Configure Atlas¶
Remove reads from Host¶
One of the most important steps in the Quality control is to remove host genome. You can add any number of genomes to be removed.
We recommend you to use genomes where repetitive sequences are masked. See here for more details human genome.
Long reads¶
Limitation: Hybrid assembly of long and short reads is supported with spades and metaSpades. However metaSpades needs a paired-end short-read library.
The path of the (preprocessed) long reads should be added manually to the the sample table under a new column heading ‘longreads’.
In addition the type of the long reads should be defined in the config file:
longread_type one of [“pacbio”, “nanopore”, “sanger”, “trusted-contigs”, “untrusted-contigs”]
Example config file¶
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# For more details about the config values see:
# https://metagenome-atlas.rtfd.io
########################
# Execution parameters
########################
# threads and memory (GB) for most jobs especially from BBtools, which are memory demanding
threads: 8
mem: 60
# threads and memory for jobs needing high amount of memory. e.g GTDB-tk,checkm or assembly
large_mem: 250
large_threads: 16
assembly_threads: 8
assembly_memory: 250
simplejob_mem: 10
simplejob_threads: 4
#Runtime only for cluster execution
runtime: #in h
default: 5
assembly: 48
long: 24
simplejob: 1
# Local directory for temp files, useful for cluster execution without shared file system
tmpdir: /tmp
# directory where databases are downloaded with 'atlas download'
database_dir: databases
########################
# Quality control
########################
data_type: metagenome # metagenome or metatranscriptome
interleaved_fastqs: false
# remove (PCR)-duplicated reads using clumpify
deduplicate: true
duplicates_only_optical: false
duplicates_allow_substitutions: 2
# used to trim adapters from reads and read ends
preprocess_adapters: /path/to/databases/adapters.fa
preprocess_minimum_base_quality: 10
preprocess_minimum_passing_read_length: 51
# 0.05 requires at least 5 percent of each nucleotide per sequence
preprocess_minimum_base_frequency: 0.05
preprocess_adapter_min_k: 8
preprocess_allowable_kmer_mismatches: 1
preprocess_reference_kmer_match_length: 27
# error correction where PE reads overlap
error_correction_overlapping_pairs: true
#contamination references can be added such that -- key: /path/to/fasta
contaminant_references:
PhiX: /path/to/databases/phiX174_virus.fa
# host:/path/to/host_genome.fasta
# We won't allow large indels
contaminant_max_indel: 20
contaminant_min_ratio: 0.65
contaminant_kmer_length: 13
contaminant_minimum_hits: 1
contaminant_ambiguous: best
########################
# Pre-assembly-processing
########################
# Advanced Error correction
error_correction_before_assembly : true
spades_skip_BayesHammer: true # Skip error correction in spades assembler
error_correction_kmer: 31 # can be longer e.g. 62 but takes more memory
# remove reads with k-mers that cannot be used for assembly.
# Filter reads that have a 10% of k-mers below a minimum depth.
error_correction_remove_lowdepth: false
error_correction_minimum_kmer_depth: 1 #
error_correction_aggressive: false
# Merging of pairs
# join R1 and R2 at overlap; unjoined reads are still utilized
merge_pairs_before_assembly : true
merging_k: 62
########################
# Assembly
########################
# megahit OR spades
assembler: spades
minimum_contig_length: 1000
# Megahit
#-----------
# 2 is for metagenomes, 3 for genomes with 30x coverage
megahit_min_count: 2
megahit_k_min: 21
megahit_k_max: 121
megahit_k_step: 20
megahit_merge_level: 20,0.98
megahit_prune_level: 2
megahit_low_local_ratio: 0.2
# ['default','meta-large','meta-sensitive']
megahit_preset: default
# Spades
#------------
spades_use_scaffolds: true # if false use contigs
#Comma-separated list of k-mer sizes to be used (all values must be odd, less than 128 and listed in ascending order).
spades_k: auto
spades_preset: meta # meta, ,normal, rna single end libraries doesn't work for metaspades
spades_extra: ""
longread_type: none # [none,"pacbio", "nanopore", "sanger", "trusted-contigs", "untrusted-contigs"]
# Preprocessed long reads can be defined in the sample table with 'longreads' , for more info see the spades manual
# Filtering
#------------
# filter out assembled noise
# this is more important for assemblys from megahit
filter_contigs: false
# trim contig tips
contig_trim_bp: 0
# require contigs to have read support
minimum_average_coverage: 1
minimum_percent_covered_bases: 20
minimum_mapped_reads: 0
########################
# Quantification
########################
# Mapping reads to contigs
#--------------------------
contig_min_id: 0.9
contig_map_paired_only: true
contig_max_distance_between_pairs: 1000
maximum_counted_map_sites: 10
########################
# Binning
########################
final_binner: DASTool # [SemiBin, DASTool, vamb, maxbin]
binner: # If DASTool is used as final_binner, use predictions of this binners
- metabat
- maxbin
# - vamb
metabat:
sensitivity: sensitive
min_contig_length: 1500 # metabat needs >1500
maxbin:
max_iteration: 50
prob_threshold: 0.9
min_contig_length: 1000
DASTool:
search_engine: 'diamond'
score_threshold: 0.5 # Score threshold until selection algorithm will keep selecting bins [0..1].
genome_filter_criteria: "(Completeness-5*Contamination >50 ) & (Length_scaffolds >=50000) & (Ambigious_bases <1e6) & (N50 > 5*1e3) & (N_scaffolds < 1e3)"
genome_dereplication:
ANI: 0.95 ## Genome dreplication threshold 0.95 is more or less species
overlap: 0.2 # See more on https://drep.readthedocs.io/en/latest/module_descriptions.html
greedy_clustering: "auto" # Add options for greedy clustering 'auto' when using more than 5k bins
score:
completeness: 1
contamination: 5
N50: 0.5
length: 0
centrality: 1
rename_mags_contigs: true #Rename contigs of representative MAGs
########################
# Annotations
#######################
annotations:
- gtdb_tree
- gtdb_taxonomy
- genes
# - kegg_modules
# - dram
gene_annotations:
- eggNOG
########################
# Gene catalog
#######################
genecatalog:
source: contigs # [contigs, genomes] Predict genes from all contigs or only from the representative genomes
clustermethod: linclust # [mmseqs or linclust] see mmseqs for more details
minlength_nt: 270 # min length
minid: 0.90 # min id for gene clustering for the main gene catalog used for annotation
coverage: 0.9
extra: " "
SubsetSize: 500000
eggNOG_use_virtual_disk: false # coping the eggNOG DB to a virtual disk can sppeed up the annotation
virtual_disk: "/dev/shm" # But you need 37G extra ram