The repository contains descriptions of the tools and methods used to analyze metagenomic data from surface water sampled from the city of Pointe-Noire, Congo. These analyses were published in this paper published in Data in Brief
Metagenomic data from gutter water in the city of Pointe-Noire, Republic of Congo
Authors
Bouziane Moumen, Céline Samba-Louaka, Victoire Aubierge Matondo Kimpamboudi, Anicet Magloire Boumba, Hervé Sabin Ngoma, Ascel Samba-Louaka
To reproduce these analyses you need the following tools installed in your local machine (Linux) (For our side we used: CentOS Linux release 7.5.1804 (Core) Maipo) ....
bash available by default in all Linux distributions (version used 4.2.46(2)-release )
FastQC available at https://github.com/s-andrews/FastQC (Version used 0.11.8)
fastp available at https://github.com/OpenGene/fastp (Version used 0.23.0)
Bowtie 2 available at https://github.com/BenLangmead/bowtie2 (Version used 2.3.4.3)
Kaiju available at https://github.com/bioinformatics-centre/kaiju (Version used v1.9.0)
Kraken2 available at https://github.com/DerrickWood/kraken2 (Version used 2.1.2)
KronaTools available at https://github.com/marbl/Krona (Version used v 2.8.1)
seqtk available at https://github.com/lh3/seqtk (Vresion v 1.3-r106)
seqkit available at https://github.com/shenwei356/seqkit/ (Version used v2.5.1 )
Samtools available at https://github.com/samtools/samtools (Version used v1.9)
bamtools available at https://github.com/pezmaster31/bamtools (Version used 2.5.1)
Megahit available at https://github.com/voutcn/megahit (Version used 1.2.9)
MetaWRAP availbale at https://github.com/bxlab/metaWRAP (Version used v1.1.2)
gtdb available at https://github.com/Ecogenomics/GTDBTk (version used 1.4.1)
MMseqs2 available at https://github.com/soedinglab/MMseqs2 (Version used release 14-7e284)
metabat2 available at https://bitbucket.org/berkeleylab/metabat/src/master/ (Version used 2.12.1)
Maxbin2 available at https://sourceforge.net/projects/maxbin2/ (Version used2.2.7)
Some modules from MetaWRAP Pipeline (but you have to install the whole pipeline if you want to use this ....)
metaSPAdes available at https://github.com/ablab/spades (Version used 3.15.2)
kaiju databases
Please, feel free to use recent databases if you want, Newer versions are available from the kaiju webserver https://kaiju.binf.ku.dk/server
Versions Used In the Paper are :
kaiju_db_nr_euk_2022-03-10 (71GB) available at https://kaiju-idx.s3.eu-central-1.amazonaws.com/2022/kaiju_db_nr_euk_2022-03-10.tgz
kaiju_db_plasmids_2022-04-10 (966MB) available at https://kaiju-idx.s3.eu-central-1.amazonaws.com/2022/kaiju_db_plasmids_2022-04-10.tgz
kaiju_db_rvdb_2022-04-07 (983MB) available at https://kaiju-idx.s3.eu-central-1.amazonaws.com/2022/kaiju_db_rvdb_2022-04-07.tgz
NB: for the Kaiju database we need 3 files for each db: database.fmi, nodes.dmp and names.dmp
kraken2 databases
The Version used in this study is (PlusPF) which means Standard plus Refeq protozoa & fungi available at https://benlangmead.github.io/aws-indexes/k2, we used the March 14, 2023 version but feel free to use the recent version if available...
Human genome reference index for bowtie2 decontamination
The index used is available here: https://benlangmead.github.io/aws-indexes/bowtie
#############################################################################################################################################################
- The technology used is Illumina, in paired-end mode (2X150).
- The data are made available in Sequence Read Archive from NCBI from bioproject: PRJNA1021800
- Feel free to use your favorite tools to get these files locally.
- If you want to reproduce these analyses, please rename these files according to this manual:
# Feel free to organize your project as you want ...
$ mkdir -p workspaces/Metacongo_Paper && cd workspaces/Metacongo_Paper
If you choose to keep the original filenames, as downloaded from ncbi, feel free to track that change over the whole pipeline
# make a directory to store the raw data
$ mkdir RAW_DATA
$ mv XXXX_R1.fastq.gz EPNC_R1.fq.gz
$ mv XXXX_R2.fastq.gz EPNC_R2.fq.gz
# Get Reads count (Optional) from the fastq files
$ for i in *gz; do printf $i"\t"; gzip -cd $i | grep -c "@GWNJ-"; done
EPNC_R1.fq.gz 84886827
EPNC_R2.fq.gz 84886827
/workspaces/Metacongo_Paper/QC_RAW_DATA (main) $ pwd
/workspaces/Metacongo_Paper/QC_RAW_DATA
# Call fastqc program on your fastq files
# In standard Linux (Pc or simple server)
$ fastqc *gz -t X # when X is the number of threads you want to use
# If you have a cluster with slurm (see Scripts folder for a script named fastqc_slurm.sh)
As mentioned in the material & methods section of the paper, we used fastp to trim and filter the raw reads.
Here is the script used for that purpose:
# Create a folder to hold the filtered data
$ mkdir FASTP_FILTERING && cd FASTP_FILTERING
# Symlink files here ...
$ for i in ../RAW_DATA/*fg.gz; do ln -$i; done
# Check if OK
$ ll
lrwxrwxrwx 1 foo users 33 Jun 20 14:03 EPNC_R1.fq.gz -> ../RAW_DATA/EPNC_R1.fq.gz
lrwxrwxrwx 1 foo users 33 Jun 20 14:03 EPNC_R2.fq.gz -> ../RAW_DATA/EPNC_R2.fq.gzFor removing adaptors and filtering by quality, please see metacongo_fastp_slurm.sh in the Script folder
The slurm script used, with this --adapter_sequence AGATCGGAAGAGCACACGTCTGAACTCCAGTCA --adapter_sequence_r2 AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT
# Run the script
$ sbatch metacongo_fastp_slurm.shHow to use fastp if you do not have slurm or HPC
# Fastp cmd line
$ fastp -i EPNC_R1.fq.gz -o EPNC_trim_R1.fq.gz -I EPNC_R2.fq.gz -O EPNC_trim_R2.fq.gz --unpaired1 EPNC_orphan_1.fq.gz --unpaired2 EPNC_orphan_2.fq.gz -z 4 --trim_poly_g --trim_poly_x --detect_adapter_for_pe -l 50 -c -p -h metacongo_fastp_report.html--json metacongo_fastp_report.json --overrepresentation_analysis -w 2 --adapter_sequence AGATCGGAAGAGCACACGTCTGAACTCCAGTCA --adapter_sequence_r2 AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT
Doing sanity check to check the output of fastp (get and idea about the filtering process by counting before/after )
############ Before fastp #################
EPNC_R1.fq.gz 84886827
EPNC_R2.fq.gz 84886827
TOTAL REAS COUNT BEFORE FASTP¨
>>> 84886827+84886827=== 169773654
############ After fastp #################
# Count was done as previous (using grep -c "@GWNJ-")
____________________________________
EPNC_orphan_1.fq.gz 4678717
EPNC_orphan_2.fq.gz 49618
EPNC_trim_R1.fq.gz 80014112
EPNC_trim_R2.fq.gz 80014112
____________________________________
TOTAL READS COUNT AFTER FASTP
>>> 4678717+49618+80014112+80014112===161779130
REMOVED READS >>> 169773654-164756559=5017095
Optional: but it is always good to see before/after (we will count the adaptors) in files before/after
$ for i in *gz; do printf $i"\t"; zcat $i |grep -c "AGATCGGAAGAGCACACGTCTGAACTCCAGTCA"; done
EPNC_orphan_1.fq.gz 0
EPNC_orphan_2.fq.gz 0
EPNC_R1.fq.gz 157789 (original file)
EPNC_R2.fq.gz 0
EPNC_trim_R1.fq.gz 1
EPNC_trim_R2.fq.gz 0
$ for i in *gz; do printf $i"\t"; zcat $i |grep -c "AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT"; done
EPNC_orphan_1.fq.gz 0
EPNC_orphan_2.fq.gz 0
EPNC_R1.fq.gz 0
EPNC_R2.fq.gz 106984 (Original file)
EPNC_trim_R1.fq.gz 0
EPNC_trim_R2.fq.gz 3
QC the data again (Optional since fastp has a report with before/after)
$ mkdir QC_FILTERED_DATA && cd QC_FILTERED_DATA
#Symlink filtered data as usual (using ln -s)
$ ll
lrwxrwxrwx 1 foo users 40 Jan 22 2021 EPNC_orphan_1.fq.gz -> ../4.FASTP_FILTERING/EPNC_orphan_1.fq.gz
lrwxrwxrwx 1 foo users 40 Jan 22 2021 EPNC_orphan_2.fq.gz -> ../4.FASTP_FILTERING/EPNC_orphan_2.fq.gz
lrwxrwxrwx 1 foo users 39 Jan 22 2021 EPNC_trim_R1.fq.gz -> ../4.FASTP_FILTERING/EPNC_trim_R1.fq.gz
lrwxrwxrwx 1 foo users 39 Jan 22 2021 EPNC_trim_R2.fq.gz -> ../4.FASTP_FILTERING/EPNC_trim_R2.fq.gz
#fastqc on these files
$ fastqc *gz -t X # when X is the number of threads you want to use
# If you have a cluster with slurm (see Scripts folder for a script named fastqc_filtered_data_slurm.sh) and sbatch fastqc_filtered_data_slurm.sh to run it
# Folers/subfolders
$ mkdir HUMAN_CONTA_REMOVAL && cd HUMAN_CONTA_REMOVAL
$ mkdir REF && cd REF
#Bowtie index was downloaded from here
https://genome-idx.s3.amazonaws.com/bt/GRCh38_noalt_as.zip
$ wget https://genome-idx.s3.amazonaws.com/bt/GRCh38_noalt_as.zip
#Extact and delete unwanted folders, files
$ unzip GRCh38_noalt_as.zip
$ mv GRCh38_noalt_as/GRCh38_noalt_as.* .
$ rm -rf GRCh38_noalt_as GRCh38_noalt_as.zip
$ cd ../
# Symlink fastq files
$ for i in ../QC_FILTERED_DATA/*gz; do ln -s $i ; done
$ ll
drwxr-xr-x 2 foo users 216 Nov 21 10:48 6.1.REF
lrwxrwxrwx 1 foo users 41 Nov 21 10:58 EPNC_orphan_1.fq.gz -> ../QC_FILTERED_DATA/EPNC_orphan_1.fq.gz
lrwxrwxrwx 1 foo users 41 Nov 21 10:58 EPNC_orphan_2.fq.gz -> ../QC_FILTERED_DATA/EPNC_orphan_2.fq.gz
lrwxrwxrwx 1 foo users 40 Nov 21 10:58 EPNC_trim_R1.fq.gz -> ../QC_FILTERED_DATA/EPNC_trim_R1.fq.gz
lrwxrwxrwx 1 foo users 40 Nov 21 10:58 EPNC_trim_R2.fq.gz -> ../QC_FILTERED_DATA/EPNC_trim_R2.fq.gz
# Get data for input (Note orphan reads from R1 and R2 are merged with simple cat command)
$ pwd
/home/foo/Metacongo_Paper/HUMAN_CONTA_REMOVAL
# Symlink files as usual (using ln -s)
$ for i in ../QC_FILTERED_DATA/*gz; do ln -s $i ; done
$ ll
drwxr-xr-x 2 foo users 216 Nov 21 2022 6.1.REF
lrwxrwxrwx 1 foo users 41 Nov 21 2022 EPNC_orphan_1.fq.gz -> ../QC_FILTERED_DATA/EPNC_orphan_1.fq.gz
lrwxrwxrwx 1 foo users 41 Nov 21 2022 EPNC_orphan_2.fq.gz -> ../QC_FILTERED_DATA/EPNC_orphan_2.fq.gz
-rw-r--r-- 1 foo users 468447598 Nov 21 2022 EPNC_orphan.fq.gz #This file is from cat EPNC_orphan_1.fq.gz EPNC_orphan_2.fq.gz> EPNC_orphan.fq.gz
lrwxrwxrwx 1 foo users 40 Nov 21 2022 EPNC_trim_R1.fq.gz -> ../QC_FILTERED_DATA/EPNC_trim_R1.fq.gz
lrwxrwxrwx 1 foo users 40 Nov 21 2022 EPNC_trim_R2.fq.gz -> ../QC_FILTERED_DATA/EPNC_trim_R2.fq.gz
# TO FACILITATE THIS STEP WE HAVE TO EXPORT SOME VAR DIRECTLY FROM CMD (IN BASH)
$ F_READS='EPNC_trim_R1.fq.gz'
$ R_READS='EPNC_trim_R2.fq.gz'
$ ORPHAN_READS='EPNC_orphan.fq.gz'
$ HUMAN_BW2_INDEX='REF/GRCh38_noalt_as'
OUT_SAM='Read_vs_human.sam'
OUT_BAM='Read_vs_human.bam'
OUT_UNMAPPED_BAM='Unmapped.bam'
OUT_BAM_SORTED='Unmapped_sorted.bam'
UNMAPPED_LIST='Unmapped.list'
# Run bowtie2 to map reads to the human genome index
$ bowtie2 --very-sensitive-local -x REF/GRCh38_noalt_as -1 EPNC_trim_R1.fq.gz -2 EPNC_trim_R2.fq.gz -U EPNC_orphan.fq.gz -S Read_vs_human.sam -p 4
# Converting sam to bam
$ samtools view -S -bh Read_vs_human.sam > Read_vs_human.bam
# Sort bam file
$ samtools sort Read_vs_human.bam -o Read_vs_human_sorted.bam
# Getting some stats from the whole BAM file (Optional)
$ bamtools stats -in Read_vs_human_sorted.bam >mapping_stat_from_whole_bam.stats
# Getting unmapped reads from the bam file ....
$ samtools view -b -f 4 Read_vs_human_sorted.bam > Unmapped.bam
# This line produces the unmapped list BUT with duplication
# Have to remove duplicated reads at the end using seqkit
$ samtools view Unmapped.bam |awk '{print $1}' > Unmapped.list
# Using seqtk to get unmapped reads in fastq format ...
$ seqtk subseq EPNC_trim_R1.fq.gz Unmapped.list | gzip > EPNC_trim_no_human_R1.fq.gz
$ seqtk subseq EPNC_trim_R2.fq.gz Unmapped.list | gzip > EPNC_trim_no_human_R2.fq.gz
$ seqtk subseq EPNC_orphan.fq.gz Unmapped.list | gzip > EPNC_orphan_no_human.fq.gz
# Rename files (Optional)
$ mv EPNC_trim_no_human_R1.fq.gz EPNC_trim_ready_R1.fq.gz
$ mv EPNC_trim_no_human_R2.fq.gz EPNC_trim_ready_R2.fq.gz
$ mv EPNC_orphan_no_human.fq.gz EPNC_orphan_ready.fq.gz
# Puts these files in another folder
$ mkdir READY_FASTQ_FILES
mv EPNC_trim_ready_R1.fq.gz EPNC_trim_ready_R2.fq.gz EPNC_orphan_ready.fq.gz READY_FASTQ_FILES
# If you have a cluster with slurm (see Scripts folder for a script named fastqc_filtered_data_slurm.sh) and sbatch bowtie2_vs_human_slurm.sh to run itAt this step data are ready to analyze, and we will profile the metagenomic read using two profilers (Kaiju and Kraken2).
kaiju uses amino acid database, so there is a six-frame translation of our reads then compared to proteins DB
kraken2 uses nucleotide database.
All reads that are not assigned/classified by kaiju were passed to kraken2
This step needs databases so be sure to have enough space in your local server ...
$ mkdir PROFILING && cd PROFILING
$ mkdir USING_KAIJU USING_KRAKEN2 && cd USING_KAIJU
# Symlink files as usual
$ ln -s ../../HUMAN_CONTA_REMOVAL/READY_FASTQ_FILES_CLEAN/EPNC_orphan_ready_clean.fq.gz
$ ln -s ../../HUMAN_CONTA_REMOVAL/READY_FASTQ_FILES_CLEAN/EPNC_trim_ready_clean_R1.fq.gz
$ ln -s ../../HUMAN_CONTA_REMOVAL/READY_FASTQ_FILES_CLEAN/EPNC_trim_ready_clean_R2.fq.gz
# For informations (Read count)
EPNC_orphan_ready_clean.fq.gz 4699049
EPNC_trim_ready_clean_R1.fq.gz 79712668
EPNC_trim_ready_clean_R2.fq.gz 79712668
# Run kaiju on the data using databases: kaiju_db_nr_euk_2022-03-10 then the unclassified using this database will be used with
# kaiju_db_plasmids_2022-04-10 then the unclassified will be used with kaiju_db_rvdb_2022-04-07 database.
# If you have a cluster with slurm (see Scripts folder for a script named kaiju_profiling_all_slurm__last.sh) and sbatch kaiju_profiling_all_slurm__last.sh to run it
Using ############# Using NR_EUK DATABASES #####################
# Running kaiju-multi on paired end reads ...
# KAIJU_NR_EUK_DB='/kaiju_db/kaiju_db_nr_euk_2022-03-10/kaiju_db_nr_euk.fmi'
# KAIJU_NR_EUK_NODES='/kaiju_db/kaiju_db_nr_euk_2022-03-10/nodes.dmp'
# KAIJU_NR_EUK_NAMES='/kaiju_db/kaiju_db_nr_euk_2022-03-10/names.dmp'
# F_READS='EPNC_trim_ready_clean_R1.fq.gz'
# R_READS='EPNC_trim_ready_clean_R2.fq.gz'
# ORPHAN_READS='EPNC_orphan_ready_clean.fq.gz'
#Output for the profiling using NR_EUK
# OUTPUT_PE_NR_EUK='Metacongo_kaiju__PE_NR_EUK.out'
# OUTPUT_SE_NR_EUK='Metacongo_kaiju__SE_NR_EUK.out'
# OUTPUT_ALL_NR_EUK='Metacongo_kaiju__ALL_NR_EUK.out'
# After merging the output of PE and SE, we have to create krona file
# OUTPUT_ALL_NR_EUK_KR='Metacongo_kaiju__ALL_NR_EUK.krona'
$ kaiju-multi -z 4 -E 0.01 -t /kaiju_db/kaiju_db_nr_euk_2022-03-10/nodes.dmp \
-f /kaiju_db/kaiju_db_nr_euk_2022-03-10/kaiju_db_nr_euk.fmi \
-i EPNC_trim_ready_clean_R1.fq.gz\
-j EPNC_trim_ready_clean_R2.fq.gz > Metacongo_kaiju__PE_NR_EUK.out
# Running kaiju (not multi) on orphan merged reads ...
$ kaiju -z 4 -E 0.01 -t /kaiju_db/kaiju_db_nr_euk_2022-03-10/nodes.dmp \
-f /kaiju_db/kaiju_db_nr_euk_2022-03-10/kaiju_db_nr_euk.fmi \
-i EPNC_orphan_ready_clean.fq.gz > $Metacongo_kaiju__SE_NR_EUK.out
# Combining the output for PE and ORPHAN (SE) ...
$ cat Metacongo_kaiju__PE_NR_EUK.out Metacongo_kaiju__SE_NR_EUK.out > Metacongo_kaiju__ALL_NR_EUK.out
# Adding full taxa names ... to output ...........
$ kaiju-addTaxonNames -p -t /kaiju_db/kaiju_db_nr_euk_2022-03-10/nodes.dmp \
-n /kaiju_db/kaiju_db_nr_euk_2022-03-10/names.dmp \
-i Metacongo_kaiju__ALL_NR_EUK.out -o Metacongo_kaiju__ALL_NR_EUK.out"_with_name.tsv"
# Get read count for classified reads (Optional)
$ echo "TOTAL REDS COUNT: " $(wc -l Metacongo_kaiju__ALL_NR_EUK.out |awk '{print $1}')
$ echo "READ CLASSIFIED COUNT: " $(grep -w -c "C" Metacongo_kaiju__ALL_NR_EUK.out )
# Converting to kaiju output to krona file
$ kaiju2krona -t /kaiju_db/kaiju_db_nr_euk_2022-03-10/nodes.dmp \
-n /kaiju_db/kaiju_db_nr_euk_2022-03-10/names.dmp \
-i Metacongo_kaiju__ALL_NR_EUK.out -o Metacongo_kaiju__ALL_NR_EUK.krona
# Creating HTML report from the krona file
$ ktImportText -o Metacongo_kaiju__ALL_NR_EUK.krona.html Metacongo_kaiju__ALL_NR_EUK.krona
# Creating classification summary for phylum, class, order family, genus, and species ...
# using loop in bash
$ for i in phylum class order family genus species; do kaiju2table \
-t /kaiju_db/kaiju_db_nr_euk_2022-03-10/nodes.dmp \
-n /kaiju_db/kaiju_db_nr_euk_2022-03-10/names.dmp \
-r $i -o Metacongo_kaiju__ALL_NR_EUK.out"_"$i"__summary.tsv" Metacongo_kaiju__ALL_NR_EUK.out; done
# At this stage profiling the data using kaiju_db_nr_euk_2022-03-10 is done we will get un_profiled data from this step and proceed with another kaijudb
################# Using RVDB DATABASE ##################################
We are going to extract the unclassified reads from the output and re_run on Virus db
#Names after the First run for RVBD classification F_Unc_from_nr_euk='F_Unc_for_rvbd.fq.gz' R_Unc_from_nr_euk='R_Unc_for_rvbd.fq.gz' O_Unc_from_nr_euk='O_Unc_for_rvbd.fq.gz'
#Output for the profiling using RVBD OUTPUT_PE_RVDB='Metacongo_kaiju__PE_RVDB.out' OUTPUT_SE_RVDB='Metacongo_kaiju__SE_RVDB.out' OUTPUT_ALL_RVDB='Metacongo_kaiju__ALL_RVDB.out'
#After merging the output of PE and SE have to create krona file OUTPUT_ALL_RVDB_KR='Metacongo_kaiju__ALL_RVDB.krona'
KAIJU_RVDB_DB='/home/databases/kaiju_db/kaiju_db_rvdb_2022-04-07/kaiju_db_rvdb.fmi' KAIJU_RVDB_NODES='/home/databases/kaiju_db/kaiju_db_rvdb_2022-04-07/nodes.dmp' KAIJU_RVDB_NAMES='/home/databases/kaiju_db/kaiju_db_rvdb_2022-04-07/names.dmp'
# Getting list (Read ID)
$ grep -w 'U' Metacongo_kaiju__ALL_NR_EUK.out |awk '{print $2}' > Unclassified_from_nr_euk.list
# Extracting Unclassified reads from original fastq
$ seqtk subseq EPNC_trim_ready_clean_R1.fq.gz Unclassified_from_nr_euk.list | gzip > F_Unc_for_rvbd.fq.gz
$ seqtk subseq REPNC_trim_ready_clean_R2.fq.gz Unclassified_from_nr_euk.list | gzip > $R_Unc_for_rvbd.fq.gz
$ seqtk subseq EPNC_orphan_ready_clean.fq.gz Unclassified_from_nr_euk.list | gzip > O_Unc_for_rvbd.fq.gz
# Files are ready for the second round of profiling
# Running kaiju-multi on paired-end reads .....
$ kaiju-multi -z 2 -E 0.01 -t /kaiju_db/kaiju_db_rvdb_2022-04-07/nodes.dmp \
-f /kaiju_db/kaiju_db_rvdb_2022-04-07/kaiju_db_rvdb.fmi \
-i F_Unc_for_rvbd.fq.gz -j R_Unc_for_rvbd.fq.gz > Metacongo_kaiju__PE_RVDB.out
$ Running kaiju (not multi) on orphan merged reads ......
$ kaiju -z 4 -E 0.01 -t /kaiju_db/kaiju_db_rvdb_2022-04-07/nodes.dmp \
-f /kaiju_db/kaiju_db_rvdb_2022-04-07/kaiju_db_rvdb.fmi \
-i O_Unc_for_rvbd.fq.gz > Metacongo_kaiju__SE_RVDB.out
# Combining the output for PE and ORPHAN (SE) here...
$ cat Metacongo_kaiju__PE_RVDB.out Metacongo_kaiju__SE_RVDB.out > Metacongo_kaiju__ALL_RVDB.out
# Adding full taxa names ... to output
$ kaiju-addTaxonNames -p -t /kaiju_db/kaiju_db_rvdb_2022-04-07/nodes.dmp \
-n /kaiju_db/kaiju_db_rvdb_2022-04-07/names.dmp \
-i Metacongo_kaiju__ALL_RVDB.out -o Metacongo_kaiju__ALL_RVDB.out"_with_name.tsv"
# How many reads are classified....
$ echo "TOTAL REDS COUNT: " $(wc -l Metacongo_kaiju__ALL_RVDB.out |awk '{print $1}')
$ echo "READ CLASSIFIED COUNT: " $(grep -w -c "C" Metacongo_kaiju__ALL_RVDB.out ) |tee -a analysis.log
# converting to kaiju output to krona file"
$ kaiju2krona -t /kaiju_db/kaiju_db_rvdb_2022-04-07/nodes.dmp \
-n /kaiju_db/kaiju_db_rvdb_2022-04-07/names.dmp \
-i Metacongo_kaiju__ALL_RVDB.out -o Metacongo_kaiju__ALL_RVDB.krona
# Creating an HTML report from the krona file ...
$ ktImportText -o Metacongo_kaiju__ALL_RVDB.krona.html Metacongo_kaiju__ALL_RVDB.krona
# Creating classification summary for phylum, class, order family, genus and species ..
$ for i in phylum class order family genus species; do kaiju2table \
-t /kaiju_db/kaiju_db_rvdb_2022-04-07/nodes.dmp \
-n /kaiju_db/kaiju_db_rvdb_2022-04-07/names.dmp \
-r $i -o Metacongo_kaiju__ALL_RVDB.out"_"$i"__summary.tsv" Metacongo_kaiju__ALL_RVDB.out; done
################################## Using RVDB Plasmid database ##################################
Using PL (for plasmids) DATABASE
We are going to extract the unclassified reads from the previous analysis output and re_run on Plasmid db
KAIJU_PL_DB='/home/databases/kaiju_db/kaiju_db_plasmids_2022-04-10/kaiju_db_plasmids.fmi' KAIJU_PL_NODES='/home/databases/kaiju_db/kaiju_db_plasmids_2022-04-10/nodes.dmp' KAIJU_PL_NAMES='/home/databases/kaiju_db/kaiju_db_plasmids_2022-04-10/names.dmp'
#Names after the First run for PLASMID classification F_Unc_from_rvbd='F_Unc_for_pl.fq.gz' R_Unc_from_rvbd='R_Unc.for_pl.fq.gz' O_Unc_from_rvbd='O_Unc.for_pl.fq.gz'
#Output for the profiling using PL OUTPUT_PE_PL='Metacongo_kaiju__PE_PL.out' OUTPUT_SE_PL='Metacongo_kaiju__SE_PL.out' OUTPUT_ALL_PL='Metacongo_kaiju__ALL_PL.out'
#After merging the output of PE and SE, we have to create the krona file OUTPUT_ALL_PL_KR='Metacongo_kaiju__ALL_PL.krona'
# getting reads list"
$ grep -w 'U' Metacongo_kaiju__ALL_RVDB.out |awk '{print $2}' > Unclassified_from_rvbd.list
$ # Extracting Unclassified reads from original fastq
# Extract reads
$ seqtk subseq EPNC_trim_ready_clean_R1.fq.gz Unclassified_from_rvbd.list | gzip > F_Unc_for_pl.fq.gz
$ seqtk subseq EPNC_trim_ready_clean_R2.fq.gz Unclassified_from_rvbd.list | gzip > R_Unc.for_pl.fq.gz
$ seqtk subseq EPNC_orphan_ready_clean.fq.gz Unclassified_from_rvbd.list | gzip > O_Unc.for_pl.fq.gz
# Files ready for second round using rvdb..........
echo "1:Runing kaiju-multi on paired-end reads ..."
$ kaiju-multi -z 4 -E 0.01 -t /kaiju_db/kaiju_db_plasmids_2022-04-10/nodes.dmp \
-f /kaiju_db/kaiju_db_plasmids_2022-04-10/kaiju_db_plasmids.fmi \
-i F_Unc_for_pl.fq.gz -j R_Unc.for_pl.fq.gz > Metacongo_kaiju__PE_PL.out
# Runing kaiju (not multi) on orphan merged reads ...
$ kaiju -z 4 -E 0.01 -t /kaiju_db/kaiju_db_plasmids_2022-04-10/nodes.dmp \
-f /kaiju_db/kaiju_db_plasmids_2022-04-10/kaiju_db_plasmids.fmi \
-i O_Unc.for_pl.fq.gz > Metacongo_kaiju__SE_PL.out
# Combining the output for PE and ORPHAN (SE) here ....
$ cat Metacongo_kaiju__PE_PL.out Metacongo_kaiju__SE_PL.out > Metacongo_kaiju__ALL_PL.out
# Adding full taxa names ... to output ...
$ kaiju-addTaxonNames -p -t /kaiju_db/kaiju_db_plasmids_2022-04-10/nodes.dmp \
-n /kaiju_db/kaiju_db_plasmids_2022-04-10/names.dmp
-i Metacongo_kaiju__ALL_PL.out -o Metacongo_kaiju__ALL_PL.out"_with_name.tsv"
# How many reads are classified .....
$ echo "TOTAL REDS COUNT: " $(wc -l $Metacongo_kaiju__ALL_PL.out |awk '{print $1}')
$ echo "READ CLASSIFIED COUNT: " $(grep -w -c "C" $Metacongo_kaiju__ALL_PL.out )
# Converting to kaiju output to Krona file
$ kaiju2krona -t /kaiju_db/kaiju_db_plasmids_2022-04-10/nodes.dmp \
-n /home/databases/kaiju_db/kaiju_db_plasmids_2022-04-10/names.dmp \
-i Metacongo_kaiju__ALL_PL.out -o Metacongo_kaiju__ALL_PL.krona
# Creating html from Krona file...
$ ktImportText -o Metacongo_kaiju__ALL_PL.krona.html Metacongo_kaiju__ALL_PL.krona
# Creating classification summary for phylum, class, order family, genus, and species ...
$ for i in phylum class order family genus species; do kaiju2table -t /kaiju_db/kaiju_db_plasmids_2022-04-10/nodes.dmp \
-n /kaiju_db/kaiju_db_plasmids_2022-04-10/names.dmp \
-r $i -o Metacongo_kaiju__ALL_PL.out"_"$i"__summary.tsv" Metacongo_kaiju__ALL_PL.out; done
# All runs finished .....
From here all the profiling step was done using kaiju, will get unprofiled reads from this step and then convert them to fastq and profile them by another profiler with Kraken2
# Extracting reads from PL output
# Going to extract reads not assigned from PL profiling ...
$ grep -w 'U' Metacongo_kaiju__ALL_PL.out |awk '{print $2}' > Unclassified_from_PL.list
echo "Extracting Unclassified reads from original fastq" |tee -a analysis.log
seqtk subseq EPNC_trim_ready_clean_R1.fq.gz Unclassified_from_PL.list | gzip > R1_to_kraken2.fq.gz
seqtk subseq EPNC_trim_ready_clean_R1.fq.gz Unclassified_from_PL.list | gzip > R2_to_kraken2.fq.gz
seqtk subseq EPNC_orphan_ready_clean.fq.gz Unclassified_from_PL.list | gzip > orphan_to_kraken2.fq.gz
#################################################################
We will use these inputs for Kraken2
- kraken2_db_path='/home/databases/kraken2_bracken_ref_seq/standard_plus_PF/'
- R1='R1_to_kraken2.fq.gz'
- R2='R2_to_kraken2.fq.gz'
- orphan='orphan_to_kraken2.fq.gz'
#kraken2_report='metacongo_remaining_krake2.report' #kraken2_output='metacongo_remaining_krake2.out'
#classified and unclassified reads #uncla_reads='unclassified#.fq' #cla_reads='classified#.fq' #$SLURM_CPUS_PER_TASK
# Profiling the remaining reads from kaiju using kraken2 Using default parameters
# PAIRED
$ kraken2 --use-names --threads 10 --db /home/databases/kraken2_bracken_ref_seq/standard_plus_PF/ \
--report metacongo_remaining_krake2.report \
--gzip-compressed --use-names \
--paired R1_to_kraken2.fq.gz R2_to_kraken2.fq.gz \
--output metacongo_remaining_krake2.out \
--unclassified-out unclassified#.fq --classified-out classified#.fq
# ORPHANS
$ kraken2 --db home/databases/kraken2_bracken_ref_seq/standard_plus_PF/ \
orphan_to_kraken2.fq.gz --use-names --report orphan_kraken.report \
--output orphan_kraken2.output \
--gzip-compressed --threads 10 \
--classified_from_orphan.fq unclassified_from_orphan.fq
# zip fastq (fq) files
gzip *fq
Now, using kreport2krona.py from krakentools (to be added in REF)
# Paired
$ kreport2krona.py -r metacongo_remaining_kraken2.report --intermediate-ranks -o metacongo_remaining_kraken2.KRONA
# Single/orphans
$ kreport2krona.py -r orphan_kraken2.report --intermediate-ranks -o orphan_kraken2.KRONA
#Generate an HTML for visualization (you can use the script available with kaiju package ktImportText from KronaTools ...)
$ ktImportText -o metacongo_remaining_kraken2.KRONA.html metacongo_remaining_kraken2.KRONA
$ ktImportText -o orphan_kraken2.KRONA.html orphan_kraken2.KRONA
- Here we will assemble the data using two assemblers, and bin assemblies using two binning tools.
- Megahit and Metaspades will be used as assemblers
- Maxbin2 and Metabat2 will be used as binning tools
$ mkdir -p ASSEMBLY/MEGAHIT_ASSEMBLY && cd ASSEMBLY/MEGAHIT_ASSEMBLY/
#Get files symlinked here ....
$ ln -s ../../HUMAN_CONTA_REMOVAL/READY_FASTQ_FILES_CLEAN/EPNC_orphan_ready_clean.fq.gz
$ ln -s ../../HUMAN_CONTA_REMOVAL/READY_FASTQ_FILES_CLEAN/EPNC_trim_ready_clean_R1.fq.gz
$ ln -s ../../HUMAN_CONTA_REMOVAL/READY_FASTQ_FILES_CLEAN/EPNC_trim_ready_clean_R2.fq.gz
#Run megahit
$ megahit -1 EPNC_trim_ready_clean_R1.fq.gz -2 EPNC_trim_ready_clean_R2.fq.gz -r EPNC_orphan_ready_clean.fq.gz \
-o metacongo_final_assembly \
-t 10 \
--min-count 2 --k-min 21 --k-max 127 --k-step 2 --min-contig-len 200 --tmp-dir .
Bonus (assembly statistics)
Feel free to do some statistics from your assembly ...
$ mkdir METASPADES_ASSEMBLY && cd METASPADES_ASSEMBLY
$ pwd
/workspaces/Metacongo_Paper/ASSEMBLY/METASPADES_ASSEMBLY
# Symlink files as usual
$ ln -s ../../HUMAN_CONTA_REMOVAL/READY_FASTQ_FILES_CLEAN/EPNC_orphan_ready_clean.fq.gz
$ ln -s ../../HUMAN_CONTA_REMOVAL/READY_FASTQ_FILES_CLEAN/EPNC_trim_ready_clean_R1.fq.gz
$ ln -s ../../HUMAN_CONTA_REMOVAL/READY_FASTQ_FILES_CLEAN/EPNC_trim_ready_clean_R2.fq.gz
#Run metaspades
$ k_mer='21,23,25,27,29,31,33,35,37,39,41,43,45,47,49,51,53,55,57,59,61,63,65,67,69,71,73,75,77,79,81,83,85,87,91,93,95,97,99,101,103,105,107,109,111,113,115,117,119,121,123,125,127'
metaspades.py --pe1-1 EPNC_trim_ready_clean_R1.fq.gz --pe1-2 EPNC_trim_ready_clean_R2.fq.gz \
-s EPNC_orphan_ready_clean.fq.gz -k $k_mer -o metaspades_final_assembly_results
Bonus (assembly statistics)
$ mkdir MEGAHIT_ASSEM_BIN
# Symlink fastq files and assembly
$ ln -s ../../ASSEMBLY/MEGAHIT_ASSEMBLY/EPNC_orphan_ready_clean.fq.gz
$ ln -s ../../ASSEMBLY/MEGAHIT_ASSEMBLY/EPNC_trim_ready_clean_R1.fq.gz
$ ln -s ../../ASSEMBLY/MEGAHIT_ASSEMBLY/EPNC_trim_ready_clean_R2.fq.gz
$ ln -s ../../ASSEMBLY/MEGAHIT_ASSEMBLY/metacongo_final_assembly/final.contigs.fa
# Symlink assembly
$ mv final.contigs.fa metacong_megahit_assembly.fsa
# Reference ... assembly file Indexing ...............
$ bowtie2-build -f metacong_megahit_assembly.fsa metacong_megahit_assembly.fsa__indexed --threads 10
# MAPP READS AGAINST THE INDEX
$ bowtie2 -p 10 --very-sensitive-local -x metacong_megahit_assembly.fsa__indexed -1 EPNC_trim_ready_clean_R1.fq.gz -2 EPNC_trim_ready_clean_R2.fq.gz \
-U EPNC_orphan_ready_clean.fq.gz |samtools view -bS - > metacongo.bam
# SORTING bam file
$ samtools sort metacongo.bam -o metacongo_sorted.bam
#Indexing ...
$ samtools index metacongo_sorted.bam
# Removing unwanted file
$ rm metacongo.bam
# Generating stats from the mapping ..............
bamtools stats -in metacongo_sorted.bam >mapping.stats
# Start binning contigs using metabat2
$ runMetaBat.sh -m 1500 -t 10 metacong_megahit_assembly.fsa metacongo_sorted.bam
# Start binning using maxbin2 ...........
$ run_MaxBin.pl -contig metacong_megahit_assembly.fsa -out maxbin2_results \
-reads EPNC_trim_ready_clean_R1.fq.gz \
-reads2 EPNC_trim_ready_clean_R2.fq.gz \
-reads3 EPNC_orphan_ready_clean.fq.gz \
-min_contig_length 200 -thread 10 \
-prob_threshold 0.7 -plotmarker -markerset 40 -verbose
$ mkdir Maxbin2_binning_results && mv maxbin* Maxbin2_binning_results
# Index the ref
$ bowtie2-build --large-index -f metacongo_metaspades_assembly.fsa metacongo_metaspades_assembly.fsa__indexed --threads 10
# Map reads against the assembly
$ bowtie2 -p 10 --very-sensitive-local -x metacongo_metaspades_assembly.fsa__indexed -1 EPNC_trim_ready_clean_R1.fq.gz \
-2 EPNC_trim_ready_clean_R2.fq.gz \
-U EPNC_orphan_ready_clean.fq.gz |samtools view -bS - > metacongo.bam
# Sort mapping file
$ samtools sort metacongo.bam -o metacongo_sorted.bam
# Index bam file
$ samtools index metacongo_sorted.bam
# Delete unwanted bam
rm metacongo.bam
# Get stats from bam file ....
$ bamtools stats -in metacongo_sorted.bam >mapping.stats
# start binning contigs using metabat2
$ runMetaBat.sh -m 1500 -t 10 $REF metacongo_sorted.bam |tee -a binning_analysis.log
$ mkdir Metabat2_binning_results && mv metacongo_metaspades_assembly.fsa.depth.txt \
metacongo_metaspades_assembly.fsa.paired.txt *bt2l \
metacongo_sorted.bam metacongo_sorted.bam.bai \
mapping.stats metacongo_metaspades_assembly.fsa.metabat-bins20 Metabat2_binning_results
# Start binning using maxbin2 ...........
run_MaxBin.pl -contig metacongo_metaspades_assembly.fsa -out maxbin2_results -reads EPNC_trim_ready_clean_R1.fq.gz \
-reads2 EPNC_trim_ready_clean_R2.fq.gz \
-reads3 EPNC_orphan_ready_clean.fq.gz \
-min_contig_length 200 -thread 10 \
-prob_threshold 0.7 -plotmarker -markerset 40
For bin refinement, we used metawrap (some modules). The refinement was done on each assembly-bin
# Set the env for metawrap
$ source ~/miniconda2/bin/activate
$ conda activate metawrap-env
$ metawrap bin_refinement -o MEGAHIT_ASSEMB_REFINED -A MEGAHIT_ASSEM_BIN/Maxbin2_binning_results -B MEGAHIT_ASSEM_BIN/Metabat2_binning_results/ metacong_megahit_assembly.fsa.metabat-bins32
$ source ~/miniconda2/bin/activate
$ conda activate metawrap-env
$ metawrap bin_refinement -o METASPADES_ASSEMB_REFINED/ -A METASPADES_ASSEMB_BIN/Maxbin2_binning_results -B METASPADES_ASSEMB_BIN/Metabat2_binning_results/metacongo_metaspades_assembly.fsa.metabat-bins20/
Please Note, that we did the assembly with two assemblers, the binning with two binner, and then refine bins.
To track the assemblers (as info) in the assembly results (contigs), we renamed files as follows:
form bin.XX.fa to mghit_bin.XX.fa
As previous steps, renaming and to track the assemblers (as info) in the assembly results (contigs), we renamed files as follows: form bin.XX.fa to mtspades_bin.XX.fa
cp the two folders (files of mghit_bin.XX.fa && mtspades_bin.XX.fa) in one folder
Metawrap quant_bins use only F and R or 1 and 2 files for reads, so we extracted each pair from orphan and appended to each file (1 or 2)
# Extract reads from orphan files ... and save them according to pairing info ..
cat EPNC_orphan_ready_clean.fastq|paste - - - - |awk '$2~ /1:N/ {print $1,$2"\n"$3"\n"$4"\n"$5}' >1.fq
cat EPNC_orphan_ready_clean.fastq|paste - - - - |awk '$2~ /2:N/ {print $1,$2"\n"$3"\n"$4"\n"$5}' >2.fq
# Concatenate files ...
cat EPNC_trim_ready_clean_1.fastq 1.fq> test_1.fastq
cat EPNC_trim_ready_clean_2.fastq 2.fq> test_2.fastq
# Finally remove unwanted files ....... and rename
rm 1.fq 2.fq EPNC_orphan_ready_clean.fastq EPNC_trim_ready_clean_1.fastq EPNC_trim_ready_clean_2.fastq
# Rename ........
mv test_1.fastq All_EPNC_trim_ready_clean_1.fastq
mv test_2.fastq All_EPNC_trim_ready_clean_2.fastq
Then we quantify bins by the module quant_bins from the metawrap pipeline ...
metawrap quant_bins -b MERGED_REFINED_RENAMED -o QUANT_MERGED_REFINED_RENAMED All_EPNC_trim_ready_clean_1.fastq All_EPNC_trim_ready_clean_2.fastq
$ metawrap blobology -a QUANT_MERGED_REFINED_RENAMED/assembly.fa -o BLOBOLOGY --bins MERGED_REFINED_RENAMED/ All_EPNC_trim_ready_clean_1.fastq All_EPNC_trim_ready_clean_2.fastq
$ metawrap classify_bins -b MERGED_REFINED_RENAMED -o CLASSIFY_BIN -t 40
$ metaWRAP annotate_bins -o FUNCT_ANNOT -t 40 -b MERGED_REFINED_RENAMED
GENOME_DIR='MERGED_REINED_RENAMED/'
OUTDIR='GTDB_TAX'
EXTENSION='fa'
# Get conda ENV
source /home/bioinf/apps/anaconda3/bin/activate
# Activate gtdb
conda activate gtdbtk
gtdbtk classify_wf --genome_dir $GENOME_DIR --out_dir $OUTDIR --extension $EXTENSION --cpus $SLURM_CPUS_PER_TASK --pplacer_cpus 1
## Clustering assembly
For this step, we want to explore the presence of AMR genes in all assembly
$ mkdir WHOLE_ASSEMBLY && cd WHOLE_ASSEMBLY
# Symlink assemblies (from megahit and metaspades ...)
$ ln -s ../../ASSEMBLY/MEGAHIT_ASSEMBLY/metacongo_final_assembly/final.contigs.fa
$ ln -s ../../ASSEMBLY/METASPADES_ASSEMBLY/metaspades_final_assembly_results/contigs.fasta
# Renamed assemblies file for good tracking
$ mv final.contigs.fa megahit_assembly.fa && mv contigs.fasta metaspades_assembly.fa
# Concatenate assembly files
cat megahit_assembly.fa metaspades_assembly.fa > all_assembly.fasta
$ mkdir Clustered_using_mmseq && cd Clustered_using_mmseq
$ ln -s ../all_assembly.fasta
# Run mmseq2 to cluster the assembly (remove redundant contigs)
$ mmseqs easy-cluster all_assembly.fasta clusterRes tmp --min-seq-id 0.8 -c 0.8 --cov-mode 1
$ ll
clusterRes_all_seqs.fasta
clusterRes_cluster.tsv
clusterRes_rep_seq.fasta (This file will be used to scan for AMR)
$ mkdir AMR_DETECT && cd AMR_DETECT
$ ln -s ../WHOLE_ASSEMBLY/clusterRes_rep_seq.fasta
# Run abricate
abricate clusterRes_rep_seq.fasta --threads 10 > Abricate_ouput.tab
# Generate summary
abricate --summary Abricate_ouput.tab> Abricate_ouput_summary.tab
If you use this protocol in your data analyses, please cite the paper : Metagenomic data from gutter water in the city of Pointe-Noire, Republic of Congo
How to cite: {the analysis of our data was done as described in Moumen et al,2024}
-
Andrews, S. (2010). FastQC: A Quality Control Tool for High Throughput Sequence Data [Online]. Available online at: http://www.bioinformatics.babraham.ac.uk/projects/fastqc/ Shifu Chen. 2023.
-
Ultrafast one-pass FASTQ data preprocessing, quality control, and deduplication using fastp. iMeta 2: e107. https://doi.org/10.1002/imt2.107.
-
Shifu Chen, Yanqing Zhou, Yaru Chen, Jia Gu; fastp: an ultra-fast all-in-one FASTQ preprocessor, Bioinformatics, Volume 34, Issue 17, 1 September 2018, Pages i884–i890, https://doi.org/10.1093/bioinformatics/bty560
-
Langmead B, Wilks C., Antonescu V., Charles R. Scaling read aligners to hundreds of threads on general-purpose processors. Bioinformatics. bty648.
-
Langmead B, Salzberg S. Fast gapped-read alignment with Bowtie 2. Nature Methods. 2012, 9:357-359.
-
Langmead B, Trapnell C, Pop M, Salzberg SL. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biology 10:R25.
-
Menzel, P. et al. (2016) Fast and sensitive taxonomic classification for metagenomics with Kaiju. Nat. Commun. 7:11257
-
Wood, D.E., Lu, J. & Langmead, B. Improved metagenomic analysis with Kraken 2. Genome Biol 20, 257 (2019). https://doi.org/10.1186/s13059-019-1891-0
-
Ondov BD, Bergman NH, and Phillippy AM. Interactive metagenomic visualization in a Web browser. BMC Bioinformatics. 2011 Sep 30; 12(1):385.
-
W Shen, S Le, Y Li*, F Hu*. SeqKit: a cross-platform and ultrafast toolkit for FASTA/Q file manipulation. PLOS ONE. doi:10.1371/journal.pone.0163962
-
Petr Danecek, James K Bonfield, Jennifer Liddle, John Marshall, Valeriu Ohan, Martin O Pollard, Andrew Whitwham, Thomas Keane, Shane A McCarthy, Robert M Davies, Heng Li . Twelve years of SAMtools and BCFtools GigaScience, Volume 10, Issue 2, February 2021, giab008, https://doi.org/10.1093/gigascience/giab008.
-
Barnett DW, Garrison EK, Quinlan AR, Strömberg MP, Marth GT. BamTools: a C++ API and toolkit for analyzing and managing BAM files. Bioinformatics. 2011 Jun 15;27(12):1691-2. doi: 10.1093/bioinformatics/btr174. Epub 2011 Apr 14. PMID: 21493652; PMCID: PMC3106182.
-
Li, D., Liu, C-M., Luo, R., Sadakane, K., and Lam, T-W., (2015) MEGAHIT: An ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics, doi: 10.1093/bioinformatics/btv033 [PMID: 25609793].
-
Li, D., Luo, R., Liu, C.M., Leung, C.M., Ting, H.F., Sadakane, K., Yamashita, H. and Lam, T.W., 2016. MEGAHIT v1.0: A Fast and Scalable Metagenome Assembler driven by Advanced Methodologies and Community Practices. Methods.
-
Uritskiy, G.V., DiRuggiero, J. & Taylor, J. MetaWRAP—a flexible pipeline for genome-resolved metagenomic data analysis. Microbiome 6, 158 (2018). https://doi.org/10.1186/s40168-018-0541-1.
-
Steinegger M and Soeding J. MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nature Biotechnology, doi: 10.1038/nbt.3988 (2017).
-
Steinegger M and Soeding J. Clustering huge protein sequence sets in linear time. Nature Communications, doi: 10.1038/s41467-018-04964-5 (2018).
-
Mirdita M, Steinegger M and Soeding J. MMseqs2 desktop and local web server app for fast, interactive sequence searches. Bioinformatics, doi: 10.1093/bioinformatics/bty1057 (2019).
-
Mirdita M, Steinegger M, Breitwieser F, Soding J, Levy Karin E: Fast and sensitive taxonomic assignment to metagenomic contigs. Bioinformatics, doi: 10.1093/bioinformatics/btab184 (2021)
-
Kang DD, Li F, Kirton E, Thomas A, Egan R, An H, Wang Z. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ. 2019 Jul 26;7:e7359. doi: 10.7717/peerj.7359. PMID: 31388474; PMCID: PMC6662567.
-
Yu-Wei Wu, Blake A. Simmons, Steven W. Singer, MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets, Bioinformatics, Volume 32, Issue 4, February 2016, Pages 605–607, https://doi.org/10.1093/bioinformatics/btv638.
-
Nurk S, Meleshko D, Korobeynikov A, Pevzner PA. metaSPAdes: a new versatile metagenomic assembler. Genome Res. 2017 May;27(5):824-834. doi: 10.1101/gr.213959.116. Epub 2017 Mar 15. PMID: 28298430; PMCID: PMC5411777.
-
Chaumeil PA, et al. 2022. GTDB-Tk v2: memory friendly classification with the Genome Taxonomy Database. Bioinformatics, btac672.
-
Chaumeil PA, et al. 2019. GTDB-Tk: A toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics, btz848.
-
Parks, D.H., et al. (2021). GTDB: an ongoing census of bacterial and archaeal diversity through a phylogenetically consistent, rank normalized and complete genome-based taxonomy. Nucleic Acids Research, 50: D785–D794.
-
Rinke, C, et al. (2021). A standardized archaeal taxonomy for the Genome Taxonomy Database. Nature Microbiology, 6: 946–959.
-
Parks, D.H., et al. 2020. A complete domain-to-species taxonomy for Bacteria and Archaea. Nature Biotechnology, https://doi.org/10.1038/s41587-020-0501-8.
-
Parks DH, et al. 2018. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nature Biotechnology, http://dx.doi.org/10.1038/nbt.4229. We strongly encourage you to cite the following 3rd party dependencies:
-
Matsen FA, et al. 2010. pplacer: linear time maximum-likelihood and Bayesian phylogenetic placement of sequences onto a fixed reference tree. BMC Bioinformatics, 11:538.
-
Jain C, et al. 2019. High-throughput ANI Analysis of 90K Prokaryotic Genomes Reveals Clear Species Boundaries. Nat. Communications, doi: 10.1038/s41467-018-07641-9.
-
Hyatt D, et al. 2010. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics, 11:119. doi: 10.1186/1471-2105-11-119.
-
Price MN, et al. 2010. FastTree 2 - Approximately Maximum-Likelihood Trees for Large Alignments. PLoS One, 5, e9490.
-
Eddy SR. 2011. Accelerated profile HMM searches. PLOS Comp. Biol., 7:e1002195.
-
Ondov BD, et al. 2016. Mash: fast genome and metagenome distance estimation using MinHash. Genome Biol 17, 132. doi: 10.1186/s13059-016-0997-x.