I am sharing my experince with various Suffix Array Construction Algorithms(SACA).
Visualizer for Suffix Tree https://visualgo.net/en/suffixtree
Few years back I stumbled upon this nice data structure Suffix Tree. I must admit its construction is a bit complex. While studying more about it, I read about Suffix Array. There are so many algorithm being developed considering vast application of Suffix Array in patter matching, compression, Genome Sequencing.These Algorithm can be categorised into broadly three category.
- Prefix Doubling - Manaber -meyers/Larsson-Sadakane
- Recursive Algorithm - DC3, KA Algroithm, NZC Algorithm
- Induced Sorting - S Algorith/MF Algorithm
Complete taxonomy of these algorithm can be found here
Key idea is if you take cyclic rotation at each position of a given string and sort it and remove everything after $ from each string you will have a sorted suffix. Lets see this with an example String = ababa Append $ at the end this will help in indetifying each uique suffix, for example try to make suffix tree for papa it will be a pa apa papa
a & apa share common prefix a , so suffix 'a' is invisible, to fix this we add $ now suffixs are $ a$ pa$ apa$ papa$
So our input string is ababaa$ Lets do cyclic rotation , sort it and remove everything after $ symbol. Cyclic rotation is nothing but starting at each index , taking into account each symbol and finally ending at one previous position.
| index | string | index | string | index | string |
|---|---|---|---|---|---|
| 0 | ababaa$ | 6 | $ababaa | 6 | $ |
| 1 | babaa$a | 5 | a$ababa | 5 | a$ |
| 2 | abaa$ab | 4 | aa$abab | 4 | aa$ |
| 3 | baa$aba | 2 | abaa$ab | 2 | abaa$ |
| 4 | aa$abab | 0 | ababaa$ | 0 | ababaa$ |
| 5 | a$ababa | 3 | baa$aba | 3 | baa$ |
| 6 | $ababaa | 1 | babaa$a | 1 | babaa$ |
So final suffix array would be [6, 5, 4, 2, 0, 3, 1] Now how to convert this into an efficient algorithm. We will first sort 1 characters and then at each iteration double up, so total iterations would be log(n) , in each iteration we will do 'n' cycle to calculate 'order' and 'rank'. So overall complexity would be O(n log(n)) Lets explain this order and rank.
order: index of suffix in lexigoraphic order in this scan cycle. rank: different types of string we encountered in this scan cycle.
Lets start with 1
ababaa$
Counting sort Partial Sum [count]
$ -> 1 $ -> 1
a -> 4 a -> 5
b -> 2 b -> 7
Scan from backward Each count[symbol]-- order [count[symbol]-- ] = pos
ababaa$
| i | str | Count | Order |
|---|---|---|---|
| 6 | $ | 0 | 6, , , , , , |
| 5 | a | 4 | 6, , , ,5, , |
| 4 | a | 3 | 6, , ,4,5, , , |
| 3 | b | 6 | 6, , ,4,5, ,3 |
| 2 | a | 2 | 6, ,2,4,5, ,3 |
| 1 | b | 5 | 6, ,2,4,5,1,3 |
| 0 | a | 1 | 6,0,2,4,5,1,3 |
We scan the input string from backward and we know based on couting sort that their is 1
rank array : each distinguised symbol occur in order rank = [0,1,1,1,1,2,2] because there are only 3 different elements i.e. $, a, b
Similar in next iteration we will take 2 symbol at a time from the order/rank of previous cycle. 0123456 Original = ababaa$ order = [6, 0, 2, 4, 5, 1, 3] rank = [0, 1, 1, 1, 1, 2, 2]
Scan the order of previous cycle from back Get its order and move L position back and get the class of this index. order['b']= 3 move L symbol back i.e. 'a' newOrder[rank['a']]== pos
| i | order [i] | back symbol | count | order |
|---|---|---|---|---|
| 6 | 3 | a(2) | 4 | , , , ,2, , |
| 5 | 1 | a(0) | 3 | , , ,0,2, , , |
| 4 | 5 | a(4) | 2 | , ,4,0,2, , |
| 3 | 4 | b(3) | 6 | , ,4,0,2, ,3 |
| 2 | 2 | b(1) | 5 | , ,4,0,2,1,3 |
| 1 | 0 | $(6) | 0 | 6, ,4,0,2,1,3 |
| 0 | 6 | a(5) | 1 | 6,5,4,0,2,1,3 |
So after this iteratior we have SA when 2 symbol taken at a time. Next calculate rank for these , by coparing first half and second half for next string. Repeat the same process now L =2 and in next iteratior we double up i.e 2 symbol back.
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BWT
BWT are helpful in converting and inpur string containing repeated subsequence into runs and then run length encoding can be used to allow compression. For a given input string, sort all cyclic rotation in lexigoraphical order and the last column give you BWT. For example ababaa$ Refer the table above , BWT(ababaa$) = aabb$aa Actually they are one previous character in suffix array. Trick is in English language any common word like and when suffix sorted its previous characters(in last column) would be a and that would contribute to run.
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Inverting Lets see if we have BWT can we recover original string. For sake of illustration lets number a and b. String = a0b0a1b1a2a3$
BWT = a3a2b1b0$a1a0
Naive Way.
As we know BWT is last column in sorted cyclic rotation.We also know that first column is sorted.
F L $ ----- a3 a3 ----- a2 a2 ----- b1 a1 ----- b0 a0 ----- $ b0 ----- a1 b1 ----- a0 combine them because we know what preceeds each sorted suffix and then sort
a$ $a---a a$a $ab--a a$ab $aba--a a$aba $abab-a a$abab
$ababaa aa a$ ---a aa$ a$a--a aa$a a$ab--a aa$ab a$aba-a aa$aba a$ababa ba aa---b baa aa$--b baa$ aa$a--b baa$a aa$ab-b baa$ab aa$abab ba Sort->ab---b Join -> bab Sort-->aba--b Join->baba Sort->abaa--b Join->babaa Sort->abaa$-bJoin->babaa$Sort->abaa$ab
$a ab---$ $ab aba--$ $aba abab--$ $abab ababa-$ $ababa ababaa$ ab ba---a aba baa--a abaa baa$--a abaa$ baa$a-a abaa$a baa$aba ab ba---a aba bab--a abab baba--a ababa babaa-a ababaa babaa$aKeep doing this and eventually we get the input string. But the problem is it take lot more memory.
First last property.
Occurence of any symbol in first and last column matches. Trick is we know that BWT contains symbol one previous that of suffix array (which are in lexigraphical order). So start with $ (because we know for sure that this is last symbol in original string). Now BWT corresponding to this $ tells what lies before $ so that symbol preceed $ in original string, take that lookup i first column and find again the correspoding BWT symbol. Repeating this process we spell out original string. Take example above $ ->a1 -> a2-> b1->a3->b2-> a4->$ So original string is ababaa$
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Pattern Matching using LF mapping Next lets see how BWT is useful in pattern matching. Key is to sart searching for pattern in backward fashion because BWT stores what lies before any given symbol. For example to search aba in ababaaa start with aba and look in first column. There are 4 places starts with a but only 2 has last columns as b (row 2 & 3) Selct those b's row and look which of them has last column as aba , both b rows has, so aba occurs 2 times in original text. Problem with this approach is one has to linearly scan the whole last column range to find the symbol. This can be solved using Couting filter which store at each row , for each symbol , its count in last column. So now while looking for last column look that the symbol transition range. For example
Fst Last $ a b $ a 0 0 0 a a 0 1 0 a b 0 2 0 a b 0 2 1 a $ 0 2 2 b a 1 2 2 b a 1 3 2 X X 1 4 2 For a in first column b changes from 0 to 2 , that means there are 2 b's in last column in the given first column of a. Goto B column and then look for 'a' and it changes 2 to 3 that means their is 1 a before a so pattern aba occurs once in string.
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FM-Index There are still 3 issues with above way of pattern matching.
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Scanning in last column can be linear scan and in worst case can be O(n). Key idea to fix is to store a tally table for occurence of each symbol in last column. For example
a b 0 0 1 0 2 0 2 1 2 2 3 2 4 2 This basically tells at each row how many a and b are seen, for example row = 3 , a=2 and b=1 i.e. 2 a's and 1 b's are seen. Now storing this kind of table also can take space, instead we store every 5th or so(checkpoint) entried and infer the requried value from nearest checkpoint. This also solved 2nd issue of saving space.
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Storing count array for each symbol take O(n) * |alphabet|
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This kind of pattern matching didnt tell posiition at which the match occurs. Use Suffix Array but store suffix's at every other 'k' position with respect to n not suffix Array. This is important because storing every kth suffix wrt to T give us a constant time to k to find suffix array t didnt available at a given row position.We will use LF mapping to find the row which has Suffix Array value present.
SA(row)= SA(k) + number of LF mapping jumps.
Memory Foot print of FM-Index First we store F and L rows, F is basically symbol in sorted occurence, just store start index for each symbol. L is basically BWT of N , so its size will be O(n) For checkpoint we save fraction of tally , lets say we wanted to a fraction, so total size will be n * a * symbol_size
For SA , since we are storing every kth suffix in T , total size would be n * k
Reference: https://www.youtube.com/watch?v=kvVGj5V65io&t=824s https://www.cs.jhu.edu/~langmea/resources/lecture_notes/bwt_and_fm_index.pdf
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Wavelet Tree During pattern matching we have to search last column from 0 to start/end of index of first character. Like searching aba , we first goto first column and find range of a i.e. 1,4 during in last column we have to find b rank(1, b) and rank(4, b) , we wanted to find number of b in last column from 0 to 1 and 0 to 4. We achieved this by using count array and some optimization in earlier section. Here we will see alternative approach using wavelet tree to answer these queries in O(1) time Broadly these are the steps.
- Encode first half of string as 0 and second half as 1
- Group each 0-encoded symbol as left subtree
- Group 1-encoded symbol as right subtree
- Recursively do that.
Lets see how we create Wavelet Tree of BWT aabb$aa {$,a} -> 0 {b} -> 1 aabb$aa 0011000 / / aa$aa 11011
aabb$aa 0123456 Suppose we need to find rank(4,a) , since a is encoded as 0 find rank(0, 4) which would be 2. At this level 1, a is encoded as 1 so find rank(1,2), which would be 2. Take right child path this will be all a i.e. 1111, do a rank query of rank(1,2) which is 2 hence we rank(4,a) would be 2. http://alexbowe.com/wavelet-trees/ https://github.com/alexbowe/wavelet-paper/blob/master/thesis.pdf -
RRR Data structure. http://alexbowe.com/rrr/ wavelet tree node when stored as RRR sequence can answer rank queries in O(1) time