Quality Control in any high-throughput sequencing technology is a critical step, which if overlooked can compromise an experiment and the resulting conclusions. A number of methods exist to identify biases during sequencing or alignment, yet not many tools exist to interpret biases due to outliers.https://github.com/gkumar09/iSeqQC) or web-interface (http://cancerwebpa.jefferson.edu/iSeqQC). A local shiny installation can also be obtained from github (https://github.com/gkumar09/iSeqQC).Hence, we developed iSeqQC, an expression-based QC tool that detects outliers either produced due to variable laboratory conditions or due to dissimilarity within a phenotypic group. iSeqQC implements various statistical approaches including unsupervised clustering, agglomerative hierarchical clustering and correlation coefficients to provide insight into outliers. It can be utilized through command-line (Github: iSeqQC is a fast, light-weight, expression-based QC tool that detects outliers by implementing various statistical approaches. High-throughput experiments are complex and prone to numerous biases during sample preparation, library preparation and sequencing. Therefore, Quality Control (QC) is critical and if overlooked, can compromise the data. To reduce false discoveries from any quantitative sequencing experiment such as RNA-seq, miRNA-seq and ATAC-seq, QC can be categorized in three different phases. In phase one, quality of raw read sequences is analyzed to detect bad quality bases. This is mainly performed on raw FASTQ files using tools including FastQC , FASTX-THere, we present iSeqQC- a simple expression-based quality control tool to detect outliers either produced due to variable laboratory conditions, reagent lots, personnel differences, different experiment times, or merely due to dissimilarity within a phenotypic group. Very straight-forward to use, iSeqQC uses a raw read count matrix or normalized transcript expression data to produce QC metrics in the form of graphical plots defining relationships of all samples.http://cancerwebpa.jefferson.edu/iSeqQC), command-line or the source package can be downloaded from https://github.com/gkumar09/iSeqQC.iSeqQC provides comprehensive information to identify any outliers in the sequencing experiment due to any technical biases. Developed in R, it can be utilized through a Shiny Server web interface  normalization on raw expression matrix using the following formula, density of mapped reads is estimated for each sampleiSeqQC requires two tab-delimited text files to execute: 1) a sample phenotype file with information on sample names, phenotypes and/or any confounding factors, if available; 2) a count matrix  with gene id or symbol from any read summarization tool such as RSEM , HTseq , feature3.Housekeeping gene expression: Expression profile  of two housekeeping genes, GAPDH and ACTB for all samples.4.Principal Component analysis (PCA)- Normalized: After z-transforming the expression matrix so that each row has a mean of 0 and a variance of 1, PCA, a dimensionality reduction algorithm is implemented to linear transform the data and observe the variance between samples. Z-score normalization is performed usingwhere μ is mean and σ is variance.where c is the total number of reads mapped to a gene and l is the length of a gene. For other organisms, it uses DESeq2 variance stabilized normalization.5.Multiple factor analysis: In a sequencing experiment, external factors that are not of biological interest can affect the expression of individual samples . To obse6.Hierarchical relationship: Measuring the distance of similarity between samples, agglomerative hierarchical clustering using Euclidean distance method is also implemented on the normally distributed expression matrix.7.Correlation: For correlation association, Pearson (quantity-based) and Spearman (rank-based) correlation using Ward’s method is implemented on the expression matrix.8.GC-bias: To assess if the bias is due to any error in library preparation step, the Locally Weighted Scatterplot Smoothing (LOWESS) fit of gene-count for each sample is plotted against GC-content. To obtain the obtain gene length and GC content for both human (GRCh38) and mouse (GRCm38), ‘getGeneLengthAndGCContent’ algorithm from EDAseq  was used9.Expression plot: Expression profile of any gene of interest.Principal component analysis (PCA)- Un- normalized: To observe the variance between samples without any standardization, PCA on raw expression matrix without any normalization is also implemented.After successfully executed, iSeqQC provides QC metrics in the form of a table and several graphical plots. First, it uses expression data to provide descriptive statistics, output in a form of a ‘summary statistics’ table. For each sample, it provides number of detected genes, mean expression, standard deviation, median expression, minimum expression, maximum expression, range of expression, skewness (symmetry of expression distribution), kurtosis (tails of distribution), library size and number of expressed genes (genes with greater than 0 reads). Next, the expression data is displayed as a ‘count-distribution’ box-plot to provide overall distribution of the expression of each sample with minimum, maximum and median expression. Further, the expression data is normalized  and density distribution of mapped reads is provided in a form of ‘mapped read density’ plot to observe any sample with no or low expressing reads. Due to stable expression of housekeeping genes, they are often used in sequencing experiments to normalize mRNA levels between different samples. iSeqQC uses GAPDH and ACTB expression data to detect whether samples show high expression of these two genes. All this information is used as a first sign to detect any outlier and could be further investigated for any possible biasness.After implementing basic statistics algorithms, iSeqQC implements various dimensionality reduction approaches to extract any technical bias in the sequencing experiment. Here, it first z-score normalizes the expression data and implements PCA unsupervised clustering method to identify the principal directions or variations called as components. The first two principal components which mainly are the highest source of variance are then displayed as a plot. This plot further segregates the samples based on their phenotype (data obtained from sample phenotype sheet). In some cases, normalizing the data can mask systematic bias, hence, iSeqQC also implements PCA on un-normalized data. To test the effect of external factors that are not of biological interest, but are possible sources of systematic bias, iSeqQC implements multifactor PCA. Here, multiple variables are weighted and assigned a weight equal to the inverse of the first eigenvalue of the analysis. Further, hierarchical clustering is implemented to provide the distance of similarity between replicates in a specific phenotypic group. Using the agglomerative method, it assigns each sample to its own cluster and then computes distance between each cluster and joins the two most similar clusters together. Next, iSeqQC utilizes correlation coefficients to detect the strength and direction of the relationship between the samples. It uses Pearson correlation, which evaluates the linear relationship between the samples and Spearman correlation, which is a rank-based method that can range from − 1 to + 1. The direction of the relationship is indicated by the value of the coefficient; samples with a close relationship tend to be in the positive range and vice versa. Next, relationship of read counts and GC-content for each sample is plotted to illustrate any bias in sequencing libraries. Output in the form of a plot, all this comprehensive information cumulatively provides sufficient indication of any outlier sample or cross-sample contamination.Additionally, iSeqQC provides an option to plot expression levels of any gene of interest for all samples in a sequencing experiment.The blood samples from Depuytrens affected patients and controls were collected and stored in RNAlater  at -80 °C. The samples were collected under the Institutional Review Board (IRB) approval #17D.510 of Thomas Jefferson University Hospital and informed consent was obtained from each participant. Under sterile conditions, total RNA was extracted using the Qiagen miRNeasy mini-kit .4 ng of total RNA was used to prepare libraries using the Takara Bio SMARTer Stranded Total RNA-Seq Kit  following manufacturer’s protocol. The final libraries were sequenced on NextSeq 500 using 75 bp paired-end chemistry.Homo sapiens Ensembl version GRCh38 utilizing further information from the gene transfer format (.gtf) annotation from GENCODE version GRCh38.p12 using STAR aligner [Raw FASTQ sequencing reads were mapped against the reference genome of  aligner  utilized aligner . Total rSequencing QC to obtain any read errors, poor quality reads and primer or adapter contamination was observed using FastQC . Inconsip value ≤0.05 and absolute fold change ≥2. All plots were constructed using R/Bioconductor.Differential gene expression was performed using diseased and control samples using the DESeq2  package Mus musculus (GRCm38) using STAR aligner implemented through RSEM. Total read counts were obtained using RSEM. This dataset was also used by Love et al. [Saccharomyces cerevisiae (R64–1-1) to obtain raw expression matrix. Additionally, raw count matrix of ENCODE dataset of human B-cells [http://bioinfo.cipf.es/noiseq/doku.php).To demonstrate the utility of iSeqQC, we also used previously studied datasets. We obtained sequence read archive FASTQ files of the Bottomly et al.  mouse RNe et al.  to test e et al. , were al B-cells  used by  B-cells  was obtaTo demonstrate the importance of expression QC and performance of iSeqQC, we first utilized RNA-seq samples sequenced in our laboratory to study Dupuytren’s disease.Following our laboratory standard protocols, all samples were tested to access RNA Integrity Number (RIN) and were within range of the requirements of the library preparation kit (> 2). The disease samples had a RIN score between 4 and 5 and control samples were between 2 and 3.Samples were sequenced and resultant FASTQ files were examined for sequencing errors using FastQC in phase one of QC. Here, for all samples, per base sequence quality for all bases at each position was observed to be > 30, demonstrating a base call accuracy > 99.9% Fig. a. AdditiIn phase two, QC was observed on the aligned BAM files using QC3 and RSeqQC. QC3 was used to observe mapping statistics, where all diseased samples had > 70% reads mapped to the reference genome showing high quality RNA samples. However, control samples had an overall low mapping percentage  as shown in Fig. iSeqQC_outputs). A comparison of existing QC tools and iSeqQC is provided in Table Next, in phase three, QC was observed on expression data using iSeqQC, which generates a summary table and 10 different plots to infer QC. Upon investigating the Principal Component Analysis (PCA) clustering  Fig. a and hiep value< 0.05) when compared to control. Here, we obtained 10,203 differentially expressed genes (DEGs), where 1278 were significantly up-regulated and 8925 were significantly down-regulated  from the differential expression analysis and observed only 5311 DEGs, where 856 genes were significantly up-regulated and 4455 were significantly down-regulated . As reported originally, iSeqQC also didn’t detect any bias in the GC-content metrics . These results by iSeqQC are in accordance with previous findings by Bottomly et al. [Saccharomyces cerevisiae grown in 3 different growth conditions: standard YP Glucose (YPD), Delft Glucose (Delft) and YP Glycerol (YP-Gly) with different library preparation methods and protocols. All the samples were sequenced on different flow cells. Z-score normalized PCA from iSeqQC showed Delft group to be tightly clustered, 2 out of 3 samples from YP-GLY were properly clustered. However, the majority of samples from YPD were dissimilar . Upon observing the multifactor PCA, this variation could be due to significant technical variation during the experiment and library protocol/preparation. Here, iSeqQC showed library protocol method to be the major source of variation, following the use of different flow cells and library preparation . These results are in accordance with findings by Risso et al. [PublicDatasetResults- Fig. E), pointing technical bias in the dataset . The results achieved by iSeqQC are in accordance with findings by Tarazona et al. [Next, we tested the performance of iSeqQC on previously published datasets. In mouse dataset by Bottomly et al. , we obsey et al. . In the y et al. , there wo et al. . Finallyo et al. , two difa et al. .Due to the complexity of high-throughput sequencing experiments, several phases of QC are required to identify any bias in the data. iSeqQC was designed to obtain comprehensive information on sample heterogeneity to detect outliers or cross-sample contamination in an expression-based sequencing experiment by implementing various statistical approaches including descriptive and dimensional reduction algorithms.In our dataset, iSeqQC was successful in identifying an outlier that was missed by existing sequencing and library QC tools. It indicated control4 to be an outlier due to its lower expression as indicated by summary statistics, housekeeping gene expression and PCA on unnormalized metrics. Upon assessing the GC-content metrics, we believed the bias could be due to library preparation step in control4 sample. We would like to note here that upon initial look at the PCA-normalized plot, one could flag diseased3 to be an outlier. However, when metrics for this sample are evaluated as a whole, they are representative of the phenotypic group and placement on the PCA plot can be attributed to biological variation.Data generated in our laboratory was sufficient to assess the utility of iSeqQC, but we also benchmark our tool using 3 different publicly available datasets that were tested by others previously. The well-characterized technical variance in these datasets offered high value in demonstrating the consistent performance of iSeqQC in a variety of scenarios. As expected, the results provided by iSeqQC were in accordance with the results previously reported for these datasets.At present, there are no defined rules to perform QC on expression matrices for the detection of outliers in any sequencing experiment. As shown in the results, existing tools and algorithms may not be sufficient. iSeqQC uses ensemble of various statistical methods to provide a detailed QC metrics in the form of a table and several graphical plots to identify any outliers. Additionally, at present while performing QC using stand-alone tools researchers have to re-run several lines of codes to re-evaluate the QC after removing the outliers. With iSeqQC, it is effortless as sample can be removed from the QC analysis by simply doing a minor change in sample phenotype sheet. Also, researchers spend hours generating publishable quality QC figures such as PCA and correlations plot, however iSeqQC by-default provides high-quality publication-ready figures. We would like to note here that for count matrix with gene id as input, iSeqQC requires only Ensembl annotations. If other annotations are used, all the metrics should work except ‘housekeeping’ and ‘GC bias’. However, in that case, ‘expression plot’ can be used to obtain the expression of housekeeping genes. Also, TPM normalization in ‘mapped read density’ plot is only compatible with human and mouse data, if any other organism is used, iSeqQC uses and DESeq2 variance stabilized normalization.While there exist several tools for assessing QC of sequencing experiments, each is limited to observe either sequencing and/or library quality. A few tools including QC3, QoRTs, RSeQC, and RNA-SeQC provide some information on outliers and cross-sample contamination but are not sufficient to provide in-depth sample qualities. QoRTs detects sample heterogeneity by analyzing read mapping, insert size distribution, cigar profile, and alignment clipping profile. RSeQC and RNA-SeQC uses Spearman and Pearson correlations to detect any outliers. QC3 is mainly focused to perform phase three QC only on Whole Exome Sequencing (WES) or Whole Genome Sequencing (WGS) data and does not include any quantitative sequencing technology such as RNA-seq. While EDASeq, NOISeq and DEGreport can utilize expression matrix as an input, they are mainly restricted to GC content, feature biotype and PCA at a basic level to explore the bias. Additionally, all these tools either require high-end computational resources or computational savviness to operate. As shown in the results, iSeqQC is simple, light-weight and accessible, yet powerful, approach to perform QC on expression-based sequencing technology,We acknowledge that due to the complexity of wet-lab protocols in sequencing technology, there are certain biasness that can evade any standardized QC approach. Implementing statistical approaches gives only an idea of overall sample heterogeneity and is not sufficient to remove the samples from study. Use of additional methods such as Real time- Polymerase Chain Reaction (RT-PCR) is recommended to validate the findings.iSeqQC is a simple, fast, light-weight, expression-based QC tool that detects outliers by implementing various statistical approaches. Implemented through web-interface and command-line interface, it generates high-quality publication-ready QC metrics for cross-comparison of samples.Project name: iSeqQC.Project home page:http://cancerwebpa.jefferson.edu/iSeqQChttps://github.com/gkumar09/iSeqQCOperating system(s): Not Applicable.Programming language: R.Other requirements: Web browsers equal or higher Safari v-12.1, Chrome v-79.0, Firefox v-72.2.License: MIT.Any restrictions to use by non-academics: None.Additional file 1. iSeqQC outputs. Remaining iSeqQC outputs  Counts distribution plot showing several low-expressed samples on Bottomly dataset; B) GC-bias plot showing no GC-content bias in any samples on Bottomly dataset; C) Unsupervised PCA clustering  showing variation in several samples in Risso dataset; D) Multifactor PCA showing library protocol method and different flow cell to be the major source of the variation; E) Unsupervised PCA clustering  showing samples clustered based on RNA extraction method in Tarazona dataset; F) Multifactor PCA showing RNA-extraction method to be the major source of variation.


Recently, the rise in the incidences of thyroid cancer worldwide renders it to be the sixth most common cancer among women. Commonly, Fine Needle Aspiration biopsy predominantly facilitates the diagnosis of the nature of thyroid nodules. However, it is inconsiderable in determining the tumor’s state, i.e., benign or malignant. This study aims to identify the key RNA transcripts that can segregate the early and late-stage samples of Thyroid Carcinoma (THCA) using RNA expression profiles.In this study, we used the THCA RNA-Seq dataset of The Cancer Genome Atlas, consisting of 500 cancer and 58 normal (adjacent non-tumorous) samples obtained from the Genomics Data Commons (GDC) data portal. This dataset was dissected to identify key RNA expression features using various feature selection techniques. Subsequently, samples were classified based on selected features employing different machine learning algorithms.DCN transcript that can classify the early-stage samples from late-stage samples with 0.66 AUROC. To further improve the performance, we identified a panel of 36 RNA transcripts that achieved F1 score of 0.75 with 0.73 AUROC (95% CI: 0.62–0.84) on the validation dataset. Moreover, prediction models based on 18-features from this panel correctly predicted 75% of the samples of the external validation dataset. In addition, the multiclass model classified normal, early, and late-stage samples with AUROC of 0.95 (95% CI: 0.84–1), 0.76 (95% CI: 0.66–0.85) and 0.72 (95% CI: 0.61–0.83) on the validation dataset. Besides, a five protein-coding transcripts panel was also recognized, which segregated cancer and normal samples in the validation dataset with F1 score of 0.97 and 0.99 AUROC (95% CI: 0.91–1).Single gene ranking based on the Area Under the Receiver Operating Characteristics (AUROC) curve identified the http://webs.iiitd.edu.in/raghava/cancertsp/).We identified 36 important RNA transcripts whose expression segregated early and late-stage samples with reasonable accuracy. The models and dataset used in this study are available from the webserver CancerTSP ( HBME-1 with CK19 combination , c ∈ [1–10] using grid search for optimizing the SVC performance. In addition, random forests, sequential minimal optimization (SMO), Naïve Bayes, and J48 were employed using WEKA software.We have developed machine learning models using two software; Scikit-learn package and Waikato Environment for Knowledge Analysis (WEKA) . We emplThe validation is an indispensable part of evaluating the performance of a prediction method. In this direction, the ten-fold cross-validation technique is exploited to calculate the performance of early vs. late-stage and cancer vs. normal classification models. Here, the dataset is randomly divided into ten sets, from which nine sets are used as training sets and the leftover tenth set as a testing dataset. This process is repeated ten times in such a manner that each set is used once as a testing dataset.In the present study, the performance of different models was measured by employing threshold-dependent and threshold-independent parameters. In case of threshold-dependent parameters, sensitivity (Sens), specificity (Spec), overall accuracy (Acc (%)), Matthews correlation coefficient (MCC), Precision, Recall and F1 score were calculated by using Eqs We also calculated a threshold-independent parameter called AUROC value, which is computed from the receiver operating characteristic (ROC) plot in this study. The ROC curve is produced by plotting true positive rate against the false positive rate at different thresholds. In addition, to ascertain the reliability of prediction, we also calculated PPV  and NPV  at various thresholds using the probability score obtained by employing SVC.The multiclass classification was implemented using the approach of one vs. rest multiclass SVC classifier employing the Scikit-learn package .Enrichment of genes was done using the Enrichr tool , 39. Onlin silico prediction models were developed using key RNA transcripts implementing various machine learning techniques. Notably, in the current study, we have considered the expression values of RNA transcripts as features for the analysis. To rule out the batch effects in the subsequent analysis, we calculated the Dispersion Separability Criterion (DSC) value. The DSC value for the TCGA-THCA data is 0.26 , which is less than 0.5, a commonly used DSC threshold for indicating batch effects  coding transcript shows the highest AUROC of 0.66. It is a proteoglycan whose role is well established in discriminating benign and metastatic thyroid and other tumors [To rank the classification potential of each RNA transcript for segregating early and late-stage samples, we developed a stage classification method using the expression threshold of each RNA transcript see . Brieflyr tumors , 41. OutThe 166 protein-coding transcripts are significantly enriched in nine oncogenic signatures from MSigDB Database  S3 Tabl that poiAs shown in the above section and From the previous results, it is evident that protein-coding transcripts were the major type of transcripts in THCA-EL-AUROC signature, therefore in this analysis, at first, we selected 19,814 protein-coding transcripts from 60,483 transcripts. Subsequently, different feature selection techniques like FCBF-WEKA (Fast correlation-based feature selection method present in WEKA)  and F_ANHIST1H2BJ, the transcript present in our signature forms a cluster, and this cluster is enriched in nucleosome cellular component. This cluster has also shown to be related to the progression of prostate cancer [DBT is present in our original signature. In addition, one more cluster of three genes is a part of the checkpoint clamp complex, out of which RAD1 is present in the original signature, and is involved in DNA damage response [To understand the interaction network among the key identified protein-coding genes, interaction analysis was performed in the STRING database  S5 Fig)S5 Fig) we cancer . Anotherresponse .In addition, the top 100 features  were selHanahan and Weinberg uncovered the importance of eight biological processes that played a vital role in tumor growth and metastatic propagation and called them as cancer hallmark processes . Thus, gPROC and NLK  are involved in the developmental pathway of the Wnt signaling, and are shown to be dysregulated in cancer [CYSLTR1 and ADRB1, are enriched in GPCRs terms . CYSLTR1 is upregulated in colon cancer patients and associated with poor prognosis [To develop prediction models based on the cancer hallmark genes, initially, 4,814 cancer hallmark specific genes extracted from 60,483 transcripts. Subsequently, the number of features was reduced using various features selection techniques followed by the development of prediction models. Towards this, the 15 transcripts selected by FCBF-WEKA (THCA-EL-H) from cancer hallmark genes were used to develop models. The F1 score of 0.68 with AUROC of 0.71 (95% CI: 0.66–0.77) is attained on the training data, while the F1 score of 0.69 with AUROC of 0.73 (95% CI: 0.61–0.85) is obtained on the validation dataset . Out of rognosis . A similFurther to ascertain the role of both coding and non-coding transcripts, we explored all the 60,483 transcripts to identify relevant features that can segregate early and late stage samples. The 78 transcripts (THCA-EL-All-WEKA) were chosen using FCBF-WEKA based feature selection algorithm. The SVC model based on the THCA-EL-All-WEKA panel performed well and attained F1 score of 0.78 and 0.86 AUROC (95% CI: 0.83–0.90) on the training dataset and F1 score of 0.70 with 0.73 AUROC (95% CI: 0.63–0.84) on the validation dataset . Among tNest we applied another feature selection method called F_ANOVA to select top 100 features. The SVC model using these 100 features achieved F1 score of 0.72 with 0.77 AUROC (95% CI: 0.66–0.77) on the training data and F1 score of 0.63 with 0.68 AUROC (95% CI: 0.56–0.79) on the validation data  as compared to 78 features (THCA-EL-All-WEKA) selected by WEKA. These 36 transcripts consist of 17 protein-coding genes, six long non-coding RNAs, and rest other types of non-coding RNA transcripts . The TERe of PTC . Overexpelopment .This model is deemed as the paramount model in our analysis for binary classification for early and late-stage samples. This also points out that both protein-coding and non-coding transcripts play an important role in tumor development.Eventually, to assess the classification potential of these features, validation performance is also evaluated on the external validation dataset, i.e., GSE48953, in addition to the independent dataset from TCGA. The GSE48953 dataset contains only 18 common features with that of 36-features signature. Therefore, we developed a model based on only those common 18 features. This model correctly predicted 70.6% (12 samples out of 17 samples) of early samples and 100% (3 samples) of late-stage samples of external validation dataset, as shown in The performance of different prediction models based on various feature sets for the segregation of early-stage and late-stage samples of training and validation datasets in terms of AUROC curves is depicted in One of the limitations of binary classification is that it would force even normal samples into either early or late-stage samples. Therefore, we implemented the multiclass classification by considering the normal samples available in TCGA for thyroid cancer.From the above analysis, it has been observed that the prediction model developed based on features selected by the SVC-L1 method has higher performance with the minimum number of features. Therefore, we employed the SVC-L1 to select 107 RNA-transcripts . The prediction models were developed based on these five RNA-transcripts using various machine-learning algorithms. The SVC model attained F1 score of 0.98 and 0.97 AUROC (95% CI: 0.93–1) on the training dataset and F1 score of 0.97 and 0.99 AUROC (95% CI: 0.91–1) on the validation dataset ( dataset . As ther dataset . As therRELN and RPS6KA5 are associated with activation of cyclic AMP (cAMP) response element-binding protein (CREB) transcription factor , which is responsible for tumor initiation, progression, and metastasis [RELN is an extracellular glycoprotein that plays a vital role in neuronal migration and has been shown to be downregulated in many cancers [Gene enrichment analysis revealed that tastasis . RELN is cancers , 60.We established a web server, CancerTSP (Thyroid cancer stage prediction), that implements models developed in the present study for investigation and estimation of cancer stage from the transcripts’ expression data. The CancerTSP consists of two key modules consisting of prediction and analysis.The prediction module consists of two modules for predicting the stage of the THCA cancer sample. One of the models is based on protein-coding features (THCA-EL-PC), and the other model is based on both protein-coding and non-protein coding transcripts (THCA-All-SVC-L1). We also provide a third prediction module based on the THCA-NEL-M features, which can predict whether the sample is normal, early-stage, or late-stage sample. The results are displayed on the score thresholds that exhibited a minimum difference in recall and specificity with maximum accuracy. The user can change the threshold for less or more stringent results in terms of recall and specificity. The lower threshold will increase the recall but decrease the specificity and a higher threshold will do the opposite to prediction outcomes .The user needs to provide transcript expression  of potential biomarker genes for every patient. The number of patients corresponds to the number of columns in a file. The output includes a list of patients and corresponding predicting stage of cancer (early or late-stage) along with the prediction score .http://webs.iiitd.edu.in/raghava/cancertsp/ for public use.Another module is dedicated for analysis which is helpful in evaluating the role of each transcript in discrimination of early-stage from the late-stage. This module gives p-value  for each transcript that signifies whether the transcript’s expression varies in the early and late-stage significantly. It also gives expression threshold and classifying AUROC of each transcript along with the average expression of that gene in the early and late-stage of cancer. The CancerTSP webserver is available from URL The current study is an attempt for the identification of reliable RNA expression-based genomic markers that are capable of segregating early-stage patients from late-stage patients of thyroid cancer. Despite the benefits of FNA for diagnosing papillary, medullary, and anaplastic thyroid cancer, it has limited utility in determining the stage and benign or malignant status of thyroid tumors. In addition, some FNA results suggest but not definitively diagnose papillary thyroid cancer . The diaDCN, at the threshold of 3.01 (log2 FPKM), showed maximum AUROC of 0.66. The DCN gene is a member of the extracellular small leucine-rich proteoglycan family present in connective tissues. Arnaldi et al. showed that DCN could be a potential diagnostic marker and therapeutic target for PTC [DCN leads to decreased adhesion and increased migration of glioma cells by downregulation of TGF-β signaling [In the current study, we tried to understand how well (prediction power in terms of AUROC) the expression of a gene or RNA transcript can predict the stage of the PTC tumor samples. First, we ranked all the transcripts on the basis of AUROC, calculated based on simple expression-based threshold models. Here, the expression of a single gene, i.e.,  for PTC . It alsoignaling .Furthermore, the gene-enrichment analysis of 166 protein-coding genes from the THCA-EL-AUROC signature set revealed their significant enrichment in various KEGG pathways including the Focal adhesion pathway, PI3K-Akt signaling pathway, and Proteoglycans in cancer, etc. Notably, the Focal adhesion kinase has already been shown to be overexpressed in thyroid cancers . There iTERT [FLT4 [DUSP6 [USP10 [POMC [TERT, is part of this signature and its promoter mutations are closely associated with aggressive clinicopathological characteristics and poor prognosis in PTC earlier [Next, various combinations are tested to elucidate potential biomarker subset for segregating early and late-stage samples. Towards this, we explored various feature spaces like protein-coding transcripts only, cancer hallmark transcripts, and both types of transcripts (protein-coding and non-coding transcripts) from the 60,483 RNA transcripts. The SVC model based on the THCA-EL-All-WEKA (78 features), resulted in F1 score of 0.70 on the validation data. The various types of features in this signature reveal the role of various non-coding transcripts along with protein-coding transcripts in the progression of cancer. Out of 28 protein-coding, five genes; TERT , FLT4 [6RT [FLT4 , DUSP6 [6 [USP10  and POMC10 [POMC  have alr10 [POMC . This sh earlier . Next, wRP11-363E7.4 showed the highest AUROC of 0.96 in classifying cancer samples from normal samples. It has been already demonstrated in the literature that sense to antisense transcript ratio increases in cancer [FAM84A shows 0.95 AUROC and has already been reported to play a role in metastasis of liver and colon cancer [Additionally, RNA transcripts having high prediction capability in terms of AUROC for categorizing cancer and normal samples also have been derived. Interestingly overlapping sense transcript n cancer . Other pn cancer , 77. In n cancer . FurtherEventually, a web server CancerTSP is developed, where the user can provide the transcripts’ expression  and can predict whether the cancer is in the early or late stage. This type of application where expression of transcripts is used to demarcate the early and late stage of cancer using machine learning can provide better understandings about the role of diverse transcripts responsible for the development of cancer from early to the late stage. Hence, this resource will help the scientific community in making preliminary hypotheses regarding cancer progression.In conclusion, 36 RNA-transcripts based SVC prediction model attained considerable performance in segregating the early-stage and late-stage PTC tissue samples with F1 score of 0.75. In addition, prediction models based on five protein-coding transcripts categorized tumorous and non-tumorous samples of patients with high F1 score of 0.97. Eventually, all prediction models based on identified candidate markers are integrated into CancerTSP webserver for the classification of early-stage from late-stage and PTC tumors from normal samples to facilitate the research community engaged in this field. We anticipate the current study might prove to be helpful in recognition of the potential of these new transcriptomic markers for early diagnosis of PTC. Additionally, further investigation of these markers on larger cohorts is required to confirm their potential clinical utility.In this study, we have scrutinized potential transcriptomic signatures to distinguish early and late-stage samples of PTC. One of the limitations associated with these signatures is that they are derived from tissue samples only, which is an invasive technique for biomarker discovery. Further, the external dataset used in the current study contains only 20 samples. Thus, field can be advanced by adopting non-invasive biomarkers from specimens like blood, urine, cell-free DNA, etc. along with the validation on large sampled cohorts to confirm their clinical utility.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.S6 Table(DOCX)Click here for additional data file.S7 Table(DOCX)Click here for additional data file.S8 Table(DOCX)Click here for additional data file.S9 Table(DOCX)Click here for additional data file.S10 Table(DOCX)Click here for additional data file.S11 Table(DOCX)Click here for additional data file.S12 Table(DOCX)Click here for additional data file.S13 Table(DOCX)Click here for additional data file.S14 Table(DOCX)Click here for additional data file.S15 Table(DOCX)Click here for additional data file.S16 Table(DOCX)Click here for additional data file.S17 Table(DOCX)Click here for additional data file.S1 Fig(PNG)Click here for additional data file.S2 Fig(PNG)Click here for additional data file.S3 Fig(TIFF)Click here for additional data file.S4 Fig(PNG)Click here for additional data file.S5 FigThe coloured nodes represent the genes in the signature and white nodes are indirect neighbours added to the network.(PNG)Click here for additional data file.S6 Fig(PNG)Click here for additional data file.S7 Fig(PNG)Click here for additional data file.S8 FigThe threshold of the selected models on the webservers (a) all transcript (THCA-EL-SVC-L1) training model (b) all transcript (THCA-EL-SVC-L1) validation model (c) protein coding (THCA-EL-PC) training model (d) protein coding (THCA-EL-PC) validation model (e) Cancer Normal (THCA-CN-F) training model (f) Cancer Normal (THCA-CN-F) validation model.(PNG)Click here for additional data file.S1 Dataset(ZIP)Click here for additional data file.


The precision evaluation of prognosis is crucial for clinical treatment decision of bladder cancer (BCa). Therefore, establishing an effective prognostic model for BCa has significant clinical implications. We performed WGCNA and DEG screening to initially identify the candidate genes. The candidate genes were applied to construct a LASSO Cox regression analysis model. The effectiveness and accuracy of the prognostic model were tested by internal/external validation and pan‐cancer validation and time‐dependent ROC. Additionally, a nomogram based on the parameter selected from univariate and multivariate cox regression analysis was constructed. Eight genes were eventually screened out as progression‐related differentially expressed candidates in BCa. LASSO Cox regression analysis identified 3 genes to build up the outcome model in E‐MTAB‐4321 and the outcome model had good performance in predicting patient progress free survival of BCa patients in discovery and test set. Subsequently, another three datasets also have a good predictive value for BCa patients' OS and DFS. Time‐dependent ROC indicated an ideal predictive accuracy of the outcome model. Meanwhile, the nomogram showed a good performance and clinical utility. In addition, the prognostic model also exhibits good performance in pan‐cancer patients. Our outcome model was the first prognosis model for human bladder cancer progression prediction via integrative bioinformatics analysis, which may aid in clinical decision‐making. In 2017, the number of deaths caused by BCa has reached 16 870 in US.The evaluation of prognosis is crucial for clinical treatment decision of BCa.Recently, many researchers focus on the exploration of microarray data.In the seven datasets of BCa, Weighted gene correlation network analysis (WGCNA) and differentially expressed genes (DEGs) screening was applied to exploring the candidate genes. Based on the candidate genes, the prognostic model was constructed by LASSO cox regression in the discovery dataset of BCa. Then, we validate the performance and accuracy of the prognostic model by several test sets and time‐dependent ROC respectively. Subsequently, univariate and multivariate cox regression analysis were performed and the nomogram was established to validate the clinical utility of the prognostic model. In addition, the performance of the model was also validated in pan‐cancer in TCGA data. Taken together, these results show the three gene signatures can be applied as new independent prognostic biomarkers for predicting the survival of BCa and pan‐cancer patients.22.1http://www.ncbi.nlm.nih.gov/geo/) and ArrayExpress database (https://www.ebi.ac.uk/arrayexpress/). Meanwhile, our group established a microarray analysis using mRNA isolated from the three stage II bladder cancer tissues and the three normal bladder tissues, as described by Wang et alhttp://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE76211). Detailed information for the 9 independent datasets including E‐MTAB‐4321, http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE13507, http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE32548, http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE32894, http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE40355, http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE48075, http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE5287, http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE7476 and http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE76211 were listed in Table The flow chart was showed in Figure 2.2For WGCNA analysis, we first calculated the standard deviation values for gene expression in each dataset, ranked by it and chose the top 25% for the following analysis.2.3http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE13507, http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE40355 and http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE7476; for http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE76211, we chose |log2FC| > 1 and P value <.05 as the cut‐off.RMA background correction for the raw expression data was first applied for the DEGs screening. Meanwhile, we performed log2 transformation and normalization for processed signals. Then, ‘affy’ R package was applied to summarize the median‐polish probe sets and the Affymetrix annotation files were used for probe annotation. Before the mRNA sequencing analyses, mRNA‐seq data was first normalized by ‘limma’ package and the expression value of each mRNA for each sample were normalized to generate the expression matrix. The normalized data were arranged in the order of tumour tissues and paracancerous normal tissues. In addition, the DEGs were screened by making comparisons between tumour tissues and paracancerous normal tissues. |log2FC| > 1 and FDR < 0.05 were used as the cut‐off threshold for datasets 2.4∑i=1n (coefi × Expri), where Expri is the relative expression of the gene in the signature for patient i, coefi is the LASSO coefficient of the gene i. Since no other dataset including progression free survival data for BCa, we only did the internal validation for PFS in E‐MTAB‐4321. Meanwhile, to further validate the clinical use for our model, prediction for overall survival (OS) and disease free survival (DFS) were tested in other independent datasets . Besides, to prove the wide utility of our model, pan‐cancer outcome validation was performed via KMPlotter database .LASSO (least absolute shrinkage and selection operator) is a regression analysis method that performs both variable selection and regularization in order to enhance the prediction accuracy and interpretability of the statistical model. Here, based on ‘glmnet’ R package, LASSO Cox regression analysis was applied to build an optimal prognostic signature for BCa by using candidate biomarkers.2.5Patients from different datasets were equally divided into low‐risk group and high‐risk group due to the risk score of our outcome signature. Then time ‐ dependent receiver operating characteristic curve (ROC) analysis by using ‘survivalROC’ package in R, was used to calculate the area under curve (AUC) for 1‐year, 3‐year and 5‐year PFS, OS and DFS, and check the prediction accuracy for our model.2.6P value, HR and 95% CI of each variable through ‘forestplot’ R package. We use ‘rms’ and ‘rmda’ packages of R software (version3.5.0) to perform the nomogram, calibration plots and decision curve. Nomogram was applied as a predict device to evaluate the prognosis of patients, which has the ability to generate an individual probability of a clinical event by integrating various prognostic factors. Calibration was performed to evaluate the performance of the 5‐year PFS nomogram. The x‐axis indicated nomogram‐predicted survival and the y‐axis represented observed outcome, and the 45° line represented the best prediction. Additionally, decision curve analysis (DCA) was also applied to evaluate the clinical utility of the nomogram, which could evaluate and compare prediction models which incorporated clinical consequences. The percentage of threshold probability and the net benefit were plotted on the x‐axis and y‐axis, respectively.First, univariate and multivariate cox regression analysis was performed to identify the proper terms to build the nomogram. The forest was used to show the 2.7http://software.broadinstitute.org/gsea/index.jsp) was used to explore biological function of prognostic signature for 2 groups.T value >2 and FDR < 0.05 were chosen as the cut‐off. Then, we merged the common pathways from 2 analyses.According to the median risk score in discovery set, we divided the samples into high‐risk and low‐risk groups. GSEA (2.8Bladder cancer tissue samples and paracancerous tissues (n = 12) were obtained from bladder cancer patients and stored in liquid nitrogen or fixed in 4% paraformaldehyde (PFA) for further analysis. Informed consent was obtained from all subjects. The Ethics Committee at Zhongnan Hospital of Wuhan University approved the experiments using human bladder tissue samples for RNA isolation and immunofluorescence staining analysis . All methods used for human bladder tissue samples were performed in accordance with the approved guidelines and regulations.2.9® ND‐1000 UV‐Vis spectrophotometer (Thermo Scientific). The cDNA was synthesized using 1 μg of total RNA isolated from bladder cancer and paracancerous tissues by ReverTra Ace qPCR RT Kit (Toyobo) and qRT‐PCR was performed using 500 ng cDNA per 20 μL reaction. Each reaction was conducted with iQTM SYBR® Green Supermix (Bio‐Rad) using 500 ng of cDNA in a final volume of 20 μL. Primer sequences and annealing temperatures are summarized in Table Total RNA from bladder cancer and paracancerous tissues was isolated using RNeasy Mini Kit  according to the manufacturer's instruction. DNase I digestion  was used in each RNA preparation to remove genomic DNA. After that, the quantity of isolated RNA was measured by a NanoDrop2.10The samples were fixed by 4% PFA at 4°C overnight and embedded into paraffin  by tissue processor . Then the rotation microtome  was used to cut paraffin sections (4 µm). The sections were incubated with indicated primary antibody and FITC‐labelled secondary antibody in humidified atmosphere . As for the prediction accuracy for the OS, the 1‐, 3‐, and 5‐year AUC were 0.724, 0.658 and 0.626 in http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE13507 and none, 0.656 and 0.790 in http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE5287, which represented high accuracy for OS prediction  and http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE32894   in our prognostic model have been previously reported to be associated with BCa. Topoisomerase‐II alpha (TOP2A) is essential enzyme for chromosome condensation and chromatid separation by regulating DNA topological state in the process of replication and transcription.In conclusion, our study revealed that the prognostic model based on comprehensive study including WGCNA, DEGs screening and LASSO Cox regression has reliable reproducibility and accuracy in predicting prognosis of BCa and even pan‐cancer.The authors declare that there is no conflict of interests.GW, LJ, YX and XW conceived and designed the study. YX, LY and JX provided bioinformatics and statistical analysis. HX and YL collected and organized specimens with clinical parameters. All authors have approved this manuscript. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file.


These eQTLs that we have highlighted are extremely promising candidates that merit further investigation.In recent years, the advantages of RNA-sequencing (RNA-Seq) have made it the platform of choice for measuring gene expression over traditional microarrays. However, RNA-Seq comes with bioinformatical challenges and higher computational costs. Therefore, this study set out to assess whether the increased depth of transcriptomic information facilitated by RNA-Seq is worth the increased computation over microarrays, specifically at three levels: absolute expression levels, differentially expressed genes identification, and expression QTL (eQTL) mapping in regions of the human brain. Using the United Kingdom Brain Expression Consortium (UKBEC) dataset, there is high agreement of gene expression levels measured by microarrays and RNA-seq when quantifying absolute expression levels and when identifying differentially expressed genes. These findings suggest that depending on the aims of a study, the relative ease of working with microarray data may outweigh the computational time and costs of RNA-Seq pipelines. On the other, there was low agreement when mapping eQTLs. However, a number of eQTLs associated with genes that play important roles in the brain were found in both platforms. For example, a  Gene expression analyses such as differential expression (DE) analysis were developed to understand the patterns of gene expression which in turn can serve as indicators of biological activity. Recently, gene expression data have been integrated with genomic data in the form of expression quantitative trait loci (eQTL) analyses, providing further insights into disease aetiology and complex regulatory networks ,2.High-throughput microarray technology has facilitated large-scale gene expression studies, especially as the cost of arrays decrease while becoming more flexible in their design. Another added benefit of microarray technology is that the analysis of microarray expression data is relatively straightforward, as the output from such analyses is in the form of continuous numerical measurements of probe intensities . HoweverRNA-Seq presents many bioinformatical challenges and requires many computationally costly steps and results in integer counts for each genomic feature of interest ,8. ThereTo date, there have been a limited number of studies ,10 on hoThis study therefore set out to assess the agreement between microarrays and RNA-Seq technology at these three levels in the human brain using genotype, microarray, and RNA-Seq expression data from two brain regions, the putamen (PUTM) and substantia nigra (SNIG) available as part of the United Kingdom Brain Expression Consortium (UKBEC) dataset.All bioinformatics and statistical computing were completed in the R environment v 3.6.0 . Due to The generation of the gene expression array data are as described by Trabzuni, et al.  followedThe same two regions (PUTM and SNIG) from the same RNA samples as the microarray study were selected for the RNA-seq platforms. However, due to difficulties in sample collection, instead of 134 samples, there was a total of 114 samples of which 62 samples had both PUTM and SNIG regional data. The remaining samples had data from only one region: 7 from SNIG and 45 from PUTM which ultimately, resulted in a total sample size of 107 for PUTM and 69 for SNIG.Briefly, of these brain samples, RNA-seq library preparation and sequencing were prepared by the UKBEC in conjunction with AROS Applied Biotechnology. In summary, 100 ng total RNA was used as input for cDNA generation using the NuGen’s Ovation RNA-Seq System V2 (NuGen Technologies). The RNA was processed according to the manufacturer’s protocol resulting in amplified cDNA from total RNA and concomitant de-selection of rRNA. Notably, reverse transcription in this protocol was carried out using both oligo(dT) and random primers. This allowed total RNA profile patterns to be assessed with the latter and locations of splicing to be inferred. Next, 1 μg of the cDNA was fragmented using a Covaris S220 Ultrasonicator and the fragmented cDNA was used as the starting point for Illumina’s TruSeq DNA library preparation. Finally, library molecules containing adaptor molecules on both ends were amplified through 10 cycles of PCR. The libraries were sequenced using Illumina’s TruSeq V3 chemistry/HiSeq2000 and 100 base pair paired-end reads. The sequencing data were then converted to FASTQ files using Illumina’s CASAVA Software. For more details, refer to Guelfi, et al. .The generated FASTQ files of paired-end reads were put through the FastQC program (version 0.11.8) for quality control . Some saRsubread :: buildindex and reads from each sample were than mapped to this reference using Rsubread :: align resulting in BAM files. The resulting alignments were then assigned at the gene-level based on the same annotations used for the microarray as described above. This was to reduce variability between technologies and to allow comparability. This resulted in raw read counts for 26,493 genes across the genome.The Rsubread  workflowedgeR :: filterByExpr where genes with a log-CPM ≥ 1.0945 across the minimum group sample size (i.e. SNIG=69 samples) were kept. This reduced the number of genes to 17,485 . Trimmed mean of M-values (TMM) normalisation was then applied but the effect was slight given that scaling factors were close to 1.These counts were then put through the edgeR-limma workflow as described by Law, et al.  for the Voom was then applied on the resulting read counts to obtain log-CPM gene expression values with associated weights [ weights . This me weights .The 17,485 genes that were kept after the filtering step in the RNA-Seq data pre-processing stage were subsetted out of the microarray data. Furthermore, as only the RNA from some samples were sequenced from PUTM and SNIG (62 samples), the corresponding samples from the two regions were subsetted out from the microarray data as well. This was to allow for comparability across the technologies.voom-transformed RNA-Seq expression data were calculated across the samples for each region separately, i.e. each correlation was based on 17,485 gene expression pairs. A linear mixed model was then fitted to investigate the effect of age, gender, cause of death, region and post-mortem interval (PMI) on the calculated correlation coefficients. Random brain sample effects were included to allow for the paired data (PUTM and SNIG) when they occurred. Note that the correlations were transformed using Fisher’s Z-transformation and splines were applied to the continuous explanatory variables (Age and PMI) to allow for a possible nonlinear effect on the correlation. This analysis was undertaken using the commercial R package ASReml-R [Spearman correlations between normalized microarray expression data and the ASReml-R .voom RNA-Seq expression data to classify DEGs. The 17,485 genes across the two regions (PUTM and SNIG) were used to fit a linear mixed model (LMM) to assess sources of variation at an overall level. Again, this was analysed using the commercial R package ASReml-R [ijky = microarray/voom transformed RNA-Seq expression data as described above for brain sample k; μ = overall mean expression value; iGene = random effect due to gene, assumed ijGene. Region = random effect due to gene i in a region j, assumed j = 1,2; and ijkε = random error, assumed voom-calculated precision weights at the observation level were added to the linear mixed model for RNA-Seq data. Because of the different measurement scale of microarray vs RNA-Seq data, levels of variation between platforms were compared using coefficients of variation, (σ/μ) ×100.The analysis method described in Trabzuni, et al.  was applASReml-R . The fori) of each gene being DE was returned and a threshold of τi ≥ 0.8 was used to determine DE status of each gene. This is similar to the method described by Trabzuni, United Kingdom Brain Expression and Thomson [The difference between the two regions (PUTM – SNIG) using the best linear unbiased predictions (BLUPs) of the gene × region random effects were then obtained and modelled as a finite mixture distribution of DE vs non-DE genes and fitted using the expectation-maximisation (EM) algorithm . The pos Thomson .2>0.5.Overall, nearly 1 million SNPs were genotyped on the Illumina Infinium Omni1-Quad BeadChip array. The first filter of the major allele frequency (MAF)>5% reduced the SNP set to 787,220. Following this, any SNP that was missing in any of the sample was omitted leaving a final set of 720,851 SNPs. Linkage disequilibrium (LD) analysis was then carried out and haplotype blocks were identified using a threshold of RThe R package MatrixEQTL  was usedvoom-transformed RNA-Seq expression data were regressed on SNP genotypes separately for each region. However, due to the observation-level weights that need to be included in the linear model, MatrixEQTL could not be used. Instead, R code was written using the lm function from the base R package that allows for specific weights. The same adjusted Benjamini-Hochberg FDR was then calculated and the threshold of 0.01 was used to determine significant RNA-Seq eQTLs. Note the need for an adjusted FDR was because a preliminary p-value threshold of 1 × 10-4 was applied before FDR was calculated. This was to decrease the size of the output files.Similarly, voom-transformed RNA-Seq expression data calculated for each region across samples were moderately high  (see p=0.17), age (p=0.59), PMI (p=0.65), gender (p=0.30) or cause of death (p=0.58) on the correlation between platforms. This finding was expected given that the RNA material was extracted from the same individuals and both RNA-Seq and microarray data underwent extensive quality control measures. However, it is still important to note that these variables did not play a significant role for this dataset, and therefore can be disregarded as possible factors for the high correlations calculated across the samples.Results show that the average Spearman correlations between microarray expression data and the A comparable number of DEGs were found using microarray data (1553) and RNA-Seq data (1632) with 1002 DEGs identified in both platforms. Encouragingly, both platforms agreed on the direction of the effects of these 1002 DEGs. This is unsurprising given the high Spearman’s correlation between the absolute microarray expression and RNA-Seq expression levels. These findings suggest that the relative ease of using microarrays may outweigh the computational time and cost of RNA-Seq pipelines for quantifying absolute expression levels or identifying DEGs in the regions of the human brain. Although, this would also depend on other qualifications of the study including using only known transcripts.p-value ≤ 10-4) more eQTLs were identified using microarray data  than RNA-Seq data . Furthermore, in this study we considered that eQTLs identified as significant by both platforms are true eQTLs and we classified them into two categories as described in Sng, Thomson and Trabzuni [cis- and trans-eQTL distinctions.The total number of significant eQTLs (FDR ≤ 0.01) found across both regions using microarray expression data was much lower compared to using RNA-Seq data . Interestingly, before the FDR threshold was applied  were identified as SR-eQTL in PUTM.There were 231 eQTLs identified by both platforms for PUTM and 27 for SNIG , i.e. trTrabzuni  and GTExcis-eQTLs in the PUTM, some were previously reported to be significant using only microarray data with inferences to brain functions [GSTT1)  due to the eQTL rs5760176     was sugg-11) see . This eQ1 region ,28. The ubstrate . Furthertrans-eQTL identified by both platforms. This trans-eQTL (SNP rs11002001) was found in SNIG and was paired with myelin protein zero (MPZ)  and retinol dehydrogenase 10 (RDH10) . Specifically, the A minor allele of this eQTL was associated with a decrease in both MPZ  in the brain in the GTEx dataset. The  Type 1b ,32. Furt Type 1b .An important limitation in this study was the smaller than ideal minimum sample size advised for eQTL mapping due to the subsetting of samples from the UKBEC dataset. However, the sample size of the current study is still larger than the sample sizes (n ≤ 50) which were shown to lead to unreliable results . There itrans-eQTL associated with the neurologically important gene MPZ as a strong candidate in addition to the previous reported cis-eQTLs associated with GSTT1 and NUCKS1-RAB29-PM20D1 locus. The authors are aware that trans-eQTLs tend to be taken as false positives by the majority of researchers due to the low replication of trans-eQTLs across studies [trans-eQTLs is based on patterns of observed data [trans-eQTL given that it was mapped using both platforms: microarray and RNA-Seq, and significant in GTEx dataset as well. Further validation with other human brain datasets is needed and a case versus control study would be essential.Despite the exploratory nature, this study has provided a comprehensive look at how mRNA expression levels measured by microarray and RNA-Seq technologies compare in two regions of the human brain and it demonstrates a high agreement between the two platforms at the absolute expression level and classifying DEGs. More importantly, this study identified a promising ved data  rather tSupplementary Material


Gentiana, which is one of the largest genera of Gentianoideae, most of which had potential pharmaceutical value, and applied to local traditional medical treatment. Because of the phytochemical diversity and difference of bioactive compounds among species, which makes it crucial to accurately identify authentic Gentiana species. In this paper, the feasibility of using the infrared spectroscopy technique combined with chemometrics analysis to identify Gentiana and its related species was studied. A total of 180 batches of raw spectral fingerprints were obtained from 18 species of Gentiana and Tripterospermum by near-infrared  and Fourier transform mid-infrared (MIR: 4000–600 cm−1) spectrum. Firstly, principal component analysis (PCA) was utilized to explore the natural grouping of the 180 samples. Secondly, random forests (RF), support vector machine (SVM), and K-nearest neighbors (KNN) models were built while using full spectra . The MIR-SVM model had a higher classification accuracy rate than the other models that were based on the results of the calibration sets and prediction sets. The five feature selection strategies, VIP (variable importance in the projection), Boruta, GARF , GASVM , and Venn diagram calculation, were used to reduce the dimensions of the data variable in order to further reduce numbers of variables for modeling. Finally, 101 NIR and 73 FT-MIR bands were selected as the feature variables, respectively. Thirdly, stacking models were built based on the optimal spectral dataset. Most of the stacking models performed better than the full spectra-based models. RF and SVM (as base learners), combined with the SVM meta-classifier, was the optimal stacked generalization strategy. For the SG-Ven-MIR-SVM model, the accuracy (ACC) of the calibration set and validation set were both 100%. Sensitivity (SE), specificity (SP), efficiency (EFF), Matthews correlation coefficient (MCC), and Cohen’s kappa coefficient (K) were all 1, which showed that the model had the optimal authenticity identification performance. Those parameters indicated that stacked generalization combined with feature selection is probably an important technique for improving the classification model predictive accuracy and avoid overfitting. The study result can provide a valuable reference for the safety and effectiveness of the clinical application of medicinal Gentiana. Gentiana is one of the largest groups in Gentianaceae, comprising 360 species that are widespread across Northwest of Africa, Europe, America, East of Australia, and Asia [G. lutea (Yellow Gentian) are traditional materials for alcoholic bitter beverages and have a function of being appetite stimulating and improving digestion [Gentiana has a long history in use for medicine [Gentiana plants found in these countries [Gentiana are diverse (about 248 species) and some of them have been an important part of traditional Chinese medicine (TCM) for a long time [G rigescens, G. trifloral, G. manshurica and G. scabra) and Qinjiao  [Gentiana have been recorded as the official drug of Pharmacopoeia of the People’s Republic of China (Ch.P. 2015 edition) [G. cephalantha, G. davidii, G. loureirii, G. rubicunda G, lawrencei var. farreri, and other species have been used as a popular herb in folk medicine and many other ethnomedicines for remedy digestive and respiratory illnesses [Gentiana and its related species are extensively used for various health disorders due to the cheap price of traditional herbs [Genus and Asia . Many spand Asia ,3,4. In igestion . In Asiamedicine ,3,6. Plaountries ,8. In Chong time ,9. Approahurica) . Presentllnesses ,12,13. GGentiana species [Gentiana species have different therapeutic properties and medicinal functions because of the complicated chemical profiles [G. lute and G. rigescens could be used as raw materials for the preparation of the therapeutic drug for Alzheimer’s disease because of neuritogenic compounds that were isolated from the two species [G. straminea and G. scabra are rich in iridoids, chemical composition and traditional uses are different between the two species. G. straminea is used for treating rheumatic arthritis, while G. scabra is used for liver protection [G. rhodantha and G. rigescens usually are often confused in traditional medicine markets in southwest China. In fact, the former is good at treating cough and other throat illnesses that are caused by fever, and, while the latter is used for chronic liver disease, inflammatory skin diseases, and clearing away heat [Gentiana species is crucial for keeping the clinical effect consistent and ensuring patients’ medication safety.Chemical and pharmacological researches have indicated that the composition of bioactive compounds is diverse according to different  species ,6. Until species . Those c species ,14,15. Gprofiles ,13,14. F species ,17. Althway heat ,13. ThosGentiana species show extremely high morphological similarity and their Chinese names of species are often used in confusion in the market (see sample information). Furthermore, the powder of medicinal materials of Gentiana species is difficult for achieving the identification. Although pharmacognosy morphology identification or microscopic identification based on inner structural composition features and the inclusions of medicinal materials may be used for this purpose [Gentiana and its relatives were focused on DNA barcoding, ISSR amplification, and other molecular identification technologies [Gentiana. In the past few decades, ultraviolet-visible (UV-Vis), Raman, and infrared (IR) spectroscopic have gained the attention of various botany scientists and pharmacognosists [Dendrobium, Paris, Rhodiola, Ganoderma, and the other genus based on IR spectroscopy has been reported [ purpose ; these wnologies ,19,20,21nologies ,22,23. Hgnosists ,25,26. Agnosists ,28,29,30gnosists ,32,33,34gnosists . This isgnosists ,32,33,34gnosists ,36. The gnosists ,36. Recereported ,36,37,38In the process of spectral discrimination, it is necessary to establish a relationship between the chemical information and sample categories by chemometrics then to establish a classification model for the class identification of unknown samples . AdditioGentiana and its wild relatives; (2) to select the optimal bands that identify the differences among different species; and, (3) to examine the feasibility of using stacked generalization combined with infrared spectral data to identify Gentiana species. The results of the study may provide some basis for the safety and effectiveness utilization of medicinal Gentiana resources in China.The aim of this research was (1) to investigate the application of NIR (near-infrared) and FT-MIR (Fourier transform mid-infrared) spectroscopies to the classification of medicinal G. rigescens and their relatives. It can be seen from the raw NIR spectra that there are seven distinct absorption bands, which are located at 6920, 5781, 5669, 5174, 4761, 4331, and 4260 cm−1, respectively  could be clearly separated from other species (G stragulata (5) and T. cordatum (18) were clustered and samples from the G. crassicaulis (6) were more easily differentiated from other samples  on the NIR data set are shown in  species A. In Sco samples B.G. stragulata (5) and G. pseudosquarrosa (12) were clustered. The samples of G. lawrencei var. farreri (4), G. rhodantha (15), and G. striata (16) were both located in the middle of the PC1 and PC2 axes. Most of the samples of G. squarrosa (11) were significantly different from other species and they were located on the negative side of PC1 and PC2. With the exception of the above species, all of the other species are grouped into one group (G. stragulata (5) and G. crassicaulis (6) were each separately clustered. Additionally, samples from the G. squarrosa (11) could be distinguished from those of the G. pseudosquarrosa (12) (ne group A. From tosa (12) B.Gentiana and its related species. Nonetheless, most of Gentiana species would be difficult to differentiate from one another, due to the overlap of their sample score. Hence, the application of supervised pattern recognition methods, such as random forest (RF), support vector machines (SVM), and k-nearest neighbors (KNN), for the development of classification models were required for enabling one to distinguish the samples.The grouping results indicated a potential application value of NIR and FT-MIR fingerprint for the discrimination of medicinal In the section, all of the classification models were established by full spectra data  and 180 samples were separated into a calibration set (108 samples) and a validation set (72 samples) by the Kennard–Stone algorithm ,50. Thostreen and trym [For RF models, model performance depends on the proper selection of the hyperparameters, which are and mtry . Figuresand mtry . Table 1For the SVM models, the optimum kernel function  and the cost function were important for modeling ,51. Hypek is critical for KNN [k value for NIR and FT-MIR data set were both one , the best performing classifier was scenario A. The next best-performing classifiers were scenario B and C, respectively. For the Ven-MIR data set (73 variables), a model of scenario E showed the highest classification rates and second were scenario D and F (94.00% and 90.00% classification rate respectively). Comparing the performance of different stacking models , SVM comGentiana species.Presently, the application of stacked generalization for establishing classification models of different medicinal plants or herbs is rather scarce. On the contrary, another modeling approach, data fusion strategy, has been widely used for classification and geographical origin traceability of herbs and foods ,49,53,54In this study, the FT-MIR and NIR spectral signals were straightforwardly concatenated and they constitute a low-level fusion data set . The mid-level data fusion data set (174 variables) was made up of feature important variables from Ven-NIR (101 variables) and Ven-MIR (73 variables) subsets . FinallyGentiana samples, as shown in The low and mid-level data fusion approach improved the discrimination capacity of the developed models to classify Gentiana and its relatives, only 73 variables used in the SG-Ven-MIR-SVM model, while low-level and mid-level data fusion models utilized 2701 and 174 variables for modeling, respectively. The variables number and modeling results indicated that the stacked generalization strategy is probably an important technique for improving species classification model predictive accuracy and avoiding overfitting.As we know, the data fusion (low-level and mid-level) approaches present a fusion of all variables or most important variables (feature variables) to create a model in order to exploit the synergy of the multispectral information to obtain an optimized model ,54,55,56Gentiana and Tripterospermum) of Gentianaceae . All sample powders were screened through a 100-mesh stainless sieve to obtain same-sized particles. The powders after sieving were stored in dry zip-lock bags for a further spectra scan of NIR and FT-MIR.−1 with a resolution of 4 cm−1 until 16 scans were averaged.The samples were scanned in the Antaris II spectrometer . Each powdered sample was scanned from 10,000 to 4000 cm−1 while using a resolution of 4 cm−1 and an accumulation of 16 scans. The ATR accessory is equipped with a unique metal O-ring for sample holding in order to control the path length and thickness of the sample  that was equipped with a deuterated triglycine sulfate (DTGS) detector and a ZnSe ATR  accessory . The spectral fingerprint of every sample was recorded bands from 4000–600 cme sample . In the −1 were not considered for further analysis due to strong crystal absorbance [−1 (baseline area and did not provide relevant information) and 682–653 cm−1 (disturbing absorption band of CO2) were excluded prior to chemometric analysis [Spectrum signals from 2500 to 1800 cmsorbance . Furtheranalysis .The principal component analysis (PCA), unsupervised technique, has been widely applied in data dimension reduction and exploratory data analysis ,63. FromRandom forests (RF) or decision tree forests is an ensemble learning technique . This alThe support vector machine (SVM) algorithm is a non-parametric supervised classification . Many prThe k-nearest neighbors (KNN) algorithm is a distance-based non-parametric discriminant technique . As its treen and trym), SVM (kernel function and cost function), and KNN (k) were optimized by using Bayesian optimization of mlr package combined with the MMCE model [Hyperparameters of RF  is a critical step in the modeling process . There mStacked generalization (stacking) is one of the ensemble learning . The essIn our study, the first level (level-0) of stacking model is composed of several weak classifiers  . SubsequUnlike stacked generalization, the data fusion strategy focus is on improving the model through best combine the subset. Most of the reported data fusion strategies include low-level data fusion and mid-level data fusion (feature-level data fusion) ,53.Low-level data fusion, as its name suggests, subsets are straightforwardly concatenated and reconstitute an independent data matrix. Subsequently, the new dataset is used to establish the classification models . In the The values of TP (Correctly identified samples of positive class), TN (correctly identified samples of negative class), FN (incorrectly identified samples of positive class), and FP (incorrectly identified samples of negative class) were calculated according to the confusion matrices of the classification models . Subsequ+ 14.0 Software . In the study, a strategy of two levels stacked generalization was used and the models were developed with R [https://www.r-project.org/) base on randomForest, e1071, Boruta, mlr, and class package. The Venn diagrams were completed by the tools on BMKCloud (www.biocloud.net).ATR correction of the FT-MIR spectra was completed by OMNIC 9.7.7 software . The other spectral data preprocessing (SNV and 2nd derivative), PCA, and VIP analysis were performed by SIMCA-Pd with R . KennardGentiana and their related species. Exploratory data analysis showed the NIR and FT-MIR spectroscopy indirect reflection interspecific phytochemistry diversity of medicinal Gentiana among the genera level and species level. Hence, there was a potential application value of NIR and FT-MIR fingerprint for the identification of medicinal Gentiana and its related species. Subsequently, supervised methods of pattern recognition were used for further analysis of spectra data. Firstly, six classification models based on RF, SVM and KNN algorithms were built on the full spectra data set that was obtained by the NIR and FT-MIR spectroscopy technique, respectively. The FT-MIR-SVM model performed more effectively than other classification models. Five approaches were applied to select optimal wavenumbers in order to improve the performance of the models and filter irrelevant or noisy features in data sets. In the end, the stacking models were built by stacked generalization combined with NIR and FT-MIR feature data sets. The modeling results suggest that RF and SVM were the best combinations of base learners (level-0). When compared the performance of six stacking models, SVM comes out to be the best algorithm at level 1 and the stacking model using the Ven-MIR data set had the highest accuracy of calibrations and validation sets. In conclusion, stacked generalization combined with feature selection is probably an important technique for improving the classification model predictive accuracy and to avoid overfitting.The results of this study indicated that NIR and FT-MIR spectroscopic techniques combined with chemometrics could successfully discriminate Chinese medicinal


S. aureus RNA-Seq data sets and reanalyzed them to generate data for sRNAs. To do so, we utilized an updated S. aureus genome annotation file, previously generated by our group, which contains annotations for 303 sRNAs. The data generated (which were previously discarded) shed new light on sRNAs in S. aureus, most of which are unstudied, and highlight certain sRNAs that are likely to play important roles in the cell.Regulatory small RNAs (sRNAs) are a class of RNA molecules that are produced in bacterial cells but that typically do not encode proteins. Instead, they perform a variety of critical functions within the cell as RNA. Most bacterial genomes do not include annotations for sRNA genes, and any type of analysis that is performed using a bacterial genome as a reference will therefore overlook data for sRNAs. In this study, we reexamined hundreds of previously generated  Staphylococcus aureus; however, their existence is often overlooked, primarily because sRNA genes are absent from genome annotation files. Consequently, transcriptome sequencing (RNA-Seq)-based experimental approaches, performed using standard genome annotation files as a reference, have likely overlooked data for sRNAs. Previously, we created an updated S. aureus genome annotation file, which included annotations for 303 known sRNAs in USA300. Here, we utilized this updated reference file to reexamine publicly available RNA-Seq data sets in an attempt to recover lost information on sRNA expression, stability, and potential to encode peptides. First, we used transcriptomic data from 22 studies to identify how the expression of 303 sRNAs changed under 64 different experimental conditions. Next, we used RNA-Seq data from an RNA stability assay to identify highly stable/unstable sRNAs. We went on to reanalyze a ribosome profiling (Ribo-seq) data set to identify sRNAs that have the potential to encode peptides and to experimentally confirm the presence of three of these peptides in the USA300 background. Interestingly, one of these sRNAs/peptides, encoded at the tsr37 locus, influences the ability of S. aureus cells to autoaggregate. Finally, we reexamined two recently published in vivo RNA-Seq data sets, from the cystic fibrosis (CF) lung and a murine vaginal colonization study, and identified 29 sRNAs that may play a role in vivo. Collectively, these results can help inform future studies of these important regulatory elements in S. aureus and highlight the need for ongoing curating and updating of genome annotation files.Regulatory small RNAs (sRNAs) are known to play important roles in the Gram-positive bacterial pathogen IMPORTANCE Regulatory small RNAs (sRNAs) are a class of RNA molecules that are produced in bacterial cells but that typically do not encode proteins. Instead, they perform a variety of critical functions within the cell as RNA. Most bacterial genomes do not include annotations for sRNA genes, and any type of analysis that is performed using a bacterial genome as a reference will therefore overlook data for sRNAs. In this study, we reexamined hundreds of previously generated S. aureus RNA-Seq data sets and reanalyzed them to generate data for sRNAs. To do so, we utilized an updated S. aureus genome annotation file, previously generated by our group, which contains annotations for 303 sRNAs. The data generated (which were previously discarded) shed new light on sRNAs in S. aureus, most of which are unstudied, and highlight certain sRNAs that are likely to play important roles in the cell. In bacterial cells, the best-studied and most abundant RNA molecules  play prominent roles in protein synthesis. Members of a less-well-studied class of RNA molecules that perform a variety of functions in the cell are collectively referred to as small RNAs (sRNAs). Many of these sRNAs are regulatory RNAs that influence gene expression by interacting with mRNA transcripts and/or proteins  are a valuable resource and have allowed us to (i) identify new sRNAs , and (ii) analyze global sRNA gene expression by RNA-Seq (S. aureus (In the Gram-positive bacterium entified . To facis genome . The res RNA-Seq . Recentl. aureus . Our stu. aureus .cis-acting and trans-acting sRNAs). The rationale behind this was to provide us with a more informative map of the S. aureus genome in which the locations of key RNA features were more apparent and to generate a tool that could be used for further studies. Undoubtedly, the list of “sRNAs” that we annotated is imperfect. sRNAs that are expressed only under specific conditions (not thus far tested) are likely to be missing from the list. Furthermore, it is also likely that some of the annotations added do not represent genuine sRNAs. As mentioned above, we did not restrict our annotation to trans-acting sRNAs; therefore, many of the annotated “sRNA genes” are riboswitches, UTRs, and cis-acting elements. Furthermore, we consider it likely that many of the annotations added in fact represent protein coding genes. Consistent with this idea, we noted that certain loci where sRNA annotations were added in USA300 FPR3757 correspond to loci where coding DNA sequence (CDS) genes had been annotated in other S. aureus strains  database, to mine them for sRNA data. Using our annotated sRNA genome file as a reference, we examine (i) RNA-Seq transcriptomic data sets to analyze sRNA gene expression levels in a variety of strain backgrounds and under a variety of environmental conditions; (ii) RNA-Seq data from rifampin-treated cultures to analyze the rates of sRNA decay and stability; (iii) a ribosome profiling (Ribo-seq) data set to determine which of the 303 previously annotated sRNAs are bound by ribosomes and therefore potentially encode peptides; and (iv) two recently published in vivo RNA-Seq studies to search for sRNAs that are upregulated during infection.As mentioned above, the absence of sRNA annotations in genome reference files has meant that all previously published RNA-Seq studies in S. aureus gene expression using tiling microarrays  in S. aureus led to increased “clumping” of the bacterial cells , which may indicate a potential biological role. While imperfect, these results were generated using freely available, previously discarded data and can serve as a valuable guide to inform future studies and facilitate improved analysis of sRNAs in S. aureus.Unlike the study by Mäder et al., the results presented here do not represent a comprehensive global transcriptomic analysis of isogenic strains across different growth media. Consequently, we have restricted our analysis to compare only RNA-Seq data sets from studies performed by the same research group from projects submitted to GEO. This ensures that all comparisons are performed in the same S. aureus genome files, we reasoned (i) that all previously published RNA-Seq data sets/experiments from S. aureus would have generated reads corresponding to sRNAs but (ii) that such data would have been discarded during the data analysis step. Consequently, a reanalysis of existing data sets performed using our sRNA annotated genome files would likely give information on the expression of sRNAs that had previously been missed. A flowchart outlining the reanalysis procedure is provided in S. aureus  and to perform only direct comparisons of sRNA gene expression between data sets generated in the same study (and therefore within the same genetic background). We hypothesized that the degree of sequence similarity between sRNAs from different S. aureus backgrounds would be sufficiently high that RNA-Seq reads generated in one strain  would map to the corresponding sRNA gene on a different S. aureus reference genome . Therefore, we elected to analyze all data sets using the USA300 FPR3757 genome (to which we previously added sRNA annotations ) found within the sRNA can encode functional peptides. Perhaps the best example of such a dual-function sRNA in on study , could ron study . Our goaS. aureus genome (containing sRNA annotations) and calculating RPKM (reads per kilobase per million) values for each sRNA. Next, RNA-Seq transcriptomic data sets, corresponding to the Ribo-seq data sets, were analyzed and expression values were calculated for each sRNA. RPKM values from the Ribo-seq experiment were divided by expression values from the RNA-Seq analysis, resulting in an RPF/expression ratio  to the on ratio . Normalion ratio . The majon ratio , indicaton ratio . To inve10.1128/mSphere.00439-20.7DATA SET S4Data Set S4, XLSX file, 0.1 MB.RPF/expression ratio for 303 sRNAs. Download Copyright © 2020 Sorensen et al.2020Sorensen et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the S. aureus. The RPF/expression ratio for RNAIII was 4.33, which was slightly lower than expected. An examination of the RPF/RNA-Seq read alignment data for the RNAIII locus shows a clear increase in the number of reads mapping to the hld locus in the RPF data set compared to the RNA-Seq data set . It also implies that any RNA with an RPF/expression ratio of >3 may potentially encode a protein/peptide.To further investigate the validity of the approach outlined above, we examined the RPF/expression data for RNAIII (which is known to encode a peptide) and also examined RPF/RNA-Seq read alignment data for the RNAIII locus in data set . The lowTaken together, the results from our reanalysis of the ribosome profiling data set indicate that the absolute RPF/expression ratio does not automatically pinpoint translated and untranslated RNAs; however, they do indicate that a RPF/expression ratio value below 3 is suggestive of noncoding transcripts whereas values above 3 may indicate the potential to encode proteins.tsr1 to tsr39) in S. aureus strain USA300 FPR . ORFs identical to the genes annotated at the corresponding loci in MRSA252 and NCTC 8325 were identified in each of the trs genes. We next cloned each tsr gene along with its native promoter into the high-copy-number pMK4 plasmid and tagged the predicted ORF on the C terminus with a 6× histidine tag. The tsr21 locus contained two unannotated potential ORFs (in addition to one annotated ORF); therefore, we individually tagged the previously unannotated ORFs and named them tsr21B and tsr21C, bringing the total number of tagged potential tsr peptides to eight.To experimentally investigate this hypothesis, we first examined the sequence of each S. aureus (USA300 AH1263) and the resulting strains grown for 4 h. Cultures were centrifuged to collect bacterial cells, and protein samples were prepared from the cytoplasmic, cell envelope, and secreted fractions. Western blot analysis was performed using an antihistidine antibody to identify putative small proteins. Bands corresponding in size to the predicted peptides encoded by tsr21C, tsr22, and tsr37 were identified in the cell envelope fraction . An analysis of the predicted amino acid sequence of the tsr21C, tsr22, and tsr37 peptides shows that all three peptides have predicted transmembrane helices , and so the failure to detect tsr9, tsr17, tsr18, tsr21B, or tsr35 in any of the protein fractions may simply indicate that these proteins were not expressed under the conditions tested.The overexpression plasmids were transformed into wild-type fraction  and B. N helices . CollectS. aureus cultures overexpressing histidine-tagged tsr37 rapidly autoaggregated and settled to the bottom of the culture tube. For simplicity, we refer to this process as “clumping,” but note that no agglutinating agent (such as fibrinogen or plasma) had been added to these cultures. To quantify the results, an aggregation assay , and an initial measurement of the optical density at 600 nm (OD600) of the sample was performed. After 2 h, 100 μl was withdrawn from the top of each sample and the OD600 measurement performed again. Percent clumping of each strain was then determined. A 10-fold increase in clumping was observed in the strain overexpressing the His-tagged tsr37 peptide (79.2% clumping at 2 h) compared to empty vector controls (7.9% clumping at 2 h)  that tsr37 has a biologically meaningful role in the cell. While it is tempting to speculate that the clumping phenotype observed upon tsr37 overexpression was due to the activity of the tsr37-encoded peptide, it remains possible that tsr37 functions exclusively as an RNA molecule and that the clumping phenotype is mediated via the tsr37 sRNA. Work is ongoing in our lab to investigate the nature and biological activity of tsr37.Interestingly, in growing the strains outlined above for the Western blot analysis, we observed that eviously ) was per at 2 h) . These din vivo to perform transcriptomic analysis of bacteria during the infectious process. Recently, two studies have utilized RNA-Seq to examine the transcriptional response of S. aureusin vivo. Work by Ibberson and Whiteley examined the S. aureus transcriptome in the lung of patients with cystic fibrosis (CF) ; therefore, we analyzed both data sets with our updated reference genome file. For analysis of the CF data set, we compared the transcriptomes of the nine samples isolated from CF patients with transcriptomes from nine S. aureus cultures grown in chemically defined media (CDM) and from nine S. aureus cultures grown in synthetic cystic fibrosis media (SCFM). We examined the data for sRNAs that were upregulated a minimum of 2-fold in the CF lung compared to both CDM and SCFM and found that a total of 122 sRNAs met our cutoffs . A total of 29 sRNAs were identified as upregulated in both the CF lung and the vagina compared to the corresponding in vitro conditions  of the 29 sRNAs identified have been studied in detail or have any known role in S. aureus. With that in mind, we compiled all of the data generated in this study for these 29 in vivo-expressed sRNAs, including their relative stabilities, RPF/expression ratios, and numbers of pairwise analyses where they demonstrated >3-fold variation in expression  , while Dnization . The RNA cutoffs . Similarsistence . Finallynditions . These spression . Of part10.1128/mSphere.00439-20.8DATA SET S5Data Set S5, XLSX file, 0.2 MB.Expression values of 303 sRNAs in CF lung isolates. Download Copyright © 2020 Sorensen et al.2020Sorensen et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSphere.00439-20.9DATA SET S6Data Set S6, XLSX file, 0.1 MB.Expression values of 303 sRNAs in vaginal colonization model. Download Copyright © 2020 Sorensen et al.2020Sorensen et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the in vivo and identified sRNAs that are upregulated in each niche. The results generated represent a useful guide for future studies, but we also acknowledge the limitations regarding their interpretation. In certain cases, the experimental data analyzed arose from a single set of growth conditions . The stabilities of sRNAs and their potentials to bind ribosomes and to be translated might, and most likely do, differ under differing growth conditions ; therefore, any error introduced  should be consistent throughout.The variety of ways in which RNA-Seq can be used is rapidly expanding, and the versatility of the technique is reflected in the variety of analyses presented here. We examined variation in expression of sRNAs under different environmental/growth conditions and in different genetic backgrounds and then went on to analyze sRNA stability/degradation and binding of ribosomes. Finally, we reexamined two recently published studies that used RNA-Seq to examine bacterial gene expression nditions –11. In aS. aureus and E. coli strains were grown routinely at 37°C with shaking in tryptic soy broth (TSB) and lysogeny broth (LB), respectively. When necessary, antibiotics were added at the following concentrations: chloramphenicol at 10 μg/ml and ampicillin at 100 μg/ml.The bacterial strains and plasmids used in this study are listed in S. aureus USA300 was previously published by our group and is available online . The genome file containing sRNA annotations for e online . RNA-Seqe online . The resSRP058249)  by the expression value of the same sRNA at time point 0 min (t0) to generate a t10/t0 value. Selected stable sRNAs  had a t10/t0 value greater than 0.5, and the selected unstable sRNAs  had a t10/t0 value of less than 0.01.To evaluate RNA stability, data were downloaded from GEO (accession number P058249) . Resultsg for 1 min. The supernatant was immediately aspirated, and the pellet was flash frozen using liquid nitrogen. Also at time zero, rifampin  was added to the culture. The sample preparation process was repeated at time points 2.5, 5, and 10 min. Pellets were stored at −80°C for subsequent RNA isolation. RNA was isolated as previously described (Cultures of USA300 strain AH1263 were grown in tryptic soy broth (TSB) for 2.5 h. At time point 0, 3 ml of culture was removed and placed into two 1.5-ml tubes and the cells were pelleted by centrifugation at 15,000 × escribed , and sam32P using a random primed DNA labeling kit (Roche) and purified with illustra MicroSpin G-25 columns . Membranes were prehybridized  in ULTRAhyb-Oligo buffer (Ambion) and then incubated with radiolabeled probe overnight. After incubation, membranes were washed with 2×, 1×, and 0.5× SSC buffer (1× SSC is 0.15 M NaCl plus 0.015 M sodium citrate) at 45°C for 15 min each time and visualized using a phosphorimager screen.Northern blotting to monitor the stability of selected sRNAs over time was performed as described previously . BrieflySRP065033). Both the Ribo-seq data and the accompanying RNA-Seq data were analyzed as described previously (Ribosome profiling (Ribo-seq) data were downloaded from GEO . Cells were pelleted and the supernatant was subjected to trichloroacetic acid (TCA) precipitation as described previously . BacteriIn vivo RNA-Seq data sets were acquired from GEO under accession number SRP222773 for CF data and accession number SRP229518 for vaginal data  as well as to those from nine samples from synthetic cystic fibrosis media (SCFM). A 2-fold cutoff value was employed to identify sRNAs with increased expression in the CF lung compared to both media. For the vaginal data, results from S. aureus growing in laboratory media were compared to results from samples isolated from the vagina of mice at 5 h, 1 day, and 3 days postinoculation. A 2-fold cutoff value was used to identify sRNAs with consistently increased expression in the vagina.nal data , 15. For