Bioinformatics generated tools is now mostly used for analysis in modern research but statistics also have impact to analyse data what is the relationship of both in Research or how interact both of them for analysis?
These are so different things. Bioinformatics serves a set of tools to calculation in order to achievement "Data". While Statistics uses these "Data" as raw input and after analysis gives output, that it is statistical results. For instance: In the studying a gene expression we use "bioinformatics" tools of Roche lightcycler real time system to calculate CT and copy number of our favor genes. While we use statistics to understand what the data mean.
The question is not very clear and relevant. Both the subjects, their direction, dimension is different although in several occasions bioinformatic computations are based on advanced statistical calculations.Therefore, I would like to request Mr.Naved to make the question more clear and elaborate the relevant objective of the question.
thanks dears and Babak answer related to my query but need to elaborate it in detail and Amit i want to know>
Bioinformatics generated tools is now mostly used for analysis in modern research but statistics also have impact to analyse data what is the relationship of both in Research or how interact both of them for analysis?
I think your query is very nicely answered by Mr. Babak. The output towards certain biological understanding is generated through bioinformatics approaches, where as the reliability of that output is further assessed by statistical means.
In other way, I would like to say I good statistician can become an able bioinformatician only the terminologies and way of looking towards the problem differs.
Many bioinformatics tools are based on statistics. I think the question is ill defined: as it is posed it cannot be answered.
Julian,yup Many bioinformatics tools are based on statistics and to know the relationship where it based and on which tools as mentioned by Babak
As I understand it, Babak says that bioinformatics is "data gathering" tools (i.e. databases, etc) whereas statistics is "data analysis". That is wrong. The analysis of biological data is both bioinformatics and statistics.
Julian,
I think the question is valid, since pairwise sequence alignment is mostly based on Dynamic programming methods (or you can use machine learning for that matter. The selection criteria will depend on the complexity and the accuracy of the algorithm) and if you have a characteristic sequence that you want to search through multiple sequences you can use regular expressions that have nothing to do with statistics.
After you find similarities the you have to ask the question on whether this pattern is significant or not and this is where traditional statistical methods come into play( Statistical significance in biological sequence analysis. by Mitrophanov and Borodovsky).
Arturo, I don't think I understand how your comment relates to the question or to my observations. I never said all bioinformatics involves statistics. I just said that some bioinformatic tools involve statistics. I insist that the question of the difference between bioinformatic tools and statistical tools makes no sense. And unsay this in a purely technical sense: attempting to compare bioinformatics and statistics is a categorical error.
Going back to the question on "what is the relationship between bioinformatic tools and statistics" I would say, briefly, the following: some bioinformatic tools involve statistics, others do not.
As an example, Arturo's comment is perfect: aligning to sequences to maximize some measure of similarity is a "bioinformatics problem" that does not involve statistics, whereas assessing the significance of such maximum similarity score must involve comparing with the expected similarity distribution over some suitable "random" zeroth-order hypothesis, which is a statistical problem. Frequently, both parts, the "non-statistical" and the "statistical" parts, are joined: thus when the similarity score is a Z-score, it includes the expected "random" distribution by normalizing the "raw" similarity score: Z=(raw-mean(raw))/sqrt(variance(raw)), where "mean" and "variance" are the values that characterize the "random" distribution.
thanks Arturo and Julian
my query is not about their differences but how they worked combine or separately for Analysis with regard to each others like some bioinformatic tools involve statistics and some not.
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