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T5 2017 database_searching_v_upload

Database searching including Burrows-Wheeler

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T5 2017 database_searching_v_upload

  1. 1. FBW 07-11-2017 Wim Van Criekinge
  2. 2. Google Calendar
  3. 3. DataBase Searching Dynamic Programming Reloaded Mapping short Read Bowtie / BWA Database Searching Fasta Blast Statistics Practical Guide Extentions PSI-Blast PHI-Blast Local Blast BLAT
  4. 4. Needleman-Wunsch-Complete.py The Score Matrix ---------------- Seq1(j)1 2 3 4 5 6 7 Seq2 * C K H V F C R (i) * 0 -1 -2 -3 -4 -5 -6 -7 1 C -1 1 0 -1 -2 -3 -4 -5 2 K -2 0 2 1 0 -1 -2 -3 3 K -3 -1 1 1 0 -1 -2 -3 4 C -4 -2 0 0 0 -1 0 -1 5 F -5 -3 -1 -1 -1 1 0 -1 6 C -6 -4 -2 -2 -2 0 2 1 7 K -7 -5 -3 -3 -3 -1 1 1 8 C -8 -6 -4 -4 -4 -2 0 0 9 V -9 -7 -5 -5 -3 -3 -1 -1 a bc A: matrix(i,j) = matrix(i-1,j-1) + (MIS)MATCH if (substr(seq1,j-1,1) eq substr(seq2,i-1,1) B: up_score = matrix(i-1,j) + GAP C: left_score = matrix(i,j-1) + GAP
  5. 5. Extensions to basic dynamic programming method use gap penalties – constant gap penalty for gap > 1 – gap penalty proportional to gap size • one penalty for starting a gap (gap opening penalty) • different (lower) penalty for adding to a gap (gap extension penalty) use blosum62 • instead of MATCH and MISMATCH Dynamic Programming: Needleman-Wunsch-Complete.py
  6. 6. Needleman-Wunsch-Complete.py
  7. 7. Needleman-Wunsch-Complete.py
  8. 8. Needleman-Wunsch-Complete.py
  9. 9. •The most practical and widely used method in multiple sequence alignment is the hierarchical extensions of pairwise alignment methods. •The principal is that multiple alignments is achieved by successive application of pairwise methods. • First do all pairwise alignments (not just one sequence with all others) • Then combine pairwise alignments to generate overall alignment Multiple Alignment Method
  10. 10. • Multiple Sequence Alignment: –ClustalW, MSA • Short Read Sequence Alignment: –BWA, Bowtie • Database Search: –BLAST, FASTA, HMMER • Genomic Analysis: –BLAT Sequence Alignment Tools
  11. 11. Read Length is Not As Important For Resequencing 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 8 10 12 14 16 18 20 Length of K-mer Reads (bp) %ofPairedK-merswithUniquely AssignableLocation E.COLI HUMAN Jay Shendure
  12. 12. Short Read Alignment Software Bowtie: memory-‐efficientshort read aligner. It aligns short DNA sequences (reads) to the human genome at a rate of over 25 million 35-‐bpreads per hours Burrows-‐Wheeler Aligner (BWA): an aligner that implements two algorithms: bwa-‐shortand BWA-‐ SW. The former works for query sequences shorter than 200 bp and the latter for longer sequences up to around 100 kbp.
  13. 13. Mapping Reads Back • Hash Table (Lookup table) – FAST, but requires perfect matches. [O(m n + N)] • Array Scanning – Can handle mismatches, but not gaps. [O(m N)] • Dynamic Programming (Smith Waterman) – Indels – Mathematically optimal solution – Slow (most programs use Hash Mapping as a prefilter) [O(mnN)] • Burrows-Wheeler Transform (BW Transform) – FAST. [O(m + N)] (without mismatch/gap) – Memory efficient. – But for gaps/mismatches, it lacks sensitivity
  14. 14. Why Burrows-Wheeler? • BWT very compact: – Approximately ½ byte per base – As large as the original text, plus a few “extras” – Can fit onto a standard computer with 2GB of memory • Linear-time search algorithm – proportional to length of query for exact matches
  15. 15. Burrows-Wheeler Transform (BWT) acaacg$ $acaacg aacg$ac acaacg$ acg$aca caacg$a cg$acaa g$acaac gc$aaac Burrows-Wheeler Matrix (BWM) BWT
  16. 16. Burrows-Wheeler Transform a ba a ba $ T Sort a bba $ a a BWT(T) Last column $ a b a a b a a $ a b a a b a a b a $ a b a b a $ a b a a b a a b a $ b a $ a b a a b a a b a $ a Burrows-Wheeler Matrix Burrows M,Wheeler DJ: A block sorting lossless data compression algorithm. Digital Equipment Corporation, Palo Alto, CA 1994, Technical Report 124; 1994 Reversible permutation of the characters of a string, used originally for compression How is itreversible?How is it useful forcompression? How is it anindex?
  17. 17. Burrows-Wheeler Transform def rotations(t): """ Return list of rotations of input string t """ tt = t * 2 return [ tt[i:i+len(t)] for i in xrange(0, len(t)) ] def bwm(t): """ Return lexicographically sorted list of t’s rotations """ return sorted(rotations(t)) def bwtViaBwm(t): """ Given T, returns BWT(T) by way of the BWM """ return ''.join(map(lambda x: x[-‐1], bwm(t))) Make list of all rotations Sort them Take last column >>> bwtViaBwm("Tomorrow_and_tomorrow_and_tomorrow$") 'w$wwdd nnoooaattTmmmrrrrrrooo ooo' >>> bwtViaBwm("It_was_the_best_of_times_it_was_the_worst_of_times$") 's$esttssfftteww_hhmmbootttt_ii woeeaaressIi ' >>> bwtViaBwm('in_the_jingle_jangle_morning_Ill_come_following_you$') 'u_gleeeengj_mlhl_nnnnt$nwj lggIolo_iiiiarfcmylo_oo_' Python example: BWT_v2.py
  18. 18. Key observation – T ranking 1$acaacg1 2aacg$ac1 1acaacg$1 3acg$aca2 1caacg$a1 2cg$acaa3 1g$acaac2 a1c1a2a3c2g1$1 “last first (LF) mapping” The i-th occurrence of character X in the last column corresponds to the same text character as the i-th occurrence of X in the first column.
  19. 19. Burrows-Wheeler Transform: LF Mapping BWM with T-ranking: $ a0 b0 a1 a2 b1 a3 a3 $ a0 b0 a1 a2 b1 a1 a2 b1 a3 $ a0 b0 a2 b1 a3 $ a0 b0 a1 a0 b0 a1 a2 b1 a3 $ b1 a3 $ a0 b0 a1 a2 b0 a1 a2 b1 a3$ a0 F L LF Mapping: The ith occurrence of a character c in L and the ith occurrence of c in F correspond to the same occurrence in T However we rank occurrences of c, ranks appear in the same order in F and L
  20. 20. Why does the LF Mapping hold ?
  21. 21. Burrows-Wheeler Transform: LF Mapping BWM with B-ranking: a3 b1 a1 a2 b0 $ a3 b1 a1 a2 a2 b0 a3 $ a3 b0 a0 $ a3 b1 b1 a1 a2 b0 a0 a0 $ a3 b1 a1 a1 a2 b0 a0 $ F $ a0 a1 a2 a3 b0 b1 L a0 b0 b1 a1 $ a2 a3 Ascending rank F now has very simple structure: a $, a block of as with ascending ranks, a block of bs with ascending ranks
  22. 22. Burrows-Wheeler Transform F L $ a0 a0 b0 a1 b1 a2 a1 a3 $ b0 a2 b1 a3row 6 Which BWM row begins with b1? Skip row starting with $ (1 row) Skip rows starting with a (4 rows) Skip row starting with b0 (1 row) Answer: row 6
  23. 23. Burrows-Wheeler Transform Say T has 300 As, 400 Cs, 250 Gs and 700 Ts and $ < A < C < G < T Which BWM row (0-based) begins with G100? (Ranks areB-ranks.) Skip row starting with $ (1 row) Skip rows starting with A (300 rows) Skip rows starting with C (400 rows) Skip first 100 rows starting with G (100 rows) Answer: row 1 + 300 + 400 + 100 = row 801
  24. 24. Burrows-Wheeler Transform:reversing Reverse BWT(T) starting at right-hand-side of T and moving left L a0 b0 b1 a1 $ a2 a3 F $ a0 a1 a2 a3 b0 b1 Start in first row. F must have $. L contains character just prior to $: a0 a0: LF Mapping says this is same occurrence of a as first a in F.Jump to row beginning with a0. L contains character just prior to a0:b0. Repeat for b0, get a2 Repeat for a2, get a1 Repeat for a1, get b1 Repeat for b1, get a3 Repeat for a3, get$, done Reverse of chars we visited = a3 b1 a1 a2 b0 a0 $ = T
  25. 25. Burrows-Wheeler Transform:reversing Another way to visualize reversing BWT(T): F L $ a0 a0 b0 a1 b1 a2 a1 a3 $ b0 a2 b1 a3 $ a0 a1 b1 a2 a1 a3 $ b0 a2 b1 a3 $ a0 a0 b0 a1 b1 a2 a1 a3 $ b1 a3 $ a0 a0 b0 a1 b1 a3 $ b0 a2 b1 a3 $ a0 a0 b0 a2 a1 a3 $ b0 a2 b1 a3 $ a0 a0 b0 a1 b1 a2 a1 a3 $ b0 a2 b1 a3 F L F L F L F L F L F L a0 b0 a1 b1 a2 a1 b0 a2 $ a0 a0 b0 a1 b1 a2 a1 a3 $ b0 a2 b1 a3 T: a3 b1 a1 a2 b0 a0 $
  26. 26. BWT is useful forcompression: Sorts characters by right-context, making a more compressible string It’sreversible: Repeated applications of LF Mapping, recreating T from right to left FM - Index Burrows-Wheeler Transform
  27. 27. Steps in using BWA Download and install BWA on Linux/Mac. Export the path or use the exact path. bunzip2 bwa-0.5.9.tar.bz2 tar xvf bwa-0.5.9.tar cd bwa-0.5.9 | make make Download the reference genome using wget.
  28. 28. Create the index for the reference genome (assuming the reference sequences are in wg.fa). Only needs to be performed once for each genome. Use –a for small genomes. • Mapping short reads to the reference genome. • 1. Align sequences using mul0ple threads (eg 4 CPUs). Assume the short reads are in the s_3_sequence.txt.gz file. • bwa aln -t 4 hg19bwaidx s_3_sequence.txt.gz > s_3_sequence.txt.bwa bwa index -p hg19bwaidx -a bwtsw wg.fa
  29. 29. 2. Create alignment in the SAM format (a generic format for storing large nucleo0de sequence alignments): bwa samse hg19bwaidx s_3_sequence.txt.bwa s_3_sequence.txt.gz > s_3_sequence.txt.sam Mapping long reads can be done using the bwasw command: bwa bwasw hg19bwaidx 454seqs.txt > 454seqs.sam
  30. 30. Sequence Alignment/Map Format Sequence Reads + Reference Sequence Alignment Software SAM File Resequencing RNA Seq SNPs Reads: Illumina reads. Reference: whole genome, contig, chromosome. BWA, Bowtie Most of the analysis happens when considering the SAM files.
  31. 31. SAM format “A tab-‐delimitedtext format consisting of a header section, which is optional, and an alignment section” https://samtools.github.io/hts-specs/SAMv1.pdf
  32. 32. Example of CIGAR RefPos: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 Reference: C C A T A C T G A A C T G A C T A A C Read: A C T A G A A T G G C T In the SAM file you will have the following fields: • POS:5 • CIGAR: 3M1I3M1D5M The POS indicates that the read aligns starting at position 5 on the reference. The CIGAR says that the first 3 bases in the read sequence align with the reference. The next base in the read does not exist in the reference. Then 3 bases align with the reference. The next reference base does not exist in the read sequence, then 5 more bases align with the reference. Note that at position 14, the base in the read is different than the reference, but it still counts as an M since it aligns to that position.
  33. 33. Harvesting Information from SAM • Query name, QNAME (SAM)/read_name (BAM). • FLAG provides the following informa0on: – are there multiple fragments? – are all fragments properly aligned? – is this fragment unmapped? – is the next fragment unmapped? – is this query the reverse strand? – is the next fragment the reverse strand? – is this the last fragment? – is this a secondary alignment? – did this read fail quality controls? – is this read a PCR or optical duplicate?
  34. 34. BAM • BAM is a compressed version of the SAM file format. • BAM is compressed in the BGZF format. All multi-byte numbers in BAM are little-endian, regardless of the machine endianness. As an example, suppose we have the hexadecimal number 12345678. • There are multiple programs that convert BAM files to SAM files and vice versa (eg samtools)
  35. 35. DataBase Searching Dynamic Programming Reloaded Mapping short Read Bowtie / BWA Database Searching Fasta Blast Statistics Practical Guide Extentions PSI-Blast PHI-Blast Local Blast BLAT
  36. 36. • Consider the task of searching SWISSPROT against a query sequence: • say our query sequence is 362 amino acids long • SWISSPROT release 38 contains 29,085,265 amino acids • finding local alignments via dynamic programming would entail O(1010) matrix operations • Given size of databases, more efficient methods needed Database Searching
  37. 37. FASTA (Pearson 1995) Uses heuristics to avoid calculating the full dynamic programming matrix Speed up searches by an order of magnitude compared to full Smith- Waterman The statistical side of FASTA is still stronger than BLAST BLAST (Altschul 1990, 1997) Uses rapid word lookup methods to completely skip most of the database entries Extremely fast One order of magnitude faster than FASTA Two orders of magnitude faster than Smith- Waterman Almost as sensitive as FASTA Heuristic approaches to DP for database searching
  38. 38. « Hit and extend heuristic» • Problem: Too many calculations “wasted” by comparing regions that have nothing in common • Initial insight: Regions that are similar between two sequences are likely to share short stretches that are identical • Basic method: Look for similar regions only near short stretches that match exactly FASTA
  39. 39. FASTA-Stages 1. Find k-tups in the two sequences (k=1,2 for proteins, 4-6 for DNA sequences) 2. Score and select top 10 scoring “local diagonals” 3. Rescan top 10 regions, score with PAM250 (proteins) or DNA scoring matrix. Trim off the ends of the regions to achieve highest scores. 4. Try to join regions with gapped alignments. Join if similarity score is one standard deviation above average expected score 5. After finding the best initial region, FASTA performs a global alignment of a 32 residue wide region centered on the best initial region, and uses the score as the optimized score.
  40. 40. • Sensitivity: the ability of a program to identify weak but biologically significant sequence similarity. • Selectivity: the ability of a program to discriminate between true matches and matches occurring by chance alone. • A decrease in selectivity results in more false positives being reported. FastA
  41. 41. FastA (http://www.ebi.ac.uk/fasta33/) Blosum50 default. Lower PAM higher blosum to detect close sequences Higher PAM and lower blosum to detect distant sequences Gap opening penalty -12, -16 by default for fasta with proteins and DNA, respectively Gap extension penalty -2, -4 by default for fasta with proteins and DNA, respectively The larger the word-length the less sensitive, but faster the search will be Max number of scores and alignments is 100
  42. 42. FastA Output Database code hyperlinked to the SRS database at EBI Accession number Description Length Initn, init1, opt, z- score calculated during run E score - expectation value, how many hits are expected to be found by chance with such a score while comparing this query to this database. E() does not represent the % similarity
  43. 43. Query: DNA Protein Database:DNA Protein FastA is a family of programs FastA, TFastA, FastX, FastY
  44. 44. FASTA can miss significant similarity since • For proteins, similar sequences do not have to share identical residues •Asp-Lys-Valis quite similar to •Glu-Arg-Ileyet it is missed even with ktuple size of 1 since no amino acid matches •Gly-Asp-Gly-Lys-Glyis quite similar to Gly-Glu- Gly-Arg-Glybut there is no match with ktuple size of 2 FASTA problems
  45. 45. FASTA can miss significant similarity since • For nucleic acids, due to codon “wobble”, DNA sequences may look like XXyXXyXXy where X’s are conserved and y’s are not •GGuUCuACgAAgand GGcUCcACaAAA both code for the same peptide sequence (Gly-Ser-Thr-Lys) but they don’t match with ktuple size of 3 or higher FASTA problems
  46. 46. DataBase Searching Dynamic Programming Reloaded Mapping short Read Bowtie / BWA Database Searching Fasta Blast Statistics Practical Guide Extentions PSI-Blast PHI-Blast Local Blast BLAT
  47. 47. BLAST - Basic Local Alignment Search Tool
  48. 48. What does BLAST do? • Search a large target set of sequences... • …for hits to a query sequence... • …and return the alignments and scores from those hits... • Do it fast. Show me those sequences that deserve a second look. Blast programs were designed for fast database searching, with minimal sacrifice of sensitivity to distant related sequences.
  49. 49. The big red button Do My Job It is dangerous to hide too much of the underlying complexity from the scientists.
  50. 50. • Approach: find segment pairs by first finding word pairs that score above a threshold, i.e., find word pairs of fixed length w with a score of at least T • Key concept “Neigborhood”: Seems similar to FASTA, but we are searching for words which score above T rather than that match exactly • Calculate neigborhood (T) for substrings of query (size W) Overview
  51. 51. Compile a list of words which give a score above T when paired with the query sequence. • Example using PAM-120 for query sequence ACDE (w=4, T=17): A C D E A C D E = +3 +9 +5 +5 = 22 • try all possibilities: A A A A = +3 -3 0 0 = 0 no good A A A C = +3 -3 0 -7 = -7 no good • ...too slow, try directed change Overview
  52. 52. A C D E A C D E = +3 +9 +5 +5 = 22 • change 1st pos. to all acceptable substitutions g C D E = +1 +9 +5 +5 = 20 ok n C D E = +0 +9 +5 +5 = 19 ok I C D E = -1 +9 +5 +5 = 18 ok k C D E = -2 +9 +5 +5 = 17 ok • change 2nd pos.: can't - all alternatives negative and the other three positions only add up to 13 • change 3rd pos. in combination with first position gCnE = 1 9 2 5 = 17 ok • continue - use recursion • For "best" values of w and T there are typically about 50 words in the list for every residue in the query sequence Overview
  53. 53. Blast_Neighbourhood.py
  54. 54. Blast_Neighbourhood.py
  55. 55. Blast_Neighbourhood.py
  56. 56. Blast_Neighbourhood.py
  57. 57. Blast_Neighbourhood.py
  58. 58. Blast_Neighbourhood.py
  59. 59. Blast_Neighbourhood.py
  60. 60. Blast_Neighbourhood.py
  61. 61. BLOSUM62 RGD 11 RGD 17 KGD 14 QGD 13 RGE 13 EGD 12 HGD 12 NGD 12 RGN 12 AGD 11 MGD 11 RAD 11 RGQ 11 RGS 11 RND 11 RSD 11 SGD 11 TGD 11 PAM200 RGD 13 RGD 18 RGE 17 RGN 16 KGD 15 RGQ 15 KGE 14 HGD 13 KGN 13 RAD 13 RGA 13 RGG 13 RGH 13 RGK 13 RGS 13 RGT 13 RSD 13 WGD 13
  62. 62. S Length of extension Score Trim to max indexed * *Two non-overlapping HSP’s on a diagonal within distance A
  63. 63. S Length of extension Score Trim to max indexed * *Two non-overlapping HSP’s on a diagonal within distance A
  64. 64. The BLAST algorithm • Break the search sequence into words • W = 3 for proteins, W = 12 for DNA • Include in the search all words that score above a certain value (T) for any search word MCGPFILGTYC MCG CGP MCG, CGP, GPF, PFI, FIL, ILG, LGT, GTY, TYC MCG CGP MCT MGP … MCN CTP … … This list can be computed in linear time
  65. 65. The Blast Algorithm (2) • Search for the words in the database • Word locations can be precomputed and indexed • Searching for a short string in a long string • HSP (High Scoring Pair) = A match between a query word and the database • Find a “hit”: Two non-overlapping HSP’s on a diagonal within distance A • Extend the hit until the score falls below a threshold value, S
  66. 66. True positives False positives False negatives Sequences reported as related Sequences reported as unrelated True negatives homologous sequences non-homologous sequences Sensitivity: ability to find true positives Specificity: ability to minimize false positives
  67. 67. BLAST parameters • Lowering the neighborhood word threshold (T) allows more distantly related sequences to be found, at the expense of increased noise in the results set. • Choosing a value for w • small w: many matches to expand • big w: many words to be generated • w=4 is a good compromise • Lowering the segment extension cutoff (S) returns longer extensions for each hit. • Changing the minimum E-value changes the threshold for reporting a hit.
  68. 68. Critical parameters: T,W and scoring matrix •The proper value of T depends ons both the values in the scoring matrix and balance between speed and sensitivity •Higher values of T progressively remove more word hits and reduce the search space. •Word size (W) of 1 will produce more hits than a word size of 10. In general, if T is scaled uniformly with W, smaller word sizes incraese sensitivity and decrease speed. •The interplay between W,T and the scoring matrix is criticial and choosing them wisely is the most effective way of controlling the speed and sensiviy of blast
  69. 69. DataBase Searching Dynamic Programming Reloaded Mapping short Read Bowtie / BWA Database Searching Fasta Blast Statistics Practical Guide Extentions PSI-Blast PHI-Blast Local Blast BLAT
  70. 70. Database Searching • How can we find a particular short sequence in a database of sequences (or one HUGE sequence)? • Problem is identical to local sequence alignment, but on a much larger scale. • We must also have some idea of the significance of a database hit. • Databases always return some kind of hit, how much attention should be paid to the result? • How can we determine how “unusual” a particular alignment score is?
  71. 71. Sentence 1: “These algorithms are trying to find the best way to match up two sequences” Sentence 2: “This does not mean that they will find anything profound” ALIGNMENT: THESEALGRITHMARETR--YINGTFINDTHEBESTWAYTMATCHPTWSEQENCES :: :.. . .. ...: : ::::.. :: . : ... THISDESNTMEANTHATTHEYWILLFINDAN-------YTHIN-GPRFND------ 12 exact matches 14 conservative substitutions Is this a good alignment? Significance
  72. 72. • A key to the utility of BLAST is the ability to calculate expected probabilities of occurrence of Maximum Segment Pairs (MSPs) given w and T • This allows BLAST to rank matching sequences in order of “significance” and to cut off listings at a user- specified probability Overview
  73. 73. Mathematical Basis of BLAST •Model matches as a sequence of coin tosses •Let p be the probability of a “head” • For a “fair” coin, p = 0.5 •(Erdös-Rényi) If there are n throws, then the expected length R of the longest run of heads is R = log1/p (n). •Example: Suppose n = 20 for a “fair” coin R=log2(20)=4.32 •Trick is how to model DNA (or amino acid) sequence alignments as coin tosses.
  74. 74. Mathematical Basis of BLAST •To model random sequence alignments, replace a match with a “head” and mismatch with a “tail”. •For DNA, the probability of a “head” is 1/4 • What is it for amino acid sequences? AATCAT ATTCAG HTHHHT
  75. 75. Mathematical Basis of BLAST • So, for one particular alignment, the Erdös-Rényi property can be applied • What about for all possible alignments? • Consider that sequences are being shifted back and forth, dot matrix plot • The expected length of the longest match is R=log1/p(mn) where m and n are the lengths of the two sequences.
  76. 76. Analytical derivation Erdös-Rényi … … … Karlin-Alschul
  77. 77. Karlin-Alschul Statistics E=kmn-λS This equation states that the number of alignments expected by chance (E) during the sequence database search is a function of the size of the search space (m*n), the normalized score (λS) and a minor constant (k mostly 0.1) E-Value grows linearly with the product of target and query sizes. Doubling target set size and doubling query length have the same effect on e- value
  78. 78. Analytical derivation Erdös-Rényi … … … Karlin-Alschul R=log1/p(mn) E=kmn-λS
  79. 79. Scoring alignments •Score: S (~R) •S=SM(qi,ti) - Sgaps •Any alignment has a score •Any two sequences have a(t least one) optimal alignment
  80. 80. • For a particular scoring matrix and its associated gap initiation and extention costs one must calculate λand k • Unfortunately (for gapped alignments), you can’t do this analytically and the values must be estimated empirically • The procedure involves aligning random sequences (Monte Carlo approach) with a specific scoring scheme and observing the alignment properties (scores, target frequencies and lengths)
  81. 81. “Monte Carlo” Approach: •Compares result to randomized result, similarly to results generated by a roulette wheel at Monte Carlo •Typical procedure for alignments • Randomize sequence A • Align to sequence B • Repeat many times (hundreds) • Keep track op optimal score • Histogram of scores … Significance
  82. 82. Assessing significance requires a distribution •I have an pumpkin of diameter 1m. Is that unusual? Diameter (m) Frequency
  83. 83. • In seeking optimal Alignments between two sequences, one desires those that have the highest score - i.e. one is seeking a distribution of maxima • In seeking optimal Matches between an Input Sequence and Sequence Entries in a Database, one again desires the matches that have the highest score, and these are obtained via examination of the distribution of such scores for the entries in the database - this is again a distribution of maxima. “A Normal Distribution is a distribution of Sums of independent variables rather than a sum of their Maxima.“ Normal Distribution does NOT Fit Alignment Scores !! Significance
  84. 84. Comparing distributions                x e x eexf 1    2 2 2 2 1      x exf Extreme Value:Gaussian:
  85. 85. P(xS) = 1-exp(-kmne-S) m, n: sequence lengths. k, : free parameters. This can be shown analytically for ungapped alignments and has been found empirically to also hold for gapped alignments under commonly used conditions. Alignment of unrelated/random sequences result in scores following an extreme value distribution Alignment scores follow extreme value distributions E x P = 1 –e-E E=-ln(1-P)
  86. 86. Alignment algorithms will always produce alignments, regardless of whether it is meaningful or not => important to have way of selecting significant alignments from large set of database hits. Solution: fit distribution of scores from database search to extreme value distribution; determine p-value of hit from this fitted distribution. Example: scores fitted to extreme value distribution. 99.9% of this distribution is located below score=112 => hit with score = 112 has a p-value of 0.1% Alignment scores follow extreme value distributions
  87. 87. BLAST uses precomputed extreme value distributions to calculate E- values from alignment scores For this reason BLAST only allows certain combinations of substitution matrices and gap penalties This also means that the fit is based on a different data set than the one you are working on A word of caution: BLAST tends to overestimate the significance of its matches E-values from BLAST are fine for identifying sure hits One should be careful using BLAST’s E-values to judge if a marginal hit can be trusted (e.g., you may want to use E-values of 10-4 to 10-5). Significance
  88. 88. • The distribution of scores graph of frequency of observed scores • expected curve (asterisks) according to the extreme value distribution • the theoretic curve should be similar to the observed results • deviations indicate that the fitting parameters are wrong • too weak gap penalties • compositional biases FastA Output
  89. 89. < 20 222 0 :* 22 30 0 :* 24 18 1 :* 26 18 15 :* 28 46 159 :* 30 207 963 :* 32 1016 3724 := * 34 4596 10099 :==== * 36 9835 20741 :========= * 38 23408 34278 :==================== * 40 41534 47814 :=================================== * 42 53471 58447 :============================================ * 44 73080 64473 :====================================================*======= 46 70283 65667 :=====================================================*==== 48 64918 62869 :===================================================*== 50 65930 57368 :===============================================*======= 52 47425 50436 :======================================= * 54 36788 43081 :=============================== * 56 33156 35986 :============================ * 58 26422 29544 :====================== * 60 21578 23932 :================== * 62 19321 19187 :===============* 64 15988 15259 :============*= 66 14293 12060 :=========*== 68 11679 9486 :=======*== 70 10135 7434 :======*== FastA Output
  90. 90. 72 8957 5809 :====*=== 74 7728 4529 :===*=== 76 6176 3525 :==*=== 78 5363 2740 :==*== 80 4434 2128 :=*== 82 3823 1628 :=*== 84 3231 1289 :=*= 86 2474 998 :*== 88 2197 772 :*= 90 1716 597 :*= 92 1430 462 :*= :===============*======================== 94 1250 358 :*= :============*=========================== 96 954 277 :* :=========*======================= 98 756 214 :* :=======*=================== 100 678 166 :* :=====*================== 102 580 128 :* :====*=============== 104 476 99 :* :===*============= 106 367 77 :* :==*========== 108 309 59 :* :==*======== 110 287 46 :* :=*======== 112 206 36 :* :=*====== 114 161 28 :* :*===== 116 144 21 :* :*==== 118 127 16 :* :*==== >120 886 13 :* :*============================== Related FastA Output
  91. 91. Complete version !
  92. 92. • A summary of the statistics and of the program parameters follows the histogram. • An important number in this summary is the Kolmogorov-Smirnov statistic, which indicates how well the actual data fit the theoretical statistical distribution. The lower this value, the better the fit, and the more reliable the statistical estimates. • In general, a Kolmogorov-Smirnov statistic under 0.1 indicates a good fit with the theoretical model. If the statistic is higher than 0.2, the statistics may not be valid, and it is recommended to repeat the search, using more stringent (more negative) values for the gap penalty parameters. FastA Output
  93. 93. Statistics summary • Optimal local alignment scores for pairs of random amino acid sequences of the same length follow and extreme-value distribution. For any score S, the probability of observing a score >= S is given by the Karlin-Altschul statistic (P(score>=S)=1-exp(-kmne(- lambda.S)) • k en Lambda are parameters related to the position of the maximum and the with of the distribution, • Note the long tail at the right. This means that a score serveral standard deviations above the mean has higher probability of arising by chance (that is, it is less significant) than if the scores followed a normal distribution.
  94. 94. P-values • Many programs report P = the probability that the alignment is no better than random. The relationship between Z and P depends on the distribution of the scores from the control population, which do NOT follow the normal distributions • P<=10E-100 (exact match) • P in range 10E-100 10E-50 (sequences nearly identical eg. Alleles or SNPs • P in range 10E-50 10E-10 (closely related sequenes, homology certain) • P in range 10-5 10E-1 (usually distant relatives) • P > 10-1 (match probably insignificant)
  95. 95. E • For database searches, most programs report E-values. The E-value of an alignemt is the expected number of sequences that give the same Z-score or better if the database is probed with a random sequence. E is found by multiplying the value of P by the size of the database probed. Note that E but not P depends on the size of the database. Values of P are between 0 and 1. Values of E are between 0 and the number of sequences in the database searched: • E<=0.02 sequences probably homologous • E between 0.02 and 1 homology cannot be ruled out • E>1 you would have to expect this good a match by just chance
  96. 96. DataBase Searching Dynamic Programming Reloaded Mapping short Read Bowtie / BWA Database Searching Fasta Blast Statistics Practical Guide Extentions PSI-Blast PHI-Blast Local Blast BLAT
  97. 97. BLAST is actually a family of programs: • BLASTN - Nucleotide query searching a nucleotide database. • BLASTP - Protein query searching a protein database. • BLASTX - Translated nucleotide query sequence (6 frames) searching a protein database. • TBLASTN - Protein query searching a translated nucleotide (6 frames) database. • TBLASTX - Translated nucleotide query (6 frames) searching a translated nucleotide (6 frames) database. Blast
  98. 98. Blast
  99. 99. Blast
  100. 100. Blast
  101. 101. Blast
  102. 102. Blast
  103. 103. Blast
  104. 104. Blast
  105. 105. • Be aware of what options you have selected when using BLAST, or FASTA implementations. • Treat BLAST searches as scientific experiments • So you should try your searches with the filters on and off to see whether it makes any difference to the output Tips
  106. 106. Tips: Low-complexity and Gapped Blast Algorithm • The common, Web-based ones often have default settings that will affect the outcome of your searches. By default all NCBI BLAST implementations filter out biased sequence composition from your query sequence (e.g. signal peptide and transmembrane sequences - beware!). • The SEG program has been implemented as part of the blast routine in order to mask low- complexity regions • Low-complexity regions are denoted by strings of Xs in the query sequence
  107. 107. •The sequence databases contain a wealth of information. They also contain a lot of errors. Contaminants … •Annotation errors, frameshifts that may result in erroneous conceptual translations. •Hypothetical proteins ? •In the words of Fox Mulder, "Trust no one." Tips
  108. 108. • Once you get a match to things in the databases, check whether the match is to the entire protein, or to a domain. Don't immediately assume that a match means that your protein carries out the same function (see above). Compare your protein and the match protein(s) along their entire lengths before making this assumption. Tips
  109. 109. • Domain matches can also cause problems by hiding other informative matches. For instance if your protein contains a common domain you'll get significant matches to every homologous sequence in the database. BLAST only reports back a limited number of matches, ordered by P value. • If this list consists only of matches to the same domain, cut this bit out of your query sequence and do the BLAST search again with the edited sequence (e.g. NHR). Tips
  110. 110. • Do controls wherever possible. In particular when you use a particular search software for the first time. • Suitable positive controls would be protein sequences known to have distant homologues in the databases to check how good the software is at detecting such matches. • Negative controls can be employed to make sure the compositional bias of the sequence isn't giving you false positives. Shuffle your query sequence and see what difference this makes to the matches that are returned. A real match should be lost upon shuffling of your sequence. Tips
  111. 111. Tips: Blast-shuffle.py
  112. 112. Tips: Blast-shuffle.py
  113. 113. Tips: Blast-shuffle.py
  114. 114. Tips: Blast-shuffle.py
  115. 115. Tips: Blast-shuffle.py
  116. 116. Tips: Blast-shuffle.py
  117. 117. •BLAST's major advantage is its speed. • 2-3 minutes for BLAST versus several hours for a sensitive FastA search of the whole of GenBank. •When both programs use their default setting, BLAST is usually more sensitive than FastA for detecting protein sequence similarity. • Since it doesn't require a perfect sequence match in the first stage of the search. FastA vs. Blast
  118. 118. Weakness of BLAST: • The long word size it uses in the initial stage of DNA sequence similarity searches was chosen for speed, and not sensitivity. • For a thorough DNA similarity search, FastA is the program of choice, especially when run with a lowered KTup value. • FastA is also better suited to the specialised task of detecting genomic DNA regions using a cDNA query sequence, because it allows the use of a gap extension penalty of 0. BLAST, which only creates ungapped alignments, will usually detect only the longest exon, or fail altogether. • In general, a BLAST search using the default parameters should be the first step in a database similarity search strategy. In many cases, this is all that may be required to yield all the information needed, in a very short time. FastA vs. Blast
  119. 119. DataBase Searching Dynamic Programming Reloaded Mapping short Read Bowtie / BWA Database Searching Fasta Blast Statistics Practical Guide Extentions PSI-Blast PHI-Blast Local Blast BLAT
  120. 120. 1. Old (ungapped) BLAST 2. New BLAST (allows gaps) 3. Profile -> PSI Blast - Position Specific Iterated  Strategy:Multiple alignment of the hits Calculates a position-specific score matrix Searches with this matrix  In many cases is much more sensitive to weak but biologically relevant sequence similarities  PSSM !!! PSI-Blast
  121. 121. • Patterns of conservation from the alignment of related sequences can aid the recognition of distant similarities. • These patterns have been variously called motifs, profiles, position-specific score matrices, and Hidden Markov Models. For each position in the derived pattern, every amino acid is assigned a score. (1) Highly conserved residue at a position: that residue is assigned a high positive score, and others are assigned high negative scores. (2) Weakly conserved positions: all residues receive scores near zero. (3) Position-specific scores can also be assigned to potential insertions and deletions. PSI-Blast
  122. 122. Pattern •a set of alternative sequences, using “regular expressions” •Prosite (http://www.expasy.org/pr osite/)
  123. 123. PSSM (Position Specific Scoring Matrice)
  124. 124. PSSM (Position Specific Scoring Matrice)
  125. 125. PSSM (Position Specific Scoring Matrice)
  126. 126. •The power of profile methods can be further enhanced through iteration of the search procedure. • After a profile is run against a database, new similar sequences can be detected. A new multiple alignment, which includes these sequences, can be constructed, a new profile abstracted, and a new database search performed. • The procedure can be iterated as often as desired or until convergence, when no new statistically significant sequences are detected. PSI-Blast
  127. 127. (1) PSI-BLAST takes as an input a single protein sequence and compares it to a protein database, using the gapped BLAST program. (2) The program constructs a multiple alignment, and then a profile, from any significant local alignments found. The original query sequence serves as a template for the multiple alignment and profile, whose lengths are identical to that of the query. Different numbers of sequences can be aligned in different template positions. (3) The profile is compared to the protein database, again seeking local alignments using the BLAST algorithm. (4) PSI-BLAST estimates the statistical significance of the local alignments found. Because profile substitution scores are constructed to a fixed scale, and gap scores remain independent of position, the statistical theory and parameters for gapped BLAST alignments remain applicable to profile alignments. (5) Finally, PSI-BLAST iterates, by returning to step (2), a specified number of times or until convergence. PSI-Blast
  128. 128. From: http://bioweb.pasteur.fr/seqanal/blast/intro-uk.html PSI-BLAST PSSM PSSM
  129. 129. PSI-BLAST
  130. 130. PSI-BLAST
  131. 131. PSI-BLAST
  132. 132. PSI-BLAST
  133. 133. PSI-BLAST pitfalls •Avoid too close sequences: overfit! •Can include false homologous! Therefore check the matches carefully: include or exclude sequences based on biological knowledge. •The E-value reflects the significance of the match to the previous training set not to the original sequence! •Choose carefully your query sequence. •Try reverse experiment to certify.
  134. 134. • A single sequence is selected from a set of blocks and enriched by replacing the conserved regions delineated by the blocks by consensus residues derived from the blocks. • Embedding consensus residues improves performance • S. Henikoff and J.G. Henikoff; Protein Science (1997) 6:698-705. Reduce overfitting risk by Cobbler
  135. 135. DataBase Searching Dynamic Programming Reloaded Mapping short Read Bowtie / BWA Database Searching Fasta Blast Statistics Practical Guide Extentions PSI-Blast PHI-Blast Local Blast BLAT
  136. 136. PHI-Blast Local Blast (Pattern-Hit Initiated BLAST)
  137. 137. PHI-Blast Local Blast From: http://bioweb.pasteur.fr/seqanal/blast/intro-uk.html
  138. 138. PHI-Blast Local Blast
  139. 139. PHI-Blast Local Blast
  140. 140. PHI-Blast Local Blast
  141. 141. DataBase Searching Dynamic Programming Reloaded Mapping short Read Bowtie / BWA Database Searching Fasta Blast Statistics Practical Guide Extentions PSI-Blast PHI-Blast Local Blast BLAT
  142. 142. Installing Blast Locally • 2 flavors: NCBI/WuBlast • Excutables: • ftp://ftp.ncbi.nih.gov/blast/executables/ • Database: • ftp://ftp.ncbi.nih.gov/blast/db/ • Formatdb • formatdb -i ecoli.nt -p F • formatdb -i ecoli.protein -p T • For options: blastall - • blastall -p blastp -i query -d database -o output
  143. 143. DataBase Searching Dynamic Programming Reloaded Mapping short Read Bowtie / BWA Database Searching Fasta Blast Statistics Practical Guide Extentions PSI-Blast PHI-Blast Local Blast BLAT
  144. 144. Main database: BLAT • BLAT: BLAST-Like Alignment Tool • Aligns the input sequence to the Human Genome • Connected to several databases, like: • mRNAs - GenScan • ESTs - TwinScan • RepeatMasker - UniGene • RefSeq - CpG Islands
  145. 145. -BLAT(compared with existing tools) -more accurate -500 times faster in mRNA/DNA alignment -50 times faster in protein/protein alignment -BLAT’s steps 1.using nonoverlapping k-mers to create index 2.using index to find homologous region 3.aligning these regions seperately 4.stiches these aligned region into larger alignment 5.revisit small internal exons possibly missed in first stage and adjusts large gap boundaries that have canonical splice sites where feasible
  146. 146. Weblems W5.1: Submit the amino acid sequence of papaya papein to a BLAST (gapped and ungapped) and to a PSI-BLAST search. What are the main difference in results? W5.2: Is there a relationship between Klebsiella aerogenes urease, Pseudomonas diminuta phosphotriesterase and mouse adenosine deaminase ? Also use DALI, ClustalW and T-coffee. W5.3: Yeast two-hybrid typically yields DNA sequences. How would you find the corresponding protein ? W5.4: When and why would you use tblastn ? W5.5: How would you search a database if you want to restrict the search space to those entries having a secretion signal consisting of 4 consecutive (N- terminal) basic residues ?

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