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Plant genomics plays a pivotal role in enhancing global food security and sustainability by offering innovative solutions for improving crop yield, disease resistance, and stress tolerance. As the number of sequenced genomes grows and the accuracy and contiguity of genome assemblies improve, structural annotation of plant genomes continues to be a significant challenge due to their large size, polyploidy, and rich repeat content. In this paper, we present an overview of the current landscape in crop genomics research, highlighting the diversity of genomic characteristics across various crop species. We also assessed the accuracy of popular gene prediction tools in identifying genes within crop genomes and examined the factors that impact their performance. Our findings highlight the strengths and limitations of BRAKER2 and Helixer as leading structural genome annotation tools and underscore the impact of genome complexity, fragmentation, and repeat content on their performance. Furthermore, we evaluated the suitability of the predicted proteins as a reliable search space in proteomics studies using mass spectrometry data. Our results provide valuable insights for future efforts to refine and advance the field of structural genome annotation. [ABSTRACT FROM AUTHOR]

This document is a response to concerns raised about a previous article on B chromosomes and their evolutionary dynamics. The authors acknowledge the valid concerns and provide corrections and clarifications. They address issues with the availability of sequenced data and inaccuracies in supplementary figures and tables. The authors also discuss errors and corrections in a study on B chromosomes in different species, including mistakes in referencing, figure citation, and data analysis. They emphasize the need for standardized protocols and bioinformatic tools to improve the study of B chromosomes and acknowledge the challenges and gaps in understanding their genomic content. [Extracted from the article]

This article examines the reproducibility of a previous study on B chromosomes in three species. The authors discovered inconsistencies and errors in the original study, resulting in false positive results and unsupported claims. They stress the significance of rigorous reproducibility practices in bioinformatics analyses and propose enhancements to the review process for such studies. The article includes supplementary materials that compare the authors' analyses with those of the original study. [Extracted from the article]

Background: Various analytical methods exist that first quantify gene expression and then analyze differentially expressed genes from Affymetrix GeneChip® gene expression analysis array data. These methods differ in the choice of probe measure (quantification of probe hybridization), summarizing multiple probe intensities into a gene expression value, and analysis of differential gene expression. Research papers that describe these methods focus on performance, and how their approaches differ from others. To better understand the common features and differences between various methods, and to evaluate their impact on the results of gene expression analysis, we describe a class of models, referred to as generalized probe models (GPMs), which encompass various currently available methods. Results: Using an empirical dataset, we compared different formulations of GPMs, and GPMs with three other commonly used methods, i.e. MAS 5.0, dChip, and RMA. The comparison shows that, on a genome-wide scale , different methods yield similar results if the same probe measures are chosen. Conclusion: In this paper we present a general framework, i.e. GPMs, which encompasses various methods. GPMs permit the use of a wide range of probe measures and facilitate appropriate comparison between commonly used methods. We demonstrate that the dissimilar results stem primarily from different choice of probe measures, rather than other factors. [ABSTRACT FROM AUTHOR]

Background: Gmelina arborea Roxb is a fast-growing tree species of commercial importance for tropical countries due to multiple industrial uses of its wood. Wood is primarily composed of thick secondary cell walls of xylem cells which imparts the strength to the wood. Identification of the genes involved in the secondary cell wall biosynthesis as well as their cognate regulators is crucial to understand how the production of wood occurs and serves as a starting point for developing breeding strategies to produce varieties with improved wood quality, better paper pulping or new potential uses such as biofuel production. In order to gain knowledge on the molecular mechanisms and gene regulation related with wood development in white teak, a de novo sequencing and transcriptome assembly approach was used employing secondary cell wall synthesizing cells from young white teak trees. Results: For generation of transcriptome, RNA-seq reads were assembled into 110,992 transcripts and 49,364 genes were functionally annotated using plant databases; 5071 GO terms and 25,460 SSR markers were identified within xylem transcripts and 10,256 unigenes were assigned to KEGG database in 130 pathways. Among transcription factor families, C2H2, C3H, bLHLH and MYB were the most represented in xylem. Differential gene expression analysis using leaves as a reference was carried out and a total of 20,954 differentially expressed genes were identified including monolignol biosynthetic pathway genes. The differential expression of selected genes (4CL, COMT, CCoAOMT, CCR and NST1) was validated using qPCR. Conclusions: We report the very first de novo transcriptome of xylem-related genes in this tropical timber species of commercial importance and constitutes a valuable extension of the publicly available transcriptomic resource aimed at fostering both basic and breeding studies. [ABSTRACT FROM AUTHOR]

Background: Small interfering RNA (siRNA) can be used to post-transcriptional gene regulation by knocking down targeted genes. In functional genomics, biomedical research and cancer therapeutics, siRNA design is a critical research topic. Various computational algorithms have been developed to select the most effective siRNA, whereas the efficacy prediction accuracy is not so satisfactory. Many existing computational methods are based on feature engineering, which may lead to biased and incomplete features. Deep learning utilizes non-linear mapping operations to detect potential feature pattern and has been considered perform better than existing machine learning method. Results: In this paper, to further improve the prediction accuracy and facilitate gene functional studies, we developed a new powerful siRNA efficacy predictor based on a deep architecture. First, we extracted hidden feature patterns from two modalities, including sequence context features and thermodynamic property. Then, we constructed a deep architecture to implement the prediction. On the available largest siRNA database, the performance of our proposed method was measured with 0.725 PCC and 0.903 AUC value. The comparative experiment showed that our proposed architecture outperformed several siRNA prediction methods. Conclusions: The results demonstrate that our deep architecture is stable and efficient to predict siRNA silencing efficacy. The method could help select candidate siRNA for targeted mRNA, and further promote the development of RNA interference. [ABSTRACT FROM AUTHOR]

Background: Asparagine is a very important nitrogen transport and storage compound in plants due to its high nitrogen/carbon ratio and stability. Asparagine intracellular concentration depends on a balance between asparagine biosynthesis and degradation. The main enzymes involved in asparagine metabolism are asparagine synthetase (ASN), asparaginase (NSE) and serine-glyoxylate aminotransferase (SGAT). The study of the genes encoding for these enzymes in the model legume Lotus japonicus is of particular interest since it has been proposed that asparagine is the principal molecule used to transport reduced nitrogen within the plant in most temperate legumes. Results: A differential expression of genes encoding for several enzymes involved in asparagine metabolism was detected in L. japonicus. ASN is encoded by three genes, LjASN1 was the most highly expressed in mature leaves while LjASN2 expression was negligible and LjASN3 showed a low expression in this organ, suggesting that LjASN1 is the main gene responsible for asparagine synthesis in mature leaves. In young leaves, LjASN3 was the only ASN gene expressed although at low levels, while all the three genes encoding for NSE were highly expressed, especially LjNSE1. In nodules, LjASN2 and LjNSE2 were the most highly expressed genes, suggesting an important role for these genes in this organ. Several lines of evidence support the connection between asparagine metabolic genes and photorespiration in L. japonicus: a) a mutant plant deficient in LjNSE1 showed a dramatic decrease in the expression of the two genes encoding for SGAT; b) expression of the genes involved in asparagine metabolism is altered in a photorespiratory mutant lacking plastidic glutamine synthetase; c) a clustering analysis indicated a similar pattern of expression among several genes involved in photorespiratory and asparagine metabolism, indicating a clear link between LjASN1 and LjSGAT genes and photorespiration. Conclusions: The results obtained in this paper indicate the existence of a differential expression of asparagine metabolic genes in L. japonicus and point out the crucial relevance of particular genes in different organs. Moreover, the data presented establish clear links between asparagine and photorespiratory metabolic genes in this plant. [ABSTRACT FROM AUTHOR]

Background: Genetic resources are important natural assets. Discovery of new enzyme gene sequences has been an ongoing effort in biotechnology industry. In the genomic age, genomes of microorganisms from various environments have been deciphered. Increasingly, it has become more and more difficult to find novel enzyme genes. In this work, we attempted to use the easily accessible banknotes to search for novel microbial gene sequences. Results: We used high-throughput genomic sequencing technology to comprehensively characterize the diversity of microorganisms on the US dollars and Chinese Renminbis (RMBs). In addition to finding a vast diversity of microbes, we found a significant number of novel gene sequences, including an unreported superoxide dismutase (SOD) gene, whose catalytic activity was further verified by experiments. Conclusions: We demonstrated that banknotes could be a good and convenient genetic resource for finding economically valuable biologicals. [ABSTRACT FROM AUTHOR]

Background: Gene expression data extracted from microarray experiments have been used to study the difference between mRNA abundance of genes under different conditions. In one of such experiments, thousands of genes are measured simultaneously, which provides a high-dimensional feature space for discriminating between different sample classes. However, most of these dimensions are not informative about the between-class difference, and add noises to the discriminant analysis. Results: In this paper we propose and study feature selection methods that evaluate the "informativeness" of a set of genes. Two measures of information based on multigene expression profiles are considered for a backward information-driven screening approach for selecting important gene features. By considering multigene expression profiles, we are able to utilize interaction information among these genes. Using a breast cancer data, we illustrate our methods and compare them to the performance of existing methods. Conclusion: We illustrate in this paper that methods considering gene-gene interactions have better classification power in gene expression analysis. In our results, we identify important genes with relative large p-values from single gene tests. This indicates that these are genes with weak marginal information but strong interaction information, which will be overlooked by strategies that only examine individual genes. [ABSTRACT FROM AUTHOR]

Background: Annotating genomes remains an hazardous task. Mistakes or gaps in such a complex process may occur when relevant knowledge is ignored, whether lost, forgotten or overlooked. This paper exemplifies an approach which could help to ressucitate such meaningful data. Results: We show that a set of closely related sequences which have been annotated as ornithine carbamoyltransferases are actually putrescine carbamoyltransferases. This demonstration is based on the following points : (i) use of enzymatic data which had been overlooked, (ii) rediscovery of a short NH2-terminal sequence allowing to reannotate a wrongly annotated ornithine carbamoyltransferase as a putrescine carbamoyltransferase, (iii) identification of conserved motifs allowing to distinguish unambiguously between the two kinds of carbamoyltransferases, and (iv) comparative study of the gene context of these different sequences. Conclusions: We explain why this specific case of misannotation had not yet been described and draw attention to the fact that analogous instances must be rather frequent. We urge to be especially cautious when high sequence similarity is coupled with an apparent lack of biochemical information. Moreover, from the point of view of genome annotation, proteins which have been studied experimentally but are not correlated with sequence data in current databases qualify as "orphans", just as unassigned genomic open reading frames do. The strategy we used in this paper to bridge such gaps in knowledge could work whenever it is possible to collect a body of facts about experimental data, homology, unnoticed sequence data, and accurate informations about gene context. [ABSTRACT FROM AUTHOR]

At the 3' end of the C2 gene in the mammalian TRB locus, a distinct reverse TRBV30 gene (named TRBV31 in mice) has been conserved throughout evolution. In the fully annotated TRB locus of 14 mammals (including six orders), we observed noteworthy variations in the localization and quality of the reverse V30 genes and Recombination Signal Sequences (RSSs) in the gene trees of 13 mammals. Conversely, the forward V29 genes and RSSs were generally consistent with the species tree of their corresponding species. This finding suggested that the evolution of the reverse V30 gene was not synchronous and likely played a crucial role in regulating adaptive immune responses. To further investigate this possibility, we utilized single-cell TCR sequencing (scTCR-seq) and high-throughput sequencing (HTS) to analyze TCRβ CDR3 repertoires from both central and peripheral tissues of Primates (Homo sapiens and Macaca mulatta), Rodentia (Mus musculus: BALB/c, C57BL/6, and Kunming mice), Artiodactyla (Bos taurus and Bubalus bubalis), and Chiroptera (Rhinolophus affinis and Hipposideros armige). Our investigation revealed several novel observations: (1) The reverse V30 gene exhibits classical rearrangement patterns adhering to the '12/23 rule' and the 'D-J rearrangement preceding the V-(D-J) rearrangement'. This results in the formation of rearranged V30-D2J2, V30-D1J1, and V30-D1J2. However, we also identified 'special rearrangement patterns' wherein V30-D rearrangement preceding D-J rearrangement, giving rise to rearranged V30-D2-J1 and forward Vx-D2-J. (2) Compared to the 'deletional rearrangement' (looping out) of forward V1-V29 genes, the reverse V30 gene exhibits preferential utilization with 'inversional rearrangement'. This may be attributed to the shorter distance between the V30 gene and D gene and the 'inversional rearrangement' modes. In summary, in the mammalian TRB locus, the reverse V30 gene has been uniquely preserved throughout evolution and preferentially utilized in V(D)J recombination, potentially serving a significant role in adaptive immunity. These results will pave the way for novel and specialized research into the mechanisms, efficiency, and function of V(D)J recombination in mammals. [ABSTRACT FROM AUTHOR]

Background: Real-time quantitative PCR (RT-qPCR) is one of the most widely used gene expression analyses for validating RNA-seq data. This technique requires reference genes that are stable and highly expressed, at least across the different biological conditions present in the transcriptome. Reference and variable candidate gene selection is often neglected, leading to misinterpretation of the results. Results: We developed a software named "Gene Selector for Validation" (GSV), which identifies the best reference and variable candidate genes for validation within a quantitative transcriptome. This tool also filters the candidate genes concerning the RT-qPCR assay detection limit. GSV was compared with other software using synthetic datasets and performed better, removing stable low-expression genes from the reference candidate list and creating the variable-expression validation list. GSV software was used on a real case, an Aedes aegypti transcriptome. The top GSV reference candidate genes were selected for RT-qPCR analysis, confirming that eiF1A and eiF3j were the most stable genes tested. The tool also confirmed that traditional mosquito reference genes were less stable in the analyzed samples, highlighting the possibility of inappropriate choices. A meta-transcriptome dataset with more than ninety thousand genes was also processed successfully. Conclusion: The GSV tool is a time and cost-effective tool that can be used to select reference and validation candidate genes from the biological conditions present in transcriptomic data. [ABSTRACT FROM AUTHOR]

Background: The ability to predict the interaction of drugs with target proteins is essential to research and development of drug. However, the traditional experimental paradigm is costly, and previous in silico prediction paradigms have been impeded by the wide range of data platforms and data scarcity. Results: In this paper, we modeled the prediction of drug-target interactions as a binary classification task. Using transcriptome data from the L1000 database of the LINCS project, we developed a framework based on a deep-learning algorithm to predict potential drug target interactions. Once fully trained, the model achieved over 98% training accuracy. The results of our research demonstrated that our framework could discover more reliable DTIs than found by other methods. This conclusion was validated further across platforms with a high percentage of overlapping interactions. Conclusions: Our model's capacity of integrating transcriptome data from drugs and genes strongly suggests the strength of its potential for DTI prediction, thereby improving the drug discovery process. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via the original article. [ABSTRACT FROM AUTHOR]

Background: The domain of unknown function 247 (DUF247) proteins is involved in plant development and stress response. Rice is an important worldwide cereal crop, although an increasing number of DUF proteins have been identified, the understanding of DUF proteins is still very limited in rice. Results: In this study, we identified 69 genes that encode DUF247 proteins in the rice (Oryza sativa) genome by homology searches and domain prediction. All the OsDUF247 proteins were classified into four major groups (I, II, III and IV) by phylogenetic analysis. Remarkably, OsDUF247 genes clustered on the chromosomes solely show close phylogenetic relationships, suggesting that gene duplications have driven the expansion of the DUF247 gene family in the rice genome. Tissue profile analysis showed that most DUF247 genes expressed at constitutive levels in seedlings, roots, stems, and leaves, except for seven genes (LOC_Os01g21670, LOC_Os03g19700, LOC_Os05g04060, LOC_Os08g26820, LOC_Os08g26840, LOC_Os08g26850 and LOC_Os09g13410) in panicles. These seven genes were induced by various abiotic stress, including cold, drought, heat, hormone treatment, and especially salt, as demonstrated by further experimental analysis. DUF247 proteins contain transmembrane domains located on the membrane, suggesting their significant roles in rice development and adaptation to the environment. Conclusions: These findings lay the foundation for functional characterizations of DUF247 genes to unravel their exact role in rice cultivars. [ABSTRACT FROM AUTHOR]

Background: Tubulins play crucial roles in numerous fundamental processes of plant development. In flowering plants, tubulins are grouped into α-, β- and γ-subfamilies, while α- and β-tubulins possess a large isotype diversity and gene number variations among different species. This circumstance leads to insufficient recognition of orthologous isotypes and significantly complicates extrapolation of obtained experimental results, and brings difficulties for the identification of particular tubulin isotype function. The aim of this research is to identify and characterize tubulins of an emerging biofuel crop Camelina sativa. Results: We report comprehensive identification and characterization of tubulin gene family in C. sativa, including analyses of exon-intron organization, duplicated genes comparison, proper isotype designation, phylogenetic analysis, and expression patterns in different tissues. 17 α-, 34 β- and 6 γ-tubulin genes were identified and assigned to a particular isotype. Recognition of orthologous tubulin isotypes was cross-referred, involving data of phylogeny, synteny analyses and genes allocation on reconstructed genomic blocks of Ancestral Crucifer Karyotype. An investigation of expression patterns of tubulin homeologs revealed the predominant role of N6 (A) and N7 (B) subgenomes in tubulin expression at various developmental stages, contrarily to general the dominance of transcripts of H7 (C) subgenome. Conclusions: For the first time a complete set of tubulin gene family members was identified and characterized for allohexaploid C. sativa species. The study demonstrates the comprehensive approach of precise inferring gene orthology. The applied technique allowed not only identifying C. sativa tubulin orthologs in model Arabidopsis species and tracking tubulin gene evolution, but also uncovered that A. thaliana is missing orthologs for several particular isotypes of α- and β-tubulins. [ABSTRACT FROM AUTHOR]

Background: Disease can drastically impair common bean (Phaseolus vulgaris L.) production. Anthracnose, caused by the fungal pathogen Colletotrichum lindemuthianum (Sacc. and Magnus) Briosi and Cavara, is one of the diseases that are widespread and cause serious economic loss in common bean. Results: Transcriptome analysis of the early response of common bean to anthracnose was performed using two resistant genotypes, Hongyundou and Honghuayundou, and one susceptible genotype, Jingdou. A total of 9,825 differentially expressed genes (DEGs) responding to pathogen infection and anthracnose resistance were identified by differential expression analysis. By using weighted gene coexpression network analysis (WGCNA), 2,051 DEGs were found to be associated with two resistance-related modules. Among them, 463 DEGs related to anthracnose resistance were considered resistance-related candidate genes. Nineteen candidate genes were coexpressed with three resistance genes, Phvul.001G243600, Phvul.001G243700 and Phvul.001G243800. To further identify resistance genes, 46 candidate genes were selected for experimental validation using salicylic acid (SA) and methyl jasmonate (MeJA). The results indicated that 38 candidate genes that responded to SA/MeJA treatment may be involved in anthracnose resistance in common bean. Conclusions: This study identified 38 resistance-related candidate genes involved in the early response of common bean, and 19 resistance-related candidate genes were coexpressed with anthracnose resistance genes. This study identified putative resistance genes for further resistance genetic investigation and provides an important reference for anthracnose resistance breeding in common bean. [ABSTRACT FROM AUTHOR]

Background: Several core breeding and supporting lines of the Qingyuan partridge chicken, a representative local chicken breed in China, have been developed over 20 years. Consequently, its economic traits related to growth and reproduction have been significantly improved by breeding selection and commercial utilization, but some characteristic traits, such as partridge feathers, high meat quality and sufficient flavor, have always been retained. However, effective methods for genetic assessment and functional gene exploration of similar trait groups are lacking. The presence of identical haplotype fragments transmitted from parent to offspring results in runs of homozygosity (ROH), which offer an efficient solution. In this study, genomes of 134 Qingyuan partridge chickens representing two breeding populations and one preserved population were re-sequenced to evaluate the genetic diversity and explore functional genes by analyzing the diversity, distribution, and frequency of ROH. Results: The results showed a low level of genomic linkage and degree of inbreeding within both the bred and preserved populations, suggesting abundant genetic diversity and an adequate genetic potential of the Qingyuan partridge chicken. Throughout the long-term selection process, 21 genes, including GLI3, ANO5, BLVRA, EFNB2, SLC5A12, and SVIP, associated with breed-specific characteristics were accumulated within three ROH islands, whereas another 21 genes associated with growth traits including IRX1, IRX2, EGFR, TPK1, NOVA1, BDNF and so on were accumulated within five ROH islands. Conclusions: These findings provide new insights into the genetic assessment and identification of genes with breed-specific and selective characteristics, offering a solid genetic basis for breeding and protection of Qingyuan partridge chickens. [ABSTRACT FROM AUTHOR]

Background: Broussonetia papyrifera is an economically significant tree with high utilization value, yet its cultivation is often constrained by soil contamination with heavy metals (HMs). Effective scientific cultivation management, which enhances the yield and quality of B. papyrifera, necessitates an understanding of its regulatory mechanisms in response to HM stress. Results: Twelve Metallothionein (MT) genes were identified in B. papyrifera. Their open reading frames ranged from 186 to 372 bp, encoding proteins of 61 to 123 amino acids with molecular weights between 15,473.77 and 29,546.96 Da, and theoretical isoelectric points from 5.24 to 5.32. Phylogenetic analysis classified these BpMTs into three subclasses: MT1, MT2, and MT3, with MT2 containing seven members and MT3 only one. The expression of most BpMT genes was inducible by Cd, Mn, Cu, Zn, and abscisic acid (ABA) treatments, particularly BpMT2e, BpMT2d, BpMT2c, and BpMT1c, which showed significant responses and warrant further study. Yeast cells expressing these BpMT genes exhibited enhanced tolerance to Cd, Mn, Cu, and Zn stresses compared to control cells. Yeasts harboring BpMT1c, BpMT2e, and BpMT2d demonstrated higher accumulation of Cd, Cu, Mn, and Zn, suggesting a chelation and binding capacity of BpMTs towards HMs. Site-directed mutagenesis of cysteine (Cys) residues indicated that mutations in the C domain of type 1 BpMT led to increased sensitivity to HMs and reduced HM accumulation in yeast cells; While in type 2 BpMTs, the contribution of N and C domain to HMs' chelation possibly corelated to the quantity of Cys residues. Conclusion: The BpMT genes are crucial in responding to diverse HM stresses and are involved in ABA signaling. The Cys-rich domains of BpMTs are pivotal for HM tolerance and chelation. This study offers new insights into the structure-function relationships and metal-binding capabilities of type-1 and − 2 plant MTs, enhancing our understanding of their roles in plant adaptation to HM stresses. [ABSTRACT FROM AUTHOR]

Background: Dendrobium huoshanense, a traditional medicinal and food plant, has a rich history of use. Recently, its genome was decoded, offering valuable insights into gene function. However, there is no comprehensive gene functional analysis platform for D. huoshanense. Result: To address this, we created a platform for gene function analysis and comparison in D. huoshanense (DhuFAP). Using 69 RNA-seq samples, we constructed a gene co-expression network and annotated D. huoshanense genes by aligning sequences with public protein databases. Our platform contained tools like Blast, gene set enrichment analysis, heatmap analysis, sequence extraction, and JBrowse. Analysis revealed co-expression of transcription factors (C2H2, GRAS, NAC) with genes encoding key enzymes in alkaloid biosynthesis. We also showcased the reliability and applicability of our platform using Chalcone synthases (CHS). Conclusion: DhuFAP (www.gzybioinformatics.cn/DhuFAP) and its suite of tools represent an accessible and invaluable resource for researchers, enabling the exploration of functional information pertaining to D. huoshanense genes. This platform stands poised to facilitate significant biological discoveries in this domain. [ABSTRACT FROM AUTHOR]

Background: Cancer and other gene related diseases are usually caused by a failure in the signaling pathway between genes and cells. These failures can occur in different areas of the gene regulatory network, but can be abstracted as faults in the regulatory function. For effective cancer treatment, it is imperative to identify faults and select appropriate drugs to treat the faults. In this paper, we present an extensible Max-SAT based automatic test pattern generation (ATPG) algorithm for cancer therapy. This ATPG algorithm is based on Boolean Satisfiability (SAT) and utilizes the stuck-at fault model for representing signaling faults. A weighted partial Max-SAT formulation is used to enable efficient selection of the most effective drug. Results: Several usage cases are presented for fault identification and drug selection. These cases include the identification of testable faults, optimal drug selection for single/multiple known faults, and optimal drug selection for overall fault coverage. Experimental results on growth factor (GF) signaling pathways demonstrate that our algorithm is flexible, and can yield an exact solution for each feature in much less than 1 second. [ABSTRACT FROM AUTHOR]

Background: Proximity-based methods and co-evolution-based phylogenetic profiles methods have been successfully used for the identification of functionally related genes. Proximity-based methods are effective for physically clustered genes while the phylogenetic profiles method is effective for co-occurring gene sets. However, both methods predict many false positives and false negatives. In this paper, we propose the Gene Cluster Profile Vector (GCPV) method, which combines these two methods by using phylogenetic profiles of whole gene clusters. The GCPV method is, currently, the only genome comparison based method that allows for the characterization of relationships between gene clusters based profiles of individual genes in clusters. Results: The GCPV method groups together reasonably related operons in E. coli about 60% of the time. The method is not sensitive to the choice of a reference genome set used and it outperforms the conventional phylogenetic profiles method. Finally, we show that the method works well for predicted gene clusters from C. crescentus and can serve as an important tool not only for understanding gene function, but also for elucidating mechanisms of general biological processes. Conclusions: The GCPV method has shown to be an effective and robust approach to the prediction of functionally related gene sets from proximity-based gene clusters or operons. [ABSTRACT FROM AUTHOR]

Background: Newly microarray technologies yield large-scale datasets. The microarray datasets are usually presented in 2D matrices, where rows represent genes and columns represent experimental conditions. Systematic analysis of those datasets provides the increasing amount of information, which is urgently needed in the postgenomic era. Biclustering, which is a technique developed to allow simultaneous clustering of rows and columns of a dataset, might be useful to extract more accurate information from those datasets. Biclustering requires the optimization of two conflicting objectives (residue and volume), and a multi-objective artificial immune system capable of performing a multi-population search. As a heuristic search technique, artificial immune systems (AISs) can be considered a new computational paradigm inspired by the immunological system of vertebrates and designed to solve a wide range of optimization problems. During biclustering several objectives in conflict with each other have to be optimized simultaneously, so multi-objective optimization model is suitable for solving biclustering problem. Results: Based on dynamic population, this paper proposes a novel dynamic multi-objective immune optimization biclustering (DMOIOB) algorithm to mine coherent patterns from microarray data. Experimental results on two common and public datasets of gene expression profiles show that our approach can effectively find significant localized structures related to sets of genes that show consistent expression patterns across subsets of experimental conditions. The mined patterns present a significant biological relevance in terms of related biological processes, components and molecular functions in a species-independent manner. Conclusions: The proposed DMOIOB algorithm is an efficient tool to analyze large microarray datasets. It achieves a good diversity and rapid convergence. [ABSTRACT FROM AUTHOR]

Background: It is always difficult to interpret microarray results. Recently, a handful of tools have been developed to meet this need, but almost none of them were designed to support agronomical species. Description: This paper presents EasyGO, a web server to perform Gene Ontology based functional interpretation on groups of genes or GeneChip probe sets. EasyGO makes a special contribution to the agronomical research community by supporting Affymetrix GeneChips of both crops and farm animals and by providing stronger capabilities for results visualization and user interaction. Currently it supports 11 agronomical plants, 3 farm animals, and the model plant Arabidopsis. The authors demonstrated EasyGO's ability to uncover hidden knowledge by analyzing a group of probe sets with similar expression profiles. Conclusion: EasyGO is a good tool for helping biologists and agricultural scientists to discover enriched biological knowledge that can provide solutions or suggestions for original problems. It is freely available to all users at http://bioinformatics.cau.edu.cn/easygo/. [ABSTRACT FROM AUTHOR]

Background: Sequencing variable regions of the 16S rRNA gene (≃300 bp) with Illumina technology is commonly used to study the composition of human microbiota. Unfortunately, short reads are unable to differentiate between highly similar species. Considering that species from the same genus can be associated with health or disease it is important to identify them at the lowest possible taxonomic rank. Third-generation sequencing platforms such as PacBio SMRT, increase read lengths allowing to sequence the whole gene with the maximum taxonomic resolution. Despite its potential, full length 16S rRNA gene sequencing is not widely used yet. The aim of the current study was to compare the sequencing output and taxonomic annotation performance of the two approaches (Illumina short read sequencing and PacBio long read sequencing of 16S rRNA gene) in different human microbiome samples. DNA from saliva, oral biofilms (subgingival plaque) and faeces of 9 volunteers was isolated. Regions V3-V4 and V1-V9 were amplified and sequenced by Illumina Miseq and by PacBio Sequel II sequencers, respectively. Results: With both platforms, a similar percentage of reads was assigned to the genus level (94.79% and 95.06% respectively) but with PacBio a higher proportion of reads were further assigned to the species level (55.23% vs 74.14%). Regarding overall bacterial composition, samples clustered by niche and not by sequencing platform. In addition, all genera with > 0.1% abundance were detected in both platforms for all types of samples. Although some genera such as Streptococcus tended to be observed at higher frequency in PacBio than in Illumina (20.14% vs 14.12% in saliva, 10.63% vs 6.59% in subgingival plaque biofilm samples) none of the differences were statistically significant when correcting for multiple testing. Conclusions: The results presented in the current manuscript suggest that samples sequenced using Illumina and PacBio are mostly comparable. Considering that PacBio reads were assigned at the species level with higher accuracy than Illumina, our data support the use of PacBio technology for future microbiome studies, although a higher cost is currently required to obtain an equivalent number of reads per sample. [ABSTRACT FROM AUTHOR]

Background: Larch is an important component of northern forests and a major cultivated tree species in restoration of forest cover using improved seed material. In recent years, the continuous low seed production has severely affected the production of improved variety seedlings and natural regeneration. However, research on the reproductive growth of gymnosperms is extremely scarce. Results: In this study, based on differential transcriptome analysis of two asexual reproductive phases, namely high-yield and low-yield, we further screened 5 ERF family genes that may affect the reproductive development of larch. We analyzed their genetic relationships and predicted their physicochemical properties. The expression patterns of these genes were analyzed in different tissues, developmental stages, hormone treatments, and environmental conditions in hybrid larch. Conclusion: The results showed that all 5 genes were induced by low temperature and ABA, and their expression patterns in different tissues suggested a suppressive role in the development of female cones in larch. Among them, LkoERF3-like1 and LkoERF071 may be involved in the flowering age pathway. This study enriches the scarce research on reproductive development in gymnosperms and provides a theoretical basis and research direction for regulating the reproductive development of larch in seed orchards. [ABSTRACT FROM AUTHOR]

Background: Tuber starch and steroidal glycoalkaloid (SGA)-related traits have been consistently prioritized in potato breeding, while allelic variation pattern of genes that underlie these traits is less explored. Results: Here, we focused on the genes involved in two important metabolic pathways in the potato: starch metabolism and SGA biosynthesis. We identified 119 genes consisting of 81 involved in starch metabolism and 38 in the biosynthesis of steroidal glycoalkaloids, and discovered 96,166 allelic variants among 2,169 gene haplotypes in six autotetraploid potato genomes. Comparative analyses revealed an uneven distribution of allelic variants among gene haplotypes and that the vast majority of deleterious mutations in these genes are retained in heterozygous state in the autotetraploid potato genomes. Leveraging full-length cDNA sequencing data, we find that approximately 70% of haplotypes of the 119 genes are transcribable. Population genetic analyses identify starch and SGA biosynthetic genes that are potentially conserved or diverged between potato varieties with varying starch or SGA content. Conclusions: These results deepen the understanding of haplotypic diversity within functionally important genes in autotetraploid genomes and may facilitate functional characterization of genes or haplotypes contributing to traits related to starch and SGA in potato. [ABSTRACT FROM AUTHOR]

Background: Nonspecific orbital inflammation (NSOI) is an idiopathic, persistent, and proliferative inflammatory condition affecting the orbit, characterized by polymorphous lymphoid infiltration. Its pathogenesis and progression have been linked to imbalances in tumor metabolic pathways, with glutamine (Gln) metabolism emerging as a critical aspect in cancer. Metabolic reprogramming is known to influence clinical outcomes in various malignancies. However, comprehensive research on glutamine metabolism's significance in NSOI is lacking. Methods: This study conducted a bioinformatics analysis to identify and validate potential glutamine-related molecules (GlnMgs) associated with NSOI. The discovery of GlnMgs involved the intersection of differential expression analysis with a set of 42 candidate GlnMgs. The biological functions and pathways of the identified GlnMgs were analyzed using GSEA and GSVA. Lasso regression and SVM-RFE methods identified hub genes and assessed the diagnostic efficacy of fourteen GlnMgs in NSOI. The correlation between hub GlnMgs and clinical characteristics was also examined. The expression levels of the fourteen GlnMgs were validated using datasets GSE58331 and GSE105149. Results: Fourteen GlnMgs related to NSOI were identified, including FTCD, CPS1, CTPS1, NAGS, DDAH2, PHGDH, GGT1, GCLM, GLUD1, ART4, AADAT, ASNSD1, SLC38A1, and GFPT2. Biological function analysis indicated their involvement in responses to extracellular stimulus, mitochondrial matrix, and lipid transport. The diagnostic performance of these GlnMgs in distinguishing NSOI showed promising results. Conclusions: This study successfully identified fourteen GlnMgs associated with NSOI, providing insights into potential novel biomarkers for NSOI and avenues for monitoring disease progression. [ABSTRACT FROM AUTHOR]

Background: Single-cell RNA sequencing (scRNA-seq) has been widely applied to dissect cellular heterogeneity in normal and diseased skin. Sebaceous glands, essential skin components with established functions in maintaining skin integrity and emerging roles in systemic energy metabolism, have been largely neglected in scRNA-seq studies. Methods: Departing from mouse and human skin scRNA-seq datasets, we identified gene sets expressed especially in sebaceous glands with the open-source R-package oposSOM. Results: The identified gene sets included sebaceous gland-typical genes as Scd3, Mgst1, Cidea, Awat2 and KRT7. Surprisingly, however, there was not a single overlap among the 100 highest, exclusively in sebaceous glands expressed transcripts in mouse and human samples. Notably, both species share a common core of only 25 transcripts, including mitochondrial and peroxisomal genes involved in fatty acid, amino acid, and glucose processing, thus highlighting the intense metabolic rate of this gland. Conclusions: This study highlights intrinsic differences in sebaceous lipid synthesis between mice and humans, and indicates an important role for peroxisomal processes in this context. Our data also provides attractive starting points for experimentally addressing novel candidates regulating sebaceous gland homeostasis. [ABSTRACT FROM AUTHOR]

Background: Cytochrome P450 monooxygenases (CYP450s) play a crucial role in various biochemical reactions involved in the synthesis of antioxidants, pigments, structural polymers, and defense-related compounds in plants. As sweet potato (Ipomoea batatas L.) holds significant economic importance, a comprehensive analysis of CYP450 genes in this plant species can offer valuable insights into the evolutionary relationships and functional characteristics of these genes. Results: In this study, we successfully identified and categorized 95 CYP450 genes from the sweet potato genome into 5 families and 31 subfamilies. The predicted subcellular localization results indicate that CYP450s are distributed in the cell membrane system. The promoter region of the IbCYP450 genes contains various cis-acting elements related to plant hormones and stress responses. In addition, ten conserved motifs (Motif1-Motif10) have been identified in the IbCYP450 family proteins, with 5 genes lacking introns and only one exon. We observed extensive duplication events within the CYP450 gene family, which may account for its expansion. The gene duplication analysis results showed the presence of 15 pairs of genes with tandem repeats. Interaction network analysis reveals that IbCYP450 families can interact with multiple target genes and there are protein-protein interactions within the family. Transcription factor interaction analysis suggests that IbCYP450 families interact with multiple transcription factors. Furthermore, gene expression analysis revealed tissue-specific expression patterns of CYP450 genes in sweet potatoes, as well as their response to abiotic stress and plant hormones. Notably, quantitative real-time polymerase chain reaction (qRT‒PCR) analysis indicated the involvement of CYP450 genes in the defense response against nonbiological stresses in sweet potatoes. Conclusions: These findings provide a foundation for further investigations aiming to elucidate the biological functions of CYP450 genes in sweet potatoes. [ABSTRACT FROM AUTHOR]

Background: Essential genes encode functions that play a vital role in the life activities of organisms, encompassing growth, development, immune system functioning, and cell structure maintenance. Conventional experimental techniques for identifying essential genes are resource-intensive and time-consuming, and the accuracy of current machine learning models needs further enhancement. Therefore, it is crucial to develop a robust computational model to accurately predict essential genes. Results: In this study, we introduce GCNN-SFM, a computational model for identifying essential genes in organisms, based on graph convolutional neural networks (GCNN). GCNN-SFM integrates a graph convolutional layer, a convolutional layer, and a fully connected layer to model and extract features from gene sequences of essential genes. Initially, the gene sequence is transformed into a feature map using coding techniques. Subsequently, a multi-layer GCN is employed to perform graph convolution operations, effectively capturing both local and global features of the gene sequence. Further feature extraction is performed, followed by integrating convolution and fully-connected layers to generate prediction results for essential genes. The gradient descent algorithm is utilized to iteratively update the cross-entropy loss function, thereby enhancing the accuracy of the prediction results. Meanwhile, model parameters are tuned to determine the optimal parameter combination that yields the best prediction performance during training. Conclusions: Experimental evaluation demonstrates that GCNN-SFM surpasses various advanced essential gene prediction models and achieves an average accuracy of 94.53%. This study presents a novel and effective approach for identifying essential genes, which has significant implications for biology and genomics research. [ABSTRACT FROM AUTHOR]

Glycoside hydrolase family 1 (GH1) β-glucosidases (BGLUs), are encoded by a large number of genes, which participate in the development and stress response of plants, particularly under biotic and abiotic stresses through the activation of phytohormones. However, there are few studies systematically analyzing stress or hormone-responsive BGLU genes in alfalfa. In this study, a total of 179 BGLU genes of the glycoside hydrolase family 1 were identified in the genome of alfalfa, and then were classified into five distinct clusters. Sequence alignments revealed several conserved and unique motifs among these MsBGLU proteins. Many cis-acting elements related to abiotic stresses and phytohormones were identified in the promoter of some MsBGLUs. Moreover, RNA-seq and RT-qPCR analyses showed that these MsBGLU genes exhibited distinct expression patterns in response to different abiotic stress and hormonal treatments. In summary, this study suggests that MsBGLU genes play crucial roles in response to various abiotic stresses and hormonal responses, and provides candidate genes for stress tolerance breeding in alfalfa. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via the original article. [ABSTRACT FROM AUTHOR]

Background: Root-knot nematode Meloidogyne graminicola has emerged as a major threat in rice agroecosystems owing to climate change-induced changes in cultivation practices. Synthetic nematicides are continually being withdrawn from the nematode management toolbox because of their ill effects on the environment. A sustainable strategy would be to develop novel nematicides or resistant plants that would target nematode sensory perception, which is a key step in the host finding biology of plant-parasitic nematodes (PPNs). However, compared to the extensive literature on the free-living nematode Caenorhabditis elegans, negligible research has been performed on PPN chemosensory biology. Results: The present study characterizes the five chemosensory genes (Mg-odr-7, Mg-tax-4, Mg-tax-4.1, Mg-osm-9, and Mg-ocr-2) from M. graminicola that are putatively associated with nematode host-finding biology. All the genes were highly transcribed in the early life stages, and RNA interference (RNAi)-induced downregulation of each candidate gene perturbed the normal behavioural phenotypes of M. graminicola, as determined by examining the tracking pattern of juveniles on Pluronic gel medium, attraction to and penetration in rice root tip, and developmental progression in rice root. In addition, a detrimental effect on nematode chemotaxis towards different volatile and nonvolatile organic compounds and host root exudates was documented. Conclusion: Our findings enrich the existing literature on PPN chemosensory biology and can supplement future research aimed at identifying a comprehensive chemosensory signal transduction pathway in PPNs. [ABSTRACT FROM AUTHOR]

Background: There has been a gradual increase in the occurrence of cardiovascular and cerebrovascular ischemic diseases, particularly as comorbidities. Yet, the mechanisms underlying these diseases remain unclear. Ferroptosis has emerged as a potential contributor to cardio-cerebral ischemic processes. Therefore, this study investigated the shared biological mechanisms between the two processes, as well as the role of ferroptosis genes in cardio-cerebral ischemic damage, by constructing co-expression modules for myocardial ischemia (MI) and ischemic stroke (IS) and a network of protein–protein interactions, mRNA-miRNA, mRNA-transcription factors (TFs), mRNA-RNA-binding proteins (RBPs), and mRNA-drug interactions. Results: The study identified seven key genes, specifically ACSL1, TLR4, ADIPOR1, G0S2, PDK4, HP, PTGS2, and subjected them to functional enrichment analysis during ischemia. The predicted miRNAs were found to interact with 35 hub genes, and interactions were observed between 11 hub genes and 30 TF transcription factors. Additionally, 10 RBPs corresponding to 16 hub genes and 163 molecular compounds corresponding to 30 hub genes were identified. This study also clarified the levels of immune infiltration between MI and IS and different subtypes. Finally, we identified four hub genes, including TLR4, by using a diagnostic model constructed by Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis; ADIPOR1, G0S2, and HP were shown to have diagnostic value for the co-pathogenesis of MI and cerebral ischemia by both validation test data and RT-qPCR assay. Conclusions: To the best our knowledge, this study is the first to utilize multiple algorithms to comprehensively analyze the biological processes of MI and IS from various perspectives. The four hub genes, TLR4, ADIPOR1, G0S2, and HP, have proven valuable in offering insights for the investigation of shared injury pathways in cardio-cerebral injuries. Therefore, these genes may serve as diagnostic markers for cardio-cerebral ischemic diseases. [ABSTRACT FROM AUTHOR]

Gene similarity networks play important role in unraveling the intricate associations within diverse cancer types. Conventionally, gauging the similarity between genes has been approached through experimental methodologies involving chemical and molecular analyses, or through the lens of mathematical techniques. However, in our work, we have pioneered a distinctive mathematical framework, one rooted in the co-occurrence of attribute values and single point mutations, thereby establishing a novel approach for quantifying the dissimilarity or similarity among genes. Central to our approach is the recognition of mutations as key players in the evolutionary trajectory of cancer. Anchored in this understanding, our methodology hinges on the consideration of two categorical attributes: mutation type and nucleotide change. These attributes are pivotal, as they encapsulate the critical variations that can precipitate substantial changes in gene behavior and ultimately influence disease progression. Our study takes on the challenge of formulating similarity measures that are intrinsic to genes' categorical data. Taking into account the co-occurrence probability of attribute values within single point mutations, our innovative mathematical approach surpasses the boundaries of conventional methods. We thereby provide a robust and comprehensive means to assess gene similarity and take a significant step forward in refining the tools available for uncovering the subtle yet impactful associations within the complex realm of gene interactions in cancer. [ABSTRACT FROM AUTHOR]

Background: Epilepsy is a prevalent neurological disorder, and while its precise mechanism remains elusive, a connection to ferroptosis has been established. This study investigates the potential clinical diagnostic significance of ferroptosis-related genes (FRGs) during the acute phase of temporal lobe epilepsy. Methods: To identify differentially expressed genes (DEGs), we accessed data from the GEO database and performed an intersection analysis with the FerrDB database to pinpoint FRGs. A protein-protein interaction (PPI) network was constructed. To assess the diagnostic utility of the discovered feature genes for the disease, ROC curve analysis was conducted. Subsequently, qRT-PCR was employed to validate the expression levels of these feature genes. Results: This study identified a total of 25 FRGs. PPI network analysis revealed six feature genes: IL6, PTGS2, HMOX1, NFE2L2, TLR4, and JUN. ROC curve analysis demonstrated that the combination of these six feature genes exhibited the highest diagnostic potential. qRT-PCR validation confirmed the expression of these feature genes. Conclusion: We have identified six feature genes (IL6, PTGS2, HMOX1, NFE2L2, TLR4, and JUN) strongly associated with ferroptosis in epilepsy, suggesting their potential as biomarkers for the diagnosis of temporal lobe epilepsy. [ABSTRACT FROM AUTHOR]

Background: The names of genes are central in describing their function and relationship. However, gene symbols are often a subject of controversy. In addition, the discovery of mammalian genes is now so rapid that a proper use of gene symbol nomenclature rules tends to be overlooked. This is currently the situation in the rat and there is a need for a cohesive and unifying overview of all rat gene symbols in use. Based on the experiences in rat gene symbol curation that we have gained from running the "Ratmap" rat genome database, we have now developed a database that unifies different rat gene naming attempts with the accepted rat gene symbol nomenclature rules., Description: This paper presents a newly developed database known as RGST (Rat Gene Symbol Tracker). The database contains rat gene symbols from three major sources: the Rat Genome Database (RGD), Ensembl, and NCBI-Gene. All rat symbols are compared with official symbols from orthologous human genes as specified by the Human Gene Nomenclature Committee (HGNC). Based on the outcome of the comparisons, a rat gene symbol may be selected. Rat symbols that do not match a human ortholog undergo a strict procedure of comparisons between the different rat gene sources as well as with the Mouse Genome Database (MGD). For each rat gene this procedure results in an unambiguous gene designation. The designation is presented as a status level that accompanies every rat gene symbol suggested in the database. The status level describes both how a rat symbol was selected, and its validity., Conclusion: This database fulfils the important need of unifying rat gene symbols into an automatic and cohesive nomenclature system. The RGST database is available directly from the RatMap home page: http://ratmap.org.

Background: High-throughput RNA interference (RNAi) screens have been used to find genes that, when silenced, result in sensitivity to certain chemotherapy drugs. Researchers therefore can further identify drug-sensitive targets and novel drug combinations that sensitize cancer cells to chemotherapeutic drugs. Considerable uncertainty exists about the efficiency and accuracy of statistical approaches used for RNAi hit selection in drug sensitivity studies. Researchers require statistical methods suitable for analyzing high-throughput RNAi screening data that will reduce false-positive and false-negative rates. Results: In this study, we carried out a simulation study to evaluate four types of statistical approaches (foldchange/ ratio, parametric tests/statistics, sensitivity index, and linear models) with different scenarios of RNAi screenings for drug sensitivity studies. With the simulated datasets, the linear model resulted in significantly lower false-negative and false-positive rates. Based on the results of the simulation study, we then make recommendations of statistical analysis methods for high-throughput RNAi screening data in different scenarios. We assessed promising methods using real data from a loss-of-function RNAi screen to identify hits that modulate paclitaxel sensitivity in breast cancer cells. High-confidence hits with specific inhibitors were further analyzed for their ability to inhibit breast cancer cell growth. Our analysis identified a number of gene targets with inhibitors known to enhance paclitaxel sensitivity, suggesting other genes identified may merit further investigation. Conclusions: RNAi screening can identify druggable targets and novel drug combinations that can sensitize cancer cells to chemotherapeutic drugs. However, applying an inappropriate statistical method or model to the RNAi screening data will result in decreased power to detect the true hits and increase false positive and false negative rates, leading researchers to draw incorrect conclusions. In this paper, we make recommendations to enable more objective selection of statistical analysis methods for high-throughput RNAi screening data. [ABSTRACT FROM AUTHOR]

Background: One method to understand and evaluate an experiment that produces a large set of genes, such as a gene expression microarray analysis, is to identify overrepresentation or enrichment for biological pathways. Because pathways are able to functionally describe the set of genes, much effort has been made to collect curated biological pathways into publicly accessible databases. When combining disparate databases, highly related or redundant pathways exist, making their consolidation into pathway concepts essential. This will facilitate unbiased, comprehensive yet streamlined analysis of experiments that result in large gene sets. Methods: After gene set enrichment finds representative pathways for large gene sets, pathways are consolidated into representative pathway concepts. Three complementary, but different methods of pathway consolidation are explored. Enrichment Consolidation combines the set of the pathways enriched for the signature gene list through iterative combining of enriched pathways with other pathways with similar signature gene sets; Weighted Consolidation utilizes a Protein-Protein Interaction network based gene-weighting approach that finds clusters of both enriched and non-enriched pathways limited to the experiments' resultant gene list; and finally the de novo Consolidation method uses several measurements of pathway similarity, that finds static pathway clusters independent of any given experiment. Results: We demonstrate that the three consolidation methods provide unified yet different functional insights of a resultant gene set derived from a genome-wide profiling experiment. Results from the methods are presented, demonstrating their applications in biological studies and comparing with a pathway web-based framework that also combines several pathway databases. Additionally a web-based consolidation framework that encompasses all three methods discussed in this paper, Pathway Distiller (http://cbbiweb.uthscsa.edu/PathwayDistiller), is established to allow researchers access to the methods and example microarray data described in this manuscript, and the ability to analyze their own gene list by using our unique consolidation methods. Conclusions: By combining several pathway systems, implementing different, but complementary pathway consolidation methods, and providing a user-friendly web-accessible tool, we have enabled users the ability to extract functional explanations of their genome wide experiments [ABSTRACT FROM AUTHOR]

Background: In the biomedical domain, there are immense data and tremendous increase of genomics and biomedical relevant publications. The wealth of information has led to an increasing amount of interest in and need for applying information retrieval techniques to access the scientific literature in genomics and related biomedical disciplines. In many cases, the desired information of a query asked by biologists is a list of a certain type of entities covering different aspects that are related to the question, such as cells, genes, diseases, proteins, mutations, etc. Hence, it is important of a biomedical IR system to be able to provide relevant and diverse answers to fulfill biologists' information needs. However traditional IR model only concerns with the relevance between retrieved documents and user query, but does not take redundancy between retrieved documents into account. This will lead to high redundancy and low diversity in the retrieval ranked lists. Results: In this paper, we propose an approach which employs a topic generative model called Latent Dirichlet Allocation (LDA) to promoting ranking diversity for biomedical information retrieval. Different from other approaches or models which consider aspects on word level, our approach assumes that aspects should be identified by the topics of retrieved documents. We present LDA model to discover topic distribution of retrieval passages and word distribution of each topic dimension, and then re-rank retrieval results with topic distribution similarity between passages based on N-size slide window. We perform our approach on TREC 2007 Genomics collection and two distinctive IR baseline runs, which can achieve 8% improvement over the highest Aspect MAP reported in TREC 2007 Genomics track. Conclusions: The proposed method is the first study of adopting topic model to genomics information retrieval, and demonstrates its effectiveness in promoting ranking diversity as well as in improving relevance of ranked lists of genomics search. Moreover, we proposes a distance measure to quantify how much a passage can increase topical diversity by considering both topical importance and topical coefficient by LDA, and the distance measure is a modified Euclidean distance. [ABSTRACT FROM AUTHOR]

Background: Bayesian networks (BNs) have been widely used to estimate gene regulatory networks. Many BN methods have been developed to estimate networks from microarray data. However, two serious problems reduce the effectiveness of current BN methods. The first problem is that BN-based methods require huge computational time to estimate large-scale networks. The second is that the estimated network cannot have cyclic structures, even if the actual network has such structures. Results: In this paper, we present a novel BN-based deterministic method with reduced computational time that allows cyclic structures. Our approach generates all the combinational triplets of genes, estimates networks of the triplets by BN, and unites the networks into a single network containing all genes. This method decreases the search space of predicting gene regulatory networks without degrading the solution accuracy compared with the greedy hill climbing (GHC) method. The order of computational time is the cube of number of genes. In addition, the network estimated by our method can include cyclic structures. Conclusions: We verified the effectiveness of the proposed method for all known gene regulatory networks and their expression profiles. The results demonstrate that this approach can predict regulatory networks with reduced computational time without degrading the solution accuracy compared with the GHC method [ABSTRACT FROM AUTHOR]

Background: Head-to-head (h2h) genes are prone to have association in expression and in functionality and have been shown conserved in evolution. Currently there are many studies on such h2h gene pairs. We found that the previous studies extremely focused on human genome. Furthermore, they only focused on analyses that require only gene or protein sequences but not conducted a systematic investigation on other promoter features such as the binding evidence of specific transcription factors (TFs). This is mainly because of the incomplete resources of higher organisms, though they are relatively of interest, than model organisms such as Saccharomyces cerevisiae. The authors of this study recently integrated nine promoter features of 6603 genes of S. cerevisiae from six databases and five papers. These resources are suitable to conduct a comprehensive analysis of h2h genes in S. cerevisiae. Results: This study analyzed various promoter features, including transcription boundaries (TSS, 5'UTR and 3'UTR), TATA box, TF binding evidence, TF regulation evidence, DNA bendability and nucleosome occupancy. The expression profiles and gene ontology (GO) annotations were used to measure if two genes are associated. Based on these promoter features, we found that i) the frequency of h2h genes was close to the expectation, namely they were not relatively frequent in genome; ii) the distance between the TSSs of most h2h genes fell into the range of 0-600 bps and was more centralized in 0-200 bps of the highly associated ones; iii) the number of TFs that regulate both h2h genes influenced the co-expression and co-function of the genes, while the number of TFs that bind both h2h genes influenced only the co-expression of the genes; iv) the association of two h2h genes was influenced by the existence of specific TFs such as STP2; v) the association of h2h genes whose bidirectional promoters have no TATA box was slightly higher than those who have TATA boxes; vi) the association of two h2h genes was not influenced by the DNA bendability and nucleosome occupancy. Conclusions: This study analyzed h2h genes with various promoter features that have not been used in analyzing h2h genes. The results can be applied to other genomes to confirm if the observations of this study are limited to S. cerevisiae or universal in most organisms [ABSTRACT FROM AUTHOR]

Background: Driven essentially by random genetic drift, subfunctionalization has been identified as a possible non-adaptive mechanism for the retention of duplicate genes in small-population species, where widespread deleterious mutations are likely to cause complementary loss of subfunctions across gene copies. Through subfunctionalization, duplicates become indispensable to maintain the functional requirements of the ancestral locus. Yet, gene duplication produces a dosage imbalance in the encoded proteins and thus, as investigated in this paper, subfunctionalization must be subject to the selective forces arising from the fitness bottleneck introduced by the duplication event. Results: We show that, while arising from random drift, subfunctionalization must be inescapably subject to selective forces, since the diversification of expression patterns across paralogs mitigates duplication-related dosage imbalances in the concentrations of encoded proteins. Dosage imbalance effects become paramount when proteins rely on obligatory associations to maintain their structural integrity, and are expected to be weaker when protein complexation is ephemeral or adventitious. To establish the buffering effect of subfunctionalization on selection pressure, we determine the packing quality of encoded proteins, an established indicator of dosage sensitivity, and correlate this parameter with the extent of paralog segregation in humans, using species with larger population -and more efficient selection- as controls. Conclusions: Recognizing the role of subfunctionalization as a dosage-imbalance buffer in gene duplication events enabled us to reconcile its mechanistic nonadaptive origin with its adaptive role as an enabler of the evolution of genetic redundancy. This constructive role was established in this paper by proving the following assertion: If subfunctionalization is indeed adaptive, its effect on paralog segregation should scale with the dosage sensitivity of the duplicated genes. Thus, subfunctionalization becomes adaptive in response to the selection forces arising from the fitness bottleneck imposed by gene duplication. [ABSTRACT FROM AUTHOR]

Background: Research using the Pacific oyster Crassostrea gigas as a model organism has experienced rapid growth in recent years due to the development of high-throughput molecular technologies. As many as 56,268 EST sequences have been sequenced to date, representing a genome-wide resource that can be used for transcriptomic investigations. Results: In this paper, we developed a Pacific oyster microarray containing oligonucleotides representing 31,918 transcribed sequences selected from the publicly accessible GigasDatabase. This newly designed microarray was used to study the transcriptome of male and female gonads, mantle, gills, posterior adductor muscle, visceral ganglia, hemocytes, labial palps and digestive gland. Statistical analyses identified genes differentially expressed among tissues and clusters of tissue-enriched genes. These genes reflect major tissue-specific functions at the molecular level, such as tissue formation in the mantle, filtering in the gills and labial palps, and reproduction in the gonads. Hierarchical clustering predicted the involvement of unannotated genes in specific functional pathways such as the insulin/NPY pathway, an important pathway under study in our model species. Microarray data also accurately identified reference genes whose mRNA level appeared stable across all the analyzed tissues. Adp-ribosylation factor 1 (arf1) appeared to be the most robust reference for normalizing gene expression data across different tissues and is therefore proposed as a relevant reference gene for further gene expression analysis in the Pacific oyster. Conclusions: This study provides a new transcriptomic tool for studies of oyster biology, which will help in the annotation of its genome and which identifies candidate reference genes for gene expression analysis. [ABSTRACT FROM AUTHOR]

Background: Doubly Uniparental Inheritance (DUI) is a fascinating exception to matrilinear inheritance of mitochondrial DNA (mtDNA). Species with DUI are characterized by two distinct mtDNAs that are inherited either through females (F-mtDNA) or through males (M-mtDNA). DUI sex-linked mitochondrial genomes share several unusual features, such as additional protein coding genes and unusual gene duplications/structures, which have been related to the functionality of DUI. Recently, new evidence for DUI was found in the mytilid bivalve Musculista senhousia. This paper describes the complete sex-linked mitochondrial genomes of this species. Results: Our analysis highlights that both M and F mtDNAs share roughly the same gene content and order, but with some remarkable differences. The Musculista sex-linked mtDNAs have differently organized putative control regions (CR), which include repeats and palindromic motifs, thought to provide sites for DNA-binding proteins involved in the transcriptional machinery. Moreover, in male mtDNA, two cox2 genes were found, one (M-cox2b) 123bp longer. Conclusions: The complete mtDNA genome characterization of DUI bivalves is the first step to unravel the complex genetic signals allowing Doubly Uniparental Inheritance, and the evolutionary implications of such an unusual transmission route in mitochondrial genome evolution in Bivalvia. The observed redundancy of the palindromic motifs in Musculista M-mtDNA may have a role on the process by which sperm mtDNA becomes dominant or exclusive of the male germline of DUI species. Moreover, the duplicated M-COX2b gene may have a different, still unknown, function related to DUI, in accordance to what has been already proposed for other DUI species in which a similar cox2 extension has been hypothesized to be a tag for male mitochondria. [ABSTRACT FROM AUTHOR]

Background: In order to carry out experimental gene annotation, DNA encoding open reading frames (ORFs) derived from real genes (termed "genic") in the correct frame is required. When genes are correctly assigned, isolation of genic DNA for functional annotation can be carried out by PCR. However, not all genes are correctly assigned, and even when correctly assigned, gene products are often incorrectly folded when expressed in heterologous hosts. This is a problem that can sometimes be overcome by the expression of protein fragments encoding domains, rather than full-length proteins. One possible method to isolate DNA encoding such domains would to "filter" complex DNA (cDNA libraries, genomic and metagenomic DNA) for gene fragments that confer a selectable phenotype relying on correct folding, with all such domains present in a complex DNA sample, termed the "domainome". Results: In this paper we discuss the preparation of diverse genic ORF libraries from randomly fragmented genomic DNA using ß-lactamase to filter out the open reading frames. By cloning DNA fragments between leader sequences and the mature ß-lactamase gene, colonies can be selected for resistance to ampicillin, conferred by correct folding of the lactamase gene. Our experiments demonstrate that the majority of surviving colonies contain genic open reading frames, suggesting that ß-lactamase is acting as a selectable folding reporter. Furthermore, different leaders (Sec, TAT and SRP), normally translocating different protein classes, filter different genic fragment subsets, indicating that their use increases the fraction of the "domainone" that is accessible. Conclusions: The availability of ORF libraries, obtained with the filtering method described here, combined with screening methods such as phage display and protein-protein interaction studies, or with protein structure determination projects, can lead to the identification and structural determination of functional genic ORFs. ORF libraries represent, moreover, a useful tool to proceed towards high-throughput functional annotation of newly sequenced genomes. [ABSTRACT FROM AUTHOR]

Background: Bacterial artificial chromosome (BAC) libraries continue to be invaluable tools for the genomic analysis of complex organisms. Complemented by the newly and fast growing deep sequencing technologies, they provide an excellent source of information in genomics projects. Results: Here, we report the construction and characterization of the CHORI-231 BAC library constructed from a Danish-farmed, male American mink (Neovison vison). The library contains approximately 165,888 clones with an average insert size of 170 kb, representing approximately 10-fold coverage. High-density filters, each consisting of 18,432 clones spotted in duplicate, have been produced for hybridization screening and are publicly available. Overgo probes derived from expressed sequence tags (ESTs), representing 21 candidate genes for traits important for the mink industry, were used to screen the BAC library. These included candidate genes for coat coloring, hair growth and length, coarseness, and some receptors potentially involved in viral diseases in mink. The extensive screening yielded positive results for 19 of these genes. Thirty-five clones corresponding to 19 genes were sequenced using 454 Roche, and large contigs (184 kb in average) were assembled. Knowing the complete sequences of these candidate genes will enable confirmation of the association with a phenotype and the finding of causative mutations for the targeted phenotypes. Additionally, 1577 BAC clones were end sequenced; 2505 BAC end sequences (80% of BACs) were obtained. An excess of 2 Mb has been analyzed, thus giving a snapshot of the mink genome. Conclusions: The availability of the CHORI-321 American mink BAC library will aid in identification of genes and genomic regions of interest. We have demonstrated how the library can be used to identify specific genes of interest, develop genetic markers, and for BAC end sequencing and deep sequencing of selected clones. To our knowledge, this is the first report of 454 sequencing of selected BAC clones in mammals and re-assures the suitability of this technique for obtaining the sequence information of genes of interest in small genomics projects. The BAC end sequences described in this paper have been deposited in the GenBank data library [HN339419- HN341884, HN604664-HN604702]. The 454 produced contigs derived from selected clones are deposited with reference numbers [GenBank: JF288166-JF288183 &JF310744]. [ABSTRACT FROM AUTHOR]