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MBB267 > Chaudhuri > Flashcards

Chaudhuri Flashcards

1
Q

What is bioinformatics?

A
  • science of collecting and analysing complex biological data, eg. genetic codes
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2
Q

What does bioinformatics exist at the interface of?

A
  • computing, biology and maths
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3
Q

Why are bioinformatics skills so in demand?

A
  • seq data accum faster than ability to analyse it and even to store it
  • transferable an necessary
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4
Q

How quickly has cost of sequencing decreased?

A
  • quicker than Moore’s Law (= computational power ≈ x2 every 18 months
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5
Q

What caused a large decrease in seq cost in 2008?

A
  • Illumina
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6
Q

How is Illumina paired-end sequencing carried out? (overview)

A
  • in library prep, fragments of ≈500bp selected
  • bridge amplification results in clusters, each w/ many copies of both strands of fragment
  • sequencing reads gen separately using primers complementary to both adaptors
  • expect those read pairs to map 500bp apart on opp strands
  • 3rd primer used to seq index barcode present in 1 of adaptors to enable identification of sample
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7
Q

What does homologous mean?

A
  • same reaction, relative position or structure
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8
Q

If 2 seqs have 12/16 of the same bases what can you say about identity and homologous?

A
  • 75% identity

- NOT 75% homologous –> either homologous or not

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9
Q

What does aligning 2 seqs tell you?

A
  • how many changes would be req to get those seqs, under assumption that aligned positions share common origin
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10
Q

What does introducing gaps when aligning seqs allow?

A
  • max no. matches

- represents insertions/deletions = indents (a imposs to distinguish)

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11
Q

What is seq alignment used for in bioinformatics?

A
  • identify homologous seqs w/ common ancestor
  • assess how similar homologous seqs are to infer evo relationships between groups of seqs
  • assemble short reads into contiguous seqs and ultimately seq entire chromosomes/genomes
  • map seq reads to reference genome
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12
Q

What is the more likely option when deciding how to align seqs?

A
  • one explained by less evolutionary events
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13
Q

How is seq alignment decided?

A
  • scoring system
  • matches assigned +ve score
  • mismatches/gaps assigned -ve score
  • sometimes 1 penalty for opening new gap and 2nd lower penalty for extending growth (as bigger gaps favoured over several small gaps)
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14
Q

What are scoring matrices and why are they used?

A
  • for nt alignment mismatches usually all treated the same
  • for AAs, scoring matrix used so biochemically conservative AA subs penalised less than subs likely to affect protein structure
  • eg. BLOSUM62, PAM70
  • constructed empirically by examining freq of each AA sub across large collection of protein alignments
  • eg. Ile for Leu is match
  • eg. Trp v unique and doesn’t like to be sub
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15
Q

What is global alignment, and when is it suitable?

A
  • attempt made to align seqs across entire length
  • assumes seqs equivalent
  • not suitable for aligning full length seq w/ partial seq
  • 1st global alignment algorithm proposed by Needleman and Wunsch
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16
Q

What is local alignment?

A
  • searches subsequences of full length seq to max alignment score
  • 1st algorithm by Smith and Waterman
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17
Q

How does BLAST work?

A
  • widely used method of searching database to rapidly identify seqs similar to query seqs
  • user supplies query seq and BLAST searches for similar seqs
  • performs local alignment to identify regions of hit that match query seq
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18
Q

What is the output of BLAST?

A
  • E value = P value normalised to database size and length of query seq
  • effectively no. hits expected to be found by chance in this database
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19
Q

What are the difficulties w/ de novo assembly?

A
  • unknown target
  • coverage bias
  • sequencing errors
  • repeats
  • multiple replicons
  • contamination
  • circular genomes
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20
Q

Why is genome assembly difficult w/ short reads?

A
  • resolving repeats esp hard –> paired end reads can help, but only for repeats smaller than insert size
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21
Q

What is overlap layout consensus seq, and when would it work?

A
  • looks for overlaps between adj reads
  • would work well if genomes non repetitive and seq error free
  • repeats can result in mis-assembly errors
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22
Q

What are de Bruijn graphs?

A
  • common approach to assembling short reads, to take account of seq errors and repeats in genome
  • break read up into Kmers
  • K = no. of bases, usually 51/99 (usually odd no.)
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23
Q

What are the advs of de Bruijn graphs?

A
  • stops assembly errors as allow repeats to be identified
  • each K-mer in seq once and expect at least 30x coverage for each Kmer and even more for repeat seq
  • Kmers only need to be stored in memory once so less RAM needed
  • removal of rare Kmers corrects for seq errors
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24
Q

How can bubbles be resolved using read pairs in de Bruijn graphs?

A
  • read pairs can provide info which spans repeat seqs, helping resolve order of contigs and close the assembly
  • resolving 1 of key functions of genome assembly software
  • if can’t be resolved, results in break in assembly
  • as reads get longer, graph gets simpler
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25
Q

How many reading frames are there for DNA?

A
  • 6
  • triplet genetic code, so 3 distinct ways DNA strand can encode a protein and 3 more in reverse direction on complementary strand
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26
Q

How can you identify gene by looking at ORFs?

A
  • longest ORF likely to be gene
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27
Q

The presence of what can identify large genes in bacteria/archaea?

A
  • long ORFs
  • AUG start codon
  • Shine-Dalgarno site
  • Pribnow box
  • characteristic base composition due to biases in codon usage
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28
Q

What are strategies for finding genes in euks?

A
  • introns so harder
  • algorithms exist but are hit and miss
  • look for
  • -> Kozak seq
  • -> euk terminator consensus
  • -> polyA adenylation signal
  • -> splice donor and acceptor sites can indicate inton presence
  • RNA seq data can reveal which regions present in mature RNAs so assist w/ identification of genes and introns
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29
Q

How can genomes be annotated?

A
  • used to be manual
  • pipelines, eg. Prokka and MAKER, provide automated annotation
  • apply no. programs to predict positions of protein coding genes, tRNA and rRNA genes
  • also use BLAST to identify homologues from which functional annotation can be transferred
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30
Q

Why is resequencing useful?

A
  • simpler to reseq from species already seq than seq new genome
  • investigate genomic variation w/in pop of species
  • understanding single gene, complex disorders and cancer
  • identifying variants for diagnosis –> may allow personalised medicine or genome editing based cures in future
  • relied on by functional genomic techs, like RNA seq and ChIP seq
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31
Q

How are reads mapped to a reference genome?

A
  • each read compared w/ sorted index derived from reference genome seq to identify short identical matches (“seed seqs”)
  • don’t use whole read as seed seq, as looking for differences, only small chunks will match
  • alignment from all seed matches extended to inc rets of read
  • alignment scoring system used to identify best mapping position, accounting for no. matches, mismatches and base qualities (mismatches at low quality bases penalised less than high quality mismatches)
  • each mapped read given mapping score, indicating confidence that read is derived from that position in genome (uniquely mapped reads have high score and ambiguously mapped have low score)
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32
Q

What are most common read mapping softwares?

A
  • BWA and Bowtie2
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33
Q

How are mapped reads stored?

A
  • usually BAM file

- contains details of which position on which chromosome mapped to and how good alignment is

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34
Q

What is the depth of coverage?

A
  • no. reads which overlap particular position
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35
Q

What is a pile up plot?

A
  • bar chart showing variation in coverage depth
36
Q

How can ambiguities be solved by paired reads?

A
  • reads in pair mapped independently, but if 1 maps to multiple positions, position of other can be used to determine correct position
  • if both reads w/in repeat, then mapping ambiguous (then 1 usually chosen and given low mapping score)
  • for some apps, low mapping score reads excluded
37
Q

How can mapped reads be visualised?

A
  • look at overview of whole chromosome, w/ focused region highlighted and ruler to show genomic coords
  • pile up plot of read depth at each position
  • positions w/ mapped reads highlighted
  • can also show equivalent into from 2nd highlighted biological sample
38
Q

Why do you need more than 1 read for each base?

A
  • if only 1x coverage would be imposs to distinguish errors from real diffs between seq sample and ref genome
  • so seq each base many times
39
Q

What assumption does having higher coverage make, and is this true?

A
  • errors random

- broadly but always

40
Q

What does variant detection software do?

A
  • uses probabilistic models to distinguish errors from homozygous and heterozygous SNPs
41
Q

How are SNPs identified by IGV?

A
  • coloured lines mean varies from ref genome
  • homozygous SNPs present in all reads, seq errors only in 1
  • heterozygous SNPs ≈ half match ref and ≈ show SNP
42
Q

How are SNP effects annotated?

A
  • programs, such as SNPeff predict SNP effects

- eg. intergenic, intron, regulatory, synonymous (doesn’t change encoded protein), non synonymous

43
Q

What are the poss sources of errors in variant callers?

A
  • random or systematic seq errors
  • mapping errors (mapper placed read in incorrect position)
  • sample contamination (contains DNA from another source w/ diff seq)
  • seq contamination (reads from another sample mislabelled)
  • errors or omissions in ref genome
44
Q

What structural variants are poss as result of larger scale chromosomal rearrangements?

A
  • insertions
  • deletions
  • duplications
  • inversions
  • translocations
45
Q

What are the diff methods of detecting structural variants?

A
  • using coverage depth
  • -> duplication where coverage depth higher and deletion where no reads
  • using read pairs
  • -> no structural variation if map to same place on ref and sample
  • -> deletion if further apart on ref
  • -> mobile element insertion if only one maps to ref
  • using split reads (for deletions)
  • -> look for indiv reads that overlap deletio
  • -> can see if read matches 2 positions, so see where gap is
  • using assembly
  • -> might get read w/ 1 end matching ref genome
  • -> then look for overlapping read pairs to assemble and see what insertion could be
46
Q

What are genome wide association studies?

A
  • simplest form of functional genomics
  • identifies regions of genome pot assoc w/ particular phenotype by statistical assoc
  • look for variants over represented
  • datasets often shown in Manhattan plot
  • can be misleading, correlation NOT causation and further studies req to establish mol mechanism underlying phenotype
47
Q

What does transcriptomics tell us?

A
  • tells us which genes expressed under particular conditions
48
Q

How is transcriptomics done?

A
  • traditionally done gene by gene, using N blotting or RT-qPCR
  • N blots use radiolabeled probes to detect specific seqs from whole RNA cell extracts
  • RT-qPCR uses fluorescently labelled primers to quantify levels of specific transcript during PCR amplification
49
Q

What are DNA microarrays used for?

A
  • measuring transcrip levels for whole genome
50
Q

How are DNA microarrays carried out?

A
  • like reverse N blot
  • can be rep 100s times on slide w/ ordered array of diff probes at known positions on slide surface
  • probes can be designed for every gene, so poss to measure global gene expression of sample
  • fluorescently labelled RNA sample added to surface of array
  • if transcript complementary to particular probe, will hybridise and spot lights up
  • measure fluorescence level of particular spot to assess abundance of transcript in sample
51
Q

How are experiments designed using high throughput methods (eg. microarrays, RNA-seq)?

A
  • same principles as any other
  • techs more expensive, so mistakes cost more, temptation to cut corners
  • as always approp controls and rep essential
  • simplest case is comparing 2 conditions (eg. treatment and control)
  • can be done of 2 separate microarrays or use 2 colour microarrays, to allow direct comparison
  • more complex designs such as courses also poss
  • can use microarrays in which red spot indicates transcript more abundant in sample, green indicated less abundant in sample and yellow indicates similar levels
52
Q

What is the output of a transcriptomic experiment?

A
  • common to focus on top few upreg and downreg genes by choosing arbitrary fold change cut off
53
Q

What are the limitations of microarrays?

A
  • low resolution sequencing tech
  • if get signal for particular prob, know that seq present in sample
  • don’t usually know if that is exact seq
  • don’t know if any seqs present not covered by microarray probes
54
Q

How is RNA-seq now carried out?

A
  • reverse transcrip, fragmentation and amplification to make cDNA library
  • high throughput seq
  • map reads to ref genome
55
Q

How do RNA-seq and microarrays compare?

A
  • both well dev w/ min technical variation
  • RNA-seq has larger dynamic range (greater ability to distinguish diff levels of expression
  • microarrays only give info for pre selected regions of genome, RNA-seq genome wide and can detect novel transcripts
  • microarrays can have dye bias effects (diff intensity for diff colour dyes)
  • RNA-seq allows detection of diff from ref genome
  • RNA-seq can be done w/o ref genome
56
Q

What is mRNA splicing?

A
  • introns spliced out of pre-mRNA to get exonic seq in mRNA
57
Q

How are reads mapped in RNA-seq?

A
  • map to ref genome to quantify expression of each gene
  • if bacterial genome, can be done using standard read-mapping software
  • not for euk, due to mRNA splicing –> need splice aware read mapper, eg. TopHat
58
Q

How is de novo transcriptome assembly carried out?

A
  • poss in absence of ref genome
  • similar to genome assembly but more complex, as not all transcripts present at same level and some genes may prod multiple diff transcripts
  • most popular software package is Trinity
59
Q

How are experiments designed for transcriptomic studies?

A
  • rep essential, typically 3x biological replicates
  • important to isolate variable of interest –> not diff person doing all WT samples and someone else mutant, as could be diffs in procedure
60
Q

How do you test for differential expression?

A
  • ratio of sample to control signal = fold change
  • usually expressed as log2(fold change) pr logFC (fold change of 1/2 = 1/2 as mich mutant as WT)
  • adv is that its symmetrical –> so genes upreg or downreg 2 fold have ratio of 2 and 1/2, respectively, but log2(fold change) of +1 and -1
  • interested if logFC signif diff from 0 –> t test
  • usually relatively few replicates, so t test lacks power
  • modern analysis programs, eg. Limma (microarray) and DEseq2 (RNA-seq) solve this by taking adv of large no. parallel experiments
61
Q

What is the diff between technical and biological replicates?

A
  • technical = doing experiment once and arraying/seq extracted RNA multiple times (tests reproducibility of techniques, not important now, as know good)
  • biological = doing experiment multiple times
62
Q

Why do we need to take multiple tests into account?

A
  • if use signif threshold of 5% , expect to see signif effects n 5% of experiments by chance
  • can lead to many false +ves when performing 1000s tests in parallel
63
Q

How are multiple tests taken into account?

A
  • usually “false discovery rate” adjustment made to P values, control % of false +ves to be equal to chosen P value cut off
64
Q

What is DNA methylation?

A
  • mostly involves add of methyl group to 5C red of cytosine
  • cat by DNA methyltransferases
  • eg. of epigenetics
65
Q

Why is DNA methylation an important epigenetic mod in higher euks?

A

Involved in many processes:

  • reg of gene expression
  • imprinting
  • X chromosome inactivation
  • silencing of germline specific genes and repeat genes
66
Q

What is DNA methylation used for in bacteria?

A
  • distinguish self DNA from non-self
  • non-self can be digested w/ REs, acting as IS
  • also important role in controlling bacterial DNA rep, limiting it to single rep per cell cycle
67
Q

What are the 3 poss contexts for methylation, and why is this important?

A
  • downstream context of cytosine critical for determining its methylation status
  • CpG (C adj to G)
  • CHG (C, any base but G, G)
  • CHH (C, 2x not G bases)
68
Q

Why are CpG dinucleotides underrepresented in genomes?

A
  • methylated cytosine easily mutates to uracil, which is repaired to thymine
69
Q

What is bisulphite sequencing?

A
  • bisulphite treatment converts unmethylated cytosine to uracil
  • methylated cytosines protected and no converted, so detected
  • can be targeted to CpG islands
70
Q

What is reduced representation bisulphite sequencing?

A
  • targets BS-seq analysis to regions of genome likely to have high CpG content
  • allows us to make most of sequencing run, particularly using lower-yield sequencing platforms
  • exploits REs w/ recognition site containing CpG
71
Q

How is direct methylation sequencing used in PacBio?

A
  • SMRT-seq allows methylated bases to be distinguished, as their presence delays progress of pol
72
Q

How is direct methylation sequencing used in Oxford Nanopore?

A
  • directly detects disruption in electrical current caused by base passing through pore in membrane
  • methylated bases give distinct signal from unmethylated
73
Q

What is ChIP used for?

A
  • isolate DNA bound by specific protein
74
Q

How is ChIP carried out?

A
  • proteins covalently crosslinked to DNA by treating w/ formaldehyde (get protein physically attached to DNA)
  • chromatin sheared by sonication or using endonuclease –> use of exonuclease allows bound DNA to be trimmed to binding site
  • immunoprecipitation and purification of bound DNA using antibody specific to protein of interest
75
Q

What is ChIP-chip (ChIP on chip), and how is it carried out?

A
  • involves identification of ChIP purified DNA using microarray
  • usually tiling microarray. w/ probes designed at regular intervals across region or whole genome
76
Q

What is ChIP-seq, and how is carried out?

A
  • DNA purified from ChIP can be identified using Illumina
  • reads mapped to ref genome and binding sites identifies as peaks in signal
  • 5’ –> 3’ exonuclease used to trim DNA before binding site
  • means offset between reads on forward and reverse strand, allows exact boundaries of binding site to be determines
  • binding site is overlap between forward and reverse peaks
77
Q

What is chromosome conformation capture (3C)?

A
  • 3C similar to ChIP-seq, but cross-links remote regions of DNA instead of DNA and protein
  • allows investigation of long range interactions between diff genomic regions, such as interaction of enhancer elements w/ target gene
78
Q

How did 3C develop further?

A
  • 3C enhanced by self-circularisation (4C) = seq info only req from 1 of interacting loci (need to know seqs of regions of gene interested in)
  • carbon copy chromosome capture (5C) = allows massively parallel analysis of ligation junction through incorp of universal primer seq
  • Hi-C = uses biotin capture of ligation junctions followed by high throughput seq
79
Q

What are the diff types of chromosome conformation capture?

A
  • 3C = 1 to 1
  • 4C = 1 to all
  • 5C = many to many
  • Hi-C = all to all
80
Q

What was the aim of the ENCODE project?

A
  • aimed at identifying all functional elements in human genome
81
Q

What were the findings of ENCODE project?

A
  • ≈80% of human genome has some biochemical function

- controversial as liberal definition of function

82
Q

What are CLIP-seq and RIP -seq similar to?

A
  • essentially same as ChIP, but w/ RNA
83
Q

What are transposon mutant screens used for?

A
  • identify genetic changes assoc w/ particular phenotype

- esp in bacteria, common to exploit transposons to gen random insertion mutations

84
Q

What are custom transposons, and how are they used?

A
  • if transposase gene removed, transposon can still move if transposase supplied, but stable otherwise
  • inc antibiotic resistance gene allows mutants to be selected
  • poss to insert at random into target gene by supplying transposase
  • once transposase removed, mutant strains will harbour stable antibiotic resistance gene at random position w/ genome
  • if gene inactivated by transposon, will be inactivated
  • -> if gene essential, mutant won’t survive
  • -> if make millions of mutants, transposons found in every gene poss to disrupt
  • -> so genes w/o insertions likely to be essential
85
Q

What is transposon directed insertion site sequencing (TraDIS)?

A
  • primers recognise inverted repeat seq and seq outwards into flanking chromosomal DNA
86
Q

What happens when there are insertions at 3’ end?

A
  • in C. dif polA gene
  • -> insertions not found w/in 5’ –> 3’ exonuclease domain but tolerated w/in rest of gene
  • TraDIS can provide info about essential regions w/in gene
87
Q

How does TraDIS work?

A
  • get input pool of random transposon mutants
  • inoculate
  • put through screen to recover = output pool
  • get TraDIS data, showing which genes essential

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