SemiBin2

Sample 1 gut microbiome (3 samples from PRJNA795985, 1M read pairs each) co-assembled with Assembly and binned with SemiBin2 v2.2.1 in self-supervised mode, producing 38 bins.

1. 1

   Download 3 samples from PRJNA795985

   for SRR in SRR17531757 SRR17531762 SRR17531772; do fastq-dump --split-files --gzip -X 1000000 --outdir reads/ $SRR; done

   Same co-assembly as VAMB — reuses the same reads. Downloads 1M read pairs per sample (~70 MB compressed each).

2. 2

   Co-assemble all samples with MEGAHIT

   megahit -1 reads/SRR17531757_1.fastq.gz,reads/SRR17531762_1.fastq.gz,reads/SRR17531772_1.fastq.gz -2 reads/SRR17531757_2.fastq.gz,reads/SRR17531762_2.fastq.gz,reads/SRR17531772_2.fastq.gz -o assembly --min-contig-len 1500 -t 8 --presets meta-sensitive

   Produces 14,934 contigs (≥1,500 bp). If already done for VAMB, reuse the same assembly.

3. 3

   Map each sample to contigs

   for SRR in SRR17531757 SRR17531762 SRR17531772; do minimap2 -ax sr -t 8 assembly/final.contigs.fa reads/${SRR}_1.fastq.gz reads/${SRR}_2.fastq.gz | samtools sort -@ 4 -o bams/${SRR}.sorted.bam && samtools index bams/${SRR}.sorted.bam; done

4. 4

   Run SemiBin2 self-supervised binning

   SemiBin2 single_easy_bin -i assembly/final.contigs.fa -b bams/SRR17531757.sorted.bam bams/SRR17531762.sorted.bam bams/SRR17531772.sorted.bam -o semibin2_out --self-supervised -m 1500 -t 8

   Uses single_easy_bin with multiple BAMs for co-abundance. Self-supervised mode trains a sample-specific model (~38 min on CPU). Produces 38 bins after HMM-based reclustering.