Lecture 6: An Intro to Clinical Bioinformatics Flashcards

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1
Q

Clinical bioinformatics

A
  • putting together a puzzle and seeing how that effects the patient
  • sample little pieces of genome
    • some parts will easily recover and some will not
  • some parts of the genome difficult to sequence
  • hard to know where to look
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2
Q

sequencing bits of

A
  • matter that we can probably understand

- coding called the exome

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3
Q

sequencing

A
  • cutting genome section to bits and find the coding
  • align reads back to the genome (bam file)
  • undergo some checking
  • remove over sequenced reads (duplicate)
  • poor quality reads
  • complexity of sequence matters
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4
Q

variance

A
  • real deletion in DNA

- variation in genomes and across genomes

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5
Q

local realignment

A
  • to see that there is a deletion
  • pick up small deletion at about 18 to 20 base pairs
  • insertion and deletion-indels
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6
Q

Phreds (quality) scores

A
  • individual bases-look at score of reads across a sample
  • mapping qualities
  • base qualities
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7
Q

How do you know when variant is real?

A
  • fewer reads lead susceptibility
  • begging or ending reads have little cover
  • blacklisted-genomic community notes that specify regions performed badly
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8
Q

A gene may have multiple ways a machinery reads it

A

-this give rise to a new transcript

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9
Q

Effect of protein for that transcript

A
  • silicone-algorithm approach to figure out what might happen to the protein
  • seen these variant in a particular gene
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10
Q

codons

A
-read in groups of three
synonymous-amino acid is the same
-missense or non synonymous
-frameshift (indel)
 -codon for the stop get introduce
 -protein degraded and not used
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11
Q

24,000 variance in genome

A
  • filter coding region and gene likely for the disease based on the person
  • splice kind of variance we see
  • deal with the artifact of the way we see things
  • different per territory
  • focus on gene relevant to patient’s disease
  • deal w/current condition instead of secondary or incidental findingd
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12
Q

gene list

A
  • based on phenotype of that patient

- used for babies and children

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13
Q

want to get to the proteins more that are:

A

-more rare and damaging

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14
Q

take into account the quality of info

A
  • ask:
  • have we seen the variant in that family before
  • is it seen before in pop. database
  • or was hat region covered in the population database or did the person ethnicity make them susceptible to it
  • region never seen change then?
  • some known mutations in region where they are not fully concern in other species?
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15
Q

All humans are:

A

variation, but most are fine

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16
Q

big problem in completion methods to figure out what a protein will do

A

-65,000 individuals in database to figure out what variance is tolerated

17
Q

find region that are:

A

-restricted and areas that are not

18
Q

common variance

A

-predicted to have high damaging effect

19
Q

in-silicon prediction methods are:

A

-not independent

20
Q

length of sequence

A

-change predictability