The families in ISfinder
are defined using an initial manual BLAST analysis often followed by
reiterative BLAST analyses with the primary transposase sequence of
representative elements used as a query in a BLASTP (Altschul, et al., 1990) search of microbial genomes. Potential full-length Tpases are retained and that
with the lowest score then used as a query in a second BLASTP search. This is
continued until no new potential candidates are detected. The ClustalW multiple
alignment algorithm (Thompson, et al., 1994) is then used and
the results displayed using the Jalview alignment editor (Clamp, et al.,
2004) for assessment. The corresponding DNA together with 1000 base
pairs up- and down-stream is then extracted and examined manually for the IRs
or other typical features such as secondary structures and flanking DRs. This,
together with comparison of the DNA extremities of various elements, allows
identification of both ends of the collected elements. In cases where more than
a single IS copy is identified, BLASTN can be used to define the IS ends. Where
only a single copy is found, the ends can often be defined by identifying and
comparing with empty sites.
In a second step, we use the Markov Cluster Algorithm (MCL) (http://micans.org/mcl/) (Van Dongen, 2000, Enright, et al., 2002) to weigh the relationships between clusters
of ISs and to validate prior ISfinder classification of ISs into families and
subgroups (Siguier, et al., 2009). This is explained in detail in Siguier, et al. (2009) and is based on the parameters used in the MCL (Fig 1.5.1) in addition to characteristics such as the
specificity of target site duplications, the detailed sequence of the ends,
genetic organisation. It
should be understood that the distinction between families and subgroups can
evolve as the number of ISs in the database increases.
Several semi-automatic IS annotation
pipelines are now available. The interested reader is directed to three of
these: ISsaga (Varani, et al., 2011) which is now integrated into the ISfinder
platform (Siguier, et al., 2006), ISScan (Wagner, et al., 2007) and Oasis (Robinson, et al., 2012). At present, de novo prediction of ISs is not efficient and these pipelines all
employ the ISfinder database to function. While all three pipelines permit
identification of IS fragments as well as full length ISs, a certain level of
manual assessment is essential.
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