This Hospital Management System project features a fully normalized relational database designed to manage hospital data including patients, doctors, appointments, diagnoses, medications, and billing. The schema applies database normalization (1NF, 2NF, 3NF) to reduce redundancy and maintain data integrity, providing an efficient, scalable structure for healthcare data management. Included are SQL scripts to create tables and insert sample data, making it a useful resource for learning practical database design and normalization in a healthcare context.

Background The Infinium EPIC array measures the methylation status of > 850,000 CpG sites. The EPIC BeadChip uses a two-array design: Infinium Type I and Type II probes. These probe types exhibit different technical characteristics which may confound analyses. Numerous normalization and pre-processing methods have been developed to reduce probe type bias as well as other issues such as background and dye bias.
Methods This study evaluates the performance of various normalization methods using 16 replicated samples and three metrics: absolute beta-value difference, overlap of non-replicated CpGs between replicate pairs, and effect on beta-value distributions. Additionally, we carried out Pearson’s correlation and intraclass correlation coefficient (ICC) analyses using both raw and SeSAMe 2 normalized data.
Results The method we define as SeSAMe 2, which consists of the application of the regular SeSAMe pipeline with an additional round of QC, pOOBAH masking, was found to be the best-performing normalization method, while quantile-based methods were found to be the worst performing methods. Whole-array Pearson’s correlations were found to be high. However, in agreement with previous studies, a substantial proportion of the probes on the EPIC array showed poor reproducibility (ICC < 0.50). The majority of poor-performing probes have beta values close to either 0 or 1, and relatively low standard deviations. These results suggest that probe reliability is largely the result of limited biological variation rather than technical measurement variation. Importantly, normalizing the data with SeSAMe 2 dramatically improved ICC estimates, with the proportion of probes with ICC values > 0.50 increasing from 45.18% (raw data) to 61.35% (SeSAMe 2). Methods

Study Participants and Samples

The whole blood samples were obtained from the Health, Well-being and Aging (Saúde, Ben-estar e Envelhecimento, SABE) study cohort. SABE is a cohort of census-withdrawn elderly from the city of São Paulo, Brazil, followed up every five years since the year 2000, with DNA first collected in 2010. Samples from 24 elderly adults were collected at two time points for a total of 48 samples. The first time point is the 2010 collection wave, performed from 2010 to 2012, and the second time point was set in 2020 in a COVID-19 monitoring project (9±0.71 years apart). The 24 individuals were 67.41±5.52 years of age (mean ± standard deviation) at time point one; and 76.41±6.17 at time point two and comprised 13 men and 11 women.

All individuals enrolled in the SABE cohort provided written consent, and the ethic protocols were approved by local and national institutional review boards COEP/FSP/USP OF.COEP/23/10, CONEP 2044/2014, CEP HIAE 1263-10, University of Toronto RIS 39685.

Blood Collection and Processing

Genomic DNA was extracted from whole peripheral blood samples collected in EDTA tubes. DNA extraction and purification followed manufacturer’s recommended protocols, using Qiagen AutoPure LS kit with Gentra automated extraction (first time point) or manual extraction (second time point), due to discontinuation of the equipment but using the same commercial reagents. DNA was quantified using Nanodrop spectrometer and diluted to 50ng/uL. To assess the reproducibility of the EPIC array, we also obtained technical replicates for 16 out of the 48 samples, for a total of 64 samples submitted for further analyses. Whole Genome Sequencing data is also available for the samples described above.

Characterization of DNA Methylation using the EPIC array

Approximately 1,000ng of human genomic DNA was used for bisulphite conversion. Methylation status was evaluated using the MethylationEPIC array at The Centre for Applied Genomics (TCAG, Hospital for Sick Children, Toronto, Ontario, Canada), following protocols recommended by Illumina (San Diego, California, USA).

Processing and Analysis of DNA Methylation Data

The R/Bioconductor packages Meffil (version 1.1.0), RnBeads (version 2.6.0), minfi (version 1.34.0) and wateRmelon (version 1.32.0) were used to import, process and perform quality control (QC) analyses on the methylation data. Starting with the 64 samples, we first used Meffil to infer the sex of the 64 samples and compared the inferred sex to reported sex. Utilizing the 59 SNP probes that are available as part of the EPIC array, we calculated concordance between the methylation intensities of the samples and the corresponding genotype calls extracted from their WGS data. We then performed comprehensive sample-level and probe-level QC using the RnBeads QC pipeline. Specifically, we (1) removed probes if their target sequences overlap with a SNP at any base, (2) removed known cross-reactive probes (3) used the iterative Greedycut algorithm to filter out samples and probes, using a detection p-value threshold of 0.01 and (4) removed probes if more than 5% of the samples having a missing value. Since RnBeads does not have a function to perform probe filtering based on bead number, we used the wateRmelon package to extract bead numbers from the IDAT files and calculated the proportion of samples with bead number < 3. Probes with more than 5% of samples having low bead number (< 3) were removed. For the comparison of normalization methods, we also computed detection p-values using out-of-band probes empirical distribution with the pOOBAH() function in the SeSAMe (version 1.14.2) R package, with a p-value threshold of 0.05, and the combine.neg parameter set to TRUE. In the scenario where pOOBAH filtering was carried out, it was done in parallel with the previously mentioned QC steps, and the resulting probes flagged in both analyses were combined and removed from the data.

Normalization Methods Evaluated

The normalization methods compared in this study were implemented using different R/Bioconductor packages and are summarized in Figure 1. All data was read into R workspace as RG Channel Sets using minfi’s read.metharray.exp() function. One sample that was flagged during QC was removed, and further normalization steps were carried out in the remaining set of 63 samples. Prior to all normalizations with minfi, probes that did not pass QC were removed. Noob, SWAN, Quantile, Funnorm and Illumina normalizations were implemented using minfi. BMIQ normalization was implemented with ChAMP (version 2.26.0), using as input Raw data produced by minfi’s preprocessRaw() function. In the combination of Noob with BMIQ (Noob+BMIQ), BMIQ normalization was carried out using as input minfi’s Noob normalized data. Noob normalization was also implemented with SeSAMe, using a nonlinear dye bias correction. For SeSAMe normalization, two scenarios were tested. For both, the inputs were unmasked SigDF Sets converted from minfi’s RG Channel Sets. In the first, which we call “SeSAMe 1”, SeSAMe’s pOOBAH masking was not executed, and the only probes filtered out of the dataset prior to normalization were the ones that did not pass QC in the previous analyses. In the second scenario, which we call “SeSAMe 2”, pOOBAH masking was carried out in the unfiltered dataset, and masked probes were removed. This removal was followed by further removal of probes that did not pass previous QC, and that had not been removed by pOOBAH. Therefore, SeSAMe 2 has two rounds of probe removal. Noob normalization with nonlinear dye bias correction was then carried out in the filtered dataset. Methods were then compared by subsetting the 16 replicated samples and evaluating the effects that the different normalization methods had in the absolute difference of beta values (|β|) between replicated samples.

Normalization of RNA-Seq data has proven essential to ensure accurate inferences and replication of findings. Hence, various normalization methods have been proposed for various technical artifacts that can be present in high-throughput sequencing transcriptomic studies. In this study, we set out to compare the widely used library size normalization methods (UQ, TMM, and RLE) and across sample normalization methods (SVA, RUV, and PCA) for RNA-Seq data using publicly available data from The Cancer Genome Atlas (TCGA) cervical cancer study. Additionally, an extensive simulation study was completed to compare the performance of the across sample normalization methods in estimating technical artifacts. Lastly, we investigated the effect of reduction in degrees of freedom in the normalized data and their impact on downstream differential expression analysis results. Based on this study, the TMM and RLE library size normalization methods give similar results for CESC dataset. In addition, the simulated datasets results show that the SVA (“BE”) method outperforms the other methods (SVA “Leek”, PCA) by correctly estimating the number of latent artifacts. Moreover, ignoring the loss of degrees of freedom due to normalization results in an inflated type I error rates. We recommend adjusting not only for library size differences but also the assessment of known and unknown technical artifacts in the data, and if needed, complete across sample normalization. In addition, we suggest that one includes the known and estimated latent artifacts in the design matrix to correctly account for the loss in degrees of freedom, as opposed to completing the analysis on the post-processed normalized data.

Table S1 and Figures S1–S6. Table S1. List of primers. Forward and reverse primers used for qPCR. Figure S1. Changes in total and polyA+ RNA during development. a) Amount of total RNA per embryo at different developmental stages. b) Amount of polyA+ RNA per 100 embryos at different developmental stages. Vertical bars represent standard errors. Figure S2. The TMM scaling factor. a) The TMM scaling factor estimated using dataset 1 and 2. We observe very similar values. b) The TMM scaling factor obtained using the replicates in dataset 2. The TMM values are very reproducible. c) The TMM scale factor when RNA-seq data based on total RNA was used. Figure S3. Comparison of scales. We either square-root transformed or used that scales directly and compared the normalized fold-changes to RT-qPCR results. a) Transcripts with dynamic change pre-ZGA. b) Transcripts with decreased abundance post-ZGA. c) Transcripts with increased expression post-ZGA. Vertical bars represent standard deviations. Figure S4. Comparison of RT-qPCR results depending on RNA template (total or poly+ RNA) and primers (random or oligo(dT) primers) for setd3 (a), gtf2e2 (b) and yy1a (c). The increase pre-ZGA is dependent on template (setd3 and gtf2e2) and not primer type. Figure S5. Efficiency calibrated fold-changes for a subset of transcripts. Vertical bars represent standard deviations. Figure S6. Comparison normalization methods using dataset 2 for transcripts with decreased expression post-ZGA (a) and increased expression post-ZGA (b). Vertical bars represent standard deviations. (PDF)

Dataset for human osteoarthritis (OA) — microarray gene expression (Affymetrix GPL570) PMC +1

Contains expression data for 7 healthy control (normal) tissue samples and 7 osteoarthritis patient tissue samples from synovial / joint tissue. PMC +1

Pre-processed for normalization (background correction, log-transformation, normalization) to remove technical variation.

Suitable for downstream analyses: differential gene expression (normal vs OA), subtype- or phenotype-based classification, machine learning.

Can act as a validation dataset when combining with other GEO datasets to increase sample size or test reproducibility. SpringerLink +1

Useful for biomarker discovery, pathway enrichment analysis (e.g., GO, KEGG), immune infiltration analysis, and subtype analysis in osteoarthritis research.

FPKM normalized data from whole transcriptome sequencing of corpus luteum tissue from lactating holstein cows in the following physiologic states: late luteal phase (control), early regression, late regression, first month pregnancy (day 20), second month pregnancy (day 55+/-3 days)

1. Microbiome sequencing data often need to be normalized due to differences in read depths, and recommendations for microbiome analyses generally warn against using proportions or rarefying to normalize data and instead advocate alternatives, such as upper quartile, CSS, edgeR-TMM, or DESeq-VS. Those recommendations are, however, based on studies that focused on differential abundance testing and variance standardization, rather than community-level comparisons (i.e., beta diversity), Also, standardizing the within-sample variance across samples may suppress differences in species evenness, potentially distorting community-level patterns. Furthermore, the recommended methods use log transformations, which we expect to exaggerate the importance of differences among rare OTUs, while suppressing the importance of differences among common OTUs. 2. We tested these theoretical predictions via simulations and a real-world data set. 3. Proportions and rarefying produced more accurate comparisons among communities and were the only methods that fully normalized read depths across samples. Additionally, upper quartile, CSS, edgeR-TMM, and DESeq-VS often masked differences among communities when common OTUs differed, and they produced false positives when rare OTUs differed. 4. Based on our simulations, normalizing via proportions may be superior to other commonly used methods for comparing ecological communities.

(1) qPCR Gene Expression Data The THP-1 cell line was sub-cloned and one clone (#5) was selected for its ability to differentiate relatively homogeneously in response to phorbol 12-myristate-13-acetate (PMA) (Sigma). THP-1.5 was used for all subsequent experiments. THP-1.5 cells were cultured in RPMI, 10% FBS, Penicillin/Streptomycin, 10mM HEPES, 1mM Sodium Pyruvate, 50uM 2-Mercaptoethanol. THP-1.5 were treated with 30ng/ml PMA over a time-course of 96h. Total cell lysates were harvested in TRIzol reagent at 1, 2, 4, 6, 12, 24, 48, 72, 96 hours, including an undifferentiated control. Undifferentiated cells were harvested in TRIzol reagent at the beginning of the LPS time-course. One biological replicate was prepared for each time point. Total RNA was purified from TRIzol lysates according to manufacturer’s instructions. Genespecific primer pairs were designed using Primer3 software, with an optimal primer size of 20 bases, amplification size of 140bp, and annealing temperature of 60°C. Primer sequences were designed for 2,396 candidate genes including four potential controls: GAPDH, beta actin (ACTB), beta-2-microglobulin (B2M), phosphoglycerate kinase 1 (PGK1). The RNA samples were reverse transcribed to produce cDNA and then subjected to quantitative PCR using SYBR Green (Molecular Probes) using the ABI Prism 7900HT system (Applied Biosystems, Foster City, CA, USA) with a 384-well amplification plate; genes for each sample were assayed in triplicate. Reactions were carried out in 20μL volumes in 384-well plates; each reaction contained: 0.5 U of HotStar Taq DNA polymerase (Qiagen) and the manufacturer’s 1× amplification buffer adjusted to a final concentration of 1mM MgCl2, 160μM dNTPs, 1/38000 SYBR Green I (Molecular Probes), 7% DMSO, 0.4% ROX Reference Dye (Invitrogen), 300 nM of each primer (forward and reverse), and 2μL of 40-fold diluted first-strand cDNA synthesis reaction mixture (12.5ng total RNA equivalent). Polymerase activation at 95ºC for 15 min was followed by 40 cycles of 15 s at 94ºC, 30 s at 60ºC, and 30 s at 72ºC. The dissociation curve analysis, which evaluates each PCR product to be amplified from single cDNA, was carried out in accordance with the manufacturer’s protocol. Expression levels were reported as Ct values. The large number of genes assayed and the replicates measures required that samples be distributed across multiple amplification plates, with an average of twelve plates per sample. Because it was envisioned that GAPDH would serve as a single-gene normalization control, this gene was included on each plate. All primer pairs were replicated in triplicates. Raw qPCR expression measures were quantified using Applied Biosystems SDS software and reported as Ct values. The Ct value represents the number of cycles or rounds of amplification required for the fluorescence of a gene or primer pair to surpass an arbitrary threshold. The magnitude of the Ct value is inversely proportional to the expression level so that a gene expressed at a high level will have a low Ct value and vice versa. Replicate Ct values were combined by averaging, with additional quality control constraints imposed by a standard filtering method developed by the RIKEN group for the preprocessing of their qPCR data. Briefly this method entails: 1. Sort the triplicate Ct values in ascending order: Ct1, Ct2, Ct3. Calculate differences between consecutive Ct values: difference1 = Ct2 – Ct1 and difference2 = Ct3 – Ct2. 2. Four regions are defined (where Region4 overrides the other regions): Region1: difference ≦ 0.2, Region2: 0.2 < difference ≦ 1.0, Region3: 1.0 < difference, Region4: one of the Ct values in the difference calculation is 40 If difference1 and difference2 fall in the same region, then the three replicate Ct values are averaged to give a final representative measure. If difference1 and difference2 are in different regions, then the two replicate Ct values that are in the small number region are averaged instead. This particular filtering method is specific to the data set we used here and does not represent a part of the normalization procedure itself; Alternate methods of filtering can be applied if appropriate prior to normalization. Moreover while the presentation in this manuscript has used Ct values as an example, any measure of transcript abundance, including those corrected for primer efficiency can be used as input to our data-driven methods. (2) Quantile Normalization Algorithm Quantile normalization proceeds in two stages. First, if samples are distributed across multiple plates, normalization is applied to all of the genes assayed for each sample to remove plate-to-plate effects by enforcing the same quantile distribution on each plate. Then, an overall quantile normalization is applied between samples, assuring that each sample has the same distribution of expression values as all of the other samples to be compared. A similar approach using quantile ormalization has been previously described in the context of microarray normalization. Briefly, our method entails the following steps: i) qPCR data from a single RNA sample are stored in a matrix M of dimension k (maximum number of genes or primer pairs on a plate) rows by p (number of plates) columns. Plates with differing numbers of genes are made equivalent by padded plates with missing values to constrain M to a rectangular structure. ii) Each column is sorted into ascending order and stored in matrix M’. The sorted columns correspond to the quantile distribution of each plate. The missing values are placed at the end of each ordered column. All calculations in quantile normalization are performed on non-missing values. iii) The average quantile distribution is calculated by taking the average of each row in M’. Each column in M’ is replaced by this average quantile distribution and rearranged to have the same ordering as the original row order in M. This gives the within-sample normalized data from one RNA sample. iv) Steps analogous to 1 – 3 are repeated for each sample. Between-sample normalization is performed by storing the within-normalized data as a new matrix N of dimension k (total number of genes, in our example k = 2,396) rows by n (number of samples) columns. Steps 2 and 3 are then applied to this matrix. (3) Rank-Invariant Set Normalization Algorithm We describe an extension of this method for use on qPCR data with any number of experimental conditions or samples in which we identify a set of stably-expressed genes from within the measured expression data and then use these to adjust expression between samples. Briefly, i) qPCR data from all samples are stored in matrix R of dimension g (total number of genes or primer pairs used for all plates) rows by s (total number of samples). ii) We first select gene sets that are rank-invariant across a single sample compared to a common reference. The reference may be chosen in a variety of ways, depending on the experimental design and aims of the experiment. As described in Tseng et al., the reference may be designated as a particular sample from the experiment (e.g. time zero in a time course experiment), the average or median of all samples, or selecting the sample which is closest to the average or median of all samples. Genes are considered to be rank-invariant if they retain their ordering or rank with respect to expression across the experimental sample versus the common reference sample. We collect sets of rank-invariant genes for all of the s pairwise comparisons, relative to a common reference. We take the intersection of all s sets to obtain the final set of rank-invariant genes that is used for normalization. iii) Let αj represent the average expression value of the rank-invariant genes in sample j. (α1, …, αs) then represents the vector of rank-invariant average expression values for all conditions 1 to s iv) We calculate the scale f The THP-1 cell line was sub-cloned and one clone (#5) was selected for its ability to differentiate relatively homogeneously in response to phorbol 12-myristate-13-acetate (PMA) (Sigma). THP-1.5 was used for all subsequent experiments. THP-1.5 cells were cultured in RPMI, 10% FBS, Penicillin/Streptomycin, 10mM HEPES, 1mM Sodium Pyruvate, 50uM 2-Mercaptoethanol. THP-1.5 were treated with 30ng/ml PMA over a time-course of 96h. Total cell lysates were harvested in TRIzol reagent at 1, 2, 4, 6, 12, 24, 48, 72, 96 hours, including an undifferentiated control. Total RNA was purifed from TRIzol lysates according to manufacturer’s instructions. The RNA samples were reverse transcribed to produce cDNA and then subjected to quantitative PCR using SYBR Green (Molecular Probes) using the ABI Prism 7900HT system (Applied Biosystems, Foster City, CA,USA) with a 384-well amplification plate; genes for each sample were assayed in triplicate.

Foraminiferal samples were collected from Chincoteague Bay, Newport Bay, and Tom’s Cove as well as the marshes on the back-barrier side of Assateague Island and the Delmarva (Delaware-Maryland-Virginia) mainland by U.S. Geological Survey (USGS) researchers from the St. Petersburg Coastal and Marine Science Center in March, April (14CTB01), and October (14CTB02) 2014. Samples were also collected by the Woods Hole Coastal and Marine Science Center (WHCMSC) in July 2014 and shipped to the St. Petersburg office for processing. The dataset includes raw foraminiferal and normalized counts for the estuarine grab samples (G), terrestrial surface samples (S), and inner shelf grab samples (G). For further information regarding data collection and sample site coordinates, processing methods, or related datasets, please refer to USGS Data Series 1060 (https://doi.org/10.3133/ds1060), USGS Open-File Report 2015–1219 (https://doi.org/10.3133/ofr20151219), and USGS Open-File Report 2015-1169 (https://doi.org/10.3133/ofr20151169). Downloadable data are available as Excel spreadsheets, comma-separated values text files, and formal Federal Geographic Data Committee metadata.

BackgroundGene expression analysis is an essential part of biological and medical investigations. Quantitative real-time PCR (qPCR) is characterized with excellent sensitivity, dynamic range, reproducibility and is still regarded to be the gold standard for quantifying transcripts abundance. Parallelization of qPCR such as by microfluidic Taqman Fluidigm Biomark Platform enables evaluation of multiple transcripts in samples treated under various conditions. Despite advanced technologies, correct evaluation of the measurements remains challenging. Most widely used methods for evaluating or calculating gene expression data include geNorm and ΔΔCt, respectively. They rely on one or several stable reference genes (RGs) for normalization, thus potentially causing biased results. We therefore applied multivariable regression with a tailored error model to overcome the necessity of stable RGs.ResultsWe developed a RG independent data normalization approach based on a tailored linear error model for parallel qPCR data, called LEMming. It uses the assumption that the mean Ct values within samples of similarly treated groups are equal. Performance of LEMming was evaluated in three data sets with different stability patterns of RGs and compared to the results of geNorm normalization. Data set 1 showed that both methods gave similar results if stable RGs are available. Data set 2 included RGs which are stable according to geNorm criteria, but became differentially expressed in normalized data evaluated by a t-test. geNorm-normalized data showed an effect of a shifted mean per gene per condition whereas LEMming-normalized data did not. Comparing the decrease of standard deviation from raw data to geNorm and to LEMming, the latter was superior. In data set 3 according to geNorm calculated average expression stability and pairwise variation, stable RGs were available, but t-tests of raw data contradicted this. Normalization with RGs resulted in distorted data contradicting literature, while LEMming normalized data did not.ConclusionsIf RGs are coexpressed but are not independent of the experimental conditions the stability criteria based on inter- and intragroup variation fail. The linear error model developed, LEMming, overcomes the dependency of using RGs for parallel qPCR measurements, besides resolving biases of both technical and biological nature in qPCR. However, to distinguish systematic errors per treated group from a global treatment effect an additional measurement is needed. Quantification of total cDNA content per sample helps to identify systematic errors.

Gamification is a strategy to stimulate social and human factors (SHF) that influence software development productivity. However, software development teams must improve their productivity to face the challenges of software development organizations. Traditionally, productivity analysis only includes technical factors. Literature shows the importance of SHFs in productivity. Furthermore, gamification elements can contribute to enhancing such factors to improve performance. Thus, to design strategies to enhance a specific SHF, it is essential to identify how gamification elements are related to these factors. The objective of this research is to determine the relationship between gamification elements and SHF that influence the productivity of software development teams. This research included the design of a scoring template to collect data from the experts. The importance was calculated using the Simple Additive Weighting (SAW) method as a tool framed in decision theory. Three criteria were considered: cumulative score, matches in inclusion, and values. The relationships of importance serve as a reference value in designing gamification strategies that promote improved productivity. It extends the path toward analyzing the effect of gamification on the productivity of software development. This relationship facilitates designing and implementing gamification strategies to improve productivity.

Multi-OEM VRF Data Normalization Market Outlook

According to our latest research, the global Multi-OEM VRF Data Normalization market size reached USD 1.14 billion in 2024, with a robust year-on-year growth trajectory. The market is expected to expand at a CAGR of 12.6% during the forecast period, reaching a projected value of USD 3.38 billion by 2033. This impressive growth is primarily fueled by the increasing adoption of Variable Refrigerant Flow (VRF) systems across multiple sectors, the proliferation of multi-OEM environments, and the rising demand for seamless data integration and analytics within building management systems. The market’s expansion is further supported by advancements in IoT, AI-driven analytics, and the urgent need for energy-efficient HVAC solutions worldwide.

One of the primary growth drivers for the Multi-OEM VRF Data Normalization market is the rapid digital transformation in the HVAC industry. Organizations are increasingly deploying VRF systems from multiple original equipment manufacturers (OEMs) to optimize performance, reduce costs, and future-proof their infrastructure. However, the lack of standardization in data formats across different OEMs presents significant integration challenges. Data normalization solutions bridge this gap by ensuring interoperability, enabling seamless aggregation, and facilitating advanced analytics for predictive maintenance and energy optimization. As facilities managers and building operators seek to harness actionable insights from disparate VRF systems, the demand for sophisticated data normalization platforms continues to rise, driving sustained market growth.

Another significant factor propelling market expansion is the growing emphasis on energy efficiency and sustainability. Regulatory mandates and green building certifications are pushing commercial, industrial, and residential end-users to adopt smart HVAC solutions that minimize energy consumption and carbon emissions. Multi-OEM VRF Data Normalization platforms play a pivotal role in this transition by enabling real-time monitoring, granular energy management, and automated system optimization across heterogeneous VRF networks. The ability to consolidate and analyze operational data from multiple sources not only enhances system reliability and occupant comfort but also helps organizations achieve compliance with stringent environmental standards, further fueling market adoption.

The proliferation of cloud computing, IoT connectivity, and AI-powered analytics is also transforming the Multi-OEM VRF Data Normalization landscape. Cloud-based deployment models offer unparalleled scalability, remote accessibility, and cost-efficiency, making advanced data normalization solutions accessible to a broader spectrum of users. Meanwhile, the integration of AI and machine learning algorithms enables predictive maintenance, anomaly detection, and automated fault diagnosis, reducing downtime and optimizing lifecycle costs. As more organizations recognize the strategic value of unified, normalized VRF data, investments in next-generation data normalization platforms are expected to accelerate, driving innovation and competitive differentiation in the market.

Regionally, the Asia Pacific market dominates the Multi-OEM VRF Data Normalization sector, accounting for the largest share in 2024, driven by rapid urbanization, robust construction activity, and widespread adoption of VRF technology in commercial and residential buildings. North America and Europe follow closely, fueled by stringent energy efficiency standards, a mature building automation ecosystem, and strong investments in smart infrastructure. Latin America and the Middle East & Africa are also witnessing steady growth, underpinned by rising demand for modern HVAC solutions and increasing awareness about the benefits of data-driven facility management. The regional outlook remains highly positive, with each geography contributing uniquely to the global market’s upward trajectory.

Component Analysis

The Mul

EV Charging Data Normalization Middleware Market Outlook

According to our latest research, the global EV Charging Data Normalization Middleware market size reached USD 1.12 billion in 2024, reflecting a strong surge in adoption across the electric vehicle ecosystem. The market is projected to expand at a robust CAGR of 18.7% from 2025 to 2033, reaching a forecasted size of USD 5.88 billion by 2033. This remarkable growth is primarily driven by the exponential increase in electric vehicle (EV) adoption, the proliferation of charging infrastructure, and the need for seamless interoperability and data integration across disparate charging networks and platforms.

One of the primary growth factors fueling the EV Charging Data Normalization Middleware market is the rapid expansion of EV charging networks, both public and private, on a global scale. As governments and private entities accelerate investments in EV infrastructure to meet ambitious decarbonization and electrification goals, the resulting diversity of hardware, software, and communication protocols creates a fragmented ecosystem. Middleware solutions play a crucial role in standardizing and normalizing data from these heterogeneous sources, enabling unified management, real-time analytics, and efficient billing processes. The demand for robust data normalization is further amplified by the increasing complexity of charging scenarios, such as dynamic pricing, vehicle-to-grid (V2G) integration, and multi-operator roaming, all of which require seamless data interoperability.

Another significant driver is the rising emphasis on data-driven decision-making and predictive analytics within the EV charging sector. Stakeholders, including automotive OEMs, charging network operators, and energy providers, are leveraging normalized data to optimize charging station utilization, forecast energy demand, and enhance customer experiences. With the proliferation of IoT-enabled charging stations and smart grid initiatives, the volume and variety of data generated have grown exponentially. Middleware platforms equipped with advanced data normalization capabilities are essential for aggregating, cleansing, and harmonizing this data, thereby unlocking actionable insights and supporting the development of innovative value-added services. This trend is expected to further intensify as the industry moves towards integrated energy management and smart city initiatives.

The regulatory landscape is also playing a pivotal role in shaping the EV Charging Data Normalization Middleware market. Governments across regions are introducing mandates for open data standards, interoperability, and secure data exchange to foster competition, enhance consumer choice, and ensure grid stability. These regulatory requirements are compelling market participants to adopt middleware solutions that facilitate compliance and enable seamless integration with national and regional charging infrastructure registries. Furthermore, the emergence of industry consortia and standardization bodies is accelerating the development and adoption of common data models and APIs, further boosting the demand for middleware platforms that can adapt to evolving standards and regulatory frameworks.

Regionally, Europe and North America are at the forefront of market adoption, driven by mature EV markets, supportive policy frameworks, and advanced digital infrastructure. However, Asia Pacific is emerging as the fastest-growing region, propelled by aggressive electrification targets, large-scale urbanization, and significant investments in smart mobility solutions. Latin America and the Middle East & Africa, while currently at a nascent stage, are expected to witness accelerated growth as governments and private players ramp up efforts to expand EV charging networks and embrace digital transformation. The interplay of these regional dynamics is shaping a highly competitive and innovation-driven global market landscape.

Component Analysis

The Component segment of the EV C

Dataset Description This record is a collection of Whole-genome sequencing (WGS), RNA sequencing (RNA-seq), NanoString's nCounter® Breast Cancer 360 (BC360) Panel and cell viability assay data, generated as part of the study “Breast cancer patient-derived whole-tumor cell culture model for efficient drug profiling and treatment response prediction" by Chen et al., 2022. The WGS dataset contains raw sequencing data (BAM files) from tumor scraping cells (TSCs) at the time of surgical resection, derived whole-tumor cell (WTC) cultures from each patient's specimen, and normal skin biopsy for germline control, from five (5) breast cancer (BC) patients. Genomic DNA samples were isolated by using the QIAamp DNA mini kit (QIAGEN). The library was prepared by using Illumina TruSeq PCR-free (350 bp) according to the manufacturer’s protocol. The bulk DNA samples were then sequenced by Illumina Hiseq X and processed via the Science for Life Laboratory CAW workflow version 1.2.362 (Stockholm, Sweden; https://github.com/SciLifeLab/Sarek). The RNA-seq dataset contains raw sequencing data (fastq files) from the TSC pellets at the time of surgical resection, and the pellets of derived WTC cultures with or without tamoxifen metabolites treatment (1 nM 4OHT and 25 nM Z-Endoxifen), from 16 BC patients. 2000 ng RNA was extracted using the RNeasy mini kit (QIAGEN) from each sample, and 1 μg of total RNA was used for rRNA depletion using RiboZero (Illumina). Stranded RNA-seq libraries were constructed using TruSeq Stranded Total RNA Library Prep Kit (Illumina), and paired-end sequencing was performed on HiSeq 2500 with a 2 x 126 setup using the Science for Life Laboratory platform (Stockholm, Sweden). The NanoString's nCounter® BC360 Panel dataset contains normalized data from FFPE tissue samples of 43 BC patients. RNA was extracted from the macrodissected sections using the High Pure FFPET RNA Isolation Kit (Roche) following the manufacturer's protocols. Then, 200 ng of RNA per sample were loaded and further analyzed according to the manufacturer’s recommendation on a NanoString nCounter® system using the Breast Cancer 360 code set, which is comprised of 18 housekeeping genes and 752 target genes covering key pathways in tumor biology, microenvironment, and immune response. Raw data was assessed using several quality assurance (QA) metrics to measure imaging quality, oversaturation, and overall signal-to-noise ratio. All samples satisfying QA metric checks were background corrected (background thresholding) using the negative probes and normalized with their mean minus two standard deviations. The background-corrected data were then normalized by calculating the geometric mean of five housekeeper genes, namely ACTB, MRPL19, PSMC4, RPLP0, and SF3A1. The cell viability assay dataset for the main study contains drug sensitivity score (DSS) values for each of the tested drugs derived from the WTC spheroids of 45 BC patients. For patient DP-45, multiple regions were sampled to establish WTCs and perform drug profiling. For the neoadjuvant setting validation study, DSS values correspond to WTCs of 15 BC patients. For the drug profiling assay, each compound covered five concentrations ranging from 10 μM to 1 nM (2 μM to 0.2 nM for trastuzumab and pertuzumab) in 10-fold dilutions and was dispensed using the acoustic liquid handling system Echo 550 (Labcyte Inc) to make spotted 384-well plates. For the neoadjuvant setting validation assay, we updated the cyclophosphamide into its active metabolite form 4-hydroperoxy cyclophosphamide (4-OOH-cyclophosphamide). Each relevant compound covered eight concentrations ranging from 10 μM to 1 nM (2 μM to 0.2 nM for trastuzumab and pertuzumab) and was dispensed using the Tecan D300e Digital Dispenser (Tecan) to make spotted 384-well plates. In both experiment settings, a total volume of 40 nl of each compound condition was dispensed into each well, for limiting the final DMSO concentration to 0.1% during the treatment period. Further details on the cell viability assay, as well as the DSS estimation are available in the Materials & Methods part of Chen et al., 2022.

Telemetry Normalization Pipelines Market Outlook

According to our latest research, the global Telemetry Normalization Pipelines market size reached USD 1.18 billion in 2024. The market is set to exhibit robust growth, with a projected compound annual growth rate (CAGR) of 13.9% from 2025 to 2033. By 2033, the Telemetry Normalization Pipelines market is forecasted to reach USD 3.69 billion. This growth trajectory is primarily driven by the increasing complexity of IT infrastructures, the exponential rise in telemetry data generated by connected devices, and the critical need for unified data processing to support advanced analytics and security operations. As organizations across industries prioritize real-time insights and automated decision-making, the demand for scalable and efficient telemetry normalization solutions continues to accelerate.

The primary growth factor fueling the Telemetry Normalization Pipelines market is the rapid digital transformation occurring across industries. Enterprises are increasingly adopting cloud-native architectures, microservices, and distributed systems, which generate vast volumes of telemetry data from disparate sources. This proliferation of data silos creates significant challenges in terms of data integration, consistency, and quality. Telemetry normalization pipelines address these challenges by aggregating, transforming, and standardizing data streams, enabling organizations to harness actionable intelligence from their IT environments. As businesses strive to enhance operational efficiency, reduce downtime, and optimize resource allocation, the adoption of telemetry normalization solutions is becoming a strategic imperative.

Another significant driver is the escalating cybersecurity landscape. With the surge in sophisticated cyber threats, organizations are under mounting pressure to implement comprehensive security analytics and compliance management frameworks. Telemetry normalization pipelines play a pivotal role in this context by ensuring that security-related data from various endpoints, networks, and applications is normalized and correlated effectively. This enables security teams to detect anomalies, respond to incidents in real time, and maintain regulatory compliance. The growing emphasis on proactive threat detection and regulatory mandates, such as GDPR and HIPAA, is compelling enterprises to invest in advanced telemetry normalization technologies.

The rise of artificial intelligence (AI) and machine learning (ML) applications is also catalyzing market growth. AI-driven analytics require high-quality, normalized data to deliver accurate predictions and insights. Telemetry normalization pipelines facilitate the seamless ingestion, cleansing, and enrichment of data, thereby powering intelligent automation across IT operations, network monitoring, and business analytics. The convergence of AI, cloud computing, and telemetry normalization is fostering a new era of data-driven decision-making, where organizations can anticipate issues, automate remediation, and drive innovation at scale.

From a regional perspective, North America currently dominates the Telemetry Normalization Pipelines market, accounting for the largest revenue share in 2024. This leadership position is attributed to the region's advanced IT infrastructure, early adoption of cloud technologies, and a robust ecosystem of technology vendors. However, the Asia Pacific region is expected to witness the highest growth rate over the forecast period, driven by rapid digitalization, increasing investments in smart infrastructure, and the expanding footprint of multinational enterprises. Europe is also a significant market, propelled by stringent data protection regulations and a strong focus on cybersecurity. As emerging economies continue to modernize their digital landscapes, the global market for telemetry normalization pipelines is poised for sustained expansion.

Component Analysis

The Component segment of the Telemetry Nor

Supplementary Table 4A: Metatranscriptome data summary for cellular activities presented and statistics on sequencing and removal of potential contaminant sequences: FPKM values. Samples taken on board of the R/V JOIDES Resolution between November 30, 2015 and January 30, 2016. access_formats=.htmlTable,.csv,.json,.mat,.nc,.tsv acquisition_description=Frozen rock material was crushed as above, and then ground quickly into a fine powder using a precooled sterilized mortar and pestle, and then RNA extraction started immediately. The jaw crusher was cleaned and rinsed with 70% ethanol and RNaseZap\u2122 RNase Decontamination Solution (Invitrogen, USA) between samples. About 40 g of material was extracted for each sample using the RNeasy PowerSoil Total RNA Isolation Kit (Qiagen, USA) according to the manufacturer\u2019s protocol with the following modifications.

Each sample was evenly divided into 8 Bead Tubes (Qiagen, USA) and then 2.5 mL of Bead Solution were added into the Bead Tube followed by 0.25 mL of Solution SR1 and 0.8 mL of Solution SR2. Bead Tubes were frozen in liquid nitrogen and then thawed at 65\u00b0C in a water bath three times. RNA was purified using the MEGAclear Transcription Clean-up Kit (Ambion, USA) and concentrated with an overnight isopropanol precipitation at 4 \u00b0C. Trace amounts of contaminating DNA were removed from the RNA extracts using TURBO DNA free\u2122 (Invitrogen, USA) as directed by the manufacturer. To ensure DNA was removed thoroughly, each RNA extract was treated twice with TURBO DNase (Invitrogen, USA). A nested PCR reaction (2 x 35 cycles) using bacterial primers was used to confirm the absence of DNA in our RNA solutions. RNA was converted to cDNA using the Ovation\u00ae RNA-Seq System V2 kit (NuGEN, USA) according to the manufacturer\u2019s protocol to preferentially prime non-rRNA sequences.

The cDNA was purified with the MinElute Reaction Cleanup Kit (Qiagen, USA) and eluted into 20 \u03bcL elution buffer. Extracts were quantified using a Qubit Fluorometer (Life Technologies, USA) and cDNAs were stored at -80 \u00b0C until sequencing using 150 bp paired-end Illumina NextSeq 550.

To control for potential contaminants introduced during drilling, sample handling, and laboratory kit reagents, we sequenced a number of control samples as above. Two samples controlled for potential nucleic acid contamination; a \u201cmethod\u201d control to monitor possible contamination from our laboratory extractions, which included ~ 40 g sterilized glass beads processed through the entire protocol in place of rock, and a \u201ckit\u201d control to account for any signal coming from trace contaminants in kit reagents, which received no addition. In addition, 3 more controls were extracted: a sample of the drilling mud (Sepiolite), and two drilling seawater samples collected during the first and third weeks of drilling. cDNA obtained from these controls were sequenced together with the rock samples and co- assembled. Trimmomatic (v. 0.32) was used to trim adapter sequences (leading=20, trailing=20, sliding window=04:24, minlen=50).

Paired reads were further quality checked and trimmed using FastQC (v. 0.11.7) and FASTX-toolkit (v. 0.014). Downstream analyses utilized paired reads. After co-assembling reads with Trinity (v. 2.4.0) from all controls (min length 150 bp), Bowtie2 (v. 2.3.4.1, 50) was used (with the parameter \u2018un- conc\u2019) to align all sample reads to this co-assembly. Reads that mapped to our control co-assembly allowing 1 mismatch were removed from further analysis (23.5-68.5% of sequences remained in sample data sets, see Supplementary Table 4). Trinity (v. 2.4.0) was used for de novo assembly of the remaining reads in sample data sets (min. length 150 bp). Bowtie aligner was used to align reads to assembled contigs, RSEM was used to estimate the expression level of these reads, and TMM was used to perform cross sample normalization and to generate a TMM-normalized expression matrix. Within the Trinotate suite, TransDecoder (v. 3.0.1) was used to identify coding regions within contigs and functional and taxonomic annotation was made 622 by BLASTx and BLASTp against UniProt, Swissprot (release 2018_02) and RefSeq non- redundant protein sequence (nr) databases (e-value threshold of 1e-5). BLASTp was used to look for sequence homologies with the same e-values. HMMER (v. 3.1b2) was used to identify conserved domains by searching against the Pfam (v31.0) database. SignalP (v. 4.1) and TMHMM (2.0c) were used to predict signal peptides and transmembrane domains. RNAMMER (v.1.2) was used to identify rRNA homologies of archaea, bacteria and eukaryotes.

Because the Swissprot database does not have extensive representation of protein sequences from environmental samples, particularly deep-sea and deep biosphere samples, annotations of contigs utilized for analyses of selected processes were manually cross checked by BLASTx against GenBank nr database. Aside from removing any reads that mapped well to our control co-assembly (1 mismatch), as an extra precaution, any sequence that exhibited \u2265 95% sequence identity over \u2265 80% of the sequence length to suspected contaminants (e.g., human pathogens, plants, or taxa known to be common molecular kit reagent contaminants, and not described from the marine environment) as in Salter et al. and Glassing et al. were removed.

This conservative approach potentially removed environmentally relevant data that were annotated to suspected contaminants due to poor taxonomic representation from environmental taxa in public databases, however it affords the highest possible confidence about any transcripts discussed. Additional functional annotations of contigs were obtained by BLAST against the KEGG, COG, SEED, and MetaCyc databases using MetaPathways (v. 2.0) to gain insights into particular cellularprocesses, and to provide overviews of metabolic functions across samples based on comparisons of FPKM-normalized data. All annotations were integrated into a SQLite database for further analysis. awards_0_award_nid=709555 awards_0_award_number=OCE-1658031 awards_0_data_url=http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1658031 awards_0_funder_name=NSF Division of Ocean Sciences awards_0_funding_acronym=NSF OCE awards_0_funding_source_nid=355 awards_0_program_manager=David L. Garrison awards_0_program_manager_nid=50534 cdm_data_type=Other Conventions=COARDS, CF-1.6, ACDD-1.3 data_source=extract_data_as_tsv version 2.3 19 Dec 2019 dataset_current_state=Final and no updates defaultDataQuery=&time<now infoUrl=https://www.bco-dmo.org/dataset/812936 institution=BCO-DMO instruments_0_acronym=Automated Sequencer instruments_0_dataset_instrument_description=RNA sequencing was performed using the Illumina NextSeq 550 platform (Univ. of Georgia).v instruments_0_dataset_instrument_nid=813310 instruments_0_description=General term for a laboratory instrument used for deciphering the order of bases in a strand of DNA. Sanger sequencers detect fluorescence from different dyes that are used to identify the A, C, G, and T extension reactions. Contemporary or Pyrosequencer methods are based on detecting the activity of DNA polymerase (a DNA synthesizing enzyme) with another chemoluminescent enzyme. Essentially, the method allows sequencing of a single strand of DNA by synthesizing the complementary strand along it, one base pair at a time, and detecting which base was actually added at each step. instruments_0_instrument_name=Automated DNA Sequencer instruments_0_instrument_nid=649 instruments_0_supplied_name=Illumina NextSeq 550 platform metadata_source=https://www.bco-dmo.org/api/dataset/812936 param_mapping={'812936': {}} parameter_source=https://www.bco-dmo.org/mapserver/dataset/812936/parameters people_0_affiliation=Woods Hole Oceanographic Institution people_0_affiliation_acronym=WHOI people_0_person_name=Virginia P. Edgcomb people_0_person_nid=51284 people_0_role=Principal Investigator people_0_role_type=originator people_1_affiliation=Woods Hole Oceanographic Institution people_1_affiliation_acronym=WHOI people_1_person_name=Virginia P. Edgcomb people_1_person_nid=51284 people_1_role=Contact people_1_role_type=related people_2_affiliation=Woods Hole Oceanographic Institution people_2_affiliation_acronym=WHOI BCO-DMO people_2_person_name=Karen Soenen people_2_person_nid=748773 people_2_role=BCO-DMO Data Manager people_2_role_type=related project=Subseafloor Lower Crust Microbiology projects_0_acronym=Subseafloor Lower Crust Microbiology projects_0_description=NSF abstract: The lower ocean crust has remained largely unexplored and represents one of the last frontiers for biological exploration on Earth. Preliminary data indicate an active subsurface biosphere in samples of the lower oceanic crust collected from Atlantis Bank in the SW Indian Ocean as deep as 790 m below the seafloor. Even if life exists in only a fraction of the habitable volume where temperatures permit and fluid flow can deliver carbon and energy sources, an active lower oceanic crust biosphere would have implications for deep carbon budgets and yield insights into microbiota that may have existed on early Earth. This is all of great interest to other research disciplines, educators, and students alike. A K-12 education program will capitalize on groundwork laid by outreach collaborator, A. Martinez, a 7th grade teacher in Eagle Pass, TX, who sailed as outreach expert on Drilling Expedition 360. Martinez works at a Title 1 school with ~98% Hispanic and ~2% Native American students and a high number of English Language Learners and migrants. Annual school visits occur during which the project investigators present hands on-activities introducing students to microbiology, and talks on marine microbiology, the project, and how to pursue science related careers. In addition, monthly Skype meetings with students and PIs update them on project progress.

Diversity analysis of amplicon sequencing data has mainly been limited to plug-in estimates calculated using normalized data to obtain a single value of an alpha diversity metric or a single point on a beta diversity ordination plot for each sample. As recognized for count data generated using classical microbiological methods, amplicon sequence read counts obtained from a sample are random data linked to source properties (e.g., proportional composition) by a probabilistic process. Thus, diversity analysis has focused on diversity exhibited in (normalized) samples rather than probabilistic inference about source diversity. This study applies fundamentals of statistical analysis for quantitative microbiology (e.g., microscopy, plating, and most probable number methods) to sample collection and processing procedures of amplicon sequencing methods to facilitate inference reflecting the probabilistic nature of such data and evaluation of uncertainty in diversity metrics. Following description of types of random error, mechanisms such as clustering of microorganisms in the source, differential analytical recovery during sample processing, and amplification are found to invalidate a multinomial relative abundance model. The zeros often abounding in amplicon sequencing data and their implications are addressed, and Bayesian analysis is applied to estimate the source Shannon index given unnormalized data (both simulated and experimental). Inference about source diversity is found to require knowledge of the exact number of unique variants in the source, which is practically unknowable due to library size limitations and the inability to differentiate zeros corresponding to variants that are actually absent in the source from zeros corresponding to variants that were merely not detected. Given these problems with estimation of diversity in the source even when the basic multinomial model is valid, diversity analysis at the level of samples with normalized library sizes is discussed.

Permafrost degradation influences the morphology, biogeochemical cycling and hydrology of Arctic landscapes over a range of time scales. To reconstruct temporal patterns of early to late Holocene permafrost and thermokarst dynamics, site-specific palaeo-records are needed. Here we present a multi-proxy study of a 350-cm-long permafrost core from a drained lake basin on the northern Seward Peninsula, Alaska, revealing Lateglacial to Holocene thermokarst lake dynamics in a central location of Beringia. Use of radiocarbon dating, micropalaeontology (ostracods and testaceans), sedimentology (grain-size analyses, magnetic susceptibility, tephra analyses), geochemistry (total nitrogen and carbon, total organic carbon, d13Corg) and stable water isotopes (d18O, dD, d excess) of ground ice allowed the reconstruction of several distinct thermokarst lake phases. These include a pre-lacustrine environment at the base of the core characterized by the Devil Mountain Maar tephra (22 800±280 cal. a BP, Unit A), which has vertically subsided in places due to subsequent development of a deep thermokarst lake that initiated around 11 800 cal. a BP (Unit B). At about 9000 cal. a BP this lake transitioned from a stable depositional environment to a very dynamic lake system (Unit C) characterized by fluctuating lake levels, potentially intermediate wetland development, and expansion and erosion of shore deposits. Complete drainage of this lake occurred at 1060 cal. a BP, including post-drainage sediment freezing from the top down to 154 cm and gradual accumulation of terrestrial peat (Unit D), as well as uniform upward talik refreezing. This core-based reconstruction of multiple thermokarst lake generations since 11 800 cal. a BP improves our understanding of the temporal scales of thermokarst lake development from initiation to drainage, demonstrates complex landscape evolution in the ice-rich permafrost regions of Central Beringia during the Lateglacial and Holocene, and enhances our understanding of biogeochemical cycles in thermokarst-affected regions of the Arctic.

Raw and preprocessed microarray expression data from the GSE65194 cohort.

Includes samples from triple-negative breast cancer (TNBC), other breast cancer subtypes, and normal breast tissues.

Expression profiles generated using the “Affymetrix Human Genome U133 Plus 2.0 Array (GPL570)” platform. tcr.amegroups.org +2 Journal of Cancer +2

Provides normalized gene expression values suitable for downstream analyses such as differential expression, subtype classification, and clustering.

Supports the identification of differentially expressed genes (DEGs) between TNBC, non-TNBC subtypes, and normal tissue. Aging-US +2 tcr.amegroups.org +2

Useful for transcriptomic analyses in breast cancer research, including subtype analysis, biomarker discovery, and comparative studies.

Ever-increasing affordability of next-generation sequencing makes whole-metagenome sequencing an attractive alternative to traditional 16S rDNA, RFLP, or culturing approaches for the analysis of microbiome samples. The advantage of whole-metagenome sequencing is that it allows direct inference of the metabolic capacity and physiological features of the studied metagenome without reliance on the knowledge of genotypes and phenotypes of the members of the bacterial community. It also makes it possible to overcome problems of 16S rDNA sequencing, such as unknown copy number of the 16S gene and lack of sufficient sequence similarity of the “universal” 16S primers to some of the target 16S genes. On the other hand, next-generation sequencing suffers from biases resulting in non-uniform coverage of the sequenced genomes. To overcome this difficulty, we present a model of GC-bias in sequencing metagenomic samples as well as filtration and normalization techniques necessary for accurate quantification of microbial organisms. While there has been substantial research in normalization and filtration of read-count data in such techniques as RNA-seq or Chip-seq, to our knowledge, this has not been the case for the field of whole-metagenome shotgun sequencing. The presented methods assume that complete genome references are available for most microorganisms of interest present in metagenomic samples. This is often a valid assumption in such fields as medical diagnostics of patient microbiota. Testing the model on two validation datasets showed four-fold reduction in root-mean-square error compared to non-normalized data in both cases. The presented methods can be applied to any pipeline for whole metagenome sequencing analysis relying on complete microbial genome references. We demonstrate that such pre-processing reduces the number of false positive hits and increases accuracy of abundance estimates.