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New in version 0.5.1
---------------------
+ bug fixes in bams2msa.py
+ default behaviour of s2f.py is now not to propagate gaps
New in version 0.5
---------------------
+ SAM/BAM format added! Users can now produce MSA files
from their alignments in SAM format
+ README and INSTALL have been updated, and more
information is available on the web, at the ShoRAH
documentation page
https://wiki-bsse.ethz.ch/display/ShoRAH/Documentation
New in version 0.4
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+ dpm_sampler now computes Hamming distance in
time proportional to the distance rather than
to the length of the sequences.
+ dpm_sampler now clubs identical reads into
objects. This method achieves a significant speed-up
with Illumina datasets. Up to 65000 Illumina reads were
analysed in a single window with good results.
+ freqEst sorts the output according to the frequency
New in version 0.3
---------------------
+ dpm_sampler is now C++! Thanks to the structures
defined in C++ libraries (map and multimap), we
are able to run requiring much less memory
+ Parallelization! dec.py now calls diri_sampler using
a pool of independent workers, exploiting all the
available compuational power, as well as s2f.py does
+ The sampling has been improved, now we can have a more
reliable estimate of the quality of our local
haplotype reconstruction
+ The output of diri_sampler is now better organized
(if -k is given, all intermediate files are saved in
subdirectories of the current)
+ The alignment program runs now in linear time (with
respect to the number of reads), and deals with indels
in a more clever way
+ All python programs now use the logging module to
write logs of their operations
+ plot_sampling.py and plot_stat.py can be used to produce
graph showing the behaviour of the Gibbs sampling
New in version 0.2
---------------------
+ New method to assign the reads after the sampling
+ The alignment is now provided by a separate
program (step2far.py), so that the user can input
his/her own alignment and install EMBOSS only if
really necessary
+ Fixed numerical bugs when dealing with very high or
very low probabilities