Kernel-based testing for single-cell differential analysis

Single-cell technologies offer insights into molecular feature distributions, but comparing them poses challenges. We propose a kernel-testing framework for non-linear cell-wise distribution comparison, analyzing gene expression and epigenomic modifications. Our method allows feature-wise and global transcriptome/epigenome comparisons, revealing cell population heterogeneities. Using a classifier based on embedding variability, we identify transitions in cell states, overcoming limitations of traditional single-cell analysis. Applied to single-cell ChIP-Seq data, our approach identifies untreated breast cancer cells with an epigenomic profile resembling persister cells. This demonstrates the effectiveness of kernel testing in uncovering subtle population variations that might be missed by other methods.

A systematic comparison of salt removal efficiency in washing treatment by using fly ashes from 13 MSWI plants in China

Municipal solid waste incineration (MSWI) fly ash contains large amounts of Ca, Si, and other elements, giving it the potential to be used as a raw material for cement production. However, fly ash often contains a high content of salts, which greatly limits its blending ratio during cement production. These salts are commonly removed via water washing, but this process is affected by the nature and characteristics of fly ash. To clarify the influence of the ash characteristics on salt removal, a total of 60 fly ash samples from 13 incineration plants were collected, characterized, and washed. The ash characterization and cluster analysis showed that the incinerator type and flue gas purification technology/process significantly influenced the ash characteristics. Washing removed a high percentage of salts from fly ash, but the removal efficiencies varied significantly from each other, with the chlorine removal efficiency ranging from 73.76% to 96.48%, while the sulfate removal efficiency ranged from 6.92% to 51.47%. Significance analysis further revealed that the salt removal efficiency varied not only between the ash samples from different incinerators, but also between samples collected at different times from the same incinerator. The high variance of the 60 ash samples during salt removal was primarily ascribed to their different mineralogical and chemical characteristics. Mineralogical analysis of the raw and washed ash samples showed that the mineralogical forms and proportion of these salts in each ash sample greatly influenced their removal. The presence of less-soluble and insoluble chloride salts (e.g., CaClOH, Ca2Al(OH)6(H2O)2Cl etc.) in fly ash significantly affected the chlorine removal efficiency. This study also found that Fe, Mn, and Al in fly ash were negatively correlated with the dechlorination efficiency of fly ash. In summary, the different physical and chemical properties of fly ash caused great discrepancies in salt removal. Consequently, it is suggested to consider the potential impact of the ash source and ash generation time on salt removal to ensure a reliable treatment efficiency for engineering applications.

Effects of exogenous N-acyl homoserine lactones (AHLs) on methanogenic activities and microbial community differences during anaerobic digestion

N-acyl homoserine lactones (AHLs) function as signaling molecules influencing microbial community dynamics. This study investigates the impact of exogenously applied AHLs on methane production during waste-activated sludge (WAS) anaerobic digestion (AD). Nine AHL types, ranging from 10-4 to 10 μg/g VSS, were applied, comparing microbial community composition under optimal AHL concentrations. Firmicutes, Bacteroidetes, Chloroflexi, and Proteobacteria were identified in anaerobic digesters with C4-HSL, C6-HSL, and C8-HSL. Compared to the control, Halobacterota increased by 19.25%, 20.87%, and 9.33% with C7-HSL, C10-HSL, and C12-HSL. Exogenous C7-HSL enhanced the relative abundance of Methanosarcina, Romboutsia, Sedimentibacter, Proteiniclasticum, Christensenellaceae_R-7_group. C10-HSL increased Methanosarcina abundance. C4-HSL, C6-HSL, C8-HSL, C10-HSL, and C12-HSL showed potential to increase unclassified_Firmicutes. Functional Annotation of Prokaryotic Taxa (FAPROTAX) predicted AHLs' impact on related functional genes, providing insights into their role in AD methanogenesis regulation. This study aimed to enhance the understanding of the influence of different types of exogenous AHLs on AD and provide technical support for regulating the methanogenesis efficiency of AD by exogenous AHLs.

Toxin metalloproteinases exert a dominant influence on pro-inflammatory response and anti-inflammatory regulation in jellyfish sting dermatitis

Toxin metalloproteinases are the primary components responsible for various toxicities in jellyfish venom, and there is still no effective specific therapy for jellyfish stings. The comprehension of the pathogenic mechanisms underlying toxin metalloproteinases necessitates further refinement. In this study, we conducted a differential analysis of a dermatitis mouse model induced by jellyfish Nemopilema nomurai venom (NnNV) samples with varying levels of metalloproteinase activity. Through skin tissue proteomics and serum metabolomics, the predominant influence of toxin metalloproteinase activity on inflammatory response was revealed, and the signal pathway involved in its regulation was identified. In skin tissues, many membrane proteins were significantly down-regulated, which might cause tissue damage. The expression of pro-inflammatory factors was mainly regulated by PI3K-Akt signaling pathway. In serum, many fatty acid metabolites were significantly down-regulated, which might be the anti-inflammation feedback regulated by NF-κB p65 signaling pathway. These results reveal the dermatitis mechanism of toxin metalloproteinases and provide new therapeutic targets for further studies. SIGNIFICANCE: Omics is an important method to analyze the pathological mechanism and discover the key markers, which can reveal the pathological characteristics of jellyfish stings. Our research first analyzed the impact of toxin metalloproteinases on jellyfish sting dermatitis by skin proteomics and serum metabolomics. The present results suggest that inhibition of toxin metalloproteinases may be an effective treatment strategy, and provide new references for further jellyfish sting studies.

Weighted gene co-expression network analysis for hub genes in colorectal cancer

BACKGROUND

This study is designed to explore hub genes participating in colorectal cancer (CRC) development through weighted gene co-expression network analysis (WGCNA).

METHODS

Expression profiles of CRC and normal samples were retrieved from the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA), and were subjected to WGCNA to filter differentially expressed genes with significant association with CRC. Functional enrichment analysis and protein-protein interaction (PPI) analysis were carried out to filter the candidate genes, further and survival analysis was performed for the candidate genes to obtain potential regulatory hub genes in CRC. Expression analysis was conducted for the candidate genes and a multifactor model was established.

RESULTS

After differential analysis and WGCNA, 289 candidate genes were filtered from the GEO and TCGA. Further functional enrichment analysis demonstrated possible regulatory pathways and functions. PPI analysis filtered 15 hub genes and survival analysis indicated a significant correlation of CLCA1, CLCA4, and CPT1A with prognosis of patients with CRC. The multifactor Cox risk model established based on the three genes revealed that if the three genes were a gene set, they had well predictive capacity for the prognosis of patients with CRC.

CONCLUSIONS

CLCA1, CLCA4, and CPT1A express at low levels in CRC and function as core anti-tumor genes. As a gene set, they can predict prognosis well.

Differential Poly(A) Tail Length Analysis Using Nanopore Sequencing

The poly(A) tail is a sequence of several adenosine nucleotides added to the 3' end of RNA molecules transcribed by polymerase II. The dynamics of poly(A) tail length play a significant role in regulating post-transcriptional gene expression by regulating the stability, translation, and decay of messenger RNAs. As a result, an accurate measurement of poly(A) tail length changes is important for understanding its regulatory function in different cellular contexts. Here, we outline a method for using nanopore sequencing and linear mixed models to analyze differences in poly(A) tail length across conditions.

Characterization and comparison of cardiomyocyte protection activities of non-starch polysaccharides from six ginseng root herbal medicines

Ginseng is rich of polysaccharides, however, the evidence supporting polysaccharides to distinguish various ginseng species is rarely reported. Focusing on six root ginseng (e.g., Panax ginseng-PG, P. quinquefolius-PQ, P. notoginseng-PN, red ginseng-RG, P. japonicus-PJ, and P. japonicus var. major-PJM), the contained non-starch polysaccharides (NPs) were structurally characterized and compared by both the chemical and biological evaluation. Holistic fingerprinting at three levels (the NPs and the acid hydrolysates involving oligosaccharides and monosaccharides) utilized various chromatography methods, and the treatment of H9c2 cells with the NPs by OGD and H2O2-induced injury models was used to assess the protective effect. NPs from six Panax herbal medicines occupied about 20 % of the total polysaccharides, which were of the highest content in RG and the lowest in PN. NPs from six ginseng exhibited weak differentiations in the molecular weight distribution, while marker oligosaccharides were found to distinguish PN and RG from the others. Glc and GalA were more abundant in the NPs for PG and RG, respectively. NPs from PQ (100/200 μg/mL) showed significant cardiomyocyte protection effect by regulating the mitochondrial functions. This work further testifies the role of polysaccharides in quality control of herbal medicine, with new markers discovered beneficial to distinguish the ginseng.

Prospective Identification of Prognostic Hot-Spot Mutant Gene Signatures for Leukemia: A Computational Study Based on Integrative Analysis of TCGA and cBioPortal Data

The advantage of an increasing amount of bioinformatics data on leukemias intrigued us to explore the hot-spot mutation profiles and investigate the implications of those hot-spot mutations in patient survival. We retrieved somatic mutations and their distribution in protein domains through data analysis of The Cancer Genome Atlas and cBioPortal databases. After determining differentially expressed mutant genes related to leukemia, we further conducted principal component analysis and single-factor Cox regression analyses. Moreover, survival analysis was performed for the obtained candidate genes, followed by a multi-factor Cox proportional hazard model method for the impacts of the candidate genes on the survival and prognosis of patients with leukemia. At last, the signaling pathways involved in leukemia were investigated by gene set enrichment analysis. There were 223 somatic missense mutation hot-spots identified with pertinence to leukemia, which were distributed in 41 genes. Differential expression in leukemia was witnessed in 39 genes. We found a close correlation between seven genes and the prognosis of leukemia patients, among which, three genes could significantly influence the survival rate. In addition, among these three genes, CD74 and P2RY8 were highlighted due to close pertinence with survival conditions of leukemia patients. Finally, data suggested that B cell receptor, Hedgehog, and TGF-beta signaling pathways were enriched in low-hazard patients. In conclusion, these data underline the involvement of hot-spot mutations of CD74 and P2RY8 genes in survival status of leukemia patients, highlighting their as novel therapeutic targets or prognostic indicators for leukemia patients. Summary of Graphical Abstract: We identified 223 leukemia-associated somatic missense mutation hotspots concentrated in 41 different genes from 2297 leukemia patients in the TCGA database. Differential analysis of leukemic and normal samples from the TCGA and GTEx databases revealed that 39 of these 41 genes showed significant differential expression in leukemia. These 39 genes were subjected to PCA analysis, univariate Cox analysis, survival analysis, multivariate Cox regression analysis, GSEA pathway enrichment analysis, and then the association with leukemia survival prognosis and related pathways were investigated.

MBE: model-based enrichment estimation and prediction for differential sequencing data

Characterizing differences in sequences between two conditions, such as with and without drug exposure, using high-throughput sequencing data is a prevalent problem involving quantifying changes in sequence abundances, and predicting such differences for unobserved sequences. A key shortcoming of current approaches is their extremely limited ability to share information across related but non-identical reads. Consequently, they cannot use sequencing data effectively, nor be directly applied in many settings of interest. We introduce model-based enrichment (MBE) to overcome this shortcoming. We evaluate MBE using both simulated and real data. Overall, MBE improves accuracy compared to current differential analysis methods.

A distinct circular DNA profile intersects with proteome changes in the genotoxic stress-related hSOD1G93A model of ALS

BACKGROUND

Numerous genes, including SOD1, mutated in familial and sporadic amyotrophic lateral sclerosis (f/sALS) share a role in DNA damage and repair, emphasizing genome disintegration in ALS. One possible outcome of chromosomal instability and repair processes is extrachromosomal circular DNA (eccDNA) formation. Therefore, eccDNA might accumulate in f/sALS with yet unknown function.

METHODS

We combined rolling circle amplification with linear DNA digestion to purify eccDNA from the cervical spinal cord of 9 co-isogenic symptomatic hSOD1G93A mutants and 10 controls, followed by deep short-read sequencing. We mapped the eccDNAs and performed differential analysis based on the split read signal of the eccDNAs, referred as DifCir, between the ALS and control specimens, to find differentially produced per gene circles (DPpGC) in the two groups. Compared were eccDNA abundances, length distributions and genic profiles. We further assessed proteome alterations in ALS by mass spectrometry, and matched the DPpGCs with differentially expressed proteins (DEPs) in ALS. Additionally, we aligned the ALS-specific DPpGCs to ALS risk gene databases.

RESULTS

We found a six-fold enrichment in the number of unique eccDNAs in the genotoxic ALS-model relative to controls. We uncovered a distinct genic circulome profile characterized by 225 up-DPpGCs, i.e., genes that produced more eccDNAs from distinct gene sequences in ALS than under control conditions. The inter-sample recurrence rate was at least 89% for the top 6 up-DPpGCs. ALS proteome analyses revealed 42 corresponding DEPs, of which 19 underlying genes were itemized for an ALS risk in GWAS databases. The up-DPpGCs and their DEP tandems mainly impart neuron-specific functions, and gene set enrichment analyses indicated an overrepresentation of the adenylate cyclase modulating G protein pathway.

CONCLUSIONS

We prove, for the first time, a significant enrichment of eccDNA in the ALS-affected spinal cord. Our triple circulome, proteome and genome approach provide indication for a potential importance of certain eccDNAs in ALS neurodegeneration and a yet unconsidered role as ALS biomarkers. The related functional pathways might open up new targets for therapeutic intervention.

An evaluation of statistical differential analysis methods in single-cell RNA-seq data

Background

Single-cell RNA Sequencing is gaining popularity in recent years. Compared to bulk RNA-Seq, single-cell RNA Sequencing allows the gene expression being measured within individual cells instead of mean gene expression levels across all cells in the sample. Thus, cell-to-cell variation of gene expressions could be examined. Gene differential expression analysis remains the major purpose in most single-cell RNA sequencing experiments and many methods have been developed in recent years to conduct gene differential expression analysis for single-cell RNA sequencing data.

Results

Through simulation studies and real data examples, we evaluated the performance of five open-source popular methods used for gene differential expression analysis in single-cell RNA sequencing data. The five methods included DEsingle (Zero-inflated negative binomial model), Linnorm (Empirical Bayes method on transformed count data using the limma package), monocle (An approximate Chi-Square likelihood ratio test), MAST (A generalized linear hurdle model), and DESeq2 (A generalized linear model with empirical Bayes approach and also commonly used for bulk RNA sequencing differential express analyses). We assessed the false discovery rate (FDR) control, sensitivity, specificity, accuracy, and area under the receiver operating characteristics (AUROC) curve for all five methods under different sample sizes, distribution assumptions, and proportions of zeros in the data.

Conclusions

We found the MAST method performed the best among the five methods compared with the largest AUROC values across all tested sample sizes and different proportion of truly differential expressed genes, when the data followed negative binomial distributions. When the sample size increased to 100 in each group, the MAST method showed the best performance with the highest AUROC regardless of the data distributions. If the excess zeros were first filtered out before the gene differential analyses, the DESingle, Linnorm, and DESeq2 performed relatively better than the MAST and the monocle methods with higher AUROC values.

Controlling taxa abundance improves metatranscriptomics differential analysis

BACKGROUND

A common task in analyzing metatranscriptomics data is to identify microbial metabolic pathways with differential RNA abundances across multiple sample groups. With information from paired metagenomics data, some differential methods control for either DNA or taxa abundances to address their strong correlation with RNA abundance. However, it remains unknown if both factors need to be controlled for simultaneously.

RESULTS

We discovered that when either DNA or taxa abundance is controlled for, RNA abundance still has a strong partial correlation with the other factor. In both simulation studies and a real data analysis, we demonstrated that controlling for both DNA and taxa abundances leads to superior performance compared to only controlling for one factor.

CONCLUSIONS

To fully address the confounding effects in analyzing metatranscriptomics data, both DNA and taxa abundances need to be controlled for in the differential analysis.

Statistical Analysis of Quantitative Peptidomics and Peptide-Level Proteomics Data with Prostar

Prostar is a software tool dedicated to the processing of quantitative data resulting from mass spectrometry-based label-free proteomics. Practically, once biological samples have been analyzed by bottom-up proteomics, the raw mass spectrometer outputs are processed by bioinformatics tools, so as to identify peptides and quantify them, notably by means of precursor ion chromatogram integration. From that point, the classical workflows aggregate these pieces of peptide-level information to infer protein-level identities and amounts. Finally, protein abundances can be statistically analyzed to find out proteins that are significantly differentially abundant between compared conditions. Prostar original workflow has been developed based on this strategy. However, recent works have demonstrated that processing peptide-level information is often more accurate when searching for differentially abundant proteins, as the aggregation step tends to hide some of the data variabilities and biases. As a result, Prostar has been extended by workflows that manage peptide-level data, and this protocol details their use. The first one, deemed "peptidomics," implies that the differential analysis is conducted at peptide level, independently of the peptide-to-protein relationship. The second workflow proposes to aggregate the peptide abundances after their preprocessing (i.e., after filtering, normalization, and imputation), so as to minimize the amount of protein-level preprocessing prior to differential analysis.

Towards a More Accurate Differential Analysis of Multiple Imputed Proteomics Data with mi4limma

Imputing missing values is a common practice in label-free quantitative proteomics. Imputation replaces a missing value by a user-defined one. However, the imputation itself is not optimally considered downstream of the imputation process. In particular, imputed datasets are considered as if they had always been complete. The uncertainty due to the imputation is not properly taken into account. Hence, the mi4p package provides a more accurate statistical analysis of multiple-imputed datasets. A rigorous multiple imputation methodology is implemented, leading to a less biased estimation of parameters and their variability, thanks to Rubin's rules. The imputation-based peptide's intensities' variance estimator is then moderated using Bayesian hierarchical models. This estimator is finally included in moderated t-test statistics to provide differential analyses results.

DIGE Analysis of Clinical Specimens

Two-dimensional difference gel electrophoresis (2D-DIGE) is an elegant gel electrophoretic analytical tool for comparative protein assessment. It is based on two-dimensional gel electrophoresis (2D-GE) separation of fluorescently labeled protein extracts. The tagging procedures are designed to not interfere with the chemical properties of proteins with respect to their pI and electrophoretic mobility, once a proper labeling protocol is followed. The use of an internal pooled standard makes 2D-DIGE a highly accurate quantitative method enabling multiple protein samples to be separated on the same two-dimensional gel. Technical limitations of this technique (i.e., underrating of low abundant, high molecular mass and integral membrane proteins) are counterbalanced by the incomparable separation power which allows proteoforms and unknown PTM (posttranslational modification) identification. Moreover, the image matching and cross-gel statistical analysis generates robust quantitative results making data validation by independent technologies successful.

Exploring Protein Interactome Data with IPinquiry: Statistical Analysis and Data Visualization by Spectral Counts

Immunoprecipitation mass spectrometry (IP-MS) is a popular method for the identification of protein-protein interactions. This approach is particularly powerful when information is collected without a priori knowledge and has been successively used as a first key step for the elucidation of many complex protein networks. IP-MS consists in the affinity purification of a protein of interest and of its interacting proteins followed by protein identification and quantification by mass spectrometry analysis. We developed an R package, named IPinquiry, dedicated to IP-MS analysis and based on the spectral count quantification method. The main purpose of this package is to provide a simple R pipeline with a limited number of processing steps to facilitate data exploration for biologists. This package allows to perform differential analysis of protein accumulation between two groups of IP experiments, to retrieve protein annotations, to export results, and to create different types of graphics. Here we describe the step-by-step procedure for an interactome analysis using IPinquiry from data loading to result export and plot production.

Recent insights on gene expression studies on Hevea Brasiliensis fatal leaf fall diseases

Hevea brasiliensis is one of the most important agricultural commodities globally, heavily cultivated in Southeast Asia. Fatal leaf fall diseases cause aggressive leaf defoliation, linked to lower latex yield and death of crops before maturity. Due to the significant consequences of the disease to H. brasiliensis, the recent gene expression studies from four fall leaf diseases of H. brasiliensis were gathered; South American leaf blight, powdery mildew, Corynespora cassiicola and Phytophthora leaf fall disease. The differential analysis observed the pattern of commonly expressed genes upon fungi triggers using RT-PCR, DDRT-PCR, Real-time qRT-PCR and RNA-Seq. We have observed that RNA-Seq is the best tool to seek novel genes. Among the identified genes with defence-against fungi were pathogenesis-related genes such as β-1,3-glucanase and chitinase, the reactive oxygen species, and the phytoalexin biosynthesis. This manuscript also provided functional elaboration on the responsive genes and predicted possible biosynthetic pathways to identify and characterise novel genes in the future. At the end of the manuscript, the PCR methods and proteomic approaches were presented for future molecular and biochemical studies in the related diseases to H. brasiliensis.

Microarray Data Preprocessing: From Experimental Design to Differential Analysis

DNA microarray data preprocessing is of utmost importance in the analytical path starting from the experimental design and leading to a reliable biological interpretation. In fact, when all relevant aspects regarding the experimental plan have been considered, the following steps from data quality check to differential analysis will lead to robust, trustworthy results. In this chapter, all the relevant aspects and considerations about microarray preprocessing will be discussed. Preprocessing steps are organized in an orderly manner, from experimental design to quality check and batch effect removal, including the most common visualization methods. Furthermore, we will discuss data representation and differential testing methods with a focus on the most common microarray technologies, such as gene expression and DNA methylation.

BCurve: Bayesian Curve Credible Bands Approach for the Detection of Differentially Methylated Regions

High-throughput assays have been developed to measure DNA methylation, among which bisulfite-based sequencing (BS-seq) and microarray technologies are the most popular for genome-wide profiling. A major goal in DNA methylation analysis is the detection of differentially methylated genomic regions under two different conditions. To accomplish this, many state-of-the-art methods have been proposed in the past few years; only a handful of these methods are capable of analyzing both types of data (BS-seq and microarray), though. On the other hand, covariates, such as sex and age, are known to be potentially influential on DNA methylation; and thus, it would be important to adjust for their effects on differential methylation analysis. In this chapter, we describe a Bayesian curve credible bands approach and the accompanying software, BCurve, for detecting differentially methylated regions for data generated from either microarray or BS-Seq. The unified theme underlying the analysis of these two different types of data is the model that accounts for correlation between DNA methylation in nearby sites, covariates, and between-sample variability. The BCurve R software package also provides tools for simulating both microarray and BS-seq data, which can be useful for facilitating comparisons of methods given the known "gold standard" in the simulated data. We provide detailed description of the main functions in BCurve and demonstrate the utility of the package for analyzing data from both platforms using simulated data from the functions provided in the package. Analyses of two real datasets, one from BS-seq and one from microarray, are also furnished to further illustrate the capability of BCurve.

Panproteome Analysis of the Human Antibody Response to Bacterial Vaccines and Challenge

High-density protein microarray is an established technology for characterizing host antibody profiles against entire pathogen proteomes. As one of the highest throughput technologies for antigen discovery, proteome microarrays are a translational research tool for identification of vaccine candidates and biomarkers of susceptibility or protection from microbial challenge. The application has been expanded in recent years due to increased availability of bacterial genomic sequences for a broader range of species and strain diversity. Panproteome microarrays now allow for fine characterization of antibody specificity and cross-reactivity that may be relevant to vaccine design and biomarker discovery, as well as a fuller understanding of factors underlying themes of bacterial evolution and host-pathogen interactions. In this chapter, we present a workflow for design of panproteome microarrays and demonstrate statistical analysis of panproteomic human antibody responses to bacterial vaccination and challenge. Focus is particularly drawn to the bioinformatics and statistical tools and providing nontrivial, real examples that may help foster hypotheses and rational design of panproteomic studies.

AIAP: A Quality Control and Integrative Analysis Package to Improve ATAC-seq Data Analysis

Assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) is a technique widely used to investigate genome-wide chromatin accessibility. The recently published Omni-ATAC-seq protocol substantially improves the signal/noise ratio and reduces the input cell number. High-quality data are critical to ensure accurate analysis. Several tools have been developed for assessing sequencing quality and insertion size distribution for ATAC-seq data; however, key quality control (QC) metrics have not yet been established to accurately determine the quality of ATAC-seq data. Here, we optimized the analysis strategy for ATAC-seq and defined a series of QC metrics for ATAC-seq data, including reads under peak ratio (RUPr), background (BG), promoter enrichment (ProEn), subsampling enrichment (SubEn), and other measurements. We incorporated these QC tests into our recently developed ATAC-seq Integrative Analysis Package (AIAP) to provide a complete ATAC-seq analysis system, including quality assurance, improved peak calling, and downstream differential analysis. We demonstrated a significant improvement of sensitivity (20%–60%) in both peak calling and differential analysis by processing paired-end ATAC-seq datasets using AIAP. AIAP is compiled into Docker/Singularity, and it can be executed by one command line to generate a comprehensive QC report. We used ENCODE ATAC-seq data to benchmark and generate QC recommendations, and developed qATACViewer for the user-friendly interaction with the QC report. The software, source code, and documentation of AIAP are freely available at https://github.com/Zhang-lab/ATAC-seq_QC_analysis.

Uncovering the pharmacological mechanisms of Xijiao Dihuang decoction combined with Yinqiao powder in treating influenza viral pneumonia by an integrative pharmacology strategy

Xijiao Dihuang decoction combined with Yinqiao powder (XDD-YQP) is a classical combination formula; however, its therapeutic effects in treating influenza viral pneumonia and the pharmacological mechanisms remain unclear. The therapeutic effect of XDD-YQP in influenza viral pneumonia was evaluated in mice. Subsequently, an everted gut sac model coupled with UPLC/Q-TOF MS were used to screen and identify the active compounds of XDD-YQP. Furthermore, network pharmacological analysis was adopted to probe the mechanisms of the active compounds. Lastly, we verified the targets predicted from network pharmacological analysis by differential bioinformatics analysis. Animal experiments showed that XDD-YQP has a therapeutic effect on influenza viral pneumonia. Moreover, 113 active compounds were identified from intestinal absorbed solutions of XDD-YQP. Using network pharmacological analysis, 90 major targets were selected as critical in the treatment of influenza viral pneumonia through 12 relevant pathways. Importantly, the MAPK signaling pathway was found to be closely associated with the other 11 pathways. Moreover, seven key targets, EGFR, FOS, MAPK1, MAP2K1, HRAS, NRAS, and RELA, which are common targets in the MAPK signaling pathway, were investigated. These seven key targets were identified as differentially expressed genes (DEGs) between influenza virus-infected and uninfected individuals. Hence, the seven key targets in the MAPK signaling pathway may play a vital role in the treatment of influenza viral pneumonia with XDD-YQP. This research may offer an integrative pharmacology strategy to clarify the pharmacological mechanisms of traditional Chinese medicines. The results provide a theoretical basis for a broader clinical application of XDD-YQP.

A Tutorial for Variance-Sensitive Clustering and the Quantitative Analysis of Protein Complexes

Data clustering facilitates the identification of biologically relevant molecular features in quantitative proteomics experiments with thousands of measurements over multiple conditions. It finds groups of proteins or peptides with similar quantitative behavior across multiple experimental conditions. This co-regulatory behavior suggests that the proteins of such a group share their functional behavior and thus often can be mapped to the same biological processes and molecular subnetworks.While usual clustering approaches dismiss the variance of the measured proteins, VSClust combines statistical testing with pattern recognition into a common algorithm. Here, we show how to use the VSClust web service on a large proteomics data set and present further tools to assess the quantitative behavior of protein complexes.

MEIRLOP: improving score-based motif enrichment by incorporating sequence bias covariates

BACKGROUND

Motif enrichment analysis (MEA) identifies over-represented transcription factor binding (TF) motifs in the DNA sequence of regulatory regions, enabling researchers to infer which transcription factors can regulate transcriptional response to a stimulus, or identify sequence features found near a target protein in a ChIP-seq experiment. Score-based MEA determines motifs enriched in regions exhibiting extreme differences in regulatory activity, but existing methods do not control for biases in GC content or dinucleotide composition. This lack of control for sequence bias, such as those often found in CpG islands, can obscure the enrichment of biologically relevant motifs.

RESULTS

We developed Motif Enrichment In Ranked Lists of Peaks (MEIRLOP), a novel MEA method that determines enrichment of TF binding motifs in a list of scored regulatory regions, while controlling for sequence bias. In this study, we compare MEIRLOP against other MEA methods in identifying binding motifs found enriched in differentially active regulatory regions after interferon-beta stimulus, finding that using logistic regression and covariates improves the ability to call enrichment of ISGF3 binding motifs from differential acetylation ChIP-seq data compared to other methods. Our method achieves similar or better performance compared to other methods when quantifying the enrichment of TF binding motifs from ENCODE TF ChIP-seq datasets. We also demonstrate how MEIRLOP is broadly applicable to the analysis of numerous types of NGS assays and experimental designs.

CONCLUSIONS

Our results demonstrate the importance of controlling for sequence bias when accurately identifying enriched DNA sequence motifs using score-based MEA. MEIRLOP is available for download from https://github.com/npdeloss/meirlop under the MIT license.

Seasonal Ely Copper Mine Superfund site shotgun metagenomic and metatranscriptomic data analysis

High throughput sequencing data collected from acid rock drainage (ARD) communities can reveal the active taxonomic and functional diversity of these extreme environments, which can be exploited for bioremediation, pharmaceutical, and industrial applications. Here, we report a seasonal comparison of a microbiome and transcriptome in Ely Brook (EB-90M), a confluence of clean water and upstream tributaries that drains the Ely Copper Mine Superfund site in Vershire, VT, USA. Nucleic acids were extracted from EB-90M water and sediment followed by shotgun sequencing using the Illumina NextSeq platform. Approximately 575,933 contigs with a total length of 1.54 Gbp were generated. Contigs of at least a size of 3264 (N50) or greater represented 50% of the sequences and the longest contig was 488,568 bp in length. Using Centrifuge against the NCBI “nt” database 141 phyla, including candidate phyla, were detected. Roughly 380,000 contigs were assembled and ∼1,000,000 DNA and ∼550,000 cDNA sequences were identified and functionally annotated using the Prokka pipeline. Most expressed KEGG-annotated microbial genes were involved in amino acid metabolism and several KEGG pathways were differentially expressed between seasons. Biosynthetic gene clusters involved in secondary metabolism as well as metal- and antibiotic-resistance genes were annotated, some of which were differentially expressed, colocalized, and coexpressed. These data can be used to show how ecological stimuli, such as seasonal variations and metal concentrations, affect the ARD microbiome and select taxa to produce novel natural products. The data reported herein is supporting information for the research article “Characterization of an acid rock drainage microbiome and transcriptome at the Ely Copper Mine Superfund site” by Giddings et al. [1].

SHAMAN: a user-friendly website for metataxonomic analysis from raw reads to statistical analysis

Background

Comparing the composition of microbial communities among groups of interest (e.g., patients vs healthy individuals) is a central aspect in microbiome research. It typically involves sequencing, data processing, statistical analysis and graphical display. Such an analysis is normally obtained by using a set of different applications that require specific expertise for installation, data processing and in some cases, programming skills.

Results

Here, we present SHAMAN, an interactive web application we developed in order to facilitate the use of (i) a bioinformatic workflow for metataxonomic analysis, (ii) a reliable statistical modelling and (iii) to provide the largest panel of interactive visualizations among the applications that are currently available. SHAMAN is specifically designed for non-expert users. A strong benefit is to use an integrated version of the different analytic steps underlying a proper metagenomic analysis. The application is freely accessible at http://shaman.pasteur.fr/, and may also work as a standalone application with a Docker container (aghozlane/shaman), conda and R. The source code is written in R and is available at https://github.com/aghozlane/shaman. Using two different datasets (a mock community sequencing and a published 16S rRNA metagenomic data), we illustrate the strengths of SHAMAN in quickly performing a complete metataxonomic analysis.

Conclusions

With SHAMAN, we aim at providing the scientific community with a platform that simplifies reproducible quantitative analysis of metagenomic data.

[Differential analysis of gene expression profiles in hepatocellular carcinoma patients with high and low levels of alpha-fetoprotein]

Objective: To investigate the differential gene expression profiles of alpha-fetoprotein (AFP) high- and low-expressing hepatocellular carcinoma (HCC), and to provide a theoretical basis for the molecular mechanism and prognosis analysis of HCC. Methods: The transcriptome data and related clinical information from 368 HCC cases were obtained from the Cancer Gene Atlas (TCGA) public database. The samples were divided into AFP high expression (AFP(high)) group and low expression (AFP(low)) group according to the quartile of AFP mRNA expression, with 92 cases in each group. The differential gene analysis was carried out using the DEseq2 package in the R software. The functional and KEGG pathway enrichment analysis of the differential genes was performed using ClusterProfiler package. The protein-protein interaction network was constructed to screen hub genes using the String database and Cytoscape software. The single-sample GSEA analysis was performed to enrich and score signature gene sets using the GSVA package. And then RNAseq data and real-time quantitative polymerase chain reaction (RT-qPCR) were used for independent dataset validation and tissue validation. Results: The clinical analysis showed that high expression of AFP was significantly associated with poor pathological differentiation and ethnicity (P<0.05 for both). A total of 1 382 differential genes were obtained by bioinformatics analysis, of which 931 genes were up-regulated and 451 genes were down-regulated in AFP(high) group. GO enrichment analysis showed that the highly expressed genes were mainly correlated with the processes of appendage development, limb development, and skeletal system development, while lowly expressed genes were related to metabolic-related processes such as xenobiotic metabolism, steroid metabolism, and cellular response to xenobiotic stimuli. KEGG pathway enrichment analysis revealed that highly expressed genes were mainly involved in primary immunodeficiency, neuroactive ligand-receptor interaction, and cytokine-cytokine receptor interaction, while lowly expressed genes were mainly involved in retinol metabolism, chemical carcinogenesis, steroid hormone biosynthesis and other pathways. A prognostic related gene set that was consisted of AURKB, TTK, CENPA, UBE2C, HJURP, and KIF15 was identified. And the high expression of this gene set was related to the shorter recurrence-free survival and overall survival time in HCC patients, and its enrichment score was positively correlated with AFP expression (r=0.475, P<0.001). The validation results of RNAseq data were basically consistent with the TCGA data. The RT-qPCR results showed that AURKB, KIF15, and UBE2C were significantly overexpressed in HCC tissues with high AFP expression. Although the expression of AURKB, TTK, KIF15, and UBE2C was not related to recurrence-free survival and overall survival of HCC patients, there was a tendency that the patients with high AFP levels showed relatively shorter recurrence-free survival time and overall survival time. Conclusions: There is a large difference in gene expression profiles between AFP(high) and AFP(low) HCC. The prognostic signature may cooperate with AFP to promote the initiation and development of HCC. It also may explain the tumorigenesis in HCC with different AFP levels, and provide new clues for the prognosis of HCC.

SURF: integrative analysis of a compendium of RNA-seq and CLIP-seq datasets highlights complex governing of alternative transcriptional regulation by RNA-binding proteins

Advances in high-throughput profiling of RNA-binding proteins (RBPs) have resulted inCLIP-seq datasets coupled with transcriptome profiling by RNA-seq. However, analysis methods that integrate both types of data are lacking. We describe SURF, Statistical Utility for RBP Functions, for integrative analysis of large collections of CLIP-seq and RNA-seq data. We demonstrate SURF's ability to accurately detect differential alternative transcriptional regulation events and associate them to local protein-RNA interactions. We apply SURF to ENCODE RBP compendium and carry out downstream analysis with additional reference datasets. The results of this application are browsable at http://www.statlab.wisc.edu/shiny/surf/.

Whole blood transcriptomic investigation identifies long non-coding RNAs as regulators in sepsis

Background

Sepsis is a fatal disease referring to the presence of a known or strongly suspected infection coupled with systemic and uncontrolled immune activation causing multiple organ failure. However, current knowledge of the role of lncRNAs in sepsis is still extremely limited.

Methods

We performed an in silico investigation of the gene coexpression pattern for the patients response to all-cause sepsis in consecutive intensive care unit (ICU) admissions. Sepsis coexpression gene modules were identified using WGCNA and enrichment analysis. lncRNAs were determined as sepsis biomarkers based on the interactions among lncRNAs and the identified modules.

Results

Twenty-three sepsis modules, including both differentially expressed modules and prognostic modules, were identified from the whole blood RNA expression profiling of sepsis patients. Five lncRNAs, FENDRR, MALAT1, TUG1, CRNDE, and ANCR, were detected as sepsis regulators based on the interactions among lncRNAs and the identified coexpression modules. Furthermore, we found that CRNDE and MALAT1 may act as miRNA sponges of sepsis related miRNAs to regulate the expression of sepsis modules. Ultimately, FENDRR, MALAT1, TUG1, and CRNDE were reannotated using three independent lncRNA expression datasets and validated as differentially expressed lncRNAs.

Conclusion

The procedure facilitates the identification of prognostic biomarkers and novel therapeutic strategies of sepsis. Our findings highlight the importance of transcriptome modularity and regulatory lncRNAs in the progress of sepsis.

Suspect and non-target screening of acutely toxic Prymnesium parvum

Harmful algal blooms (HABs) are increasing in frequency, magnitude, and duration around the world. Prymnesium parvum is a HAB species known to cause massive fish kills, but the toxin(s) it produces contributing to this acute toxicity to fish have not been confirmed. In the present study, a 2 × 2 factorial design was employed to examine influences of salinity (2.4 or 5 ppt) and nutrient limitation (f/2 or f/8) on P. parvum acute toxicity to fish and produced molecules. Acute toxicity (LC50) of these cultures, following a 48-h mortality assay, ranged from 10,213 to 96,816 cells mL-1. Non-targeted analysis was performed using liquid chromatography high-resolution mass spectrometry (LC-HRMS) to investigate compounds contributing to the differential toxicological responses. When P. parvum elicited toxicity to fish, suspect screening confirmed the presence of several prymnesins, and the peak area of PRM-A (3 Cl; prymnesin2aglycone) was significantly (p < 0.05) and positively related to acute toxicity. In addition, a non-targeted approach to highlighting peaks that differ between two chemical fingerprints was developed, termed a relative difference plot, and used to search for peaks co-varying with P. parvum induced acute toxicity to fish. Several peaks were highlighted along with the prymnesins identified through suspect screening when acute toxicity to fish was observed.

Exploring the molecular disorder and dysfunction mechanism of human dental pulp cells under hypoxia by comprehensive multivariate analysis

Dental pulp cells (DPCs) are multipotent cells, which can differentiate into various tissues and have the potential to treat many diseases. However, little is known about the molecular disorder mechanism. To explore the mechanism of molecular disorders and dysfunction of DPCs under hypoxia, we investigated the molecular effects of two hypoxic time lengths on DPCs. Differential analysis, protein interaction network (PPI), enrichment analysis and coupling analysis were further synthesized to identify human dental pulp cell dysfunction modules under hypoxic conditions. Based on the module aggregation of 579 genes, 13 dental pulp cell dysfunction modules were obtained. Importantly, we found that up to 12 modules were significantly involved in positive regulation of neurogenesis, positive regulation of nervous system development. Based on the predictive analysis of regulators, we identified a series of ncRNAs (including CRNDE, MALAT1, microRNA-140-5p, microRNA-300 and microRNA-30a-5p) and transcription factors (including E2F1). Based on the comprehensive functional module analysis, we identified the dysfunction module of human dental pulp cells (HDPCs) under hypoxia. The results suggest that nerve regulation plays an important role in regulating the dysfunction module of DPCs. These prominent pivotal regulators in the module were used as an important part of the molecular disorders of DPCs, may be an important part of the subnetwork of the manipulation module and affect the molecular dysregulation mechanism of DPCs. This study provides new directions and potential targets for further research.

High-Parameter Immune Profiling with CyTOF

Mass cytometry, or CyTOF, is a useful technology for high-parameter single-cell phenotyping, especially from suspension cells such as blood or PBMC. It is particularly appealing to monitor the systemic immune changes that could accompany cancer immunotherapy. Here we present a reference panel for identification of all major immune cell populations, with flexibility for addition of trial-specific markers. We also describe best-practice measures for minimizing and tracking batch variability. These include: sample barcoding, use of spiked-in reference cells, and lyophilization of the antibody cocktail. Our protocol assumes the use of cryopreserved PBMC, both for convenience of batching samples and for maximum comparability across patients and time points. Finally, we show an option for automated analysis using the Astrolabe platform (Astrolabe Diagnostics, Inc.).

Network Modeling of Complex Data Sets

We demonstrate a selection of network and machine learning techniques useful in the analysis of complex datasets, including 2-way similarity networks, Markov clustering, enrichment statistical networks, FCROS differential analysis, and random forests. We demonstrate each of these techniques on the Populus trichocarpa gene expression atlas.

Untargeted Differential Metabolomics Analysis Using Drift Tube Ion Mobility-Mass Spectrometry

Mass spectrometry-based metabolomics is being increasingly applied to a number of applications, including the fields of clinical, industrial, plant, and nutritional science. Several improvements have advanced the field considerably over the past decade, including ultra-high performance liquid chromatography (uHPLC), column chemistries, instruments, software, and molecular databases. However, challenges remain, including how to separate small molecules that are part of highly complex samples; this can be accomplished using chromatographic techniques or through improved resolution in the gas phase. Ion mobility-mass spectrometry (IM-MS) provides an extra dimension of gas phase separation that can result in improvements to both quantitation and compound identification. Here we describe a typical drift tube IM-MS metabolomics workflow, which includes the following steps: (1) Data acquisition, (2) Data preprocessing, (3) Molecular feature finding, and (4) Differential analysis and Molecular annotation. Overall, these methods can help investigators from a variety of scientific fields use IM-MS metabolomics as part of their own workflow.

CRUP: a comprehensive framework to predict condition-specific regulatory units

We present the software Condition-specific Regulatory Units Prediction (CRUP) to infer from epigenetic marks a list of regulatory units consisting of dynamically changing enhancers with their target genes. The workflow consists of a novel pre-trained enhancer predictor that can be reliably applied across cell types and species, solely based on histone modification ChIP-seq data. Enhancers are subsequently assigned to different conditions and correlated with gene expression to derive regulatory units. We thoroughly test and then apply CRUP to a rheumatoid arthritis model, identifying enhancer-gene pairs comprising known disease genes as well as new candidate genes.

PEPA test: fast and powerful differential analysis from relative quantitative proteomics data using shared peptides

We propose a new hypothesis test for the differential abundance of proteins in mass-spectrometry based relative quantification. An important feature of this type of high-throughput analyses is that it involves an enzymatic digestion of the sample proteins into peptides prior to identification and quantification. Due to numerous homology sequences, different proteins can lead to peptides with identical amino acid chains, so that their parent protein is ambiguous. These so-called shared peptides make the protein-level statistical analysis a challenge and are often not accounted for. In this article, we use a linear model describing peptide-protein relationships to build a likelihood ratio test of differential abundance for proteins. We show that the likelihood ratio statistic can be computed in linear time with the number of peptides. We also provide the asymptotic null distribution of a regularized version of our statistic. Experiments on both real and simulated datasets show that our procedures outperforms state-of-the-art methods. The procedures are available via the pepa.test function of the DAPAR Bioconductor R package.

Five simple yet essential steps to correctly estimate the rate of false differentially abundant proteins in mass spectrometry analyses

Results from mass spectrometry based quantitative proteomics analysis correspond to a subset of proteins which are considered differentially abundant relative to a control. Their selection is delicate and often requires some statistical expertise in addition to a refined knowledge of the experimental data. To facilitate the selection process, we have considered differential analysis as a five-step process, and here we present the practical aspects of the different steps. Prostar software is used throughout this article for illustration, but the general methodology is applicable with many other tools. By applying the approach detailed here, researchers who may be less familiar with statistical considerations can be more confident in the results they present.

lncDIFF: a novel quasi-likelihood method for differential expression analysis of non-coding RNA

BACKGROUND

Long non-coding RNA (lncRNA) expression data have been increasingly used in finding diagnostic and prognostic biomarkers in cancer studies. Existing differential analysis tools for RNA sequencing do not effectively accommodate low abundant genes, as commonly observed in lncRNAs.

RESULTS

We investigated the statistical distribution of normalized counts for low expression genes in lncRNAs and mRNAs, and proposed a new tool lncDIFF based on the underlying distribution pattern to detect differentially expressed (DE) lncRNAs. lncDIFF adopts the generalized linear model with zero-inflated Exponential quasi-likelihood to estimate group effect on normalized counts, and employs the likelihood ratio test to detect differential expressed genes. The proposed method and tool are applicable to data processed with standard RNA-Seq preprocessing and normalization pipelines. Simulation results showed that lncDIFF was able to detect DE genes with more power and lower false discovery rate regardless of the data pattern, compared to DESeq2, edgeR, limma, zinbwave, DEsingle, and ShrinkBayes. In the analysis of a head and neck squamous cell carcinomas data, lncDIFF also appeared to have higher sensitivity in identifying novel lncRNA genes with relatively large fold change and prognostic value.

CONCLUSIONS

lncDIFF is a powerful differential analysis tool for low abundance non-coding RNA expression data. This method is compatible with various existing RNA-Seq quantification and normalization tools. lncDIFF is implemented in an R package available at https://github.com/qianli10000/lncDIFF .

Differential analysis of combinatorial protein complexes with CompleXChange

BACKGROUND

Although a considerable number of proteins operate as multiprotein complexes and not on their own, organism-wide studies so far are only able to quantify individual proteins or protein-coding genes in a condition-specific manner for a sizeable number of samples, but not their assemblies. Consequently, there exist large amounts of transcriptomic data and an increasing amount of data on proteome abundance, but quantitative knowledge on complexomes is missing. This deficiency impedes the applicability of the powerful tool of differential analysis in the realm of macromolecular complexes. Here, we present a pipeline for differential analysis of protein complexes based on predicted or manually assigned complexes and inferred complex abundances, which can be easily applied on a whole-genome scale.

RESULTS

We observed for simulated data that results obtained by our complex abundance estimation algorithm were in better agreement with the ground truth and physicochemically more reasonable compared to previous efforts that used linear programming while running in a fraction of the time. The practical usability of the method was assessed in the context of transcription factor complexes in human monocyte and lymphoblastoid samples. We demonstrated that our new method is robust against false-positive detection and reports deregulated complexomes that can only be partially explained by differential analysis of individual protein-coding genes. Furthermore we showed that deregulated complexes identified by the tool potentially harbor significant yet unused information content.

CONCLUSIONS

CompleXChange allows to analyze deregulation of the protein complexome on a whole-genome scale by integrating a plethora of input data that is already available. A platform-independent Java binary, a user guide with example data and the source code are freely available at https://sourceforge.net/projects/complexchange/ .

Genome-wide analysis of epigenetic dynamics across human developmental stages and tissues

BACKGROUND

Epigenome is highly dynamic during the early stages of embryonic development. Epigenetic modifications provide the necessary regulation for lineage specification and enable the maintenance of cellular identity. Given the rapid accumulation of genome-wide epigenomic modification maps across cellular differentiation process, there is an urgent need to characterize epigenetic dynamics and reveal their impacts on differential gene regulation.

METHODS

We proposed DiffEM, a computational method for differential analysis of epigenetic modifications and identified highly dynamic modification sites along cellular differentiation process. We applied this approach to investigating 6 epigenetic marks of 20 kinds of human early developmental stages and tissues, including hESCs, 4 hESC-derived lineages and 15 human primary tissues.

RESULTS

We identified highly dynamic modification sites where different cell types exhibit distinctive modification patterns, and found that these highly dynamic sites enriched in the genes related to cellular development and differentiation. Further, to evaluate the effectiveness of our method, we correlated the dynamics scores of epigenetic modifications with the variance of gene expression, and compared the results of our method with those of the existing algorithms. The comparison results demonstrate the power of our method in evaluating the epigenetic dynamics and identifying highly dynamic regions along cell differentiation process.

Differentially mutated subnetworks discovery

PROBLEM

We study the problem of identifying differentially mutated subnetworks of a large gene-gene interaction network, that is, subnetworks that display a significant difference in mutation frequency in two sets of cancer samples. We formally define the associated computational problem and show that the problem is NP-hard.

ALGORITHM

We propose a novel and efficient algorithm, called DAMOKLE, to identify differentially mutated subnetworks given genome-wide mutation data for two sets of cancer samples. We prove that DAMOKLE identifies subnetworks with statistically significant difference in mutation frequency when the data comes from a reasonable generative model, provided enough samples are available.

EXPERIMENTAL RESULTS

We test DAMOKLE on simulated and real data, showing that DAMOKLE does indeed find subnetworks with significant differences in mutation frequency and that it provides novel insights into the molecular mechanisms of the disease not revealed by standard methods.

dStruct: identifying differentially reactive regions from RNA structurome profiling data

RNA biology is revolutionized by recent developments of diverse high-throughput technologies for transcriptome-wide profiling of molecular RNA structures. RNA structurome profiling data can be used to identify differentially structured regions between groups of samples. Existing methods are limited in scope to specific technologies and/or do not account for biological variation. Here, we present dStruct which is the first broadly applicable method for differential analysis accounting for biological variation in structurome profiling data. dStruct is compatible with diverse profiling technologies, is validated with experimental data and simulations, and outperforms existing methods.

Protein-Level Statistical Analysis of Quantitative Label-Free Proteomics Data with ProStaR

ProStaR is a software tool dedicated to differential analysis in label-free quantitative proteomics. Practically, once biological samples have been analyzed by bottom-up mass spectrometry-based proteomics, the raw mass spectrometer outputs are processed by bioinformatics tools, so as to identify peptides and quantify them, by means of precursor ion chromatogram integration. Then, it is classical to use these peptide-level pieces of information to derive the identity and quantity of the sample proteins before proceeding with refined statistical processing at protein-level, so as to bring out proteins which abundance is significantly different between different groups of samples. To achieve this statistical step, it is possible to rely on ProStaR, which allows the user to (1) load correctly formatted data, (2) clean them by means of various filters, (3) normalize the sample batches, (4) impute the missing values, (5) perform null hypothesis significance testing, (6) check the well-calibration of the resulting p-values, (7) select a subset of differentially abundant proteins according to some false discovery rate, and (8) contextualize these selected proteins into the Gene Ontology. This chapter provides a detailed protocol on how to perform these eight processing steps with ProStaR.

MicroRNA dysregulational synergistic network: discovering microRNA dysregulatory modules across subtypes in non-small cell lung cancers

Background

The majority of cancer-related deaths are due to lung cancer, and there is a need for reliable diagnostic biomarkers to predict stages in non-small cell lung cancer cases. Recently, microRNAs were found to have potential as both biomarkers and therapeutic targets for lung cancer. However, some of the microRNA’s functions are unknown, and their roles in cancer stage progression have been mostly undiscovered in this clinically and genetically heterogeneous disease. As evidence suggests that microRNA dysregulations are implicated in many diseases, it is essential to consider the changes in microRNA-target regulation across different lung cancer subtypes.

Results

We proposed a pipeline to identify microRNA synergistic modules with similar dysregulation patterns across multiple subtypes by constructing the MicroRNA Dysregulational Synergistic Network. From the network, we extracted microRNA modules and incorporated them as prior knowledge to the Sparse Group Lasso classifier. This leads to a more relevant selection of microRNA biomarkers, thereby improving the cancer stage classification accuracy. We applied our method to the TCGA Lung Adenocarcinoma and the Lung Squamous Cell Carcinoma datasets. In cross-validation tests, the area under ROC curve rate for the cancer stages prediction has increased considerably when incorporating the learned microRNA dysregulation modules. The extracted modules from multiple independent subtypes differential analyses were found to have high agreement with microRNA family annotations, and they can also be used to identify mutual biomarkers between different subtypes. Among the top-ranked candidate microRNAs selected by the model, 87% were reported to be related to Lung Adenocarcinoma. The overall result demonstrates that clustering microRNAs from the dysregulation pattern between microRNAs and their targets leads to biomarkers with high precision and recall rate to known differentially expressed disease-associated microRNAs.

Conclusions

The results indicated that our method improves microRNA biomarker selection by detecting similar microRNA dysregulational synergistic patterns across the multiple subtypes. Since microRNA-target dysregulations are implicated in many cancers, we believe this tool can have broad applications for discovery of novel microRNA biomarkers in heterogeneous cancer diseases.

Extensive characterization and differential analysis of endogenous peptides from Bombyx batryticatus using mass spectrometric approach

Bombyx batryticatus, the dried larva of Bombyx mori L. (4th-5th instars) infected with Beauveria bassiana Vuill, is an important animal-derived medicine effective against several diseases. The metamorphosis of silkworm can result insignificant changes in the levels of proteins and polypeptides in the 4^th and 5^th instar larvae. Here, we performed extensive characterization of Bombyx batryticatus peptides, including polypeptides containing cysteines, using an MS-based data mining strategy. A total of 779 peptides with various PTMs (post-translational modifications) were identified through database search and de novo sequencing. Some of these peptides might have important biological activities. Besides, the differential analysis of polypeptides between the head and body of Bombyx batryticatus was performed to provide a clinical basis for rational use of the drugs derived from it. This study illustrates the abundance and sequences of endogenous Bombyx batryticatus polypeptides, and thus, provides potential candidates for the screening of active compounds for future biological research and drug discovery studies.

Responses of Patients with Disorders of Consciousness to Habit Stimulation: A Quantitative EEG Study

Whether habit stimulation is effective in DOC patient arousal has not been reported. In this paper, we analyzed the responses of DOC patients to habit stimulation. Nineteen DOC patients with alcohol consumption or smoking habits were recruited and 64-channel EEG signals were acquired both at the resting state and at three stimulation states. Wavelet transformation and nonlinear dynamics were used to extract the features of EEG signals and four brain lobes were selected to investigate the degree of EEG response to habit stimulation. Results showed that the highest degree of EEG response was from the call-name stimulation, followed by habit and music stimulations. Significant differences in EEG wavelet energy and response coefficient were found both between habit and music stimulation, and between habit and call-name stimulation. These findings prove that habit stimulation induces relatively more intense EEG responses in DOC patients than music stimulation, suggesting that it may be a relevant additional method for eliciting patient arousal.

HiCcompare: an R-package for joint normalization and comparison of HI-C datasets

Background

Changes in spatial chromatin interactions are now emerging as a unifying mechanism orchestrating the regulation of gene expression. Hi-C sequencing technology allows insight into chromatin interactions on a genome-wide scale. However, Hi-C data contains many DNA sequence- and technology-driven biases. These biases prevent effective comparison of chromatin interactions aimed at identifying genomic regions differentially interacting between, e.g., disease-normal states or different cell types. Several methods have been developed for normalizing individual Hi-C datasets. However, they fail to account for biases between two or more Hi-C datasets, hindering comparative analysis of chromatin interactions.

Results

We developed a simple and effective method, HiCcompare, for the joint normalization and differential analysis of multiple Hi-C datasets. The method introduces a distance-centric analysis and visualization of the differences between two Hi-C datasets on a single plot that allows for a data-driven normalization of biases using locally weighted linear regression (loess). HiCcompare outperforms methods for normalizing individual Hi-C datasets and methods for differential analysis (diffHiC, FIND) in detecting a priori known chromatin interaction differences while preserving the detection of genomic structures, such as A/B compartments.

Conclusions

HiCcompare is able to remove between-dataset bias present in Hi-C matrices. It also provides a user-friendly tool to allow the scientific community to perform direct comparisons between the growing number of pre-processed Hi-C datasets available at online repositories. HiCcompare is freely available as a Bioconductor R package https://bioconductor.org/packages/HiCcompare/.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2288-x) contains supplementary material, which is available to authorized users.

2D-DIGE and Fluorescence Image Analysis

2D-DIGE is still a very widespread technique in proteomics for the identification of panels of biomarkers, allowing to tackle with some important drawback of classical two-dimensional gel-electrophoresis. However, once 2D-gels are obtained, they must undergo a quite articulated multistep image analysis procedure before the final differential analysis via statistical mono- and multivariate methods. Here, the main steps of image analysis software are described and the most recent procedures reported in the literature are briefly presented.

DIGE Analysis of Human Tissues

Two-dimensional difference gel electrophoresis (2-D DIGE) is an advanced and elegant gel electrophoretic analytical tool for comparative protein assessment. It is based on two-dimensional gel electrophoresis (2-DE) separation of fluorescently labeled protein extracts. The tagging procedures are designed to not interfere with the chemical properties of proteins with respect to their pI and electrophoretic mobility, once a proper labeling protocol is followed. The two-dye or three-dye systems can be adopted and their choice depends on specific applications. Furthermore, the use of an internal pooled standard makes 2-D DIGE a highly accurate quantitative method enabling multiple protein samples to be separated on the same two-dimensional gel. The image matching and cross-gel statistical analysis generates robust quantitative results making data validation by independent technologies successful.

Utilizing networks for differential analysis of chromatin interactions

Chromatin conformation capture with high-throughput sequencing (Hi-C) is a powerful technique to detect genome-wide chromatin interactions. In this paper, we introduce two novel approaches to detect differentially interacting genomic regions between two Hi-C experiments using a network model. To make input data from multiple experiments comparable, we propose a normalization strategy guided by network topological properties. We then devise two measurements, using local and global connectivity information from the chromatin interaction networks, respectively, to assess the interaction differences between two experiments. When multiple replicates are present in experiments, our approaches provide the flexibility for users to either pool all replicates together to therefore increase the network coverage, or to use the replicates in parallel to increase the signal to noise ratio. We show that while the local method works better in detecting changes from simulated networks, the global method performs better on real Hi-C data. The local and global methods, regardless of pooling, are always superior to two existing methods. Furthermore, our methods work well on both unweighted and weighted networks and our normalization strategy significantly improves the performance compared with raw networks without normalization. Therefore, we believe our methods will be useful for identifying differentially interacting genomic regions.