Next Gen DNAseq analysis
Next Gen RNAseq analysis
Illumina sequence
Quality trimming
Mapping-based assembly
De-novo assembly
Data interpretation
Terminal-based information
Other useful tools
EXE
Day 1
EXE1_Quality Trimming (FastQC)
EXE2_barcode trimming
EXE3_mapping-based assembly to identify microbial taxa (root sample):
EXE3-1: Fungal rRNA/D2 (Bowtie mapping; read extraction; check through RDP identification)
EXE3-2: Bacteria rRNA (Bowtie mapping; read extraction; check through RDP identification)
EXE3-3: Option 1: Collect the reads based upon mapping:
Option 2: De-novo assembly using Trinity for pooled rRNA reads collected from individual samples. Use as reference to map back to the individual samples
EXE3-4: Multidata set to compare microbial abundance : (% read for rRNA)
EXE4_Reference preparation for Genome free data analysis
EXE4_1: database generation for fungi: Trinity
**Students practice EXE3_for a random sample
Day 2
**Students finalize the data for EXE3
EXE5: _mapping-based assembly to identify key genes of dominant fungi(root sample):
EXE5-1: Mapto fungalRNA_Trinity reference (Bowtie)
EXE5-2: Mapto fungal genome database (Tophat)
EXE5-3: Data normalization using DEseq (after Bowtie)/or Cuff-packages, FPKM (after Tophat)
EXE6: _mapping-based assembly to identify key genes of dominant plant(root sample):
EXE6-1: Mapto_Plant genes_Trinity reference (Bowtie); Gene identification
EXE6-2: Mapto_Plant genome database (Tophat); Gene identification
EXE6-3: Data normalization using DEseq (after Bowtie)/or FPKM (after Tophat)
EXE7_Apply the metatranscriptomic to the complex soil microbicrobiome (combined ref-based and de-novo approaches)
EXE8_Data plot
EXE9_Relative gene expression (i.e. Heatmap)
EXE10_KEGG pathway network; ClueGO
**Student practice EXE 3-6 and complete the question sheet.
Quality trimming
Mapping-based assembly
De-novo assembly
Data interpretation
Terminal-based information
Other useful tools
EXE
Day 1
EXE1_Quality Trimming (FastQC)
EXE2_barcode trimming
EXE3_mapping-based assembly to identify microbial taxa (root sample):
EXE3-1: Fungal rRNA/D2 (Bowtie mapping; read extraction; check through RDP identification)
EXE3-2: Bacteria rRNA (Bowtie mapping; read extraction; check through RDP identification)
EXE3-3: Option 1: Collect the reads based upon mapping:
Option 2: De-novo assembly using Trinity for pooled rRNA reads collected from individual samples. Use as reference to map back to the individual samples
EXE3-4: Multidata set to compare microbial abundance : (% read for rRNA)
EXE4_Reference preparation for Genome free data analysis
EXE4_1: database generation for fungi: Trinity
**Students practice EXE3_for a random sample
Day 2
**Students finalize the data for EXE3
EXE5: _mapping-based assembly to identify key genes of dominant fungi(root sample):
EXE5-1: Mapto fungalRNA_Trinity reference (Bowtie)
EXE5-2: Mapto fungal genome database (Tophat)
EXE5-3: Data normalization using DEseq (after Bowtie)/or Cuff-packages, FPKM (after Tophat)
EXE6: _mapping-based assembly to identify key genes of dominant plant(root sample):
EXE6-1: Mapto_Plant genes_Trinity reference (Bowtie); Gene identification
EXE6-2: Mapto_Plant genome database (Tophat); Gene identification
EXE6-3: Data normalization using DEseq (after Bowtie)/or FPKM (after Tophat)
EXE7_Apply the metatranscriptomic to the complex soil microbicrobiome (combined ref-based and de-novo approaches)
EXE8_Data plot
EXE9_Relative gene expression (i.e. Heatmap)
EXE10_KEGG pathway network; ClueGO
**Student practice EXE 3-6 and complete the question sheet.
Computational workflow/Exercises