**Homolog.us blog is written by professional janitors dedicated to clean up US science. During lunch breaks and other time off from the job, we discuss bioinformatics. The name 'homolog.us' is not a spelling mistake, but is derived by taking Arabic translation of the 'O' in the original word.**

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Our dear reader Nils Homer suggested FM-index as a follow-up topic in our discussions on Burrows Wheeler transform. FM-index is an important component of Bowtie algorithm allowing it to obtain small memory footprint for the indices. In this commentary, we shall give you a list of important developments in computer science related to pattern searching so that you know the historical context of those discoveries. The presentation here will consist of a set of links and brief explanation of why the algorithm was a major improvement over the existing landscape. In the subsequent posts, we shall go into details of some of those algorithms related to NGS search.

Before I start, here are few things to keep in mind about computer algorithms. You may wonder about what factors lead to selection of one algorithm over other competing ones. Speed of the algorithm is one obvious factor. The algorithm must do its task much faster than other alternatives, and computer scientists have a mathematical way to define what is ‘much faster’. It is called order of the algorithm, and is measured by the increase in time to operate on bigger and bigger sets. If an algorithm takes 10 hours to process 1 million reads, 40 hours to process 2 million reads and 90 hours to process 3 million reads, the order of the algorithm is O(N^2). The amount of time grows quadratically with the number of reads. That is less preferable than an O(N) algorithm, where the amount of time is proportional to the number of reads.

The second factor in deciding about an algorithm is the amount of RAM it takes. We talked about this extensively in our discussions on de Bruijn assemblers. Here also you can define order of the algorithm in terms of growth of RAM usage with bigger and bigger input sets.

The third factor that is never mentioned in computer science textbooks is whether the algorithm is patented. This was a key factor in widespread adoption of Burrows Wheeler transform in data compression. BWT algorithm was not patented, and Mr. Burrows and Mr. Wheeler never intended (‘threatened’) to do so, whereas arithmetic compression algorithms were patented by IBM and other parties.

Next, we shall discuss various important discoveries related to string search algorithms. Let me define the problem at the outset. We want to find out, whether a string such as ‘ATGGTGTGGAT’ is present within a very large string ‘TTATAGGT……………GTGGA’ – likely a genome. At this point, we only care about exact matches. Insertions, deletions and modifications will be handled later.

**Dumbest algorithm**

The dumbest algorithm that you can think of will start from one end of the genome and look for pattern matches, until it finds one or reaches the other end of the genome. For a large genome and large number of reads, this process takes for ever and you realize that a smart solution is needed. Computer scientists reached that point in 1970s and came up with the solution called ‘suffix tree’.

**Suffix tree – 1976**

If you are unfamiliar with the term computer science term ‘suffix’, if means all subwords of a word that go from anywhere in the middle of the word to its end. For ‘homolog.us’, the suffixes are ‘homolog.us’, ‘omolog.us’, ‘molog.us’, ‘olog.us’, ‘log.us’, ‘og.us’, ‘g.us’, ‘.us’, ‘us’ and ‘s’.

Before covering suffix tree, let me give you the general idea behind speeding up the search process. As we explained before, you need to construct an index of the genome and utilize the index to guide your search. For example, you can store the locations of all 11-mers in the genome in an ‘index array’. For searching, you first compare the read with the ‘index array’, and then conduct full pattern matching sin genomic regions referenced by the index array. This method definitely speeds up the search process, but it adds two additional complexities – (i) the **time** taken to build up the index and (ii) **space** needed for the index.

Suffix trees were the most elegant approach for building and storing the index. A greatly simplified version of constructing suffix trees was presented in a paper by Edward McCreight in 1976. I found this link most helpful on suffix trees.

**Suffix array- 1989**

As you remember, space and time were two important factors for constructing suffix trees or any other indices of large strings. The space issue was greatly improved by discovery of suffix arrays by Gene Myers and Udi Manber in 1989. Does the name Gene Myers seem familiar? He was one of the authors of the seminal BLAST paper by Altschul.

**Ukkonen’s algorithm – 1995**

Ukkonen developed a linear time algorithm for generating suffix trees and dramatically improved the time needed to construct the trees.

**Burrows Wheeler transform – 1994**

We already covered Burrows Wheeler transform in our previous commentaries. This transform takes an input string and turns it into something else. Why is that great? (i) because the procedure is reversible, (ii) the transformed string can be compressed more easily than the original one.

Burrows Wheeler transform may appear unrelated to the previous discoveries that we listed until you realize that the procedure for generating Burrows Wheeler transform is very similar to the method for generating suffix array.

**FM index – 2000**

‘FM’ in the name stands for the names of the authors. In 2000, Ferragina and Manzini published a paper titled ‘Opportunistic Data Structures with Applications’, where they proposed a data structure to combine the best of Burrows Wheeler transformation-based data compression and suffix arrays. In their method, the reference sequence can be kept compressed, and there is no need to decompress the whole sequence for pattern searching. One can search for pattern in a large reference by only decompressing a small part of it and comparing for matches. This resulted in significant reduction in storing the index without any cost to time needed for pattern searching.

Few other algorithms appear promising and we may update the above list by adding them, but for the time being, we shall leave you with the above set of historical developments.

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[...] us in homolog.us – part II Finding us in homolog.us III (Search Algorithm and Indexing) Burrows Wheeler transform – Suffix Arrays and FM Index Data Compression [...]

[...] on Burrows-wheeler transform (Finding us in homolog.us, Finding us in homolog.us – part II, Burrows Wheeler transform – Suffix Arrays and FM Index and Suffix [...]

The link to “Searching through BWT-compressed Data using FM-index” is not effective now, but never mind, I can find the article.

I also tried to learn alignment algorithms, and I read some papers from Nature Reviews. But I was still confused after reading those papers.

I couldn’t learn alignment algorithms as good as you. You know the development of alignment algorithms, and you know intuitive feeling of the algorithm development. I am a lot interested in how your learn, how you find the ideas behind alignment algorithms.

Thanks Kevin for the compliment !!

The best way to understand the algorithms is to use Feynman approach, which is to stop reading papers and try very hard to design your own algorithm. Imagine there is nothing out there and you are the first person to solve the problem.

Maybe you will find something really clever or maybe you will not, but the process will orient your brain in such a way that when you go back to read papers, you will appreciate the cleverness of other solutions very quickly.

[...] Best Way to Understand Algorithms Reader KC asked: I also tried to learn alignment algorithms, and I read some papers from Nature Reviews. But I was [...]

[...] us in homolog.us – part II Finding us in homolog.us III (Search Algorithm and Indexing) Burrows Wheeler transform – Suffix Arrays and FM Index Data Compression Algorithms Suffix Arrays Burrow Wheeler Transform – Matlab Code, [...]

[…] That competitor of Gene Myers is none other than Gene Myers, 20 years younger. In early 90s, he and Udi Manber (currently leading the search group at Google) introduced the concept of suffix arrays. Fast forward by two decades, every nucleotide search program for new sequencing technologies uses suffix arrays, combined with BWT transform and FM indices. We covered that history in an earlier commentary (check this blog post from 2011). […]