Fig 2: Polymorphisms in E. coli 16S rRNA gene sequences. a The position and frequency of substitutions appearing in E. coli strain K-12 MG1655 V1–V9 amplicons generated from our mock community and sequenced on the PacBio RS II platform. b The position and frequency of substitutions in reads generated from genomic sequencing of the isolated E. coli strain K-12 MG1655 on the Illumina MiSeq platform. Magnified regions show respective positions in the alignment of all seven 16S genes present in the E. coli K-12 MG1655 reference genome. The 16S sequence from the rrnD operon (**) is used as the reference for all SNP phasing. c The predicted nucleotide substitution profile of E. coli K-12 MG1655 based on aligning the seven 16S gene sequences present in the reference genome. d The predicted substitution profile of E. coli O157 Sakai based on aligning the seven 16S gene sequences present in the reference genome. Gray panels depict variable regions defined by commonly used primer-binding sites (Supplementary Table 1). Dashed lines indicate the expected proportion of nucleotide substitutions, given there are seven 16S gene copies within each genome. Source data are provided as a Source Data file

Fig 3: Research design and general effect of DSN-based cDNA-normalized PacBio RNA-seq.a Overall design of the research. Generally, cDNA generated from human peripheral blood, two SRCCs, and their adjacent non-malignant gastric mucosa samples (as listed in the white box) were normalized by DSN digestion and non-normalized, followed by PacBio RS II SMS. The effect was compared between normalization and non-normalization, mainly in respects of the four items as indicated in the gray box. Each original cDNA sample was also sequenced using Illumina SGS platforms for assisting data analysis. The SGS and normalized SMS data of SRCCs and paired samples were also used for SRCC transcriptome profiling and comparison (shown in purple). b General experimental and data analytic procedure of cDNA-normalized SMS. The step in the box with dash lines was optional. c The principle of DSN-based cDNA normalization (referring to Zhulidov et al.13). Basically, DSN specifically digests the incompletely hybridized double-strand (ds) cDNA molecules, which mostly represent the genes with high expression, and eventually, the transcripts of genes with uneven expression originally turn equal. d–e General effect of cDNA normalization in reducing the highly expressed genes and enriching the lowly expressed genes in human blood samples. d The representative genes with varied original expression (quantified by SGS, left gray bars) were quantified with the number of ROIs mapped to the corresponding genes (normalized as per 100k total ROIs) in the normalized or non-normalized SMS libraries. e Expression analysis of ERCC genes with real-time quantitative PCR. The graph represents the mRNA expression levels of indicated genes that were normalized to the Ct values of ERCC-00076. The experiments were performed in triplicates. “Standard” showed the actual ratio of molecular concentrations in the original reagent.

Fig 4: Workflow for linearizing, pooling, and sequencing plasmids on the PacBio RS II and ONT MinION long-read platforms. Each plasmid was cut with the restriction enzyme identified in the respective plasmid maps (Fig. 1), and at the specified location. After linearizing each plasmid independently, the plasmids were pooled and cleaned. We then sequenced the same pool on the PacBio RS II and Oxford Nanopore Technologies’ (ONT) MinION using their respective library preparation protocols. After sequencing, reads from each plasmid were identified using BLAST and then aligned to their respective reference sequences using graphmap, as preparation for downstream comparisons