v3.2 [2021-01-13]

DESeq2 preprocess : Compute differential abundancy analysis
DESeq2 visualization : Create table and plots to explore and illustrate the differential abundant OTUs
Filters has been splitted into to new tools : FROGS OTU Filters and FROGS Affiliations Filters.
- FROGS OTU Filters filters OTU on presence/absence, abundances and contamination as Filters did. For contamination research, user may now use a personnal multifasta contaminant reference.
- FROGS Affiliation Filters delete OTU or mask affiliation that do not respect affiliation metrics criteria, or affiliated to undesirable (partial) taxon.

Affiliation_postprocess : taxon-ignore option added, to ignore some taxon like "unknown species" during the aggreagation process. Multiple taxon may be provided as well as partial taxon, like "sp."
Preprocess : now accept input sequence file as Fasta format (format automatically detected) for already contiged input.
Affiliations_stat : check that the number of rank name correspond to the number of ranks in input biom file
FROGSSTAT Phyloseq import : check sample names consistency between sample metadata and input biom file
FROGS OTU Filters filters OTU on presence/absence, abundances and contamination as Filters did.

Affiliation_postprocess : correctly compare de %coverage and the coverage threshold.
FROGSSTAT Structure : plot_heatmap now take into account ordination method and dissimilarity matrix
addAffiliation2biom : do not split partial description from blast reference ID
tsv_2_biom now keep initial OTU order (Cluster_1 is the most abundant one and Cluster_X the less abundant one)

All python scripts are now in python 3
FROGSSTAT Phyloseq and FROGSSTAT DESeq now generate notebook_html instead of classical HTML output file. This facilitates code maintenance.

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