As the amount of metagenomic sequencing continues to increase, there is a growing need for tools that help biologists make sense of the data. Specifically, researchers are often interested in the potential of a microbial community to carry out a metabolic reaction, but this analysis requires knitting together multiple software tools into a complex pipeline. Thanos offers a user-friendly R package designed for the pathway-centric analysis and visualization of the functions encoded within metagenomic samples. It allows researchers to go beyond taxonomic profiles and find out, quantitatively, which pathways are prevalent in an environment, as well as comparing different environments in terms of their functional potential. The analysis is based on the sequencing depth of the genes of interest, either in the metagenome-assembled genomes (MAGs) or in the assembled reads (contigs), using a normalization strategy that enables comparison across samples. The package can import the data from multiple formats and offers functions for the visualization of the results as bar plots of the functional profile, box plots of compare functions across samples, and annotated pathway graphs. By streamlining the analysis of the functional potential encoded in microbial communities, Thanos can enable impactful discoveries in all the fields touched by metagenomics, from human health to the environmental sciences.

OBJECTIVE: To evaluate non-invasive prenatal testing (NIPT) and expanded non-invasive prenatal testing (NIPT-plus) for detecting aneuploidies at different sequencing depths and assess Z-score accuracy in predicting trisomies 21, 18, 13, 45X, and 47XXX.
METHODS: Pregnancies with positive NIPT or NIPT-plus results detected at the prenatal diagnosis center of Nanfang Hospital were included in this retrospective study, between January 2017 and December 2022. Invasive prenatal diagnostic results were collected. Logistic regression analyses were used to study the relationship between Z-score and positive predictive value (PPV). Optimal cut-off values were obtained based on receiver operating characteristic analysis, and PPVs were calculated in different groups.
RESULTS: We evaluated 1348 pregnant women with positive results, including 930 reported by NIPT and 418 reported by NIPT-plus. NIPT reported significantly more rare chromosomal aneuploidies (RCAs), and NIPT-plus had a significantly higher PPV for trisomy 21 (T21). Logistic regression analyses showed a significant association (P < 0.001) between Z-score and PPVs for T21 and trisomy 18 (T18). A linear relationship was observed between fetal fraction (FF) and Z-values in the true positive cases of T21 and T18.The high Z-score group had significantly higher PPVs than the low Z-score group for T21, T18, trisomy 13, and 47XXX, but not for 45X.
CONCLUSIONS: The Z-score is helpful in assessing NIPT or NIPT-plus results. Therefore, we suggest including the Z-score and FF in the results. By combining the Z-score, FF, and maternal age, clinicians can interpret NIPT results more accurately and improve personal counsel to reduce patients\' anxiety.

Revealing the mechanisms governing the complex community assembly over space and time is a central issue in ecology. Null models have been developed to quantitatively disentangle the relative importance of deterministic vs. stochastic processes in structuring the compositional variations of biological communities. Similar approaches have been recently extended to the field of microbial ecology. However, the profiling of highly diverse biological communities (e.g., microbial communities) is severely influenced by random sampling issues, leading to undersampled community profiles and overestimated β-diversity, which may further affect stochasticity inference in community assembly. By implementing simulated datasets, this study demonstrate that microbial stochasticity inference is also affected due to random sampling issues associated with microbial profiling. The effects on microbial stochasticity inference for the whole community and the abundant subcommunities were different using different randomization methods in generating null communities. The stochasticity of rare subcommunities, however, was persistently overestimated irrespective of which randomization method was used. Comparatively, the stochastic ratio approach was more sensitive to random sampling issues, whereas the Raup-Crick metric was more affected by randomization methods. As more studies begin to focus on the mechanisms governing abundant and rare subcommunities, we urge cautions be taken for microbial stochasticity inference based on β-diversity, especially for rare subcommunities. Randomization methods to generate null communities shall also be carefully selected. When necessary, the cutoff used for judging the relative importance of deterministic vs. stochastic processes shall be redefined.

Our study was to analyze and evaluate the impact of different shotgun metagenomic sequencing depths from 5 to 20 million in metagenome-wide association studies (MWASs), and to determine the optimal minimum sequencing depth. We included a set of 200 previously published gut microbial shotgun metagenomic sequencing data on obesity (100 obese vs. 100 non-obese). The reads with original sequencing depths >20 million were downsized into seven experimental groups with depths from 5 to 20 million (interval 2.5 million). Using both integrated gene cluster (IGC) and metagenomic phylogenetic analysis 2 (MetaPhlAn2), we obtained and analyzed the read matching rates, gene count, species richness and abundance, diversity, and clinical biomarkers of the experimental groups with the original depth as the control group. An additional set of 100 published data from a colorectal cancer (CRC) study was included for validation (50 CRC vs. 50 CRC-free). Our results showed that more genes and species were identified following the increase in sequencing depths. When it reached 15 million or higher, the species richness became more stable with changing rate of 5% or lower, and the species composition more stable with ICC intraclass correlation coefficient (ICC) higher than 0.75. In terms of species abundance, 81% and 97% of species showed significant differences in IGC and MetaPhlAn2 among all groups with p < 0.05. Diversity showed significant differences across all groups, with decreasing differences of diversity between the experimental and the control groups following the increase in sequencing depth. The area under a receiver operating characteristic curve, AUC, of the obesity classifier for running the obesity testing samples showed an increasing trend following the increase in sequencing depth (τ = 0.29). The validation results were consistent with the above results. Our study found that the higher the sequencing depth is, the more the microbial information in structure and composition it provides. We also found that when sequencing depth was 15 million or higher, we obtained more stable species compositions and disease classifiers with good performance. Therefore, we recommend 15 million as the optimal minimum sequencing depth for an MWAS.

Investigating the functions and activities of genes requires proper annotation of the transcribed units. However, transcript assembly efforts have produced a surprisingly large variation in the number of transcripts, and especially so for noncoding transcripts. This heterogeneity in assembled transcript sets might be partially explained by sequencing depth. Here, we used real and simulated short-read sequencing data as well as long-read data to systematically investigate the impact of sequencing depths on the accuracy of assembled transcripts. We assembled and analyzed transcripts from 671 human short-read data sets and four long-read data sets. At the first level, there is a positive correlation between the number of reads and the number of recovered transcripts. However, the effect of the sequencing depth varied based on cell or tissue type, the type of read and the nature and expression levels of the transcripts. The detection of coding transcripts saturated rapidly with both short and long-reads, however, there was no sign of early saturation for noncoding transcripts at any sequencing depth. Increasing long-read sequencing depth specifically benefited transcripts containing transposable elements. Finally, we show how single-cell RNA-seq can be guided by transcripts assembled from bulk long-read samples, and demonstrate that noncoding transcripts are expressed at similar levels to coding transcripts but are expressed in fewer cells. This study highlights the impact of sequencing depth on transcript assembly.

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.

High-throughput experiments are an essential part of modern biological and biomedical research. The outcomes of high-throughput biological experiments often have a lot of missing observations due to signals below detection levels. For example, most single-cell RNA-seq (scRNA-seq) protocols experience high levels of dropout due to the small amount of starting material, leading to a majority of reported expression levels being zero. Though missing data contain information about reproducibility, they are often excluded in the reproducibility assessment, potentially generating misleading assessments. In this article, we develop a regression model to assess how the reproducibility of high-throughput experiments is affected by the choices of operational factors (eg, platform or sequencing depth) when a large number of measurements are missing. Using a latent variable approach, we extend correspondence curve regression, a recently proposed method for assessing the effects of operational factors to reproducibility, to incorporate missing values. Using simulations, we show that our method is more accurate in detecting differences in reproducibility than existing measures of reproducibility. We illustrate the usefulness of our method using a single-cell RNA-seq dataset collected on HCT116 cells. We compare the reproducibility of different library preparation platforms and study the effect of sequencing depth on reproducibility, thereby determining the cost-effective sequencing depth that is required to achieve sufficient reproducibility.

To evaluate the performance of noninvasive prenatal testing (NIPT) and NIPT-PLUS for the detection of genome-wide microdeletion and microduplication syndromes (MMSs) at different sequencing depths. The NIPT sequencing depth was 0.15X, and the data volume was 3 million reads; the NIPT-PLUS sequencing depth was 0.4X, and the data volume was 8 million reads.
A cohort of 50,679 pregnancies was recruited. A total of 42,969 patients opted for NIPT, and 7710 patients opted for NIPT-PLUS. All high-risk cases were advised to undergo invasive prenatal diagnosis and were followed up.
A total of 373 cases had a high risk of a copy number variation (CNV) as predicted by NIPT and NIPT-PLUS: NIPT predicted 250 high-risk CNVs and NIPT-PLUS predicted 123. NIPT-PLUS increased the detection rate by 1.02% (0.58% vs 1.60%, p < 0.001). A total of 291 cases accepted noninvasive prenatal diagnosis, with 197 cases of NIPT and 94 cases of NIPT-PLUS. The PPV of CNV > 10 Mb for NIPT-PLUS was significantly higher than that for NIPT (p = 0.02). The total PPV of NIPT-PLUS was 12.56% higher than that of NIPT (43.61% vs 30.96%, p = 0.03).
NIPT-PLUS had a better performance in detecting CNVs in terms of the total detection rate and total PPV. However, great care must be taken in presenting results and providing appropriate counseling to patients when deeper sequencing is performed in clinical practice.

Non-invasive prenatal testing (NIPT) for common fetal trisomies is effective. However, the usefulness of cell-free DNA testing to detect other chromosomal abnormalities is poorly understood. We analyzed the positive rate at different read depths in next-generation sequencing (NGS) and identified a strategy for fetal copy number variant (CNV) detection in NIPT. Pregnant women who underwent NIPT by NGS at read depths of 4-6 M and fetuses with suspected CNVs were analyzed by amniocentesis and chromosomal microarray analysis (CMA). These fetus samples were re-sequenced at a read depth of 25 M and the positive detection rate was determined. With the increase in read depth, the positive CNV detection rate increased. The positive CNV detection rates at 25 M with small fragments were higher by NGS than by karyotype analysis. Increasing read depth in NGS improves the positive CNV detection rate while lowering the false positive detection rate. NIPT by NGS may be an accurate method of fetal chromosome analysis and reduce the rate of birth defects.

BACKGROUND: Structural variations (SVs) have been reported to play an important role in genetic diversity and trait regulation. Many computer algorithms detecting SVs have recently been developed, but the use of multiple algorithms to detect high-confidence SVs has not been studied. The most suitable sequencing depth for detecting SVs in pear is also not known.
RESULTS: In this study, a pipeline to detect SVs using next-generation and long-read sequencing data was constructed. The performances of seven types of SV detection software using next-generation sequencing (NGS) data and two types of software using long-read sequencing data (SVIM and Sniffles), which are based on different algorithms, were compared. Of the nine software packages evaluated, SVIM identified the most SVs, and Sniffles detected SVs with the highest accuracy (> 90%). When the results from multiple SV detection tools were combined, the SVs identified by both MetaSV and IMR/DENOM, which use NGS data, were more accurate than those identified by both SVIM and Sniffles, with mean accuracies of 98.7 and 96.5%, respectively. The software packages using long-read sequencing data required fewer CPU cores and less memory and ran faster than those using NGS data. In addition, according to the performances of assembly-based algorithms using NGS data, we found that a sequencing depth of 50× is appropriate for detecting SVs in the pear genome.
CONCLUSIONS: This study provides strong evidence that more than one SV detection software package, each based on a different algorithm, should be used to detect SVs with higher confidence, and that long-read sequencing data are better than NGS data for SV detection. The SV detection pipeline that we have established will facilitate the study of diversity in other crops.