Coming of age: ten years of next-generation sequencing technologies

Comparative analysis of HiSeq3000 and BGISEQ-500 sequencing platform over whole genome sequencing metagenomics data

Recent advances in sequencing technologies and platforms have enabled to generate metagenomics sequences using different sequencing platforms. In this study, we analyzed and compared shotgun metagenomic sequences generated by HiSeq3000 and BGISEQ-500 platforms from 12 sediment samples collected across the Norwegian coast. Metagenomics DNA sequences were normalized to an equal number of bases for both platforms and further evaluated by using different taxonomic classifiers, reference databases, and assemblers. Normalized BGISEQ-500 sequences retained more reads and base counts after preprocessing, while a slightly higher fraction of HiSeq3000 sequences were taxonomically classified. Kaiju classified a higher percentage of reads relative to Kraken2 for both platforms, and comparison of reference database for taxonomic classification showed that MAR database outperformed RefSeq. Assembly using MEGAHIT produced longer assemblies and higher total contigs count in majority of HiSeq3000 samples than using metaSPAdes, but the assembly statistics notably improved with unprocessed or normalized reads. Our results indicate that both platforms perform comparably in terms of the percentage of taxonomically classified reads and assembled contig statistics for metagenomics samples. This study provides valuable insights for researchers in selecting an appropriate sequencing platform and bioinformatics pipeline for their metagenomics studies.

Evaluation of recombination detection methods for viral sequencing

Recombination is a key evolutionary driver in shaping novel viral populations and lineages. When unaccounted for, recombination can impact evolutionary estimations or complicate their interpretation. Therefore, identifying signals for recombination in sequencing data is a key prerequisite to further analyses. A repertoire of recombination detection methods (RDMs) have been developed over the past two decades; however, the prevalence of pandemic-scale viral sequencing data poses a computational challenge for existing methods. Here, we assessed eight RDMs: PhiPack (Profile), 3SEQ, GENECONV, recombination detection program (RDP) (OpenRDP), MaxChi (OpenRDP), Chimaera (OpenRDP), UCHIME (VSEARCH), and gmos; to determine if any are suitable for the analysis of bulk sequencing data. To test the performance and scalability of these methods, we analysed simulated viral sequencing data across a range of sequence diversities, recombination frequencies, and sample sizes. Furthermore, we provide a practical example for the analysis and validation of empirical data. We find that RDMs need to be scalable, use an analytical approach and resolution that is suitable for the intended research application, and are accurate for the properties of a given dataset (e.g. sequence diversity and estimated recombination frequency). Analysis of simulated and empirical data revealed that the assessed methods exhibited considerable trade-offs between these criteria. Overall, we provide general guidelines for the validation of recombination detection results, the benefits and shortcomings of each assessed method, and future considerations for recombination detection methods for the assessment of large-scale viral sequencing data.

Genome-wide association analysis unveils candidate genes and loci associated with aplasia cutis congenita in pigs

BACKGROUND

Aplasia cutis congenita (ACC) is a rare genetic disorder characterized by the localized or widespread absence of skin in humans and animals. Individuals with ACC may experience developmental abnormalities in the skeletal and muscular systems, as well as potential complications. Localized and isolated cases of ACC can be treated through surgical and medical interventions, while extensive cases of ACC may result in neonatal mortality. The presence of ACC in pigs has implications for animal welfare. It contributes to an elevated mortality rate among piglets at birth, leading to substantial economic losses in the pig farming industry. In order to elucidate candidate genetic loci associated with ACC, we performed a Genome-Wide Association Study analysis on 216 Duroc pigs. The primary goal of this study was to identify candidate genes that associated with ACC.

RESULTS

This study identified nine significant SNPs associated with ACC. Further analysis revealed the presence of two quantitative trait loci, 483 kb (5:18,196,971-18,680,098) on SSC 5 and 159 kb (13:20,713,440-207294431 bp) on SSC13. By annotating candidate genes within a 1 Mb region surrounding the significant SNPs, a total of 11 candidate genes were identified on SSC5 and SSC13, including KRT71, KRT1, KRT4, ITGB7, CSAD, RARG, SP7, PFKL, TRPM2, SUMO3, and TSPEAR.

CONCLUSIONS

The results of this study further elucidate the potential mechanisms underlying and genetic architecture of ACC and identify reliable candidate genes. These results lay the foundation for treating and understanding ACC in humans.

A preliminary metabarcoding analysis of Portuguese raw honeys

The microbial diversity in Portuguese raw honeys remains largely uncharacterized, constituting a serious knowledge gap in one of the country's most important resources. This work provides an initial investigation with amplicon metabarcoding analysis of two Lavandula spp. from different geographical regions of Portugal and one Eucalyptus spp. honey. The results obtained allowed to identify that each honey harbors diverse microbiomes with taxa that can potentially affect bee and human health, cause spoilage, and highlight bad bee-hive management practices. We verified that prokaryotes had a tendency towards a more marked core bacterial and a relative homogenous taxa distribution, and that the botanical origin of honey is likely to have a stronger impact on the fungal community. Thus, the results obtained in this work provide important information that can be helpful to improve this critical Portuguese product and industry.

Trio-binning of a hinny refines the comparative organization of the horse and donkey X chromosomes and reveals novel species-specific features

We generated single haplotype assemblies from a hinny hybrid which significantly improved the gapless contiguity for horse and donkey autosomal genomes and the X chromosomes. We added over 15 Mb of missing sequence to both X chromosomes, 60 Mb to donkey autosomes and corrected numerous errors in donkey and some in horse reference genomes. We resolved functionally important X-linked repeats: the DXZ4 macrosatellite and ampliconic Equine Testis Specific Transcript Y7 (ETSTY7). We pinpointed the location of the pseudoautosomal boundaries (PAB) and determined the size of the horse (1.8 Mb) and donkey (1.88 Mb) pseudoautosomal regions (PARs). We discovered distinct differences in horse and donkey PABs: a testis-expressed gene, XKR3Y, spans horse PAB with exons1-2 located in Y and exon3 in the X-Y PAR, whereas the donkey XKR3Y is Y-specific. DXZ4 had a similar ~ 8 kb monomer in both species with 10 copies in horse and 20 in donkey. We assigned hundreds of copies of ETSTY7, a sequence horizontally transferred from Parascaris and massively amplified in equids, to horse and donkey X chromosomes and three autosomes. The findings and products contribute to molecular studies of equid biology and advance research on X-linked conditions, sex chromosome regulation and evolution in equids.

Spiralian genomics and the evolution of animal genome architecture

Recent developments in sequencing technologies have greatly improved our knowledge of phylogenetic relationships and genomic architectures throughout the tree of life. Spiralia, a diverse clade within Protostomia, is essential for understanding the evolutionary history of parasitism, gene conversion, nervous systems and animal body plans. In this review, we focus on the current hypotheses of spiralian phylogeny and investigate the impact of long-read sequencing on the quality of genome assemblies. We examine chromosome-level assemblies to highlight key genomic features that have driven spiralian evolution, including karyotype, synteny and the Hox gene organization. In addition, we show how chromosome rearrangement has influenced spiralian genomic structures. Although spiralian genomes have undergone substantial changes, they exhibit both conserved and lineage-specific features. We recommend increasing sequencing efforts and expanding functional genomics research to deepen insights into spiralian biology.

Identification of loci associated with water use efficiency and symbiotic nitrogen fixation in soybean

Soybean (Glycine max) production is greatly affected by persistent and/or intermittent droughts in rainfed soybean-growing regions worldwide. Symbiotic N2 fixation (SNF) in soybean can also be significantly hampered even under moderate drought stress. The objective of this study was to identify genomic regions associated with shoot carbon isotope ratio (δ13C) as a surrogate measure for water use efficiency (WUE), nitrogen isotope ratio (δ15N) to assess relative SNF, N concentration ([N]), and carbon/nitrogen ratio (C/N). Genome-wide association mapping was performed with 105 genotypes and approximately 4 million single-nucleotide polymorphism markers derived from whole-genome resequencing information. A total of 11, 21, 22, and 22 genomic loci associated with δ13C, δ15N, [N], and C/N, respectively, were identified in two environments. Nine of these 76 loci were stable across environments, as they were detected in both environments. In addition to the 62 novel loci identified, 14 loci aligned with previously reported quantitative trait loci for different C and N traits related to drought, WUE, and N2 fixation in soybean. A total of 58 Glyma gene models encoding for different genes related to the four traits were identified in the vicinity of the genomic loci.

Genome interpretation in a federated learning context allows the multi-center exome-based risk prediction of Crohn's disease patients

High-throughput sequencing allowed the discovery of many disease variants, but nowadays it is becoming clear that the abundance of genomics data mostly just moved the bottleneck in Genetics and Precision Medicine from a data availability issue to a data interpretation issue. To solve this empasse it would be beneficial to apply the latest Deep Learning (DL) methods to the Genome Interpretation (GI) problem, similarly to what AlphaFold did for Structural Biology. Unfortunately DL requires large datasets to be viable, and aggregating genomics datasets poses several legal, ethical and infrastructural complications. Federated Learning (FL) is a Machine Learning (ML) paradigm designed to tackle these issues. It allows ML methods to be collaboratively trained and tested on collections of physically separate datasets, without requiring the actual centralization of sensitive data. FL could thus be key to enable DL applications to GI on sufficiently large genomics data. We propose FedCrohn, a FL GI Neural Network model for the exome-based Crohn's Disease risk prediction, providing a proof-of-concept that FL is a viable paradigm to build novel ML GI approaches. We benchmark it in several realistic scenarios, showing that FL can indeed provide performances similar to conventional ML on centralized data, and that collaborating in FL initiatives is likely beneficial for most of the medical centers participating in them.

Insights gained from single-cell analysis of chimeric antigen receptor T-cell immunotherapy in cancer

Advances in chimeric antigen receptor (CAR)-T cell therapy have significantly improved clinical outcomes of patients with relapsed or refractory hematologic malignancies. However, progress is still hindered as clinical benefit is only available for a fraction of patients. A lack of understanding of CAR-T cell behaviors in vivo at the single-cell level impedes their more extensive application in clinical practice. Mounting evidence suggests that single-cell sequencing techniques can help perfect the receptor design, guide gene-based T cell modification, and optimize the CAR-T manufacturing conditions, and all of them are essential for long-term immunosurveillance and more favorable clinical outcomes. The information generated by employing these methods also potentially informs our understanding of the numerous complex factors that dictate therapeutic efficacy and toxicities. In this review, we discuss the reasons why CAR-T immunotherapy fails in clinical practice and what this field has learned since the milestone of single-cell sequencing technologies. We further outline recent advances in the application of single-cell analyses in CAR-T immunotherapy. Specifically, we provide an overview of single-cell studies focusing on target antigens, CAR-transgene integration, and preclinical research and clinical applications, and then discuss how it will affect the future of CAR-T cell therapy.

Enhanced annotation of CD45RA to distinguish T cell subsets in single-cell RNA-seq via machine learning

Motivation

T cell heterogeneity presents a challenge for accurate cell identification, understanding their inherent plasticity, and characterizing their critical role in adaptive immunity. Immunologists have traditionally employed techniques such as flow cytometry to identify T cell subtypes based on a well-established set of surface protein markers. With the advent of single-cell RNA sequencing (scRNA-seq), researchers can now investigate the gene expression profiles of these surface proteins at the single-cell level. The insights gleaned from these profiles offer valuable clues and a deeper understanding of cell identity. However, CD45RA, the isoform of CD45 which distinguishes between naive/central memory T cells and effector memory/effector memory cells re-expressing CD45RA T cells, cannot be well profiled by scRNA-seq due to the difficulty in mapping short reads to genes.

Results

In order to facilitate cell-type annotation in T cell scRNA-seq analysis, we employed machine learning and trained a CD 45 RA + / - classifier on single-cell mRNA count data annotated with known CD45RA antibody levels provided by cellular indexing of transcriptomes and epitopes sequencing data. Among all the algorithms we tested, the trained support vector machine with a radial basis function kernel with optimized hyperparameters achieved a 99.96% accuracy on an unseen dataset. The multilayer perceptron classifier, the second most predictive method overall, also achieved a decent accuracy of 99.74%. Our simple yet robust machine learning approach provides a valid inference on the CD45RA level, assisting the cell identity annotation and further exploring the heterogeneity within human T cells. Based on the overall performance, we chose the support vector machine with a radial basis function kernel as the model implemented in our Python package scCD45RA.

Availability and implementation

The resultant package scCD45RA can be found at https://github.com/BrubakerLab/ScCD45RA and can be installed from the Python Package Index (PyPI) using the command "pip install sccd45ra."

A Critical Analysis of the FDA's Omics-Driven Pharmacodynamic Biomarkers to Establish Biosimilarity

Demonstrating biosimilarity entails comprehensive analytical assessment, clinical pharmacology profiling, and efficacy testing in patients for at least one medical indication, as required by the U.S. Biologics Price Competition and Innovation Act (BPCIA). The efficacy testing can be waived if the drug has known pharmacodynamic (PD) markers, leaving most therapeutic proteins out of this concession. To overcome this, the FDA suggests that biosimilar developers discover PD biomarkers using omics technologies such as proteomics, glycomics, transcriptomics, genomics, epigenomics, and metabolomics. This approach is redundant since the mode-action-action biomarkers of approved therapeutic proteins are already available, as compiled in this paper for the first time. Other potential biomarkers are receptor binding and pharmacokinetic profiling, which can be made more relevant to ensure biosimilarity without requiring biosimilar developers to conduct extensive research, for which they are rarely qualified.

Coarse-grained model simulation-guided localized DNA signal amplification probe for miRNA detection

DNA-based enzyme-free signal amplification strategies are widely employed to detect biomarkers in low abundance. To enhance signal amplification, localized DNA reaction units which increases molecular collision probability is commonly utilized. However, the current understanding of the structure-function relationships in localized DNA signal amplification probes is limited, leading to unsatisfied performance. In this study, we introduced a coarse-grained molecular model to simulate the dynamic behavior of two DNA reaction units within a DNA enzyme-free signal amplification circuit called Localized Catalytic Hairpin Assembly (LCHA). We investigated the impact of localized distance and flexibility on reaction performance. The most efficient LCHA probe guided by simulation exhibits sensitivity 28 times greater that of free CHA, with a detection limit of miR-21 reaching 16 pM, while the least effective LCHA probe demonstrated a modest improvement of only 7 times. We successfully employed the optimized probe to differentiate cancer cells from normal cells based on their miR-21 expression levels, showcasing its quantification ability. By elucidating the mechanistic insights and structure-function relationship in our work, we aim to contribute valuable information that can save users' time and reduce costs when designing localized DNA probes. With a comprehensive understanding of how the localization affects probe performance, researchers can now make more informed and efficient decisions during the design process. This work would find broad applications of DNA nanotechnology in biosensing, biocomputing, and bionic robots.

Genetic susceptibility to post-endoscopic retrograde cholangiopancreatography pancreatitis identified in propensity score-matched analysis

BACKGROUND/AIMS

A previous history of post-endoscopic retrograde cholangiopancreatography (ERCP) pancreatitis (PEP) is a risk factor for PEP, suggesting that there may be a genetic predisposition to PEP. However, nothing is known about this yet. The aim of this study was to identify genetic variations associated with PEP.

METHODS

A cohort of high-risk PEP patients was queried from December 2016 to January 2019. For each PEP case, two propensity score-matched controls were selected. Whole exome sequencing was performed using blood samples. Genetic variants reported to be related to pancreatitis were identified. To discover genetic variants that predispose to PEP, a logistic regression analysis with clinical adjustment was performed. Gene-wise analyses were also conducted.

RESULTS

Totals of 25 PEP patients and 50 matched controls were enrolled. Among the genetic variants reported to be associated with pancreatitis, only CASR rs1042636 was identified, and it showed no significant difference between the case and control groups. A total of 54,269 non-synonymous variants from 14,313 genes was identified. Logistic regression analysis of these variants showed that the IRF2BP1 rs60158447 GC genotype was significantly associated with the occurrence of PEP (odds ratio 2.248, FDR q value = 0.005). Gene-wise analyses did not show any significant results.

CONCLUSION

This study found that the IRF2BP1 gene variant was significantly associated with PEP. This genetic variant is a highly targeted PEP risk factor candidate and can be used for screening high-risk PEP groups before ERCP through future validation. (ClinicalTrials.gov no. NCT02928718).

MOMS: A pipeline for scaffolding using multi-optical maps

Here, we report a new multi-optical maps scaffolder (MOMS) aiming at utilizing complementary information among optical maps labelled by distinct enzymes. This pipeline was designed for data structure organization, scaffolding by path traversal, gap-filling and molecule reuse of optical maps. Our testing showed that this pipeline has uncapped enzyme tolerance in scaffolding. This means that there are no inbuilt limits as to the number of maps generated by different enzymes that can be utilized by MOMS. For the genome assembly of the human GM12878 cell line, MOMS significantly improved the contiguity and completeness with an up to 144-fold increase of scaffold N50 compared with initial assemblies. Benchmarking on the genomes of human and O. sativa showed that MOMS is more effective and robust compared with other optical-map-based scaffolders. We believe this pipeline will contribute to high-fidelity chromosome assembly and chromosome-level evolutionary analysis.

Upfront liquid next-generation sequencing in treatment-naïve advanced non-small cell lung cancer patients: A prospective randomised study in the Taiwanese health system

BACKGROUND

Next-generation sequencing (NGS) of plasma cell-free DNA identifies driver mutations in advanced non-small cell lung cancer (NSCLC) and may complement routine molecular evaluation. The utility of liquid NGS at the start of tumour workup is undetermined.

METHODS

This is a randomised study of patients with suspected advanced NSCLC. All patients received blood liquid NGS testing at their first clinic visit and underwent standard histological diagnosis and tissue genotyping, encompassing polymerase chain reaction based methods for EGFR mutation, immunohistochemical (IHC) staining for ALK fusion and BRAF V600E mutation, and an IHC screening followed by confirmation using fluorescence in situ hybridization confirmation for ROS1 fusion. They were then randomly assigned to receive NGS results either after tissue genotyping (Group A) or as soon as possible after histological diagnosis of advanced NSCLC (Group B). The study measured time to start of systemic treatment as the primary endpoint and secondary endpoints included biomarker discovery rate, objective response rate (ORR), and progression-free survival (PFS).

RESULTS

This study enroled 180 patients with suspected advanced NSCLC, randomised into two groups. 63 patients in Group A and 59 in Group B with advanced NSCLC were confirmed as advanced NSCLC and analysed. Most had adenocarcinoma (Group A: 77.8%, Group B: 79.7%). The prevalence of EGFR mutations in the two groups was similar (Group A: 57.1%; Group B: 56.6%). Other driver alterations were rare. The median time to treatment was shorter in Group B (20 days) than in Group A (28 days). ORR and PFS did not differ between groups significantly. Liquid NGS had high concordance with tissue testing and identified driver mutations in 42.6% (20/47) of tissue-negative cases.

CONCLUSION

Performing liquid NGS at the initial clinic visit for suspected advanced NSCLC identifies more patients suitable for targeted therapies and shortens time to the start of treatment.

CmVCall: An automated and adjustable nanopore analysis pipeline for heteroplasmy detection of the control region in human mitochondrial genome

Genetic associations between human mitochondrial DNA (mtDNA) heteroplasmy and mitochondrial diseases, aging, and cancer have been elaborated, contributing a lot to the further understanding of mtDNA polymorphic spectrum in anthropology, population, and forensic genetics. In the past decade, heteroplasmy detection using Sanger sequencing and next generation sequencing (NGS) was hampered by the former's inefficiency and the latter's inherent bias due to amplification and mapping of short reads, respectively. Nanopore sequencing stands out for its ability to yield long contiguous segments of DNA, providing a new insight into heterogeneity authentication. In addition to MinION from Oxford Nanopore Technologies, an alternative nanopore sequencer QNome (Qitan Technology) has also been applied to various biological research and the forensic applicability of this platform has been proved recently. In this study, we evaluated the performance of four commonly used variant callers in the heterogeneity authentication of the control region of human mtDNA based on simulations of different ratios generated by mixing QNome nanopore sequencing reads of two synthetic sequences. Then, an open-source and python-based nanopore analytics pipeline, CmVCall was developed and incorporated multiple programs including reads filtering, removal of nuclear mitochondrial sequences (NUMTs), alignment, optional 'Correction' mode, and heterogeneity identification. CmVCall can achieve high precision, accuracy, and recall of 100%, 99.9%, and 92.3% with a 5% heteroplasmy level in 'Correction' mode. Moreover, blood, saliva, and hair shaft samples from monozygotic (MZ) twins were used for heterogeneity evaluation and comparison with the NGS data. Results of MZ twin samples showed that CmVCall could identify more point heteroplasmy sites, revealing significant levels of inter- and intra-individual mtDNA polymorphism. In conclusion, we believe that this analysis pipeline will lay a solid foundation for the development of a comprehensive nanopore analysis pipeline targeting the whole mitochondrial genome.

Third-generation sequencing for genetic disease

Third-generation sequencing (TGS) has led to a brave new revolution in detecting genetic diseases over the last few years. TGS has been rapidly developed for genetic disease applications owing to its significant advantages such as long read length, rapid detection, and precise detection of complex and rare structural variants. This approach greatly improves the efficiency of disease diagnosis and complements the shortcomings of short-read sequencing. In this paper, we first briefly introduce the working mechanism of one of the most important representatives of TGS, single-molecule real-time (SMRT) sequencing by Pacific Bioscience (PacBio), followed by a review and comparison of the advantages and disadvantages of different sequencing technologies. Finally, we focused on the progress of SMRT sequencing applications in genetic disease detection. Future perspectives on the applications of TGS in other fields were also presented. With the continuous innovation of the SMRT technologies and the expansion of their fields of application, SMRT sequencing has broad clinical application prospects in genetic diseases detection, and is expected to become an important tool for the molecular diagnosis of other diseases.

Discovery medicine - the HVTN's iterative approach to developing an HIV-1 broadly neutralizing vaccine

PURPOSE OF REVIEW

In the past two decades, there has been an explosion in the discovery of HIV-1 broadly neutralizing antibodies (bnAbs) and associated vaccine strategies to induce them. This abundance of approaches necessitates a system that accurately and expeditiously identifies the most promising regimens. We herein briefly review the background science of bnAbs, provide a description of the first round of phase 1 discovery medicine studies, and suggest an approach to integrate these into a comprehensive HIV-1-neutralizing vaccine.

RECENT FINDINGS

With recent preclinical success including induction of early stage bnAbs in mouse knockin models and rhesus macaques, successful priming of VRC01-class bnAbs with eOD-GT8 in a recent study in humans, and proof-of-concept that intravenous infusion of VRC01 prevents sexual transmission of virus in humans, the stage is set for a broad and comprehensive bnAb vaccine program. Leveraging significant advances in protein nanoparticle science, mRNA technology, adjuvant development, and B-cell and antibody analyses, the HVTN has reconfigured its HIV-1 vaccine strategy by developing the Discovery Medicine Program to test promising vaccine candidates targeting six key epitopes.

SUMMARY

The HVTN Discovery Medicine program is testing multiple HIV-1-neutralizing vaccine candidates.

A molecular classification system for estimating radiotherapy response and anticancer immunity for individual breast cancer patients

Objective

Radiotherapy is a cornerstone of breast cancer therapy, but radiotherapy resistance is a major clinical challenge. Herein, we show a molecular classification approach for estimating individual responses to radiotherapy.

Methods

Consensus clustering was adopted to classify radiotherapy-sensitive and -resistant clusters in the TCGA-BRCA cohort based upon prognostic differentially expressed radiotherapy response-related genes (DERRGs). The stability of the classification was proven in the GSE58812 cohort via NTP method and the reliability was further verified by quantitative RT-PCR analyses of DERRGs. A Riskscore system was generated through Least absolute shrinkage and selection operator (LASSO) analysis, and verified in the GSE58812 and GSE17705. Treatment response and anticancer immunity were evaluated via multiple well-established computational approaches.

Results

We classified breast cancer patients as radiotherapy-sensitive and -resistant clusters, namely C1 and C2, also verified by quantitative RT-PCR analyses of DERRGs. Two clusters presented heterogeneous clinical traits, with poorer prognosis, older age, more advanced T, and more dead status in the C2. The C1 tumors had higher activity of reactive oxygen species and response to X-ray, proving better radiotherapeutic response. Stronger anticancer immunity was found in the C1 tumors that had rich immune cell infiltration, similar expression profiling to patients who responded to anti-PD-1, and activated immunogenic cell death and ferroptosis. The Riskscore was proposed for improving patient prognosis. High Riskscore samples had lower radiotherapeutic response and stronger DNA damage repair as well as poor anticancer immunity, while low Riskscore samples were more sensitive to docetaxel, doxorubicin, and paclitaxel.

Conclusion

Our findings propose a novel radiotherapy response classification system based upon molecular profiles for estimating radiosensitivity for individual breast cancer patients, and elucidate a methodological advancement for synergy of radiotherapy with ICB.

An ultrasensitive method for detecting mutations from short and rare cell-free DNA

Circulating tumor DNA (ctDNA) was short and rare, making the detection performance of the current targeted sequencing methods unsatisfying. We developed the One-PrimER Amplification (OPERA) system and examined its performance in detecting mutations of low variant allelic frequency (VAF) in various samples with short-sized DNA fragments. In cell line-derived samples containing sonication-sheared DNA fragments with 50-150 bp, OPERA was capable of detecting mutations as low as 0.0025% VAF, while CAPP-Seq only detected mutations of >0.03% VAF. Both single nucleotide variant and insertion/deletion can be detected by OPERA. In synthetic fragments as short as 80 bp with low VAF (0.03%-0.1%), the detection sensitivity of OPERA was significantly higher compared to that of droplet digital polymerase chain reaction. The error rate was 5.9×10-5 errors per base after de-duplication in plasma samples collected from healthy volunteers. By suppressing "single-strand errors", the error rate can be further lowered by >5 folds in EGFR T790M hotspot. In plasma samples collected from lung cancer patients, OPERA detected mutations in 57.1% stage I patients with 100% specificity and achieved a sensitivity of 30.0% in patients with tumor volume of less than 1 cm3. OPERA can effectively detect mutations in rare and highly-fragmented DNA.

Genome sequencing and analysis of Bacillus velezensis VJH504 reveal biocontrol mechanism against cucumber Fusarium wilt

One major issue in reducing cucumber yield is the destructive disease Cucumber (Cucumis sativus L.) wilt disease caused by Fusarium oxysporum f. sp. cucumerinum (Foc). When using the isolate VJH504 isolated from cucumber rhizosphere soil and identified as Bacillus velezensis, the growth of Foc in the double culture experiment was effectively inhibited. Phenotypic, phylogenetic, and genomic analyses were conducted to enhance understanding of its biocontrol mechanism. According to the result of the phenotype analysis, B. velezensis VJH504 could inhibit cucumber fusarium wilt disease both in vitro and in vivo, and significantly promote cucumber seed germination and seedling growth. Additionally, the tests of growth-promoting and biocontrol characteristics revealed the secretion of proteases, amylases, β-1,3-glucanases, cellulases, as well as siderophores and indole-3-acetic acid by B. velezensis VJH504. Using the PacBio Sequel II system, we applied the complete genome sequencing for B. velezensis VJH504 and obtained a single circular chromosome with a size of 3.79 Mb. A phylogenetic tree was constructed based on the 16S rRNA gene sequences of B. velezensis VJH504 and 13 other Bacillus species, and Average Nucleotide Identity (ANI) analysis was performed using their whole-genome sequences, confirming isolateVJH504 as B. velezensis. Following this, based on the complete genome sequence od B. velezensis VJH504, specific functional analysis, Carbohydrate-Active Enzymes (CAZymes) analysis, and secondary metabolite analysis were carried out, predicting organism's abilities for biofilm formation, production of antifungal CAZymes, and synthesis of antagonistic secondary metabolites against pathogens. Afterwards, a comparative genomic analysis was performed between B. velezensis VJH504 and three other B. velezensis strains, revealing subtle differences in their genomic sequences and suggesting the potential for the discovery of novel antimicrobial substances in B. velezensis VJH504. In conclusion, the mechanism of B. velezensis VJH504 in controlling cucumber fusarium wilt was predicted to appear that B. velezensis VJH504is a promising biocontrol agent, showcasing excellent application potential in agricultural production.

Genomic variant benchmark: if you cannot measure it, you cannot improve it

Genomic benchmark datasets are essential to driving the field of genomics and bioinformatics. They provide a snapshot of the performances of sequencing technologies and analytical methods and highlight future challenges. However, they depend on sequencing technology, reference genome, and available benchmarking methods. Thus, creating a genomic benchmark dataset is laborious and highly challenging, often involving multiple sequencing technologies, different variant calling tools, and laborious manual curation. In this review, we discuss the available benchmark datasets and their utility. Additionally, we focus on the most recent benchmark of genes with medical relevance and challenging genomic complexity.

Data pre-processing for analyzing microbiome data - A mini review

The human microbiome is an emerging research frontier due to its profound impacts on health. High-throughput microbiome sequencing enables studying microbial communities but suffers from analytical challenges. In particular, the lack of dedicated preprocessing methods to improve data quality impedes effective minimization of biases prior to downstream analysis. This review aims to address this gap by providing a comprehensive overview of preprocessing techniques relevant to microbiome research. We outline a typical workflow for microbiome data analysis. Preprocessing methods discussed include quality filtering, batch effect correction, imputation of missing values, normalization, and data transformation. We highlight strengths and limitations of each technique to serve as a practical guide for researchers and identify areas needing further methodological development. Establishing robust, standardized preprocessing will be essential for drawing valid biological conclusions from microbiome studies.

The Integration of Proteogenomics and Ribosome Profiling Circumvents Key Limitations to Increase the Coverage and Confidence of Novel Microproteins

There has been a dramatic increase in the identification of non-conical translation and a significant expansion of the protein-coding genome and proteome. Among the strategies used to identify novel small ORFs (smORFs), Ribosome profiling (Ribo-Seq) is the gold standard for the annotation of novel coding sequences by reporting on smORF translation. In Ribo-Seq, ribosome-protected footprints (RPFs) that map to multiple sites in the genome are computationally removed since they cannot unambiguously be assigned to a specific genomic location, or to a specific transcript in the case of multiple isoforms. Furthermore, RPFs necessarily result in short (25-34 nucleotides) reads, increasing the chance of ambiguous and multi-mapping alignments, such that smORFs that reside in these regions cannot be identified by Ribo-Seq. Here, we show that the inclusion of proteogenomics to create a Ribosome Profiling and Proteogenomics Pipeline (RP3) bypasses this limitation to identify a group of microprotein-encoding smORFs that are missed by current Ribo-Seq pipelines. Moreover, we show that the microproteins identified by RP3 have different sequence compositions from the ones identified by Ribo-Seq-only pipelines, which can affect proteomics identification. In aggregate, the development of RP3 maximizes the detection and confidence of protein-encoding smORFs and microproteins.

Precision medicine in the era of multi-omics: can the data tsunami guide rational treatment decision?

Precision medicine for cancer is rapidly moving to an approach that integrates multiple dimensions of the biology in order to model mechanisms of cancer progression in each patient. The discovery of multiple drivers per tumor challenges medical decision that faces several treatment options. Drug sensitivity depends on the actionability of the target, its clonal or subclonal origin and coexisting genomic alterations. Sequencing has revealed a large diversity of drivers emerging at treatment failure, which are potential targets for clinical trials or drug repurposing. To effectively prioritize therapies, it is essential to rank genomic alterations based on their proven actionability. Moving beyond primary drivers, the future of precision medicine necessitates acknowledging the intricate spatial and temporal heterogeneity inherent in cancer. The advent of abundant complex biological data will make artificial intelligence algorithms indispensable for thorough analysis. Here, we will discuss the advancements brought by the use of high-throughput genomics, the advantages and limitations of precision medicine studies and future perspectives in this field.

Merging short and stranded long reads improves transcript assembly

Long-read RNA sequencing has arisen as a counterpart to short-read sequencing, with the potential to capture full-length isoforms, albeit at the cost of lower depth. Yet this potential is not fully realized due to inherent limitations of current long-read assembly methods and underdeveloped approaches to integrate short-read data. Here, we critically compare the existing methods and develop a new integrative approach to characterize a particularly challenging pool of low-abundance long noncoding RNA (lncRNA) transcripts from short- and long-read sequencing in two distinct cell lines. Our analysis reveals severe limitations in each of the sequencing platforms. For short-read assemblies, coverage declines at transcript termini resulting in ambiguous ends, and uneven low coverage results in segmentation of a single transcript into multiple transcripts. Conversely, long-read sequencing libraries lack depth and strand-of-origin information in cDNA-based methods, culminating in erroneous assembly and quantitation of transcripts. We also discover a cDNA synthesis artifact in long-read datasets that markedly impacts the identity and quantitation of assembled transcripts. Towards remediating these problems, we develop a computational pipeline to "strand" long-read cDNA libraries that rectifies inaccurate mapping and assembly of long-read transcripts. Leveraging the strengths of each platform and our computational stranding, we also present and benchmark a hybrid assembly approach that drastically increases the sensitivity and accuracy of full-length transcript assembly on the correct strand and improves detection of biological features of the transcriptome. When applied to a challenging set of under-annotated and cell-type variable lncRNA, our method resolves the segmentation problem of short-read sequencing and the depth problem of long-read sequencing, resulting in the assembly of coherent transcripts with precise 5' and 3' ends. Our workflow can be applied to existing datasets for superior demarcation of transcript ends and refined isoform structure, which can enable better differential gene expression analyses and molecular manipulations of transcripts.

The pig pangenome provides insights into the roles of coding structural variations in genetic diversity and adaptation

Structural variations have emerged as an important driving force for genome evolution and phenotypic variation in various organisms, yet their contributions to genetic diversity and adaptation in domesticated animals remain largely unknown. Here we constructed a pangenome based on 250 sequenced individuals from 32 pig breeds in Eurasia and systematically characterized coding sequence presence/absence variations (PAVs) within pigs. We identified 308.3-Mb nonreference sequences and 3438 novel genes absent from the current reference genome. Gene PAV analysis showed that 16.8% of the genes in the pangene catalog undergo PAV. A number of newly identified dispensable genes showed close associations with adaptation. For instance, several novel swine leukocyte antigen (SLA) genes discovered in nonreference sequences potentially participate in immune responses to productive and respiratory syndrome virus (PRRSV) infection. We delineated previously unidentified features of the pig mobilome that contained 490,480 transposable element insertion polymorphisms (TIPs) resulting from recent mobilization of 970 TE families, and investigated their population dynamics along with influences on population differentiation and gene expression. In addition, several candidate adaptive TE insertions were detected to be co-opted into genes responsible for responses to hypoxia, skeletal development, regulation of heart contraction, and neuronal cell development, likely contributing to local adaptation of Tibetan wild boars. These findings enhance our understanding on hidden layers of the genetic diversity in pigs and provide novel insights into the role of SVs in the evolutionary adaptation of mammals.

Power of neutrality tests for detecting natural selection

Detection of natural selection is one of the main interests in population genetics. Thus, many tests have been developed for detecting natural selection using genomic data. Although it is recognized that the utility of tests depends on several evolutionary factors, such as the timing of selection, strength of selection, frequency of selected alleles, demographic events, and initial frequency of selected allele when selection started acting (softness of selection), the relationships between such evolutionary factors and the power of tests are not yet entirely clear. In this study, we investigated the power of 4 tests: Tajiama's D, Fay and Wu's H, relative extended haplotype homozygosity (rEHH), and integrated haplotype score (iHS), under ranges of evolutionary parameters and demographic models to quantitatively expand the understanding of approaches for detecting selection. The results show that each test detects selection within a limited parameter range, and there are still wide ranges of parameters for which none of these tests work effectively. In addition, the parameter space in which each test shows the highest power overlaps the empirical results of previous research. These results indicate that our present perspective of adaptation is limited to only a part of actual adaptation.

De novo assembly and annotation of the singing mouse genome

BACKGROUND

Developing genomic resources for a diverse range of species is an important step towards understanding the mechanisms underlying complex traits. Specifically, organisms that exhibit unique and accessible phenotypes-of-interest allow researchers to address questions that may be ill-suited to traditional model organisms. We sequenced the genome and transcriptome of Alston's singing mouse (Scotinomys teguina), an emerging model for social cognition and vocal communication. In addition to producing advertisement songs used for mate attraction and male-male competition, these rodents are diurnal, live at high-altitudes, and are obligate insectivores, providing opportunities to explore diverse physiological, ecological, and evolutionary questions.

RESULTS

Using PromethION, Illumina, and PacBio sequencing, we produced an annotated genome and transcriptome, which were validated using gene expression and functional enrichment analyses. To assess the usefulness of our assemblies, we performed single nuclei sequencing on cells of the orofacial motor cortex, a brain region implicated in song coordination, identifying 12 cell types.

CONCLUSIONS

These resources will provide the opportunity to identify the molecular basis of complex traits in singing mice as well as to contribute data that can be used for large-scale comparative analyses.

Screening for Novel Fluorescent Nucleobase Analogues Using Computational and Experimental Methods: 2-Amino-6-chloro-8-vinylpurine (2A6Cl8VP) as a Case Study

Novel fluorescent nucleic acid base analogues (FBAs) with improved optical properties are needed in a variety of biological applications. 2-Amino-6-chloro-8-vinylpurine (2A6Cl8VP) is structural analogue of two existing highly fluorescent FBAs, 2-aminopurine (2AP) and 8-vinyladenine (8VA), and can therefore be expected to have similar base pairing as well as better optical properties compared to its counterparts. In order to determine the absorption and fluorescence properties of 2A6Cl8VP, as a first step, we used TD-DFT calculations and the polarizable continuum model for simulating the solvents and computationally predicted absorption and fluorescence maxima. To test the computational predictions, we also synthesized 2A6Cl8VP and measured its UV/vis absorbance, fluorescence emission, and fluorescence lifetime. The computationally predicted absorbance and fluorescence maxima of 2A6Cl8VP are in reasonable agreement to the experimental values and are significantly redshifted compared to 2AP and 8VA, allowing for its specific excitation. The fluorescence quantum yield of 2A6Cl8VP, however, is significantly lower than those of 2AP and 8VA. Overall, 2A6Cl8VP is a novel fluorescent nucleobase analogue, which can be useful in studying structural, biophysical, and biochemical applications.

COWID: an efficient cloud-based genomics workflow for scalable identification of SARS-COV-2

Implementing a specific cloud resource to analyze extensive genomic data on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses a challenge when resources are limited. To overcome this, we repurposed a cloud platform initially designed for use in research on cancer genomics (https://cgc.sbgenomics.com) to enable its use in research on SARS-CoV-2 to build Cloud Workflow for Viral and Variant Identification (COWID). COWID is a workflow based on the Common Workflow Language that realizes the full potential of sequencing technology for use in reliable SARS-CoV-2 identification and leverages cloud computing to achieve efficient parallelization. COWID outperformed other contemporary methods for identification by offering scalable identification and reliable variant findings with no false-positive results. COWID typically processed each sample of raw sequencing data within 5 min at a cost of only US$0.01. The COWID source code is publicly available (https://github.com/hendrick0403/COWID) and can be accessed on any computer with Internet access. COWID is designed to be user-friendly; it can be implemented without prior programming knowledge. Therefore, COWID is a time-efficient tool that can be used during a pandemic.

A denoised multi-omics integration framework for cancer subtype classification and survival prediction

The availability of high-throughput sequencing data creates opportunities to comprehensively understand human diseases as well as challenges to train machine learning models using such high dimensions of data. Here, we propose a denoised multi-omics integration framework, which contains a distribution-based feature denoising algorithm, Feature Selection with Distribution (FSD), for dimension reduction and a multi-omics integration framework, Attention Multi-Omics Integration (AttentionMOI) to predict cancer prognosis and identify cancer subtypes. We demonstrated that FSD improved model performance either using single omic data or multi-omics data in 15 The Cancer Genome Atlas Program (TCGA) cancers for survival prediction and kidney cancer subtype identification. And our integration framework AttentionMOI outperformed machine learning models and current multi-omics integration algorithms with high dimensions of features. Furthermore, FSD identified features that were associated to cancer prognosis and could be considered as biomarkers.

Sequence Alignment/Map format: a comprehensive review of approaches and applications

The Sequence Alignment/Map (SAM) format file is the text file used to record alignment information. Alignment is the core of sequencing analysis, and downstream tasks accept mapping results for further processing. Given the rapid development of the sequencing industry today, a comprehensive understanding of the SAM format and related tools is necessary to meet the challenges of data processing and analysis. This paper is devoted to retrieving knowledge in the broad field of SAM. First, the format of SAM is introduced to understand the overall process of the sequencing analysis. Then, existing work is systematically classified in accordance with generation, compression and application, and the involved SAM tools are specifically mined. Lastly, a summary and some thoughts on future directions are provided.

Trends and Challenges in the Surveillance and Control of Avian Metapneumovirus

Among the respiratory pathogens of birds, the Avian Metapneumovirus (aMPV) is one of the most relevant, as it is responsible for causing infections of the upper respiratory tract and may induce respiratory syndromes. aMPV is capable of affecting the reproductive system of birds, directly impacting shell quality and decreasing egg production. Consequently, this infection can cause disorders related to animal welfare and zootechnical losses. The first cases of respiratory syndromes caused by aMPV were described in the 1970s, and today six subtypes (A, B, C, D, and two more new subtypes) have been identified and are widespread in all chicken and turkey-producing countries in the world, causing enormous economic losses for the poultry industry. Conventionally, immunological techniques are used to demonstrate aMPV infection in poultry, however, the identification of aMPV through molecular techniques helped in establishing the traceability of the virus. This review compiles data on the main aMPV subtypes present in different countries; aMPV and bacteria co-infection; vaccination against aMPV and viral selective pressure, highlighting the strategies used to prevent and control respiratory disease; and addresses tools for viral diagnosis and virus genome studies aiming at improving and streamlining pathogen detection and corroborating the development of new vaccines that can effectively protect herds, preventing viral escapes.

Exploration of whole genome amplification generated chimeric sequences in long-read sequencing data

MOTIVATION

Multiple displacement amplification (MDA) has become the most commonly used method of whole genome amplification, generating a vast amount of DNA with higher molecular weight and greater genome coverage. Coupling with long-read sequencing, it is possible to sequence the amplicons of over 20 kb in length. However, the formation of chimeric sequences (chimeras, expressed as structural errors in sequencing data) in MDA seriously interferes with the bioinformatics analysis but its influence on long-read sequencing data is unknown.

RESULTS

We sequenced the phi29 DNA polymerase-mediated MDA amplicons on the PacBio platform and analyzed chimeras within the generated data. The 3rd-ChimeraMiner has been constructed as a pipeline for recognizing and restoring chimeras into the original structures in long-read sequencing data, improving the efficiency of using TGS data. Five long-read datasets and one high-fidelity long-read dataset with various amplification folds were analyzed. The result reveals that the mis-priming events in amplification are more frequently occurring than widely perceived, and the propor tion gradually accumulates from 42% to over 78% as the amplification continues. In total, 99.92% of recognized chimeric sequences were demonstrated to be artifacts, whose structures were wrongly formed in MDA instead of existing in original genomes. By restoring chimeras to their original structures, the vast majority of supplementary alignments that introduce false-positive structural variants are recycled, removing 97% of inversions on average and contributing to the analysis of structural variation in MDA-amplified samples. The impact of chimeras in long-read sequencing data analysis should be emphasized, and the 3rd-ChimeraMiner can help to quantify and reduce the influence of chimeras.

AVAILABILITY AND IMPLEMENTATION

The 3rd-ChimeraMiner is available on GitHub, https://github.com/dulunar/3rdChimeraMiner.

Current Status of Next-Generation Sequencing in Bone Genetic Diseases

The development of next-generation sequencing (NGS) has dramatically increased the speed and volume of genetic analysis. Furthermore, the range of applications of NGS is rapidly expanding to include genome, epigenome (such as DNA methylation), metagenome, and transcriptome analyses (such as RNA sequencing and single-cell RNA sequencing). NGS enables genetic research by offering various sequencing methods as well as combinations of methods. Bone tissue is the most important unit supporting the body and is a reservoir of calcium and phosphate ions, which are important for physical activity. Many genetic diseases affect bone tissues, possibly because metabolic mechanisms in bone tissue are complex. For instance, the presence of specialized immune cells called osteoclasts in the bone tissue, which absorb bone tissue and interact with osteoblasts in complex ways to support normal vital functions. Moreover, the many cell types in bones exhibit cell-specific proteins for their respective activities. Mutations in the genes encoding these proteins cause a variety of genetic disorders. The relationship between age-related bone tissue fragility (also called frailty) and genetic factors has recently attracted attention. Herein, we discuss the use of genomic, epigenomic, transcriptomic, and metagenomic analyses in bone genetic disorders.

Advances in novel strategies for isolation, characterization, and analysis of CTCs and ctDNA

Over the past decade, the detection and analysis of liquid biopsy biomarkers such as circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA) have advanced significantly. They have received recognition for their clinical usefulness in detecting cancer at an early stage, monitoring disease, and evaluating treatment response. The emergence of liquid biopsy has been a helpful development, as it offers a minimally invasive, rapid, real-time monitoring, and possible alternative to traditional tissue biopsies. In resource-limited settings, the ideal platform for liquid biopsy should not only extract more CTCs or ctDNA from a minimal sample volume but also accurately represent the molecular heterogeneity of the patient's disease. This review covers novel strategies and advancements in CTC and ctDNA-based liquid biopsy platforms, including microfluidic applications and comprehensive analysis of molecular complexity. We discuss these systems' operational principles and performance efficiencies, as well as future opportunities and challenges for their implementation in clinical settings. In addition, we emphasize the importance of integrated platforms that incorporate machine learning and artificial intelligence in accurate liquid biopsy detection systems, which can greatly improve cancer management and enable precision diagnostics.

Microfluidic delivery of cutting enzymes for fragmentation of surface-adsorbed DNA molecules

We describe a method for fragmenting, in-situ, surface-adsorbed and immobilized DNAs on polymethylmethacrylate(PMMA)-coated silicon substrates using microfluidic delivery of the cutting enzyme DNase I. Soft lithography is used to produce silicone elastomer (Sylgard 184) gratings which form microfluidic channels for delivery of the enzyme. Bovine serum albumin (BSA) is used to reduce DNase I adsorption to the walls of the microchannels and enable diffusion of the cutting enzyme to a distance of 10mm. Due to the DNAs being immobilized, the fragment order is maintained on the surface. Possible methods of preserving the order for application to sequencing are discussed.

Comparison of three massively parallel sequencing platforms for single nucleotide polymorphism (SNP) genotyping in forensic genetics

Three MPS platforms are being used in forensic genetic analysis, i.e., MiSeq FGx, Ion S5 XL, and MGISEQ-2000. However, few studies compared their performance. In this study, we sequenced 83 common SNPs of 71 samples using the ForenSeq™ DNA Signature Prep Kit on MiSeq FGx, the Precision ID Identity Panel on Ion S5 XL, and the MGIEasy Signature Identification Library Prep Kit on MGISEQ-2000 and then the performance was compared. Results showed that the MiSeq FGx had the highest sequence quality but the lowest sequencing depth and allele balance. Discordant genotypes were observed at six SNPs, which may be caused by variants at primer binding regions, indel errors, or misalignments. Besides, two kinds of background noises, allele-specific miscalled reads (ASMR) and allele-nonspecific miscalled reads (ANMR), were characterized. MGISEQ-2000 showed the highest level of ASMR while Ion S5 XL had the highest level of ANMR. Site- and genotype-dependent miscalled patterns were observed at several SNPs on Ion S5 XL and MGISEQ-2000, but few on MiSeq FGx. In conclusion, the three MPS platforms perform differently with respect to sequencing quality, sequencing depth, allele balance, concordance, and background noise. These findings may be useful for data comparison, mixture deconvolution, and heteroplasmy analysis in forensic genetics.

Eye and hair color prediction of an early medieval adult and subadult skeleton using massive parallel sequencing technology

Phenotypic trait prediction in ancient DNA analysis can provide information about the external appearance of individuals from past human populations. Some studies predicting eye and hair color in ancient adult skeletons have been published, but not for ancient subadult skeletons, which are more prone to decay. In this study, eye and hair color were predicted for an early medieval adult skeleton and a subadult skeleton that was anthropologically characterized as a middle-aged man and a subadult of unknown sex about 6 years old. When processing the petrous bones, precautions were taken to prevent contamination with modern DNA. The MillMix tissue homogenizer was used for grinding, 0.5 g of bone powder was decalcified, and DNA was purified in Biorobot EZ1. The PowerQuant System was used for quantification and a customized version of the HIrisPlex panel for massive parallel sequencing (MPS) analysis. Library preparation and templating were performed on the HID Ion Chef Instrument and sequencing on the Ion GeneStudio S5 System. Up to 21 ng DNA/g of powder was obtained from ancient petrous bones. Clean negative controls and no matches with elimination database profiles confirmed no contamination issue. Brown eyes and dark brown or black hair were predicted for the adult skeleton and blue eyes and brown or dark brown hair for the subadult skeleton. The MPS analysis results obtained proved that it is possible to predict hair and eye color not only for an adult from the Early Middle Ages, but also for a subadult skeleton dating to this period.

Exploring the Microbiome in Human Reproductive Tract: High-Throughput Methods for the Taxonomic Characterization of Microorganisms

Microorganisms are important due to their widespread presence and multifaceted roles across various domains of life, ecology, and industries. In humans, they underlie the proper functioning of multiple systems crucial to well-being, including immunological and metabolic functions. Emerging research addressing the presence and roles of microorganisms within human reproduction is increasingly relevant. Studies implementing new methodologies (e.g., to investigate vaginal, uterine, and semen microenvironments) can now provide relevant insights into fertility, reproductive health, or pregnancy outcomes. In that sense, cutting-edge sequencing techniques, as well as others such as meta-metabolomics, culturomics, and meta-proteomics, are becoming more popular and accessible worldwide, allowing the characterization of microbiomes at unprecedented resolution. However, they frequently involve rather complex laboratory protocols and bioinformatics analyses, for which researchers may lack the required expertise. A suitable pipeline would successfully enable both taxonomic classification and functional profiling of the microbiome, providing easy-to-understand biological interpretations. However, the selection of an appropriate methodology would be crucial, as it directly impacts the reproducibility, accuracy, and quality of the results and observations. This review focuses on the different current microbiome-related techniques in the context of human reproduction, encompassing niches like vagina, endometrium, and seminal fluid. The most standard and reliable methods are 16S rRNA gene sequencing, metagenomics, and meta-transcriptomics, together with complementary approaches including meta-proteomics, meta-metabolomics, and culturomics. Finally, we also offer case examples and general recommendations about the most appropriate methods and workflows and discuss strengths and shortcomings for each technique.

Going circular: history, present, and future of circRNAs in cancer

To date, thousands of highly abundant and conserved single-stranded RNA molecules shaped into ring structures (circRNAs) have been identified. CircRNAs are multifunctional molecules that have been shown to regulate gene expression transcriptionally and post-transcriptionally and exhibit distinct tissue- and development-specific expression patterns associated with a variety of normal and disease conditions, including cancer pathogenesis. Over the past years, due to their intrinsic stability and resistance to ribonucleases, particular attention has been drawn to their use as reliable diagnostic and prognostic biomarkers in cancer diagnosis, treatment, and prevention. However, there are some critical caveats to their utility in the clinic. Their circular shape limits their annotation and a complete functional elucidation is lacking. This makes their detection and biomedical application still challenging. Herein, we review the current knowledge of circRNA biogenesis and function, and of their involvement in tumorigenesis and potential utility in cancer-targeted therapy.

Copy Number Variations (CNVs) Account for 10.8% of Pathogenic Variants in Patients Referred for Hereditary Cancer Testing

BACKGROUND/AIM

Germline copy number variation (CNV) is a type of genetic variant that predisposes significantly to inherited cancers. Today, next-generation sequencing (NGS) technologies have contributed to multi gene panel analysis in clinical practice.

MATERIALS AND METHODS

A total of 2,163 patients were screened for cancer susceptibility, using a solution-based capture method. A panel of 52 genes was used for targeted NGS. The capture-based approach enables computational analysis of CNVs from NGS data. We studied the performance of the CNV module of the commercial software suite SeqPilot (JSI Medical Systems) and of the non-commercial tool panelcn.MOPS. Additionally, we tested the performance of digital multiplex ligation-dependent probe amplification (digitalMLPA).

RESULTS

Pathogenic/likely pathogenic variants (P/LP) were identified in 464 samples (21.5%). CNV accounts for 10.8% (50/464) of pathogenic variants, referring to deletion/duplication of one or more exons of a gene. In patients with breast and ovarian cancer, CNVs accounted for 10.2% and 6.8% of pathogenic variants, respectively. In colorectal cancer patients, CNV accounted for 28.6% of pathogenic/likely pathogenic variants.

CONCLUSION

In silico CNV detection tools provide a viable and cost-effective method to identify CNVs from NGS experiments. CNVs constitute a substantial percentage of P/LP variants, since they represent up to one of every ten P/LP findings identified by NGS multigene analysis; therefore, their evaluation is highly recommended to improve the diagnostic yield of hereditary cancer analysis.

Lipid-correlated alterations in the transcriptome are enriched in several specific pathways in the postmortem prefrontal cortex of Japanese patients with schizophrenia

AIMS

Schizophrenia is a chronic relapsing psychiatric disorder that is characterized by many symptoms and has a high heritability. There were studies showing that the phospholipid abnormalities in subjects with schizophrenia (Front Biosci, S3, 2011, 153; Schizophr Bull, 48, 2022, 1125; Sci Rep, 7, 2017, 6; Anal Bioanal Chem, 400, 2011, 1933). Disturbances in prefrontal cortex phospholipid and fatty acid composition have been reported in subjects with schizophrenia (Sci Rep, 7, 2017, 6; Anal Bioanal Chem, 400, 2011, 1933; Schizophr Res, 215, 2020, 493; J Psychiatr Res, 47, 2013, 636; Int J Mol Sci, 22, 2021). For exploring the signaling pathways contributing to the lipid changes in previous study (Sci Rep, 7, 2017, 6), we performed two types of transcriptome analyses in subjects with schizophrenia: an unbiased transcriptome analysis solely based on RNA-seq data and a correlation analysis between levels of gene expression and lipids.

METHODS

RNA-Seq analysis was performed in the postmortem prefrontal cortex from 10 subjects with schizophrenia and 5 controls. Correlation analysis between the transcriptome and lipidome from 9 subjects, which are the same samples in the previous lipidomics study (Sci Rep, 7, 2017, 6).

RESULTS

Extraction of differentially expressed genes (DEGs) and further sequence and functional group analysis revealed changes in gene expression levels in phosphoinositide 3-kinase (PI3K)-Akt signaling and the complement system. In addition, a correlation analysis clarified alterations in ether lipid metabolism pathway, which is not found as DEGs in transcriptome analysis alone.

CONCLUSIONS

This study provided results of the integrated analysis of the schizophrenia-associated transcriptome and lipidome within the PFC and revealed that lipid-correlated alterations in the transcriptome are enriched in specific pathways including ether lipid metabolism pathway.

Beyond assembly: the increasing flexibility of single-molecule sequencing technology

The maturation of high-throughput short-read sequencing technology over the past two decades has shaped the way genomes are studied. Recently, single-molecule, long-read sequencing has emerged as an essential tool in deciphering genome structure and function, including filling gaps in the human reference genome, measuring the epigenome and characterizing splicing variants in the transcriptome. With recent technological developments, these single-molecule technologies have moved beyond genome assembly and are being used in a variety of ways, including to selectively sequence specific loci with long reads, measure chromatin state and protein-DNA binding in order to investigate the dynamics of gene regulation, and rapidly determine copy number variation. These increasingly flexible uses of single-molecule technologies highlight a young and fast-moving part of the field that is leading to a more accessible era of nucleic acid sequencing.

Investigation of the vaginal microbiota of dairy cows through genetic sequencing of short (Illumina) and long (PacBio) reads and associations with gestational status

The vaginal microbiota has been shown to be important in local immune regulation and may play a role in reproduction and fertility. Next-generation sequencing (NGS) technologies have been used to characterize the bovine vaginal microbiota, mainly using short-read sequencing (Illumina). However, the main limitation of this technique is its inability to classify bacteria at the species level. The objective of this study was to characterize the bovine vaginal microbiota at the species level using long-read sequencing (PacBio) and to compare it with the results of short-read sequencing. In addition, the vaginal microbiota of cows that became pregnant after artificial insemination (AI) was compared with that of infertile animals. Thirteen Holstein cows had vaginal swabs collected prior to AI. DNA was extracted and subjected to Illumina and PacBio sequencing to characterize the V4 region and the entire 16S rRNA gene, respectively. PacBio sequencing yielded 366,509 reads that were assigned to 476 species from 27 phyla. However, none of the most abundant reads (>1%) could be classified at the species level. Illumina sequencing yielded more reads and consequently was able to detect a more observed species, but PacBio sequencing was able to detect more unique and rare species. The composition of the vaginal microbiota varies according to the sequencing method used, which might complicate the interpretation of results obtained in the majority of the current studies. The present study expands on the current knowledge of bovine microbiota, highlighting the need for further efforts to improve the current databanks.

Long-read assembled metagenomic approaches improve our understanding on metabolic potentials of microbial community in mangrove sediments

BACKGROUND

Mangrove wetlands are coastal ecosystems with important ecological features and provide habitats for diverse microorganisms with key roles in nutrient and biogeochemical cycling. However, the overall metabolic potentials and ecological roles of microbial community in mangrove sediment are remained unanswered. In current study, the microbial and metabolic profiles of prokaryotic and fungal communities in mangrove sediments were investigated using metagenomic analysis based on PacBio single-molecule real time (SMRT) and Illumina sequencing techniques.

RESULTS

Comparing to Illumina short reads, the incorporation of PacBio long reads significantly contributed to more contiguous assemblies, yielded more than doubled high-quality metagenome-assembled genomes (MAGs), and improved the novelty of the MAGs. Further metabolic reconstruction for recovered MAGs showed that prokaryotes potentially played an essential role in carbon cycling in mangrove sediment, displaying versatile metabolic potential for degrading organic carbons, fermentation, autotrophy, and carbon fixation. Mangrove fungi also functioned as a player in carbon cycling, potentially involved in the degradation of various carbohydrate and peptide substrates. Notably, a new candidate bacterial phylum named as Candidatus Cosmopoliota with a ubiquitous distribution is proposed. Genomic analysis revealed that this new phylum is capable of utilizing various types of organic substrates, anaerobic fermentation, and carbon fixation with the Wood-Ljungdahl (WL) pathway and the reverse tricarboxylic acid (rTCA) cycle.

CONCLUSIONS

The study not only highlights the advantages of HiSeq-PacBio Hybrid assembly for a more complete profiling of environmental microbiomes but also expands our understanding of the microbial diversity and potential roles of distinct microbial groups in biogeochemical cycling in mangrove sediment. Video Abstract.

Comparing antimicrobial resistant genes and phenotypes across multiple sequencing platforms and assays for Enterobacterales clinical isolates

INTRODUCTION

Whole genome sequencing (WGS) of bacterial isolates can be used to identify antimicrobial resistance (AMR) genes. Previous studies have shown that genotype-based AMR has variable accuracy for predicting carbapenem resistance in carbapenem-resistant Enterobacterales (CRE); however, the majority of these studies used short-read platforms (e.g. Illumina) to generate sequence data. In this study, our objective was to determine whether Oxford Nanopore Technologies (ONT) long-read WGS would improve detection of carbapenem AMR genes with respect to short-read only WGS for nine clinical CRE samples. We measured the minimum inhibitory breakpoint (MIC) using two phenotype assays (MicroScan and ETEST) for six antibiotics, including two carbapenems (meropenem and ertapenem) and four non-carbapenems (gentamicin, ciprofloxacin, cefepime, and trimethoprim/sulfamethoxazole). We generated short-read data using the Illumina NextSeq and long-read data using the ONT MinION. Four assembly methods were compared: ONT-only assembly; ONT-only assembly plus short-read polish; ONT + short-read hybrid assembly plus short-read polish; short-read only assembly.

RESULTS

Consistent with previous studies, our results suggest that the hybrid assembly produced the highest quality results as measured by gene completeness and contig circularization. However, ONT-only methods had minimal impact on the detection of AMR genes and plasmids compared to short-read methods, although, notably, differences in gene copy number differed between methods. All four assembly methods showed identical presence/absence of the blaKPC-2 carbapenemase gene for all samples. The two phenotype assays showed 100% concordant results for the non-carbapenems, but only 65% concordance for the two carbapenems. The presence/absence of AMR genes was 100% concordant with AMR phenotypes for all four non-carbapenem drugs, although only 22%-50% sensitivity for the carbapenems.

CONCLUSIONS

Overall, these findings suggest that the lack of complete correspondence between CRE AMR genotype and phenotype for carbapenems, while concerning, is independent of sequencing platform/assembly method.

Resolving complex structural variants via nanopore sequencing

The recent development of high-throughput sequencing platforms provided impressive insights into the field of human genetics and contributed to considering structural variants (SVs) as the hallmark of genome instability, leading to the establishment of several pathologic conditions, including neoplasia and neurodegenerative and cognitive disorders. While SV detection is addressed by next-generation sequencing (NGS) technologies, the introduction of more recent long-read sequencing technologies have already been proven to be invaluable in overcoming the inaccuracy and limitations of NGS technologies when applied to resolve wide and structurally complex SVs due to the short length (100-500 bp) of the sequencing read utilized. Among the long-read sequencing technologies, Oxford Nanopore Technologies developed a sequencing platform based on a protein nanopore that allows the sequencing of "native" long DNA molecules of virtually unlimited length (typical range 1-100 Kb). In this review, we focus on the bioinformatics methods that improve the identification and genotyping of known and novel SVs to investigate human pathological conditions, discussing the possibility of introducing nanopore sequencing technology into routine diagnostics.