Extend the benchmarking indel set by manual review using the individual cell line sequencing data from the Sequencing Quality Control 2 (SEQC2) project.

Accurate indel calling plays an important role in precision medicine. A benchmarking indel set is essential for thoroughly evaluating the indel calling performance of bioinformatics pipelines. A reference sample with a set of known-positive variants was developed in the FDA-led Sequencing Quality Control Phase 2 (SEQC2) project, but the known indels in the known-positive set were limited. This project sought to provide an enriched set of known indels that would be more translationally relevant by focusing on additional cancer related regions. A thorough manual review process completed by 42 reviewers, two advisors, and a judging panel of three researchers significantly enriched the known indel set by an additional 516 indels. The extended benchmarking indel set has a large range of variant allele frequencies (VAFs), with 87% of them having a VAF below 20% in reference Sample A. The reference Sample A and the indel set can be used for comprehensive benchmarking of indel calling across a wider range of VAF values in the lower range. Indel length was also variable, but the majority were under 10 base pairs (bps). Most of the indels were within coding regions, with the remainder in the gene regulatory regions. Although high confidence can be derived from the robust study design and meticulous human review, this extensive indel set has not undergone orthogonal validation. The extended benchmarking indel set, along with the indels in the previously published known-positive set, was the truth set used to benchmark indel calling pipelines in a community challenge hosted on the precisionFDA platform. This benchmarking indel set and reference samples can be utilized for a comprehensive evaluation of indel calling pipelines. Additionally, the insights and solutions obtained during the manual review process can aid in improving the performance of these pipelines.

Sweat as a source of non-invasive biomarkers for clinical diagnosis: An overview.

Sweat has excellent potential as one of the sources of non-invasive biomarkers for clinical diagnosis. It is relatively easy to collect and process and may contain different disease-specific markers and drug metabolites, making it ideal for various clinical applications. This article discusses the anatomy of sweat glands and their role in sweat production, as well as the history and development of multiple sweat sample collection and analysis techniques. Another primary focus of this article is the application of sweat detection in clinical disease diagnosis and other life scenarios. Finally, the limitations and prospects of sweat analysis are discussed.

Quartet RNA reference materials improve the quality of transcriptomic data through ratio-based profiling.

Certified RNA reference materials are indispensable for assessing the reliability of RNA sequencing to detect intrinsically small biological differences in clinical settings, such as molecular subtyping of diseases. As part of the Quartet Project for quality control and data integration of multi-omics profiling, we established four RNA reference materials derived from immortalized B-lymphoblastoid cell lines from four members of a monozygotic twin family. Additionally, we constructed ratio-based transcriptome-wide reference datasets between two samples, providing cross-platform and cross-laboratory 'ground truth'. Investigation of the intrinsically subtle biological differences among the Quartet samples enables sensitive assessment of cross-batch integration of transcriptomic measurements at the ratio level. The Quartet RNA reference materials, combined with the ratio-based reference datasets, can serve as unique resources for assessing and improving the quality of transcriptomic data in clinical and biological settings.

Multi-omics data integration using ratio-based quantitative profiling with Quartet reference materials.

Characterization and integration of the genome, epigenome, transcriptome, proteome and metabolome of different datasets is difficult owing to a lack of ground truth. Here we develop and characterize suites of publicly available multi-omics reference materials of matched DNA, RNA, protein and metabolites derived from immortalized cell lines from a family quartet of parents and monozygotic twin daughters. These references provide built-in truth defined by relationships among the family members and the information flow from DNA to RNA to protein. We demonstrate how using a ratio-based profiling approach that scales the absolute feature values of a study sample relative to those of a concurrently measured common reference sample produces reproducible and comparable data suitable for integration across batches, labs, platforms and omics types. Our study identifies reference-free 'absolute' feature quantification as the root cause of irreproducibility in multi-omics measurement and data integration and establishes the advantages of ratio-based multi-omics profiling with common reference materials.

Correcting batch effects in large-scale multiomics studies using a reference-material-based ratio method.

BACKGROUND: Batch effects are notoriously common technical variations in multiomics data and may result in misleading outcomes if uncorrected or over-corrected. A plethora of batch-effect correction algorithms are proposed to facilitate data integration. However, their respective advantages and limitations are not adequately assessed in terms of omics types, the performance metrics, and the application scenarios. RESULTS: As part of the Quartet Project for quality control and data integration of multiomics profiling, we comprehensively assess the performance of seven batch effect correction algorithms based on different performance metrics of clinical relevance, i.e., the accuracy of identifying differentially expressed features, the robustness of predictive models, and the ability of accurately clustering cross-batch samples into their own donors. The ratio-based method, i.e., by scaling absolute feature values of study samples relative to those of concurrently profiled reference material(s), is found to be much more effective and broadly applicable than others, especially when batch effects are completely confounded with biological factors of study interests. We further provide practical guidelines for implementing the ratio based approach in increasingly large-scale multiomics studies. CONCLUSIONS: Multiomics measurements are prone to batch effects, which can be effectively corrected using ratio-based scaling of the multiomics data. Our study lays the foundation for eliminating batch effects at a ratio scale.

Overexpression of CD99 is associated with tumor adaptiveness and indicates the tumor recurrence and therapeutic responses in gliomas.

Glioma undergoes adaptive changes, leading to poor prognosis and resistance to treatment. CD99 influences the migration and invasion of glioma cells and plays an oncogene role. However, whether CD99 can affect the adaptiveness of gliomas is still lacking in research, making its clinical value underestimated. Here, we enrolled our in-house and public multiomics datasets for bioinformatic analysis and conducted immunohistochemistry staining to investigate the role of CD99 in glioma adaptive response and its clinical implications. CD99 is expressed in more adaptative glioma subtypes and cell states. Under hypoxic conditions, CD99 is upregulated in glioma cells and is associated with angiogenesis and metabolic adaptations. Gliomas with over-expressed CD99 also increased the immunosuppressive tumor-associated macrophages. The relevance with tumor adaptiveness of CD99 presented clinical significance. We discovered that CD99 overexpression is associated with short-time recurrence and validated its prognostic value. Additionally, Glioma patients with high expression of CD99 were resistant to chemotherapy and radiotherapy. The CD99 expression was also related to anti-angiogenic and immune checkpoint inhibitor therapy response. Inhibitors of the PI3K-AKT pathway have therapeutic potential against CD99-overexpressing gliomas. Our study identified CD99 as a biomarker characterizing the adaptive response in glioma. Gliomas with high CD99 expression are highly tolerant to stress conditions such as hypoxia and antitumor immunity, making treatment responses dimmer and tumor progression. Therefore, for patients with CD99-overexpressing gliomas, tumor adaptiveness should be fully considered during treatment to avoid drug resistance, and closer clinical monitoring should be carried out to improve the prognosis.

Type I interferon score is associated with the severity and poor prognosis in anti-MDA5 antibody-positive dermatomyositis patients.

Objectives: To investigate the clinical significance of the interferon (IFN) score, especially the IFN-I score, in patients with anti-melanoma differentiation-associated gene 5 (MDA5) antibody-positive dermatomyositis (anti-MDA5+ DM). Methods: We enrolled 262 patients with different autoimmune diseases, including idiopathic inflammatory myopathy, systemic lupus erythematosus, rheumatoid arthritis, adult-onset Still's disease, and Sjögren's syndrome, as well as 58 healthy controls. Multiplex quantitative real-time polymerase chain reaction (RT-qPCR) using four TaqMan probes was used to evaluate type I IFN-stimulated genes (IFI44 and MX1), one type II IFN-stimulated gene (IRF1), and one internal control gene (HRPT1), which were used to determine the IFN-I score. The clinical features and disease activity index were compared between the high and low IFN-I score groups in 61 patients with anti-MDA5+ DM. The associations between laboratory findings and the predictive value of the baseline IFN-I score for mortality were analyzed. Results: The IFN score was significantly higher in patients with anti-MDA5+ DM than in healthy controls. The IFN-I score was positively correlated with the serum IFN-α concentration, ferritin concentration, and Myositis Disease Activity Assessment Visual Analogue Scale (MYOACT) score. Compared with patients with a low IFN-I score, patients with a high IFN-I score showed a higher MYOACT score, C-reactive protein concentration, aspartate transaminase concentration, ferritin concentration, plasma cell percentage, and CD3+ T-cell percentage, as well as lower lymphocyte, natural killer cell, and monocyte counts. The 3-month survival rate was significantly lower in patients with an IFN-I score of >4.9 than in those with an IFN-I score of ≤4.9 (72.9% vs. 100%, respectively; P = 0.044). Conclusion: The IFN score, especially the IFN-I score, measured by multiplex RT-qPCR is a valuable tool to monitor disease activity and predict mortality in patients with anti-MDA5+ DM.

Activation of perfluoroalkyl iodides by anions: extending the scope of halogen bond activation to C(sp3)-H amidation, C(sp2)-H iodination, and perfluoroalkylation reactions.

A simple, efficient, and convenient activation of perfluoroalkyl iodides by tBuONa or KOH, without expensive photo- or transition metal catalysts, allows the promotion of versatile α-sp3 C-H amidation reactions of alkyl ethers and benzylic hydrocarbons, C-H iodination of heteroaryl compounds, and perfluoroalkylations of electron-rich π bonds. Mechanistic studies show that these novel protocols are based on the halogen bond interaction between perfluoroalkyl iodides and tBuONa or KOH, which promote homolysis of perfluoroalkyl iodides under mild conditions.

Robust Covalent Aptamer Strategy Enables Sensitive Detection and Enhanced Inhibition of SARS-CoV-2 Proteins.

Aptamer-based detection and therapy have made substantial progress with cost control and easy modification. However, the conformation lability of an aptamer typically causes the dissociation of aptamer-target complexes during harsh washes and other environmental stresses, resulting in only moderate detection sensitivity and a decreasing therapeutic effect. Herein, we report a robust covalent aptamer strategy to sensitively detect nucleocapsid protein and potently neutralize spike protein receptor binding domain (RBD), two of the most important proteins of SARS-CoV-2, after testing different cross-link electrophilic groups via integrating the specificity and efficiency. Covalent aptamers can specifically convert aptamer-protein complexes from the dynamic equilibrium state to stable and irreversible covalent complexes even in harsh environments. Covalent aptamer-based ELISA detection of nucleocapsid protein can surpass the gold standard, antibody-based sandwich ELISA. Further, covalent aptamer performs enhanced functional inhibition to RBD protein even in a blood vessel-mimicking flowing circulation system. The robust covalent aptamer-based strategy is expected to inspire more applications in accurate molecular modification, disease biomarker discovery, and other theranostic fields.

A comprehensive genomic and transcriptomic dataset of triple-negative breast cancers.

Molecular subtyping of triple-negative breast cancer (TNBC) is essential for understanding the mechanisms and discovering actionable targets of this highly heterogeneous type of breast cancer. We previously performed a large single-center and multiomics study consisting of genomics, transcriptomics, and clinical information from 465 patients with primary TNBC. To facilitate reusing this unique dataset, we provided a detailed description of the dataset with special attention to data quality in this study. The multiomics data were generally of high quality, but a few sequencing data had quality issues and should be noted in subsequent data reuse. Furthermore, we reconduct data analyses with updated pipelines and the updated version of the human reference genome from hg19 to hg38. The updated profiles were in good concordance with those previously published in terms of gene quantification, variant calling, and copy number alteration. Additionally, we developed a user-friendly web-based database for convenient access and interactive exploration of the dataset. Our work will facilitate reusing the dataset, maximize the values of data and further accelerate cancer research.

GlioMarker: An integrated database for knowledge exploration of diagnostic biomarkers in gliomas.

Gliomas are the most frequent malignant and aggressive tumors in the central nervous system. Early and effective diagnosis of glioma using diagnostic biomarkers can prolong patients' lives and aid in the development of new personalized treatments. Therefore, a thorough and comprehensive understanding of the diagnostic biomarkers in gliomas is of great significance. To this end, we developed the integrated and web-based database GlioMarker (http://gliomarker.prophetdb.org/), the first comprehensive database for knowledge exploration of glioma diagnostic biomarkers. In GlioMarker, accurate information on 406 glioma diagnostic biomarkers from 1559 publications was manually extracted, including biomarker descriptions, clinical information, associated literature, experimental records, associated diseases, statistical indicators, etc. Importantly, we integrated many external resources to provide clinicians and researchers with the capability to further explore knowledge on these diagnostic biomarkers based on three aspects. (1) Obtain more ontology annotations of the biomarker. (2) Identify the relationship between any two or more components of diseases, drugs, genes, and variants to explore the knowledge related to precision medicine. (3) Explore the clinical application value of a specific diagnostic biomarker through online analysis of genomic and expression data from glioma cohort studies. GlioMarker provides a powerful, practical, and user-friendly web-based tool that may serve as a specialized platform for clinicians and researchers by providing rapid and comprehensive knowledge of glioma diagnostic biomarkers to subsequently facilitates high-quality research and applications.

Comprehensive microRNA-seq transcriptomic profiling across 11 organs, 4 ages, and 2 sexes of Fischer 344 rats.

Rat is one of the most widely-used models in chemical safety evaluation and biomedical research. However, the knowledge about its microRNA (miRNA) expression patterns across multiple organs and various developmental stages is still limited. Here, we constructed a comprehensive rat miRNA expression BodyMap using a diverse collection of 320 RNA samples from 11 organs of both sexes of juvenile, adolescent, adult and aged Fischer 344 rats with four biological replicates per group. Following the Illumina TruSeq Small RNA protocol, an average of 5.1 million 50 bp single-end reads was generated per sample, yielding a total of 1.6 billion reads. The quality of the resulting miRNA-seq data was deemed to be high from raw sequences, mapped sequences, and biological reproducibility. Importantly, aliquots of the same RNA samples have previously been used to construct the mRNA BodyMap. The currently presented miRNA-seq dataset along with the existing mRNA-seq dataset from the same RNA samples provides a unique resource for studying the expression characteristics of existing and novel miRNAs, and for integrative analysis of miRNA-mRNA interactions, thereby facilitating better utilization of rats for biomarker discovery.

Multiplexed Signal Ion Emission Reactive Release Amplification (SIERRA) Assay for the Culture-Free Detection of Gram-Negative and Gram-Positive Bacteria and Antimicrobial Resistance Genes.

The global prevalence of antibiotic-resistant bacteria has increased the risk of dangerous infections, requiring rapid diagnosis and treatment. The standard method for diagnosis of bacterial infections remains dependent on slow culture-based methods, carried out in central laboratories, not easily extensible to rapid identification of organisms, and thus not optimal for timely treatments at the point-of-care (POC). Here, we demonstrate rapid detection of bacteria by combining electrochemical immunoassays (EC-IA) for pathogen identification with confirmatory quantitative mass spectral immunoassays (MS-IA) based on signal ion emission reactive release amplification (SIERRA) nanoparticles with unique mass labels. This diagnostic method uses compatible reagents for all involved assays and standard fluidics for automatic sample preparation at POC. EC-IA, based on alkaline phosphatase-conjugated pathogen-specific antibodies, quantified down to 104 bacteria per sample when testing Staphylococcus aureus, Escherichia coli, and Pseudomonas aeruginosa lysates. EC-IA quantitation was also obtained for wound samples. The MS-IA using nanoparticles against S. aureus, E. coli, Klebsiella pneumoniae, and P. aeruginosa allowed selective quantitation of ∼105 bacteria per sample. This method preserves bacterial cells allowing extraction and amplification of 16S ribosomal RNA genes and antibiotic resistance genes, as was demonstrated through identification and quantitation of two strains of E. coli, resistant and nonresistant due to ß-lactamase cefotaximase genes. Finally, the combined immunoassays were compared against culture using remnant deidentified patient urine samples. The sensitivities for these immunoassays were 83, 95, and 92% for the prediction of S. aureus, P. aeruginosa, and E. coli or K. pneumoniae positive culture, respectively, while specificities were 85, 92, and 97%. The diagnostic platform presented here with fluidics and combined immunoassays allows for pathogen isolation within 5 min and identification in as little as 15 min to 1 h, to help guide the decision for additional testing, optimally only on positive samples, such as multiplexed or resistance gene assays (6 h).

A Survey of Optimization Methods From a Machine Learning Perspective.

Machine learning develops rapidly, which has made many theoretical breakthroughs and is widely applied in various fields. Optimization, as an important part of machine learning, has attracted much attention of researchers. With the exponential growth of data amount and the increase of model complexity, optimization methods in machine learning face more and more challenges. A lot of work on solving optimization problems or improving optimization methods in machine learning has been proposed successively. The systematic retrospect and summary of the optimization methods from the perspective of machine learning are of great significance, which can offer guidance for both developments of optimization and machine learning research. In this article, we first describe the optimization problems in machine learning. Then, we introduce the principles and progresses of commonly used optimization methods. Finally, we explore and give some challenges and open problems for the optimization in machine learning.

Enumeration of Rare Cells in Whole Blood by Signal Ion Emission Reactive Release Amplification with Same-Sample RNA Analysis.

Herein is presented a platform capable of detecting less than 30 cells from a whole blood sample by size-exclusion filtration, microfluidic sample handling, and mass spectrometric detection through signal ion emission reactive release amplification (SIERRA). This represents an approximate 10-fold improvement in detection limits from previous work. Detection by SIERRA is accomplished through the use of novel nanoparticle reagents coupled with custom fluidic fixtures for precise sample transfer. Sample processing is performed in standardized 96-well microtiter plates with commonly available laboratory instrumentation to facilitate assay automation. The detection system is easily amenable to multiplex detection, and compatibility with PCR-based gene assays is demonstrated.

A systematic review of questionnaires about patient's values and preferences in clinical practice guidelines.

OBJECTIVE: We conducted a systematic review to evaluate questionnaires about patient's values and preferences to provide information on the most appropriate questionnaires to be used when developing clinical practice guidelines. METHODS: A systematic literature search of the Cochrane Library, MEDLINE, Embase, Web of Science, Chinese Biomedical Database, China National Knowledge Infrastructure, and the Wanfang Database was performed to identify studies on questionnaires evaluating patient's values and preferences. The articles that used fully structured questionnaires or scales with standardized questions and answer options were included. We assessed the questionnaires' construction and content with a psychometric methodology and summarized the domains and items about patient's preferences and values. RESULTS: A total of 7,008 records were retrieved by the search strategy and scanned, and 20 articles were finally included. Of these, 10 (50%) articles described the process of item generation and only four questionnaires (20%, 4/20) mentioned the pilot testing. Regarding "validity", seven questionnaires (35%, 7/20) assessed validity and only one (5%, 1/20) questionnaire assessed internal consistency, with Cornbrash's α values of 0.74-0.87. For "acceptability", the time to complete the questionnaires ranged from 10 to 30 minutes and only nine studies (45%, 9/20) reported the response rates. In addition, the results of domains and items about patient's preferences and values showed that the "effectiveness" domain was the most considered item in the patient's value questionnaire followed by "safety", "prognosis", and others, whereas the least considered domain was "physician's experience". CONCLUSION: Only a few studies have developed questionnaires with rigorous psychometric methods to measure patient's preferences and values. Currently, still there is no valid or reliable questionnaire for patient's preferences and values for use when developing clinical practice guidelines. Further study should be conducted to develop standardized instruments to measure patient's preferences and values. This study provides the domains and items that may be used in formulating questionnaires about patient's preferences and values.

Triplex-quadruplex structural scaffold: a new binding structure of aptamer.

Apart from the canonical Watson-Crick duplex, nucleic acids can often form other structures, e.g. G-quadruplex and triplex. These structures give nucleic acid additional functions besides coding for genetic information. Aptamers are one type of functional nucleic acids that bind to specific targets with high selectivity and affinity by folding into special tertiary structures. Despite the fact that numerous aptamers have been reported, only a few different types of aptamer structures are identified. Here we report a novel triplex-quadruplex hybrid scaffold formed by a codeine binding aptamer (CBA). CBA and its derivatives are G-rich DNA sequences. Codeine binding can induce the formation of a complex structure for this aptamer containing a G-quadruplex and a G·GC triplex, while codeine is located at the junction of the triplex and quadruplex. When split CBA into two moieties, codeine does not bind either moieties individually, but can bind them together by inducing the formation of the triplex-quadruplex scaffold. This structure formation induced by codeine binding is shown to inhibit polymerase reaction, which shows a potential application of the aptamer sequence in gene regulations.

A Cyanine Dye to Probe Mitophagy: Simultaneous Detection of Mitochondria and Autolysosomes in Live Cells.

Mitophagy is a process in which cells remove dysfunctional mitochondria and recycle their constituents in a lysosome-dependent manner. To probe this process, two different fluorescent dyes specific for mitochondria and lysosomes, respectively, are often used in combination. However, current fluorescent dyes for lysosomes cannot distinguish mitochondria-containing autolysosomes from other lysosomes. Therefore, we herein report a cyanine dye, HQO, which can simultaneously probe mitochondria and autolysosomes in live cells by exhibiting different fluorescence properties. HQO selectively accumulates in mitochondria but then transforms to the protonated HQOH(+) form with the decrease of pH when dysfunctional mitochondria evolve into autolysosomes. Since HQO and HQOH(+) exhibit different absorption and emission with Ex/Em at 530/650 and 710/750 nm, respectively, in a low polarity environment, such as that found in micelles, they are uniquely suited to monitor mitophagy with the ability to distinguish autolysosomes from other lysosomes.

Activity Enhancement of G-Quadruplex/Hemin DNAzyme by Flanking d(CCC).

G-quadruplex (G4)/hemin DNAzymes have been extensively applied in bioanalysis and molecular devices. However, their catalytic activity is still much lower than that of proteinous enzymes. The G4/hemin DNAzyme activity is correlated with the G4 conformations and the solution conditions. However, little is known about the effect of the flanking sequences on the activity, though they are important parts of G4s. Here, we report sequences containing d(CCC), flanked on both ends of the G4-core sequences remarkably enhance their DNAzyme activity. By using circular dichroism and UV-visible spectroscopy, the d(CCC) flanking sequences were demonstrated to improve the hemin binding affinity to G4s instead of increasing the parallel G4 formation, which might explain the enhanced DNAzyme activity. Meanwhile, the increased hemin binding ability promoted the degradation of hemin within the DNAzyme by H2O2. Furthermore, the DNAzyme with d(CCC) flanking sequences showed strong tolerance to pH value changes, which makes it more suitable for applications requiring wide pH conditions. The results highlight the influence of the flanking sequences on the DNAzyme activity and provide insightful information for the design of highly active DNAzymes.