PICRUSt

[rosebaesj]

couldn't start due to connection lost. check up on Monday

[rosebaesj]

need to find making phylogenetic tree.....

[rosebaesj]

Files after PICRUSt2 in QIIME

Generate metagenome predictions

https://github.com/picrust/picrust2/wiki/PICRUSt2-Tutorial-(v2.5.0) EC_metagenome_out/pred_metagenome_unstrat.tsv.gz - overall EC number abundances per sample. EC_metagenome_out/pred_metagenome_contrib.tsv.gz - A stratified table in "contributional" format breaking down how the ASVs contribute to gene family abundances in each sample. EC_metagenome_out/seqtab_norm.tsv.gz - the ASV abundance table normalized by predicted 16S copy number. EC_metagenome_out/weighted_nsti.tsv.gz - the mean NSTI value per sample (when taking into account the relative abundance of the ASVs). This file can be useful for identifying outlier samples in your dataset. In PICRUSt1 weighted NSTI values < 0.06 and > 0.15 were suggested as good and high, respectively. The cut-offs can be useful for getting a ball-park of how your samples compare to other datasets, but a weighted NSTI score > 0.15 does not necessarily mean that the predictions are meaningless.

EC는 ASV number를 기반으로 하여 계산하는 것. 두번째의 stratified table 같은 경우 PICRUST에 추가된지 얼마 되지 않았음. BURRITO, MIMOSA 등의 툴을 이용하려면 stratified table을 사용해야하며, legacy format으로 전환해야함 근데 우리는 pathhway -level inference도 PICRUSt2에서 바로 진행하니까 이건 알필요 없을ㄱ듯?

[rosebaesj]

Files after PICRUSt2 in QIIME

Pathway-level inference

https://github.com/picrust/picrust2/wiki/PICRUSt2-Tutorial-(v2.5.0)

pathways_out directory 는 MetaCyc에 기반해 계산된 결과가 나옴

This default stratified pathway abundance table represents how much each ASV is contributing to the community-wide pathway abundance and not what the pathway abundance is predicted to be within the predicted genome of that ASV alone. 즉 단일 ASV의 역할을 보는 것이 아니라 community level에서 역할을 보는 것 같음

우리의 목적은 이것이라 생각됨

[rosebaesj]

Files after PICRUSt2 in QIIME

Add functional descriptions

해당 pathway를 이해할 수 있는 언어로 변환