
computer code
- Testing
programs on a wide range of examples to see if they perform as expected
- All
of these options
- Updating
code so that it remains compatible with other software systems.
Q6. Bowtie, TopHat, and Cufflinks are
- Programs
for analysis of RNA-seq (transcriptome) data sets
- A
web-based system for browsing genome data
- Elements
of men’s formal evening wear
- Programs
for determining the function of a gene
Q7. Sequence alignment refers to
- Lining
up two DNA sequences so that positions with the same base match one
another
- Making
sure that all the DNA sequences in a file use the same format
- Shuffling
the positions in a sequence to randomize them
- Determining
the amino acid sequence produced by translating a DNA sequence
Q8. A software pipeline for RNA sequence (RNA-seq) analysis
will
- Compare
cases to controls and determine which genes were responsible for any
differences in phenotype
- Create
a database in which one can efficiently store and retrieve results
- Process
large raw sequence files into a summary table showing which genes were
present
- Automatically
update all software to the latest version
Q9. Which of the following is not a computer operating
system?
- RedHat
Linux
- Mac
OS X
- Google
Drive
- Unix
Q10. A data set large enough to overwhelm the main memory of
a computer would
- Use
uncompressed files rather than compressed ones
- Contain
more than one genome’s worth of data
- Be
larger than the available RAM
- Be
at least 100 gigabytes in size
Introduction to Genomic Technologies Coursera Week 4 Quiz Answers
Quiz 1 Answers: Data Science Technology
Q1. Which of the following are required for sharing a data
set?
- An
explicit and exact recipe to go from the raw to the tidy data
- The
raw data
- A
code book describing each variable and its values
- All
of these options
Q2. Which of the following should be included in data
tidying recipes?
- Power
calculations
- Version
numbers for software
- Preprocessed
data
- Units
of variables
Q3. What is the central dogma of statistics?
- Using
measurements on a population to infer knowledge about a sample
- Using
Bayes rule to calculate probabilities we care about
- Estimating
parameters using frequencies of observed events
- Using
measurements on a probabilistically selected sample to infer knowledge
about a population
Q4. Which of the following are types of variability in all
genomic data?
- Phenotypic
variability
- Genetic
drift
- Variation
from changing technology
- Variability
due to dropout
Q5. Which of the following will increase power in a
statistical analysis?
- Increasing
sample size
- Using
a new technology
- Adjusting
for confounders
- Increasing
measurement variation
Q6.If 100 p-values are calculated on a data set with no
signal, how many p-values would we expect to be less than 0.05 on average?
- 100
- 20
- 10
- 5
Q7. If we report 500 results as significant out of 10,000
tests while controlling the family-wise error rate at 5%, about how many false
positives do we expect?
- 10
- 0
- 200
- 25
Q8. What is the most common confounder in genomics?
- Batch
effects
- Sex
- Genetic
background
- Population
stratification
Q9. Which of the following are benefits of making big data as small as possible as soon as possible?
- Reducing
the data will reduce the number of hypothesis tests
- Smaller
data sets will decrease false discovery rates
- Interactive
analysis can improve our ability to make discoveries
- Reduced
data will increase the power of statistical tests
Quiz 2 Answers: Course Project
Q1. Why did the authors write this paper?
- To
prove that there are a large number of genes shared between humans and
bacteria.
- To
compute the E-values for the BlastP matches to the proteins from the human
proteome.
- To
propose a plausible alternative to the hypothesis that genes had been
“laterally” transferred to humans.
- To
show that sample size formulae for “lateral” gene transfer are not
correct.
Q2. What is “lateral gene transfer”?
- When
genetic material is passed from the genome of one organism to another
through a process other than reproduction.
- When
a gene is transferred out of the DNA and permanently lost.
- When
genes are transferred out of the nucleus and into the cell.
- When
new genetic material is created and transferred to the genome of an
organism.
Q3. Why is lateral gene transfer (LGT) from bacteria to
humans unlikely?
- Because
a bacterium would have to infect a germline cell, enter the nucleus of
that cell, and insert some of its DNA into one of the host’s chromosomes,
after which the mutation would then have to provide an evolutionary
advantage to spread through the population.
- Because
bacteria never actually enter human cells during an infection.
- It
is not unlikely; in fact, LGT has occurred and it is an ongoing process in
the human population.
- There
hasn’t been sufficient time since humans and bacteria diverged for
laterally transferred genes to spread through the population.
Q4. What are homologs?
- Identical
mutations that occurred over evolutionary time.
- Two
genes in different organisms that have been mutated at the same rate.
- Genes
that are greater than 99% similar in DNA sequence
- Two
copies of a gene in different organisms that share a common ancestor.
Q5. What was the main method used to rule out lateral gene
transfers between humans and bacteria?
- If
a homolog of a bacteria was also found in humans.
- If
genes were found to have mutated between eukaryotic genomes and human
genomes.
- If
a homolog of a gene was found in prokaryotic genomes.
- If
a homolog of a gene found in humans was also found in a species of
nonvertebrate eukaryotes.
Q6. Why would this method rule out lateral gene transfers?
- Lateral
gene transfer is an unusual process compared to standard inheritance and
nonvertebrate eukaryotic organisms and humans are evolutionarily “closer”
than bacteria and humans. If humans and nonvertebrates share a homologous
gene, it was likely not directly passed from bacteria to humans.
- Inheritance
of common genes is less common than lateral gene transfer and
nonvertebrate eukaryotic organisms and humans are evolutionarily “closer”
than bacteria and humans. If humans and bacteria share a homologous gene,
it was likely directly passed from bacteria to humans.
- Humans
and bacteria are both likely to have shared an evolutionary history with
nonvertebrate eukaryotic organisms, so genes are likely to be homologous
across all three.
- Nonvertebrate
eukaryotic organisms and bacteria are evolutionarily “closer” than
invertebrate eukaryotic organisms and humans. If they share a homologous
gene, then bacteria are likely to have passed genes directly to humans.
Q7. What are the biological, computational, and statistical
parts of Figure 1?
- Biological:
the argument that lateral transfer should be ruled out if there is a
human/nonvertebrate eukaryote homologs.
- Computational:
The identification of homologs by performing Blastp searches on known
protein sets.
- Statistical:
The calculation of the standard error for the sample size curves.
- Biological:
the argument that a Blast cutoff of 10^-10 should define homologs
- Computational:
The identification of homologs by performing Blastp searches on known
protein sets.
- Statistical:
Observing and quantifying the trend in genes shared versus genome sample
size.
- Biological:
the argument that lateral transfer should be ruled out if there is a
human/nonvertebrate eukaryote homologs.
- Computational:
The identification of homologs by performing Blastp searches on known
protein sets.
- Statistical:
Observing and quantifying the trend in genes shared versus genome sample
size.
- Biological:
the argument that gene should be ruled out if there is a
human/nonvertebrate eukaryote homologs.
- Computational:
Observing and quantifying the trend in genes shared versus genome sample
size.
- Statistical:
The identification of homologs by performing Blastp searches on known
protein sets.
Q8. In the end what is the conclusion of the paper?
- That increasing the number of sequenced genomes is likely to increase the number of potential lateral gene transfer events.
- That a more plausible explanation for the observation of homologous genes found in bacteria and humans but not in non-vertebrate eukaryotes is gene loss and low sample size.
- That the argument for lateral gene transfer is statistical because we must average over multiple possible transfer events.
- That genes are more likely to be laterally transferred from certain types of bacteria to humans.
Q9. What are the biological, computational, and statistical
parts of Figure 2?
- Biological:
the argument that lateral gene transfer is less common than standard gene
flow through reproduction
- Computational:
The identification of homologs of human HAS genes by iterative BlastP
searches and application of the neighbor-joining algorithm to create the
phylogenetic tree.
- Statistical:
The inference that humans cluster more closely (have smaller distances
to) other eukaryotes than to bacteria.
- Biological:
the argument that lateral gene transfer is less common than standard gene
flow through reproduction.
- Computational:
The calculation of statistical significance of the protein hits in the
Blastp search.
- Statistical:
The statistical modeling of protein sequences via a Markov Model.
- Biological:
the argument that proteins should have more similar sequences if they are
evolutionarily closer.
- Computational:
The identification of homologs of human HAS genes by iterative BlastP
searches and application of the neighbor-joining algorithm to create the
phylogenetic tree.
- Statistical:
The inference that humans cluster more closely (have smaller distances
to) other eukaryotes than to bacteria.
- Biological:
the argument that lateral gene transfer is less common than standard gene
flow through reproduction
- Computational:
The storage of data in a low redundancy protein database.
- Statistical:
The inference that humans cluster more closely (have smaller distances
to) other eukaryotes than to bacteria.
Q10. The analysis in this paper required multiple data
sources. Which of the following data sources was not used in the paper?
- The
set of all known genes (at the time) from the malaria parasite, Plasmodium
falciparum.
- The
set of all known genes (at the time) from completed bacterial genomes.
- The
complete set of genes from the fruit fly, nematode worm, yeast, and
mustard weed genomes.
- The
complete set of noncoding RNA genes from the human genome.
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