In water samples that were nutrient-poor (as indicated by chlorophyll content) there were more pathways that had to do with amino acid synthesis than in samples that were in nutrient-rich water. have found correlations between the genomic potential of the samples and the environmental conditions. In a study of marine microbial samples, Gianoulis et al. For example, a comparative analysis of the gut microbiome of obese and lean mice has shown that the constituent bacteria of obese mice have a higher count of enzymes that break down complex carbohydrates.
Functional potential is defined as the biochemical and physiological functions that the analyzed group of genes or proteins may perform. With the advent of fast genome sequencing, and with the increasing number of studies involving metagenomics and metaproteomics, it is has become customary to talk about the functional potential of a microbiome or a genome (e.g. However, when producing large amounts of data, there is a need to provide a good visualization of the results to make them tractable. Since Interproscan also scales up to work on cluster computers, it can handle large amounts of data.
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This ability to compile results from different sequence signature methods makes Interproscan the software of choice for protein function annotation. Interproscan compares query protein sequences against Interpro -a repository of collected and annotated protein signatures- member databases using a variety of motif, pHMMs and positional specific score matrix methods. For those reasons, Interproscan is a popular function annotation program. Furthermore, a consensus method can help weed out false positives. The rationale is that by using more than one algorithm to functionally annotate a protein, we overcome the lack of sensitivity that may result from using only one program. It is therefore almost obligatory to use several function annotation programs to functionally annotate proteins. This is true for pairwise sequence alignment algorithms, simple sequence-motif algorithms, as well as for the more complex profile hidden Markov models (pHMM) and position specific sequence similarity based algorithms. However, homology-based transfer algorithms require, first and foremost, a comprehensive, accurately annotated and up-to-date reference sequence database, but no single database can boast all three traits at 100%. Many function prediction algorithms use homology-based transfer, the rationale being that functional similarity can be inferred from sequence similarity. At the same time, this knowledge does not preclude the protein's participation in other pathways, of which we know nothing about. Conversely, we may know that a protein plays a role in a specific pathway, but not its molecular function.
We may predict that a protein is a protease, but not know which protein or proteins it degrades. First, in many cases we know only certain aspects of a protein's function. Computationally annotating gene and protein function is a difficult problem for several reasons, and is best solved if attacked by several different strategies. As sequencing technology becomes cheaper, we are becoming inundated with sequencing data, which requires annotation and interpretation.
Function analysis of protein sequences is one of the primary challenges in the post-genomic era.