MiGA Workflow

This is the general overview of the MiGA workflow:

For each step, performed analyses may include the use of external Software, and produce one or more result files (indexed in a hash). In most steps, different utilities from the Enveomics Collection are used in addition to the Software detailed below. Some files are mandatory to continue with the analysis (marked with req), some can be gzipped during or after the analysis (marked with gz), and some are directories (marked with dir).

List of individual steps

• ​Dataset Results​

  • ​Raw Reads​

  • ​Trimmed Reads​

  • ​Read Quality​

  • ​Trimmed FastA​

  • ​Assembly​

  • ​CDS​

  • ​Essential Genes​

  • ​SSU​

  • ​MyTaxa​

  • ​MyTaxa Scan​

  • ​Distances​

  • ​Taxonomy​

  • ​Stats​

• ​Project Results​

  • ​hAAI Distances​

  • ​AAI Distances​

  • ​ANI Distances​

  • ​Clade Finding​

  • ​Subclades​

  • ​OGS​

  • ​Project Stats​

Dataset Results

Raw Reads

This step is never actually performed by MiGA, instead it serves as the entry point for raw reads input.

Supported file keys:

• For single reads only

  • single (req, gz): FastQ file containing the raw reads

• For paired-end reads only

  • pair1 (req, gz): FastQ file containing the raw forward reads

  • pair2 (req, gz): FastQ file containing the raw reverse reads

Statistics:

• For single reads only

  • reads: Total number of reads

  • length_average: Average read length (in bp)

  • length_standard_deviation: Standard deviation of read length (in bp)

  • g_c_content: G+C content of all reads (in %)

  • x_content: Undetermined bases content of all reads (in %)

  • a_t_skew: A-T sequence skew across all reads (in %)

  • g_c_skew: G-C sequence skew across all reads (in %)

• For paired-end reads only

  • read_pairs: Total number of read pairs

  • forward_length_average: Average forward read length (in bp)

  • forward_length_standard_deviation: Standard deviation of forward read

    length (in bp)

  • forward_g_c_content: G+C content of forward reads (in %)

  • forward_x_content: Undetermined bases content of forward reads (in %)

  • forward_a_t_skew: A-T sequence skew across forward reads (in %)

  • forward_g_c_skew: G-C sequence skew across forward reads (in %)

  • reverse_length_average, reverse_length_standard_deviation,

  • reverse_g_c_content: Same as above, for reverse reads

  • reverse_x_content: Undetermined bases content of reverse reads (in %)

  • reverse_a_t_skew: A-T sequence skew across reverse reads (in %)

  • reverse_g_c_skew: G-C sequence skew across reverse reads (in %)

MiGA symbol: raw_reads.

Trimmed Reads

This is part of Trimming & read quality in the above diagram. In this step, MiGA trims reads by Phred quality score 20 (Q20) and minimum length of 50bp using SolexaQA++, and clips potential adapter contamination using Scythe (reapplying the length filter). If the reads are paired, only pairs passing the filters are used.

Supported file keys:

• For single reads only

  • single (req, gz): FastQ file containing trimmed/clipped reads

• For paired-end reads only

  • pair1 (req, gz): FastQ file containing trimmed/clipped forward reads

  • pair2 (req, gz): FastQ file containing trimmed/clipped reverse reads

  • single (req, gz): FastQ file containing trimmed/clipped reads with

    only one sister passing quality control

• For either type

  • trimming_summary: Raw text file containing a summary of the trimmed

    sequences

MiGA symbol: trimmed_reads.

Read Quality

This is a quality-control step included as part of Trimming & read quality in the diagram above. In this step, MiGA generates quality reports of the trimmed/clipped reads using SolexaQA++ and FastQC.

Supported file keys:

• solexaqa (dir): Folder containing the SolexaQA++ quality-control

  summaries

• fastqc (dir): Folder containing the FastQC quality-control analyses

MiGA symbol: read_quality.

Trimmed FastA

This is the final step included in Trimming & read quality in the diagram above, in which MiGA generates FastA files with the trimmed/clipped reads.

Supported file keys:

• coupled (req for coupled reads, unless pair1 and pair2 exist):

  Interposed FastA file containing quality-checked paired reads. If this file

  doesn't exist, it is automatically generated from pair1 and pair2

• single (req for single reads, gz for coupled reads): FastA file with

  quality-checked single-end reads

• pair1 (gz): FastA file containing forward sisters of quality-checked

  paired-end reads

• pair2 (gz): FastA file containing reverse sisters of quality-checked

  paired-end reads

Statistics:

• reads: Total number of reads

• length_average: Average read length (in bp)

• length_standard_deviation: Standard deviation of read length (in bp)

• g_c_content: G+C content of all reads (in %)

• x_content: Undetermined bases content of all reads (in %)

• a_t_skew: A-T sequence skew across all reads (in %)

• g_c_skew: G-C sequence skew across all reads (in %)

MiGA symbol: trimmed_fasta.

Assembly

In this step MiGA assembles trimmed FastA reads using IDBA-UD.

Supported file keys:

• largecontigs (req): FastA file containing large contigs or scaffolds

  (>1Kbp)

• allcontigs: FastA file containing all contigs or scaffolds (including

  large)

• assembly_data (dir): Folder containing some intermediate files generated

  during the assembly

Statistics:

• contigs: Total number of (large) contigs

• n50: N50 of (large) contigs (in bp)

• total_length: Total length of large contigs (in bp)

• longest_sequence: Length of the longest contig (in bp)

• n_content: Undetermined bases content of large contigs (in %)

• g_c_content: G+C content of large contigs (in %)

• x_content: Undetermined bases content of large contigs (in %)

• a_t_skew: A-T sequence skew across large contigs (in %)

• g_c_skew: G-C sequence skew across large contigs (in %)

MiGA symbol: assembly.

CDS

This step corresponds to Gene prediction in the diagram above. MiGA predicts coding sequences (putative genes and proteins) using Prodigal.

Supported file keys:

• proteins (req): FastA file containing translated protein sequences

• genes: FastA file containing putative gene sequences

• gff3 (gz): GFF v3 file containing the coordinates of coding sequences

  This file is not required, but MyTaxa depends on it (or gff2 or

  tab, whichever is available)

• gff2 (gz): GFF v2 file containing the coordinates of coding sequences

  This file is not produced by MiGA, but it's supported for backwards

  compatibility with earlier versions using MetaGeneMark

• tab (gz): Tabular-delimited file containing the columns: gene ID, gene

  length, and contig ID. This file is not produced by MiGA, but it's supported

  to allow MyTaxa to run when more detailed information about the

  gene prediction is missing

Statistics:

• predicted_proteins: Total number of predicted proteins

• average_length: Average length of predicted proteins (in aa)

• coding_density: Coding density of the genome (in %)

• codon_table: Optimal coding table (4 or 11)

MiGA symbol: cds.

Essential Genes

In this step, MiGA uses HMM.essential.rb from the Enveomics Collection to identify a set of genes typically present in single-copy in Bacterial and Archaeal genomes. In this step, protein translations of those essential genes are extracted for other analyses in MiGA (e.g., hAAI in distances) or outside (e.g., phylogeny or MLSA for diversity analyses). In addition, this step generates a report that can be used for quality control including estimations of completeness and contamination (for genomes) and median number of copies of single-copy genes (for metagenomes and viromes).

Supported file keys:

• ess_genes (req): FastA file containing all extracted protein translations

  from essential genes (.faa) or archived collection (proteins.tar.gz)

• collection (req): Folder containing individual FastA files with protein

  translations from essential genes

• report (req): Raw text report including derived statistics, as well as

  essential genes missing or detected in multiple copies (for genomes) or

  copy counts (for metagenomes and viromes)

• alignments: Generated for all genomes (non-multi types). It contains the

  best matching protein for each detected model aligned to the model

• bac_report: If present, this is the original report, and it indicates that a

  corrected report has been generated to accomodate particular features of the

  dataset

Statistics:

• For metagenomes and viromes

  • mean_copies: Average copy number across essential genes

  • median_copies: Median copy number across essential genes

• For genomes

  • completeness: Estimated completeness of the genome, based on presence of

    essential genes (in %)

  • contamination: Estimated contamination of the genome, based on copy number

    of essential genes (in %)

  • quality: Completeness - 5 x Contamination

MiGA symbol: essential_genes.

SSU

In this step, MiGA detects small-subunit rRNA genes (16S) using Barrnap and extracts their sequences using Bedtools.

Supported file keys:

• longest_ssu_gene (req): FastA file containing the longest detected SSU

  gene

• gff (gz): GFF v3 file containing the location of detected SSU genes

• all_ssu_genes (gz): FastA file containing all the detected SSU genes

Statistics:

• ssu: Total number of detected SSU fragments

• complete_ssu: Number of complete SSU loci

• max_length: Length of the longest detected SSU fragment

MiGA symbol: ssu.

MyTaxa

This step is only supported for metagenomes and viromes, and it requires the (optional) MyTaxa requirements installed.

In this step, the most likely taxonomic classification of each contig is identified using MyTaxa, and a report is generated using Krona.

Supported file keys:

• mytaxa (req): Output generated by MyTaxa

• blast (gz): BLAST against the reference genomes database

• mytaxain (gz): Re-formatted BLAST used as input for MyTaxa

• nomytaxa: If it exists, MiGA assumes no support for MyTaxa modules, and none

  of the above files are required

• species: Profile of species composition (in permil) as raw tab-delimited

  text

• genus: Profile of genus composition (in permil) as raw tab-delimited text

• phylum: Profile of phylum composition (in permil) as raw tab-delimited text

• innominate: List of innominate taxa (groups without a name but containing

  lower-rank classifications) as raw text

• kronain: Raw-text list of taxa used as input for Krona

• krona: HTML output produced by Krona

MiGA symbol: mytaxa.

MyTaxa Scan

This step is only supported for genomes (dataset types genome, popgenome, and scgenome), and it requires the (optional) MyTaxa requirements installed.

In this step, the genomes are scanned in windows of ten genes. For each window, the taxonomic distribution is determined using MyTaxa and compared against the distribution for the entire genome. This is a quality-control step for manual curation.

Supported file keys:

• mytaxa (req): MyTaxa output

• report (req): PDF file containing the graphic report

• regions_archive (gz): Archived folder containing FastA files with the

  sequences of the genes in regions identified as abnormal

• nomytaxa: If it exists, MiGA assumes no support for MyTaxa modules, and none

  of the above files are required

Deprecated file keys:

• wintax: Taxonomic distribution of each window

• blast (gz): BLAST against the reference genomes database

• mytaxain (gz): Re-formatted BLAST used as input for MyTaxa

• regions (dir): Folder containing FastA files with the sequences of the

  genes in regions identified as abnormal

• gene_ids: List of genes per window

• region_ids: List of regions identified as abnormal

MiGA symbol: mytaxa_scan.

Distances

This step is only supported for genomes (dataset types genome, popgenome, and scgenome). In this step, each dataset is compared against all other datasets in the project. If the dataset is a reference dataset, it is compared against all other reference datasets in the project. If it's a query dataset, it is compared iteratively against medoids. For more details on the strategy used in this step, see the manual section on distances.

Supported file keys:

• For reference datasets

  • haai_db (req): SQLite3 database containing hAAI values

  • aai_db: SQLite3 database containing AAI values

  • ani_db: SQLite3 database containing ANI values

• For query datasets

  • aai_medoids (req except for clades projects): Best hits among medoids

    at different hierarchical levels in the AAI indexing

  • ani_medoids (req for clades projects): Best hits among medoids at

    different hierarchical levels in the ANI indexing

  • haai_db (req): SQLite3 database containing hAAI values

  • aai_db: SQLite3 database containing AAI values

  • ani_db: SQLite3 database containing ANI values

  • ref_tree: Newick file with the Bio-NJ tree including queried medoids and

    the query dataset

  • ref_tree_pdf: PDF rendering of ref_tree

  • intax: Raw text result of the taxonomy test against the reference genome

MiGA symbol: distances.

Taxonomy

This step is only supported for genomes (dataset types genome, popgenome, and scgenome) that are reference datasets, in projects with a set reference project (:ref_project in metadata).

In this step, MiGA compares the genome against a reference project using the query search method, and imports the resulting taxonomy with p-value below 0.05 (or whichever value is set as :tax_pvalue in metadata).

Supported file keys:

• intax: Raw text result of the taxonomy test against the reference genome

• aai_medoids (req except for reference clades projects): Best hits among

  medoids at different hierarchical levels in the AAI indexing

• ani_medoids (req for reference clades projects): Best hits among medoids

  at different hierarchical levels in the ANI indexing

• haai_db (req): SQLite3 database containing hAAI values

• aai_db: SQLite3 database containing AAI values

• ani_db: SQLite3 database containing ANI values

• ref_tree: Newick file with the Bio-NJ tree including queried medoids and

  the query dataset

• ref_tree_pdf: PDF rendering of ref_tree

Statistics:

• closest_relative: Name of the reference dataset with highest AAI

• aai: AAI to the closest relative

• domain_pvalue, phylum_pvalue, class_pvalue, order_pvalue,

  family_pvalue, genus_pvalue, species_pvalue, subspecies_pvalue:

  Empirical p-values for classification at each rank with respect to the closest

  relative, based on the observed AAI

MiGA symbol: taxonomy

Stats

In this step, MiGA traces back all the results of the dataset and estimates summary statistics. In addition, it cleans any stored values in the distances database including datasets no longer registered in the project.

No supported file keys.

MiGA symbol: stats.

Project Results

Once all datasets have been pre-processed (i.e., once all the results above are available for all reference datasets), MiGA executes the following project-wide steps:

hAAI Distances

Consolidation of hAAI distances.

Supported file keys:

• rdata (req): Pairwise values in a data.frame for R

• matrix (req): Pairwise values in a raw tab-delimited file

• log (req): List of datasets included in the matrix

• hist: Histogram of hAAI values as raw tab-delimited file

MiGA symbol: haai_distances.

AAI Distances

Consolidation of AAI distances.

Supported file keys:

• rdata (req): Pairwise values in a data.frame for R

• matrix (req): Pairwise values in a raw tab-delimited file

• log (req): List of datasets included in the matrix

• hist: Histogram of AAI values as raw tab-delimited file

MiGA symbol: aai_distances.

ANI Distances

Consolidation of ANI distances.

Supported file keys:

• rdata (req): Pairwise values in a data.frame for R

• matrix (req): Pairwise values in a raw tab-delimited file

• log (req): List of datasets included in the matrix

• hist: Histogram of ANI values as raw tab-delimited file

MiGA symbol: ani_distances.

Clade Finding

This step is only supported for project types genomes and clade.

In this step, MiGA attempts to identify clades at species level or above using a combination of ANI and AAI values. MiGA generates AAI clades in this step for genomes projects. Clades proposed at AAI > 90% and ANI > 95% are formed using the Markov Clustering algorithm implemented in MCL. Most distance manipulation and tree estimation and manipulation utilities use the R packages Ape and Vegan.

Supported file keys:

• report (req for genomes): PDF file including a graphic report for the

  clustering

• class_table (req for genomes): Tab-delimited file containing the

  classification of all datasets in AAI clusters

• class_tree (req for genomes): Newick file containing the classification

  of all datasets in AAI clusters as a dendrogram

• classif (req for genomes): Tab-delimited file containing the

  highest-level classification of each dataset, the medoid of the cluster, and

  the AAI against the corresponding medoid

• medoids (req for genomes): List of medoids per cluster

• aai_tree: Bio-NJ tree based on AAI distances in Newick format

• proposal (req): Proposed species-level clades in the project, based on

  clades_ani95. One line per proposed clade, with tab-delimited dataset names.

  Only clades with 5 or more members are included

• clades_aai90: Clades formed at AAI > 90%. One clade per line, with

  comma-delimited dataset names

• clades_ani95: Clades formed at ANI > 95%. One clade per line, with

  comma-delimited dataset names

• medoids_ani95: List of clades_ani95 datasets with the smallest ANI

  distance to all members of its own ANI95 clade. The list is in the same order

MiGA symbol: clade_finding.

Subclades

This step is only supported for project type clade.

In this step, MiGA attempts to identify clades below species level using ANI values. MiGA generates ANI clades in this step. Most distance manipulation and tree estimation and manipulation utilities use the R packages Ape and Vegan.

Supported file keys:

• report (req): PDF file including a graphic report for the clustering

• class_table (req): Tab-delimited file containing the classification of all

  datasets in ANI clusters

• class_tree (req): Newick file containing the classification of all

  datasets in ANI clusters as a dendrogram

• classif (req): Tab-delimited file containing the highest-level

  classification of each dataset, the medoid of the cluster, and

  the ANI against the corresponding medoid

• medoids (req): List of medoids per cluster

• ani_tree: Bio-NJ tree based on AAI distances in Newick format

MiGA symbol: subclades.

OGS

This step is only supported for project type clade.

In this step, MiGA generates groups of orthology using reciprocal best matches between all pairs of datasets in the project. Groups are generated using MCL with pairs weighted by bit score. Once computed, MiGA uses the matrix of OGS to estimate summary and rarefied statistics.

Supported file keys:

• ogs (req): Matrix of orthology groups, as tab-delimited raw file

• stats (req): Summary statistics in JSON format

• abc (gz): When available, it includes all the individual RBM files in

  ABC format. This file is typically produced as intermediate result and

  removed before finishing, but can be maintained using

  miga update -P . -m clean_ogs=false in the project folder using the

  ​CLI​

• core_pan: Summary statistics of rarefied core-genome/pangenome sizes in

  tab-delimited format

• core_pan_plot: Plot of rarefied core-genome/pangenome sizes in PDF

MiGA symbol: ogs.

Project Stats

In this step, MiGA traces back all the results of the project and estimates summary statistics.

Supported file keys:

• taxonomy_index (req): Index of datasets per taxonomy in JSON format

• metadata_index (req): Searchable index of datasets metadata as SQLite3

  database

MiGA symbol: project_stats.