BioInformatics-> HMM

In summary, HMM (Hidden Markov Models) is a statistical tool used in sequence analysis to model a system that is assumed to be a Markov process with unknown parameters. It is called "hidden" because the parameters cannot be directly measured, and is commonly used in DNA analysis for tasks such as gene recognition.
  • #1
karthik3k
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BioInformatics--> HMM

What is the use of HMM (Hidden Markov Models) ?? in sequence analysis ??
 
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  • #2
This is a statistical tool that allows you to model a system that is assumed to be a Markov process with unknow parameters. A Markov process basically means something random, stochastic, where future states do not depend on past states. It is hidden because you cannot directly measure the effect, so you are metaphorically using the shadow or acoustics to measure the real parameter.

This would apply to sequence analysis, since the order of basis is assumed to be random, if you measure Adenine.. you cannot say whether the next base is going to be A, C, G or T. So I guess that would mean it is a Markov chain..

Now I'm not all too familiar with the statistic, but think for instance that it can be used for the recognition of genes in DNA.
 
  • #3


HMMs are statistical models used in bioinformatics to analyze and interpret sequences of biological data. They are based on the concept of a hidden state, which represents an unobservable variable that affects the observed data. In sequence analysis, HMMs are used to predict the most likely sequence of states that generated a given sequence of data, such as DNA or protein sequences. This allows researchers to identify patterns, motifs, and other important features within the sequence that may have biological significance. HMMs are also used to compare sequences from different organisms and identify evolutionary relationships. Overall, HMMs play a crucial role in analyzing and understanding complex biological data, making them an essential tool in bioinformatics.
 

What is Bioinformatics?

Bioinformatics is an interdisciplinary field that combines computer science, mathematics, and biology to analyze and interpret biological data. It involves the use of computational tools and algorithms to study biological systems and processes.

What is an HMM in Bioinformatics?

HMM stands for Hidden Markov Model, which is a statistical model used to analyze sequential data. In Bioinformatics, HMMs are used to predict the presence of certain biological sequences, such as genes or protein domains, in a given sequence of DNA or protein.

How do HMMs work?

HMMs work by using a set of observed data (sequences) to predict the underlying hidden state (such as a gene or protein domain) that generated the data. This is done by assigning probabilities to different states and transitions between states, and then using those probabilities to calculate the most likely sequence of states.

What are the applications of HMMs in Bioinformatics?

HMMs have a wide range of applications in Bioinformatics, such as gene finding, protein structure prediction, sequence alignment, and phylogenetic analysis. They are also commonly used in genome annotation and functional annotation of proteins.

What are the limitations of HMMs in Bioinformatics?

Although HMMs are a powerful tool in Bioinformatics, they have some limitations. These include the need for extensive training data, the assumption of independence between states, and the difficulty in handling large and complex datasets. Other statistical models, such as deep learning, are being explored as alternatives to HMMs in certain applications.

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