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Genomics > Data analysis > Flashcards

Data analysis Flashcards

1
Q

What does data analysis consist of?

A

Turning raw image data into an extremely useful sequence data

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2
Q

What has happened following the development of bioinformatic tools?

A

The importance of data analysis has increased

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3
Q

What is bioinformatics?

A

Broad, interdisciplinary field that integrates principles from computer science, mathematics and statistics

In order to manage, mine, visualize and analyze biological data

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4
Q

Which field has bioinformatics co-evolved with?

A

Genomics

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5
Q

What is the role of a bioinformatician?

A

Develop analytical methods

Construct and curate computational tools and databases

Data mining, interpretation and analysis

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6
Q

What are the aims of a bioinformatician?

A

Identify differentially expressed genes

Identify epigenetic changes

Analyse pathways

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7
Q

Examples of ways genes can be differentially expressed

A

Somatic muations

Copy number alterations

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8
Q

What type of bioinformatician is the best bioinformatician?

A

Relevant background knowledge regarding the biological component of the data

Can differentiate between technical and biologically relevant artifacts

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9
Q

How can we increase genomic understanding of disease?

A

Pairing information obtained through genomic technologies with clinical data

This entails integrating omic and expression data

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10
Q

What are basic genome browsers?

A

Curated databases

Allow annotation of human and other species DNA

These are references or working drafts

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11
Q

What are the two main hosts of genome browsers?

A

USCS

ENSEMBL

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12
Q

Where is USCS based?

A

University of California

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13
Q

Where is ENSEMBL based?

A

Europe - UK and Germany

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14
Q

What do basic genome browsers bring together?

A

Genomic annotation from multiple species as well as many other data like transcription factor binding sites

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15
Q

What can we measure using microarrays?

A

DNA

Gene expression

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16
Q

What aspects of DNA can we measure using microarrays?

A

SNPs

Copy number variations

Methylation

Chromosome conformation

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17
Q

What aspects of gene expression can be measure using microarrays?

A

mRNA

miRNA

inRNA

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18
Q

What type of variation can be detected through SNPs, somatic mutations and CNVs ?

A

Genetic variation

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19
Q

What type of variation can be detected through DNA methylation and chromatin analysis?

A

Epigenetic variation

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20
Q

What type of information can be detected through RNA expression and gene structure?

A

Expression variation

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21
Q

Describe the 6 steps of array processing

A
  1. Experimental design
  2. Image analysis
  3. Normalisation to clean the data
  4. More low level analysis (fold change, ANOVA and data filtering)
  5. Data mining
  6. Validation
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22
Q

Why is it important to normalise the data?

A

Cleaning the data allows us to compare data across arrays without altering the interpretation of changes in gene expression

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23
Q

Why does the data need to be normalised?

A

The intensity of fluorescent markers might be different from one batch to another

Technical variation can hide real data

Unavoidable systematic bias

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24
Q

What is the main reason we normalise data?

A

Because the experimental goal is to identify biological variation and expression changes between samples

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25
Q

What is the most appropriate test used to analyse data?

A

Pairwise analysis using t-tests or ANOVA is the most appropriate

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26
Q

What is the goal of data analysis?

A

Determining the fold up or down cutoffs to figure out what is truly significant

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27
Q

What is a common theme to measure differences in gene expression in arrays and NGS?:

A

Ranking genes according to the evidence of difference in gene expression

Score the differences using fold changes, t-statistics or a combination

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28
Q

What are ways to interpret the changes in signal intensity?

A

Heat maps

Volcano plots

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29
Q

What does the p-value inform?

A

Determines how deleterious the mutation is

Smaller the p-value, the more deleterious

30
Q

What does the t-value inform?

A

The magnitude and direction of the fold-change

31
Q

What is Moore’s Law?

A

A law that describes how the computer power doubles every two year

Technologies which keep up the pace with Moore’s law are cutting edge

32
Q

Does genomics keep up with Moore’s law?

A

No

The computer power is weaker than the sequencing power

We have the sequence, but don’t know how to interpret it

33
Q

Which aspects of the genome can be investigated through sequencing genomic DNA?

A

Genomic footprinting

Epigenetic profiling

Whole genome sequencing

34
Q

Which aspects of the genome can be investigated through sequencing cDNA?

A

Transcriptome

RNA footprinting

Transcriptome expression profiling

35
Q

What sample an be used to analyse germline diseases?

A

Blood exome

36
Q

When is transcriptomics commonly used?

A

Looking at individual tissue types with certain diseases to infer the function of mutated genes

Since post-translational mutations in RNA is not present in DNA

37
Q

For what is methylation and histone modification analyses used?

A

To integrate and interpret all the information together

To look for the clinical application of data

38
Q

What are the 3 steps of NGS data analysis?

A

Primary analysis

Secondary analysis

Tertiary analysis

39
Q

What is the use of primary analysis of NGS data?

A

Determine the run/sample quality through looking at the quality values of the visual information (colours)

40
Q

What is the use of secondary analysis of NGS data?

A

Determine the sample /information quality through aligning the sequences

41
Q

What is the use of tertiary analysis of NGS data?

A

Data interpretation

42
Q

What is the output format of data obtained from sequencing ?

A

Top line = position of the flow cell and the read it comes from

Second line = sequence itself

Third line = positive or negative read

At the bottom = uality score

43
Q

What does the quality score mean?

A

How confident the software is that this is the specific base

44
Q

What are intensities turned into in NGS?

A

Count number

Number of sequences that line up the reference

45
Q

What are intensities turned into in microarrays?

A

Numerical values

46
Q

How can read number be used to quantify gene expression?

A

Count number

Counting the number of reads that map to each gene using programs

47
Q

What is a read?

A

Text-based formats for storing both a biological sequence and its corresponding quality scores

48
Q

What is the ASCII characteri?

A

Character showing the sequence letter and quality score

49
Q

What are the steps of to analysing NGS data?

A
  1. Base calling
  2. Variant calling
  3. Annotation
  4. Filtering
  5. Reporting
50
Q

What is base calling?

A

Aligning the sequence to the reference to compare

51
Q

Two steps of base calling

A

QC alignment

Alignment

52
Q

What do we need to check when aligning the sequence to the reference?

A

Percentage of reads properly or uniquely mapped

Among the mapped reads, determine the percentage or reads in the exon, intron and intergenic regions

5’ or 3’ bias

53
Q

What is IGV?

A

Intergrated genome viewer

A software that allows us to visualise the reads on the genome, highlighting potential variants

54
Q

What are counts?

A

Expression levels in IGV

55
Q

How does IGV determine whether the variants in the genome are SNPs or mutations?

A

The ratio of the commonality of alterations in the host cell

If it is a SNP = 50/50
If it is a mutation = less frequent

56
Q

What can alter the number of reads?

A

The gene expression levels

The length of the gene

57
Q

Why is the read number not always accurate?

A

Longer genes have naturally more reads, so the read number does not always reflect the expression rate

58
Q

How can read number be normalised?

A

RPKM

Reads per kilobase per million mapped reads

59
Q

Equation for RPKM

A

Counts of mapped fragments / total mapped fragments (million) X exon length of transcripts (KB)

60
Q

What information can RNA sequencing provide?

A

Fold changes in protein expression

If the gene is expressed at higher levels than the protein = splice variant

61
Q

What are variant calling methods?

A

Rather than looking at the level of expression, we look art somatic mutations

62
Q

What are the 3 ways in which variants can be detected?

A

Allele counting

Probabilistic methods - uses a bayesian model to statistically quantify the number of allelic variants

Heurisic approach - based on thresholds

63
Q

What method does varScan2 use?

A

Looks at the number of reads to determine whether there is a variant or not

Heuristic approach

64
Q

Describe what the VarScan2 procedure will determine about a cancer genome if tumour allele frequency matches normal

A

If tumour and normal match the reference = reference

If tumour and normal do not match the reference = germline

65
Q

Describe what the VarScan2 procedure will determine about a cancer genome if the tumour allele frequency does not match normal

A

Calculate the significance of allele frequency difference by Fisher’s exact test

If the difference is significant

  • if normal matches reference = somatic
  • if normal is heterozygous = LOH
  • if normal and tumour are both variants and different = unknown

If the difference is not significant
- combine the tumour and normal read counts for each allele, recalculate p-value and call germline

66
Q

What are the classes of structural variation?

A

Deletion

Novel sequence insertion

Mobile-element insertion

Interspersed duplication

Tandem duplication

Inversion

Translocation

67
Q

3 software that annotate variants

A

SeattleSeq

Oncotator

Annovar

68
Q

What does SeattleSeq annotate?

A

SNVs

Small indels

Both common and novel

69
Q

What does Oncotator annotate?

A

Human genomic point mutations and indels with relevant data to cancer researchers

70
Q

What does Annovar annotate?

A

Genetic variants

Detected from diverse genomes

71
Q

What do variant annotation software do?

A

Annotates the SNPs and informs as to how likely they are to be deleterious

Genomics (9 decks)

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