Transcriptomics in the Study of Antiviral Innate Immunity.

Transcriptomics is an extremely important area of molecular biology and is a powerful tool for studying all RNA molecules in an organism. Conventional transcriptomic technologies include microarrays and RNA sequencing, and the rapid development of single-cell sequencing and spatial transcriptomics in recent years has provided an enormous scope for research in this field. This chapter describes the application, significance, and experimental procedures of a variety of transcriptomic technologies in antiviral natural immunity.

Development and validation of a 66K SNP array for the hard clam (Mercenaria mercenaria).

BACKGROUND: The hard clam (Mercenaria mercenaria), a marine bivalve distributed along the U.S. eastern seaboard, supports a significant shellfish industry. Overharvest in the 1970s and 1980s led to a reduction in landings. While the transition of industry from wild harvest to aquaculture since that time has enhanced production, it has also exacerbated challenges such as disease outbreaks. In this study, we developed and validated a 66K SNP array designed to advance genetic studies and improve breeding programs in the hard clam, focusing particularly on the development of markers that could be useful in understanding disease resistance and environmental adaptability. RESULTS: Whole-genome resequencing of 84 individual clam samples and 277 pooled clam libraries yielded over 305 million SNPs, which were filtered down to a set of 370,456 SNPs that were used as input for the design of a 66K SNP array. This medium-density array features 66,543 probes targeting coding and non-coding regions, including 70 mitochondrial SNPs, to capture the extensive genetic diversity within the species. The SNPs were distributed evenly throughout the clam genome, with an average interval of 25,641 bp between SNPs. The array incorporates markers for detecting the clam pathogen Mucochytrium quahogii (formerly QPX), enhancing its utility in disease management. Performance evaluation on 1,904 samples demonstrated a 72.7% pass rate with stringent quality control. Concordance testing affirmed the array's repeatability, with an average agreement of allele calls of 99.64% across multiple tissue types, highlighting its reliability. The tissue-specific analysis demonstrated that some tissue types yield better genotyping results than others. Importantly, the array, including its embedded mitochondrial markers, effectively elucidated complex genetic relationships across different clam groups, both wild populations and aquacultured stocks, showcasing its utility for detailed population genetics studies. CONCLUSIONS: The 66K SNP array is a powerful and robust genotyping tool that offers unprecedented insights into the species' genomic architecture and population dynamics and that can greatly facilitate hard clam selective breeding. It represents an important resource that has the potential to transform clam aquaculture, thereby promoting industry sustainability and ecological and economic resilience.

Comment on, "Increased expression of ephrin A1 in brain arteriovenous malformation: DNA microarray analysis".

This study by Sasahara et al. explores the role of ephrin A1 in brain arteriovenous malformations (AVM) using DNA microarray analysis, quantitative real-time RT-PCR, and immunohistochemistry. The research identifies significant upregulation of ephrin A1 in AVM, suggesting its potential involvement in the abnormal vascular architecture characteristic of this condition. The study's innovative methodology and thorough exploration of gene expression patterns contribute valuable insights into AVM pathogenesis, highlighting ephrin A1 as a potential therapeutic target. However, the study's limitations include clinical variability among patient samples and the use of draining veins as controls, which may affect the robustness of the findings. Future research should address these limitations by using more homogeneous samples and expanding the investigation to include other ephrin family members. This could provide a broader understanding of ephrin signaling in AVM and guide the development of targeted therapies.

Technical and biological sources of unreliability of Infinium probes on Illumina methylation microarrays.

The Illumina Methylation array platform has facilitated countless epigenetic studies on DNA methylation (DNAme) in health and disease, yet relatively few studies have so studied its reliability, i.e., the consistency of repeated measures. Here we investigate the reliability of both type I and type II Infinium probes. We propose a method for excluding unreliable probes based on dynamic thresholds for mean intensity (MI) and 'unreliability', estimated by probe-level simulation of the influence of technical noise on methylation ß values using the background intensities of negative control probes. We validate our method in several datasets, including newly generated Illumina MethylationEPIC BeadChip v1.0 data from paired whole blood samples taken six weeks apart and technical replicates spanning multiple sample types. Our analysis revealed that specifically probes with low MI exhibit higher ß value variability between repeated samples. MI was associated with the number of C-bases in the respective probe sequence and correlated negatively with unreliability scores. The unreliability scores were substantiated through validation in a new EPIC v1.0 (blood and cervix) and a publicly available 450k (blood) dataset, as they effectively captured the variability observed in ß values between technical replicates. Finally, despite promising higher robustness, the newer version v2.0 of the MethylationEPIC BeadChip retained a substantial number of probes with poor unreliability scores. To enhance current pre-processing pipelines, we developed an R package to calculate MI and unreliability scores and provide guidance on establishing optimal dynamic score thresholds for a given dataset.

[Application of CNVPLUS-array custom microarray in genetic analysis of spinal muscular atrophy].

OBJECTIVE: To assess the application value of CNVPLUS-array for the genetic analysis of Spinal muscular atrophy (SMA). METHODS: From June 2021 to December 2022, CNVPLUS-array technique was employed to test the SMN1 and SMN2 genes among peripheral blood samples from 17 suspected SMA patients, 18 core families with suspected SMA, and 25 healthy individuals. The results were compared with those of multiple ligation-dependent probe amplification (MLPA) assay. Samples with inconsistent results were subjected to nested PCR or comprehensive analysis of SMA. RESULTS: CNVPLUS-array has identified 35 SMA patients, 36 carriers, and 25 healthy individuals. In comparison, MLPA has identified 34 SMA patients, 36 carriers, and 26 healthy individuals. The two methods demonstrated a high consistency (Kappa = 0.968, P < 0.001). Additionally, CNVPLUS-array has identified one patient with compound heterozygous variants of SMN1 and one carrier with a [2+0] genotype. CONCLUSION: CNVPLUS-array not only can accurately determine the copy numbers of SMN1 and SMN2 genes, but also identify point mutations in SMN1 and [2+0] carriers, which has offered a new method for the genetic testing of SMA.

Development and validation of a 5K low-density SNP chip for Hainan cattle.

BACKGROUND: This study aimed to design and develop a 5K low-density liquid chip for Hainan cattle utilizing targeted capture sequencing technology. The chip incorporates a substantial number of functional single nucleotide polymorphism (SNP) loci derived from public literature, including SNP loci significantly associated with immunity, heat stress, meat quality, reproduction, and other traits. Additionally, SNPs located in the coding regions of immune-related genes from the Bovine Genome Variation Database (BGVD) and Hainan cattle-specific SNP loci were included. RESULTS: A total of 5,293 SNPs were selected, resulting in 9,837 DNA probes with a coverage rate of 85.69%, thereby creating a Hainan cattle-specific 5K Genotyping by Target Sequencing (GBTS) liquid chip. Evaluation with 152 cattle samples demonstrated excellent clustering performance and a detection rate ranging from 96.60 to 99.07%, with 94.5% of SNP sites exhibiting polymorphism. The chip achieved 100% gender coverage and displayed a heterozygosity rate between 14.20% and 29.65%, with a repeatability rate of 99.65-99.85%. Analyses using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) revealed the potential regulatory roles of exonic SNPs in immune response pathways. CONCLUSION: The development and validation of the 5K GBTS liquid chip for Hainan cattle represent a valuable tool for genome analysis and genetic diversity assessment. Furthermore, it facilitates breed identification, gender determination, and kinship analysis, providing a foundation for the efficient utilization and development of local cattle genetic resources.

A gene selection algorithm for microarray cancer classification using an improved particle swarm optimization.

Gene selection is an essential step for the classification of microarray cancer data. Gene expression cancer data (deoxyribonucleic acid microarray] facilitates in computing the robust and concurrent expression of various genes. Particle swarm optimization (PSO) requires simple operators and less number of parameters for tuning the model in gene selection. The selection of a prognostic gene with small redundancy is a great challenge for the researcher as there are a few complications in PSO based selection method. In this research, a new variant of PSO (Self-inertia weight adaptive PSO) has been proposed. In the proposed algorithm, SIW-APSO-ELM is explored to achieve gene selection prediction accuracies. This novel algorithm establishes a balance between the exploitation and exploration capabilities of the improved inertia weight adaptive particle swarm optimization. The self-inertia weight adaptive particle swarm optimization (SIW-APSO) algorithm is employed for solution explorations. Each particle in the SIW-APSO increases its position and velocity iteratively through an evolutionary process. The extreme learning machine (ELM) has been designed for the selection procedure. The proposed method has been employed to identify several genes in the cancer dataset. The classification algorithm contains ELM, K-centroid nearest neighbor, and support vector machine to attain high forecast accuracy as compared to the start-of-the-art methods on microarray cancer datasets that show the effectiveness of the proposed method.

A novel parallel feature rank aggregation algorithm for gene selection applied to microarray data classification.

Microarray data often comprises numerous genes, yet not all genes are relevant for predicting cancer. Feature selection becomes a crucial step to reduce the high dimensionality in these kinds of data. While no single feature selection method consistently outperforms others across diverse domains, the combination of multiple feature selectors or rankers tends to produce more effective results compared to relying on a single ranker alone. However, this approach can be computationally expensive, particularly when handling a large quantity of features. Hence, this paper presents a parallel feature rank aggregation that utilizes borda count as the rank aggregator. The concept of vertically partitioning the data along feature space was adapted to ease the parallel execution of the aggregation task. Features were selected based on the final aggregated rank list, and their classification performances were evaluated. The model's execution time was also observed across multiple worker nodes of the cluster. The experiment was conducted on six benchmark microarray datasets. The results show the capability of the proposed distributed framework compared to the sequential version in all the cases. It also illustrated the improved accuracy performance of the proposed method and its ability to select a minimal number of genes.

A Method to Study the Meiotic Recombination Map in Human Preimplantation Blastocysts.

Homologous recombination plays pivotal roles in physical attachments and genetic diversity. In the past, it was studied among individuals from different populations. However, only few gametes from individual could generate offspring, which limits its exploration in nature selection. In the last few years, preimplantation blastocysts based on trio SNP-chip data were available in individuals for preimplantation genetic testing (PGT). In this protocol, we demonstrate how to detect meiotic recombination events and construct the genetic map based on trio SNP-chip data, obtained from biopsied blastocysts and their related individuals in PGT cycles, which may allow better understanding of recombination events in nature selection.

Application effectiveness of "Youxin-1" in genetic diversity and structure analysis of local chickens.

China's local chicken breeds are rich in resources, and have formed different germplasm characteristics in the process of long-term selection and evolution. Scientific assessment of population genetic diversity and identification of inter-breed genetic structure are of great value to the protection and innovative utilization of local chicken breed resource. In order to evaluate the application effectiveness of 23K SNP chip "Youxin-1" in the analysis of genetic diversity and genetic structure of local chickens, we used RADseq to identify genomic genetic variation of 21 local chicken breeds and developed 23K chip "Youxin-1". The genetic statistics of each variety were calculated based on two sets of SNP data, and correlation, fitting and phylogenetic analysis were carried out to evaluate the application effectiveness of the chip. The results showed that the observed heterozygosity (Ho), polymorphism information content (PIC), inbred coefficient (FROH) and genetic differentiation coefficient (Fst) calculated based on the two SNP data sets were basically consistent in the 21 local chicken breeds. The genetic diversity of Langya chicken (LA), Piao chicken (PJ) and Wenchang chicken (WC) was relatively rich. The genetic diversity of Bian chickens (BJ), Langshan chickens (LS), Gushi chickens (GS), Dongxiang blue-eggshell chickens (DX) and Beijing fatty chickens (BY) was relatively poor, and the correlation coefficients of Ho, PIC, FROH and average Fst in the two groups were 0.794, 0.901, 0.926 and 0.984, respectively, all reaching extremely significant levels (P<0.01) with a high degree of fit (P<0.001) and R2 were 0.644, 0.827, 0.916 and 0.927. For the two sets of SNP data, the evolutionary tree constructed by neighbor-joining (NJ) method and maximum likelihood (ML) method was reasonable, and the 21 local chicken breeds were generally divided into six categories, which was consistent with the formation history and geographical distribution of the varieties. The 23K chip also realized reasonable clustering of the five new varieties without individual deviation. There are some differences in the estimation of genetic statistics using SNP with different densities, and data standardization is needed. 23K chip has good efficacy in the analysis of genetic diversity and structure of local chickens.

Superior detection of low-allele burden Janus kinase 2 V617F mutation and monitoring clonal evolution in myeloproliferative neoplasms using chip-based digital PCR.

The JAK2 V617F is a prevalent driver mutation in Philadelphia chromosome-negative myeloproliferative neoplasms (Ph-MPNs), significantly affecting disease progression, immunophenotype, and patient outcomes. The World Health Organization (WHO) guidelines highlight the JAK2 V617F mutation as one of the key diagnostic criterions for Ph-MPNs. In this study, we analyzed 283 MPN samples with the JAK2 V617F mutation to assess the effectiveness of three detection technologies: chip-based digital PCR (cdPCR), real-time quantitative PCR (qPCR), and next-generation sequencing (NGS). Additionally, we investigated the relationship between JAK2 V617F mutant allele burden (% JAK2 V617F) and various laboratory characteristics to elucidate potential implications in MPN diagnosis. Our findings demonstrated high conformance of cdPCR with qPCR/NGS for detecting % JAK2 V617F, but the mutant allele burdens detected by qPCR/NGS were lower than those detected by cdPCR. Moreover, the cdPCR exhibited high sensitivity with a limit of detection (LoD) of 0.08% and a limit of quantification (LoQ) of 0.2% for detecting % JAK2 V617F in MPNs. Clinical implications were explored by correlating % JAK2 V617F with various laboratory characteristics in MPN patients, revealing significant associations with white blood cell counts, lactate dehydrogenase levels, and particularly ß2-microglobulin (ß2-MG) levels. Finally, a case report illustrated the application of cdPCR in detecting low-allele burdens in a de novo chronic myeloid leukemia (CML) patient with a hidden JAK2 V617F subclone, which expanded during tyrosine kinase inhibitor (TKI) treatment. Our findings underscore the superior sensitivity and accuracy of cdPCR, making it a valuable tool for early diagnosis and monitoring clonal evolution.

Identification of new targets for glioblastoma therapy based on a DNA expression microarray.

This study provides a comprehensive perspective on the deregulated pathways and impaired biological functions prevalent in human glioblastoma (GBM). In order to characterize differences in gene expression between individuals diagnosed with GBM and healthy brain tissue, we have designed and manufactured a specific, custom DNA microarray. The results obtained from differential gene expression analysis were validated by RT-qPCR. The datasets obtained from the analysis of common differential expressed genes in our cohort of patients were used to generate protein-protein interaction networks of functionally enriched genes and their biological functions. This network analysis, let us to identify 16 genes that exhibited either up-regulation (CDK4, MYC, FOXM1, FN1, E2F7, HDAC1, TNC, LAMC1, EIF4EBP1 and ITGB3) or down-regulation (PRKACB, MEF2C, CAMK2B, MAPK3, MAP2K1 and PENK) in all GBM patients. Further investigation of these genes and enriched pathways uncovered in this investigation promises to serve as a foundational step in advancing our comprehension of the molecular mechanisms underpinning GBM pathogenesis. Consequently, the present work emphasizes the critical role that the unveiled molecular pathways likely play in shaping innovative therapeutic approaches for GBM management. We finally proposed in this study a list of compounds that target hub of GBM-related genes, some of which are already in clinical use, underscoring the potential of those genes as targets for GBM treatment.

mLiftOver: harmonizing data across Infinium DNA methylation platforms.

MOTIVATION: Infinium DNA methylation BeadChips are widely used for genome-wide DNA methylation profiling at the population scale. Recent updates to probe content and naming conventions in the EPIC version 2 (EPICv2) arrays have complicated integrating new data with previous Infinium array platforms, such as the MethylationEPIC (EPIC) and the HumanMethylation450 (HM450) BeadChip. RESULTS: We present mLiftOver, a user-friendly tool that harmonizes probe ID, methylation level, and signal intensity data across different Infinium platforms. It manages probe replicates, missing data imputation, and platform-specific bias for accurate data conversion. We validated the tool by applying HM450-based cancer classifiers to EPICv2 cancer data, achieving high accuracy. Additionally, we successfully integrated EPICv2 healthy tissue data with legacy HM450 data for tissue identity analysis and produced consistent copy number profiles in cancer cells. AVAILABILITY AND IMPLEMENTATION: mLiftOver is implemented R and available in the Bioconductor package SeSAMe (version 1.21.13+): https://bioconductor.org/packages/release/bioc/html/sesame.html. Analysis of EPIC and EPICv2 platform-specific bias and high-confidence mapping is available at https://github.com/zhou-lab/InfiniumAnnotationV1/raw/main/Anno/EPICv2/EPICv2ToEPIC_conversion.tsv.gz. The source code is available at https://github.com/zwdzwd/sesame/blob/devel/R/mLiftOver.R under the MIT license.

A comparison of the TempO-Seq and Affymetrix microarray platform using RTqPCR validation.

Next-generation risk assessment relies on mechanistic data from new approach methods, including transcriptome data. Various technologies, such as high-throughput targeted sequencing methods and microarray technologies based on hybridization with complementary probes, are used to determine differentially expressed genes (DEGs). The integration of data from different technologies requires a good understanding of the differences arising from the use of various technologies.To better understand the differences between the TempO-Seq platform and Affymetrix chip technology, whole-genome data for the volatile compound dimethylamine were compared. Selected DEGs were also confirmed using RTqPCR validation. Although the overlap of DEGs between TempO-Seq and Affymetrix was no higher than 37%, a comparison of the gene regulation in terms of log2fold changes revealed a very high concordance. RTqPCR confirmed the majority of DEGs from either platform in the examined dataset. Only a few conflicts were found (11%), while 22% were not confirmed, and 3% were not detected.Despite the observed differences between the two platforms, both can be validated using RTqPCR. Here we highlight some of the differences between the two platforms and discuss their applications in toxicology.

Establishment of diagnostic model and identification of diagnostic markers between liver cancer and cirrhosis based on multi-chip and machine learning.

OBJECTIVE: Most cases of hepatocellular carcinoma (HCC) arise as a consequence of cirrhosis. In this study, our objective is to construct a comprehensive diagnostic model that investigates the diagnostic markers distinguishing between cirrhosis and HCC. METHODS: Based on multiple GEO datasets containing cirrhosis and HCC samples, we used lasso regression, random forest (RF)-recursive feature elimination (RFE) and receiver operator characteristic analysis to screen for characteristic genes. Subsequently, we integrated these genes into a multivariable logistic regression model and validated the linear prediction scores in both training and validation cohorts. The ssGSEA algorithm was used to estimate the fraction of infiltrating immune cells in the samples. Finally, molecular typing for patients with cirrhosis was performed using the CCP algorithm. RESULTS: The study identified 137 differentially expressed genes (DEGs) and selected five significant genes (CXCL14, CAP2, FCN2, CCBE1 and UBE2C) to construct a diagnostic model. In both the training and validation cohorts, the model exhibited an area under the curve (AUC) greater than 0.9 and a kappa value of approximately 0.9. Additionally, the calibration curve demonstrated excellent concordance between observed and predicted incidence rates. Comparatively, HCC displayed overall downregulation of infiltrating immune cells compared to cirrhosis. Notably, CCBE1 showed strong correlations with the tumour immune microenvironment as well as genes associated with cell death and cellular ageing processes. Furthermore, cirrhosis subtypes with high linear predictive scores were enriched in multiple cancer-related pathways. CONCLUSION: In conclusion, we successfully identified diagnostic markers distinguishing between cirrhosis and hepatocellular carcinoma and developed a novel diagnostic model for discriminating the two conditions. CCBE1 might exert a pivotal role in regulating the tumour microenvironment, cell death and senescence.

Metaheuristic integrated machine learning classification of colon cancer using STFT LASSO and EHO feature extraction from microarray gene expressions.

The microarray gene expression data poses a tremendous challenge due to their curse of dimensionality problem. The sheer volume of features far surpasses available samples, leading to overfitting and reduced classification accuracy. Thus the dimensionality of microarray gene expression data must be reduced with efficient feature extraction methods to reduce the volume of data and extract meaningful information to enhance the classification accuracy and interpretability. In this research, we discover the uniqueness of applying STFT (Short Term Fourier Transform), LASSO (Least Absolute Shrinkage and Selection Operator), and EHO (Elephant Herding Optimisation) for extracting significant features from lung cancer and reducing the dimensionality of the microarray gene expression database. The classification of lung cancer is performed using the following classifiers: Gaussian Mixture Model (GMM), Particle Swarm Optimization (PSO) with GMM, Detrended Fluctuation Analysis (DFA), Naive Bayes classifier (NBC), Firefly with GMM, Support Vector Machine with Radial Basis Kernel (SVM-RBF) and Flower Pollination Optimization (FPO) with GMM. The EHO feature extraction with the FPO-GMM classifier attained the highest accuracy in the range of 96.77, with an F1 score of 97.5, MCC of 0.92 and Kappa of 0.92. The reported results underline the significance of utilizing STFT, LASSO, and EHO for feature extraction in reducing the dimensionality of microarray gene expression data. These methodologies also help in improved and early diagnosis of lung cancer with enhanced classification accuracy and interpretability.

Mechanism of drug-pairs Astragalus Mongholicus-Largehead Atractylodes on treating knee osteoarthritis investigated by GEO gene chip with network pharmacology and molecular docking.

Investigations into the therapeutic potential of Astragalus Mongholicus (AM, huáng qí) and Largehead Atractylodes (LA, bái zhú) reveal significant efficacy in mitigating the onset and progression of knee osteoarthritis (KOA), albeit with an elusive mechanistic understanding. This study delineates the primary bioactive constituents and their molecular targets within the AM-LA synergy by harnessing the comprehensive Traditional Chinese Medicine (TCM) network databases, including TCMSP, TCMID, and ETCM. Furthermore, an analysis of 3 gene expression datasets, sourced from the gene expression omnibus database, facilitated the identification of differential genes associated with KOA. Integrating these findings with data from 5 predominant databases yielded a refined list of KOA-associated targets, which were subsequently aligned with the gene signatures corresponding to AM and LA treatment. Through this alignment, specific molecular targets pertinent to the AM-LA therapeutic axis were elucidated. The construction of a protein-protein interaction network, leveraging the shared genetic markers between KOA pathology and AM-LA intervention, enabled the identification of pivotal molecular targets via the topological analysis facilitated by CytoNCA plugins. Subsequent GO and KEGG enrichment analyses fostered the development of a holistic herbal-ingredient-target network and a core target-signal pathway network. Molecular docking techniques were employed to validate the interaction between 5 central molecular targets and their corresponding active compounds within the AM-LA complex. Our findings suggest that the AM-LA combination modulates key biological processes, including cellular activity, reactive oxygen species modification, metabolic regulation, and the activation of systemic immunity. By either augmenting or attenuating crucial signaling pathways, such as MAPK, calcium, and PI3K/AKT pathways, the AM-LA dyad orchestrates a comprehensive regulatory effect on immune-inflammatory responses, cellular proliferation, differentiation, apoptosis, and antioxidant defenses, offering a novel therapeutic avenue for KOA management. This study, underpinned by gene expression omnibus gene chip analyses and network pharmacology, advances our understanding of the molecular underpinnings governing the inhibitory effects of AM and LA on KOA progression, laying the groundwork for future explorations into the active components and mechanistic pathways of TCM in KOA treatment.

Detection and evaluation of parameters influencing the identification of heterozygous-enriched regions in Holstein cattle based on SNP chip or whole-genome sequence data.

BACKGROUND: A heterozygous-enriched region (HER) is a genomic region with high variability generated by factors such as balancing selection, introgression, and admixture processes. In this study, we evaluated the genomic background of HERs and the impact of different parameters (i.e., minimum number of SNPs in a HER, maximum distance between two consecutive SNPs, minimum length of a HER, maximum number of homozygous allowed in a HER) and scenarios [i.e., different SNP panel densities and whole-genome sequence (WGS)] on the detection of HERs. We also compared HERs characterized in Holstein cattle with those identified in Angus, Jersey, and Norwegian Red cattle using WGS data. RESULTS: The parameters used for the identification of HERs significantly impact their detection. The maximum distance between two consecutive SNPs did not impact HERs detection as the same average of HERs (269.31 ± 787.00) was observed across scenarios. However, the minimum number of markers, maximum homozygous markers allowed inside a HER, and the minimum length size impacted HERs detection. For the minimum length size, the 10 Kb scenario showed the highest average number of HERs (1,364.69 ± 1,483.64). The number of HERs decreased as the minimum number of markers increased (621.31 ± 1,271.83 to 6.08 ± 21.94), and an opposite pattern was observed for the maximum homozygous markers allowed inside a HER (54.47 ± 195.51 to 494.89 ± 1,169.35). Forty-five HER islands located in 23 chromosomes with high Tajima's D values and differential among the observed and estimated heterozygosity were detected in all evaluated scenarios, indicating their ability to potentially detect regions under balancing selection. In total, 3,440 markers and 28 genes previously related to fertility (e.g., TP63, ZSCAN23, NEK5, ARHGAP44), immunity (e.g., TP63, IGC, ARHGAP44), residual feed intake (e.g., MAYO9A), stress sensitivity (e.g., SERPINA6), and milk fat percentage (e.g., NOL4) were identified. When comparing HER islands among breeds, there were substantial overlaps between Holstein with Angus (95.3%), Jersey (94.3%), and Norwegian Red cattle (97.1%), indicating conserved HER across taurine breeds. CONCLUSIONS: The detection of HERs varied according to the parameters used, but some HERs were consistently identified across all scenarios. Heterozygous genotypes observed across generations and breeds appear to be conserved in HERs. The results presented could serve as a guide for defining HERs detection parameters and further investigating their biological roles in future studies.

Improving prediction of blood cancer using leukemia microarray gene data and Chi2 features with weighted convolutional neural network.

Blood cancer has emerged as a growing concern over the past decade, necessitating early diagnosis for timely and effective treatment. The present diagnostic method, which involves a battery of tests and medical experts, is costly and time-consuming. For this reason, it is crucial to establish an automated diagnostic system for accurate predictions. A particular field of focus in medical research is the use of machine learning and leukemia microarray gene data for blood cancer diagnosis. Even with a great deal of research, more improvements are needed to reach the appropriate levels of accuracy and efficacy. This work presents a supervised machine-learning algorithm for blood cancer prediction. This work makes use of the 22,283-gene leukemia microarray gene data. Chi-squared (Chi2) feature selection methods and the synthetic minority oversampling technique (SMOTE)-Tomek resampling is used to overcome issues with imbalanced and high-dimensional datasets. To balance the dataset for each target class, SMOTE-Tomek creates synthetic data, and Chi2 chooses the most important features to train the learning models from 22,283 genes. A novel weighted convolutional neural network (CNN) model is proposed for classification, utilizing the support of three separate CNN models. To determine the importance of the proposed approach, extensive experiments are carried out on the datasets, including a performance comparison with the most advanced techniques. Weighted CNN demonstrates superior performance over other models when coupled with SMOTE-Tomek and Chi2 techniques, achieving a remarkable 99.9% accuracy. Results from k-fold cross-validation further affirm the supremacy of the proposed model.