Bioinformatic Prediction and High Throughput In Vivo Screening to Identify Cis-Regulatory Elements for the Development of Algal Synthetic Promoters.

Algae biotechnology holds immense promise for revolutionizing the bioeconomy through the sustainable and scalable production of various bioproducts. However, their development has been hindered by the lack of advanced genetic tools. This study introduces a synthetic biology approach to develop such tools, focusing on the construction and testing of synthetic promoters. By analyzing conserved DNA motifs within the promoter regions of highly expressed genes across six different algal species, we identified cis-regulatory elements (CREs) associated with high transcriptional activity. Combining the algorithms POWRS, STREME, and PhyloGibbs, we predicted 1511 CREs and inserted them into a minimal synthetic promoter sequence in 1, 2, or 3 copies, resulting in 4533 distinct synthetic promoters. These promoters were evaluated in vivo for their capacity to drive the expression of a transgene in a high-throughput manner through next-generation sequencing post antibiotic selection and fluorescence-activated cell sorting. To validate our approach, we sequenced hundreds of transgenic lines showing high levels of GFP expression. Further, we individually tested 14 identified promoters, revealing substantial increases in GFP expression─up to nine times higher than the baseline synthetic promoter, with five matching or even surpassing the performance of the native AR1 promoter. As a result of this study, we identified a catalog of CREs that can now be used to build superior synthetic algal promoters. More importantly, here we present a validated pipeline to generate building blocks for innovative synthetic genetic tools applicable to any algal species with a sequenced genome and transcriptome data set.

OmicVerse: a framework for bridging and deepening insights across bulk and single-cell sequencing.

Single-cell sequencing is frequently affected by "omission" due to limitations in sequencing throughput, yet bulk RNA-seq may contain these ostensibly "omitted" cells. Here, we introduce the single cell trajectory blending from Bulk RNA-seq (BulkTrajBlend) algorithm, a component of the OmicVerse suite that leverages a Beta-Variational AutoEncoder for data deconvolution and graph neural networks for the discovery of overlapping communities. This approach effectively interpolates and restores the continuity of "omitted" cells within single-cell RNA sequencing datasets. Furthermore, OmicVerse provides an extensive toolkit for both bulk and single cell RNA-seq analysis, offering seamless access to diverse methodologies, streamlining computational processes, fostering exquisite data visualization, and facilitating the extraction of significant biological insights to advance scientific research.

Long-read RNA-Seq for the discovery of long noncoding and antisense RNAs in plant organelles.

Plant organelle transcription has been studied for decades. As techniques advanced, so did the fields of mitochondrial and plastid transcriptomics. The current view is that organelle genomes are pervasively transcribed, irrespective of their size, content, structure, and taxonomic origin. However, little is known about the nature of organelle noncoding transcriptomes, including pervasively transcribed noncoding RNAs (ncRNAs). Next-generation sequencing data have uncovered small ncRNAs in the organelles of plants and other organisms, but long ncRNAs remain poorly understood. Here, we argue that publicly available third-generation long-read RNA sequencing data from plants can provide a fine-tuned picture of long ncRNAs within organelles. Indeed, given their bloated architectures, plant mitochondrial genomes are well suited for studying pervasive transcription of ncRNAs. Ultimately, we hope to showcase this new avenue of plant research while also underlining the limitations of the proposed approach.

Comprehensive and deep evaluation of structural variation detection pipelines with third-generation sequencing data.

BACKGROUND: Structural variation (SV) detection methods using third-generation sequencing data are widely employed, yet accurately detecting SVs remains challenging. Different methods often yield inconsistent results for certain SV types, complicating tool selection and revealing biases in detection. RESULTS: This study comprehensively evaluates 53 SV detection pipelines using simulated and real data from PacBio (CLR: Continuous Long Read, CCS: Circular Consensus Sequencing) and Nanopore (ONT) platforms. We assess their performance in detecting various sizes and types of SVs, breakpoint biases, and genotyping accuracy with various sequencing depths. Notably, pipelines such as Minimap2-cuteSV2, NGMLR-SVIM, PBMM2-pbsv, Winnowmap-Sniffles2, and Winnowmap-SVision exhibit comparatively higher recall and precision. Our findings also show that combining multiple pipelines with the same aligner, like pbmm2 or winnowmap, can significantly enhance performance. The individual pipelines' detailed ranking and performance metrics can be viewed in a dynamic table: http://pmglab.top/SVPipelinesRanking . CONCLUSIONS: This study comprehensively characterizes the strengths and weaknesses of numerous pipelines, providing valuable insights that can improve SV detection in third-generation sequencing data and inform SV annotation and function prediction.

NGS mutational status on first diagnostic tissue, liquid biopsy and mastectomy in G2-G3 breast cancer.

Breast cancer is one of the more frequently diagnosed cancers leading to death in women, and, like other tumor types, it is heterogeneous in its immunophenotype. It harbors mutations that modify tumor aggressiveness, therapy responses, residual disease, drug resistance, and relapse rates in advanced stages. This study aims to assess the mutational status of G2 and G3 tumors using next-generation sequencing (NGS) on initial tissue biopsies, liquid biopsies, and mastectomy specimens. The histopathological (HP) diagnosis for the 32 selected cases was established via Hematoxylin-Eosin (HE) staining by two observers. For the immunohistochemical (IHC) testing of estrogen receptor (ER), progesterone receptor (PGR) and human epidermal growth factor receptor 2 (HER2), we used the Ventana BenchMark Ultra. Ki67 testing was conducted using Bond-III from Leica. For cases with a score of 2+, gene amplification was assessed by silver-enhanced in situ hybridization (ISH) (SISH; Inform HER2 Dual ISH) on Ventana BenchMark Ultra. NGS analysis was initially performed on biopsies and plasma, and later on mastectomy specimens. After automated deoxyribonucleic acid (DNA) and ribonucleic acid (RNA) extraction, concentrations were measured using the Invitrogen Qubit system. Libraries were created using Oncomine systems, and sequencing and analysis were done with the Ion Torrent system. Most tumors were graded as G3 (19 cases), with Luminal A being the predominant molecular subtype, and a significant number displayed HER2∕HER2-low characteristics (24 out of 32 cases). The NGS assessment showed that phosphatidylinositol-4,5-bisphosphate 3-kinase, catalytic subunit alpha (PIK3CA) mutations were the most frequent across all sample types. A significant limitation was the high number of invalid plasma tests due to pre-analytical handling errors or transport issues. Nonetheless, plasma testing (liquid biopsy) proved useful for monitoring tumor evolution and assessing residual disease.

Human B Cell Receptor Repertoire Sequencing.

Next-generation sequencing has the potential to uncover the complex nature of B cell immunity by revealing the full complexity of B cell receptor (BCR) repertoires in health and disease. However, there are drawbacks which can compromise the validity of the repertoire analysis caused by quantitative bias and accumulation of sequencing errors during the library preparation and sequencing. Here, we provide an optimized protocol designed to minimize bias for reproducible and accurate preparation of human BCR repertoire libraries for high-throughput sequencing.

Measuring B Cell Chromatin Accessibility by One-Step ATAC-seq.

The Assay for Transposase Accessible Chromatin (ATAC)-seq protocol is optimized to generate global maps of accessible chromatin using limited cell inputs. The Tn5 transposase tagmentation reaction simultaneously fragments and tags the accessible DNA with Illumina Nextera sequencing adapters. Fragmented and adapter tagged DNA is then purified and PCR amplified with dual indexing primers to generate a size-specific sequencing library. The One-Step workflow below outlines the Tn5 nuclei transposition from a range of cell inputs followed by PCR amplification to generate a sequencing library.

Haplotype-resolved assembly of the mule duck genome using high-fidelity sequencing technology.

Mule duck is vitally important to the production of global duck meat. Here, we present two high-quality haplotypes of a female mule duck (haplotype 1 (H1):1.28 Gb, haplotype 2 (H2): 1.40 Gb). The continuity (H1: contig N50 = 14.90 Mb, H2: contig N50 = 15.70 Mb) and completeness (BUSCO: H1 = 96.9%, H2 = 97.3%) are substantially better than those of other duck genomes. We detected the structural variations (SVs) in H1 and H2. We observed a positive correlation between autosome length and the number of SVs. Z chromosome was some deficient in deletions and insertions, but W chromosome was some excessive. A total of 1,451 genes were haplotype specific expression (HSEs). Among them, 737 specifically expressed in H1, and 714 specifically expressed in H2. We found that H1 and H2 HSEs tended to be involved in similar biological processes, such as myometrial relaxation and contraction pathways, muscle structure development and phosphorylation. Our haplotype-resolved genome assembly provides a powerful platform for future functional genomics, molecular breeding, and genome editing in mule duck.

Comparative analysis of gut microbiota in children with obstructive sleep apnea: assessing the efficacy of 16S rRNA gene sequencing in metabolic function prediction based on weight status.

Background: Analyzing bacterial microbiomes consistently using next-generation sequencing (NGS) is challenging due to the diversity of synthetic platforms for 16S rRNA genes and their analytical pipelines. This study compares the efficacy of full-length (V1-V9 hypervariable regions) and partial-length (V3-V4 hypervariable regions) sequencing of synthetic 16S rRNA genes from human gut microbiomes, with a focus on childhood obesity. Methods: In this observational and comparative study, we explored the differences between these two sequencing methods in taxonomic categorization and weight status prediction among twelve children with obstructive sleep apnea. Results: The full-length NGS method by Pacbio® identified 118 genera and 248 species in the V1-V9 regions, all with a 0% unclassified rate. In contrast, the partial-length NGS method by Illumina® detected 142 genera (with a 39% unclassified rate) and 6 species (with a 99% unclassified rate) in the V3-V4 regions. These approaches showed marked differences in gut microbiome composition and functional predictions. The full-length method distinguished between obese and non-obese children using the Firmicutes/Bacteroidetes ratio, a known obesity marker (p = 0.046), whereas the partial-length method was less conclusive (p = 0.075). Additionally, out of 73 metabolic pathways identified through full-length sequencing, 35 (48%) were associated with level 1 metabolism, compared to 28 of 61 pathways (46%) identified through the partial-length method. The full-length NGS also highlighted complex associations between body mass index z-score, three bacterial species (Bacteroides ovatus, Bifidobacterium pseudocatenulatum, and Streptococcus parasanguinis ATCC 15912), and 17 metabolic pathways. Both sequencing techniques revealed relationships between gut microbiota composition and OSA-related parameters, with full-length sequencing offering more comprehensive insights into associated metabolic pathways than the V3-V4 technique. Conclusion: These findings highlight disparities in NGS-based assessments, emphasizing the value of full-length NGS with amplicon sequence variant analysis for clinical gut microbiome research. They underscore the importance of considering methodological differences in future meta-analyses.

Identification of the novel HLA-A*30:221 allele by next-generation sequencing.

The novel HLA-A*30:221 allele differs from HLA-A*30:01:01:01 by one nucleotide substitution in Exon 7.

Characterisation of the novel HLA-B*46:96 allele by next-generation sequencing.

HLA-B*46:96 differs from HLA-B*46:01:01:01 by a single nucleotide substitution at position 479 C>T.

Metagenomic Next-Generation Sequencing as an Effective Diagnostic Tool for Talaromycosis in HIV-Negative Patients.

The diagnosis of Talaromyces marneffei infection in HIV-negative patients remains challenging. There is an urgent need for rapid and convenient methods to diagnose this complicated disease. The aim of this study was to evaluate the diagnostic efficiency of metagenomic next-generation sequencing (mNGS) for talaromycosis in non-HIV-infected patients by comparing mNGS with traditional microbial culture. In total, 66 samples from 57 patients were analyzed via both mNGS and microbial culture. The ROC curve showed a sensitivity for mNGS of 97.22%, which was greater than that of microbial culture (61.11%). Samples from the respiratory tract, infectious skin lesions, and lymph nodes are recommended as routine samples for talaromycosis detection via mNGS. Furthermore, mNGS significantly reduced the diagnostic time compared to microbial culture. Overall, our study demonstrated that mNGS is a promising tool for rapid and accurate pathogenic detection in HIV-negative patients with talaromycosis.

The novel HLA-C*04:01:01:182 allele identified in a candidate bone marrow donor by next-generation sequencing.

HLA-C*04:01:01:182 differs from the HLA-C*04:01:01:06 allele by one nucleotide substitution in the 5'UTR.

ProcaryaSV: structural variation detection pipeline for bacterial genomes using short-read sequencing.

BACKGROUND: Structural variations play an important role in bacterial genomes. They can mediate genome adaptation quickly in response to the external environment and thus can also play a role in antibiotic resistance. The detection of structural variations in bacteria is challenging, and the recognition of even small rearrangements can be important. Even though most detection tools are aimed at and benchmarked on eukaryotic genomes, they can also be used on prokaryotic genomes. The key features of detection are the ability to detect small rearrangements and support haploid genomes. Because of the limiting performance of a single detection tool, combining the detection abilities of multiple tools can lead to more robust results. There are already available workflows for structural variation detection for long-reads technologies and for the detection of single-nucleotide variation and indels, both aimed at bacteria. Yet we are unaware of structural variations detection workflows for the short-reads sequencing platform. Motivated by this gap we created our workflow. Further, we were interested in increasing the detection performance and providing more robust results. RESULTS: We developed an open-source bioinformatics pipeline, ProcaryaSV, for the detection of structural variations in bacterial isolates from paired-end short sequencing reads. Multiple tools, starting with quality control and trimming of sequencing data, alignment to the reference genome, and multiple structural variation detection tools, are integrated. All the partial results are then processed and merged with an in-house merging algorithm. Compared with a single detection approach, ProcaryaSV has improved detection performance and is a reproducible easy-to-use tool. CONCLUSIONS: The ProcaryaSV pipeline provides an integrative approach to structural variation detection from paired-end next-generation sequencing of bacterial samples. It can be easily installed and used on Linux machines. It is publicly available on GitHub at https://github.com/robinjugas/ProcaryaSV .

Assessment of performance and comparison of three commercial HDV RNA detection assays: implications for diagnosis and treatment monitoring.

Objectives: Precise HDV-RNA detection and quantification are pivotal for diagnosis and monitoring of response to newly approved treatment. We evaluate the performance of three HDV RNA detection and quantification assays. Methods: Hepatitis Delta RT-PCR system kit, EurobioPlex HDV assay, and RoboGene HDV RNA Quantification kit 2.0 were used for testing 151 HBsAg-positive samples, 90 HDV-RNA negative and 61 HDV-RNA positive. We also evaluated serial dilutions of the WHO international standard for HDV, PEI 7657/12. All HDV-RNA positive samples were genotyped using a next-generation sequencing strategy. Results: Qualitative results indicated a 100% concordance between tests. Quantitative results correlated well, r2 = 0.703 (Vircell-vs-Eurobio), r2 = 0.833 (Vircell-vs-RoboGene), r2 = 0.835 (Robogene-vs-Eurobio). Bias index was 2.083 (Vircell-vs-Eurobio), -1.283 (Vircell-vs-RoboGene), and -3.36 (Robogene-vs-Eurobio). Using the WHO IS, Vircell overestimated the viral load by 0.98 log IU/mL, Eurobio by 1.46 log IU/mL, and RoboGene underestimated it by 0.98 log IU/mL. Fifty-nine samples were successfully genotyped (Genotype 1, n=52; Genotype 5, n=7; Genotype 6, n=1), with similar results for correlation and bias. Conclusion: This study underscores the necessity of using reliable HDV-RNA detection and quantification assays, as evidenced by the high concordance rates in qualitative detection and the observed variability in quantitative results. These findings highlight the importance of consistent assay use in clinical practice to ensure accurate diagnosis and effective treatment monitoring of HDV infection.

[Carrier screening for 223 monogenic diseases in Chinese population: a multi-center study in 33 104 individuals].

OBJECTIVE: To investigate the epidemiological characteristics and mutation spectrum of monogenic diseases in Chinese population through a large-scale, multicenter carrier screening. METHODS: This study was conducted among a total of 33 104 participants (16 610 females) from 12 clinical centers across China.Carrier status for 223 genes was analyzed using high-throughput sequencing and different PCR methods. RESULTS: The overall combined carrier frequency was 55.58% for 197 autosomal genes and 1.84% for 26 X-linked genes in these participants.Among the 16 669 families, 874 at-risk couples (5.24%) were identified.Specifically, 584 couples (3.50%) were at risk for autosomal genes, 306(1.84%) for X-linked genes, and 16 for both autosomal and X-linked genes.The most frequently detected autosomal at-risk genes included GJB2(autosomal recessive deafness type 1A, 393 couples), HBA1/HBA2(α-thalassemia, 36 couples), PAH (phenylketonuria, 14 couples), and SMN1(spinal muscular atrophy, 14 couples).The most frequently detected X-linked at-risk genes were G6PD (G6PD deficiency, 236 couples), DMD (Duchenne muscular dystrophy, 23 couples), and FMR1(fragile X syndrome, 17 couples).After excluding GJB2 c.109G>A, the detection rate of at-risk couples was 3.91%(651/16 669), which was lowered to 1.72%(287/16 669) after further excluding G6PD.The theoretical incidence rate of severe monogenic birth defects was approximately 4.35‰(72.5/16 669).Screening for a battery of the top 22 most frequent genes in the at-risk couples could detect over 95% of at-risk couples, while screening for the top 54 genes further increased the detection rate to over 99%. CONCLUSION: This study reveals the carrier frequencies of 223 monogenic genetic disorders in the Chinese population and provides evidence for carrier screening strategy development and panel design tailored to the Chinese population.In carrier testing, genetic counseling for specific genes or gene variants can be challenging, and the couples need to be informed of these difficulties before testing and provided with options for not screening these genes or gene variants.

The polygenic implication of clopidogrel responsiveness: Insights from platelet reactivity analysis and next-generation sequencing.

Clopidogrel is widely used worldwide as an antiplatelet therapy in patients with acute coronary disease. Genetic factors influence interindividual variability in response. Some studies have explored the polygenic contributions in the drug response, generating pharmacogenomic risk scores (PgxPRS). Importantly, these factors are less explored in underrepresented populations, such as Latin-American countries. Identifying patients at risk of high-on-treatment platelet reactivity (HTPR) is highly valuable in translational medicine. In this study we used a custom next-generation sequencing (NGS) panel composed of 91 single nucleotide polymorphisms (SNPs) and 28 genes related to clopidogrel metabolism, to analyze 70 patients with platelet reactivity values, assessed through closure time (CT). Our results demonstrated the association of SNPs with HTPR and non-HTPR, revealing the strongest associations with rs2286823 (OR: 5,0; 95% CI: 1,02-24,48; p: 0,03), rs2032582 (OR: 4,41; 95% CI: 1,20-16,12; p: 0,019), and rs1045642 (OR: 3,38; 95% CI: 0,96-11,9; p: 0,05). Bivariate regression analysis demonstrated the significant association of several SNPs with the CT value, a "surrogate" biomarker of clopidogrel response. Exploratory results from the LASSO regression model showed a high discriminatory capacity between HTPR and non-HTPR patients (AUC: 0,955), and the generated PgxPRS demonstrated a significant negative association between the risk score, CT value, and the condition of HTPR and non-HTPR. To our knowledge, our study addresses for the first time the analysis of the polygenic contribution in platelet reactivity using NGS and establishes PgxPRS derived from the LASSO model. Our results demonstrate the polygenic implication of clopidogrel response and offer insights applicable to the translational medicine of antiplatelet therapy in an understudied population.

Omada: robust clustering of transcriptomes through multiple testing.

BACKGROUND: Cohort studies increasingly collect biosamples for molecular profiling and are observing molecular heterogeneity. High-throughput RNA sequencing is providing large datasets capable of reflecting disease mechanisms. Clustering approaches have produced a number of tools to help dissect complex heterogeneous datasets, but selecting the appropriate method and parameters to perform exploratory clustering analysis of transcriptomic data requires deep understanding of machine learning and extensive computational experimentation. Tools that assist with such decisions without prior field knowledge are nonexistent. To address this, we have developed Omada, a suite of tools aiming to automate these processes and make robust unsupervised clustering of transcriptomic data more accessible through automated machine learning-based functions. FINDINGS: The efficiency of each tool was tested with 7 datasets characterized by different expression signal strengths to capture a wide spectrum of RNA expression datasets. Our toolkit's decisions reflected the real number of stable partitions in datasets where the subgroups are discernible. Within datasets with less clear biological distinctions, our tools either formed stable subgroups with different expression profiles and robust clinical associations or revealed signs of problematic data such as biased measurements. CONCLUSIONS: In conclusion, Omada successfully automates the robust unsupervised clustering of transcriptomic data, making advanced analysis accessible and reliable even for those without extensive machine learning expertise. Implementation of Omada is available at http://bioconductor.org/packages/omada/.

Molecular profiling of 888 pediatric tumors informs future precision trials and data-sharing initiatives in pediatric cancer.

To inform clinical trial design and real-world precision pediatric oncology practice, we classified diagnoses, assessed the landscape of mutations, and identified genomic variants matching trials in a large unselected institutional cohort of solid tumors patients sequenced at Dana-Farber / Boston Children's Cancer and Blood Disorders Center. Tumors were sequenced with OncoPanel, a targeted next-generation DNA sequencing panel. Diagnoses were classified according to the International Classification of Diseases for Oncology (ICD-O-3.2). Over 6.5 years, 888 pediatric cancer patients with 95 distinct diagnoses had successful tumor sequencing. Overall, 33% (n = 289/888) of patients had at least 1 variant matching a precision oncology trial protocol, and 14% (41/289) were treated with molecularly targeted therapy. This study highlights opportunities to use genomic data from hospital-based sequencing performed either for research or clinical care to inform ongoing and future precision oncology clinical trials. Furthermore, the study results emphasize the importance of data sharing to define the genomic landscape and targeted treatment opportunities for the large group of rare pediatric cancers we encounter in clinical practice.

Establishing Silphids in the invertebrate DNA toolbox: a proof of concept.

Environmental DNA (eDNA) analyses are an increasingly popular tool for assessing biodiversity. eDNA sampling that uses invertebrates, or invertebrate DNA (iDNA), has become a more common method in mammal biodiversity studies where biodiversity is assessed via diet analysis of different coprophagous or hematophagous invertebrates. The carrion feeding family of beetles (Silphidae: Coleoptera, Latreille (1807)), have not yet been established as a viable iDNA source in primary scientific literature, yet could be useful indicators for tracking biodiversity in forested ecosystems. Silphids find carcasses of varying size for both food and reproduction, with some species having host preference for small mammals; therefore, iDNA Silphid studies could potentially target small mammal communities. To establish the first valid use of iDNA methods to detect Silphid diets, we conducted a study with the objective of testing the validity of iDNA methods applied to Silphids using both Sanger sequencing and high throughput Illumina sequencing. Beetles were collected using inexpensive pitfall traps in Alberta, Michigan in 2019 and 2022. We successfully sequenced diet DNA and environmental DNA from externally swabbed Silphid samples and diet DNA from gut dissections, confirming their potential as an iDNA tool in mammalian studies. Our results demonstrate the usefulness of Silphids for iDNA research where we detected species from the genera Anaxyrus, Blarina, Procyon, Condylura, Peromyscus, Canis, and Bos. Our results highlight the potential for Silphid iDNA to be used in future wildlife surveys.