Using Bloom Filter - A Simple Introduction for Bioinformaticians

Using Bloom Filter - A Simple Introduction for Bioinformaticians


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Among various techniques for computing k-mer distribution, bloom filter method is the most memory efficient. We set to write an article about bloom filter, but found several excellent sources online. Please follow the links at the bottom of the page to find them. Instead in this space, we shall give you a very simple analogy to explain the positives and negatives of bloom filter.

Suppose you are writing a paper and want to include references about other papers in your article. There is no point in including title, authors, year, journal and every other detail about every reference. You like your reference to be short, yet informative. One common method is to choose the last name of the first author and year of publication. This what ‘hashing’ approach does.

At times, you encounter two papers by two authors with last name Smith published in the same year 2002. Hashing method will try to break the tie by including the first name, such as Smith, John 2002 and Smith, Adams 2002. Instead bloom filter method will not break the tie and refer to both of them as Smith, 2002.

Bloom filter allows for some false positives. That means if someone wants to check whether the 2002 paper by a different author, Christopher Smith, has been cited in your manuscript, he will get ‘YES’. On the other hand, bloom filter method does not allow for false negatives. If your paper does not cite Donaldson 2007, then checking for Donaldson 2007 will give ‘NO’ and the answer is definitive.

Bloom filter is helpful in applications, where your RAM or storage is very expensive and you want to cut down its usage at the cost of some false positives. Let us present an example. Suppose you are building a website for a journal, where readers can access and read all bioinformatics-related papers published the journal. Every time someone accesses information about a paper, you also show information on other papers in your journal related to the requested paper such as ‘people who read this paper also read those papers’. The above calculation is done in real time and is computationally intensive. So, you want to run it, only if you know that the original paper indeed exists in your database.

If you have limited storage in your web server, you can only store the last name of authors and years for all papers in your journal and compare those with new requests. If a new request matches the stored information, you initiate the resource-intensive calculation. From time to time, false positives will make you initiate the resource-intensive calculation even though the requested paper does not exist in your journal. However, you correct for that error in reporting time. That is a small cost for savings in storage for your web-server.

Overall, bloom filter offers a trade-off in the above example. That trade-off is not beneficial to you in the above example, if the cost of performing resource-intensive calculation is more than additional storage in your web server. Given that RAM has become the most expensive overhead for NGS bioinformatics programs, bloom filter seems to offer substantial benefit.

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You will also find the following pages helpful -

1. Using Bloom Filters at Perl.com

2. All you ever wanted to know about writing bloom filters - Spyced Blog

3. The Bloom Filter - Mike James


Written by M. //

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