"If a naturalistic dogma of the origin of biological life was going to be successfully sold to the academic community in the new age of biotechnology, a solution to cloak and obfuscate the evolution-negating inconvenient data needed to be achieved.
The response to this dilemma was undertaken, not by biologists, but by mathematicians such as Felsenstein and Kingman who essentially pushed the biological reality and inconvenience of ILS off to the side and used statistical models that manipulated the data by a -
--combination of boot-strapping, 
--subjective data selection,
--and injecting biased evolutionary presuppositions into the overall analysis methodology.
The result was that the real meaning of so-called discordant or discontinuous data was mathematically smoothed over and manipulated to fit a preconceived Darwinian outcome.
Nevertheless, the ongoing inability of mathematicians to satisfy the nature of real world biology continues to grow as an increasingly glaring evolutionary problem and is exacerbated by the genomics revolution and the escalating amounts of discordant data.
Nevertheless, the ongoing inability of mathematicians to satisfy the nature of real world biology continues to grow as an increasingly glaring evolutionary problem and is exacerbated by the genomics revolution and the escalating amounts of discordant data.
A 2006 article by Maddison and Knowles, aptly titled ‘Inferring phylogeny despite incomplete lineage sorting’ made the following statement in regard to the inability of prevalent mathematical models to deal with the exponential increase in so-called phylogenic anomalies: “Although phylogenetic patterns generated by incomplete lineage sorting have been discussed for many years, considerable work remains to develop and assess methods that consider these issues during phylogenetic reconstruction."
Despite the widespread use of various types of data-smoothing
techniques, the mosaic pattern of life presents many difficulties for evolutionary scientists to overcome.
Despite the widespread use of various types of data-smoothing
In 1996, Wilkinson introduced the terms ‘rogue data’ and ‘rogue taxa’ to describe discordant biological sequence that negated the development of inferred evolutionary trees.
This rogue data is typically given other terms like ‘ambiguous’ or it is considered to provide ‘insufficient phylogenetic signal’.
In fact, the concept of developing bioinformatics filters to remove unruly ‘rogue’ data that does not produce favorable evolutionary outcomes has been an important goal of bioinformatics software developers as the genomics revolution progressed.
In 2002, a filtering algorithm was developed that would assess evolutionarily unfavorable DNA that the authors defined as ‘phylogenetically discordant sequence (PDS)’ and eliminate them if they fell outside a pre-set similarity threshold. It is now very common to use various types of software programs that ‘prune’ (eliminate) insertions, deletions, and other types of discordant data to produce more favorable multiple sequence alignments and then evolutionary trees.
Recently, software was developed and made available via web server interface that removes unruly non-evolutionary genomic sequence from your data set to give you what is termed the ‘true tree’, otherwise known as the inferred hypothetical phylogeny.
*While commonalities across the spectrum of life can be observed in
many gene sequences, this is a common theme inherent to engineered systems whereby similar mechanisms along with their control sequences show similar design.
*While commonalities across the spectrum of life can be observed in
*However, life is a mosaic of patterns as revealed in the many new genome sequences being produced and is not supportive of universal common ancestry, but rather the distinct creation of separate kinds of life as depicted in the Genesis account of origins.
Creationists maintain that the original ‘created kinds’ have diversified (horizontally) over time and through such genetic bottlenecks as the global flood. Thus, the mosaic of life observed in DNA sequence fits well with this model."
ICR