bluuebunny/crossref_metadata_2025 huggingface dataset:
A Calcium Dependent De-Adhesion Mechanism Regulates the Direction	and Rate of Cell Migration: A Mathematical Model
Authors: Valeyev Najl V. , Downing A. Kristina , Skorinkin Andrei I. , Campbell Iain D. , Kotov Nikolai V.
Published: January 2025

Cell migration has long been studied by a variety of techniques and many proteins have been implicated in its regulation. Integrins, key proteins that link the cell to the extracellular matrix, are central to adhesion complexes whose turnover defines the rate of cell locomotion. The formation and disassembly of these adhesions is regulated by both intracellular and extracellular factors. In this study we have focused on the Ca $^{2+}$ -dependent protein network (module) that disassembles the adhesion complexes. We have developed a mathematical model that includes the Ca $^{2+}$ -dependent enzymes μ-calpain and phospholipase C (PLC) as well as IP $_{3}$ receptors and stretch activated Ca $^{2+}$ channels, all of which have been reported to regulate migration. The model also considers the spatial effects of Ca $^{2+}$ propagation into lamella. Our model predicts differential activation of calpain at the leading and trailing edges of the cell. Since disassembly of integrin adhesive contacts is proportional to the degree of calpain activation, this leads to cell migration in a preferred direction. We show how the dynamics of Ca $^{2+}$ spiking affects calpain activation and thus changes the disassembly rate of adhesions. The spiking is controlled by PLC activity and currents through stretch-activated Ca $^{2+}$ channels. Our model thus combines the effects of various molecular factors and leads to a consistent explanation of the regulation of the rate and direction of cell migration.
Sequence-Structure-Function Relation Characterized in silico
Authors: Brylinski Michal , Kochanczyk Marek , Konieczny Leszek , Roterman Irena
Published: January 2025

Methods for biological function recognition in silico appeared to be useful also for identifying characteristics of structure-to-function relations. The introduction of a three-dimensional Gauss function was assumed to represent the hydrophobic core in a protein molecule. The discrepancy between idealized "fuzzy oil-drop" and the observed one in real proteins appeared to be localized in the ligation site or in the area of biological function related part of protein molecule. The examples of proteins presented in this paper reveal that the structure-function relation can be evaluated and characterized also using the profile of the difference in value between idealized and real hydrophobicity distribution along the polypeptide chain. The specificity of particular polypeptide chain fragments in respect to their biological function and their specific participation in active site creation is discussed in this paper. The scale allowing comparison of different proteins in respect to their ligand-binding sites characteristics is introduced.
MEGU: Pathway Mapping Web-Service Based on KEGG and SVG
Authors: Kono Nobuaki , Arakawa Kazuharu , Tomita Masaru
Published: January 2025

Scientific visualization is a key technology to enhance our understanding of huge masses of omics data, especially for cell-wide, high-throughput measurements of the physiological networks. We have developed a web application that visualizes complex omics data of multiple layers simultaneously, including transcriptome, proteome, and metabolome, onto an integrated pathway diagram derived by connecting the individual KEGG pathway maps; the mapped images are generated in Scalable Vector Graphics for easy editing by hand or with computer programs or drawing software. The web application is available at http://megu.iab.keio.ac.jp/.
Transcriptional Regulatory Networks via Gene Ontology and	Expression Data
Authors: Tuncay Kagan , Ensman Lisa , Sun Jingjun , Haidar Alaa Abi , Stanley Frank , Trelinski Michael , Ortoleva Peter
Published: January 2025

Transcriptional regulatory network (TRN) discovery using information from a single source does not seem feasible due to lack of sufficient information, resulting in the construction of spurious or incomplete TRNs. A methodology, TRND, that integrates a preliminary TRN, gene expression data and gene ontology is developed to discover TRNs. The method is applied to a comprehensive set of expression data on B cell and a preliminary TRN that included 1,335 genes, 443 transcription factors (TFs) and 4032 gene/TF interactions. Predictions were obtained for 443 TFs and 9,589 genes. 14,616 of 4,247,927 possible gene/TF interactions scored higher than the imposed threshold. Results for three TFs, E2F-4, p130 and c-Myc, were examined in more detail to assess the accuracy of the integrated methodology. Although the training sets for E2F-4 and p130 were rather limited, the activities of these two TFs were found to be highly correlated and a large set of coregulated genes is predicted. These predictions were confirmed with published experimental results not used in the training set. A similar test was run for the c-Myc TF using the comprehensive resource www.myccancergene.org. In addition, correlations between expression of genes that encode TFs and TF activities were calculated and showed that the assumption of TF activity correlates with encoding gene expression might be misleading. The constructed B cell TRN, and scores for individual methodologies and the integrated approach are available at systemsbiology.indiana.edu/trndresults.
In Silico Analysis of P53 Using the P53	Knowledgebase: Mutations, Polymorphisms, MicroRNAs and Pathways
Authors: Yang Yuchen , Tantoso Erwin , Chua Gek Huey , Yeo Zhen Xuan , Ng Felicia Soo Lee , Wong Sum Thai , Chung Cheuk Wang , Li Kuo-Bin
Published: January 2025

P53 is probably the most important tumor suppressor known. Over the years, information about this gene has increased dramatically. We have built a comprehensive knowledgebase of p53, which aims to facilitate wet-lab biologists to formulate their experiments and new-comers to learn whatever they need about the gene and bioinformaticians to make new discoveries through data analysis. Using the information curated, including mutation information, transcription factors, transcriptional targets, and single nucleotide polymorphisms, we have performed extensive bioinformatics analysis, and made several new discoveries about P53. We have identified point missense mutations that are over-represented in cancers, but lack of functional studies. By assessing the capability of six p53 transcriptional targets' tag SNPs selected from HapMap to capture SNPs obtained from National Institute of Environmental Health Sciences (NIEHS) Environmental Genome project and vice versa, we conclude that NIEHS data is a better source for tagSNP selections of these genes in future association studies. Analysis of microRNA regulation in the transcriptional network of the p53 gene reveals potentially important regulatory relationships between oncogenic microRNAs and transcription factors of p53. By mapping transcription factors of p53 to pathways involved in cell cycle and apoptosis, we have identified distinctive transcriptional controls of p53 in these two physiological states.
LyM: A Tool to Reach the Best Factor in Gene Expression	Comparison
Authors: de Souza Peres Tarcísio , Costa Fernando Ferreira , Alberto Fernando Lopes
Published: January 2025

We developed a Perl-based tool called LyM to determine the best factor for changes in the expression level for each transcript across two sets of expression libraries. LyM includes a Bayesian framework that analyzes the prior and posterior probability density function for each transcript considering the size of the libraries. To find out the best factor for change in each distribution, LyM was implemented with a binary search. In this work we aimed to validate the performance of LyM tool using SAGE libraries from different human tissues. The results were compared with those generated by DGED (Digital Gene Expression Displayer), which worked as the gold standard, on the same data set, to assess accuracy. SAGE libraries were selected from CGAP for the following tissues (normal versus tumor): breast, colon, lung and stomach, consisting of eight SAGE libraries and 381,569 tags. DGED analyses were performed with five arbitrary factors for gene expression in two expression libraries: 2, 4, 8, 16 and 32. The results were confronted using the ratio between LyM and DGED factors and were quantitatively well-matched. LyM was capable of retrieving the best value of F, a factor that represents the fold difference in the expression of a specific gene between two expression libraries, represented by its SAGE tags. However, the optimal value of F is only shown in DGED output after multiple manual interactions. As a result, there was a significant economy of time with the LyM binary search algorithm. In some anecdotal cases we observed that the differential expression levels reached values above 100-fold for a fixed value of P=0.05, an information that initially remained hidden in DGED. Finally, LyM proved to be relatively fast, portable to the standard workstation present in the molecular biology laboratory, assisting accurate and convenient gene search in expression experiments with minimal user interactions.
eXpanda: An Integrated Platform for Network Analysis and	Visualization
Authors: Negishi Yoshiteru , Nakamura Hiroyuki , Yachie Nozomu , Saito Rintaro , Tomita Masaru
Published: January 2025

Analysis and visualization of biological networks, such as protein-protein and protein-DNA interactions, are crucially important toward obtaining a thorough understanding of living systems. Here, we present an integrative software platform, eXpanda, which enables an analysis of a very broad range of biological networks, with a special focus on the extraction of characteristic topologies which potentially function as units in the networks. eXpanda is provided as a Perl library which gives full-automatic connections to various biological databases via a Perl programmable interface and can perform topological analysis based on graph theory. The results of these analyses are visualizable by vector graphics. eXpanda is under GNU General Public License. Software package, detailed documentations, source codes, and some sample scripts are downloadable at http://medcd.iab.keio.ac.jp/expanda/.
Evolutionary Origin of the Protozoan Parasites Histone-like	Proteins (HU)
Authors: Arenas Aylan Farid , Escobar Andrés J. Gutierrez , Gómez-Marin Jorge Enrique
Published: January 2025

The histone-like proteins (HU) belong to a family of DNA architectural proteins that stabilize nucleoprotein complexes. We found a putative HU protein (TgGlmHMM_3045) in Toxoplasma gondii genome that was homologous to the bacterial HU protein. This putative sequence was located in the scaffold TGG_995361 of the chromosome 10. The sequence included the prokaryotic bacterial histone-like domain, KFGSLGlRRRGERVARNPRT (ID number PS00045). HU protein sequences were also found in Plasmodium falciparum, Neospora caninum, Theileria parva and Theileria annulata. We found that the homology of the putative HU protein in Apicomplexa was greater with bacterial histone-like proteins than with eukaryotic histone proteins. The phylogenetic tree indicated that the putative HU protein genes were acquired in Apicomplexa by means of a secondary endosymbiotic event from red algae and later they were transfered from the apicoplast organelle to the nuclear genome.
Computational Identification of Three-Way Junctions in Folded	RNAs: A Case Study in Arabidopsis
Authors: Cohen Adaya , Bocobza Samuel , Veksler Isana , Gabdank Idan , Barash Danny , Aharoni Asaph , Shapira Michal , Kedem Klara
Published: January 2025

Three-way junctions in folded RNAs have been investigated both experimentally and computationally. The interest in their analysis stems from the fact that they have significantly been found to possess a functional role. In recent work, three-way junctions have been categorized into families depending on the relative lengths of the segments linking the three helices. Here, based on ideas originating from computational geometry, an algorithm is proposed for detecting three-way junctions in data sets of genes that are related to a metabolic pathway of interest. In its current implementation, the algorithm relies on a moving window that performs energy minimization folding predictions, and is demonstrated on a set of genes that are involved in purine metabolism in plants. The pattern matching algorithm can be extended to other organisms and other metabolic cycles of interest in which three-way junctions have been or will be discovered to play an important role. In the test case presented here with, the computational prediction of a three-way junction in Arabidopsis that was speculated to have an interesting functional role is verified experimentally.
ROSY – A Flexible and Universal Database and Bioinformatics Tool	Platform for Roseobacter Related Species
Authors: Pommerenke Claudia , Gabriel Inga , Bunk Boyke , M^uuml;nch Richard , Haddad Isam , Tielen Petra , Wagner-Döbler Irene , Jahn Dieter
Published: January 2025

Systems biology approaches to bacteria require an integrated database and a bioinformatics tool platform to enable automated and manual annotation, regulatory and metabolic network deduction, and the storage of related experimental as well as predicted data. In this context ROSY – the Roseobacter SYstems biology database – was developed for completed and draft genomes of representatives of the marine Roseobacter clade, which constitutes one of the most abundant bacterial clades in the ocean. ROSY provides an integrative view on comprehensive data collections such as KEGG, GenBank, RoseoBase, BRENDA, and PRODORIC as well as mediates the use of connected tools for promoter analysis (Virtual Footprint), genome and pathway visualization (CGView, PathCompare), and prediction of signal peptides (PrediSi). Moreover, metabolome, transcriptome, and proteome data can be stored in ROSY, supplying an integrated platform for comparative genomics and systems biology. This entire database system along with the data retrieval, comparative analysis, and website presentation tools (http://rosy.tu-bs.de) can be easily adopted for the systems biological analysis of other bacterial groups.
COPid: Composition Based Protein Identification
Authors: Kumar Manish , Thakur Varun , Raghava Gajendra P.S.
Published: January 2025

In the past, a large number of methods have been developed for predicting various characteristics of a protein from its composition. In order to exploit the full potential of protein composition, we developed the web-server COPid to assist the researchers in annotating the function of a protein from its composition using whole or part of the protein. COPid has three modules called search, composition and analysis. The search module allows searching of protein sequences in six different databases. Search results list database proteins in ascending order of Euclidian distance or descending order of compositional similarity with the query sequence. The composition module allows calculation of the composition of a sequence and average composition of a group of sequences. The composition module also allows computing composition of various types of amino acids (e.g. charge, polar, hydrophobic residues). The analysis module provides the following options; i) comparing composition of two classes of proteins, ii) creating a phylogenetic tree based on the composition and iii) generating input patterns for machine learning techniques. We have evaluated the performance of composition-based (or alignment-free) similarity search in the subcellular localization of proteins. It was found that the alignment free method performs reasonably well in predicting certain classes of proteins. The COPid web-server is available at http://www.imtech.res.in/raghava/copid/.
PredictBias: A Server for the Identification of Genomic and	Pathogenicity Islands in Prokaryotes
Authors: Pundhir Sachin , Vijayvargiya Hemant , Kumar Anil
Published: January 2025

Pathogenicity Islands (PAIs) are the sub-sets of Genomic Islands (GIs) that are acquired by horizontal gene transfer (HGT) and are generally shown to have a significant deviation in G + C, dinucleotide or codon frequency from core genome. Major approaches used for PAI identification are based on composition bias and/or similarity with known PAIs. These approaches either limit the search to GIs or to regions similar to previously annotated PAIs. PredictBias is a web application for the identification of genomic and pathogenicity islands in prokaryotes based on composition bias, presence of insertion elements, proximity with virulence-associated genes and absence in related non-pathogenic species. A profile database of virulence factors (VFPD) has been developed using 213 protein families associated to virulence retrieved from Pfam and PRINTS database. PredictBias performs a RPSBLAST search for regions with significant composition bias against VFPD. If a region encodes for at least one protein related to virulence then it is marked as potential PAI (biased-composition) otherwise as GI. Regions involved in virulence but having unsuspicious composition bias due to ancient HGT are identified by scanning genome segments (8 ORFs) with more than four significant hits to VFPD and are marked as potential PAI (unbiased-composition). The relative absence of potential PAIs in related non-pathogenic species can be investigated using 'compare genome feature' of PredictBias that further aids in validating the results and defining boundaries for PAIs. Performance measure analysis showed that the output of PredictBias is in agreement with the known findings. PredictBias is available at www.davvbiotech.res.in/PredictBias.
Computational Prediction of Candidate miRNAs and their Targets	from Medicago truncatula Non-Protein-Coding	Transcripts
Authors: Wen Jiayu , Frickey Tancred , Weiller Georg F.
Published: January 2025

Identification and analysis of miRNAs enhances our understanding of the important roles that small RNAs play in complex regulatory networks. It is often difficult to perform large-scale validation of miRNA expression that is predicted from genomic regions. Expressed transcripts provide an alternative resource to facilitate identification of miRNAs and their targets. We developed a computational pipeline to scan for miRNA genes from polyadenylated transcripts that were associated with limited protein coding potentials, corresponding to the intergenic regions of Medicago truncatula genomic sequences. Each predicted miRNA was required to have a near perfect match with target genes. We also searched for miRNA conservation in other plant species, clustered highly similar miRNAs, and provided a functional classification of target genes.

The data is represented in the Medicago-MIRATdb (MiRNA And Target gene Data Base). The database provides detailed information on the sequences of the predicted miRNAs, their precursors, and potential target genes. It also details sequence source information such as the EST library, tissue category, and the number of EST clones. Information regarding miRNA conservation in other species, functional classification of target genes, and clusters of similar miRNAs is also provided. The web interface to the database provides researchers with the ability to narrow their search for miRNAs and target genes of interest by using a variety of filters. To our knowledge, this work represents the first systematic effort to identify candidate miRNAs and targets in the model legume M. truncatula. The database is freely accessible at http://bioinfoserver.rsbs.anu.edu.au/utils/MIRATdb/Medicago/v1/.
MaXlab: A Novel Application for the Cross Comparison and	Integration of Biological Signatures from Microarray Studies
Authors: Khalid Sabah , Khan Mohsin , Gorle Chandrasekhar Babu , Fraser Karl , Wang Ping , Liu Xiaohui , Li Suling
Published: January 2025

Microarray gene expression datasets are continually being placed in public repositories. As a result, one of the most important emerging challenges is that which enables researchers to take full advantage of such previously accumulated data to discover or validate common genes in similar biological systems. In light of this we have designed the MaXlab software to not only cross-compare available array data from different laboratories but also extract further knowledge from gene expression patterns embedded within published data. More importantly MaXlab offers a flexible and automated solution applicable for microarray technologies including cDNA and Affymetrix gene chips generating expression profiles for common genes with biological significance. We have identified several sets of genes previously unknown to be commonly expressed across studies investigating related biological questions. Among them is the identification of 17 genes involved in the dysregulation of immune tolerance including the crucial transcription factor Egr2. In addition, we have identified 175 genes commonly expressed in basal and luminal breast tumours in response to the chemotherapeutic drug doxorubicin. The universal expression and characterisation of these encouraging genes identified through MaXlab suggests that they may play a common role in the mechanism of disease and hence act as an incentive for further investigation for identifying potential therapeutic targets. Overall, MaXlab is an attractive application for molecular biologists extracting the intersection between microarray datasets together with the gene expression profiles, from which biologists are able to infer further biological insights.

The software together with file formats and additional material is freely available at http://www.immuno-software.org.
Identification and Characterization of Polyadenylation Signal	(PAS) Variants in Human Genomic Sequences Based on Modified EST	Clustering
Authors: Kamasawa Masami , Horiuchi Jun-ichi
Published: January 2025

A large-scale analysis of human polyadenylation signals was carried out in silico. The most canonical AAUAAA hexamer and its 11 single-nucleotide variants that are most frequent in human genes were used to search for polyadenylation signals in the terminal sequences. Out of 18,277 poly(A) sites that were identified from 26,414 human genes, 82.5% of the sites were found to contain at least one of these 12 hexamers as a polyadenylation signal within 40 nucleotides upstream of the poly(A) site. The rest (17.5%) did not contain any of these hexamers, which suggests the existence of yet unknown signals. A total of 20,347 terminal sequences in close proximity to 12 polyadenylation signals were collected using modified EST clustering technique to establish a large-scale database of polyadenylation signals. To characterize the 12 hexamers, the locations of polyadenylation signals that were identified as "authentic" and the uracil contents of the downstream region of the signal were examined. Based on this analysis, the 11 variants of the canonical AAUAAA were identified as possibly forming "functional" signals as AAUAAA. Moreover, the observed frequency of 41.9% for AAUAAA was significantly lower than those of other reports, suggesting that the non-canonical variants are more important in the polyadenylation process than frequently recognized. Since the poly(A) sites processed by those non-canonical variants have not been generally annotated in major gene databases, it is important to determine whether the variant hexamers could work as polyadenylation signals that may be responsible for generating heterogeneity of mRNAs by alternative polyadenylation.
TRABAS: A Database for Transcription Regulation by ABA	Signaling
Authors: Choudhury Ananyo , Lahiri Ansuman
Published: January 2025

The effects of abscisic acid (ABA) induction on Arabidopsis thaliana transcriptome have been investigated by various expression studies. We have assembled and analyzed data from available expression studies related to ABA signaling in Arabidopsis along with other available microarray data, functional annotations and information related to occurrence of cis-regulatory elements in promoters of Arabidopsis genes in a database called TRABAS. TRABAS is expected to provide a simple, user-friendly platform to facilitate the study of different aspects of ABA mediated transcription regulation and is freely available at http://www.bioinformatics.org/trabas/.
Structural Analysis of Catalase from Two Musa	Accessions, FHIA18 and Williams, and from Ravenala	madagascariensis
Authors: Khoyratty Sher-ullah S.S. , Souza Jr. Manoel T. , Jaufeerally-Fakim Yasmina
Published: January 2025

Nucleotide sequences of catalase were obtained following amplification using specific primers and were blasted against Musa acuminata catalase 2 mRNA from NCBI (157418810). Clustering of the amino acid sequences from NCBI was done using Clustal X. The latter revealed that FHIA18 catalase is more related to Ravenala madagascariensis (Musa relative) catalase while the Williams catalase is more related to a clade containing a Musa acuminata (Musa ancestor) catalase from NCBI. The tertiary structures and the catalase consensus functional sites, based on the Pseudomonas syringae catalase structural template, were obtained for FHIA18, Williams, Ravenala madagascariensis and Musa acuminata catalases. They were found to differ slightly. Using known features of catalase active sites, four pre-requisite criteria were defined to find such sites: (1) Position of tyrosine axial to heme determined by X-ray diffraction, (2) 7 conserved amino acids in the active site found by sequence alignment, (3) favourable docking energy, and (4) presence of an unobstructed long tunnel that leads the ligand to the active site. Two differing potential docking sites were found for both FHIA18 and Williams that fit a maximum number of criteria. In terms of 1D sequence, the region of the docking site for Williams is within the catalase domains as seen upon NCBI blast. The counterpart of FHIA18 for the same region is not. This sequence difference between FHIA18 and Williams affects the best docking site in FHIA18 and Williams in silico.
Characterization of Sequence and Structural Features of the	Candida krusei Enolase
Authors: Gandhi Neha S. , Young Kathy , Warmington John R. , Mancera Ricardo L.
Published: January 2025

The incidence of human infections by the fungal pathogen Candida species has been increasing in recent years. Enolase is an essential protein in fungal metabolism. Sequence data is available for human and a number of medically important fungal species. An understanding of the structural and functional features of fungal enolases may provide the structural basis for their use as a target for the development of new anti-fungal drugs. We have obtained the sequence of the enolase of Candida krusei (C. krusei), as it is a significant medically important fungal pathogen. We have then used multiple sequence alignments with various enolase isoforms in order to identify C. krusei specific amino acid residues. The phylogenetic tree of enolases shows that the C. krusei enolase assembles on the tree with the fungal genes. Importantly, C. krusei lacks four amino acids in the active site compared to human enolase, as revealed by multiple sequence alignments. These differences in the substrate binding site may be exploited for the design of new anti-fungal drugs to selectively block this enzyme. The lack of the important amino acids in the active site also indicates that C. krusei enolase might have evolved as a member of a mechanistically diverse enolase superfamily catalying somewhat different reactions.
On Multiple Regulatory Mechanisms in the Tryptophan Operon System	in Escherichia coli: In silico Study of	Perturbation Dynamics
Authors: Nguyen Lan K. , Kulasiri Don
Published: January 2025

Living organisms often exist in uncertain environments where changes are the norm. Cellular systems therefore require resilient regulatory mechanisms for timely and stable adaptation. Among various regulation motifs, multiple feedback control emerges as a common theme. The tryptophan operon system in Escherichia coli regulates the production of intracellular tryptophan using an apparatus of three feedback mechanisms: repression, attenuation and enzyme inhibition; each provides essentially the same function but operates in distinctly different ways. Here we aim to understand the roles of each loop by studying transient dynamics of the system to perturbations of different types; to reveal the underlying relationships between individual control mechanisms and macroscopic behaviour. We develop an S-systems approximation of an existing model for the system and characterise transient dynamics by introducing two measurable quantities: maximum disturbance (MD) and recovery time (RT). Our simulation results showed that combined regulation using all three feedback mechanisms significantly increases system stability, broadening the range of kinetic parameters for stable behaviour. Enzyme inhibition was shown to directly control the disturbance level in system variables after perturbations. Attenuation, on the other hand, was found to speed up system recovery whereas repression lengthens recovery time. The method developed in this paper and the defined transient dynamics measurements can be applied to other cellular systems.
Identification of Common Tumor Signatures Based on Gene Set	Enrichment Analysis
Authors: Wang Xiaosheng
Published: January 2025

The identification of common tumor signatures can discover the shared molecular mechanisms underlying tumorgenesis whereby we can prevent and treat tumors by a system intervention. We identified tumor-associated signatures including pathways, transcription factors, microRNAs and gene ontology categories by analyzing gene sets for differential expression between normal vs. tumor phenotypes classes in various tumor gene expression datasets. We obtained the common tumor signatures based on their identified frequencies for different tumor types. Some shared signatures important for various tumor types were uncovered and discussed. We proposed that the interventions aiming at both the shared tumor signatures and the tissue-specific tumor signatures might be a potential approach to overcoming cancer.
Gene Expression Data Analysis Using Multiobjective Clustering	Improved with SVM Based Ensemble
Authors: Mukhopadhyay Anirban , Maulik Ujjwal , Bandyopadhyay Sanghamitra
Published: January 2025

Microarray technology facilitates the monitoring of the expression levels of thousands of genes over different experimental conditions simultaneously. Clustering is a popular data mining tool which can be applied to microarray gene expression data to identify co-expressed genes. Most of the traditional clustering methods optimize a single clustering goodness criterion and thus may not be capable of performing well on all kinds of datasets. Motivated by this, in this article, a multiobjective clustering technique that optimizes cluster compactness and separation simultaneously, has been improved through a novel support vector machine classification based cluster ensemble method. The superiority of MOCSVMEN (MultiObjective Clustering with Support Vector Machine based ENsemble) has been established by comparing its performance with that of several well known existing microarray data clustering algorithms. Two real-life benchmark gene expression datasets have been used for testing the comparative performances of different algorithms. A recently developed metric, called Biological Homogeneity Index (BHI), which computes the clustering goodness with respect to functional annotation, has been used for the comparison purpose.
miRNA-mRNA network detects hub mRNAs and cancer specific miRNAs in lung cancer
Authors: Devaraj Saranya , Natarajan Jeyakumar
Published: January 2025

MicroRNA expression profiles can improve classification, diagnosis, and prognostic information of malignancies, including lung cancer. In this paper, we undertook to develop a miRNA-mRNA network and uncover unique growth suppressive miRNAs in lung cancer using microarray data. The miRNA-mRNA network was developed based on a bipartite graph theory approach, and a number of miRNA-mRNA modules have been identified to mine associations between miRNAs and mRNAs. From the network, we identified totally 29 protective miRNA-mRNA regulatory modules, since we restricted our search to protective miRNAs. Subsequently we analyzed the pathways for the target genes in the protective miRNA-mRNA modules using Pathway-Express. The miRNA-mRNA network efficiently detects hub mRNAs deregulated by the protective miRNAs and identifies cancer specific miRNAs in lung cancer. From the pathway analysis results, the ECM receptor pathway, Focal adhesion pathway and cell adhesion molecules pathway seem to be more interesting to investigate, since these pathways were related to all the ten protective miRNAs. Furthermore, protective miRNA target analysis revealed that genes VCAN, SIL, CD44 and MMP14 were found to have an important role in these pathways. Hence, it was inferred that these genes can be important putative targets for those protective miRNAs. A greater understanding of the mechanisms regulating VCAN, SIL, CD44 and MMP14 expression and activity will assist in the development of specific inhibitors of cancer cell metastasis. Thus these observations are expected to have an intense implication in cancer and may be useful for further research.
Differences in variability of hypervariable region 1 of hepatitis C virus (HCV) between acute and chronic stages of HCV infection
Authors: Astrakhantseva I.V. , Campo D.S. , Araujo A. , Teo C.-G. , Khudyakov Y. , Kamili S.
Published: January 2025

Distinguishing between acute and chronic HCV infections is clinically important given that early treatment of infected patients leads to high rates of sustained virological response. Analysis of 2179 clonal sequences derived from hypervariable region 1 (HVR1) of the HCV genome in samples obtained from patients with acute (n = 49) and chronic (n = 102) HCV infection showed that intra-host HVR1 diversity was 1.8 times higher in patients with chronic than acute infection. Significant differences in frequencies of 5 amino acids (positions 5, 7, 12, 16 and 18) and the average genetic distances among intra-host HVR1 variants were found using analysis of molecular variance. Differences were also observed in the polarity, volume and hydrophobicity of 10 amino acids (at positions 1, 4, 5, 12, 14, 15, 16, 21, 22 and 29). Based on these properties, a classification model could be constructed, which permitted HVR1 variants from acute and chronic cases to be discriminated with an accuracy of 88%. Progression from acute to chronic stage of HCV infection is accompanied by characteristic changes in amino acid composition of HVR1. Identifying these changes may permit diagnosis of recent HCV infection.
QColors: An algorithm for conservative viral quasispecies reconstruction from short and non-contiguous next generation sequencing reads
Authors: Huang Austin , Kantor Rami , DeLong Allison , Schreier Leeann , Istrail Sorin
Published: January 2025

Next generation sequencing technologies have recently been applied to characterize mutational spectra of the heterogeneous population of viral genotypes (known as a quasispecies) within HIV-infected patients. Such information is clinically relevant because minority genetic subpopulations of HIV within patients enable viral escape from selection pressures such as the immune response and antiretroviral therapy. However, methods for quasispecies sequence reconstruction from next generation sequencing reads are not yet widely used and remains an emerging area of research. Furthermore, the majority of research methodology in HIV has focused on 454 sequencing, while many next-generation sequencing platforms used in practice are limited to shorter read lengths relative to 454 sequencing. Little work has been done in determining how best to address the read length limitations of other platforms.

The approach described here incorporates graph representations of both read differences and read overlap to conservatively determine the regions of the sequence with sufficient variability to separate quasispecies sequences. Within these tractable regions of quasispecies inference, we use constraint programming to solve for an optimal quasispecies subsequence determination via vertex coloring of the conflict graph, a representation which also lends itself to data with non-contiguous reads such as paired-end sequencing. We demonstrate the utility of the method by applying it to simulations based on actual intra-patient clonal HIV-1 sequencing data.
Coordinated evolution among hepatitis C virus genomic sites is coupled to host factors and resistance to interferon
Authors: Lara James , Tavis John E. , Donlin Maureen J. , Lee William M. , Yuan He-Jun , Pearlman Brian L. , Vaughan Gilberto , Forbi Joseph C. , Xia Guo-Liang , Khudyakov Yury E.
Published: January 2025

Machine-learning methods in the form of Bayesian networks (BN), linear projection (LP) and self-organizing tree (SOT) models were used to explore association among polymorphic sites within the HVR1 and NS5a regions of the HCV genome, host demographic factors (ethnicity, gender and age) and response to the combined interferon (IFN) and ribavirin (RBV) therapy. The BN models predicted therapy outcomes, gender and ethnicity with accuracy of 90%, 90% and 88.9%, respectively. The LP and SOT models strongly confirmed associations of the HVR1 and NS5A structures with response to therapy and demographic host factors identified by BN. The data indicate host specificity of HCV evolution and suggest the application of these models to predict outcomes of IFN/RBV therapy.
Improved transcriptome quantification and reconstruction from RNA-Seq reads using partial annotations
Authors: Mangul Serghei , Caciula Adrian , Glebova Olga , Mandoiu Ion , Zelikovsky Alex
Published: January 2025

The paper addresses the problem of how to use RNA-Seq data for transcriptome reconstruction and quantification, as well as novel transcript discovery in partially annotated genomes. We present a novel annotation-guided general framework for transcriptome discovery, reconstruction and quantification in partially annotated genomes and compare it with existing annotation-guided and genome-guided transcriptome assembly methods. Our method, referred as Discovery and Reconstruction of Unannotated Transcripts (DRUT), can be used to enhance existing transcriptome assemblers, such as Cufflinks [3], as well as to accurately estimate the transcript frequencies. Empirical analysis on synthetic datasets confirms that Cufflinks enhanced by DRUT has superior quality of reconstruction and frequency estimation of transcripts.
Positive Darwinian Selection on Crustacean Hyperglycemic Hormone	(CHH) of the Green Shore Crab, Carcinus maenas
Authors: Padhi Abinash , Verghese Bindhu , Otta Subhendu K. , Varghese Binu , Ramu Karri
Published: January 2025

The tissue-specific expression and differential function of the crustacean hyperglycemic hormone (CHH) in Carcinus maenas indicate an interesting evolutionary history. Previous studies have shown that CHH from the sinus gland X-organ (XO-type) has hyperglycemic activity, whereas the CHH from the pericardial organ (PO-type) neither shows hyperglycemic activity nor it inhibits Y-organ ecdysteroid synthesis. Here we examined the types of selective pressures operating on the variants of CHH in Carcinus maenas. Maximum likelihood-based codon substitution analyses revealed that the variants of this neuropeptide in C. maenas have been subjected to positive Darwinian selection indicating adaptive evolution and functional divergence among the CHH variants leading to two unique groups (PO and XO-type). Although the average ratio of nonsynonymous to synonymous substitution (ω) for the entire coding region is 0.5096, few codon sites showed significantly higher ω (10.95). Comparison of models that incorporate positive selection (ω > 1) with models not incorporating positive selection (ω <1) at certain codon sites failed to reject (p=0) evidence of positive Darwinian selection.
In Silico Identification and Characterization	of mRNA-Like Noncoding Transcripts in Medicago truncatula
Authors: Wen Jiayu , Parker Brian J. , Weiller Georg F.
Published: January 2025

Accumulating evidence suggests that that non-coding RNAs (ncRNAs) play key roles in gene regulation and may form the basis of an inter-gene communication system. Many ncRNAs are synthesized similar to mRNAs and can be detected through screening of polyA-rich EST or cDNA libraries. We developed a computational pipeline to screen EST and genomic sequence data for those transcribed genes with limited protein coding potential and applied this pipeline to the model legume Medicago truncatula. This process identified a set of 503 mRNA-like transcripts that appear not to encode proteins. Further computational analysis showed that many of these ncRNA candidates share structural similarities to known ncRNAs and that they clearly differ from protein coding genes and non-transcribed regions in their base and oligonucleotide compositions, as well as in aspects of secondary structure. By using a machine learning approach, we show that the distinctive ncRNA features presented in this study can be used to discriminate most ncRNAs and may thus be useful for improving ncRNA prediction. Computational analysis of EST isolation frequencies in various plant tissues showed that the expression levels and expression profiles of the putative ncRNAs and mRNAs differ – most interestingly, the putative ncRNAs are highly expressed relative to mRNAs in the root nodule tissue and conserved only in closely related plants. The work presented here constitutes the first large-scale prediction and characterization of ncRNAs in legumes, and provides a basis for further research on elucidating ncRNA function in legume genomics.
PREDICTOR: A Web-Based Tool for the Prediction of Atomic Structure	from Sequence for Double Helical DNA with up to 150 Base Pairs
Authors: Farwer Jochen , Packer Martin J. , Hunter Christopher A.
Published: January 2025

We announce the release of a web-based tool for DNA structure prediction (PREDICTOR) which allows the calculation of atomic structures of double-helical DNA with up to 150 Watson-Crick base pairs (http://farwer.staff.shef.ac.uk/ PREDICTOR). The semi-empirical method uses computational chemistry to extrapolate knowledge of sequence-dependent DNA structure contained in the X-ray crystal structure database. The properties of the base stacking interactions are treated theoretically, and an empirical model is used to add the conformational constraints imposed by the backbone. For DNA oligomers in the X-ray crystal structure database that were not used for parameterisation of the model, the method distinguishes A and B form DNA reasonably reliably, and the final structures are accurate to 2 å rmsd. Simulation of a 150mer and a 494mer with experimentally confirmed bending clearly reproduces the bending whereas the predicted structure of a random 150mer does not show any curvature. Calculation times are 90 seconds for an octamer and 7 minutes for a 30mer.
Cell System Ontology: Representation for Modeling, Visualizing,	and Simulating Biological Pathways
Authors: Jeong Euna , Nagasaki Masao , Saito Ayumu , Miyano Satoru
Published: January 2025

With the rapidly accumulating knowledge of biological entities and networks, there is a growing need for a general framework to understand this information at a system level. In order to understand life as system, a formal description of system dynamics with semantic validation will be necessary. Within the context of biological pathways, several formats have been proposed, e.g., SBML, CellML, and BioPAX. Unfortunately, these formats lack the formal definitions of each term or fail to capture the system dynamics behavior. Thus, we have developed a new system dynamics centered ontology called Cell System Ontology (CSO). As an exchange format, the ontology is implemented in the Web Ontology Language (OWL), which enables semantic validation and automatic reasoning to check the consistency of biological pathway models. The features of CSO are as follows: (1) manipulation of different levels of granularity and abstraction of pathways, e.g., metabolic pathways, regulatory pathways, signal transduction pathways, and cell-cell interactions; (2) capture of both quantitative and qualitative aspects of biological function by using hybrid functional Petri net with extension (HFPNe); and (3) encoding of biological pathway data related to visualization and simulation, as well as modeling. The new ontology also predefines mature core vocabulary, which will be necessary for creating models with system dynamics. In addition, each of the core terms has at least one standard icon for easy modeling and accelerating the exchangeability among applications. In order to demonstrate the potential of CSO-based pathway modeling, visualization, and simulation, we present an HFPNe model for the ASEL and ASER regulatory networks in Caenorhabditis elegans.
Highly dispersed chromium(III) molybdate powders obtained by solid phase synthesis
Authors: Miroshnichenko M. N. , Kolosov V. N.
Published: January 2025

Objectives. To obtain highly dispersed powders of chromium(III) molybdate Cr2(MoO4)3 by solid phase synthesis and to study their porous structure.Methods. After stirring in water, a mixture of Cr2O3 and MoO3 oxide powders was dried in air and subjected to heat treatment in the temperature range of 600–800°C. After heat treatment, the products were identified by X-ray phase and sedimentation analysis. The specific surface area was measured using the Brunauer–Emmett–Teller static adsorption method. Porosity parameters were measured using the Barrett–Joyner–Halenda (BJH) method.Results. The Gibbs free energy ΔG of the reaction between chromium and molybdenum oxides was calculated and it was shown that the process is characterized by a significant negative value of ΔG. Concurrently, the Gibbs energy exhibits a relatively weak dependence on temperature. The highly dispersed chromium(III) molybdate powders with specific surface area of 15.3–29.7 m2·g−1 obtained in this way were pure according to X-ray diffraction analysis. A study of the volume, diameter, and pore size distribution was conducted through the utilization of nitrogen adsorption–desorption isotherms in accordance with the BJH model.Conclusions. It was demonstrated that Cr2(MoO4)3 powders possess a mesoporous structure and are distinguished by a bimodal pore system comprising small pores with a diameter of 2–3 nm and larger pores with a diameter ranging from 15 to 30 nm.