Hands-on with Bin Chicken
Bin Chicken installation and setup
See https://aroneys.github.io/binchicken for more details.
mamba create -n binchicken -c bioconda -c conda-forge 'binchicken>=0.12.5'
conda activate binchicken
binchicken build \
--conda-prefix /storage/data/.conda \
--singlem-metapackage /storage/data/metapackage \
--checkm2-db /storage/data/checkm2
Single-sample assembly with multi-sample binning
Start by running binchicken single
to prepare the data to assemble each sample individually.
binchicken single \
--forward 788707_20171213_S_R1.fastq.gz 788707_20180129_S_R1.fastq.gz 788707_20180313_S_R1.fastq.gz 788707_20181126_S_R1.fastq.gz \
--reverse 788707_20171213_S_R2.fastq.gz 788707_20180129_S_R2.fastq.gz 788707_20180313_S_R2.fastq.gz 788707_20181126_S_R2.fastq.gz \
--output single_assembly
The suggested assemblies with their respective binning samples can be found at single_assembly/coassemble/target/elusive_clusters.tsv
.
In this case, only two of the samples are considered likely to recover genomes. These samples are 788707_20180313_S and 788707_20180129_S.
The other samples are probably too small (they are heavily subsampled) to recover genomes.
The actual assembly and binning can be run by adding --run-aviary
.
Note that with 1 core, the assemblies will take ~30 minutes each.
binchicken single \
--forward 788707_20171213_S_R1.fastq.gz 788707_20180129_S_R1.fastq.gz 788707_20180313_S_R1.fastq.gz 788707_20181126_S_R1.fastq.gz \
--reverse 788707_20171213_S_R2.fastq.gz 788707_20180129_S_R2.fastq.gz 788707_20180313_S_R2.fastq.gz 788707_20181126_S_R2.fastq.gz \
--output single_assembly --run-aviary --cores 5
The assembly and binning for each sample is found at single_assembly/coassemble/coassemble/
.
Each sample should have a folder containing assemble
for the assembly and recover
for the binning.
The bins for each sample are found in recover/bins
, with genome info at recover/bins/bin_info.tsv
.
The recovered bins are likely only ~40-60% complete, with fairly high contamination. This is probably due the small sample size, but the genome could still be analysed further with e.g. GTDBtk to find out their taxonomy.
Coassembly with multi-sample binning
Now that we have run single-sample assembly for the decent samples, we can run coassembly across the dataset. Because
binchicken coassemble \
--forward 788707_20171213_S_R1.fastq.gz 788707_20180129_S_R1.fastq.gz 788707_20180313_S_R1.fastq.gz 788707_20181126_S_R1.fastq.gz \
--reverse 788707_20171213_S_R2.fastq.gz 788707_20180129_S_R2.fastq.gz 788707_20180313_S_R2.fastq.gz 788707_20181126_S_R2.fastq.gz \
--output coassembly --max-coassembly-samples 5
The suggested coassemblies with their respective binning samples can be found at coassembly/coassemble/target/elusive_clusters.tsv
.
Since they share single-copy marker genes, the samples 788707_20180129_S and 788707_20180313_S are suggested for coassembly.
We can run the actual coassembly and binning as before by adding --run-aviary
.
Note that with 1 core, the coassembly will take ~1 hour.
binchicken coassemble \
--forward 788707_20171213_S_R1.fastq.gz 788707_20180129_S_R1.fastq.gz 788707_20180313_S_R1.fastq.gz 788707_20181126_S_R1.fastq.gz \
--reverse 788707_20171213_S_R2.fastq.gz 788707_20180129_S_R2.fastq.gz 788707_20180313_S_R2.fastq.gz 788707_20181126_S_R2.fastq.gz \
--output coassembly --max-coassembly-samples 5 --run-aviary --cores 5
References
- Alneberg, J., Bjarnason, B.S., de Bruijn, I., Schirmer, M., Quick, J., Ijaz, U.Z., Lahti, L., Loman, N.J., Andersson, A.F., Quince, C., 2014. Binning metagenomic contigs by coverage and composition. Nat. Methods 11, 1144–1146. https://doi.org/10.1038/nmeth.3103
- Ayling, M., Clark, M.D., Leggett, R.M., 2020. New approaches for metagenome assembly with short reads. Brief. Bioinform. 21, 584–594. https://doi.org/10.1093/bib/bbz020
- Kang, D.D., Froula, J., Egan, R., Wang, Z., 2015. MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ. https://doi.org/10.7717/peerj.1165
- Kang, D.D., Li, F., Kirton, E., Thomas, A., Egan, R., An, H., Wang, Z., 2019. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ 7, e7359. https://doi.org/10.7717/peerj.7359
- Li, D., Liu, C.-M., Luo, R., Sadakane, K., Lam, T.-W., 2015. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31, 1674–1676. https://doi.org/10.1093/bioinformatics/btv033
- Mallawaarachchi, V., Wickramarachchi, A., Xue, H., Papudeshi, B., Grigson, S.R., Bouras, G., Prahl, R.E., Kaphle, A., Verich, A., Talamantes-Becerra, B., Dinsdale, E.A., Edwards, R.A., 2024. Solving genomic puzzles: computational methods for metagenomic binning. Brief. Bioinform. 25, bbae372. https://doi.org/10.1093/bib/bbae372
- Nissen, J.N., Johansen, J., Allesøe, R.L., Sønderby, C.K., Armenteros, J.J.A., Grønbech, C.H., Jensen, L.J., Nielsen, H.B., Petersen, T.N., Winther, O., Rasmussen, S., 2021. Improved metagenome binning and assembly using deep variational autoencoders. Nat. Biotechnol. 39, 555–560. https://doi.org/10.1038/s41587-020-00777-4
- Nurk, S., Meleshko, D., Korobeynikov, A., Pevzner, P.A., 2017. metaSPAdes: a new versatile metagenomic assembler. Genome Res. 27, 824–834. https://doi.org/10.1101/gr.213959.116
- Pan, S., Zhao, X.-M., Coelho, L.P., 2023. SemiBin2: self-supervised contrastive learning leads to better MAGs for short- and long-read sequencing. Bioinformatics 39, i21–i29. https://doi.org/10.1093/bioinformatics/btad209
- Pan, S., Zhu, C., Zhao, X.-M., Coelho, L.P., 2022. A deep siamese neural network improves metagenome-assembled genomes in microbiome datasets across different environments. Nat. Commun. 13, 2326. https://doi.org/10.1038/s41467-022-29843-y
- Sieber, C.M.K., Probst, A.J., Sharrar, A., Thomas, B.C., Hess, M., Tringe, S.G., Banfield, J.F., 2018. Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. Nat. Microbiol. 3, 836–843. https://doi.org/10/gfwwfg
- Wang, Z., You, R., Han, H., Liu, W., Sun, F., Zhu, S., 2024. Effective binning of metagenomic contigs using contrastive multi-view representation learning. Nat. Commun. 15, 585. https://doi.org/10.1038/s41467-023-44290-z
- Wu, Y.-W., Simmons, B.A., Singer, S.W., 2016. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinforma. Oxf. Engl. 32, 605–607. https://doi.org/10.1093/bioinformatics/btv638