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PubMed2026

SeqQC-former: A sequence-quality fusion framework for QC-aware review prioritization of candidate somatic SNVs in cancer genomics.

The accurate prioritization of candidate somatic single-nucleotide variants (SNVs) remains a challenge due to the substantial variability in sequencing quality across genomic loci. SeqQC-Former is a sequence-quality fusion framework that integrates the local nucleotide context with read-level quality-control (QC) covariates derived from matched tumor-normal sequencing data. This integration generates QC-aware prioritization scores for the downstream review of candidate variants. Unlike conventional variant callers, SeqQC-Former is designed not to infer biological truth but to support post-calling review and prioritization under heterogeneous sequencing conditions. The framework was trained and evaluated on a SEQC2-derived dataset comprising 89,447 candidate loci, including 1378 positive and 88,069 negative loci. In chromosome-held-out validation, which aims to reduce potential genomic-position leakage, SeqQC-Former demonstrated strong discrimination (AUROC = 0.9479; AUPRC = 0.9448), indicating good generalization to previously unseen chromosomes. Given that the SEQC2-derived labels contain QC-associated information; these results should be interpreted as an evaluation of QC-aware prioritization capability rather than an independent validation of biological variant correctness. Ablation analyses revealed that structured QC covariates provided the dominant predictive signal under the current SEQC2-derived labeling regime. SeqQC-Former achieved a significantly higher AUROC than classical machine-learning baselines, as determined by DeLong's test (p < 0.01). Application to 53,164 glioblastoma variants demonstrated that external predictions were sensitive to QC scaling and threshold selection, underscoring that model outputs should be interpreted as QC-dependent prioritization scores rather than calibrated probabilities or definitive biological classifications. Overall, SeqQC-Former offers a reproducible post-calling QC-aware prioritization framework for large-scale somatic SNV review and underscores the importance of explicitly modeling sequencing-quality information when interpreting structured cancer genomics datasets.

Humans↗

PubMed2026

NeuroOmics-Net: An interpretable multimodal deep learning framework for Alzheimer's disease diagnosis and progression prediction using neuroimaging, EEG, and genomic data.

Accurate diagnosis and progression prediction of Alzheimer's disease (AD) remain challenging due to the heterogeneous nature of the disease, which involves structural brain degeneration, electrophysiological dysfunction, and molecular dysregulation. Most existing deep learning approaches rely on a single modality or limited multimodal combinations, thereby failing to capture the complex cross-domain interactions underlying AD progression. Furthermore, the scarcity of large-scale datasets containing synchronized neuroimaging, electrophysiological, and genomic measurements restricts the development of comprehensive multimodal diagnostic systems. To address these challenges, this study proposes NeuroOmics-Net, a multimodal deep learning framework for Alzheimer's disease analysis that integrates structural magnetic resonance imaging (sMRI), electroencephalography (EEG), and gene expression data. The proposed framework combines a Hierarchical Multi-View Encoder (HME) for modality-specific feature extraction, a Cross-Omics Attention Fusion (CAF) module for adaptive integration of complementary biomarkers, and a Disease Progression Graph Learning (DPGL) module for modeling progression-related relationships across biological domains. To facilitate cross-modal integration from independent cohorts, Regularized Canonical Correlation Analysis (RCCA) is employed to align heterogeneous feature representations within a shared latent space. Experiments were conducted using publicly available datasets from ADNI, PhysioNet, and GEO repositories comprising 1120 diagnosis-aligned samples. The proposed framework achieved 94.3% classification accuracy and an AUC of 0.975 for distinguishing normal controls (NC), mild cognitive impairment (MCI), and Alzheimer's disease subjects, while attaining 93.7% accuracy for predicting conversion from stable mild cognitive impairment (sMCI) to progressive mild cognitive impairment (pMCI). However, a fairness sensitivity analysis using stratified demographic reweighting revealed accuracy ranging from 90.8% (low-education, high-comorbidity proxy subgroup) to 96.1% (low-risk, high-reserve proxy subgroup), a demographic parity gap of 5.3 percentage points, indicating that overall accuracy reflects a performance ceiling in a relatively homogeneous research cohort rather than a realistic estimate for demographically diverse clinical populations. Comparative evaluations demonstrated consistent improvements over state-of-the-art unimodal and multimodal deep learning models. Interpretability analysis further identified clinically relevant biomarkers, including hippocampal and entorhinal atrophy, theta-alpha EEG alterations, and APOE-associated molecular pathways. Because sMRI, EEG, and gene expression data were sourced from separate, unpaired cohorts with no subjects possessing all three synchronized measurements, all reported cross-modal associations reflect population-level statistical correspondence across diagnosis-matched groups rather than within-subject physiological coupling; no claim of intra-individual causal cross-modal interaction is made. These findings demonstrate that NeuroOmics-Net provides an effective computer-aided framework for multimodal biomedical data processing and Alzheimer's disease analysis. By integrating neuroimaging, electrophysiological, and genomic information, the proposed approach enables accurate diagnosis, progression prediction, and biologically interpretable decision support for clinical and translational applications.

Humans↗

PubMed2005

Monophyly of primary photosynthetic eukaryotes: green plants, red algae, and glaucophytes.

Between 1 and 1.5 billion years ago, eukaryotic organisms acquired the ability to convert light into chemical energy through endosymbiosis with a Cyanobacterium (e.g.,). This event gave rise to "primary" plastids, which are present in green plants, red algae, and glaucophytes ("Plantae" sensu Cavalier-Smith). The widely accepted view that primary plastids arose only once implies two predictions: (1) all plastids form a monophyletic group, as do (2) primary photosynthetic eukaryotes. Nonetheless, unequivocal support for both predictions is lacking (e.g.,). In this report, we present two phylogenomic analyses, with 50 genes from 16 plastid and 15 cyanobacterial genomes and with 143 nuclear genes from 34 eukaryotic species, respectively. The nuclear dataset includes new sequences from glaucophytes, the less-studied group of primary photosynthetic eukaryotes. We find significant support for both predictions. Taken together, our analyses provide the first strong support for a single endosymbiotic event that gave rise to primary photosynthetic eukaryotes, the Plantae. Because our dataset does not cover the entire eukaryotic diversity (but only four of six major groups in), further testing of the monophyly of Plantae should include representatives from eukaryotic lineages for which currently insufficient sequence information is available.

Bayes Theorem↗

PubMed2006

Differences between primiparous and multiparous dairy cows in the inter-relationships between metabolic traits, milk yield and body condition score in the periparturient period.

During the early postpartum period dairy cows mobilize fat and muscle to support lactation. This is associated with alterations in blood metabolite and hormone profiles which in turn influence milk yield and fertility. This study developed models to determine how metabolic traits, milk yield and body condition score were inter-related at different times in the periparturient period and to compare these relationships in primiparous (PP, n=188) and multiparous (MP, n=312) cows. Data from four previous studies which included information on blood metabolic parameters, parity, milk yield, body condition score and diet were collated into a single dataset. Coefficients of polynomial equations were calculated for each trait between -1 week pre-calving and week +7 postpartum using residual maximum likelihood modelling. The completed dataset was used in a multiple correlation model to determine how the best fit curves were related to each other over time. PP cows had higher concentrations of insulin-like growth factor-I and lower beta-hydroxybutyrate concentrations throughout, higher leptin concentrations pre-partum and both the peak in non-esterified fatty acids and the nadir in urea concentration occurred earlier after calving. These differences were associated with significantly lower milk production. Leptin concentrations fell at calving and were related to body condition score. Insulin was negatively correlated with yield in MP cows only. In MP cows the relationship between insulin-like growth factor-I and yield switched from negative to positive between weeks +4 and +7. Both beta-hydroxybutyrate and urea were positively related to yield in PP cows. In contrast, in MP cows beta-hydroxybutyrate was negatively correlated with yield and urea was strongly related to body condition score but not yield. These results suggest that there are differences in the control of tissue mobilization between PP and MP cows which may promote nutrient partitioning into growth as well as milk during the first lactation.

3-Hydroxybutyric Acid↗

PubMed2006

Carbon monoxide lung diffusion capacity improves risk stratification in patients without airflow limitation: evidence for systematic measurement before lung resection.

OBJECTIVE: In many centers, carbon monoxide lung diffusion capacity (DLCO) is still not routinely measured in all patients but only in patients with airflow limitation. The objective of the study was to assess the degree of correlation between forced expiratory volume in 1s (FEV1) and DLCO, and verify whether a low predicted postoperative DLCO (ppoDLCO) could have a role in predicting complications in patients without airflow limitation. METHODS: We analyzed 872 patients submitted to lung resection between January 2000 and December 2004 in two units measuring systematically DLCO before operation. Correlation between FEV1 and DLCO was assessed in the entire dataset and in different subsets of patients. A number of variables were then tested for a possible association with postoperative cardiopulmonary complications in patients with FEV1>80% by univariate analysis. Variables with p<0.10 at univariate analysis were used as independent variables in a stepwise logistic regression analysis (dependent variable: presence of cardiopulmonary morbidity), which was in turn validated by bootstrap analysis. RESULTS: The correlation coefficients between FEV1 and DLCO in the entire dataset and in different subsets of lung resection candidates (stratified by age, gender, cause of operation, airflow limitation) were all below 0.5, showing a modest degree of correlation. Two hundred and nineteen of the 508 patients (43%) with FEV1>80% had DLCO<80%. Moreover, in patients with FEV1>80%, logistic regression analysis showed that ppoDLCO<40% was a significant and reliable predictor of postoperative complications (p=0.004). CONCLUSION: The modest correlation between FEV1 and DLCO and the capacity of ppoDLCO to discriminate between patients with and without complications in subjects with a normal FEV1, warrants the routine measurement of DLCO in all candidates for lung resection, irrespective of their FEV1 value, in order to improve surgical risk stratification.

Aged↗

PubMed2026

Artificial Intelligence-Driven Multi-Omics Analysis Reveals Hydroxytyrosol Targeting of the TXNIP-NLRP3 Inflammasome Axis in Traumatic Brain Injury.

Traumatic brain injury (TBI) induces secondary neuroinflammation driven by oxidative stress, inflammasome activation, and immune remodeling, yet specific mechanism-guided pharmacological interventions remain limited. This study established an artificial intelligence (AI)-integrated network pharmacology and multi-omics framework to evaluate whether hydroxytyrosol (HT), an olive-derived natural polyphenol, may regulate TBI-related neuroinflammatory targets centered on the TXNIP/NLRP3 inflammasome axis. Starting from the SMILES structure of HT, potential targets were predicted using PharmMapper, SwissTargetPrediction, and the Similarity Ensemble Approach and were standardized to UniProt identifiers. TBI-associated genes were integrated from GeneCards, DisGeNET, OMIM, and the Therapeutic Target Database. The overlapping target set was analyzed using STRING-based protein-protein interaction (PPI) networks, MCODE, CytoHubba, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment. Public GEO transcriptomic datasets (GSE123831 and GSE104687) were used for cross-platform expression validation, differential expression analysis, and exploratory CIBERSORT-based immune infiltration estimation. Random forest (RF), multilayer perceptron (MLP), graph convolutional network (GCN), graph attention network (GAT), SHAP/LIME explainability analysis, LASSO inflammatory-risk scoring, and two-sample Mendelian randomization (MR) were further applied for target prioritization, immune phenotype mapping, and genetic association analysis. Seventy-three overlapping HT-TBI targets were identified. PPI and topology analyses prioritized TXNIP, NLRP3, CASP1, MAPK1, and TP53 as key hubs enriched in inflammasome activation, oxidative stress, apoptosis, and NOD-like receptor signaling. TXNIP, NLRP3, and CASP1 were consistently upregulated in both TBI transcriptomic datasets. LM22-based immune deconvolution suggested increased pro-inflammatory immune signatures and a positive TXNIP-M1 macrophage association (r = 0.63, p < 0.001), which should be interpreted as a transcriptome-derived hypothesis rather than validated murine immune-cell proportions. AI-based models consistently ranked TXNIP/NLRP3 as high-contribution features under internal validation, and removal of these targets reduced model performance. A five-gene inflammatory score achieved an internally evaluated AUC of 0.87, while two-sample MR supported positive genetic associations involving TXNIP expression, TBI risk, NLRP3 and IL-1β expression. Collectively, these findings prioritize the TXNIP/NLRP3/CASP1 module as a computationally supported candidate mechanism through which HT may influence oxidative stress-inflammasome-immune coupling in TBI. This study provides an interpretable drug-target-pathway-phenotype framework and identifies TXNIP, NLRP3, and CASP1 as priority nodes for future experimental validation.

Artificial Intelligence↗

PubMed2004

A study on two different CAD systems for mammography as an aid to radiological diagnosis in the search of microcalcification clusters.

OBJECTIVE: The aim of the present study was to evaluate the efficacy of two different computer aided detection (CAD) systems for mammography in improving radiological diagnosis in the search of microcalcification clusters. The CAD systems used are: the SecondLooktrade mark (CADx Medical Systems, Canada) commercial system and the CALMA (computer assisted library in MAmmography) research CAD system. Three radiologists were asked to read mammographic images with and without the support of the CAD systems. MATERIAL AND METHODS: Three radiologists with respectively 3, 5 and 7 years of practice in mammogram reading in an Italian public hospital analysed a dataset composed of 120 digitized mammograms of healthy subjects with no lesion (proven by a radiological follow up of at least 3 years) and 70 images of patients with malignant cluster of microcalcification (proven by histopathological examination) both with no CAD support as well as with the help of the SecondLooktrade mark system. After 3 months they were asked to observe the same digitized mammograms with the assistance of the CALMA system. The radiologists worked independently and were unaware of the final diagnosis. The values of the area A(z) under the ROC curve, diagnostic sensitivity, specificity, positive and negative predictive values, and diagnostic accuracy were evaluated with and without the support of the CAD systems. The reading time and qualitative evaluations of each radiologist were also reported. RESULTS: With the support of the two CAD systems an improvement in A(z) area was obtained ranging from 0.01 to 0.04. Sensitivity increased from +8.6 to +15.7% and specificity decreased from 0.8 to 4.2%. CONCLUSION: In our study, not conditioned by the dataset, the CAD systems as second reader produced an increase in overall sensitivity of up to 15.7%, with a little decrease in specificity of up to 4.2%. Based on these results both CAD systems might be used in the current practise to improve the sensitivity values of conventional reading (radiologist alone). The results of this study show that no significant differences exist in term of A(z), sensitivity and specificity between CALMA and CADx.

Breast Diseases↗

PubMed2006

Derivation of ozone flux-yield relationships for lettuce: a key horticultural crop.

Ozone flux-response relationships were derived for lettuce, employing a multiplicative approach to model the manner in which stomatal conductance is influenced by key environmental variables, using a dataset collected during field experimentation in Crete and yield-response relationships derived from parallel open-top chamber experiments. Regional agronomic practices were adopted throughout. Computed versus measured data revealed that the derived model explained 51% (P<0.001) of the observed variation in stomatal conductance. Concentration-based indices were compared with flux-based indices. Analyses revealed a significant relationship between accumulated stomatal ozone flux and yield employing flux threshold cut-offs up to 4 nmol m(-2) s(-1). Regressions employing very low or zero flux thresholds resulted in the strongest yield-flux relationships (explaining approximately 80% (P<0.05) of the variation in the dataset).

Crops, Agricultural↗

PubMed2026

Integrating structure and experimental data annotations with computational modeling framework for predicting micro-nanoplastics toxicities.

The wide use of plastic materials leads to increased emissions of micro-nanoplastics (MNPs) into the environment, raising significant concerns about their impact on human health. Traditional experimental approaches for assessing MNPs toxicity are costly, time-consuming, and there are no experimental protocols that are universally acceptable. Computational modeling using machine learning (ML) approaches provides an efficient alternative to MNP toxicity assessment. However, most modeling studies of MNPs are limited due to the lack of high-quality data and there are few previous modeling studies considering complex structures of MNPs for model training. To address this challenge, we constructed three MNP datasets with popular toxicity endpoints from various resources and used nanostructure annotation techniques to create virtual MNPs (vMNPs) for all MNP structures. The MNP structures were digitalized from annotated vMNPs, and geometrical descriptors were calculated using the Delaunay Tessellation approach. Moreover, important experimental information, such as concentrations and cell lines, were transformed into extra training variables. Partial least squares regression (PLSR) models were built using both experimental and geometrical descriptors and validated through a leave-one-out cross validation procedure. The resulting models showed reasonable performance in predicting toxicity potentials of MNPs for the three endpoints in the present datasets. Moreover, an additional library of vMNPs with their predicted properties and bioactivities was constructed, directing further research of new MNPs. This study provides three novel ML models for MNPs by integrating geometrical and experimental descriptors, which have the potential to assess new MNPs for their toxicity. The modeling strategy developed in this study can be easily expanded to model other MNP toxicity endpoints and create promising new models for MNP toxicity assessments.

Data annotation↗

PubMed2005

Prediction of protein subcellular location using a combined feature of sequence.

To understand the structure and function of a protein, an important task is to know where it occurs in the cell. Thus, a computational method for properly predicting the subcellular location of proteins would be significant in interpreting the original data produced by the large-scale genome sequencing projects. The present work tries to explore an effective method for extracting features from protein primary sequence and find a novel measurement of similarity among proteins for classifying a protein to its proper subcellular location. We considered four locations in eukaryotic cells and three locations in prokaryotic cells, which have been investigated by several groups in the past. A combined feature of primary sequence defined as a 430D (dimensional) vector was utilized to represent a protein, including 20 amino acid compositions, 400 dipeptide compositions and 10 physicochemical properties. To evaluate the prediction performance of this encoding scheme, a jackknife test based on nearest neighbor algorithm was employed. The prediction accuracies for cytoplasmic, extracellular, mitochondrial, and nuclear proteins in the former dataset were 86.3%, 89.2%, 73.5% and 89.4%, respectively, and the total prediction accuracy reached 86.3%. As for the prediction accuracies of cytoplasmic, extracellular, and periplasmic proteins in the latter dataset, the prediction accuracies were 97.4%, 86.0%, and 79.7, respectively, and the total prediction accuracy of 92.5% was achieved. The results indicate that this method outperforms some existing approaches based on amino acid composition or amino acid composition and dipeptide composition.

Amino Acid Sequence↗

PubMed2005

An all atom energy based computational protocol for predicting binding affinities of protein-ligand complexes.

We report here a computationally fast protocol for predicting binding affinities of non-metallo protein-ligand complexes. The protocol builds in an all atom energy based empirical scoring function comprising electrostatics, van der Waals, hydrophobicity and loss of conformational entropy of protein side chains upon ligand binding. The method is designed to ensure transferability across diverse systems and has been validated on a heterogenous dataset of 161 complexes consisting of 55 unique protein targets. The scoring function trained on a dataset of 61 complexes yielded a correlation of r=0.92 for the predicted binding free energies against the experimental binding affinities. Model validation and parameter analysis studies ensure the predictive ability of the scoring function. When tested on the remaining 100 protein-ligand complexes a correlation of r=0.92 was recovered. The high correlation obtained underscores the potential applicability of the methodology in drug design endeavors. The scoring function has been web enabled at as binding affinity prediction of protein-ligand (BAPPL) server.

Drug Design↗

PubMed2006

Prediction of the deleterious nsSNPs in ABCB transporters.

The non-synonymous SNPs (nsSNPs) in coding regions, neutral or deleterious, could lead to the alteration of the function or structure of proteins. We have developed the computational models to analyze the deleterious nsSNPs in the transporters and predict ones in ABCB (ATP-binding cassette B) transporters of interest. The RPLS (ridge partial least square) and LDA (linear discriminant analysis) methods were applied to the problem, by training on a selection of datasets from a specified source, i.e., human transporters. The best combination of datasets and prediction attributes was ascertained. The prediction accuracy of the theoretical RPLS model for the training and testing sets is 84.8% and 80.4%, respectively (LDA: 84.3% and 80.4%), which indicates the models are reasonable and may be helpful for pharmacogenetics studies.

ATP-Binding Cassette Transporters↗

PubMed2005

Temporal characteristics of foot roll-over during barefoot jogging: reference data for young adults.

The purpose of this study was to establish a representative reference dataset for temporal characteristics of foot roll-over during barefoot jogging, based on plantar pressure data collected from 220 healthy young adults. The subjects ran at 3.3 ms-1 over a 16.5 m long running track, having a built-in pressure platform mounted on a force platform. The initial contact, final contact, time to peak pressure and the duration of contact at the lateral and medial heel, metatarsal heads I to V and the hallux were measured. Temporal plantar pressure variables were found to be reliable (93% of ICC coefficients above 0.75) and both gender and asymmetry influences could be neglected. Foot roll-over during jogging started with heel contact followed by a latero-medial contact of the metatarsals and finally the hallux. After heel off, the forefoot started to push off at the lateral metatarsals, followed by a more central push off over the second metatarsal and finally over the hallux. Based on the plantar pressure data, the stance phase during running was divided into four distinct phases: initial contact (8.2%), forefoot contact (11.3%), foot flat (25.3%) and forefoot push off (55.1%). These findings provide a reliable and representative reference dataset for temporal characteristics of foot roll-over during jogging of young adults that may also be relevant in the evaluation of running patterns.

Adolescent↗

PubMed2005

A functional foot type classification with cluster analysis based on plantar pressure distribution during jogging.

The purpose of this study was to establish a reference dataset for peak pressures and pressure-time integrals during jogging, to compare this reference dataset with existing walking data and to develop a foot type classification, all based on plantar pressure data obtained from 215 healthy young adults. The subjects ran at 3.3 m s(-1) over a 16.5 m long running track, with a built-in pressure platform mounted on top of a force platform. Peak pressures, regional impulses and relative regional impulses were measured. These variables were found to be reliable (all intra class correlation coefficients above 0.75) and, except for the heel areas, gender and asymmetry effects could be neglected. Highest peak pressures were found under the heel due to large impact forces during initial contact phase (ICP). In the forefoot, the highest peak pressure was found under the second metatarsal (64.2 +/- 21.1 N cm(-2)). Compared to walking data, overall higher peak pressures and impulses and difference in hallux loading were found during barefoot jogging. Four pressure loading patterns were identified using a K-means cluster analysis, based on the relative regional impulses underneath the forefoot: medial M1 pattern, medial M2 pattern, central pattern and central-lateral pattern. These four pressure loading patterns could help in the functional interpretation of the foot behaviour during the stance phase in slow running.

Adolescent↗

PubMed2005

Cancer risk in hereditary nonpolyposis colorectal cancer syndrome: later age of onset.

BACKGROUND & AIMS: Mutations in the mismatch repair genes cause hereditary nonpolyposis colorectal cancer (HNPCC) syndrome and convey high lifetime cancer risks for colorectal (CRC) and endometrial cancer. Currently, cancer risks for individuals with HNPCC are based on data from clinically ascertained families. The purpose of this study was to re-examine the penetrance in HNPCC using a comprehensive dataset from a geographically defined region. METHODS: A combined dataset of 70 HNPCC families ascertained by traditional high-risk criteria and by molecular screening comprising 88 probands and 373 mutation-positive family members was used. Statistical methods were modified survival analysis techniques. RESULTS: In mutation-positive relatives (excluding probands), the median age at diagnosis of CRC was 61.2 years (confidence interval [CI], 56.3-68.0 y). The lifetime risk for CRC was 68.7% (CI, 58.6%-78.9%) for men and 52.2% (CI, 37.6%-66.9%) for women. Considering only probands, the median age at diagnosis of CRC was 44.0 years (CI, 41.0-46.3 y). Median age of onset of EC was 62.0 years (CI, 55.9 y to an upper limit too high to calculate) with a lifetime cancer risk of 54% (CI, 41.9%-66.1%). CONCLUSIONS: A markedly later age of onset for CRC at 61 y than previously reported (approximately 44 y) is suggested, resulting mainly from a more rigorous method of analysis in which all gene-positive individuals (both affected and unaffected with cancer) are considered. Lifetime cancer risks may be lower for CRC and endometrial cancer than presently assumed. If confirmed, these data suggest a need to alter counseling practices, and to consider HNPCC in older individuals than before.

Adaptor Proteins, Signal Transducing↗

PubMed2004

Insertion-deletion biases and the evolution of genome size.

Numerous theories have been proposed to account for the pronounced differences in the quantity of non-coding DNA among eukaryotic genomes, but the current repertoire remains incomplete because the only explicit mechanisms it provides involve DNA gain. It has been proposed more recently that biases in spontaneous insertions and deletions (indels) can lead to genome shrinkage by mutational mechanisms alone. The present article provides the first detailed critical discussion of this approach, and covers three different ideas related to it: (1) the general notion of DNA loss by deletion bias, (2) the "DNA loss hypothesis" which supposes that variation in genome size can be attributed to differences in DNA loss rate, and (3) the "mutational equilibrium model" which attempts to describe the long-term evolution of genome size. The mutational equilibrium model is found to be problematic, and it is noted that DNA loss by small indels is too slow in real time to determine variation in genome size above a relatively low threshold. Some alternative explanations for the observed patterns are provided, and the critique also identifies some potential problems with the current dataset. These include a failure to cite a more detailed (and somewhat contradictory) mammalian dataset, a questionable use of arithmetic means with highly skewed data, and important discrepancies among the particular DNA sequences so far analyzed. Overall, evolutionary reductions in genome size are considered important, but the specific mechanism relating to small deletion bias is far too weak to be accepted as a primary determinant of genome size variation in general.

Animals↗

PubMed2005

Broadly predicting specific gene functions with expression similarity and taxonomy similarity.

Previous studies on computational gene functional prediction have not fully exploited the taxonomy structure of Gene Ontology (GO). They just select a few classes from GO into a set, and conduct classwise learning of these classes. The pre-selection of learning classes, often done according to the annotation sizes, limits the prediction breadth and depth. This way of pre-selecting learning classes ignores the taxonomy relations among classes, and so wastes the valuable functional knowledge encoded in the DAG structure of GO. This paper proposes GESTS, a novel gene functional prediction approach based on both gene expression similarity and GO taxonomy similarity, which circumvents the problem of arbitrary learning class pre-selection. GESTS is a semi-supervised approach that reasonably and efficiently incorporates the ontology-formed gene functional knowledge into automated functional analyses of local gene clustering. By integrating both expression similarity and taxonomy similarity into the learning process, GESTS achieves better prediction breadth, depth, and precision than previous studies on the fibroblast serum response dataset and the yeast expression dataset.

Computational Biology↗

PubMed2026

MAJIQ-CLIN: A novel tool to help identify Mendelian disease-causing variants from RNA-seq data.

PURPOSE: The current diagnostic rate for patients with suspected Mendelian genetic disorders is low, despite exome/genome sequencing being the standard of care. One reason for this low diagnostic rate is that traditional exome/genome sequencing analysis methods struggle to detect RNA splicing aberrations. Causative variants often involve splicing changes, with numerous splice-altering variants being responsible for known Mendelian disorders. Therefore, it is crucial to develop reliable tools to detect, quantify, prioritize, and visualize RNA splicing aberrations from patient RNA sequencing data. METHODS: We developed Modeling Alternative Junction Inclusion Quantification for Clinical Applications (MAJIQ-CLIN), a method to identify RNA splicing aberrations in patients' RNA sequencing data compared with a cohort of control samples. MAJIQ-CLIN can efficiently process large datasets, avoiding reprocessing when new data are added, while effectively detecting local splicing variations with deviations in a given patient, termed outlier local splicing variation, or unique to the patient, termed private local splicing variation. RESULTS: We performed a systematic evaluation of the accuracy of tools for detecting patients' RNA splicing aberrations from RNA sequence using synthetic data across several aberration types and transcript inclusion levels. Then, we used several real datasets to assess MAJIQ-CLINs ability to identify solved test cases and control for the effect of confounders such as batches. We showed that MAJIQ-CLIN compares favorably to existing tools in both accuracy and efficiency. We also used MAJIQ-CLIN to investigate several unsolved patient cases from the Undiagnosed Diseases Network. CONCLUSION: MAJIQ-CLIN offers an efficient, accurate, and user-friendly tool to aid in diagnosing Mendelian disease-causing variants from RNA sequence data.

Bioinformatics↗