Annotation of polyadenylation sites from short-read RNA sequencing alone is a challenging computational task. Other algorithms rooted in DNA sequence predict potential polyadenylation sites; however, in vivo expression of a particular site varies based on a myriad of conditions. Here, we introduce aptardi , which leverages both DNA sequence and RNA sequencing in a machine learning paradigm to predict expressed polyadenylation sites. Specifically, as input aptardi takes DNA nucleotide sequence, genome-aligned RNA-Seq data, and an initial transcriptome. The program evaluates these initial transcripts to identify expressed polyadenylation sites in the biological sample and refines transcript 3′-ends accordingly. The average precision of the aptardi model is twice that of a standard transcriptome assembler. In particular, the recall of the aptardi model  is improved by over three-fold. Also, the model—trained using the Human Brain Reference RNA commercial standard—performs well when applied to RNA-sequencing samples from different tissues and different mammalian species. Finally, aptardi’s input is simple to compile and its output is easily amenable to downstream analyses such as quantitation and differential expression. Short read RNA sequencing and DNA sequence contain useful information for profiling polyadenylation sites, but each also possesses inherent limitations when examined independently. Aptardi combines these data and significantly improves annotation of polyadenylation sites in the expressed transcriptome. Most APA sites lead to identical protein products but variable 3′ untranslated region lengths2. APA has been associated with disease through many transcripts displaying APA  and APA in an individual transcript . Furthermore, differences in expression of APA transcripts have been implicated in diseases11 and are recognized as risk factors in complex diseases12. Indeed, research suggests individual susceptibility to complex diseases is mainly owing to variation in gene regulation processes—such as APA—rather than variation in protein-coding sequence16. APA’s impact is expected given that it is pervasive, with >70% of human genes subjected to APA17, and also far-reaching, as it modulates mRNA stability, translation, nuclear export, and cellular localization, as well as the localization of the encoded protein18—often times through differences in microRNA binding availability.Alternative polyadenylation (APA) is gene regulation mechanism by which a single gene encodes multiple RNA isoforms with different polyadenylation (polyA) sites20, and “choice” of polyA sites can be influenced by physiological, environment, and disease states21. This dynamic may explain—at least in part—why polyA sites are often under annotated22 and, furthermore, why (the often times sparse) prior annotation is typically not relevant to the given set of experimental conditions23. As a result, polyA sites often need to be re-defined for the sample(s) of interest to gain insight into the role of APA in various processes and diseases . There are three broad sequencing technologies utilized to identify polyA sites: (1) short-read RNA sequencing (RNA-Seq), (2) direct 3′-end RNA sequencing, and (3) DNA sequence, but each possesses inherent limitations for sample-specific identification of polyA sites.APA patterns are tissue specific27. Unlike splice junctions that can be precisely located via reads that span the junctions, polyA sites are characterized by a gradual drop off in coverage28. For assemblers that harness prior annotation to guide the reconstruction, often the annotated polyA site assumed by the assembler is not correct22. Although many transcriptome assemblers have been developed—each with its own design—to our knowledge none have demonstrated competence at annotating 3′-ends. Some assemblers, e.g., Cufflinks29/StringTie22, construct a minimum path RNA-Seq cover to the position where there is zero read coverage to annotate the 3′-end of a transcript30; but, since reads can be derived from precursor mRNA31, this often results in an overestimation of polyA sites. Others, e.g., Scripture32, calculate scan statistics above genomic background to define transcript structures, but this approach tends to produce biased estimates of polyA sites and, in general, is not well-suited for defining 3′-ends33. Importantly, these strategies are only capable of producing a single transcript stop site per intron chain structure, which tends to be the distal polyA site, thereby missing embedded proximal polyA sites, i.e., APA isoforms28. The challenge of accurately identifying polyA sites is apparent to both the developers and those evaluating assemblers by way of allowing for error at 3′-end predictions when assessing accuracy34.Next-generation RNA-Seq has become the standard technology to profile the expressed transcriptome. The resulting short reads are used by transcriptome assemblers to produce a genome-scale, sample-specific transcriptome map. Transcriptome assembly has proven a powerful approach to assess the transcriptome, but accurate determination of polyA sites from short-read RNA-Seq alone is a known shortcoming35 provided a comprehensive critical review of these methods which we will briefly highlight here. There are three main methods for characterizing polyA sites and/or quantifying APA dynamics. Those that require a priori annotated polyA sites, e.g., MISO36, QAPA37, and PARQ38, cannot identify de novo polyA sites. Others such as Kleat39 and ContextMap 240 utilize reads with strings of adenosines not derived from a DNA template, i.e., polyA tails. However, studies have demonstrated that polyA reads are scarce in RNA-Seq data42, resulting in low sensitivity and missing more weakly expressed polyA sites. Finally, those that consider fluctuations in read coverage near the 3′-ends of transcripts, e.g., DaPars43, APAtrap44, and TAPAS45, are largely interested in single gene APA switching and/or quantifying differential APA usage between two groups of samples rather than producing a complete transcriptome. Also—as noted by Chen et al.35—these tools are not user-friendly; specific input formats are required and outputs are not readily integrable into downstream studies.Acknowledging the challenges of annotating polyA sites and design shortcomings of transcriptome assemblers to do so, researchers have developed supplemental tools to characterize APA dynamics from RNA-Seq. Chen et al.46, 3′ READS47, PAS-Seq27, etc. . These methods are accurate at characterizing the genomic locations of polyA sites; however, whereas RNA-Seq data are widely available, 3′-sequencing data represents only a small fraction of available sequencing data and is costly and labor intensive to produce35.An alternative approach is to directly capture 3′-ends of mRNA with sequencing technology e.g., PolyA-Seq50 for a detailed review). Most of these methods, e.g., DeepPASTA51, Omni-PolyA52, and Conv-Net53, deploy machine learning but conspicuously do not consider in vivo expression.A final category of algorithms has sought to capitalize on the wealth of research connecting specific strings of DNA nucleotides, or DNA sequence elements, to polyadenylation . Aptardi leverages the information afforded by DNA nucleotide sequence information (from the appropriate reference genome) and RNA-Seq, as well as the predilection of transcriptome assemblers to accurately characterize splice junctions, in a use-all-data, multi-omics approach to create a modified, sample-specific transcriptome that includes information on expressed polyA sites Fig. . Specifi54 (HBR) RNA using Illumina’s TruSeq stranded mRNA sample preparation kit to generate 100 base, paired-end reads55. The transcriptome reconstruction contained 113,923 transcripts (excluding those from scaffold chromosomes) with 94,369 unique transcript termini, and the corresponding PolyA-Seq data contained 94,322 polyA sites in this sample. Throughout this manuscript, we refer to the polyA sites identified from PolyA-Seq46 as “true” polyA sites to distinguish them from polyA sites predicted by a computational algorithm, but we acknowledge that there are false negatives and false positives among the PolyA-Seq derived polyA sites. After transcript processing (see Methods) and integration with the PolyA-Seq data—generated from the same HBR RNA—70,748 transcript models with 0–50 true polyA sites per transcript model were used for learning and evaluating the aptardi prediction model. The modified 3′-terminal exons of these transcript models were binned into 100 base increments for machine learning, and 14 RNA-Seq features, 12 DNA sequence-related features, and one feature derived from the original transcriptome were calculated for each bin.The initial data set used for developing the aptardi model was derived from Human Brain Referenceχ2 = 80,837, p value < 0.0001; ATTAAA: χ2 = 15,012, p value < 0.0001; AGTAAA: χ2 = 1378, p value < 0.0001). For instance, of the 100 base bins that possessed a true polyA site via PolyA-Seq, over half also possessed AATAAA. In contrast, only ~10% of bins that did not contain a polyA site had the AATAAA signal. This enrichment was observed for all binary features, i.e., all the DNA sequence features and the original transcriptome end location feature  in each 100 base bin as a function of whether the bin also contained a polyA site are shown in Fig. When aptardi was built using only features derived from RNA-Seq or only features derived from DNA sequence, the average precision (AP) in the testing data set was significantly greater than simply relying on the polyA sites identified in the original transcriptome Fig. . FurtherThe relative contributions of the individual DNA sequence features were further explored by generating aptardi models for each DNA sequence feature that either (1) included all other features but the DNA sequence feature or (2) removed all other DNA-derived features but the DNA sequence feature  and evaluating performance on the testing split. Unsurprisingly, the greatest reduction in performance from leaving out any single DNA sequence feature came from removing the canonical polyA signal , and the greatest improvement by including any single DNA sequence feature was from this feature as well .56. To include a cross-species comparison and to examine similar tissue across two genetically different individuals, we also used data derived from two inbred rat strains; the congenic Brown Norway strain with polydactyly-luxate syndrome  and the spontaneously hypertensive rat strain . All true polyA sites were derived from 3′ sequencing PolyA-Seq data; true polyA sites for the HBR and UHR data sets were from the same corresponding RNA, whereas the true polyA sites for the two rat data sets were derived from Sprague Dawley rat brain RNA46.To evaluate the generalizability of the aptardi prediction model, we asked two questions: 1) does the performance of the aptardi prediction model, built on the HBR data set, remain consistent across diverse data sets, and (2) are the performances of prediction models built on alternative data sets comparable to the aptardi prediction model (built from the HBR data set)? To answer these questions, we analyzed four alternative data sets. These data sets were chosen because they had sufficient similarities and differences to assess the applicability of the aptardi prediction model  by comparing its performance on the four alternative data sets not used to train the model. The AP of the HBR-based aptardi prediction model across the four other data sets ranged from 0.55 to 0.63, whereas the AP of this HBR aptardi prediction model on its own HBR data set was 0.65  was minimal, i.e., ≤2 percentage points Fig. . Further57 and PolyA_DB 358. Although aptardi was designed to make sample-specific predictions, we evaluated the performance of the aptardi prediction model (built from HBR) on these more extensive true polyA sites data sets using the HBR RNA-Seq data and hg38/GRCh38 human genome. For each polyA site cluster listed by PolyASite 2.0, the representative polyA site was used. PolyASite 2.0 and PolyA_DB 3 contained 569,005 and 289,998 annotated polyA sites leading to 102,774 and 86,685 polyA site bins, respectively  out of the 778,166 bins produced from the transcript processing of HBR. As expected, the precision increased  and the recall decreased  compared with the aptardi prediction model’s performance on its testing data set .Other polyA site data sets in the literature report polyA sites aggregated from multiple samples and sources, such as PolyASite 2.022. The StringTie assembly also incorporated Ensembl59 (v.99) annotation, which helps guide its reconstruction—especially at 3′-ends. Note that aptardi outputs all original transcript structures from the input transcriptome  in addition to those annotations identified by the program.The overarching goal of aptardi is to yield an updated, sample/experiment-specific transcriptome map from the original transcriptome with more accurately annotated 3′-ends of expressed polyadenylated transcripts. As such, aptardi was primarily benchmarked by comparing how it improved upon the reconstruction generated by the popular assembler StringTieOf the 113,923 transcripts in the original transcriptome from the HBR sample , only 39,842 (35%) had a 3′ terminus that corresponded to a true polyA site (±100 bases). When the aptardi prediction model was incorporated, 27,853 transcript annotations were added to the original transcriptome where the polyA site/3′ terminus differed from its original transcript structure. Of these additional 27,853 transcripts, 22,846 (82%) matched the location of a true polyA site in the HBR PolyA-Seq data (±100 bases), meaning the majority of aptardi transcript structures incorporated into the original transcriptome had accurate polyA site annotation . The aptardi pipeline identified the 3′ termini correctly for 62,688 transcripts compared to 3′ termini of 22,804 transcripts using TAPAS. Although the number of transcripts with a false positive 3′ terminus was higher in the aptardi modified transcriptome compared with TAPAS45 owing to annotations from the original transcriptome, the positive predictive value was higher for the aptardi modified transcriptome because it added many more true positive than false positive 3′ termini to the original transcriptome . Finally, the aptardi pipeline captured more unique true polyA sites (as identified by the HBR PolyA-Seq data) compared with both TAPAS and the original transcriptome . Similar results were achieved when adjusting the base distance cutoff for true positives and/or utilizing the PolyASite 2.0 and PolyA_DB databases to define true polyA sites .For each the control and knockdown RNA-Seq data set, the aptardi modified transcriptome was compared with the original transcriptome, which contained both Ensembl annotations and sample-specific expressed transcripts identified through StringTie reconstruction. In the control RNA-Seq data set, neither aptardi nor the original transcriptome identified a shorter APA transcript for orm Fig. , demonstER1 Fig. . For TIMity Fig. . Finally64, true polyA sites were derived again from PolyA-Seq data that were specific to brain and liver, and the mouse reference mm10/GRCm38 genome was used for DNA sequence for both tissues .The above study demonstrated aptardi’s ability to differentiate polyA sites across samples; however, we extended this analysis by comparing different mouse tissues—namely liver and brain—to mimic subtle differences in polyA sites. Brain and liver RNA-Seq data were procured from Li et al.For the PolyA-Seq data, we identified polyA sites that (1) were unique to either brain or liver, (2) were within the 3′ modified terminal exon of a transcript in the StringTie generated original transcriptome  for both tissues, and (3) did not coincide with a previously annotated Ensembl transcript end. Using these restrictions, we were able to focus on unannotated polyA sites that differed across tissues but were associated with genes/transcripts expressed in both tissues. This resulted in 756 unannotated brain-specific sites and 1529 unannotated liver-specific sites. The StringTie pipeline was able to capture three of the unannotated brain-specific sites and four of the unannotated liver-specific sites. Including the aptardi prediction model (built from HBR) in the transcript discovery pipeline added 26 unannotated brain-specific sites (fold increase = 9) and 69 unannotated liver-specific sites (fold increase = 17) Furthermore, only nine of these 26 additional unannotated brain-specific polyA sites were also added to the liver data using aptardi  and only 25 of these 69 unannotated liver-specific polyA sites were identified by aptardi in the brain data.p value ≤ 0.001) but not the original transcriptome derived from the StringTie/Ensembl pipeline . Note that since aptardi incorporates transcripts into annotation, expression levels of existing transcripts can also change, i.e., transcripts present in both the aptardi modified transcriptome and the original transcriptome may be identified as differentially expressed in one and not the other. A total of 1166 out of 32,348 transcripts and 918 out of 28,329 transcripts expressed above background were differentially expressed  using the aptardi modified transcriptome and original transcriptome, respectively. A total of 40 transcripts that could be associated with an Ensembl gene symbol were differentially expressed in the aptardi modified transcriptome but not in the original transcriptome although they had identical structures, including 3′-ends, in both . Furthermore, 54 aptardi transcripts that could be associated with a gene symbol were differentially expressed and NOT measured/identified in the original transcriptome  a prediction model derived from a single data set performed well on data sets that differ on technical issues and even species, 2) the process of training the prediction model is generalizable across different types of RNA-Seq data/DNA sequence, and (3) the algorithm is not prone to overfitting. Namely, we showed that the aptardi prediction model provided for users (built from the HBR data set) performs equally well on RNA-Seq data sets derived from different library preparations, organisms, and with different RNA-sequencing depths. We note that aptardi performed modestly worse on the BNLx and SHR data sets and hypothesize this is because the true polyA sites were derived from the Sprague Dawley rat instead of the specific rat strain. This is supported by the fact that prediction models built from the BNLx and SHR data sets and tested on these same data sets performed similarly to the aptardi prediction model built on the HBR data set  sensitivity to sample-specific expression, (2) ability to identify both shortening and lengthening APA events, (3) competence across a broad range of RNA-Seq coverage depths, and (4) ability to improve upon current reconstruction methods. Of interest, in the control for rms Fig. . Of noteThe sample-specific sensitivity of aptardi was also evaluated using RNA-Seq from brain and liver mouse tissues and evaluating its ability to identify polyA sites unique to each tissue. Aptardi successfully annotated tissue-specific polyA sites not previously reported in Ensembl annotation or discovered by StringTie reconstruction. Furthermore, aptardi was able to provide a significant fold increase in polyA site annotation compared with StringTie, exemplifying its sensitivity. Although the overall number of polyA sites detected was modest, this was likely owing to the low sequencing depths of the samples  or already generated during the course of transcriptomic analysis . The output GTF file can be used in the same manner as other annotation files . Moreover, the program can be seamlessly integrated into a single operation with upstream transcriptome assembly and downstream analyses (e.g. quantitation) via piping to make for streamlined analysis. Finally, there is also the option of constructing a prediction model using the aptardi architecture, which increases the breadth of its applicability to diverse data sources.50, and as such researchers often group polyA sites within 30 bases into a single site46. Furthermore, few 100 base bins contained multiple polyA sites . However, this concern is partially mitigated because the precise location of polyA sites can “wiggle” by up to 30 nucleotides for what is considered a single isoform, i.e., not an APA eventTranscriptome profiling is one of the most-utilized approaches for investigating human diseases at the molecular level, yielding important insights into many pathologies. A prerequisite for these studies is a representative transcriptome map. Aptardi incorporates APA transcripts to produce a more-accurate transcriptome map, thereby enabling future research into the role of APA transcripts—as well as other transcripts unencumbered by convoluted annotation with APA transcripts–in human health and disease.The overall goal of aptardi is to accurately identify the polyA sites of expressed transcripts in a given biological sample. Specifically, aptardi analyzes the modified 3′-terminal exon  of previously annotated transcripts and, using relevant RNA-Seq data and DNA sequence in a machine learning environment, identifies locations of expressed polyA sites in the region. Aptardi then annotates the 3′-termini to match these locations and outputs these transcript structures to the transcriptome (in GTF format) that can be easily incorporated into downstream analyses. Note that aptardi does not evaluate the intron chain structure of transcripts, i.e., it only examines the modified 3′ terminal exon of each transcript structure and alters the 3′ terminus location(s) accordingly. Also, note that aptardi outputs all original transcript structures from the original transcriptome in addition to transcripts identified through its analysis, i.e., the program only adds transcripts.54 (see RNA-sequencing data sets for more details). BNLx and SHR represent two inbred rat strains: the congenic Brown Norway strain with polydactyly-luxate syndrome (BNLx/Cub) and the spontaneous hypertensive rat strain (SHR/OlaIpcv), respectively.A total of five unique data sets, hereafter referred to as HBR, 2nd HBR, UHR, BNLx, and SHR, were subjected to aptardi’s machine learning pipeline. In addition to RNA-Seq measurements, each data set required DNA sequence, a transcriptome, and—since each was used to build a machine learning model—a “gold standard” data source providing locations of expressed, i.e., “true” polyA sites. HBR, 2nd HBR, and UHR are well-established RNA reference samples from the MAQC/SEQC consortium69 and are publicly available on the PhenoGen website. The human reference genome (hg38/GRCh38), accessed via the UCSC Genome Browser70, was utilized for the HBR, 2nd HBR, and UHR DNA sequence data sets.For BNLx and SHR, strain-specific genomes were generated from the rn6/Rnor_6.0 version of the rat genome55: Accession: PRJNA510978, SRA runs: SRR8360036-37; 2nd HBR and UHR56: Accession: PRJNA362835, 2nd HBR SRA runs: SRR5236425-30, UHR SRA runs: SRR5236455-60; BNLx and SHR71: Accession: GSE166117, BNLx: GSM5061950-52, SHR: GSM5061947-49). In brief, all libraries were generated with the TruSeq stranded  or unstranded (BNLx and SHR) mRNA sample preparation kit (Illumina), sequenced on a HiSeq2500 Instrument (Illumina), and sequencing results processed to FASTQ files. The HBR RNA-Seq data set originated from 1 µg RNA starting material, whereas 100 ng input was used for 2nd HBR and UHR RNA-Seq data sets. For more detailed descriptions on these publicly available data, see Palomares et al.55 (HBR) and Schuierer et al.56 (2nd HBR and UHR). For BNLx and SHR, RNA‐seq libraries prepared from the polyA+ fraction were constructed using the Illumina TruSeq RNA Sample Preparation kit from 1 μg of brain RNA in accordance with the manufacturer’s instructions. Four μL of a 1:100 dilution of either ERCC Spike-In Mix 1 or Mix 2 (ThermoFisher Scientific) were added to each extracted RNA sample. An Agilent Technologies Bioanalyzer 2100 (Agilent Technologies) was utilized to assess sequencing library quality. RNA samples from three biological replicates per strain were processed and sequenced71. All reads were paired-end but differed in read length . Individual FASTQ files were assessed for quality using FastQC  and, if necessary, reads were trimmed with cutadapt72 (v.1.9.1). For the purpose of read coverage used by the aptardi algorithm, reads from technical replicates  or biological replicates  were concatenated and aligned to their respective genomes (see DNA sequence data sets section for more details) using HISAT273 (v.2.1.0) with the–rna-strandness (when appropriate) and–dta options specified as recommended for transcriptome assembly with StringTie22 (see Transcriptome data sets below for more details) and otherwise default arguments (see Supplementary Table 74 (v.1.9) was used to remove unmapped reads and convert the output to a sorted Binary Alignment Map (BAM) file required as input by aptardi.The HBR and 2nd HBR RNA-Seq data sets were derived from the Human Brain Reference , and the UHR RNA-Seq data set was derived from the Universal Human Reference . Each of these data sets were accessed from the Sequence Read Archive (SRA) using the SRA Toolkit (v.2.8.2) as publicly available data , were subjected to PolyA-Seq analysis to identify the genomic locations of expressed polyA sites in each sample . High-quality filtered polyA sites from each RNA sample were accessed using the UCSC Table Browser75, and liftOver76 (from the UCSC Genome Browser Group) was used to convert the genomic coordinates to the most recent human genome assembly (hg38/GRCh38) for the HBR and UHR samples, or rat genome assembly (rn6/Rnor_6.0) for the rat brain sample. PolyA sites identified in the HBR and UHR RNA reference samples were used for the corresponding HBR, 2nd HBR and UHR data sets, and those identified in the rat brain were used for both BNLx and SHR. Derti et al.46 uploaded technical replicates to UCSC Table Browser for each RNA sample; however, as polyA sites within 30 bases were clustered into the single site with greatest expression, and since this was done separately for each data set, we utilized only a single data set for each sample, i.e., technical replicates were not combined.True polyA sites  were taken from Derti et al.22 (v.1.3.5) was used to reconstruct the transcriptome expressed in each data set from their RNA-Seq data, hereafter referred to as the original transcriptome. Ensembl59 (v.99) reference annotation from the respective species was provided to guide the StringTie reconstruction. We note that a user can simply use reference annotation directly, i.e., Ensembl annotation, in lieu of performing transcriptome assembly that takes into account expression, i.e., StringTie. If the RNA-Seq data were stranded, the read orientation was specified as an argument to StringTie. Transcript structures from scaffold chromosomes and unstranded contigs, if present, were removed.StringTie44. Extensions overlapping any neighboring transcripts (on the same strand) were shortened to remove the overlap. RNA-Seq coverage at single-nucleotide resolution was obtained via bedtools genomecov  and, similar to the criteria employed by Ye et al.44 and Miura et al.78, used to refine each of these 3′ terminal exons, hereafter referred to as modified 3′ terminal exons (see Supplementary Methods for more details.) The refinement step either shortened the extended 3′ terminal exon or kept it the same length to give the modified 3′ terminal exon.Using the original transcriptome, the 3′ terminal exons of transcripts were isolated. Each transcript’s 3′ terminal exon was extended 10,000 bases plus two times the bin size  similar to what has been done previouslyFeatures were engineered in 100 base increments along the modified 3′ terminal exon, referred to hereafter as bins. For each bin, a total of 27 features were engineered and can be broadly classified as being derived from DNA sequence or RNA-Seq data. In both cases, information from the local environment, i.e., the 100 bases upstream and downstream the bin, as well as the bin itself,  was used.86 and evaluation of other algorithms that use DNA sequence to predict polyA sites88. Perhaps the most well-known indicator of polyadenylation is the polyadenylation signal (PAS), a conserved hexamer located ~10–35 nucleotides upstream the polyA site. Overrepresented sequences of DNA, or DNA sequence elements, also influence polyadenylation, and the location of these sequences are often described relative to the PAS. As such, DNA sequence features were engineered by first identifying the presence of several known PAS’s. Specifically, for each bin, a six base sliding window scanned a predefined region to detect the presence or absence (binary indicator of 1/−1) of (1) the canonical PAS (AATAAA), (2) its major variant (ATTAAA), (3) a second common variant (AGTAAA), and (4) any one of nine other minor variants  for four total PAS features. Subsequently, regions relative to the PAS  were likewise scanned using a sliding window approach to determine frequency of the following known DNA sequence elements: (1) a G-rich region downstream the PAS, (2) a downstream region near the PAS enriched in TTT, (3) a downstream region near the PAS enriched in GT/TG, and (4) a downstream region near the PAS enriched in GTGT/TGTG, (5) a T-rich region immediately downstream of the PAS, (6) a T-rich region upstream the PAS, (7) a TGTA/TATA-rich region upstream the PAS, and (8) a AT-rich region upstream and downstream the PAS86 for an additional eight features . If the frequency of the given DNA sequence element was above an enrichment threshold, the feature was encoded 1, otherwise −1. (See Supplementary Methods for more details.)The choice of DNA sequence features was made through a combination of an exhaustive literature review77 (v2.29.2). The approach for designing RNA-Seq features was to exploit localized fluctuations in RNA read coverage similar to that implemented by tools designed for APA-specific analysis from RNA-Seq data45. Intuitively, upstream but in close proximity to the end of a transcript, coverage is expected to begin to decrease gradually until its end. As a result, changes in expression were utilized when designing RNA-Seq features in two scenarios: (1) intra- and (2) inter-bin. In both cases, three regions were defined: an upstream region, a middle region, and a downstream region  wrapper for TensorFlow (v.2.0.0). This machine learning paradigm was chosen because of its design to analyze sequential data, i.e., it takes into account all the 100 base bins of a given transcript when learning model parameters for each individual bin. Each direction of the biLSTM consisted of 20 nodes , and this layer was followed by a fully connected dense layer with a sigmoid activation function that outputs a probability value. Of note, the biLSTM outperformed traditional classifiers such as Random Forest (RF) and Support Vector Machine (SVM) models .The machine learning task is two class classification (polyA site or no polyA site) of each 100 base bin. Supervised learning was used where labels for training were provided from the polyA sites data sets (see PolyA sites data sets for more details). A bidirectional long short term memory recurrent neural network (biLSTM)93 optimizer. Precision and recall metrics on the training and validation sets were monitored during training to prevent overfitting, and the model that produced the minimum loss was kept. For evaluation purposes (see Results), individual prediction models were generated from each of the five data sets.To prevent duplicate bins, overlapping modified 3′ terminal exons were merged prior to training. In addition, all merged modified 3′ terminal exons were masked to a length of 300 bins  to generate equal lengths, which is required for the sequential model. A total of 778,166 bins were present, of which 42,977 possessed a polyA site out of 94,322 polyA sites annotated by the PolyA-Seq data (for the HBR data set). Merged modified 3′ terminal exons were split into 60/20/20 training, validation, testing sets, respectively. Quantitative measures were standardized using the training set, and the training set was used to build the model in 25 epochs. Owing to the high imbalance of the data, class weights were used during training. Model weights were optimized using a binary cross entropy loss function and AdamTp = true positive, Fp = false positive, Fn = false negative, P = precision, R = recall, and F = F-measurePrecision, recall, and F-measure at the default probability threshold (0.5) were used to evaluate model performance defined as follows:94 (far fewer bins with polyA sites than bins without a polyA site):nth threshold, respectively.To generalize model performance over the range of probability thresholds, AP was used in place of the receiver operating curve owing to the highly imbalanced nature of the dataFor 100 base bins where a polyA site is predicted, transcript structures are annotated to the 3′ most base position unless either (1) the input transcript’s stop site is already in the region or (2) the 3′ most base position is within 100 bases of the input transcript’s stop site. Any aptardi transcript structures were added to the original transcriptome, and this aptardi modified transcriptome was outputted as a GTF file.To assess the impact of bin size, 25, 50, and 150 base bins were used to train the aptardi prediction model on the HBR data set in an otherwise identical manner to the original 100 base bin. The AP on the testing set was 0.41, 0.51, and 0.61 for the 25, 50, and 150 base bins, respectively, compared with 0.58 for the original 100 base bin. In addition, the F-measure on the testing set was 0.35, 0.46, and 0.52, respectively, compared with 0.52 for the for the original 100 base bin. Based on these results, 100 base bins were utilized to build the pre-existing aptardi prediction model. Higher resolution bin sizes may be appropriate depending on the data set , species, etc. and bin size can be specified as a parameter when using the aptardi pipeline.https://github.com/luskry/aptardi) was generated from the HBR data set55. Several other algorithm options are available.A user has the option of using the pre-existing aptardi prediction model or building a prediction model (if a reliable true polyA sites data set is available). The pre-built model, i.e., the aptardi prediction model, provided on the aptardi GitHub repository , the 3′ terminal exon of each transcript was provided as the noncoding region, and default arguments were used. The TAPAS polyA site prediction program (APA_sites_detection) was used here and accessed via its GitHub repository (https://github.com/arefeen/TAPAS).TAPAS  predicts the locations of polyA sites from RNA-Seq and reference annotation (genome or transcriptome)60. Its performance was evaluated using the human reference genome (hg38/GRCh38) and transcript structures generated from the HBR StringTie/Ensembl original transcriptome. Specifically, similar to that for aptardi, the 3′ terminal exon of each of the 113,923 transcript models in the original transcriptome were extracted. Since APARENT requires DNA sequences to be ≥205 nucleotides and ≤10,000 nucleotides, 20,658 3′ terminal exons were removed from the analysis. For the remaining 93,265 3′ terminal exons, the DNA sequence was extracted using BEDtools (v2.29.2). For 3′ terminal exons on the negative strand, the reverse complement sequence was used. The APARENT model  from its GitHub repository (https://github.com/johli/aparent) was used to predict the locations of polyA sites using default parameters.APARENT (APA REgression NeT) predicts the locations of polyA sites from DNA sequence61 for more details). Reads were processed in a manner identical to all other data sets to produce a sorted BAM file . The RNA-Seq library preparation was unstranded, and 100 base paired-end reads were sequenced on an Illumina HiSeq 2000 instrument . The two SRA runs per tissue represent technical replicates from one biological sample—namely 6-week old female C57BL/6JJcl mice. In brief, libraries were constructed using polyA selection and the Illumina TruSeq RNA-Seq library protocol and sequenced using an Illumina HiScan platform to generate 100 base, paired end, and unstranded reads. Reads were trimmed for quality and adapter content with Trimmomatic95 (v.0.39), and the two technical replicates per tissue were aligned together to the mm10/GRCm38 genome using HISAT2  followed by the production of an original transcriptome using the merged RNA-Seq data set and StringTie/Ensembl (without the read orientation argument). The aptardi modified transcriptome was produced using the original transcriptome as input, along with the merged RNA-Seq data, the rn6/Rnor_6 DNA sequence (accessed via the UCSC Genome Browser70), and the aptardi prediction model built from HBR. RSEM96 (v.1.2.31) was used to estimate the abundances of the isoforms identified within each transcriptome . Prior to quantitation, transcripts from scaffold chromosomes and unstranded contigs were removed from both transcriptomes. Isoform level expression estimates were determined for each biological sample . Isoforms without at least 50 counts in two of the three biological replicates for at least one strain were removed, and differential expression between the two strains (with BNLx as reference) were evaluated using DESeq297 (v.1.28.0) for the remaining set of isoforms in each transcriptome  that differ in the number of transcripts tested.To generate a single transcriptome representing both rat strains, their genome-aligned RNA-Seq data were merged using SAMtoolsFurther information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Reporting Summary


Malignant ovarian cancer is associated with the highest mortality of all gynecological tumors. Designing therapeutic targets that are specific to OC tissue is important for optimizing OC therapies. This study aims to identify different expression patterns of genes related to FGFR1 and the usefulness of FGFR1 as diagnostic biomarker for OC.We collected data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. In the TCGA cohort we analyzed clinical information according to patient characteristics, including age, stage, grade, longest dimension of the tumor and the presence of a residual tumor. GEO data served as a validation set. We obtained data on differentially expressed genes (DEGs) from the two microarray datasets. We then used gene set enrichment analysis (GSEA) to analyze the DEG data in order to identify enriched pathways related to FGFR1.GSE105437 and GSE12470 database highlighted the significant diagnostic value of FGFR1 in identifying OC .Differential expression analysis revealed that FGFR1 was significantly downregulated in OC specimens. 303 patients were included in the TCGA cohort. The GEO dataset confirmed these findings using information on 75 Asian patients. The Our study examined existing TCGA and GEO datasets for novel factors associated with OC and identified FGFR1 as a potential diagnostic factor. Further investigation is warranted to characterize the role played by FGFR1 in OC. Ovarian cancer is a common gynecological malignant cancer. Because its mortality rate ranks first among gynecological tumors, it has become a major health risk for women . The onsThe targeted treatment of OC plays an increasingly important role in the comprehensive treatment of OC and new tumor treatment strategies depend on the search for new targets . With thThe human fibroblast growth factor receptor (FGFR) family consists of four members: FGFR1 to FGFR4 and the native ligand of FGFRs is fibroblast growth factors (FGFs) . DysreguThis study aims to provide insight into the differential expression of FGFR-linked genes in OC and normal tissues by comparing TCGA and GEO data. The detection of DEGs between OC and non-OC tissues may facilitate the identification of novel therapeutic targets of OC. To better understand the role of FGFR1 in ovarian cancer in individual studies, a meta-analysis was designed to synthesize current findings. Here, DEGs found by comparing gene expression in OC and non-OC samples were screened using the R software. The enrichment pathway analysis of DEGs was performed using GSEA. Our results suggest that the significant diagnostic ability of FGFR1, but also note that more evidence is needed to improve disease prognosis and to design effective drugs to treat ovarian cancer.https://tcga-data.nci.nih.gov/tcga/. For the TCGA cohort, gene expression profiles were studied in 303 female patients with histologically confirmed ovarian cancer who had undergone prior surgical resection and received no pretreatment. Gene expression profiles were measured experimentally using an Illumina HiSeq2000 RNA Sequencing instrument at the University of North Carolina TCGA genome characterization center. Level 3 data was downloaded from the TCGA data coordination center. This dataset shows gene-level transcription estimates, as in log2(x+1) transformed RSEM normalized counts. Genes were mapped onto the human genome coordinates using HUGO probeMap. Reference to method description from University of North Carolina TCGA genome characterization center. All analyses were performed using FDR  corrected q-values adjusted to 0.05.To obtain a dataset based on studies listed in The Cancer Gene Atlas (TCGA) database, patients’ gene expression and clinical information were downloaded from the publicly available TCGA Data Portal at http://www.ncbi.nlm.nih.gov/geo/). The following keywords were used to query the GEO database: (ovary OR ovarium OR oophoron OR ootheca OR germarium) AND . All the functional genomics data of FGFR1 were requested and assembled from the GEO Database (http://www.ncbi.nlm.nih.gov/geo/) with the closing date of 10 February 2019. The inclusion and exclusion criteria of this study were as follows: (1) only human tissue studies were included, and samples from cell lines and animal models were excluded; (2) the expression data of the experimental and control groups must be provided or calculated; (3) dual-channel microarray studies were excluded; (4) DNA methylation arrays studies were excluded; (5) studies without a control group, as well as studies on other cancers were excluded; (6) studies with less than 20 cases were excluded. Differentially expressed genes (DEGs) identified by comparing expression in ovarian cancer and normal samples were analyzed using the R language software. Samples with both corrected P-values <0.05 and log fold changes (FC) >1 were deemed to be DEGs.We obtained OC microarray profiles from the GEO database  were used to test for differences in means among three or more groups. Standardized mean difference (SMD) was applied to evaluate the association between FGFR1 levels and OC using RevMan 5.3.0. We pooled SMDs across GEO datasets using the Mantel-Haenszel formula (fixed-effect model) or the DerSimonian-Laird formula (random-effect model). A fixed-effect model was adopted when the Q statistic was considered significant ; otherwise, a random-effect model was used.All data are displayed as mean ± standard deviation (SD) for each group. Student’s GSE105437, GSE66957, GSE66387, GSE40595, GSE29450, GSE29156, GSE27651, GSE26712, GSE18521, GSE18520, GSE17308, and GSE12470) were collected for use in our study. Two datasets (GSE105437 and GSE12470) contained detailed information on Asian populations, and these were used in this study. The platform for GSE105437 was GPL570, the [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array, includes 5 normal ovarian surface epithelial samples and 10 high-grade stage III invasive serous ovarian cancer samples. The platform for GSE12470 was GPL887, the Agilent-012097 Human 1A Microarray (V2) G4110B; this platform, included 10 normal peritoneum samples, 35 advanced stage serous ovarian cancer samples and 8 early stage serous ovarian cancer samples. In addition, 308 cases with EGFR or FGFR expression data were screened in TCGA database excluding 5 cases with recurrence, and 303 patients form this database were included in this study. A flowchart and other detailed information regarding the studies included in the meta-analysis are shown in The present study consists of several processes sequentially , that isGSE105437 and GSE12470 with analyses performing Fold change cut-off adjusted to 2.0 and corrected p-values cut-off adjusted to 0.05. Therefore, we considered FGFR1 might play an important role in diagnosing ovarian cancer. In brief, no significant differences were found between OC and nontumor groups based on the GSE105437 and GSE12470 data considered here . Finally, the funnel plot shown in To have a more comprehensive understanding of the biomedical predictive value of FGFR1, ROC Curves were provided to investigate the diagnostic value of GSE105437 and GSE12470 were analyzed using the R limma package and were sorted according to logfold-change values with corrected p-values <0.05. The results of DEGs from the two databases showed in The two ovarian cancer gene expression microarray datasets GSE105437 . In contrast, we found no statistical evidence that other characteristics, including age, stage, grade, tumor longest dimension, and ANS were independent prognostic factors. In addition, our results showed that patients with larger residual tumors, high FGFR expression, and advanced cancer stages were associated with shorter OS  were subjected to further examination. No clinical data was available for these datasets, and therefore we were not able to corroborate our previous finding. Next, we investigated the expression patterns of FGFR1 in ovarian cancer samples from both datasets. Survival analysis showed that low expression of FGFR1 was closely associated with poor OS. Moreover, using data from the GEO datasets, we also found that FGFR1 was downregulated in ovarian cancer samples relative to normal tissues. However, no evidence indicated that FGFR1 is an independent factor affecting the clinical outcomes of ovarian cancer patients. Taken together, the results of our investigation, we found that FGFR1 might have significant diagnostic value in predicting OC for Asian populations . These results suggest that FGFR1 may be a very good diagnostic biomarker but not a good prognosis marker. More samples should be examined to validate these findings.In this study, we examined FGFR1 expression in OC by examining RNA expression profiles in cancerous tissues sourced from the TCGA and GEO datasets. In samples from the TCGA database, we identified the clinical characteristics of OC patients that were found to be associated with FGFR1 expression. However we were not able to draw a strong functional link between FGFR1 and OC, since the TCGA dataset contained too fewer noncancerous samples, thus reducing the statistical power of the analysis. To gain insight into the functional molecular pathways of FGFR1 that are implicated in the development and progression of ovarian cancer, two samples from the GEO database (datasets In conclusion, this is a preliminary study designed to investigate the role played by FGFR1 in ovarian cancer. The meta analyses of ovarian cancer that we report here significantly extends our knowledge base to cognize that FGFR1 may be a good diagnostic biomarker in Asian populations . In the 10.7717/peerj.10817/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.10817/supp-2Supplemental Information 2Click here for additional data file.10.7717/peerj.10817/supp-3Supplemental Information 3Click here for additional data file.10.7717/peerj.10817/supp-4Supplemental Information 4Click here for additional data file.10.7717/peerj.10817/supp-5Supplemental Information 5Click here for additional data file.10.7717/peerj.10817/supp-6Supplemental Information 6Click here for additional data file.10.7717/peerj.10817/supp-7Supplemental Information 7Click here for additional data file.10.7717/peerj.10817/supp-8Supplemental Information 8Click here for additional data file.10.7717/peerj.10817/supp-9Supplemental Information 9Click here for additional data file.10.7717/peerj.10817/supp-10Supplemental Information 10Click here for additional data file.10.7717/peerj.10817/supp-11Supplemental Information 11Click here for additional data file.10.7717/peerj.10817/supp-12Supplemental Information 12Click here for additional data file.10.7717/peerj.10817/supp-13Supplemental Information 13Click here for additional data file.10.7717/peerj.10817/supp-14Supplemental Information 14Click here for additional data file.


We updated deepBase v3.0 to provide the most comprehensive expression atlas of small RNAs and lncRNAs by integrating ∼67 620 data from 80 normal tissues and ∼50 cancer tissues. The extracellular patterns of various ncRNAs were profiled to explore their applications for discovery of noninvasive biomarkers. Moreover, we constructed survival maps of tRNA-derived RNA Fragments (tRFs), miRNAs, snoRNAs and lncRNAs by analyzing >45 000 cancer sample data and corresponding clinical information. We also developed interactive webs to analyze the differential expression and biological functions of various ncRNAs in ∼50 types of cancers. This update is expected to provide a variety of new modules and graphic visualizations to facilitate analyses and explorations of the functions and mechanisms of various types of ncRNAs.Eukaryotic genomes encode thousands of small and large non-coding RNAs (ncRNAs). However, the expression, functions and evolution of these ncRNAs are still largely unknown. In this study, we have updated deepBase to version 3.0 (deepBase v3.0,  The vast majority of human genome and other mammalian genomes are transcribed to encode thousands of short (sRNAs) and long non-coding RNAs (lncRNAs), which have been implicated in diverse physiological and pathological processes, such as tumorigenesis, development, imprinting, apoptosis and cell differentiation . AlthougGiven the thousands of ncRNAs being discovered in various species, many databases have been developed to help researchers understand their diversity and functions in recent years. Notable examples include miRBase , a referTremendous amounts of deep-sequencing data have been generated by multiple consortium projects, such as the ENCODE , TCGA 1, ICGC 1, GTEx 1 and ERCCTo overcome the abovementioned issues, we have updated deepBase  to versiHomo sapiens) reference genome was updated to GRCh38(NCBI GRCh38) , chimp , gorilla , rhesus monkey , rat , cow , opossum , platypus , X. tropicalis , zebrafish  and C. elegans , were downloaded from the UCSC Genome Browser. Gene annotation files were downloaded from the UCSC Genome Browser corresponding to the genome versions  data from 14 species were collected from the GEO , GTEx 1, ENCODE  GRCh38) . The genns Table . Once cotRNA-derived RNA fragments(tRFs) are 14–32 nt single-stranded RNAs ,26. Theyz-score or mean and deposited in deepBase v3.0.For RNA-seq data, after downloading and classification, the raw data were recomputed as the FPKM (Fragments Per Kilobase of transcript per Million mapped reads) values to calculate the expression of genes. The expression in different tissues or life stages were normalized by For sRNA-seq data, miRNA data in RPM form were collected to determine the expression amounts. snoRNA annotations were downloaded from snoDB. We counted the snoRNAs with featureCounts (parameters: -M -s1 -fraction).For exosome data, based on a series of alignment results and a description file downloaded from ERCC, the expression levels of mRNAs and lncRNAs were integrated in RPKM format, and those of miRNAs and circRNAs were collected in RPM format.The gene expression profiles identified from RNA-seq data were reannotated to GRCh38 and were then separated into different subclasses of RNAs. EdgeR  was usedR stats package and the P-values were adjusted with the FDR   . Protein package .P-value < 0.05) were collected from the differential expression data and clinical data from TCGA to complete a survival analysis using univariate Cox regression. Once an RNA was identified as apparently differentially expressed in one kind of cancer, it was brought into the analysis. deepBase set the log-rank P-value ≤0.05 to determine whether an RNA had an influence on patient survival. A KM survival plot was drawn for the qualified lncRNAs  of RNAs in the form of a heatmap to give users a clear view of the relative expression in different tissues or samples. Users can click the gene name and jump to a detailed expression page. In the cancer part, the same normalization is applied to samples from different cancer types to show the expression differences between cancers. TCGA and ICGC data are displayed separately. The cancer small RNA page data are RNA-seq data, while the miRNA page data are sRNA-seq data, and the two types of data change in parallel. On the basis of the re-analysis of sRNA-seq data from TCGA, deepBase v3.0 also displayed the expression profiles of snoRNAs in various cancer types.The expression sections are divided into two parts: normal tissues and cancers. In the normal tissue part, deepBase provides the expression profiles of all mRNAs, lncRNAs, miRNAs, circRNAs and small ncRNAs. deepBase v3.0 shows the normalized expression values  are a type of RNA molecule that is present in various biological fluids. ExRNAs from heterogeneous populations including small RNAs, circRNAs, lncRNAs and mRNAs. They exist in free form or associate with proteins to form complexes, participating in a variety of cell-to-cell communications and play significant roles in cancer and other diseases. More than 2500 sample data downloaded from the ERCC database were analyzed to construct an expression map for different exosomes and to provide an overview of human exosomes RNA expression. Users can obtain direct views of exRNA expression in different biofluids and of the expression specificity of exRNAs.P-value < 0.05). deepBase shows all survival log-rank P-values in a data table. Genes that are not expressed in a specific cancer type or show no relationship to survival are not displayed. KM survival curve plots are provided for survival-related RNAs that pass the log-rank P-value threshold of 0.05.With RNA-seq and sRNA-seq sequencing data, we also collected clinical data from TCGA and ICGC for deepBase v3.0. Combining expression data and clinical data, we applied univariate Cox regression to all differentially expressed genes  GO terms are shown on the webpage, a text file containing all three kinds of GO terms can be downloaded for further study. The user can download the specific plot or the zipped data of all plots and data tables.We introduce deepBase v3.0, which has significantly better web modules and functionalities than deepBase v2.0 . PreviouVarious high-throughput sequencing methods, such as CLIP-seq, ChIP-seq and ribo-seq, have been developed to explore the biological function, regulatory networks and translational potential of ncRNAs. We are considering adding these kinds of data in the next version of deepBase to facilitate analyses and explorations of the complex regulation, functions and mechanisms of various types of ncRNAs. Moreover, more annotation data and additional species will be integrated to further expand this database. We will continue to improve the database to accept and analyze new data uploaded by users.http://rna.sysu.edu.cn/deepbase3/index.html. The deepBase data files can be downloaded and used in accordance with the GNU Public License and the licenses of primary data sources.deepBase v3.0 is freely available at


Determining which treatment to provide to men with prostate cancer (PCa) is a major challenge for clinicians. Currently, the clinical risk-stratification for PCa is based on clinico-pathological variables such as Gleason grade, stage and prostate specific antigen (PSA) levels. But transcriptomic data have the potential to enable the development of more precise approaches to predict evolution of the disease. However, high quality RNA sequencing (RNA-seq) datasets along with clinical data with long follow-up allowing discovery of biochemical recurrence (BCR) biomarkers are small and rare. In this study, we propose a machine learning approach that is robust to batch effect and enables the discovery of highly predictive signatures despite using small datasets. Gene expression data were extracted from three RNA-Seq datasets cumulating a total of 171 PCa patients. Data were re-analyzed using a unique pipeline to ensure uniformity. Using a machine learning approach, a total of 14 classifiers were tested with various parameters to identify the best model and gene signature to predict BCR. Using a random forest model, we have identified a signature composed of only three genes  predicting BCR with better accuracy  than the clinico-pathological variables  currently in use to predict PCa evolution. This score is in the range of the studies that predicted BCR in single-cohort with a higher number of patients. We showed that it is possible to merge and analyze different small and heterogeneous datasets altogether to obtain a better signature than if they were analyzed individually, thus reducing the need for very large cohorts. This study demonstrates the feasibility to regroup different small datasets in one larger to identify a predictive genomic signature that would benefit PCa patients. Prostate Cancer (PCa) is the most common non-cutaneous cancer in American men. Around 160 000 men were diagnosed with PCa in 2017  and arouCurrent treatments for localized PCa mainly include surgical removal or external beam radiation therapy of the prostate. If the initial treatments did not succeed to cure the patient then a recurrence will occur, revealed by an increase in seric PSA level, an event called biochemical recurrence (BCR). After surgery, about 70% of the patients will be cured and about 30% will relapse to a BCR. Since prostate tumor cells depend on androgens to grow, recurrences are treated with androgen deprivation therapy consisting in chemical or surgical castration either alone or in association with administration of anti-androgens. However, the cancer will inevitably recur and will then be called castration-resistant prostate cancer (CRPC). To treat CRPC, docetaxel  was intrOne problem generally inherent to cancer care is to orient people to the adequate treatment corresponding to the stage of the disease and the individual characteristics of the patient . In PCa,To reduce costs and continue to improve prognostic, omics data are promising. With the decreasing price of RNA sequencing (RNA-seq), the accessibility of affordable technologies , the avHence, there is a challenge to set up predictive models that could anticipate the event of BCR, thus predicting the evolution of cancer, immediately after surgery. Consequently, we propose here a method to discover a transcriptomic signature that could be used to predict BCR events using a combination of datasets to increase the discovery potential. To this purpose, we applied specific preprocessing and cleaning steps on three RNA-seq datasets and established a machine learning protocol.After recovering the raw data from the different studies, we processed them in a pipeline composed of three main steps: Samples quality control and selection, sequencing data processing, machine learning analysis . All devWe retrieved three different RNA-Seq datasets of radical prostatectomy specimens with the associated clinical features. The first dataset is from TCGA cohort in the Prostate Adenocarcinoma (PRAD) project. The second dataset (GSE54460) is from a cohort constituted by Quality of the BCR event data is dependent on patient clinical follow-up. A patient followed only a few weeks or months after surgery without showing BCR would be considered as a non-BCR case. These cases are a bias since the patient could have experienced a BCR event after the period of follow-up. Consequently, we discarded from our analysis the patients with no BCR whose follow-up was inferior to 60 months.1. According to the TCGA Research Network  and follow-up inferior to 60 months. We ended up with 52 samples after these filters.Data from 498 samples were initially recovered from the PRAD project on the TCGA data portal Network  131 sampGSE54460) where sequencing and clinical data from 106 patients were recovered. After selecting cases with a minimum of 60 months of follow-up, we ended-up with 96 patients of whom 54 had a BCR.The data were downloaded from NCBI website .Kallisto   training and testing sets, 1000 times, hence the classification algorithm is trained and tested on different sets. The measure of performance is an aggregated value  of the individual performance on the test set. Because we have no repeated measures and independent variables  we chose the subsampling method which is also the best in general in different benchmarks but is less effective computationally . The resTo evaluate the performance we used the balanced error rate (BER), the matthews correlation coefficient (MCC) and the mean misclassification error (MMCE). The BER is calculated as the average proportion of wrongly classified samples in each class and weights up small sample size classes . The areFeature selection was performed to reduce dimensionality to improve prediction performances by removing uninformative features, which has been proven successful in other studies . There aAlgorithms typically require to change the settings of parameters to optimize their performance. The optimization method was the Irace method  which isFollowing our machine learning pipeline , we firsSince our goal was to identify a very short genomic signature we looked up the BER rate and other metrics while varying the number of selected features, from 1 to 400, used in the model. We observed that the BER and MMCE dropped rapidly with a few features selected (<3) then oscillated around 0.27 .The MCC and the accuracy (ACC) went up rapidly and stabilized in the same way. After these observations, we focused the analysis on the first eight genes. The results are shown in Four hyper-parameters of the RF classifier were optimized: ntree, mtry, maxnode, and nodesize. Ntree refers to the number of decision trees in the model, mtry the number of variables selected from a decision split for the next split, maxnodes the maximal number of nodes in the forest and nodesize the minimal number of samples allowed in a node. Because we selected only three features, the parametrization step was not expected to drastically change the performance of our optimization task. First we used a grid search method to define the best setting for each parameter taken individually, letting the others at default. The grid search provided us 500 (ntree), 1 (mtry), 24 (maxnodes), and 5 (nodesize) .From these hyper-parameters an Irace search was performed around the space of those values. The best value was obtained with ntree, mtry, maxnodes and nodesize at 187, 1, 881 and 1 resp. for a BER of 0.27. We observed relative stability despite the modification of the hyperparameters.To ensure the stability of our three-gene model, a subsampling test was done 100000 times for the last part of our work. From this subsampling, the results obtained are ber = 0.274, mmce = 0.26, mcc = 0.468, fpr = 0.368, tpr = 0.82, acc = 0.739. Then we calculated the associated AUC (0.761) and plotted the ROC curve The proposed three genes signature  and a merged model combining clinic and omics data were set up following the same protocol used for the omics data. For the clinical model the best BER obtained was 0.311 and for the mixed model the best BER obtained was 0.276 .To further assess the performance of the three-gene model obtained with the combined dataset, we also performed the analysis with the individual cohorts. We used the RF algorithm iterated on the 50 best features from Information Gain on the three datasets evaluated by leave one out group validation , and the combined dataset evaluated by resampling . The results are displayed in Machine Learning is one of the fastest growing fields in bioinformatics  and its In this study, we took advantage of the power of machine learning to identify a biomarker signature composed of three genes. We showed that such short signature from omics data performs better to predict BCR than clinico-pathological features or a combination of these data . We have explored many machine learning algorithms, since each has its advantages and drawbacks in terms of computational time, hyper-parameters and range of application  and also because their performance depends on the type of data and their composition . Using tThe identified signature contains three genes: JUN, HES4, and PPDPF. Gene JUN is well known for being a transcription factor acting as an oncogene . ProteinHes Family BHLH Transcription Factor 4 (HES4) is a gene related to the PI3K-Akt signaling pathway. This gene is a transcription factor binding DNA. It is related to the NOTCH3 receptor and is a biomarker of PCa aggressiveness  hit is that the three genes exist in the Human Breast Nam08 30 genes UpregulatedGeneList signature , accession number PRJEB6530 from github.com/ArnaudDroitLab/prostate_BCR_ prediction.Publicly available datasets were analyzed in this study. This data can be found here: TCGA at GDC data portal; GEO accession This study was approved by the Research Ethics Committee of the CHU de Québec-Université Laval (Project 2018-3670). Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.BV conducted literature searches, gathered the data, wrote the code to perform the research, and wrote the manuscript. He also created figures and tables, and wrote, formatted the manuscript for submission. ML participated to design the approach. ML, M-LM-M, and AB helped to improve the manuscript. YF and AD supervised and reviewed the design of the study. CC provided the VPCC data. All authors contributed to reviewing the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Scalable, inexpensive, and secure testing for SARS-CoV-2 infection is crucial for control of the novel coronavirus pandemic. Recently developed highly multiplexed sequencing assays (HMSAs) that rely on high-throughput sequencing can, in principle, meet these demands, and present promising alternatives to currently used RT-qPCR-based tests. However, reliable analysis, interpretation, and clinical use of HMSAs requires overcoming several computational, statistical and engineering challenges. Using recently acquired experimental data, we present and validate a computational workflow based on kallisto and bustools, that utilizes robust statistical methods and fast, memory efficient algorithms, to quickly, accurately and reliably process high-throughput sequencing data. We show that our workflow is effective at processing data from all recently proposed SARS-CoV-2 sequencing based diagnostic tests, and is generally applicable to any diagnostic HMSA. Currently, SARS-CoV-2 tests are primarily based on RT-qPCR, however, several groups have recently proposed massively parallelized diagnostic assays based on high-throughput sequencing that hold the promise of greatly increased throughput, reduced cost, and improved sensitivity4. While the proposed diagnostics differ in implementation details, they share several key features:(Synthetic) sequence barcodes known as samples indices are associated with samples and are recovered by sequencing. sequences associated with genes, including viral genes, control genes, or spike-ins, are recovered by sequencing.Sequenced sample indices and biological sequences are associated with each other.Reliable, scalable, low-cost testing for SARS-CoV-2 is paramount for reducing infection rates and controlling the current pandemic7, and as a result, the bioinformatics challenges that must be overcome in analyzing the data are similar.These assays bear resemblance to multiplexed barcoding technologies used for single-cell RNA-seqProcessing of the data requires association of the biological sequences with their genes of origin, error correction of the sample indices and collation of sequences associated with a single sample to count the number of molecules from each gene that have been observed Fig. . Finally9.It is crucial that the software used to report infection status for each sample is reliable, well-documented, portable, and open source. These features of diagnostic software instill confidence in healthcare providers and patients, and ensure transparency and reproducibility in a setting where software errors can be deadly. Additionally they make it easier to adapt, improve, and expand on the capabilities of the software to handle novel use cases. Current software programs used for sequencing-based diagnostics, such as bcl2fastq, do not satisfy these crucial requirements, despite being used in FDA approved diagnostics10 and bustools12 to HMSA analysis, and coupled them in a workflow we designate “kallisto|bustools”  as the lowest concentration at which 19/20 replicates are positive. Therefore, to assess the LoD from a standard curve, we performed 99 replicates of the training–testing and identified the titer at which the mean recall was equal to or above 0.95 (= 19/20). The results .pdf). In addition, the behavior of kallisto bustools is transparent and well-documented, whereas the behavior of bcl2fastq is not. By generating BCL files from a set of FASTQ records and running the BCL files through bcl2fastq (code), we found that when the sample sheet used for demultiplexing has two index barcodes that are Hamming distance n apart, bcl2fastq exists with an error when the Hamming distance used for error correction is less than n/2 + 1. Moreover, the default Hamming distance correction for bcl2fastq 2.20 and bcl2fastq 1.8.4 differs, with 1 mismatch for the former and 0 for the latter. This issue alone can result in unwanted and unexpected behavior when using bcl2fastq.Most importantly, unlike bcl2fastq, the kallisto bustools workflow is readily accessible, open source, portable, and reliable. Obtaining a copy of bcl2fastq requires a user to create an account at Illumina.com and agree to a restrictive license agreement  and that target regions of genetic variation, both of which are promising future directions for HMSAs.19. Furthermore, the speed of the workflow allows for the processing of thousands of samples within minutes , a human gene control (RPP30), and two different lysates . The wells moreover contained barcoded primers unique to each well, synthetic RNA spike-in controls that contained the same priming regions as the target RNA from SARS-CoV-2, primers for the target SARS-CoV-2 RNA, and a one-step RT-PCR mix. Next, RT-PCR was performed on all of the wells. The wells were then pooled and sequenced on an Illumina Nextseq.Raw BCL files were converted into FASTQ files for the kallisto|bustools workflow using ‘bcl2fastq –create-fastq-for-index-reads’ with read 1 corresponding to the Illumina i5 index, read 2 corresponding to the biological read, and index 1 corresponding to the Illumina i7 index.k-mer length of 11.The genes targeted by SwabSeq were a 108 bp sequence of the SARS-CoV-2 S gene, a 6 bp modification of the SARS-CoV-2 S gene (spike-in), a 72 bp sequence of the SARS-CoV-2 N gene, a 6 bp modification of the SARS-CoV-2 N gene (spike-in), and a 65 bp sequence of the RPP30 gene . The spike-in sequence differs from the original gene at the first 6 bp. For each spike-in/viral gene, a 10 bp window around the unique stretch of the sequence was retained and the rest of the sequence removed since any 11-mer that maps outside of the unique region could have originated from either the target gene or the spike-in. A FASTA file of all Hamming one distance variants of these target genes was made and indexed with ‘kallisto index -k 11’ with a A BUS file is a columnar binary file where each row is a quadruplet of a sample index, UMI, set, and count that facilitates sample index error correction and amplicon quantification. Reads from the FASTQ files generated for the kallisto|bustools workflow were pseudoaligned using ‘kallisto bus -x SwabSeq’ to generate a BUS file where all records contain the same UMI.The BUS file was sorted with bustools sort which in addition to sorting the file lexicographically, counts and collapses the BUS records that are the same. Each half of the sample index in the BUS file was then corrected separately to a whitelist using ‘bustools correct -w whitelist.txt –split’. Correcting each half of the sample index independently is unique to SwabSeq which has a whitelist for the i5 and i7 primers, the combination of which makes the sample index. Each half of the sample index was corrected to at most Hamming distance one. The BUS file was sorted once more using ‘bustools sort’ to count and collapse any additional BUS records that are the same.The BUS file was processed with ‘bustools count—cm’ to count the number of reads per sample that map uniquely to a specific target gene. This procedure yielded a sample by gene matrix with the number of reads. For the SwabSeq assay, this matrix consisted of 768 samples by 5 genes.wi = weights, V = loge(1 + virus counts), K = loge(1 + spike-in counts) and H = loge(1 + RPP30 counts).Each sample was classified as + virus if it contained a non-zero amount of viral RNA. For each experiment, we split the data into two, half for training and half for testing, and used the log of the viral, spike-in, and RPP30 counts plus one as input. We learned the weights of a multivariate logistic regression model on the training data and used those weights to predict, for each sample, the probability that it contained virus. The logistic model used wase(1 + counts). Given the weights wi, the known viral load yi, and the log of the counts for each training sample Xij plus one, the weighted linear regression model identified the vector of parameters β minimizingWe performed a weighted linear regression on the viral and spike-in counts where the samples with known zero RNA titer were weighted by one over the number of unique titers. This was done to equalize the effect of each titer in the regression. For each experiment, we split the data into two halves: a training set and a testing set. The optimal coefficients for the linear model were learned from the training data, and the viral load was then predicted using those weights for the testing data. We performed this procedure on the logWe iteratively removed the samples corresponding to increasing amounts of RNA titer, starting with the lowest titer, and performed 99 rounds split/test and logistic regression on the remaining samples. Each run reported the recall rate, i.e. the number of true positives divided by the sum of the number of true positives and false negatives. We defined the limit of detection (LoD) as the lowest RNA titer such that the mean of the recall for that titer is greater than or equal to 19/20 (0.95).Demultiplexed FASTQ files for the orthogonal validation were generated using the Illumina Sample Sheet and ‘bcl2fastq –no-lane-splitting –sample-sheet SampleSheet.csv’. The default with bcl2fastq is error correction of 1 mismatch, this step serves to error-correct each index separately. The resultant demultiplexed FASTQ files corresponding to each sample were clustered using ‘starcode -d2 -t1 –sphere’. Sequence centroids were assigned to genes by exact matching, and the count for each gene was given by the number of reads up to Levenshtein distance 2 away from the centroid. A sample by gene matrix was then constructed from the counts.Reads were generated using the sample by genes matrix from the Plate 1 HEK293 lysate N gene Twist RNA SwabSeq experiment. This matrix consisted of 96 samples and 3 targeted genes. We obtained a list of 1000 sample indices from LAMP-Seq and 19,200 sample indices from TRB-Seq as well as their associated primer sets, N1, N2, and RPP30 for TRB-seq and B_B3 for LAMP-Seq. We generated a number of reads equal to the number of amplicon counts for each sample index and target gene pair, 12,062,027 reads in total. In addition, bases in each read were randomly mutated to another base with a probability of 0.005 to simulate Illumina sequencing errors. The read structure for TRB-seq consists of paired-end reads with read 1 corresponding to a 15 bp sample index and a 22 bp constant region, and read 2 corresponding to the target gene. The read structure for LAMP-seq consists of single-end reads where the first 20 bp correspond to the targeted gene, the next 22 bp correspond to the first forward inner primer, the subsequent 10 bp correspond to the sample index, and the last 19 bp correspond to the second forward inner primer.Reads from each assay were processed with ‘kallisto bus -x LAMPSeq’ and ‘kallisto bus -x TRBSeq’ to generate a BUS file. The BUS file was sorted with ‘bustools sort’, the sample indices were corrected to Hamming distance 1 with ‘bustools correct’ and the BUS file was sorted once more to sum duplicate records. The BUS file was then processed with ‘bustools count—cm’ to generate the technology comparisons.14, and processed the reads with ‘kallisto bus -x covEseq’. The processing time for all 2,437,573 reads was determined with the time command line utility.We downloaded data in the form of FASTQ reads for the S5 protocol21 we developed a simple tool, bcltools which is available at https://github.com/pachterlab/bcltools, to generate BCL files from a set of FASTQ files. We created three FASTQ files each containing three reads. The index FASTQs contain 8 bp sequences and the biological read contains a 26 bp sequence. The I1 FASTQ sequences are all the same and the I2 FASTQ sequences differ. In the I2 FASTQ read 1 is 3 Hamming distances away from read 2 and 1 Hamming distance away from read 3. Read 2 is 2 Hamming distances away from read 3. After generating a set of BCL files, bcl2fastq 2.20 was run both with a Sample Sheet and without a Sample Sheet. When run without the Sample Sheet, we obtained the same read sequences as those that were put in. When run with the Sample Sheet, we observed that the reads were assigned to the samples in the Sample Sheet by correcting the index sequences within 1 Hamming distance.Using Illumina documentationhttps://github.com/pachterlab/BLCSBGLKP_2020. Each notebook can be run directly on Google Colab by pressing “Open in Colab” → “Runtime” → “Run all”. The links to all FASTQ files can be found in Supplementary Table All the data, code and methods used to generate the results in this manuscript are open source freely available. The code to reproduce every figure and analysis for this manuscript is located here: https://www.notion.so/Octant-SwabSeq-Testing-9eb80e793d7e46348038aa80a5a901fd.The SwabSeq protocol is described at Software programs used are listed in Supplementary Table Supplementary Information 1.Supplementary Information 2.


Edge Contribution value (ECv) based on the estimated system, which realizes condition-specific subnetwork extraction using a limited number of samples. We applied this method to the Epithelial-Mesenchymal Transition (EMT) data set that is related to the process of metastasis and thus prognosis in cancer biology. We established our method-driven EMT network representing putative gene interactions. Furthermore, we found that the sample-specific ECv patterns of this EMT network can characterize the survival of lung cancer patients. These results show that our method unveils the explainable network differences in biological and clinical features through artificial intelligence technology.Gene network estimation is a method key to understanding a fundamental cellular system from high throughput omics data. However, the existing gene network analysis relies on having a sufficient number of samples and is required to handle a huge number of nodes and estimated edges, which remain difficult to interpret, especially in discovering the clinically relevant portions of the network. Here, we propose a novel method to extract a biomedically significant subnetwork using a Bayesian network, a type of unsupervised machine learning method that can be used as an explainable and interpretable artificial intelligence algorithm. Our method quantifies sample specific networks using our proposed  The use of high throughput technologies in molecular biology has led to the generation of large volumes of data, and thus, the development of precise methods for handling such large data is required. Understanding the cellular mechanisms at a system level forms a fundamental goal of these methods. Elucidation of the cellular mechanisms is indispensable for discovering biologically significant events, especially for clinical applications like finding new drug targets .de novo pathways and relationships.Researchers have developed several approaches to elucidate cellular mechanisms. One of the most popular methods is the conventional mRNA expression analysis to identify Differentially Expressed Genes (DEG) ,3. This Network analysis constitutes a promising method to interpret the big omics data, and researchers have applied it to many biomolecular investigations ,8. HowevEdge Contribution value (ECv) for every single edge in a gene network with respect to each sample. The proposed ECv can quantify a particular edge for each sample using the estimated model. Therefore, based on the differences in ECvs between different samples, which imitate biologically or clinically-specific phenomena, our method highlights the differentiated subnetwork. This method defines the edges out of a possible hundred thousand links between genes, and thus demonstrates major differences in regulating the basal gene network potentially associated with target diseases or phenomena. Since the extracted subnetwork highlights specific differences between samples, it may portray expression data sets that are not used in network estimation or subnetwork extraction. We proved that the network ECv pattern with respect to a patient sample for certain types of diseases can be adopted for clinical classifications by validating real data.Herein, we propose a novel method to extract a biomedically differentiated subnetwork from a huge number of edges in a gene network estimated using a relatively small number of samples. Compared to the structure-based network analysis, our approach aims to extract edges, which represent the system-level differences between the samples in cellular networks without comparing network structures. Therefore, even with a limited number of samples for conditions of interest, our method will extract system-based differential networks. The outline of the proposed method is shown in The idea of estimating sample specific gene networks has already been addressed. For instance, Shimamura et al. (2011)  proposedWe examined our method on Epithelial-Mesenchymal Transition (EMT)  to underAs described in the Introduction, we used nonparametric BN to estimate a gene-to-gene regulatory system from gene expression data . The BN X be an n-by-p data matrix whose element j-th variable; that is, the expression value of the j-th gene, at the i-th sample, where n represents the total number of samples and p the number of variables. In the nonparametric BN model, we consider the joint density of all the variables and assume that it can be decomposed as the product of the local conditional densities, such asi-th sample of j-th variable, and B-spline nonparametric regression. In the model, the gene expression is represented byB-spline function which is determined by the range of the observations and M is the number of the B-splines and we use G represents the network structure, G and the basal network in the later steps.Let . (2002) . The loclgorithm  that is k-th parent of the j-th gene to the expression value of Edge Contribution value (ECv) of edge i-th sample ask-th parent of the j-th gene.Our basic idea is to evaluate a single edge value of an observed sample through the mathematical model estimated as a BN. In the model, the expression value of a child gene is represented by a linear combination of their parent values transformed by the functions denoted as S and T are sets of indices of samples observed in particular conditions, respectively. The graphical representation of ΔECv is shown in Since ECv is calculated from the model parameters to every single edge in the estimated network, the ECv of an edge can be considered as its edge weight, representing how it contributes to the link between a node pair for a certain sample. However, this calculation of ECv alone does not represent an evaluation of the absolute importance of the edge, because it is impossible to determine the size of calculated ECv. This is similar to gene expression values where differential expression is considered. Therefore, two ECvs of an edge must be compared between samples. For instance, let us assume that there are control and drug-perturbated samples in an in vitro cell assay. To obtain the differential edge in terms of ECv, we define a variation of ECv, ΔECv, asS and T here can be 1 because we focus only on differences of ECv so that only a single pair of samples is required to extract edges.The ΔECv of an edge is the absolute difference that stands for the difference in edge contribution between samples of different conditions. Thus, an edge that shows significant ECv difference between certain samples represents a distinctive edge. In our experiment, ΔECv implementation did not result in a large number of candidate edges that were needed for following the investigation. This suggests that ΔECv functions as a screening method for candidate networks from the estimated basal networks that are important under certain conditions. The network estimation with a small number of samples generally increases the number of false positive edges due to spurious correlations between variables. It is expected that the ΔECv can overcome this problem, because ΔECv might highlight a sample difference which is determined by not only the estimated model but also the sample-specific differences through the estimated model. Note that the number of samples in ECv matrix. More precisely, the ECv matrix E is an n-by-m matrix whose element v-th edge at the i-th sample, where m represents the total number of estimated edges and n the number of samples. The ECv matrix thus can be considered as a set of each sample’s ECvs. Since ECv originally represents the sample-specific profiling for each estimated edge, clustering of the ECv matrix highlights differences according to ECvs for each sample, which then allows for the grouping of samples based on their system-level similarities.As a consequence of ECv calculation, we obtain a matrix consisting of ECvs for all of the edges and samples. We call this β-treated cells. Thus, the data consists of 2-transformed values of preprocessed data were applied to BN estimation and ECv calculation. The clinical and RNA-seq data of lung cancer patients [The microarray data for EMT analysis were acquired from Gene Expression Omnibus (GSE49644) . The datpatients  were acqpatients . NSCLC pThe differential expression gene analysis for GSE49644 microarray data was performed using R package limma . BenjamiThe functional analysis was generated through the use of Ingenuity Pathway Analysis .Unsupervised-clustering for the ECv matrix was performed using the “ward.D2” clustering method with Euclidean distance for both edges and samples in R. The following survival analysis was performed with log-rank test using R package surveminer and TCGAbiolinks ,29.The network visualization and a part of network analysis were performed using Cytoscape .All the computation for the network estimation and the ECv calculations in this study were performed by the SHIROKANE supercomputer system (Shirokane3) at Human Genome Center, the Institute of Medical Science, the University of Tokyo, where the computation nodes were equipped with dual Intel Xeon E5-2670 v3 2.3GHz CPUs and 128GB memory per node.For the basal gene network estimation using the BN, we adopted the NNSR algorithm  as descrβ to induce EMT. EMT is a cellular process in which metastasis and invasion are involved [β-treated cells represented the EMT profile, which was confirmed by the alteration in cellular morphology and expression levels of EMT markers in the previous study [u = A549, HCC827 and NCI-H358. The edges that were assigned high ΔECv can be considered as significant differences in the cellular system between control and EMT-induced samples in each cell line. The distribution of ΔECv values for each cell line is displayed in 2 Fold Change (FC) distribution of mRNAs, which is a standard indicator in DEGs, we overlapped their histograms -corrected p value < 0.01   . Eventuanetwork) . Furthernetwork) . Interes2FC > 2 and FDR-corrected p value < 0.00001, approximately following a previous report [β-treated samples. The number of shared genes obtained by ΔECv and DEG was 71  for LUAD (p = 0.011 < 0.05) in terms of their prognosis for LUSC  patients by calculating their own RNA-seq specific ECv asanalysis . The 108for LUAD , these tfor LUSC B, suggesHere, we report a novel method to obtain a sample-specific subnetwork using ECv derived from the estimated parameters of the BN. We investigated the application of this method in EMT biology and clinical data analysis. Our method shows the potential ability of capturing the subnetwork and the patient-specific ECv patterns, and these patterns further characterized the prognosis. The prognosis in cancer patients depends on more than the metastasis. This is probably one reason why the combination of clustering and survival analysis works on LUSC, but not LUAD. On the other hand, the results of conventional clustering analysis on ECv matrices showed clear discriminations into two groups both on cell line microarray data and patient tumor sample RNA-seq data. Therefore, our results imply that individuals are distinguished through network differentiation using the proposed quantification of the edges.Although translating laboratory experiments into the clinical context remains a big hurdle, this study indicates that our approach could be a powerful tool for bridging the divide between them. However, if we use it for this purpose, in vitro experimental design should at least relate directly to practical clinical realities. In addition, because some data sets in replicate experiments involving in vitro assays are markedly homogeneous, we calculate ΔECv using mean ECv difference for each condition-specific sample set in this study. This, however, would not be appropriate for a heterogeneous data set because mean ECv is not supposed to reflect a representative ECv for a particular set of samples. These issues are current limitations of our proposed method and can be improved in the future work.As discussed previously, existing gene network analyses generally focus on hub genes that are supposed to catalog important regulatory genes in a network. In the EMT network, most of the basal network hub genes were either located at the corners of the EMT network or completely isolated from the biggest connected component . Only a HS3ST3B1, FAM198B and IGFBP5 are the three top hub genes of the EMT network. Genes farthest from the hub genes were closely linked, suggesting that the subnetwork centered on the hub genes essentially represents EMT profiles depicted in the EMT data set. In particular, we found that these components are more enriched in Extracellular Matrix (ECM) genes. ECM constructs a multilayer scaffold structure located outside of the cell membrane and functions as an attachment between cells and tissues, assisting in cell growth, movement, development and differentiation [HAS3, FN1 and MMP7 are located proximally to the top three hub genes. HS3ST3B1 encodes an enzyme that controls the ECM environment following organization of heparin sulfate in GAGs, and reports show its expression level regulates EMT [FAM198B attenuates tumor growth and metastasis [IGFBP5 reduces EMT [PDK4—identified biologically as a novel EMT-associated gene [PDK4 with EMT-related genes.Within the biggest connected components in ntiation . Given tates EMT . A low ltastasis . Overexpuces EMT . Althouguces EMT , our EMTRecently, the emergence of deep learning technology has allowed us to apply it to many scientific fields, including biology and medicine. However, such a situation depends on a large number of data sets. More importantly, the explainability of deep learning technology remains elusive. This disadvantage is a key issue, especially for biological or clinical situations, because the predictive process must also have a responsibility for interpretation and the ultimate outcome. In contrast, BN has been conventionally developed as an explainable model, and thus it is considered to be more appropriate to these fields. However, BN was unable to explain the individuality of samples and additional statistical models intended to interpret the population of certain data sets. In this study, by overcoming this weakness of BN, ECv has proven to be a more powerful tool in terms of explainability in machine learning. Therefore, the application possibilities of this BN model regarding biomedical data beyond existing bioinformatics methods makes it superior to other models. Although multi-omics analysis is becoming mainstream in biology, single transcriptome analysis still possesses a broad scope. Integration of other types of omics data, such as genome, proteome and epigenome data, with transcriptome data using BN, would lead us to analyze these big data sets more precisely than in existing studies, which in turn may result in a new approach in precision medicine.Y.T. (Yoshinori Tamada) and Y.O. have a patent application on the individual profiling and sample specific network extraction method, and its applications used in the submitted study through the technology licensing organization in Kyoto University.