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CLANS Description

A reason to transfer knowledge about function or structure from known to unknown proteins

Frequently, homology is used as a reason to transfer knowledge about function or structure from known to unknown proteins. Although phylogenies are the method of choice when attempting to determine homology, the most frequently used marker is pairwise sequence similarity. Similarity search programs, such as BLAST or PSIBLAST (Altschul et al. 1997), can efficiently work with enormous datasets while phylogenetic inference and the prerequisite sequence alignments rapidly reach a point where they become unsuitable due to prohibitive calculation costs and loss of resolution. On the other hand, pairwise similarity searches are plagued by false positive matches and problems arising from amino acid composition bias causing, in many cases, the best BLAST hits not to be the closest sequence relatives (Koski & Golding 2001).
Aiming for the best of both worlds, we have implemented a version of the Fruchterman-Reingold graph-layout algorithm (1991). The program takes unaligned fasta format sequences a input, performs all-against-all BLAST searches and displays the pairwise similarities in either 2D or 3D graphs. Contrary to phylogenetic inference methods this approach uses unaligned sequences and works better the more sequences are provided as an increase in number of pairwise similarities better averages out the chance hits that plague standard BLAST comparisons.

The first version of CLANS was developed in 2004. Since, it has been extended with numerous features (and a few bells and whistles) in response to user-feedback and wish-lists. The biggest change however, came in the ability of CLANS to analyze microarray data.
The problem of finding groups of co-regulated genes across a number of microarray experiments is quite similar to the problem of finding groups of homologous proteins in a large dataset. In both cases we have huge amounts of data and are looking for those few genes that show some kind of significant similarity. Putative co-regulation as well as putative homology are generally inferred from similarities in the feature set of a gene. In the first case the feature set consists of the expression levels across the experiments, in the second case of the amino-acid sequences of the proteins. A certain similarity is also apparent when you look at the graphs generated for either protein sequence data (right,top graph) or microarray data (right,bottom graph)

While for protein data the knowledge of "homology" generally helps in formulating a hypothesis, as , most likely, some functional annotation will be available for at least some of the close or more distant homologs, the knowledge of apparent co-expression helps a lot less for microarray data. This due to two problems. First of all there is the huge amount of genes present on the microarray chip and the, generally, low number of experiments and/or replicates that are performed. The probability of two genes showing co-expression, even though they are not causally related in any way, is relatively high. The second is that knowing that a group of genes co-express does not help without having at least an inkling of what some of them do. Therefore we need to include additional data such as functional annotation, cellular localization or pathway information.
Overlaying such annotation on to a cluster map greatly enhances it's value. The map then not only tells you which genes appear co-expressed (i.e. might be causally related), but also shows which genes are thought/known to be involved in the same pathway or what their function might be.
Although it is quite possible to use other programs to find plausible "clusters" of co-expressed genes, then extract the annotation data for these genes from another source, the interactivity of CLANS is a great help. You can vary the clustering cutoffs at will and observe how the various groups associate or dissociate, what the annotations for the genes are, etc. and thereby find the most relevant clustering cutoffs for the question YOU want to answer. This interactivity is a great help when sifting through large amounts of data as no static clustering is likely to give the best results in all cases. Sometimes a bit more lenient settings may be beneficial, other time more stringent cutoffs may produce better results.
Example: Three groups of sequences are selected and the corresponding expression plots are drawn (each of the datapoints in the small, colored graphs represents one experiment for one gene; vertical bars represent the standard deviation across the replicates; the datapoints are connected by lines to better show "trends" in the cange of expression). Two groups are highlighted by black circles while the third is highlighted by red dots. The known functional annotation for the sequences in the third group is shown on the right (I had to shift the annotation a bit so that the sequence names were no longer visible, as this data has not yet been made publicly available). It is possible to search for all genes involved in a specific pathway (by selecting the pathway/bin of interest and pressing the "show bins in clans" button) or looking in what pathways/bins a given set of sequences (for example group 3) is involved (select a group of sequences and press the "show clans in bins" button). For a more detailed explanation read through the tutorial available HERE.

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