Metagenomic profiling

Requirements

Database
- Prebuilt databases are available.
- Or build custom databases.
Hardware.
- CPU: ≥ 32 cores preferred.
- RAM: ≥ 64 GB, depends on file size of the biggest database.

Datasets

Short reads, single or paired end.

Steps

Step 1. Preprocessing reads

For example, removing adapters and trimming using fastp:

fastp -i in_1.fq.gz -I in_2.fq.gz  \
    -o out_1.fq.gz -O out_2.fq.gz \
    -l 75 -q 20 -W 4 -M 20 -3 20 --thread 32 \
    --html out.fastp.html

Step 2. Removing host reads

Tools:

bowtie2 is recommended for removing host reads.
samtools is also used for processing reads mapping file.
pigz is a parallel implementation of gzip, which is much faster than gzip.

Host reference genomes:

Human: CHM13. We also provide a database of CHM13 for fast removing human reads.

Building the index (~60min):

bowtie2-build --threads 32 GCA_009914755.3_CHM13_T2T_v1.1_genomic.fna.gz chm13

Mapping and removing mapped reads:

index=~/ws/db/bowtie2/chm13

bowtie2 --threads 32 -x $index -1 in_1.fq.gz -2 in_2.fq.gz  \
    | samtools view -buS -f 4 - \
    | samtools fastq - \
    | gzip -c > sample.fq.gz

Step 3. Searching

Reads can be searched against multiple databases which can be built with different parameters, and the results can be fastly merged for downstream analysis.

Attentions

Input format should be (gzipped) FASTA or FASTQ from files or stdin. Paired-End reads should be given via -1/--read1 and -2/--read2.
```
kmcp search -d db -1 read_1.fq.gz -2 read_2.fq.gz -o read.tsv.gz
```
Single-end can be given as positional arguments or -1/-2.
```
kmcp search -d db file1.fq.gz file2.fq.gz -o result.tsv.gz
```
Single-end mode is recommended for paired-end reads, for higher sensitivity.
A long query sequence may contain duplicated k-mers, which are not removed for short sequences by default. You may modify the value of -u/--kmer-dedup-threshold (default 256) to remove duplicates.
For long reads or contigs, you should split them into short reads using seqkit sliding, e.g.,
```
seqkit sliding -s 100 -W 300
```
The values of tCov and jacc in results only apply to databases built with a single size of k-mer.

kmcp search and kmcp profile share some flags, therefore users can use stricter criteria in kmcp profile.

-t/--min-query-cov, minimum query coverage, i.e., proportion of matched k-mers and unique k-mers of a query (default 0.55, close to ~96.5% sequence similarity)
-f/--max-fpr, maximum false positive rate of a query. (default 0.05)

Index files loading modes

Using memory-mapped index files with mmap (default):
- Faster startup speed when index files are buffered in memory.
- Multiple KMCP processes can share the memory.
Loading the whole index files into memory (-w/--load-whole-db):
- This mode occupies a little more memory. And multiple KMCP processes can not share the database in memory.
- It's slightly faster due to the use of physically contiguous memory. The speedup is more significant for smaller databases.
- Please switch on this flag (-w) when searching on computer clusters, where the default mmap mode would be very slow for network-attached storage (NAS).
Low memory mode (--low-mem):
- Do not load all index files into memory nor use mmap, using file seeking.
- It's much slower, >4X slower on SSD and would be much slower on HDD disks.
- Only use this mode for small number of queries or a huge database that can't be loaded into memory.

Performance tips:

Increase the value of -j/--threads for acceleratation, but values larger than the the number of CPU cores won't bring extra speedup.

Commands

Single-end mode is recommended for paired-end reads, for higher sensitivity:

# ---------------------------------------------------
# single-end (recommended)

read1=sample_1.fq.gz
read2=sample_2.fq.gz
sample=sample

# 1. searching results against multiple databases
for db in refseq-fungi.kmcp genbank-viral.kmcp gtdb.kmcp ; do
    dbname=$(basename $db)

    kmcp search \
        --threads            32 \
        --db-dir            $db \
        --min-kmers          10 \
        --min-query-len      30 \
        --min-query-cov    0.55 \
        $read1                  \
        $read2                  \
        --out-file         $sample.kmcp@$dbname.tsv.gz \
        --log              $sample.kmcp@$dbname.tsv.gz.log
done

# 2. Merging search results against multiple databases
kmcp merge $sample.kmcp@*.tsv.gz --out-file $sample.kmcp.tsv.gz

Paired-end reads:

# ---------------------------------------------------
# paired-end

read1=sample_1.fq.gz
read2=sample_2.fq.gz
sample=sample

# 1. searching results against multiple databases
for db in refseq-fungi.kmcp genbank-viral.kmcp gtdb.kmcp ; do
    dbname=$(basename $db)

    kmcp search \
        --threads            32 \
        --db-dir            $db \
        --min-kmers          10 \
        --min-query-len      30 \
        --min-query-cov    0.55 \
        --read1          $read1 \
        --read2          $read2 \
        --out-file       $sample.kmcp@$dbname.tsv.gz \
        --log            $sample.kmcp@$dbname.tsv.gz.log
done

# 2. Merging search results against multiple databases
kmcp merge $sample.kmcp@*.tsv.gz --out-file $sample.kmcp.tsv.gz

Search result format

Tab-delimited format with 15 columns:

 1. query,    Identifier of the query sequence
 2. qLen,     Query length
 3. qKmers,   K-mer number of the query sequence
 4. FPR,      False positive rate of the match
 5. hits,     Number of matches
 6. target,   Identifier of the target sequence
 7. chunkIdx, Index of reference chunk
 8. chunks,   Number of reference chunks
 9. tLen,     Reference length
10. kSize,    K-mer size
11. mKmers,   Number of matched k-mers
12. qCov,     Query coverage,  equals to: mKmers / qKmers
13. tCov,     Target coverage, equals to: mKmers / K-mer number of reference chunk
14. jacc,     Jaccard index
15. queryIdx, Index of query sequence, only for merging

Note: The header line starts with #, you need to assign another comment charactor if using csvtk for analysis. e.g.,

csvtk filter2 -C '$' -t -f '$qCov > 0.55' mock.kmcp.gz

Demo result:

#query	qLen	qKmers	FPR	hits	target	chunkIdx	chunks	tLen	kSize	mKmers	qCov	tCov	jacc	queryIdx
NC_003197.2-64416/1	150	130	7.4626e-15	1	GCF_000006945.2	9	10	4857450	21	90	0.6923	0.0002	0.0002	1
NC_003197.2-64414/1	150	130	7.4626e-15	1	GCF_000006945.2	6	10	4857450	21	130	1.0000	0.0003	0.0003	2
NC_003197.2-64412/1	150	130	7.4626e-15	1	GCF_000006945.2	6	10	4857450	21	121	0.9308	0.0002	0.0002	3
NC_003197.2-64410/1	150	130	7.4626e-15	1	GCF_000006945.2	1	10	4857450	21	101	0.7769	0.0002	0.0002	4
NC_003197.2-64408/1	150	130	7.8754e-15	1	GCF_000006945.2	9	10	4857450	21	83	0.6385	0.0002	0.0002	5
NC_003197.2-64406/1	150	130	7.4626e-15	1	GCF_000006945.2	2	10	4857450	21	103	0.7923	0.0002	0.0002	6
NC_003197.2-64404/1	150	130	7.4671e-15	1	GCF_000006945.2	5	10	4857450	21	86	0.6615	0.0002	0.0002	7
NC_003197.2-64402/1	150	130	7.5574e-15	1	GCF_000006945.2	3	10	4857450	21	84	0.6462	0.0002	0.0002	8
NC_003197.2-64400/1	150	130	7.4626e-15	1	GCF_000006945.2	1	10	4857450	21	89	0.6846	0.0002	0.0002	9

Searching on a computer cluster

Update: We recommend analyzing one sample using one computer node, which is easier to setup up.

Here, we split genomes of GTDB into 16 partitions and build a database for every partition, so we can use computer cluster to accelerate the searching. The genbank-viral genomes are also diveded into 4 partition.

A helper script easy_sbatch is used for batch submitting Slurm jobs via script templates.

# ---------------------------------------------------
# searching


j=32
reads=reads

# -----------------
# gtdb

dbprefix=~/ws/db/kmcp/gtdb.n16-

for file in $reads/*.left.fq.gz; do
    prefix=$(echo $file | sed 's/.left.fq.gz//')
    read1=$file
    read2=$(echo $file | sed 's/left.fq.gz/right.fq.gz/')

    ls -d $dbprefix*.kmcp \
        | easy_sbatch \
            -c $j -J $(basename $prefix) \
            "kmcp search         \
                --load-whole-db  \
                --threads   $j   \
                --db-dir    {}   \
                $read1 $read2    \
                --out-file  $prefix.kmcp@\$(basename {}).tsv.gz \
                --log       $prefix.kmcp@\$(basename {}).tsv.gz.log \
                --quiet "
done

# -----------------
# viral

dbprefix=~/ws/db/kmcp/genbank-viral.n4-

for file in $reads/*.left.fq.gz; do
    prefix=$(echo $file | sed 's/.left.fq.gz//')
    read1=$file
    read2=$(echo $file | sed 's/left.fq.gz/right.fq.gz/')

    ls -d $dbprefix*.kmcp \
        | easy_sbatch \
            -c $j -J $(basename $prefix) \
            "kmcp search         \
                --load-whole-db  \
                --threads   $j   \
                --db-dir    {}   \
                $read1 $read2    \
                --out-file  $prefix.kmcp@\$(basename {}).tsv.gz \
                --log       $prefix.kmcp@\$(basename {}).tsv.gz.log \
                --quiet "
done

# -----------------
# fungi

dbprefix=~/ws/db/kmcp/refseq-fungi

for file in $reads/*.left.fq.gz; do
    prefix=$(echo $file | sed 's/.left.fq.gz//')
    read1=$file
    read2=$(echo $file | sed 's/left.fq.gz/right.fq.gz/')

    ls -d $dbprefix*.kmcp \
        | easy_sbatch \
            -c $j -J $(basename $prefix) \
            "kmcp search         \
                --load-whole-db  \
                --threads   $j   \
                --db-dir    {}   \
                $read1 $read2    \
                --out-file  $prefix.kmcp@\$(basename {}).tsv.gz \
                --log       $prefix.kmcp@\$(basename {}).tsv.gz.log \
                --quiet "
done


# ---------------------------------------------------
# wait all job being done



# ---------------------------------------------------
# merge result and profiling

# merge results
# there's no need to submit to slurm, which could make it slower, cause the bottleneck is file IO
for file in $reads/*.left.fq.gz; do
    prefix=$(echo $file | sed 's/.left.fq.gz//')

    echo $prefix; date
    kmcp merge $prefix.kmcp@*.tsv.gz --out-file $prefix.kmcp.tsv.gz \
        --quiet --log $prefix.kmcp.tsv.gz.merge.log
done

# profiling
X=taxdump/
T=taxid.map

fd kmcp.tsv.gz$ $reads/ \
    | rush -v X=$X -v T=$T \
        'kmcp profile -X {X} -T {T} {} -o {}.k.profile -C {}.c.profile -s {%:} \
            --log {}.k.profile.log'

Step 4. Profiling

Input

TaxId mapping file(s).
Taxdump files.
KMCP search results.

Methods

Reference genomes can be split into chunks when computing k-mers (sketches), which could help to increase the specificity via a threshold, i.e., the minimum proportion of matched chunks (-p/--min-chunks-fraction) (highly recommended). Another flag -d/--max-chunks-cov-stdev further reduces false positives.
We require a part of the uniquely matched reads of a reference having high similarity, i.e., with high confidence for decreasing the false positive rate.
We also use the two-stage taxonomy assignment algorithm in MegaPath to reduce the false positive of ambiguous matches. You can also disable this step by the flag --no-amb-corr. If stage 1/4 produces thousands of candidates, you can use the flag --no-amb-corr to reduce analysis time, which has very little effect on the results.
Abundance are estimated using an Expectation-Maximization (EM) algorithm..
Input files are parsed for multiple times, therefore STDIN is not supported.

Accuracy notes:

Smaller -t/--min-qcov increase sensitivity at the cost of higher false positive rate (-f/--max-fpr) of a query.
And we require part of the uniquely matched reads of a reference having high similarity, i.e., with high confidence to decrease the false positive. E.g., -H >= 0.8 and -P >= 0.1 equals to 90th percentile >= 0.8
- -U/--min-hic-ureads, minimum number, >= 1
- -H/--min-hic-ureads-qcov, minimum query coverage, >= -t/--min-qcov
- -P/--min-hic-ureads-prop, minimum proportion, higher values increase precision at the cost of sensitivity.
-R/--max-mismatch-err and -D/--min-dreads-prop is for determing the right reference for ambigous reads with the algorithm in MegaPath.
--keep-perfect-match is not recommended, which decreases sensitivity.
-n/--keep-top-qcovs is not recommended, which affects accuracy of abundance estimation.

Profiling modes

We preset six profiling modes, available with the flag -m/--mode.

0 (for pathogen detection)
1 (higher recall)
2 (high recall)
3 (default)
4 (high precision)
5 (higher precision)

You can still change the values of some options below as usual.

options                       m=0    m=1   m=2   m=3    m=4   m=5
---------------------------   ----   ---   ---   ----   ---   ----
-r/--min-chunks-reads         1      5     10    50     100   100
-p/--min-chunks-fraction      0.2    0.6   0.7   0.8    1     1
-d/--max-chunks-depth-stdev   10     2     2     2      2     1.5
-u/--min-uniq-reads           1      2     5     20     50    50
-U/--min-hic-ureads           1      1     2     5      10    10
-H/--min-hic-ureads-qcov      0.7    0.7   0.7   0.75   0.8   0.8
-P/--min-hic-ureads-prop      0.01   0.1   0.2   0.1    0.1   0.15
--keep-main-matches           true                            
--max-qcov-gap                0.4

Taxonomy data:

Mapping references IDs to TaxIds: -T/--taxid-map
NCBI taxonomy dump files: -X/--taxdump

For databases built with a custom genome collection, you can use taxonkit create-taxdump to create NCBI-style taxdump files, which also generates a TaxId mapping file.

Performance notes:

Searching results are parsed in parallel, and the number of lines proceeded by a thread can be set by the flag --line-chunk-size.
However using a lot of threads does not always accelerate processing, 4 threads with a chunk size of 500-5000 is fast enough.
If stage 1/4 produces thousands of candidates, then stage 2/4 would be very slow. You can use the flag --no-amb-corr to disable ambiguous reads correction which has very little effect on the results.

Commands

# taxid mapping files, multiple files supported.
taxid_map=gtdb.kmcp/taxid.map,refseq-viral.kmcp/taxid.map,refseq-fungi.kmcp/taxid.map

# or concatenate them into a big taxid.map
#    cat gtdb.kmcp/taxid.map refseq-viral.kmcp/taxid.map refseq-fungi.kmcp/taxid.map > taxid.map
# taxid_map=taxid.map

# taxdump directory
taxdump=taxdump

sfile=$file.kmcp.tsv.gz

kmcp profile \
    --taxid-map      $taxid_map \
    --taxdump         $taxdump/ \
    --level             species \
    --min-query-cov        0.55 \
    --min-chunks-reads       50 \
    --min-chunks-fraction   0.8 \
    --max-chunks-depth-stdev  2 \
    --min-uniq-reads         20 \
    --min-hic-ureads          5 \
    --min-hic-ureads-qcov  0.75 \
    --min-hic-ureads-prop   0.1 \
    $sfile                      \
    --out-file         $sfile.kmcp.profile \
    --metaphlan-report $sfile.metaphlan.profile \
    --cami-report      $sfile.cami.profile \
    --sample-id        "0" \
    --binning-result   $sfile.binning.gz

Profiling result formats

Taxonomic profiling output formats:

KMCP (-o/--out-file). Note that: abundances are only computed for target references rather than each taxon at all taxonomic ranks, so please output CAMI or MetaPhlAn format.
CAMI (-M/--metaphlan-report, --metaphlan-report-version, sample name: -s/--sample-id, taxonomy data: --taxonomy-id)
MetaPhlAn (-C/--cami-report, sample name: -s/--sample-id))

Taxonomic binning formats:

CAMI (-B/--binning-result)

KMCP format (Tab-delimited format with 17 columns):

 1. ref,                Identifier of the reference genome
 2. percentage,         Relative abundance of the reference
 3. coverage,           Average coverage of the reference
 4. score,              The 90th percentile of qCov of uniquely matched reads
 5. chunksFrac,         Genome chunks fraction
 6. chunksRelDepth,     Relative depths of reference chunks
 7. chunksRelDepthStd,  The standard deviation of chunksRelDepth
 8. reads,              Total number of matched reads of this reference
 9. ureads,             Number of uniquely matched reads
10. hicureads,          Number of uniquely matched reads with high-confidence
11. refsize,            Reference size
12. refname,            Reference name, optional via name mapping file
13. taxid,              TaxId of the reference
14. rank,               Taxonomic rank
15. taxname,            Taxonomic name
16. taxpath,            Complete lineage
17. taxpathsn,          Corresponding TaxIds of taxa in the complete lineage

Demo output:

ref	percentage	coverage	score	chunksFrac	chunksRelDepth	chunksRelDepthStd	reads	ureads	hicureads	refsize	taxid	rank	taxname	taxpath	taxpathsn
GCF_003697165.2	18.663804	1.864553	100.00	1.00	1.04;0.90;1.03;1.00;0.90;1.00;1.00;1.02;1.11;0.99	0.06	60952	27831	15850	4903501	4093283224	species	Escherichia coli	Bacteria;Proteobacteria;Gammaproteobacteria;Enterobacterales;Enterobacteriaceae;Escherichia;Escherichia coli	609216830;3788559933;329474883;3160438580;2234733759;3334977531;4093283224
GCF_002949675.1	18.201855	1.818404	97.69	1.00	1.04;0.93;1.02;1.03;1.04;0.97;1.04;1.02;0.95;0.98	0.04	53288	17152	8866	4395762	524994882	species	Shigella dysenteriae	Bacteria;Proteobacteria;Gammaproteobacteria;Enterobacterales;Enterobacteriaceae;Shigella;Shigella dysenteriae	609216830;3788559933;329474883;3160438580;2234733759;2258433137;524994882
GCF_000006945.2	18.143627	1.812587	100.00	1.00	1.02;0.98;0.98;0.99;1.03;0.99;0.98;1.03;0.97;1.02	0.02	58697	57300	40690	4857450	1678121664	species	Salmonella enterica	Bacteria;Proteobacteria;Gammaproteobacteria;Enterobacterales;Enterobacteriaceae;Salmonella;Salmonella enterica	609216830;3788559933;329474883;3160438580;2234733759;794943543;1678121664
GCF_000742135.1	17.738253	1.772089	100.00	1.00	1.01;1.01;1.02;0.99;1.01;1.01;1.00;0.97;0.96;1.03	0.02	65518	63665	44088	5545864	3958205156	species	Klebsiella pneumoniae	Bacteria;Proteobacteria;Gammaproteobacteria;Enterobacterales;Enterobacteriaceae;Klebsiella;Klebsiella pneumoniae	609216830;3788559933;329474883;3160438580;2234733759;2440106587;3958205156

CAMI format:

@SampleID:
@Version:0.10.0
@Ranks:superkingdom|phylum|class|order|family|genus|species|strain
@TaxonomyID:ncbi-taxonomy
@@TAXID RANK    TAXPATH TAXPATHSN   PERCENTAGE
2   superkingdom    2   Bacteria    100.000000
1224    phylum  2|1224  Bacteria|Proteobacteria 99.530189
74201   phylum  2|74201 Bacteria|Verrucomicrobia    0.469811
1236    class   2|1224|1236 Bacteria|Proteobacteria|Gammaproteobacteria 99.530189
203494  class   2|74201|203494  Bacteria|Verrucomicrobia|Verrucomicrobiae   0.469811
91347   order   2|1224|1236|91347   Bacteria|Proteobacteria|Gammaproteobacteria|Enterobacterales    99.530189
48461   order   2|74201|203494|48461    Bacteria|Verrucomicrobia|Verrucomicrobiae|Verrucomicrobiales    0.469811
543 family  2|1224|1236|91347|543   Bacteria|Proteobacteria|Gammaproteobacteria|Enterobacterales|Enterobacteriaceae 99.530189
1647988 family  2|74201|203494|48461|1647988    Bacteria|Verrucomicrobia|Verrucomicrobiae|Verrucomicrobiales|Akkermansiaceae    0.469811
561 genus   2|1224|1236|91347|543|561   Bacteria|Proteobacteria|Gammaproteobacteria|Enterobacterales|Enterobacteriaceae|Escherichia 99.530189
239934  genus   2|74201|203494|48461|1647988|239934 Bacteria|Verrucomicrobia|Verrucomicrobiae|Verrucomicrobiales|Akkermansiaceae|Akkermansia    0.469811
562 species 2|1224|1236|91347|543|561|562   Bacteria|Proteobacteria|Gammaproteobacteria|Enterobacterales|Enterobacteriaceae|Escherichia|Escherichia coli    99.530189
239935  species 2|74201|203494|48461|1647988|239934|239935  Bacteria|Verrucomicrobia|Verrucomicrobiae|Verrucomicrobiales|Akkermansiaceae|Akkermansia|Akkermansia muciniphila    0.469811
431946  strain  2|1224|1236|91347|543|561|562|431946    Bacteria|Proteobacteria|Gammaproteobacteria|Enterobacterales|Enterobacteriaceae|Escherichia|Escherichia coli|Escherichia coli SE15  48.321535
83333   strain  2|1224|1236|91347|543|561|562|83333 Bacteria|Proteobacteria|Gammaproteobacteria|Enterobacterales|Enterobacteriaceae|Escherichia|Escherichia coli|Escherichia coli K-12  46.194629
386585  strain  2|1224|1236|91347|543|561|562|386585    Bacteria|Proteobacteria|Gammaproteobacteria|Enterobacterales|Enterobacteriaceae|Escherichia|Escherichia coli|Escherichia coli O157:H7 str. Sakai    5.014025
349741  strain  2|74201|203494|48461|1647988|239934|239935|349741   Bacteria|Verrucomicrobia|Verrucomicrobiae|Verrucomicrobiales|Akkermansiaceae|Akkermansia|Akkermansia muciniphila|Akkermansia muciniphila ATCC BAA-835   0.469811

Related tools:

taxonkit profile2cami can convert any metagenomic profile table with TaxIds to CAMI format.
taxonkit cami-filter can remove taxa of given TaxIds and their descendants in CAMI metagenomic profile.

Metaphlan3 format (--metaphlan-report):

#SampleID   
#clade_name NCBI_tax_id relative_abundance  additional_species
k__Bacteria 2   100.000000  
k__Bacteria|p__Proteobacteria   1224    99.530189   
k__Bacteria|p__Verrucomicrobia  74201   0.469811    
k__Bacteria|p__Proteobacteria|c__Gammaproteobacteria    1236    99.530189   
k__Bacteria|p__Verrucomicrobia|c__Verrucomicrobiae  203494  0.469811    
k__Bacteria|p__Proteobacteria|c__Gammaproteobacteria|o__Enterobacterales    91347   99.530189   
k__Bacteria|p__Verrucomicrobia|c__Verrucomicrobiae|o__Verrucomicrobiales    48461   0.469811    
k__Bacteria|p__Proteobacteria|c__Gammaproteobacteria|o__Enterobacterales|f__Enterobacteriaceae  543 99.530189   
k__Bacteria|p__Verrucomicrobia|c__Verrucomicrobiae|o__Verrucomicrobiales|f__Akkermansiaceae 1647988 0.469811    
k__Bacteria|p__Proteobacteria|c__Gammaproteobacteria|o__Enterobacterales|f__Enterobacteriaceae|g__Escherichia   561 99.530189   
k__Bacteria|p__Verrucomicrobia|c__Verrucomicrobiae|o__Verrucomicrobiales|f__Akkermansiaceae|g__Akkermansia  239934  0.469811    
k__Bacteria|p__Proteobacteria|c__Gammaproteobacteria|o__Enterobacterales|f__Enterobacteriaceae|g__Escherichia|s__Escherichia coli   562 99.530189   
k__Bacteria|p__Verrucomicrobia|c__Verrucomicrobiae|o__Verrucomicrobiales|f__Akkermansiaceae|g__Akkermansia|s__Akkermansia muciniphila   239935  0.469811    
k__Bacteria|p__Proteobacteria|c__Gammaproteobacteria|o__Enterobacterales|f__Enterobacteriaceae|g__Escherichia|s__Escherichia coli|t__Escherichia coli SE15  431946  48.321535   
k__Bacteria|p__Proteobacteria|c__Gammaproteobacteria|o__Enterobacterales|f__Enterobacteriaceae|g__Escherichia|s__Escherichia coli|t__Escherichia coli K-12  83333   46.194629   
k__Bacteria|p__Proteobacteria|c__Gammaproteobacteria|o__Enterobacterales|f__Enterobacteriaceae|g__Escherichia|s__Escherichia coli|t__Escherichia coli O157:H7 str. Sakai    386585  5.014025    
k__Bacteria|p__Verrucomicrobia|c__Verrucomicrobiae|o__Verrucomicrobiales|f__Akkermansiaceae|g__Akkermansia|s__Akkermansia muciniphila|t__Akkermansia muciniphila ATCC BAA-835   349741  0.469811

Metaphlan2 format (--metaphlan-report-version 2 --metaphlan-report):

#SampleID   
k__Bacteria 100.000000
k__Bacteria|p__Proteobacteria   99.530189
k__Bacteria|p__Verrucomicrobia  0.469811
k__Bacteria|p__Proteobacteria|c__Gammaproteobacteria    99.530189
k__Bacteria|p__Verrucomicrobia|c__Verrucomicrobiae  0.469811
k__Bacteria|p__Proteobacteria|c__Gammaproteobacteria|o__Enterobacterales    99.530189
k__Bacteria|p__Verrucomicrobia|c__Verrucomicrobiae|o__Verrucomicrobiales    0.469811
k__Bacteria|p__Proteobacteria|c__Gammaproteobacteria|o__Enterobacterales|f__Enterobacteriaceae  99.530189
k__Bacteria|p__Verrucomicrobia|c__Verrucomicrobiae|o__Verrucomicrobiales|f__Akkermansiaceae 0.469811
k__Bacteria|p__Proteobacteria|c__Gammaproteobacteria|o__Enterobacterales|f__Enterobacteriaceae|g__Escherichia   99.530189
k__Bacteria|p__Verrucomicrobia|c__Verrucomicrobiae|o__Verrucomicrobiales|f__Akkermansiaceae|g__Akkermansia  0.469811
k__Bacteria|p__Proteobacteria|c__Gammaproteobacteria|o__Enterobacterales|f__Enterobacteriaceae|g__Escherichia|s__Escherichia coli   99.530189
k__Bacteria|p__Verrucomicrobia|c__Verrucomicrobiae|o__Verrucomicrobiales|f__Akkermansiaceae|g__Akkermansia|s__Akkermansia muciniphila   0.469811
k__Bacteria|p__Proteobacteria|c__Gammaproteobacteria|o__Enterobacterales|f__Enterobacteriaceae|g__Escherichia|s__Escherichia coli|t__Escherichia coli SE15  48.321535
k__Bacteria|p__Proteobacteria|c__Gammaproteobacteria|o__Enterobacterales|f__Enterobacteriaceae|g__Escherichia|s__Escherichia coli|t__Escherichia coli K-12  46.194629
k__Bacteria|p__Proteobacteria|c__Gammaproteobacteria|o__Enterobacterales|f__Enterobacteriaceae|g__Escherichia|s__Escherichia coli|t__Escherichia coli O157:H7 str. Sakai    5.014025
k__Bacteria|p__Verrucomicrobia|c__Verrucomicrobiae|o__Verrucomicrobiales|f__Akkermansiaceae|g__Akkermansia|s__Akkermansia muciniphila|t__Akkermansia muciniphila ATCC BAA-835   0.469811

Binning result:

# This is the bioboxes.org binning output format at
# https://github.com/bioboxes/rfc/tree/master/data-format
@Version:0.10.0
@SampleID:
@@SEQUENCEID    TAXID
NC_000913.3_sliding:1244941-1245090     511145
NC_013654.1_sliding:344871-345020       562
NC_000913.3_sliding:3801041-3801190     511145
NC_013654.1_sliding:752751-752900       562
NC_000913.3_sliding:4080871-4081020     562
NC_000913.3_sliding:3588091-3588240     511145
NC_000913.3_sliding:2249621-2249770     562
NC_013654.1_sliding:2080171-2080320     431946
NC_000913.3_sliding:2354841-2354990     511145
NC_013654.1_sliding:437671-437820       431946