Practical considerations ofworking with sequencing data
File Types Fastq - aligner - reference(genome) coordinates Coordinate files SAM/BAM – most complete, contains all of the info in fastq and more! Bedgraph – read density along the genome Bed file –Read density reported in large continuous intervals Genes/transcript and transcript structure Transcription factor binding regions If someone does a sequencing experiment usually one of these is availableand deposited in a public database
SAM/BAM
Viewing genome coordinate files with IGV Integrated Genome Browser Cross-platform application Knows about commongenomes Genome version isimportant!
Different assemblies Genome coordinates differentbetween genome assemblies Differences accumulate overchromosome length You have to know whichassembly was used Sequencing files are nonrandomly distributed relativeto genes RNAseq—should align withexons TF binding sites—biasedtowards promoter regions
Converting coordinates UCSC liftOver -- convertsgenome coordinates Convert from one assembly toanother Cross organism conversion Mammals/vertebrates
Sequence Alignment
To do: Global alignment Local alignment Scoring Gaps Scoring matrices Database Search Statistical Significance Multiple Sequence alignment
Why compare sequences Given a new sequence, infer its functionbased on similarity to another sequence Find important molecular regions –conserved across species Determine 3d structure with homologymodeling Homologs-sequences that descendedfrom a common ancestral sequence Orthologs- separated by speciation Paralogs separated by duplication in asingle genome Basic unit of protein homology is asufficient functional unit—typicallymuch smaller than a whole gene
DNA vs Protein alignments Protein coding Typically compared in amino acid space Amino acid change slower than nucleotides Some nucleotides can change without any change to a.a. sequence Different levels of amino acid similarity can be accounted for Not all a.a. changes are equally disruptive Can detect very remote homology Non-coding regions Smaller alphabet requires more matches to achieve significance No notion of similarity—match or nor match Diverge more rapidly though some are very conserved at short evolutionary distances
What is a good sequence alignment Theory: If two sequences arehomologous we want to match up theresidues such that each residue isdescendant from a common ancestralresidue Practice: approximate string matching introduce gaps and padding to find bestmatching between two strings
Efficient alignment What is the best alignment? – we need a scoring metric Basic scoring metric (1 for matching, 0 for mismatching, 0 for a gap) Number of possible alignments is exponential in string lengthScoring is localwe apply dynamic programmingdynamic programming –solve a large problem in terms of smaller subproblems Requirements There is only a polynomial number of subproblems Align x1 xi to y1 yj Original problem is one of the subproblems Align x1 xM to y1 yN Each subproblem is easily solved from smaller subproblems
Matrix representation of an alignment
Dynamical programming approach Score the optimal alignment up to every (i,j) F(i,j) Scoring is local so F(i,j) depends only 3 other values
Global alignmentTSj-1ji-1iMijFi-1, j-1 Score(Si,Tj )Fi,j MAXFi,j-1 gpGap penaltyFi-1,j gpNeedleman & Wunsch, 197015
ExampleKeep track of the argmax!
Matrix filled out
Finding the optimal alignment
Complete Algorithm Initialization.F(0,0) 0F(0, j) - j goF(i, 0) - i go Main Iteration. Filling-in partial alignmentsFor each i 1.MFor each j 1.NF(i, j) max(F(i-1,j-1) s(xi, yj) F(i-1, j) – gp, F(i, j-1) – gp)Pt art Gap in s (deletion): from middleFi,j maxF i,j-1 - eF i,j-1 -(d e)Continue Gap in t (insertion)Fi,j maxFi-1,j-1 s(vi, wj) Match or MismatchF i,jEnd deletion: from topF i,jEnd insertion: from bottomStart Gap in t (insertion):from middle
How to decide on the correct scoring metric Scoring metrics should reflect the evolutionary process What are the odds that an alignment is biologicallymeaningful – the proteins are homologous Random model: product of chance events Non-random model: two sequences derived from a commonancestor Things to consider What is the frequency of different mutations Over what time scale?
Log-odds scoring29
Log-odds scoring30
Accepted Point Mutation (PAM) model Where do we get qXY Compare closely related proteins Find substitutions that are“accepted” to natural selection Very likely mutations E to D Very unlikely: involve C and W
Conservative substitutions
PAM1 probability matrix PAM1 probability matrix Dayhoff et al (1978)estimated probability of onestep transitions Used a family of very closelyrelated proteins Corresponds to 1 change per100 a.a.
PAM1 through PAM250 We can multiply PAM1 by itselfto get a probability matrix forlonger time scales PAM is measured in number ofchanges not time Number of changes thatoccurred is not the same asnumber of observed changes
PAM250 Only 20% identity 20% identity is close towhat you might getaligning randomsequences
Choice of scale reflects the result Human and chimp beta globin—closeorthologs Human beta and alpha globin –paralogs –further apart
PAM model Assumptions Replacement at any site depends only on the a.a. on that site, giventhe mutability of the a.a. Sequences in the training set (and those compared) have averagea.a. composition. Sources of error Many proteins depart from the average a.a. composition. The a.a. composition can vary even within a protein (e.g.transmembrane proteins). A.a. positions are not “mutated” equally probably; especially in longevolutionary distances. Rare replacements are observed too infrequently and errors in PAM1 are magnified in PAM250.
Blocks Substitution Matrices (BLOSUM): Log-likelihood matrix (Henikoff & Henikoff, 1992) BLOCKS database of aligned sequences used as primary source set. Different BOLSUMn matrices are calculated independently fromBLOCKS (ungapped local alignments) BLOSUMn is based on a cluster of BLOCKS of sequences that shareat least n percent identity BLOSUM62 represents closer sequences than BLOSUM45 BLOCKS database contains large number of ungapped multiple localalignments of conserved regions of proteins Alignments include distantly related sequences in which multiplebase substitutions at the same position could be observed
PAM vs BLOSUM PAM is based on closely related sequences, thus is biased for short evolutionarydistances where number of mutations are scalable PAM is based on globally aligned sequences, thus includes conserved and nonconserved positions; BLOSUM is based on conserved positions only Lower PAM/higher BLOSUM matrices identify shorter local alignments of highlysimilar sequences Higher PAM/lower BLOSUM matrices identify longer local alignments of moredistant sequences Matrices of choice: BLOSUM62: the all-weather matrixPAM250: for distant relatives
PAM vs BLOSUM PAM is based on closely related sequences, thus is biased for short evolutionary distances where number of mutations are scalable PAM is based on globally aligned sequences, thus includes conserved and non-conserved positions; BLOSUM is based on conserved positions only
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