CoverageMaster: comprehensive CNV detection and visualization from NGS short reads for genetic medicine applications

CopyVAE: a variational autoencoder-based approach for copy number variation inference using single-cell transcriptomics

MOTIVATION

Copy number variations (CNVs) are common genetic alterations in tumour cells. The delineation of CNVs holds promise for enhancing our comprehension of cancer progression. Moreover, accurate inference of CNVs from single-cell sequencing data is essential for unravelling intratumoral heterogeneity. However, existing inference methods face limitations in resolution and sensitivity.

RESULTS

To address these challenges, we present CopyVAE, a deep learning framework based on a variational autoencoder architecture. Through experiments, we demonstrated that CopyVAE can accurately and reliably detect CNVs from data obtained using single-cell RNA sequencing. CopyVAE surpasses existing methods in terms of sensitivity and specificity. We also discussed CopyVAE's potential to advance our understanding of genetic alterations and their impact on disease advancement.

AVAILABILITY AND IMPLEMENTATION

CopyVAE is implemented and freely available under MIT license at https://github.com/kurtsemih/copyVAE.

Genetic interrogation for sequence and copy number variants in systemic lupus erythematosus

Early-onset systemic lupus erythematosus presents with a more severe disease and is associated with a greater genetic burden, especially in patients from Black, Asian or Hispanic ancestries. Next-generation sequencing techniques, notably whole exome sequencing, have been extensively used in genomic interrogation studies to identify causal disease variants that are increasingly implicated in the development of autoimmunity. This Review discusses the known casual variants of polygenic and monogenic systemic lupus erythematosus and its implications under certain genetic disparities while suggesting an age-based sequencing strategy to aid in clinical diagnostics and patient management for improved patient care.

Recurrent human 16p11.2 microdeletions in type I Mayer-Rokitansky-Küster-Hauser (MRKH) syndrome patients in Chinese Han population

BACKGROUNDS

Mayer-Rokitansky-Küster-Hauser (MRKH) syndrome, a severe congenital malformation of the female genital tract, is a highly heterogeneous disease which has no clear etiology. Previous studies have suggested that copy number variations (CNVs) and single-gene mutations might contribute to the development of MRKH syndrome. In particular, deletions in 16p11.2, which are suggested to be involved in several congenital diseases, have been reported in Chinese type II MRKH patients and European MRKH patients. However, few CNVs including 16p11.2 microdeletions were identified in Chinese type I MRKH cases although it accounted for the majority of MRKH patients in China. Thus, we conducted a retrospective study to identify whether CNVs at human chromosome 16p11.2 are risk factors of type I MRKH syndrome in the Chinese Han population.

METHODS

We recruited 143 patients diagnosed with type I MRKH between 2012 and 2014. Five hundred unrelated Chinese without congenital malformation were enrolled in control group, consisting of 197 from the 1000 Genomes Project and 303 from Fudan University. Quantitative PCR, array comparative genomic hybridization, and sanger sequencing were conducted to screen and verify candidate variant.

RESULTS

Our study identified recurrent 16p11.2 microdeletions of approximately 600 kb in two out of the 143 type I MRKH syndrome patients using high-density array-based comparative genomic hybridization (aCGH), while no 16p11.2 deletion was found in the control group. We did not find any mutations in TBX6 gene in our samples.

CONCLUSIONS

The results of the study identify 16p11.2 deletion in Chinese MRKH I patients for the first time, as well as support the contention that 16p11.2 microdeletions are associated with MRKH syndrome in both types across populations. It is suggested that 16p11.2 microdeletions should be included in molecular diagnosis and genetic counseling of female reproductive tract disorders.

A Bioinformatics Toolkit for Next-Generation Sequencing in Clinical Oncology

Next-generation sequencing (NGS) has taken on major importance in clinical oncology practice. With the advent of targeted therapies capable of effectively targeting specific genomic alterations in cancer patients, the development of bioinformatics processes has become crucial. Thus, bioinformatics pipelines play an essential role not only in the detection and in identification of molecular alterations obtained from NGS data but also in the analysis and interpretation of variants, making it possible to transform raw sequencing data into meaningful and clinically useful information. In this review, we aim to examine the multiple steps of a bioinformatics pipeline as used in current clinical practice, and we also provide an updated list of the necessary bioinformatics tools. This resource is intended to assist researchers and clinicians in their genetic data analyses, improving the precision and efficiency of these processes in clinical research and patient care.

Metagenomics next-generation sequencing for the diagnosis of central nervous system infection: A systematic review and meta-analysis

Objective

It is widely acknowledged that central nervous system (CNS) infection is a serious infectious disease accompanied by various complications. However, the accuracy of current detection methods is limited, leading to delayed diagnosis and treatment. In recent years, metagenomic next-generation sequencing (mNGS) has been increasingly adopted to improve the diagnostic yield. The present study sought to evaluate the value of mNGS in CNS infection diagnosis.

Methods

Following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) 2022 guidelines, we searched relevant articles published in seven databases, including PubMed, Web of Science, and Cochrane Library, published from January 2014 to January 2022. High-quality articles related to mNGS applications in the CNS infection diagnosis were included. The comparison between mNGS and the gold standard of CNS infection, such as culture, PCR or serology, and microscopy, was conducted to obtain true positive (TP), true negative (TN), false positive (FP), and false negative (FN) values, which were extracted for sensitivity and specificity calculation.

Results

A total of 272 related studies were retrieved and strictly selected according to the inclusion and exclusion criteria. Finally, 12 studies were included for meta-analysis and the pooled sensitivity was 77% (95% CI: 70–82%, I² = 39.69%) and specificity was 96% (95% CI: 93–98%, I² = 72.07%). Although no significant heterogeneity in sensitivity was observed, a sub-group analysis was conducted based on the pathogen, region, age, and sample pretreatment method to ascertain potential confounders. The area under the curve (AUC) of the summary receiver operating characteristic curve (SROC) of mNGS for CNS infection was 0.91 (95% CI: 0.88–0.93). Besides, Deek's Funnel Plot Asymmetry Test indicated no publication bias in the included studies (Figure 3, p > 0.05).

Conclusion

Overall, mNGS exhibits good sensitivity and specificity for diagnosing CNS infection and diagnostic performance during clinical application by assisting in identifying the pathogen. However, the efficacy remains inconsistent, warranting subsequent studies for further performance improvement during its clinical application.

Study registration number

INPLASY202120002

PEcnv: accurate and efficient detection of copy number variations of various lengths

Copy number variation (CNV) is a class of key biomarkers in many complex traits and diseases. Detecting CNV from sequencing data is a substantial bioinformatics problem and a standard requirement in clinical practice. Although many proposed CNV detection approaches exist, the core statistical model at their foundation is weakened by two critical computational issues: (i) identifying the optimal setting on the sliding window and (ii) correcting for bias and noise. We designed a statistical process model to overcome these limitations by calculating regional read depths via an exponentially weighted moving average strategy. A one-run detection of CNVs of various lengths is then achieved by a dynamic sliding window, whose size is self-adopted according to the weighted averages. We also designed a novel bias/noise reduction model, accompanied by the moving average, which can handle complicated patterns and extend training data. This model, called PEcnv, accurately detects CNVs ranging from kb-scale to chromosome-arm level. The model performance was validated with simulation samples and real samples. Comparative analysis showed that PEcnv outperforms current popular approaches. Notably, PEcnv provided considerable advantages in detecting small CNVs (1 kb–1 Mb) in panel sequencing data. Thus, PEcnv fills the gap left by existing methods focusing on large CNVs. PEcnv may have broad applications in clinical testing where panel sequencing is the dominant strategy. Availability and implementation: Source code is freely available at https://github.com/Sherwin-xjtu/PEcnv