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At least 883 records · Page 49Linked to original sources
PubMed2003

Detecting Cacopsylla pyricola (Hemiptera: Psyllidae) in predator guts using COI mitochondrial markers.

Cacopsylla pyricola (Förster) is one of the most important pests of pear in North America, where several native predators have been considered for integrated pest management (IPM) programmes. Two molecular markers of 271 and 188 bp were developed from C. pyricola cytochrome oxidase I (COI) fragments, in order to study the detection of this species in the gut of arthropod predators. Primer sensitivity and the detection period for pear psylla remains in the guts of Anthocoris tomentosus Pericart were determined. The sensitivity threshold was defined at 10-5 dilution of a C. pyricola fifth-instar nymph in all samples. Predator adults were evaluated immediately after ingestion of one to five C. pyricola nymphs (t = 0) and after 2, 4, 6, 8, 16, 24 and 32 h. Detection of the presence of C. pyricola DNA always lasted longer using the shorter fragment and was observed after 32 h of digestion using both markers. The primers amplifying the 188 bp fragment amplified all four psyllid species tested, whereas the primers designed to amplify the 271 bp fragment did so exclusively for C. pyricola and its close relative, Cacopsylla pyri (Linnaeus). Both primers failed to amplify DNA from representative species of the Coccinellidae, Chrysopidae, Hemerobiidae, Anthocoridae, Miridae, Salticidae, Aphididae, Tetranychidae and the Tortricidae, suggesting their suitability for general trophic studies.

Animals↗
PubMedDate not supplied

Mechanism of benzene-induced hematotoxicity and leukemogenicity: current review with implication of microarray analyses.

Benzene is a potent human leukemogen but the mechanism underlying benzene-induced leukemia remains an enigma due to a number of questions regarding the requirement of extraordinarily long exposure, a relatively low incidence of leukemia for genotoxicity of metabolites and a narrow dose range for leukemogenicity over marrow aplasia (overdoses tend to result in marrow aplasia). Moreover, there were previous controversies as to whether the cell cycle is upregulated or suppressed by the benzene exposure. Subsequently, it was found that the cell cycle is suppressed, but how leukemia develops under such suppression of hemopoiesis remains to be clarified. These questions were fortunately resolved with much effort. Benzene exposure was found to induce the expression of p21, an interlocking counterdevice for cell cycle: due to p53 upregulation, thereby inducing the immediate suppression of the kinetics of hemopoietic progenitors followed by the prominent suppression of hemopoiesis. Intermittent benzene exposure (i.e., cessation of exposure during weekends, for example) allowed an immediate recovery from marrow suppression after terminating exposure, which induced continuous oscillatory changes in marrow hemopoiesis. Benzene-induced leukemia was chiefly due to such an oscillatory change in hemopoiesis, which epigenetically developed leukemia more than 1 year later. The mechanisms of benzene-induced leukemogenicity seem to differ between wild-type mice and mice lacking p53. For p53 knockout mice, DNA damage such as weak mutagenicity or chromosomal damage was retained, and such damage induced consequent activation of proto-oncogenes and related genes, which led cells to undergo further neoplastic changes. In contrast, for wild-type mice carrying the p53 gene, a marked oscillatory change in the cell cycle of the stem cell compartment seems to be important. Compatible and discriminative gene expression profiling between the p53 knockout mice and wild-type mice was observed after benzene exposure by microarray analyses.

Administration, Inhalation↗
PubMedDate not supplied

Toxicogenomic approach to endocrine disrupters: identification of a transcript profile characteristic of chemicals with estrogenic activity.

Public concerns have been raised in recent years over the possible adverse effects that may result from exposure to chemicals in the environment that have the potential to interfere with the normal function of the endocrine system in wildlife and humans ("endocrine disrupters"). Regulations have been established that require the testing of pesticides used in food crops and drinking water contaminants, for estrogenicity and other hormonal activities. In the United States, the U.S. EPA proposed the Endocrine Disrupter Screening Program, which consists of a Tier 1 screening battery of tests that is designed to identify chemicals capable of interacting with various hormonal systems, and different Tier 2 testing assays that are designed to verify and broaden the Tier 1 results. We identify 2 main problems with this approach: (1) the fact that the developmental stages that are the most susceptible to endocrine disruption are not represented in the screening tier, mainly because developmental effects tend to be latent, and there is no way to economically screen in developing models; and (2) the expense to screen each chemical to be included in this program. Thus, the need arises for an accurate, rapid, and cost effective method for assessing the potential endocrine activity of multiple chemicals during development. We hypothesize that the largely latent developmental effects of some endocrine disruptors are preceded by immediate changes in gene expression in the embryo and fetus. Therefore, an approach to assess the potential estrogenic (and other steroid hormonal) activity of different compounds is to identify those patterns of gene expression elicited in a tissue/organ exposed to these particular classes of chemicals. In this paper, the potential utility of such an approach for screening and better understanding of mechanism of action for specific chemicals with endocrine disrupter activities is presented, using as an example chemicals with estrogenic activity.

Animals↗
PubMed2001

Novel approaches to tackling malarial drug resistance using yeast.

Yeasts have a justified reputation as one of the world's most versatile organisms. Baker's yeast continues to live up to this recognition by joining the war against malaria. Yeast can now be used to study antifolate drug resistance patterns that depend on the dihydrofolate reductase enzyme (DHFR) from the malaria parasite.

Antimalarials↗
PubMed2026

HyLnc: a hybrid deep learning and feature-based approach for long non-coding RNA prediction.

Long non-coding RNAs (lncRNAs) play important roles in gene regulation, development and disease, yet accurate identification of lncRNAs from transcriptomic data remains a major computational challenge. Existing methods often rely either on handcrafted sequence features or deep learning approaches, each with their inherent limitations in capturing the full complexity of RNA sequences. In this study, we proposed HyLnc, a computational framework that integrates transformer-based contextual embeddings with biologically meaningful sequence features for improved lncRNA prediction. A custom BERT-based model was first pre-trained on a large corpus of metazoan RNA sequences using a masked language modelling strategy to learn contextual nucleotide dependencies. The model was subsequently fine-tuned on curated datasets of lncRNAs and protein-coding transcripts and 256-dimensional deep sequence embeddings were extracted. Parallelly, 348 handcrafted features, including ORF characteristics, untranslated region (UTR) properties, nucleotide composition and Fickett scores, were computed. A multi-stage feature selection strategy was applied to identify the most informative features, resulting in optimized hybrid feature sets. Multiple machine learning classifiers were evaluated, with the RF model achieving the best performance. The proposed framework attained an accuracy of 91.30%, F1-score of 91.23% and MCC of 82.60 on an independent validation dataset, outperforming several existing lncRNA prediction tools. Thus, HyLnc demonstrates that integrating deep contextual representations with biologically interpretable features enhances lncRNA prediction. This approach provides a robust and scalable solution for large-scale transcriptome annotation and can be extended to other sequence-based prediction.

RNA, Long Noncoding↗
PubMed2004

Transcriptional response of T cells to IFN-alpha: changes induced in IFN-alpha-sensitive and resistant cutaneous T cell lymphoma.

Interferon-alpha (IFN-alpha) therapy is commonly used in the treatment of neoplastic and autoimmune diseases, including cutaneous T cell lymphoma (CTCL). However, the IFN-alpha response is unpredictable, and the IFN-alpha cell targets and pathways are only partially understood. To delineate the molecular mechanisms of IFN-alpha activity, gene expression profiling was performed in a time-course experiment of both IFN-alpha sensitive and IFN-alpha-resistant variants of a CTCL cell line. These experiments revealed that IFN-alpha is responsible for the regulation of hundreds of genes in both variants and predominantly involves genes implicated in signal transduction, cell cycle control, apoptosis, and transcription regulation. Specifically, the IFN-alpha response of tumoral T cells is due to a combination of induction of apoptosis in which TNFSF10 and HSXIAPAF1 may play an important role and cell cycle arrest achieved by downregulation of CDK4 and CCNG2 and upregulation of CDKN2C and tumor suppressor genes (TSGs). Resistance to IFN-alpha appears to be associated with failure to induce IRF1 and IRF7 and deregulation of the apoptotic signals of HSXIAPAF1, TRADD, BAD, and BNIP3. Additionally, cell cycle progression is heralded by upregulation of CDC25A and CDC42. A critical role of NF-kappaB in promoting cell survival in IFN-alpha-resistant cells is indicated by the upregulation of RELB and LTB.

Cell Line, Tumor↗
PubMed1997

Automated gene identification in large-scale genomic sequences.

Computational methods for gene identification in genomic sequences typically have two phases: coding region recognition and gene parsing. While there are a number of effective methods for recognizing coding regions (exons), parsing the recognized exons into proper gene structures, to a large extent, remains an unsolved problem. We have developed a computer program which can automatically parse the recognized exons into gene models that are most consistent with the available Expressed Sequence Tags (ESTs) and a set of biological heuristics, derived empirically. The gene modeling algorithm used in this program provides a general framework for applying EST information so the modeling accuracy improves as the amount of available EST information increases. Based on preliminary tests on a number of large DNA sequences, using the dbEST database, we have observed that the algorithm can (1) accurately model complicated multiple gene structures, including embedded genes, (2) identify falsely-recognized exons and locate missed exons by the initial exon recognition phase, and (3) make more accurate exon boundary predictions, if the necessary EST information is available. We have extended this EST-based gene modeling algorithm to model genes on unfinished DNA contigs at the end of the shotgun sequencing. This extended version can automatically determine the orientations and the relative order of the DNA contigs (with gaps between them) using the available ESTs as reference models, before the gene modeling phase.

Algorithms↗
PubMed2005

Explore biological pathways from noisy array data by directed acyclic Boolean networks.

We consider the structure of directed acyclic Boolean (DAB) networks as a tool for exploring biological pathways. In a DAB network, the basic objects are binary elements and their Boolean duals. A DAB is characterized by two kinds of pairwise relations: similarity and prerequisite. The latter is a partial order relation, namely, the on-status of one element is necessary for the on-status of another element. A DAB network is uniquely determined by the state space of its elements. We arrange samples from the state space of a DAB network in a binary array and introduce a random mechanism of measurement error. Our inference strategy consists of two stages. First, we consider each pair of elements and try to identify their most likely relation. In the meantime, we assign a score, s-p-score, to this relation. Second, we rank the s-p-scores obtained from the first stage. We expect that relations with smaller s-p-scores are more likely to be true, and those with larger s-p-scores are more likely to be false. The key idea is the definition of s-scores (referring to similarity), p-scores (referring to prerequisite), and s-p-scores. As with classical statistical tests, control of false negatives and false positives are our primary concerns. We illustrate the method by a simulated example, the classical arginine biosynthetic pathway, and show some exploratory results on a published microarray expression dataset of yeast Saccharomyces cerevisiae obtained from experiments with activation and genetic perturbation of the pheromone response MAPK pathway.

Algorithms↗
PubMed2006

Hidden Markov model for defining genomic changes in lung cancer using gene expression data.

The study of gene expression patterns in relationship to chromosomal position, the "transcriptome map," has become an area of active research and has revealed unexpected chromosomal regions within which gene expression levels are highly correlated. In cancer research, these regional changes in gene expression that may result from alterations at the chromosome level such as gene amplification or loss. To facilitate the search for such regions utilizing gene expression data, we have developed a hidden Markov model (HMM). Maximum penalized likelihood is used to estimate the parameters in the model. This method is applied to a lung cancer microarray experiment, including 86 human lung adenocarcinomas. Several regions identified through the HMM are consistent with known recurrent regions of amplification or deletion in this cancer. We further demonstrate the association of these abnormal expression regions with measures of disease status, such as tumor stage, differentiation, and survival. These findings suggest that genes in these regions may play a major role in the process of carcinogenesis of the lung. Our proposed method provides a valuable tool to accurately pinpoint regions of abnormal expression for further investigation.

Adenocarcinoma↗
PubMed2000

SISEQ: manipulation of multiple sequence and large database files for common platforms.

UNLABELLED: A multiple sequence file converter for common platforms, SISEQ,is described, which performs extraction of DNA sequences that correspond to CDS or RNA field of a large database file as well as subsequent multi-sequence conversions for phylogenetic or molecular biological analysis. Command-line interface as well as a GUI and a script-driven operation mode are provided. AVAILABILITY: The program is freely available to academic users in the form of Macintosh FAT binary, DOS executable, or UNIX source code at http://www.molbiol.saitama-u.ac.jp/ñaoki/ Software.html. CONTACT: naokisat@molbiol.saitama-u.ac.jp

DNA↗
PubMed2002

Building and analysing genome-wide gene disruption networks.

MOTIVATION: Microarray experiments comparing expression levels of all genes in yeast for hundreds of mutants allow us to examine properties of gene regulatory networks on a genomic scale. We can investigate questions such as network modularity, connectivity, and look for genes with particular roles in the network structure. RESULTS: We have built genome-wide disruption networks for yeast, using a representation of gene expression data as directed labelled graphs. Nodes represent genes and arcs connect nodes if the disruption of the source gene significantly alters the expression of the target gene. We are interested in features of the resulting disruption networks that are robust over a range of significance cutoffs. The networks show a significant overlap with analogous networks constructed from scientific literature. In disruption networks the number of arcs adjacent to different nodes are distributed roughly according to a power-law, like in many complex systems where the robustness against perturbations is important. The networks are dominated by a single large component and do not have an obvious modular structure. Genes with the highest outdegrees often encode proteins with regulatory functions, whereas genes with the highest indegrees are predominantly involved in metabolism. The local structure of the networks is meaningful, genes involved in the same cellular processes are close together in the network. AVAILABILITY: http://www.ebi.ac.uk/microarray/networks

Chromosome Mapping↗
PubMed2025

Soffritto: a deep learning model for predicting high-resolution replication timing.

MOTIVATION: Replication timing (RT) refers to the order in which DNA loci are replicated during S phase. RT is cell-type specific and implicated in cellular processes including transcription, differentiation, and disease. RT is typically quantified genome-wide using two-fraction assays (e.g. Repli-Seq) which sort cells into early and late S phase fractions followed by DNA sequencing, yielding a ratio as the RT signal. While two-fraction RT data are widely available in multiple cell lines, it is limited in its ability to capture high-resolution RT features. To address this, high-resolution Repli-Seq, which quantifies RT across 16 fractions, was developed, but it is costly and technically challenging with very limited data generated to date. RESULTS: Here, we developed Soffritto, a deep learning model that predicts high-resolution RT data using two-fraction RT data, histone ChIP-seq data, GC content, and gene density as input. Soffritto is composed of a Long Short-Term Memory (LSTM) module and a prediction module. The LSTM module learns long- and short-range interactions between genomic bins, while the prediction module is composed of a fully connected layer that outputs a 16-fraction probability vector for each bin using the LSTM module's embeddings as input. By performing both within cell line and cross-cell line training and testing for five human and mouse cell lines, we show that Soffritto is able to capture experimental 16-fraction RT signals with high accuracy, and the predicted signals allow detection of high-resolution RT patterns. AVAILABILITY AND IMPLEMENTATION: Soffritto is available at https://github.com/ay-lab/Soffritto.

Deep Learning↗
PubMed2025

Columba: fast approximate pattern matching with optimized search schemes.

MOTIVATION: Aligning sequencing reads to reference genomes is a fundamental task in bioinformatics. Aligners can be classified as lossy or lossless: lossy aligners prioritize speed by reporting only one or a few high-scoring alignments, whereas lossless aligners output all optimal alignments, ensuring completeness and sensitivity. RESULTS: This paper introduces Columba, a high-performance lossless aligner tailored for Illumina sequencing data. Columba processes single or paired-end reads in FASTQ format and outputs alignments in SAM format. By utilizing advanced search schemes and bit-parallel alignment techniques, Columba achieves exceptional speed. Columba is available in two variants. The first, based on the bidirectional FM-index, prioritizes speed. The second, Columba RLC, uses run-length compression using a bidirectional move structure, significantly reducing memory usage for large, repetitive datasets like pan-genomes. Benchmarks on the human genome, as well as bacterial and human pan-genome datasets, demonstrate that Columba is much faster than existing lossless aligners and even competitive with lossy tools. We integrated Columba into the OptiType HLA genotyping pipeline, where it substantially reduced computational time while maintaining accuracy. These results position Columba as a versatile, state-of-the-art tool for high-sensitivity genomic analyses. AVAILABILITY AND IMPLEMENTATION: The source code of Columba is available at https://github.com/biointec/columba under AGPL license. Scripts to reproduce the benchmarks and analyses are available at https://doi.org/10.5281/zenodo.15849246.

Software↗
PubMed2026

Base-pair resolution conservation data improves cell type specific sequence-to-expression prediction.

MOTIVATION: Genomic sequence-to-activity models can decipher gene regulatory mechanisms and predict the functional impact of regulatory variants. However, current models struggle to integrate information from sequences outside promoters, especially information from cell type specific regulatory elements. RESULTS: Here, we propose incorporating base-pair resolution evolutionary conservation data into genomic sequence-to-expression predictors. We explore two training strategies-training from scratch or fine-tuning an existing sequence-only model with additional conservation input. We find that in both cases, base-pair resolution conservation data improves cell type specific sequence-to-expression prediction, with training from scratch yielding the greatest benefit. The improvement in cell type specific expression prediction can be attributed in part to the fact that models trained on sequence and conservation data learn to better recognize cell type specific regulatory elements than models trained on sequence alone. AVAILABILITY: Code is available at https://github.com/ni-lab/basenji-phyloP.

Conserved Sequence↗
PubMed2004

Quantitative characterization of the transcriptional regulatory network in the yeast cell cycle.

MOTIVATION: Genome-wide gene expression programs have been monitored and analyzed in the yeast Saccharomyces cerevisiae, but how cells regulate global gene expression programs in response to environmental changes is still far from being understood. We present a systematic approach to quantitatively characterize the transcriptional regulatory network of the yeast cell cycle. For the interpretative purpose, 20 target genes were selected because their expression patterns fluctuated in a periodic manner concurrent with the cell cycle and peaked at different phases. In addition to the most significant five possible regulators of each specific target gene, the expression pattern of each target gene affected by synergy of the regulators during the cell cycle was characterized. Our first step includes modeling the dynamics of gene expression and extracting the transcription rate from a time-course microarray data. The second step embraces finding the regulators that possess a high correlation with the transcription rate of the target gene, and quantifying the regulatory abilities of the identified regulators. RESULTS: Our network discerns not only the role of the activator or repressor for each specific regulator, but also the regulatory ability of the regulator to the transcription rate of the target gene. The highly coordinated regulatory network has identified a group of significant regulators responsible for the gene expression program through the cell cycle progress. This approach may be useful for computing the regulatory ability of the transcriptional regulatory networks in more diverse conditions and in more complex eukaryotes. SUPPLEMENTARY INFORMATION: Matlab code and test data are available at http://www.ee.nthu.edu.tw/~bschen/quantitative/regulatory_network.htm

Algorithms↗
PubMed2004

PermutMatrix: a graphical environment to arrange gene expression profiles in optimal linear order.

PermutMatrix is a work space designed to graphically explore gene expression data. It relies on the graphical approach introduced by Eisen and also offers several methods for the optimal reorganization of rows and columns of a numerical dataset. For example, several methods are proposed for optimal reorganization of the leaves of a hierarchical clustering tree, along with several seriation or unidimensional scaling methods that do not require any preliminary hierarchical clustering. This program, developed for MS Windows, with MS-Visual C++, has a clear and efficient graphical interface. Large datasets can be thoroughly and quickly analyzed.

Algorithms↗
PubMed2004

Characterizing the dynamic connectivity between genes by variable parameter regression and Kalman filtering based on temporal gene expression data.

MOTIVATION: One popular method for analyzing functional connectivity between genes is to cluster genes with similar expression profiles. The most popular metrics measuring the similarity (or dissimilarity) among genes include Pearson's correlation, linear regression coefficient and Euclidean distance. As these metrics only give some constant values, they can only depict a stationary connectivity between genes. However, the functional connectivity between genes usually changes with time. Here, we introduce a novel insight for characterizing the relationship between genes and find out a proper mathematical model, variable parameter regression and Kalman filtering to model it. RESULTS: We applied our algorithm to some simulated data and two pairs of real gene expression data. The changes of connectivity in simulated data are closely identical with the truth and the results of two pairs of gene expression data show that our method has successfully demonstrated the dynamic connectivity between genes. CONTACT: jiangtz@nlpr.ia.ac.cn.

Computer Simulation↗