scDAC: deep adaptive clustering of single-cell transcriptomic data with coupled autoencoder and Dirichlet process mixture model.

MOTIVATION: Clustering analysis for single-cell RNA sequencing (scRNA-seq) data is an important step in revealing cellular heterogeneity. Many clustering methods have been proposed to discover heterogenous cell types from scRNA-seq data. However, adaptive clustering with accurate cluster number reflecting intrinsic biology nature from large-scale scRNA-seq data remains quite challenging. RESULTS: Here, we propose a single-cell Deep Adaptive Clustering (scDAC) model by coupling the Autoencoder (AE) and the Dirichlet Process Mixture Model (DPMM). By jointly optimizing the model parameters of AE and DPMM, scDAC achieves adaptive clustering with accurate cluster numbers on scRNA-seq data. We verify the performance of scDAC on five subsampled datasets with different numbers of cell types and compare it with 15 widely used clustering methods across nine scRNA-seq datasets. Our results demonstrate that scDAC can adaptively find accurate numbers of cell types or subtypes and outperforms other methods. Moreover, the performance of scDAC is robust to hyperparameter changes. AVAILABILITY AND IMPLEMENTATION: The scDAC is implemented in Python. The source code is available at https://github.com/labomics/scDAC.

MetaLogo: a heterogeneity-aware sequence logo generator and aligner.

Sequence logos are used to visually display conservations and variations in short sequences. They can indicate the fixed patterns or conserved motifs in a batch of DNA or protein sequences. However, most of the popular sequence logo generators are based on the assumption that all the input sequences are from the same homologous group, which will lead to an overlook of the heterogeneity among the sequences during the sequence logo making process. Heterogeneous groups of sequences may represent clades of different evolutionary origins, or genes families with different functions. Therefore, it is essential to divide the sequences into different phylogenetic or functional groups to reveal their specific sequence motifs and conservation patterns. To solve these problems, we developed MetaLogo, which can automatically cluster the input sequences after multiple sequence alignment and phylogenetic tree construction, and then output sequence logos for multiple groups and aligned them in one figure. User-defined grouping is also supported by MetaLogo to allow users to investigate functional motifs in a more delicate and dynamic perspective. MetaLogo can highlight both the homologous and nonhomologous sites among sequences. MetaLogo can also be used to annotate the evolutionary positions and gene functions of unknown sequences, together with their local sequence characteristics. We provide users a public MetaLogo web server (http://metalogo.omicsnet.org), a standalone Python package (https://github.com/labomics/MetaLogo), and also a built-in web server available for local deployment. Using MetaLogo, users can draw informative, customized and publishable sequence logos without any programming experience to present and investigate new knowledge on specific sequence sets.

Hepatic neddylation targets and stabilizes electron transfer flavoproteins to facilitate fatty acid ß-oxidation.

Neddylation is a ubiquitination-like pathway that controls cell survival and proliferation by covalently conjugating NEDD8 to lysines in specific substrate proteins. However, the physiological role of neddylation in mammalian metabolism remains elusive, and no mitochondrial targets have been identified. Here, we report that mouse models with liver-specific deficiency of NEDD8 or ubiquitin-like modifier activating enzyme 3 (UBA3), the catalytic subunit of the NEDD8-activating enzyme, exhibit neonatal death with spontaneous fatty liver as well as hepatic cellular senescence. In particular, liver-specific UBA3 deficiency leads to systemic abnormalities similar to glutaric aciduria type II (GA-II), a rare autosomal recessive inherited fatty acid oxidation disorder resulting from defects in mitochondrial electron transfer flavoproteins (ETFs: ETFA and ETFB) or the corresponding ubiquinone oxidoreductase. Neddylation inhibition by various strategies results in decreased protein levels of ETFs in neonatal livers and embryonic hepatocytes. Hepatic neddylation also enhances ETF expression in adult mice and prevents fasting-induced steatosis and mortality. Interestingly, neddylation is active in hepatic mitochondria. ETFs are neddylation substrates, and neddylation stabilizes ETFs by inhibiting their ubiquitination and degradation. Moreover, certain mutations of ETFs found in GA-II patients hinder the neddylation of these substrates. Taken together, our results reveal substrates for neddylation and add insight into GA-II.

Revealing diverse alternative splicing variants of the highly homologous SMN1 and SMN2 genes by targeted long-read sequencing.

The survival of motor neuron (SMN) genes, SMN1 and SMN2, are two highly homologous genes related to spinal muscular atrophy (SMA). Different patterns of alternative splicing have been observed in the SMN genes. In this study, the long-read sequencing technique for distinguishing SMN1 and SMN2 without any assembly were developed and applied to reveal multiple alternative splicing patterns and to comprehensively identify transcript variants of the SMN genes. In total, 36 types of transcript variants were identified, with an equal number of variants generated from both SMN1 and SMN2. Of these, 18 were novel SMN transcripts that have never been reported. The structures of SMN transcripts were revealed to be much more complicated and diverse than previously discovered. These novel transcripts were derived from diverse splicing events, including skipping of one or more exons, intron retention, and exon shortening or addition. SMN1 mainly produces FL-SMN1, SMN1Δ7, SMN1Δ5 and SMN1Δ3. The distribution of SMN2 transcripts was significantly different from those of SMN1, with the majority transcripts to be SMN2Δ7, followed by FL-SMN2, SMN2Δ3,5 and SMN2Δ5,7. Targeted long-read sequencing approach could accurately distinguish sequences of SMN1 from those of SMN2. Our study comprehensively addressed naturally occurring SMN1 and SMN2 transcript variants and splicing patterns in peripheral blood mononuclear cells (PBMCs). The novel transcripts identified in our study expanded knowledge of the diversity of transcript variants generated from the SMN genes and showed a much more comprehensive profile of the SMN splicing spectrum. Results in our study will provide valuable information for the study of low expression level of SMN proteins and SMA pathogenesis based on transcript levels.

Dissecting transcriptional heterogeneity in primary gastric adenocarcinoma by single cell RNA sequencing.

OBJECTIVE: Tumour heterogeneity represents a major obstacle to accurate diagnosis and treatment in gastric adenocarcinoma (GA). Here, we report a systematic transcriptional atlas to delineate molecular and cellular heterogeneity in GA using single-cell RNA sequencing (scRNA-seq). DESIGN: We performed unbiased transcriptome-wide scRNA-seq analysis on 27 677 cells from 9 tumour and 3 non-tumour samples. Analysis results were validated using large-scale histological assays and bulk transcriptomic datasets. RESULTS: Our integrative analysis of tumour cells identified five cell subgroups with distinct expression profiles. A panel of differentiation-related genes reveals a high diversity of differentiation degrees within and between tumours. Low differentiation degrees can predict poor prognosis in GA. Among them, three subgroups exhibited different differentiation grade which corresponded well to histopathological features of Lauren's subtypes. Interestingly, the other two subgroups displayed unique transcriptome features. One subgroup expressing chief-cell markers (eg, LIPF and PGC) and RNF43 with Wnt/ß-catenin signalling pathway activated is consistent with the previously described entity fundic gland-type GA (chief cell-predominant, GA-FG-CCP). We further confirmed the presence of GA-FG-CCP in two public bulk datasets using transcriptomic profiles and histological images. The other subgroup specifically expressed immune-related signature genes (eg, LY6K and major histocompatibility complex class II) with the infection of Epstein-Barr virus. In addition, we also analysed non-malignant epithelium and provided molecular evidences for potential transition from gastric chief cells into MUC6+TFF2+ spasmolytic polypeptide expressing metaplasia. CONCLUSION: Altogether, our study offers valuable resource for deciphering gastric tumour heterogeneity, which will provide assistance for precision diagnosis and prognosis.

Up-regulation of FAM64A promotes epithelial-to-mesenchymal transition and enhances stemness features in breast cancer cells.

FAM64A was found to be markedly up-regulated in tumor samples and associated with worse overall survival in multiple cancer types, including breast cancer. However, the functional significance of FAM64A in breast cancer remains largely unknown. In this study, we systematically investigated the expression of FAM64A in multiple public breast cancer datasets. We found that FAM64A is significantly positively correlated with tumor stemness index in breast cancer samples, corresponding with an advanced clinical grade, metastasis and unfavorable prognosis. In vitro experiments further showed an up-regulation of stemness genes after over-expressing FAM64A in breast cancer cells. FAM64A overexpression also promoted breast cancer cell proliferation, migration, accompanied by the activation of epithelial-to-mesenchymal transition (EMT). Besides, we identified a strong association of FAM64A expression with TP53 mutations in TCGA and three additional breast cancer datasets. In summary, our study revealed a novel function of FAM64A in promoting breast cancer stemness and EMT, suggesting that targeting of FAM64A may have therapeutic values in advanced breast cancer.

Predicting the regrowth of clinically non-functioning pituitary adenoma with a statistical model.

BACKGROUND: Compared with clinically functioning pituitary adenoma (FPA), clinically non-functioning pituitary adenoma (NFPA) lacks of detectable hypersecreting serum hormones and related symptoms which make it difficult to predict the prognosis and monitoring for postoperative tumour regrowth. We aim to investigate whether the expression of selected tumour-related proteins and clinical features could be used as tumour markers to effectively predict the regrowth of NFPA. METHOD: Tumour samples were collected from 295 patients with NFPA from Beijing Tiantan Hospital. The expression levels of 41 tumour-associated proteins were assessed using tissue microarray analyses. Clinical characteristics were analysed via univariate and multivariate logistic regression analyses. Logistic regression algorithm was applied to build a prediction model based on the expression levels of selected proteins and clinical signatures, which was then assessed in the testing set. RESULTS: Three proteins and two clinical signatures were confirmed to be significantly related to the regrowth of NFPA, including cyclin-dependent kinase inhibitor 2A (CDKN2A/p16), WNT inhibitory factor 1 (WIF1), tumour growth factor beta (TGF-ß), age and tumour volume. A prediction model was generated on the training set, which achieved a fivefold predictive accuracy of 81.2%. The prediction ability was validated on the testing set with an accuracy of 83.9%. The area under the receiver operating characteristic curves (AUC) for the signatures were 0.895 and 0.881 in the training and testing sets, respectively. CONCLUSION: The prediction model could effectively predict the regrowth of NFPA, which may facilitate the prognostic evaluation and guide early interventions.

[Diagnosis of two cases from one family with Joubert syndrome caused by novel mutations of TCTN1 gene by whole exome sequencing].

OBJECTIVE: To explore the pathogenesis of two fetuses from one family affected with Joubert syndrome (JS). METHODS: Whole exome sequencing was employed to screen potential mutations in both fetuses. Suspected mutations were verified by Sanger sequencing. Impact of intronic mutations on DNA transcription was validated by cDNA analysis. RESULTS: Two novel TCTN1 mutations, c.342-8A>G and c.1494+1G>A, were identified in exons 2 and 12, respectively.cDNA analysis confirmed the pathogenic nature of both mutations with interference of normal splicing resulting in production of truncated proteins. CONCLUSION: The genetic etiology of the family affected with JS has been identified.Above findings have enriched the mutation spectrum of TCTN1gene and facilitated understanding of the genotype-phenotype correlation of JS.

miR-21-mediated Radioresistance Occurs via Promoting Repair of DNA Double Strand Breaks.

miR-21, as an oncogene that overexpresses in most human tumors, is involved in radioresistance; however, the mechanism remains unclear. Here, we demonstrate that miR-21-mediated radioresistance occurs through promoting repair of DNA double strand breaks, which includes facilitating both non-homologous end-joining (NHEJ) and homologous recombination repair (HRR). The miR-21-promoted NHEJ occurs through targeting GSK3B (a novel target of miR-21), which affects the CRY2/PP5 pathway and in turn increases DNA-PKcs activity. The miR-21-promoted HRR occurs through targeting both GSK3B and CDC25A (a known target of miR-21), which neutralizes the effects of targeting GSK3B-induced CDC25A increase because GSK3B promotes degradation of both CDC25A and cyclin D1, but CDC25A and cyclin D1 have an opposite effect on HRR. A negative correlation of expression levels between miR-21 and GSK3ß exists in a subset of human tumors. Our results not only elucidate miR-21-mediated radioresistance, but also provide potential new targets for improving radiotherapy.

Systematic study of cis-antisense miRNAs in animal species reveals miR-3661 to target PPP2CA in human cells.

MicroRNAs (miRNAs) suppress targeting gene expression through blocking translation or triggering mRNA degradation and, in general, act in trans, through a partially complementary interaction with the 3' untranslated region (3' UTR) or coding regions of a target gene. Although it has been reported previously that some miRNAs suppress their target genes on the opposite strand with a fully complementary sequence (i.e., natural antisense miRNAs that act in cis), there is no report to systematically study such cis-antisense miRNAs in different animal species. Here we report that cis-antisense miRNAs do exist in different animal species: 48 in Caenorhabditis elegans, 17 in Drosophila, 36 in Mus musculus, and 52 in Homo sapiens using a systematical bioinformatics approach. We show that most of these cis-antisense miRNAs can efficiently reduce the expression levels of their target genes in human cells. We further investigate hsa-miR-3661, one of the predicted cis-antisense miRNAs, in detail and demonstrate that this miRNA directly targets the coding sequence of PPP2CA located on the opposite DNA strand and inhibits the PPP2CA expression. Taken together, these results indicate that cis-antisense miRNAs are conservative and functional in animal species including humans.

Signaling protein signature predicts clinical outcome of non-small-cell lung cancer.

BACKGROUND: Non-small-cell lung cancer (NSCLC) is characterized by abnormalities of numerous signaling proteins that play pivotal roles in cancer development and progression. Many of these proteins have been reported to be correlated with clinical outcomes of NSCLC. However, none of them could provide adequate accuracy of prognosis prediction in clinical application. METHODS: A total of 384 resected NSCLC specimens from two hospitals in Beijing (BJ) and Chongqing (CQ) were collected. Using immunohistochemistry (IHC) staining on stored formalin-fixed paraffin-embedded (FFPE) surgical samples, we examined the expression levels of 75 critical proteins on BJ samples. Random forest algorithm (RFA) and support vector machines (SVM) computation were applied to identify protein signatures on 2/3 randomly assigned BJ samples. The identified signatures were tested on the remaining BJ samples, and were further validated with CQ independent cohort. RESULTS: A 6-protein signature for adenocarcinoma (ADC) and a 5-protein signature for squamous cell carcinoma (SCC) were identified from training sets and tested in testing sets. In independent validation with CQ cohort, patients can also be divided into high- and low-risk groups with significantly different median overall survivals by Kaplan-Meier analysis, both in ADC (31 months vs. 87 months, HR 2.81; P <  0.001) and SCC patients (27 months vs. not reached, HR 9.97; P <  0.001). Cox regression analysis showed that both signatures are independent prognostic indicators and outperformed TNM staging (ADC: adjusted HR 3.07 vs. 2.43, SCC: adjusted HR 7.84 vs. 2.24). Particularly, we found that only the ADC patients in high-risk group significantly benefited from adjuvant chemotherapy (P = 0.018). CONCLUSIONS: Both ADC and SCC protein signatures could effectively stratify the prognosis of NSCLC patients, and may support patient selection for adjuvant chemotherapy.

A long non-coding RNA lncRNA-PE promotes invasion and epithelial-mesenchymal transition in hepatocellular carcinoma through the miR-200a/b-ZEB1 pathway.

Long non-coding RNAs have been revealed to play important roles in the progression of hepatocellular carcinoma. However, the detailed mechanisms underlying their activities are not fully understood. Using microarray technology, a number of long non-coding RNAs were previously identified to be aberrantly expressed in hepatocellular carcinoma. In this study, one of these long non-coding RNAs, designated lncRNA-PE (lncRNA promotes epithelial-mesenchymal transition), was further explored to study its expression profile and function. A cohort of human hepatocellular carcinoma tissue samples combined with benign controls and established human hepatocellular carcinoma cell lines were examined for the expression of lncRNA-PE. The biological functions of lncRNA-PE were examined by wound-healing and Transwell assays, which revealed that lncRNA-PE promotes cell invasion and migration. By detecting the level of epithelial-mesenchymal transition markers, lncRNA-PE was revealed to promote epithelial-mesenchymal transition in hepatocellular carcinoma cells. Further study suggested that lncRNA-PE downregulated miR-200a/b by repressing the primary transcript expression, enhanced ZEB1 expression, and promoted epithelial-mesenchymal transition of hepatocellular carcinoma cells. All these data imply that lncRNA-PE might play an important role in hepatocellular carcinoma development via the miR-200a/b-ZEB1 pathway.

Vitronectin: a promising breast cancer serum biomarker for early diagnosis of breast cancer in patients.

Breast cancer is the most common cancer in women worldwide, identification of new biomarkers for early diagnosis and detection will improve the clinical outcome of breast cancer patients. In the present study, we determined serum levels of vitronectin (VN) in 93 breast cancer patients, 30 benign breast lesions, 9 precancerous lesions, and 30 healthy individuals by enzyme-linked immunosorbent assays. Serum VN level was significantly higher in patients with stage 0-I primary breast cancer than in healthy individuals, patients with benign breast lesion or precancerous lesions, as well as those with breast cancer of higher stages. Serum VN level was significantly and negatively correlated with tumor size, lymph node status, and clinical stage (p < 0.05 in all cases). In addition, VN displayed higher area under curve (AUC) value (0.73, 95 % confidence interval (CI) [0.62-0.84]) than carcinoembryonic antigen (CEA) (0.64, 95 % CI [0.52-0.77]) and cancer antigen 15-3 (CA 15-3) (0.69, 95 % CI [0.58-0.81]) when used to distinguish stage 0-I cancer and normal control. Importantly, the combined use of three biomarkers yielded an improvement in receiver operating characteristic curve with an AUC of 0.83, 95 % CI [0.74-0.92]. Taken together, our current study showed for the first time that serum VN is a promising biomarker for early diagnosis of breast cancer when combined with CEA and CA15-3.

A Tumor-Specific Prognostic Long Non-Coding RNA Signature in Gastric Cancer.

BACKGROUND Aberrant expression of long non-coding RNAs (lncRNAs) is associated with prognosis of gastric cancer, some of which could be further evaluated as potential biomarkers. In this study, we attempted to identify a specific lncRNA signature to predict the prognosis of gastric cancer. MATERIAL AND METHODS The genome-wide lncRNA expression in the high-throughput RNA-sequencing data was retrieved from the Cancer Genome Atlas (TCGA). Differential expression of lncRNAs was identified using the Limma package. Survival analysis was conducted by use of univariate and multivariate Cox regression models. Functional enrichment analysis of lncRNAs was based on co-expressed mRNAs. DAVID was used to perform gene ontology and KEGG pathway analysis. RESULTS A total of 452 differentially expressed lncRNAs between gastric cancer and matched normal tissues were screened, of which 76 lncRNAs were identified to be gastric cancer-specific from a pan-cancer analysis of 12 types of human cancer. Among these 76 gastric cancer-specific lncRNAs, 5 lncRNAs (CTD-2616J11.14, RP1-90G24.10, RP11-150O12.3, RP11-1149O23.2, and MLK7-AS1) were significantly associated with the overall survival of patients with gastric cancer. A gastric cancer-specific 5-lncRNA signature was deduced to divide the patients into high- and low-risk groups with significantly different survival times (P<0.0001). Multivariate Cox regression analysis showed that this 5-lncRNA signature was an independent predictor of prognosis. Functional enrichment analysis of the 5 lncRNAs showed that they were mainly involved in DNA replication, mitotic cell cycle, programmed cell death, and RNA splicing. CONCLUSIONS Our results suggest that this tumor-specific lncRNA signature may be clinically useful in the prediction of gastric cancer prognosis.

Haplotype-based approach for noninvasive prenatal tests of Duchenne muscular dystrophy using cell-free fetal DNA in maternal plasma.

PURPOSE: This study demonstrates noninvasive prenatal testing (NIPT) for Duchenne muscular dystrophy (DMD) using a newly developed haplotype-based approach. METHODS: Eight families at risk for DMD were recruited for this study. Parental haplotypes were constructed using target-region sequencing data from the parents and the probands. Fetal haplotypes were constructed using a hidden Markov model through maternal plasma DNA sequencing. The presence of haplotypes linked to the maternal mutant alleles in males indicated affected fetuses. This method was further validated by comparing the inferred single-nucleotide polymorphism (SNP) genotypes to the direct sequencing results of fetal genomic DNA. Prenatal diagnosis was confirmed with amniocentesis, and those results were interpreted in a blinded fashion. RESULTS: The results showed an average accuracy of 99.98% for the total inferred maternal SNPs. With a mean depth of 30× achieved in the 10-Mb target region of each sample, the noninvasive results were consistent with those of the invasive procedure. CONCLUSION: This is the first report of NIPT for DMD and the first application of a haplotype-based approach in NIPT for X-linked diseases. With further improvements in accuracy, this haplotype-based strategy could be feasible for NIPT for DMD and even other X-linked single-gene disorders.

Mosaic integration and knowledge transfer of single-cell multimodal data with MIDAS.

Integrating single-cell datasets produced by multiple omics technologies is essential for defining cellular heterogeneity. Mosaic integration, in which different datasets share only some of the measured modalities, poses major challenges, particularly regarding modality alignment and batch effect removal. Here, we present a deep probabilistic framework for the mosaic integration and knowledge transfer (MIDAS) of single-cell multimodal data. MIDAS simultaneously achieves dimensionality reduction, imputation and batch correction of mosaic data by using self-supervised modality alignment and information-theoretic latent disentanglement. We demonstrate its superiority to 19 other methods and reliability by evaluating its performance in trimodal and mosaic integration tasks. We also constructed a single-cell trimodal atlas of human peripheral blood mononuclear cells and tailored transfer learning and reciprocal reference mapping schemes to enable flexible and accurate knowledge transfer from the atlas to new data. Applications in mosaic integration, pseudotime analysis and cross-tissue knowledge transfer on bone marrow mosaic datasets demonstrate the versatility and superiority of MIDAS. MIDAS is available at https://github.com/labomics/midas .

Discriminative Geometric-Structure-Based Deep Hashing for Large-Scale Image Retrieval.

Deep hashing reaps the benefits of deep learning and hashing technology, and has become the mainstream of large-scale image retrieval. It generally encodes image into hash code with feature similarity preserving, that is, geometric-structure preservation, and achieves promising retrieval results. In this article, we find that existing geometric-structure preservation manner inadequately ensures feature discrimination, while improving feature discrimination of hash code essentially determines hash learning retrieval performance. This fact principally spurs us to propose a discriminative geometric-structure-based deep hashing method (DGDH), which investigates three novel loss terms based on class centers to induce the so-called discriminative geometrical structure. In detail, the margin-aware center loss assembles samples in the same class to the corresponding class centers for intraclass compactness, then a linear classifier based on class center serves to boost interclass separability, and the radius loss further puts different class centers on a hypersphere to tentatively reduce quantization errors. An efficient alternate optimization algorithm with guaranteed desirable convergence is proposed to optimize DGDH. We theoretically analyze the robustness and generalization of the proposed method. The experiments on five popular benchmark datasets demonstrate superior image retrieval performance of the proposed DGDH over several state of the arts.

sRNATarBase: a comprehensive database of bacterial sRNA targets verified by experiments.

Bacterial sRNAs are an emerging class of small regulatory RNAs, 40-500 nt in length, which play a variety of important roles in many biological processes through binding to their mRNA or protein targets. A comprehensive database of experimentally confirmed sRNA targets would be helpful in understanding sRNA functions systematically and provide support for developing prediction models. Here we report on such a database--sRNATarBase. The database holds 138 sRNA-target interactions and 252 noninteraction entries, which were manually collected from peer-reviewed papers. The detailed information for each entry, such as supporting experimental protocols, BLAST-based phylogenetic analysis of sRNA-mRNA target interaction in closely related bacteria, predicted secondary structures for both sRNAs and their targets, and available binding regions, is provided as accurately as possible. This database also provides hyperlinks to other databases including GenBank, SWISS-PROT, and MPIDB. The database is available from the web page http://ccb.bmi.ac.cn/srnatarbase/.

Towards Strain-Level Complexity: Sequencing Depth Required for Comprehensive Single-Nucleotide Polymorphism Analysis of the Human Gut Microbiome.

Intestinal bacteria strains play crucial roles in maintaining host health. Researchers have increasingly recognized the importance of strain-level analysis in metagenomic studies. Many analysis tools and several cutting-edge sequencing techniques like single cell sequencing have been proposed to decipher strains in metagenomes. However, strain-level complexity is far from being well characterized up to date. As the indicator of strain-level complexity, metagenomic single-nucleotide polymorphisms (SNPs) have been utilized to disentangle conspecific strains. Lots of SNP-based tools have been developed to identify strains in metagenomes. However, the sufficient sequencing depth for SNP and strain-level analysis remains unclear. We conducted ultra-deep sequencing of the human gut microbiome and constructed an unbiased framework to perform reliable SNP analysis. SNP profiles of the human gut metagenome by ultra-deep sequencing were obtained. SNPs identified from conventional and ultra-deep sequencing data were thoroughly compared and the relationship between SNP identification and sequencing depth were investigated. The results show that the commonly used shallow-depth sequencing is incapable to support a systematic metagenomic SNP discovery. In contrast, ultra-deep sequencing could detect more functionally important SNPs, which leads to reliable downstream analyses and novel discoveries. We also constructed a machine learning model to provide guidance for researchers to determine the optimal sequencing depth for their projects (SNPsnp, https://github.com/labomics/SNPsnp). To conclude, the SNP profiles based on ultra-deep sequencing data extend current knowledge on metagenomics and highlights the importance of evaluating sequencing depth before starting SNP analysis. This study provides new ideas and references for future strain-level investigations.