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The codes, mathematical equations and data sources involved in Moutai-SME project.

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Moutai-SME

The codes, mathematical equations,softwares and data sources involved in Moutai-SME project.

The necessary resources of softwares and databases had been provided as following.

Softwares Version Reference Identifier
Trimmomatic v0.38 https://doi.org/10.1093/bioinformatics/btu170 https://github.com/usadellab/Trimmomatic
MEGAHIT v1.1.2 https://doi.org/10.1093/bioinformatics/btv033 https://github.com/voutcn/megahit
QUAST v5.0.2 https://doi.org/10.1093/bioinformatics/btt086 https://github.com/ablab/quast
MetaWRAP v1.2.2 https://doi.org/10.1186/s40168-018-0541-1 https://github.com/bxlab/metaWRAP
CheckM v1.0.18 https://doi.org/10.1101/gr.186072.114 https://github.com/tribe29/checkmk
Prodigal v2.6.3 https://doi.org/10.1186/1471-2105-11-119 https://github.com/hyattpd/Prodigal
Prokka v1.14.5 https://doi.org/10.1093/bioinformatics/btu153 https://github.com/tseemann/prokka
MMseqs2 v4.8.1 https://doi.org/10.1093/bioinformatics/btq003 https://github.com/soedinglab/MMseqs2
KofamScan v1.3.0 https://doi.org/10.1093/bioinformatics/btz859 https://github.com/rotheconrad/KEGGDecoder-binder
DIAMOND v2.0.14.152 https://doi.org/10.1038/nmeth.3176 https://github.com/python-diamond/Diamond
DeepARG v1.0.2 https://doi.org/10.1186/s40168-018-0401-z https://github.com/Deeparg/Deeparg
eggNOG-mapper v2.1.6 https://doi.org/10.1093/molbev/msab293 https://github.com/eggnogdb/eggnog-mapper
antiSMASH v6.0.0 https://doi.org/10.1093/nar/gkab335 https://github.com/antismash/antismash
BiG-SCAPE v1.1.5 https://doi.org/10.1038/s41589-019-0400-9 https://github.com/medema-group/BiG-SCAPE
dRep v3.4.0 https://doi.org/10.1038/ismej.2017.126 https://github.com/MrOlm/drep
GTDB-tk v2.1.1 https://doi.org/10.1093/bioinformatics/btac672 https://gtdb.ecogenomic.org/downloads
Databases Date for download Reference Identifier
eggNOG v5.0; 2022_12 https://doi.org/10.1093/nar/gky1085 http://eggnog5.embl.de/#/app/downloads
KEGG 2022_12 https://doi.org/10.1093/nar/gkaa970 http://kobas.cbi.pku.edu.cn/kobas3/download/
COG 2022_12 http://oi.org/10.1093/nar/gkaa1018 http://www.ncbi.nlm.nih.gov/COG/
GO 2022_12 http://oi.org/10.1093/nar/gky1055 http://geneontology.org/
CAZyDB 2022_12 https://doi.org/10.1093/nar/gkn663 http://www.cazy.org/
EC 2022_12 http://oi.org/10.1093/nar/28.1.304 https://enzyme.expasy.org/index.html
CARD 2022_12 https://doi.org/10.1093/nar/gkz935 https://card.mcmaster.ca/download
VFDB 2022_12 https://doi.org/10.1093/nar/gki008 http://www.mgc.ac.cn/VFs/download.htm
GTDB 2022_12 https://doi.org/10.1093/bioinformatics/btac672 https://gtdb.ecogenomic.org/downloads
Swiss-sport 2022_12 https://doi.org/10.1093/nar/gkac1052 https://www.uniprot.org/
TrEMBL 2022_12 https://doi.org/10.1093/nar/gkac1052 https://www.uniprot.org/
UniRef50 2022_12 https://doi.org/10.1093/bioinformatics/btm098 https://www.uniprot.org/
Pfam 2022_12 https://doi.org/10.1093/nar/gkaa913 http://pfam-legacy.xfam.org/

The concepts involved in Moutai-SME project.

Name Introduction
MSSSF (Multi-stage solid-state fermentation) SSF (Solid-state fermentation) is a process where there is a porous solid substrate or support for the growth of microorganisms, with a continuous gas phase. MSSSF is a kind of SSF, which has several fermentation stages
SME (Starter microbiota engraftment) SME means that starter is added to the fermentation system before each round of fermentation stages and mixed with fermented grains. In this process, the bacteria in the starter is transplanted into the fermentation system
Fermentation triad The pre-SME recipient, the post-SME recipient and the corresponding donor
Distance (post_FGijk, donori) The Bray-Curtis distance between post-recipient and donor
Distance (pre_FGi, post_FGijk) The Bray-Curtis distance between pre-recipient and donor
Mean distance (pre_FGi, donori) Mean of distance between post recipient and donor
Mean distance (pre_FGi, post_FGi) Mean of distance between post recipient and pre_recipient
QER (pre_FGi, donori) Quatative engraftment rate between post recipient and donor
QER (pre_FGi, post_FGi) Quatative engraftment rate between post recipient and pre_recipient
SSW (pre_FGi, donori) Sum of Squares Within groups of distance between post recipient and donor
SSW (pre_FGi,post_FGijk) Sum of Squares Within groups of distance between post_recipient and pre_recipient
dfwithini The degrees of freedom within groups
The labels of LASSO-regularized linear regression Thresholds are set based on the engraftment rate and S2FG to determine the label of lasso

The mathematical equations for calculating the QER index.

We used the relative abundance of microbes of each sample to calculate the Bray‒Curtis distance in the pair of starter vs. post-FG, as well as in the pair of post-FG vs. pre-FG (Figure 3a). The Bray‒Curtis distance between post-FG and starter samples Distance(post_FG_ijk,donor_i), and post-FG and pre-FG samples Distance(pre_FG_i,post_FG_ijk) was calculated by the vegdist() function in R “vegan” package (v2.6-2), in which i {1,2,3,4,5,6}┤ (for example, i=1 represents SME1), j represents the time point in the SMEi process, and k represents the sample k at the j time point in the SMEi process (Figure 3a). The calculation of QER index is shown as following:

  • The mean distance between post-FG and starter samples can be calculated as $$Mean distance(\post_FG_i,donor_i)=∑_j∑_kDistance(post_FG_ijk,donor_i)/n_i$$
  • The mean distance between post-FG and pre-FG samples can be calculated as
Mean distance(pre_FG_i,post_FG_i )=∑_j∑_kDistance(post_FG_ijk,pre_FG_i)/n_i
  • n_i represents the number of samples in SMEi process
  • We calculated the sum of the squares within (SSW) of sample distance to calculate the quantitative engraftment rate (QER) value in each SME process. The SSW(post_FG_i,donor_i) can be calculated as
SSW(post_FG_i,donor_i)=∑_j∑_k(Distance(post_FG_ijk,donor_i)-(∑_kDistance(post_FG_ijk,donor_i)))^2 
  • The SSW(pre_FG_i,post_FG_ijk ) can be calculated as
SSW(pre_FG_i,post_FG_i )=∑_j∑_k(Distance(post_FG_ijk,pre_FG_i )-(∑_kDistance(post_FG_ijk,pre_FG_i)))^2 
  • The inter-group degree of freedom (df_within_i) in a SME process is the total degree of freedom (n_i) minus the number of time points (m_i,{9,10,8,9,8,5}) and then minus 1, which can be expressed as
df_within_i=n_i-m_i-1
  • Then the QER(post_FG_i,donor_i ) is calculated by
QER(post_FG_i,donor_i )=SSW(post_FG_i,donor_i )/df_within_i
  • The QER(pre_FG_i,post_FG_i ) is calculated by
QER(pre_FG_i,post_FG_i )=SSW(pre_FG_i,post_FG_i )/df_within_i

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