Achilles Heel of 'Big Data' Science

Achilles Heel of 'Big Data' Science

The promoters of ‘Big Data’ science argue that by collecting increasingly large amount of data and by processing the data with clever algorithms, they can make fundamental scientific discoveries (or other social contributions). Many others point out the lack of discoveries compared to what the same people had been promising for years, to which the ‘Big Data’ supporters say that they have not collected enough data yet.

In this article, I present three rules to show that the basic premise of ‘Big Data’ is faulty and explain them with many examples. The rules are-

Rule 1.

Quality does not scale, but noise scales.

Rule 2.

The noise can only be reduced by high-quality algorithms.

Rule 3.

Rules 1 and 2 are valid at all scales.


Let me start with the simple example of genome assembly from short reads using de Bruijn graphs. What happens, when one throws in tons and tons of reads to get higher coverage? The number of high-quality k-mers (i.e. those truly matching the genome) remain the same, but the number of noisy k-mers scale with more data. As you add more coverage, the number of noisy k-mers start to overwhelm the system. First thing you see is that your computer RAM space getting filled, leading to periodic crashes. At even higher coverage, the de Bruijn graph has all kinds of tips and bubbles formed in addition to real contigs.

Both problems are solvable, but they need high-quality algorithms.


Think carefully about the implication of the last sentence within the context of Rule 3. Rule 3 says that Rule 1 and 2 apply at all scales. That means they apply for de Bruijn graph, but they also apply for researchers developing high-quality algorithms.

Let us say, the government throws in a lot of money to get high-quality algorithms. What happens? Well, high-quality algorithms reach a plateau, but noise scales with money. As a result, the space of new bioinformatics tools look similar to de Bruijn graph at 200x coverage. How to figure out what is good and what is not? Well, maybe you need high-quality algorithms to figure out which algorithms are worth using :)


I thought about the mentioned rules for months and could not find any way to get out of the constraints imposed by them. In the meanwhile, the scientific world keeps marching to the tune of ‘Big Data’ in all aspects. Every aspect of it, including ranking papers based on citation (or God forbid - retweets) is vulnerable. The same goes for automated annotation of public databases based on existing data. This last point will be the focus of another forthcoming post.



Stephano Lonardi posted a link to his paper, and believe it or not, I was looking for the same paper in my directory, while writing this blog post today, but could not locate it. That is no coincidence, because my initial thinking six months back was influenced by his paper posted at biorxiv. At that time, I was working on 1000x E. coli data posted by the SPAdes group and made similar observation in the assembly stats. The explanation (dBG getting more noise) seemed obvious, but it is also known that SPAdes manages to produce a good assembly from 1000x data. That observation inspired the rest of the thinking about need for high-quality algorithm to overcome noise.

You may also realize that throwing more money to solve assembly problem would not have obtained the high-quality solution, but instead polluted the space with too much noise (i.e. low-quality algorithm). It was rather Pevzner’s work of over two decades that got us there. That is the essence of Rule 3 in one human context.

Written by M. //