16S rrNA gene bioinformatics

Once a 16S PCR product has been sequenced and amplified it must be placed in the circumstance of its phylogenetic relationships with other sequences. The preliminary idea of the close relatives can be gained through the use of an alignment to one or more known sequences. The program like as BLAST can do this while it is extremely limited in detail and will only give information relating to one other sequence at one time.  To gain a true idea of phylogeny the 16S rRNA gene sequence should be compared to as several other sequences as possible concurrently. Only ten years ago this was impossible to achieve on anything but a supercomputer; moreover, recent advances in computing power now mean in which most personal computers can carry out some or all of this procedure. Several web-based programs also allow free access to the more powerful computers which may be required.

To start with the newly acquired sequence must be aligned with all or some of the sequences obtained in past. As there is some variation in length of 16S rRNA gaps, genes must be inserted to achieve a perfect alignment by this can be done through programs such as CLUSTAL. The aligned sequences are then clipped so in which the 5’ and 3’ ends are equivalent bases and the alignment is sent to a program capable of generating phylogenetic trees.

An ideal representation of phylogeny would be multidimensional but given the constraints of our 3-dimensional universe in common and the scientific predilection for presentation in 2-dimensional form on paper in particular the ‘tree’ is a good compromise. Two major algorithms are used with that are: maximum parsimony and neighbor joining. Neighbor joining is an evolutionary distance technique based on a matrix of differences in the dataset. The resulting tree has branches of lengths proportional to evolutionary distance statistically corrected for back mutation. Maximum parsimony is a more hard concept to grasp in which the resulting tree has branches whose length is proportional to the mini- mum amount of sequence modification necessary to enable the creation of a new branch.

For both techniques it is possible to generate trees differing in details like as the number of branches from one dataset. The process known as bootstrapping is applied to get an  idea  of the  sum  of all the  possible trees and  this  gives a confidence value  for the  presence of  each branch. In  addition parsimonious trees and  neighbor joining generated from  the  similar dataset can  give quite different results and  to  date neither technique is  considered to  be  more right  than the   other. Therefore   any tree   should be considered as the best possible result with the data available and should not necessarily over-rule any other information.