Analysis of Xenopus metagenome and metatranscriptome

Taxonomic affiliations using full-length rRNA gene sequences

-Krona plot of taxonomic affiliations obtained from MATAM on metaDNA

# Run matam on metaDNA
DBDIR=/home/Databases/MATAM
matam_assembly.py -d $DBDIR/SILVA_128_SSURef_NR95 -i reads4matam/metaDNA_reads.fastq --cpu 24 --max_memory 50000 -v --perform_taxonomic_assignment -o matam_output

You can find the assembled sequences here

-Krona plot of phyloFlash results on metaDNA

# Run phyloflash on metaDNA
phyloFlash.pl -lib metaDNA -read1 READ_QC/metatetard_DNA/final_pure_reads_1.fastq -read2 READ_QC/metatetard_DNA/final_pure_reads_2.fastq -CPUs 24 -everything

You can find the assembled sequences here

-Krona plot of taxonomic affiliations obtained from MATAM on metaRNA

# Run matam on metaRNA
# This is a very intensive computation so you may need to tune the max_memory and coverage_threshold according to your needs.
DBDIR=/home/Databases/MATAM
matam_assembly.py -d $DBDIR/SILVA_128_SSURef_NR95 -i reads4matam/metaRNA_reads.fastq --cpu 72 --max_memory 500000 -v --perform_taxonomic_assignment --coverage_threshold 500 --min_read_node 2 --min_overlap_edge 2 -o matam_output

You can find the assembled sequences here

-Krona plot of phyloFlash results on metaRNA

# Run phyloflash on metaRNA
phyloFlash.pl -lib metaRNA -read1 final_pure_reads_1.fastq -read2 final_pure_reads_2.fastq -CPUs 24 -everything -trusted Xt_metatrin.fasta

You can find the assembled sequences here

Read krona.tab output files from matam

#
library(ggplot2)
library(dplyr)
library(ggbump)
library(tidyr)

source('theme_npgray.R')

#Importing the krona.tab file produced by matam
matam_mt<-read.table("metatranscriptomic/mt.krona.tab",header=FALSE,sep="\t")
matam_mg<-read.table("metagenomic/mg.krona.tab",header=FALSE,sep="\t")

#Assign headers - MT for the numbers of reads
matam_mt<-setNames(matam_mt,c("MT","Kingdom","Phylum","Class","Order","Family","Genus"))
matam_mg<-setNames(matam_mg,c("MG","Kingdom","Phylum","Class","Order","Family","Genus"))
#Group by Phylum
mt_by_phylum <- matam_mt %>% group_by(Phylum) %>% summarise(MT=sum(MT))
mg_by_phylum <- matam_mg %>% group_by(Phylum) %>% summarise(MG=sum(MG))

#Join the (two) datasets
matam<-full_join(mg_by_phylum,mt_by_phylum,by="Phylum")
#Replace NA by 0
matam<-matam %>% mutate_if(is.numeric, ~replace(., is.na(.), 0))

#Compute percentages
matam<-matam %>% mutate(MetaRNA=100*(MT/sum(MT)),MetaDNA=100*(MG/sum(MG)))

#For working on read numbers
#  matam %>% pivot_longer(c('MT','MG'),names_to="Experiment",values_to="Nb_reads")

#Working only on percentages
matam<-matam %>% select(Phylum,MetaRNA,MetaDNA) %>% pivot_longer(c('MetaRNA','MetaDNA'),names_to="Experiment",values_to="Percentage")

# Plotting a stacked bar plot
colors<-c("Actinobacteria"="#810027","Bacteroidetes"="#92c5de","Cyanobacteria/Chloroplast"="#f7f7f7","Deferribacteres"="#f4a582","Firmicutes"="#053061","Fusobacteria"="#80cdc1","Lentisphaerae"="#ffffff","Proteobacteria"="#d6604d","Synergistetes"="#2166ac","Verrucomicrobia"="#fddbc7","unclassified"="#f0f0f0")

ggplot() +
geom_bar(data = matam,
aes(x = Experiment, y =Percentage, fill = Phylum),
colour = 'black', width = 0.6, stat="identity") +
geom_sigmoid(data = tidyr::spread(matam, Experiment, Percentage),
colour = "black",
direction="x",
smooth=6,
aes(x = 1 + 0.6/2,
xend = 2 - 0.6/2,
y = 100-cumsum(MetaDNA),
yend = 100-cumsum(MetaRNA)))+
scale_fill_manual(values=colors)+
theme_npgray()+coord_flip()

ggsave("meta_matam_abundance.pdf",width=16,height=18,units="cm")

Visualisation of metagenome bins using vizbin

-Vizbin of DASTool binning results

Metabolic pathways inference

-Krona plot of KEGG metabolic pathways

-Krona plot of Metacyc metabolic pathways

-iPath3 visualization is available here