CellSTAR: a comprehensive resource for single-cell transcriptomic annotation.

Large-scale studies of single-cell sequencing and biological experiments have successfully revealed expression patterns that distinguish different cell types in tissues, emphasizing the importance of studying cellular heterogeneity and accurately annotating cell types. Analysis of gene expression profiles in these experiments provides two essential types of data for cell type annotation: annotated references and canonical markers. In this study, the first comprehensive database of single-cell transcriptomic annotation resource (CellSTAR) was thus developed. It is unique in (a) offering the comprehensive expertly annotated reference data for annotating hundreds of cell types for the first time and (b) enabling the collective consideration of reference data and marker genes by incorporating tens of thousands of markers. Given its unique features, CellSTAR is expected to attract broad research interests from the technological innovations in single-cell transcriptomics, the studies of cellular heterogeneity & dynamics, and so on. It is now publicly accessible without any login requirement at: https://idrblab.org/cellstar.

Label-free proteome quantification and evaluation.

The label-free quantification (LFQ) has emerged as an exceptional technique in proteomics owing to its broad proteome coverage, great dynamic ranges and enhanced analytical reproducibility. Due to the extreme difficulty lying in an in-depth quantification, the LFQ chains incorporating a variety of transformation, pretreatment and imputation methods are required and constructed. However, it remains challenging to determine the well-performing chain, owing to its strong dependence on the studied data and the diverse possibility of integrated chains. In this study, an R package EVALFQ was therefore constructed to enable a performance evaluation on >3000 LFQ chains. This package is unique in (a) automatically evaluating the performance using multiple criteria, (b) exploring the quantification accuracy based on spiking proteins and (c) discovering the well-performing chains by comprehensive assessment. All in all, because of its superiority in assessing from multiple perspectives and scanning among over 3000 chains, this package is expected to attract broad interests from the fields of proteomic quantification. The package is available at https://github.com/idrblab/EVALFQ.

FASN-mediated fatty acid biosynthesis remodels immune environment in Clonorchis sinensis infection-related intrahepatic cholangiocarcinoma.

BACKGROUND & AIMS: Intrahepatic cholangiocarcinoma (iCCA) is the second most common primary liver cancer and is highly lethal. Clonorchis sinensis (C. sinensis) infection is an important risk factor for iCCA. Here we investigated the clinical impact and underlying molecular characteristics of C. sinensis infection-related iCCA. METHODS: We performed single-cell RNA sequencing, whole-exome sequencing, RNA sequencing, metabolomics and spatial transcriptomics in 251 patients with iCCA from three medical centers. Alterations in metabolism and the immune microenvironment of C. sinensis-related iCCAs were validated through an in vitro co-culture system and in a mouse model of iCCA. RESULTS: We revealed that C. sinensis infection was significantly associated with iCCA patients' overall survival and response to immunotherapy. Fatty acid biosynthesis and the expression of fatty acid synthase (FASN), a key enzyme catalyzing long-chain fatty acid synthesis, were significantly enriched in C. sinensis-related iCCAs. iCCA cell lines treated with excretory/secretory products of C. sinensis displayed elevated FASN and free fatty acids. The metabolic alteration of tumor cells was closely correlated with the enrichment of tumor-associated macrophage (TAM)-like macrophages and the impaired function of T cells, which led to formation of an immunosuppressive microenvironment and tumor progression. Spatial transcriptomics analysis revealed that malignant cells were in closer juxtaposition with TAM-like macrophages in C. sinensis-related iCCAs than non-C. sinensis-related iCCAs. Importantly, treatment with a FASN inhibitor significantly reversed the immunosuppressive microenvironment and enhanced anti-PD-1 efficacy in iCCA mouse models treated with excretory/secretory products from C. sinensis. CONCLUSIONS: We provide novel insights into metabolic alterations and the immune microenvironment in C. sinensis infection-related iCCAs. We also demonstrate that the combination of a FASN inhibitor with immunotherapy could be a promising strategy for the treatment of C. sinensis-related iCCAs. IMPACT AND IMPLICATIONS: Clonorchis sinensis (C. sinensis)-infected patients with intrahepatic cholangiocarcinoma (iCCA) have a worse prognosis and response to immunotherapy than non-C. sinensis-infected patients with iCCA. The underlying molecular characteristics of C. sinensis infection-related iCCAs remain unclear. Herein, we demonstrate that upregulation of FASN (fatty acid synthase) and free fatty acids in C. sinensis-related iCCAs leads to formation of an immunosuppressive microenvironment and tumor progression. Thus, administration of FASN inhibitors could significantly reverse the immunosuppressive microenvironment and further enhance the efficacy of anti-PD-1 against C. sinensis-related iCCAs.

MultiClassMetabo: A Superior Classification Model Constructed Using Metabolic Markers in Multiclass Metabolomics.

Multiclass metabolomics has become a popular technique for revealing the mechanisms underlying certain physiological processes, different tumor types, or different therapeutic responses. In multiclass metabolomics, it is highly important to uncover the underlying biological information on biosamples by identifying the metabolic markers with the most associations and classifying the different sample classes. The classification problem of multiclass metabolomics is more difficult than that of the binary problem. To date, various methods exist for constructing classification models and identifying metabolic markers consisting of well-established techniques and newly emerging machine learning algorithms. However, how to construct a superior classification model using these methods remains unclear for a given multiclass metabolomic data set. Herein, MultiClassMetabo has been developed for constructing a superior classification model using metabolic markers identified in multiclass metabolomics. MultiClassMetabo can enable online services, including (a) identifying metabolic markers by marker identification methods, (b) constructing classification models by classification methods, and (c) performing a comprehensive assessment from multiple perspectives to construct a superior classification model for multiclass metabolomics. In summary, MultiClassMetabo is distinguished for its capability to construct a superior classification model using the most appropriate method through a comprehensive assessment, which makes it an important complement to other available tools in multiclass metabolomics. MultiClassMetabo can be accessed at http://idrblab.cn/multiclassmetabo/.

Strategy for Identifying a Robust Metabolomic Signature Reveals the Altered Lipid Metabolism in Pituitary Adenoma.

Despite the well-established connection between systematic metabolic abnormalities and the pathophysiology of pituitary adenoma (PA), current metabolomic studies have reported an extremely limited number of metabolites associated with PA. Moreover, there was very little consistency in the identified metabolite signatures, resulting in a lack of robust metabolic biomarkers for the diagnosis and treatment of PA. Herein, we performed a global untargeted plasma metabolomic profiling on PA and identified a highly robust metabolomic signature based on a strategy. Specifically, this strategy is unique in (1) integrating repeated random sampling and a consensus evaluation-based feature selection algorithm and (2) evaluating the consistency of metabolomic signatures among different sample groups. This strategy demonstrated superior robustness and stronger discriminative ability compared with that of other feature selection methods including Student's t-test, partial least-squares-discriminant analysis, support vector machine recursive feature elimination, and random forest recursive feature elimination. More importantly, a highly robust metabolomic signature comprising 45 PA-specific differential metabolites was identified. Moreover, metabolite set enrichment analysis of these potential metabolic biomarkers revealed altered lipid metabolism in PA. In conclusion, our findings contribute to a better understanding of the metabolic changes in PA and may have implications for the development of diagnostic and therapeutic approaches targeting lipid metabolism in PA. We believe that the proposed strategy serves as a valuable tool for screening robust, discriminating metabolic features in the field of metabolomics.

LargeMetabo: an out-of-the-box tool for processing and analyzing large-scale metabolomic data.

Large-scale metabolomics is a powerful technique that has attracted widespread attention in biomedical studies focused on identifying biomarkers and interpreting the mechanisms of complex diseases. Despite a rapid increase in the number of large-scale metabolomic studies, the analysis of metabolomic data remains a key challenge. Specifically, diverse unwanted variations and batch effects in processing many samples have a substantial impact on identifying true biological markers, and it is a daunting challenge to annotate a plethora of peaks as metabolites in untargeted mass spectrometry-based metabolomics. Therefore, the development of an out-of-the-box tool is urgently needed to realize data integration and to accurately annotate metabolites with enhanced functions. In this study, the LargeMetabo package based on R code was developed for processing and analyzing large-scale metabolomic data. This package is unique because it is capable of (1) integrating multiple analytical experiments to effectively boost the power of statistical analysis; (2) selecting the appropriate biomarker identification method by intelligent assessment for large-scale metabolic data and (3) providing metabolite annotation and enrichment analysis based on an enhanced metabolite database. The LargeMetabo package can facilitate flexibility and reproducibility in large-scale metabolomics. The package is freely available from https://github.com/LargeMetabo/LargeMetabo.

ConSIG: consistent discovery of molecular signature from OMIC data.

The discovery of proper molecular signature from OMIC data is indispensable for determining biological state, physiological condition, disease etiology, and therapeutic response. However, the identified signature is reported to be highly inconsistent, and there is little overlap among the signatures identified from different biological datasets. Such inconsistency raises doubts about the reliability of reported signatures and significantly hampers its biological and clinical applications. Herein, an online tool, ConSIG, was constructed to realize consistent discovery of gene/protein signature from any uploaded transcriptomic/proteomic data. This tool is unique in a) integrating a novel strategy capable of significantly enhancing the consistency of signature discovery, b) determining the optimal signature by collective assessment, and c) confirming the biological relevance by enriching the disease/gene ontology. With the increasingly accumulated concerns about signature consistency and biological relevance, this online tool is expected to be used as an essential complement to other existing tools for OMIC-based signature discovery. ConSIG is freely accessible to all users without login requirement at https://idrblab.org/consig/.

Metabolome and Genome Analysis of a Novel Endophytic Fungus Aureobasidium pullulans KB3: Discovery of Polyketones and Polyketone Biosynthesis Pathway.

Endophytic fungi associated with plants may contain undiscovered bioactive compounds. Under standard laboratory conditions, most undiscovered compounds are inactive, whereas their production could be stimulated under different cultivation conditions. In this study, six endophytic fungi were isolated from the bark of Koelreuteria paniculata in Quancheng Park, Jinan City, Shandong Province, one of which was identified as a new subspecies of Aureobasidium pullulans, named A. pullulans KB3. Additionally, metabolomic tools were used to screen suitable media for A. pullulans KB3 fermentation, and the results showed that peptone dextrose medium (PDM) was more beneficial to culture A. pullulans KB3 for isolation of novel compounds. Sphaerolone, a polyketone compound, was initially isolated from A. pullulans KB3 via scaled up fermentation utilizing PDM. Additionally, the whole-genome DNA of A. pullulans KB3 was sequenced to facilitate compound isolation and identify the biosynthesis gene clusters (BGCs). This study reports the multi-omics (metabolome and genome) analysis of A. pullulans KB3, laying the foundation for discovering novel compounds of silent BGCs and identifying their biosynthesis pathway.

Critical Assessment of the Biomarker Discovery and Classification Methods for Multiclass Metabolomics.

Multiclass metabolomics has been widely applied in clinical practice to understand pathophysiological processes involved in disease progression and diagnostic biomarkers of various disorders. In contrast to the binary problem, the multiclass classification problem is more difficult in terms of obtaining reliable and stable results due to the increase in the complexity of determining exact class decision boundaries. In particular, methods of biomarker discovery and classification have a significant effect on the multiclass model because different methods with significantly varied theories produce conflicting results even for the same dataset. However, a systematic assessment for selecting the most appropriate methods of biomarker discovery and classification for multiclass metabolomics is still lacking. Therefore, a comprehensive assessment is essential to measure the suitability of methods in multiclass classification models from multiple perspectives. In this study, five biomarker discovery methods and nine classification methods were assessed based on four benchmark datasets of multiclass metabolomics. The performance assessment of the biomarker discovery and classification methods was performed using three evaluation criteria: assessment a (cluster analysis of sample grouping), assessment b (biomarker consistency in multiple subgroups), and assessment c (accuracy in the classification model). As a result, 13 combining strategies with superior performance were selected under multiple criteria based on these benchmark datasets. In conclusion, superior strategies that performed consistently well are suggested for the discovery of biomarkers and the construction of a classification model for multiclass metabolomics.

Evaluating Machine Learning Methods of Analyzing Multiclass Metabolomics.

Multiclass metabolomic studies have become popular for revealing the differences in multiple stages of complex diseases, various lifestyles, or the effects of specific treatments. In multiclass metabolomics, there are multiple data manipulation steps for analyzing raw data, which consist of data filtering, the imputation of missing values, data normalization, marker identification, sample separation, classification, and so on. In each step, several to dozens of machine learning methods can be chosen for the given data set, with potentially hundreds or thousands of method combinations in the whole data processing chain. Therefore, a clear understanding of these machine learning methods is helpful for selecting an appropriate method combination for obtaining stable and reliable analytical results of specific data. However, there has rarely been an overall introduction or evaluation of these methods based on multiclass metabolomic data. Herein, detailed descriptions of these machine learning methods in multiple data manipulation steps are reviewed. Moreover, an assessment of these methods was performed using a benchmark data set for multiclass metabolomics. First, 12 imputation methods for imputing missing values were evaluated based on the PSS (Procrustes statistical shape analysis) and NRMSE (normalized root-mean-square error) values. Second, 17 normalization methods for processing multiclass metabolomic data were evaluated by applying the PMAD (pooled median absolute deviation) value. Third, different methods of identifying markers of multiclass metabolomics were evaluated based on the CWrel (relative weighted consistency) value. Fourth, nine classification methods for constructing multiclass models were assessed using the AUC (area under the curve) value. Performance evaluations of machine learning methods are highly recommended to select the most appropriate method combination before performing the final analysis of the given data. Overall, detailed descriptions and evaluation of various machine learning methods are expected to improve analyses of multiclass metabolomic data.

Implant survival rate and marginal bone loss with the all-on-4 immediate-loading strategy: A clinical retrospective study with 1 to 4 years of follow-up.

STATEMENT OF PROBLEM: Assessing peri-implant marginal bone loss (MBL) and its risk factors with cone beam computed tomography (CBCT) may clarify the risk factors for the all-on-4 (5 or 6) strategy and further improve its survival rate. PURPOSE: The purpose of this retrospective clinical study was to evaluate the implant survival rate, MBL, and associated risk factors of all-on-4 (5 or 6) prostheses after 1 to 4 years of follow-up with CBCT. MATERIAL AND METHODS: A total of 56 participants rehabilitated with 325 implants by using the all-on-4 (5 or 6) concept between October 2015 and December 2019 were included. Outcome measures were cumulative implant survival (life-table analysis) and MBL. Four CBCT scans, a scan immediately after surgery (T0), a scan 1 year after surgery (T1), a scan 2 years after surgery (T2), and a scan 3 to 4 years after treatment (T3), were obtained to evaluate the MBL. The Pearson correlation coefficient analysis and linear mixed models were performed to assess the potential risk factors for MBL (α=.05). RESULTS: The implant survival rate was 99.38%, and the prosthesis survival rate was 100%. The reductions in the vertical buccal bone height (△VBBH) were 0.74 ±0.10 mm (T0-T1), 0.37 ±0.12 mm (T1-T2), and 0.15 ±0.14 mm (T2-T3). Except for T2-T3, the △VBBH showed a significant difference at T0-T1 and T1-T2 (P≤.05). The alterations in vertical mesial bone height (VMBH), vertical distal bone height (VDBH), and vertical lingual bone height (VLBH) were similar to the trend observed in VBBH. The △VBBH (T0-T3) was negatively correlated with the horizontal buccal bone thickness (HBBT) (T0) (r=-.394, P<.001). Linear mixed models revealed that factors such as smoking (P=.001), mandible implant site (P<.001), immediate implant (P=.026), tilted implant (P<.001), female sex (P=.003), systemic disease (P=.025), and bruxism (P=.022) negatively affected MBL. The cantilever length (CL) also had a negative effect on MBL around the implants at the distal extension (P<.001). CONCLUSIONS: The high implant and prosthesis survival rates and low MBL confirmed the predictability of the all-on-4 (5 or 6) concept. Smoking, mandible implant site, systemic disease, bruxism, female sex, immediate implant, tilted implant, and CL were identified as potential risk factors for MBL.

A novel bioinformatics approach to identify the consistently well-performing normalization strategy for current metabolomic studies.

Unwanted experimental/biological variation and technical error are frequently encountered in current metabolomics, which requires the employment of normalization methods for removing undesired data fluctuations. To ensure the 'thorough' removal of unwanted variations, the collective consideration of multiple criteria ('intragroup variation', 'marker stability' and 'classification capability') was essential. However, due to the limited number of available normalization methods, it is extremely challenging to discover the appropriate one that can meet all these criteria. Herein, a novel approach was proposed to discover the normalization strategies that are consistently well performing (CWP) under all criteria. Based on various benchmarks, all normalization methods popular in current metabolomics were 'first' discovered to be non-CWP. 'Then', 21 new strategies that combined the 'sample'-based method with the 'metabolite'-based one were found to be CWP. 'Finally', a variety of currently available methods (such as cubic splines, range scaling, level scaling, EigenMS, cyclic loess and mean) were identified to be CWP when combining with other normalization. In conclusion, this study not only discovered several strategies that performed consistently well under all criteria, but also proposed a novel approach that could ensure the identification of CWP strategies for future biological problems.

ANPELA: analysis and performance assessment of the label-free quantification workflow for metaproteomic studies.

Label-free quantification (LFQ) with a specific and sequentially integrated workflow of acquisition technique, quantification tool and processing method has emerged as the popular technique employed in metaproteomic research to provide a comprehensive landscape of the adaptive response of microbes to external stimuli and their interactions with other organisms or host cells. The performance of a specific LFQ workflow is highly dependent on the studied data. Hence, it is essential to discover the most appropriate one for a specific data set. However, it is challenging to perform such discovery due to the large number of possible workflows and the multifaceted nature of the evaluation criteria. Herein, a web server ANPELA (https://idrblab.org/anpela/) was developed and validated as the first tool enabling performance assessment of whole LFQ workflow (collective assessment by five well-established criteria with distinct underlying theories), and it enabled the identification of the optimal LFQ workflow(s) by a comprehensive performance ranking. ANPELA not only automatically detects the diverse formats of data generated by all quantification tools but also provides the most complete set of processing methods among the available web servers and stand-alone tools. Systematic validation using metaproteomic benchmarks revealed ANPELA's capabilities in 1 discovering well-performing workflow(s), (2) enabling assessment from multiple perspectives and (3) validating LFQ accuracy using spiked proteins. ANPELA has a unique ability to evaluate the performance of whole LFQ workflow and enables the discovery of the optimal LFQs by the comprehensive performance ranking of all 560 workflows. Therefore, it has great potential for applications in metaproteomic and other studies requiring LFQ techniques, as many features are shared among proteomic studies.

Consistent gene signature of schizophrenia identified by a novel feature selection strategy from comprehensive sets of transcriptomic data.

The etiology of schizophrenia (SCZ) is regarded as one of the most fundamental puzzles in current medical research, and its diagnosis is limited by the lack of objective molecular criteria. Although plenty of studies were conducted, SCZ gene signatures identified by these independent studies are found highly inconsistent. As one of the most important factors contributing to this inconsistency, the feature selection methods used currently do not fully consider the reproducibility among the signatures discovered from different datasets. Therefore, it is crucial to develop new bioinformatics tools of novel strategy for ensuring a stable discovery of gene signature for SCZ. In this study, a novel feature selection strategy (1) integrating repeated random sampling with consensus scoring and (2) evaluating the consistency of gene rank among different datasets was constructed. By systematically assessing the identified SCZ signature comprising 135 differentially expressed genes, this newly constructed strategy demonstrated significantly enhanced stability and better differentiating ability compared with the feature selection methods popular in current SCZ research. Based on a first-ever assessment on methods' reproducibility cross-validated by independent datasets from three representative studies, the new strategy stood out among the popular methods by showing superior stability and differentiating ability. Finally, 2 novel and 17 previously reported transcription factors were identified and showed great potential in revealing the etiology of SCZ. In sum, the SCZ signature identified in this study would provide valuable clues for discovering diagnostic molecules and potential targets for SCZ.

A critical assessment of the feature selection methods used for biomarker discovery in current metaproteomics studies.

Microbial community (MC) has great impact on mediating complex disease indications, biogeochemical cycling and agricultural productivities, which makes metaproteomics powerful technique for quantifying diverse and dynamic composition of proteins or peptides. The key role of biostatistical strategies in MC study is reported to be underestimated, especially the appropriate application of feature selection method (FSM) is largely ignored. Although extensive efforts have been devoted to assessing the performance of FSMs, previous studies focused only on their classification accuracy without considering their ability to correctly and comprehensively identify the spiked proteins. In this study, the performances of 14 FSMs were comprehensively assessed based on two key criteria (both sample classification and spiked protein discovery) using a variety of metaproteomics benchmarks. First, the classification accuracies of those 14 FSMs were evaluated. Then, their abilities in identifying the proteins of different spiked concentrations were assessed. Finally, seven FSMs (FC, LMEB, OPLS-DA, PLS-DA, SAM, SVM-RFE and T-Test) were identified as performing consistently superior or good under both criteria with the PLS-DA performing consistently superior. In summary, this study served as comprehensive analysis on the performances of current FSMs and could provide a valuable guideline for researchers in metaproteomics.

Patient-derived cancer organoids for drug screening: Basic technology and clinical application.

Cancer organoids, a three-dimensional (3D) culture system of cancer cells derived from tumor tissues, recapitulate physiological structure of the parental tumor. Different tumor organoids have been established for a variety of tumor types, such as colorectal, liver, stomach, pancreatic and brain tumors. Some tumor organoid biobanks are built to screen and discover novel antitumor drug targets. Moreover, patients-derived tumor organoids (PDOs) could predict treatment response to chemoradiotherapy, targeted therapy and immunotherapy to provide guidance for personalized cancer therapy. In this review, we provide an updated overview of tumor organoid development, summarize general approach to establish tumor organoids, and discuss the application of anti-cancer drug screening based on tumor organoid and its application in personalized therapy. We also outline the opportunities and challenges for organoids to guide precision medicine.

NOREVA: enhanced normalization and evaluation of time-course and multi-class metabolomic data.

Biological processes (like microbial growth & physiological response) are usually dynamic and require the monitoring of metabolic variation at different time-points. Moreover, there is clear shift from case-control (N=2) study to multi-class (N>2) problem in current metabolomics, which is crucial for revealing the mechanisms underlying certain physiological process, disease metastasis, etc. These time-course and multi-class metabolomics have attracted great attention, and data normalization is essential for removing unwanted biological/experimental variations in these studies. However, no tool (including NOREVA 1.0 focusing only on case-control studies) is available for effectively assessing the performance of normalization method on time-course/multi-class metabolomic data. Thus, NOREVA was updated to version 2.0 by (i) realizing normalization and evaluation of both time-course and multi-class metabolomic data, (ii) integrating 144 normalization methods of a recently proposed combination strategy and (iii) identifying the well-performing methods by comprehensively assessing the largest set of normalizations (168 in total, significantly larger than those 24 in NOREVA 1.0). The significance of this update was extensively validated by case studies on benchmark datasets. All in all, NOREVA 2.0 is distinguished for its capability in identifying well-performing normalization method(s) for time-course and multi-class metabolomics, which makes it an indispensable complement to other available tools. NOREVA can be accessed at https://idrblab.org/noreva/.

Potential Chemoprevention of Paeoniflorin in Colitis-Associated Colorectal Cancer by Network Pharmacology, Molecular Docking, and In Vivo Experiment.

Chronic inflammation plays a positive role in the development and progression of colitis-associated colorectal cancer (CAC). Medicinal plants and their extracts with anti-inflammatory and immunoregulatory properties may be an effective treatment and prevention strategy for CAC. This research aimed to explore the potential chemoprevention of paeoniflorin (PF) for CAC by network pharmacology, molecular docking technology, and in vivo experiments. The results showed that interleukin-6 (IL-6) is a key target of PF against CAC. In the CAC mouse model, PF increased the survival rate of mice and decreased the number and size of colon tumors. Moreover, reduced histological score of colitis and expression of Ki-67 and PCNA were observed in PF-treated mice. In addition, the chemoprevention mechanisms of PF in CAC may be associated with suppression of the IL-6/STAT3 signaling pathway and the IL-17 level. This research provides experimental evidence of potential chemoprevention strategies for CAC treatment.

Simultaneous Improvement in the Precision, Accuracy, and Robustness of Label-free Proteome Quantification by Optimizing Data Manipulation Chains.

The label-free proteome quantification (LFQ) is multistep workflow collectively defined by quantification tools and subsequent data manipulation methods that has been extensively applied in current biomedical, agricultural, and environmental studies. Despite recent advances, in-depth and high-quality quantification remains extremely challenging and requires the optimization of LFQs by comparatively evaluating their performance. However, the evaluation results using different criteria (precision, accuracy, and robustness) vary greatly, and the huge number of potential LFQs becomes one of the bottlenecks in comprehensively optimizing proteome quantification. In this study, a novel strategy, enabling the discovery of the LFQs of simultaneously enhanced performance from thousands of workflows (integrating 18 quantification tools with 3,128 manipulation chains), was therefore proposed. First, the feasibility of achieving simultaneous improvement in the precision, accuracy, and robustness of LFQ was systematically assessed by collectively optimizing its multistep manipulation chains. Second, based on a variety of benchmark datasets acquired by various quantification measurements of different modes of acquisition, this novel strategy successfully identified a number of manipulation chains that simultaneously improved the performance across multiple criteria. Finally, to further enhance proteome quantification and discover the LFQs of optimal performance, an online tool (https://idrblab.org/anpela/) enabling collective performance assessment (from multiple perspectives) of the entire LFQ workflow was developed. This study confirmed the feasibility of achieving simultaneous improvement in precision, accuracy, and robustness. The novel strategy proposed and validated in this study together with the online tool might provide useful guidance for the research field requiring the mass-spectrometry-based LFQ technique.

Therapeutic target database update 2018: enriched resource for facilitating bench-to-clinic research of targeted therapeutics.

Extensive efforts have been directed at the discovery, investigation and clinical monitoring of targeted therapeutics. These efforts may be facilitated by the convenient access of the genetic, proteomic, interactive and other aspects of the therapeutic targets. Here, we describe an update of the Therapeutic target database (TTD) previously featured in NAR. This update includes: (i) 2000 drug resistance mutations in 83 targets and 104 target/drug regulatory genes, which are resistant to 228 drugs targeting 63 diseases (49 targets of 61 drugs with patient prevalence data); (ii) differential expression profiles of 758 targets in the disease-relevant drug-targeted tissue of 12 615 patients of 70 diseases; (iii) expression profiles of 629 targets in the non-targeted tissues of 2565 healthy individuals; (iv) 1008 target combinations of 1764 drugs and the 1604 target combination of 664 multi-target drugs; (v) additional 48 successful, 398 clinical trial and 21 research targets, 473 approved, 812 clinical trial and 1120 experimental drugs, and (vi) ICD-10-CM and ICD-9-CM codes for additional 482 targets and 262 drugs against 98 disease conditions. This update makes TTD more useful for facilitating the patient focused research, discovery and clinical investigations of the targeted therapeutics. TTD is accessible at http://bidd.nus.edu.sg/group/ttd/ttd.asp.