Pe150 platform

After removing ribosomal RNA and building a special library, RNA-seq data collection was conducted using the Illumina PE150 platform by Novogene Bioinformatics Technology Co. Ltd. (Beijing, China). Clean reads for subsequent analyses were obtained through raw data filtering, sequencing error rate check and GC content distribution check. Clean reads were then compared and mapped to the reference genome. Find_circ and CIRI2 were used to detect and identify circRNAs (Gao et al., 2017 (link); Memczak et al., 2013 (link)). Following identification of circRNAs, length distribution and sources of known or novel circRNAs were counted. Density statistics and circRNA locations on each chromosome were identified by Circos software for all circRNAs of each sample, and compared to all chromosomes (Krzywinski et al., 2009 (link)). Normalization of readcount by TPM was conducted before expression analysis (Zhou et al., 2010 (link)).
Raw sequencing data has been uploaded to NCBI’s Gene Expression Omnibus (GEO) and is accessible through GEO Series accession number GSE208089.

Subsequent to cultivation, genomic DNA was extracted employing the QIAamp DNA Mini Kit (Qiagen, Hilden, Germany), adhering to the manufacturer’s guidelines. The concentration and purity of the DNA samples were assessed using the NanoDrop spectrophotometer (Thermo Scientific, Waltham, MA, USA). Quality criteria encompassed a concentration of ≥20 ng/μL and a total amount exceeding 2 μg. Additionally, a purity requirement dictated that the OD260/OD280 value should fall within the range of 1.6 to 1.8.
DNA sequencing was conducted using the Illumina PE150 platform (Illumina Inc., San Diego, CA, USA) at the Novogene Corporation (Beijing, China), with coverage reaching a depth of 100×. A 350 bp paired-end library was established to facilitate genome sequencing, yielding 150 bp reads. To appraise and enhance the quality of raw sequence data, FastQC 0.11.9 [31 ] and fastp v0.23.4 [32 (link)] software tools were employed. Low-quality reads, defined by a sequence quality score ≤ Q30 across ≥3 consecutive bases, were subsequently excluded. The resultant high-quality reads were then assembled using SOAPdenovo2 [33 (link)].

The microbial DNA was extracted from the soil by an E.Z.N.A.@ Soil DNA Kit (OMEGA, Norcross, GA, U.S.). The DNA integrity was assessed by 1% agarose gel electrophoresis (200 V, 30 min). To construct the paired-end library, 500-bp DNA fragments were generated using a Covaris S220 (Covaris Inc., Woburn, MA, U.S.). Afterwards, a paired-end library was constructed using the NEB Next® UltraTM DNA Library Prep Kit (Illumina, San Diego, CA, USA). Sequencing was performed on an Illumina PE150 platform (Illumina Inc., San Diego, CA, USA) at Sangon Biotech Co., Ltd. (Shanghai, China).

After activation and centrifugation, four isolates were used to perform DNA extraction and genome-wide sequences based on an Illumina PE150 platform (Illumina). Gene detection of the assembled genomes was carried out using RAST web server (https://rast.nmpdr.org/). To illustrate the overall metabolic capability of those four isolates, the function of genes encoded by the isolates was predicted using the KEGG KofamKOALA tool [80 (link)] and classified using COG categories. To explore functional differences and similarity among isolates, their pan-genome was constructed using Orthofinder2 software with the default parameters [81 (link)]. The core ortholog groups were further manually checked based on the functional assignment. Additionally, two specific genes identified in pan-genome analysis for each isolate were selected for use in the design of the specific primers for quantification of inoculant colonization. The primers were detected using Primer premier v5 [82 (link)].

Transcriptome profiling of 18 breast muscle and 18 abdominal fat tissues was performed on the Illumina PE150 platform (Berry Genomics; Beijing, China) (https://www.berrygenomics.com). RNA was extracted as described previously [55 (link)]. Adapter sequences and other low-quality data were removed using Cutadapt. Reads were aligned to the reference genome using HISAT, while clean reads obtained by filtering were compared to the reference genome according to the gene position information specified in the genome annotation file gtf, and the total comparison rate was approximately 90%.

The study was approved by the ethical committee of Jiangsu Cancer Hospital. Thirty-one patients with multiple BLCA who accepted partial and radical cystectomy at Jiangsu Cancer Hospital from December 2018 to August 2020 were recruited. All patients were diagnosed with urothelial carcinoma. Thirty-one tumor samples and normal bladder tissues were achieved for the experiment. Detailed clinical data were collected from the electronic medical records retrospectively. Sequencing was conducted using the Illumina PE150 platform at Novogene Bioinformatics Technology Co., Ltd.

All 994 DNA samples were processed in whole-genome sequencing using the Illumina PE150 platform with an average insert size and read length of 350 bp and 150 bp, respectively. Two different genome-wide resequencing strategies were applied on 994 samples, 772 of which were XGG with an average sequencing depth of 1× and 222 other geese (51 FCG, 50 GFW, 50 LXW, 50 LDG, 11 LHW, 5 ACy, and 5 AAn) with an average sequencing depth of 10×. These genome sequencing data were aligned to the XGG reference, and variants were detected with Sentieon v201711.03^{78 (link)} (twenty times faster than GATK) DNAseq pipeline following the best practices algorithms of GATK on a Tianhe-2 Supercomputer. For 772 XGG, we further used an R package “STITCH v1.68”^{79 (link)} to impute to the whole-genome level. After quality control and filtering (Supplementary Method 4), 12,415,004 SNPs (MAF >1% and call rate > 90%) were finally detected in XGG population, and 13,008,900 SNPs (MAF >5%, call rate >90%) were identified in 222 geese. After filtering repeats or closely related individuals, 11,029,910 SNPs of 845 geese with MAF >1% and call rate >90% merged from the datasets of 636 XGG, and 209 other geese were used for subsequent analysis. The final SNP datasets were further analyzed and classified by SnpEff v5.0^{80 (link)} according to the gene annotation of the XGG reference genome.