NCBI Dataset Research Articles

NCBI taxonomy: enhanced access via NCBI datasets.

The NCBI Taxonomy resource (https://www.ncbi.nlm.nih.gov/taxonomy) has long been a trusted, curated hub for organism names, classifications, and links to related data for all taxonomic nodes. NCBI Datasets (https://www.ncbi.nlm.nih.gov/datasets/) is an improved way to leverage the rich data available at NCBI so users can effectively browse, search, and download information. While taxonomy data has been a cornerstone of NCBI Datasets since its inception, we recently extended the taxonomy information available via NCBI Datasets by updating the existing NCBI Datasets taxonomy page, implementing a new taxonomy name details page, expanding programmatic access to taxonomic information via command-line tools and APIs and improving the way we handle taxonomic queries to connect users to gene and genome data. This paper highlights these improvements and provides examples to help users effectively harness these new features.

Long non-coding ribonucleic acid SNHG18 induced human granulosa cell apoptosis via disruption of glycolysis in ovarian aging

BackgroundIn-depth understanding of dynamic expression profiles of human granulosa cells (GCs) during follicular development will contribute to the diagnostic and targeted interventions for female infertility. However, genome-scale analysis of long non-coding ribonucleic acid (lncRNA) in GCs across diverse developmental stages is challenging. Meanwhile, further research is needed to determine how aberrant lncRNA expression participates in ovarian diseases.MethodsGranulosa cell-related lncRNAs data spanning five follicular development stages were retrieved and filtered from the NCBI dataset (GSE107746). Stage-specific lncRNA expression patterns and mRNA-lncRNA co-expression networks were identified with bioinformatic approaches. Subsequently, the expression pattern of SNHG18 was detected in GCs during ovarian aging. And SNHG18 siRNA or overexpression plasmids were transfected to SVOG cells in examining the regulatory roles of SNHG18 in GC proliferation and apoptosis. Moreover, whether PKCɛ/SNHG18 signaling take part in GC glycolysis via ENO1 were verified in SVOG cells.ResultsWe demonstrated that GC-related lncRNAs were specifically expressed across different developmental stages, and coordinated crucial biological functions like mitotic cell cycle and metabolic processes in the folliculogenesis. Thereafter, we noticed a strong correlation of PRKCE and SNHG18 expression in our analysis. With downregulated SNHG18 of GCs identified in the context of ovarian aging, SNHG18 knockdown could further induce cell apoptosis, retard cell proliferation and exacerbate DNA damage in SVOG cell. Moreover, downregulated PKCɛ/SNHG18 pathway interrupted the SVOG cell glycolysis by lowering the ENO1 expression.ConclusionsAltogether, our results revealed that folliculogenesis-related lncRNA SNHG18 participated in the pathogenesis of ovarian aging, which may provide novel biomarkers for ovarian function and new insights for the infertility treatment.

Whole genome–based comparative analysis of the genus Streptomyces reveals many misclassifications

Streptomyces species are experts in the production of bioactive secondary metabolites; however, their taxonomy has fallen victim of the tremendous interest shown by the scientific community, evident in the discovery of numerous synonymous in public repositories. Based on genomic data from NCBI Datasets and nomenclature from the LPSN database, we compiled a dataset of 600 Streptomyces species along with their annotations and metadata. To pinpoint the most suitable taxonomic classification method, we conducted a comprehensive assessment comparing multiple methodologies, including analysis of 16S rRNA, individual housekeeping genes, multilocus sequence analysis (MLSA), and Fast Average Nucleotide Identity (FastANI) on a subset of 409 species with complete data. Due to insufficient resolution of 16S rRNA and inconsistency observed in individual housekeeping genes, we performed a more in-depth analysis, comparing only FastANI and MLSA, which expanded our dataset to include 502 species. With FastANI validated as the preferred method, we conducted pairwise analysis on the entire dataset identifying 59 non-unique species among the 600, and subsequently refined the dataset to 541 unique species. Additionally, we collected data on 724 uncharacterized Streptomyces strains to investigate the uniqueness potential of the unannotated fraction of the Streptomyces genus. Utilizing FastANI, 289 strains could be successfully classified into one of the 541 Streptomyces species.Key points• Evaluation of taxonomic classification methods for Streptomyces species.• Whole genome analysis, specifically FastANI, has been chosen as preferred method.• Various reclassifications are proposed within the Streptomyces genus.Graphical abstract

Biomarkers in Alzheimer’s disease progression: a longitudinal cohort study of NPTX2, GRIA1, and GRIA4

Early diagnosis and intervention are vital for slowing Alzheimer’s disease progression and improving the quality of life in older people. Mild cognitive impairment, an early stage of Alzheimer’s disease, offers a key opportunity for research and intervention. However, current diagnostic methods are typically applied after significant symptoms appear, limiting the effectiveness of early intervention. The data of middle-aged and older individuals collected from the publicly available NCBI datasets GSE5281 and GSE1297 were included in this longitudinal cohort study. The cognitive status changes in participants over time were assessed using standard cognitive assessment tools, such as the Mini-Mental State Examination, along with mild cognitive impairment-specific tools. There was a significant negative correlation between Mini-Mental State Examination scores and the expression levels of neuronal pentraxin 2, glutamate receptor ionotropic AMPA 1 and 4. These results suggest that higher expression levels of these genes are associated with more severe cognitive impairment, highlighting their potential as biomarkers for the early detection of mild cognitive impairment. This study provides new insights into the molecular mechanisms underlying cognitive decline in older people and suggests directions for future research.

Regulation of Alternative Splicing of Lipid Metabolism Genes in Sepsis-Induced Liver Damage by RNA-Binding Proteins.

RNA binding proteins (RBPs) have the potential for transcriptional regulation in sepsis-induced liver injury, but precise functions remain unclear. Our aim is to conduct a genome-wide expression analysis of RBPs and illuminate changes in the regulation of alternative splicing in sepsis-induced liver injury. RNA-seq data on "sepsis and liver" from the publicly available NCBI data set was analyzed, and differentially expressed RBPs and alternative splicing events (ASEs) in the healthy and septic liver were identified. Co-expression analyses of sepsis-regulated RBPs and ASEs were performed. Models of sepsis were established to validate hepatic RBP gene expression patterns with different treatments. Pairwise analysis of gene expression profiles of sham, cecum ligation puncture (CLP), and CLP with dichloroacetate (CLPDCA) mice allowed 1208 differentially expressed genes (DEGs), of which 800 were up-regulated and 408 down-regulated, to be identified. DEGs were similar in both Sham and CLPDCA mice. TheKEGG analysis showed that up-regulated genes as being involved in cytokine-cytokine receptor interaction and IL-17 signaling pathway and down-regulated genes in metabolic pathways. Differences in lipid metabolism-related alternative splicing events, including A3SS, were also found in CLP and CLPDCA compared with sham mice. Thirty-seven RBPs, including S100a11, Ads2, Fndc3b, Fn1, Ddx28, Car2, Cisd1, and Ptms, were differentially expressed in CLP mice and the regulated alternative splicing genes(RASG) with the RBPshown to be enriched in lipid metabolic and oxidation-reduction-related processes by GO functional analysis.In KEEG analysis the RASG mainly enriched in metabolic pathway. The models of sepsis were constructed with different treatment groups, and S100a11 expression in the CLP group found to be higher than in the sham group, a change that was reversed by DCA. The alternative splicing ratio of Srebf1 and Cers2 decreased compared with the sham group increased after DCA treatment. Abnormal profiles of gene expression and alternative splicing were associated with sepsis-induced liver injury. Unusual expression of RBPs, such as S100a11, may regulate alternative splicing of lipid metabolism-associated genes, such as Srebf1 and Cers2, in the septic liver. RBPs may constitute potential treatment targets for sepsis-induced liver injury.

Identification and characterization of multiple novel picornaviruses in fecal samples of bar-headed goose.

The bar-headed goose (Anser indicus), one of the most well-known high-altitude birds, is renowned for its adaptation to high-altitude environments. Previous studies have shown that they can be infected with highly pathogenic avian influenza; however, there is currently limited research on other viruses in bar-headed geese. In this study, 10 fecal samples of healthy bar-headed geese were collected, and viral metagenomics method was conducted to identify novel picornaviruses. Seven novel picornaviruses were identified in the fecal samples of bar-headed geese. Most of these picornaviruses were genetically different from other currently known viruses in the NCBI dataset. Among them, PICV4 was determined to be a new species belonging to the Anativirus genus, PICV5 and PICV13 were classified as novel species belonging to the Hepatovirus genus, and the remaining four picornaviruses (PICV1, PICV19, PICV21, and PICV22) were identified as part of the Megrivirus A species of the Megrivirus genus. Recombinant analysis indicates that PICV21 was a potential recombinant, and the major and minor parents were PICV1 and PICV22, respectively. The findings of this study increase our understanding of the diversity of picornaviruses in bar-headed geese and provide practical viral genome information for the prevention and treatment of potential viral diseases affecting this species.

GenBank 2024 Update.

GenBank® (https://www.ncbi.nlm.nih.gov/genbank/) is a comprehensive, public database that contains 25 trillion base pairs from over 3.7 billion nucleotide sequences for 557 000 formally described species. Daily data exchange with the European Nucleotide Archive (ENA) and the DNA Data Bank of Japan (DDBJ) ensures worldwide coverage. Recent updates include policies for including spatio-temporal metadata, clarified documentation for GenBank data processing, enhanced foreign contamination screening tools, new processes in the Submission Portal, migration of Entrez Genome and Assembly displays into NCBI Datasets, and the impending retirement of tbl2asn, replaced by table2asn.

DETECTION OF EXON AND INTRON REGIONS IN DNA SEQUENCES BY THE PROPOSED HASHING FUNCTION

Chromosomes, which are formed by the combination of DNA and special proteins, are structures that can show some changes with the effect of genetic or environmental factors. The DNA molecule in these structures carries vital information in elucidating critical information about life. DNA, which is formed by the combination of sugar, phosphate and organic bases, has exon and intron regions separation. Information about the processes in the life cycle of cells, the changes experienced by stem cells, the regulations in the growth and development stage, the development status of cancer, mutation occurrences and protein synthesis are stored in exon regions. Distinguishing exon regions that form 3% of a cell's DNA is challenging. However, detecting diseases on genetically based facts offers more precise outputs. For this reason, analyses were made on the BCR-ABL gene and BRCA-1 mutation carrier genes to analyse leukemia and breast cancer, which are genetically based diseases. First, these genes obtained from the NCBI gene bank were digitized by integer mapping technique. The digitized sequences were given as input to the hash function. This proposed hash function consists of the steps of finding the logarithmic equivalent of the total number of digitized organic bases, summing all logarithmic equivalents, rounding to the nearest integer, expressing it in binary and placing it in the hash table. These outputs, which define the exon and intron regions, were shown as clusters to find the new input region easily. The collision cluster is the binary representation of key values representing both exon and intron regions for the same region. The main goal is to have a small number of elements in this cluster. With the proposed hierarchy in this study, only one collision occurred for BCR-ABL and BRCA-1 genes. Accuracy rates of the proposed approach based on a mathematical basis and independent of nucleotide length were obtained 93.33%, and 96%, respectively.

The Genetic Cross-Talk between Periodontitis and Chronic Kidney Failure Revealed by Transcriptomic Analysis.

Periodontitis and chronic kidney failure (CKF) are potentially related to each other. This bioinformatics analysis aimed at the identification of potential cross-talk genes and related pathways between periodontitis and CKF. Based on NCBI Gene Expression Omnibus (GEO), datasets GSE10334, GSE16134, and GSE23586 were extracted for periodontitis. A differential expression analysis (p < 0.05, |log2(FC)| > 0.5) was performed to assess deregulated genes (DEGs). CKF-related genes were extracted from DisGeNET and examined regarding their overlap with periodontitis-related DEGs. Cytoscape was used to construct and analyze a protein-protein interaction (PPI) network. Based on Cytoscape plugin MCODE and a LASSO regression analysis, the potential hub cross-talk genes were identified. Finally, a complex PPI of the hub genes was constructed. A total of 489 DEGs for periodontitis were revealed. With the 805 CKF-related genes, an overlap of 47 cross-talk genes was found. The PPI network of the potential cross-talk genes was composed of 1081 nodes and 1191 edges. The analysis with MCODE resulted in 10 potential hub genes, while the LASSO regression resulted in 22. Finally, five hub cross-talk genes, CCL5, FCGR3B, MMP-9, SAA1, and SELL, were identified. Those genes were significantly upregulated in diseased samples compared to controls (p ≤ 0.01). Furthermore, ROC analysis showed a high predictive value of those genes (AUC ≥ 73.44%). Potentially relevant processes and pathways were primarily related to inflammation, metabolism, and cardiovascular issues. In conclusion, five hub cross-talk genes, i.e., CCL5, FCGR3B, MMP-9, SAA1, and SELL, could be involved in the interplay between periodontitis and CKF, whereby primarily inflammation, metabolic, and vascular issues appear to be of relevance.

Classification of Exon and Intron Regions on DNA Sequences with Hybrid Use of SBERT and ANFIS Approaches

DNA is the part of the genome that contains enormous amounts of information related to life. Amino acids are formed by coding three nucleotides in this genome part, and the encoded amino acids are called codes in DNA. The frequency of the triple nucleotide in the DNA sequence allows for the evaluation of protein-coding (exon) and non-protein-coding (intron) regions. Distinguishing these regions enables the analysis of vital functions related to life. This study provides the classification of exon and intron regions for BCR-ABL and MEFV genes obtained from NCBI and Ensemble datasets, respectively. Then, existing DNA sequences are clustered using pretrained models in the scope of the SBERT approach. In the clustering process, K-Means and Agglomerative Clustering approaches are used consecutively. The frequency of repetition of codes is calculated with a representative sample selected from each cluster. The matrix is created using the frequencies of 64 different codons that constitute genetic code. This matrix is given as input to the ANFIS structure. The %88.88 accuracy rate is obtained with the ANFIS approach to classify exon and intron DNA sequences. As a result of this study, a successful result was produced independently of DNA length.

Ingenuity pathway analysis of α-synuclein predicts potential signaling pathways, network molecules, biological functions, and its role in neurological diseases.

Despite the knowledge that mutation, multiplication, and anomalous function of α-synuclein cause progressive transformation of α-synuclein monomers into toxic amyloid fibrils in neurodegenerative diseases, the understanding of canonical signaling, interaction network molecules, biological functions, and role of α-synuclein remains ambiguous. The evolution of artificial intelligence and Bioinformatics tools have enabled us to analyze a vast pool of data to draw meaningful conclusions about the events occurring in complex biological systems. We have taken the advantage of such a Bioinformatics tool, ingenuity pathway analysis (IPA) to decipher the signaling pathways, interactome, biological functions, and role of α-synuclein. IPA of the α-synuclein NCBI gene dataset revealed neuroinflammation, Huntington's disease, TREM1, phagosome maturation, and sirtuin signaling as the key canonical signaling pathways. IPA further revealed Parkinson's disease (PD), sumoylation, and SNARE signaling pathways specific to the toxicity of α-synuclein. A frequency distribution analysis of α-synuclein-associated genes from the NCBI dataset that appeared in the predicted canonical pathways revealed that NFKB1 was the most populated gene across the predicted pathways followed by FOS, PRKCD, TNF, GSK3B, CDC42, IL6, MTOR, PLCB1, and IL1B. Overlapping of the predicted top-five canonical signaling pathways and the α-synuclein NCBI gene dataset divulged that neuroinflammation signaling was the most overlapped pathway, while NFKB1, TNF, and CASP1 were the most shared molecules among the pathways. The major diseases associated with α-synuclein were predicted to be neurological diseases, organismal injury and abnormalities, skeletal and muscular disorders, psychological disorders, and hereditary disorders. The molecule activity predictor (MAP) analysis of the principal interaction network of α-synuclein gene SNCA revealed that SNCA directly interacts with APP, CLU, and NEDD4, whereas it indirectly communicates with CALCA and SOD1. Besides, IPA also predicted amyloid plaque forming APP, cytokines/inflammatory mediators IL1B, TNF, MIF, PTGS2, TP53, and CCL2, and kinases of MAPK family Mek, ERK, and P38 MAPK as the top upstream regulators of α-synuclein signaling cascades. Taken together, the first IPA analysis of α-synuclein predicted PD as the key toxicity pathway, neurodegeneration as the major pathological outcome, and inflammatory mediators as the critical interacting partners of α-synuclein.

GenBank 2023 update.

GenBank® (https://www.ncbi.nlm.nih.gov/genbank/) is a comprehensive, public database that contains 19.6 trillion base pairs from over 2.9 billion nucleotide sequences for 504 000 formally described species. Daily data exchange with the European Nucleotide Archive (ENA) and the DNA Data Bank of Japan (DDBJ) ensures worldwide coverage. Recent updates include resources for data from the SARS-CoV-2 virus, NCBI Datasets, BLAST ClusteredNR, the Submission Portal, table2asn, a Foreign Contamination Screening tooland BioSample.

DeePROG: Deep Attention-Based Model for Diseased Gene Prognosis by Fusing Multi-Omics Data.

An in-depth exploration of gene prognosis using different methodologies aids in understanding various biological regulations of genes in disease pathobiology and molecular functions. Interpreting gene functions at biological and molecular levels remains a daunting yet crucial task in domains such as drug design, personalized medicine, and next-generation diagnostics. Recent advancements in omics technologies have produced diverse heterogeneous genomic datasets like micro-array gene expression, miRNA expression, DNA sequence, 3D structures, which are significant resources for understanding the gene functions. In this paper, we propose a novel self-attention based deep multi-modal model, named DeePROG, for the prognosis of disease affected genes based on heterogeneous omics data. We use three NCBI datasets covering three modalities, namely gene expression profile, the underlying DNA sequence, and the 3D protein structures. To extract useful features from each modality, we develop several context-specific deep learning models. Besides, we develop three attention-based deep bi-modal architectures along with DeePROG to leverage the prognosis of the underlying biomedical data. We assess the performance of the models' in terms of computational assessment of function annotation (CAFA2) metrics. Moreover, we analyze the results in terms of receiver operating characteristics (ROC) curve in high-class imbalance data setting and perform statistical significance tests in terms of Welch's t-test. Experiment results show that DeePROG significantly outperforms baseline models across in terms of performance metrics. The source code and all preprocessed datasets used in this study are available at https://github.com/duttaprat/DeePROG.

The Utility of Small Fishes for the Genetic Study of Human Age-Related Disorders.

Animal models have been used to model human diseases, and among them, small fishes have been highlighted for their usefulness in various ways, such as the low cost of maintenance, ease of genetic modification, small size for easy handling, and strength in imaging studies due to their relative transparency. Recently, the use of turquoise killifish, Nothobranchius furzeri, which is known to exhibit various aging phenotypes in a short period, has attracted attention in research on aging and age-related diseases. However, when using animal models, it is important to keep their genetic background and interspecies differences in mind for translating them into human diseases. In this article, we obtained the gene symbols of protein-coding genes of turquoise killifish, medaka, zebrafish, and humans from NCBI datasets and extracted common shared genes among four species to explore the potential of interspecies translational research and to apply small fish models for human age-related disorders. Common shared protein-coding genes were analyzed with the Reactome Pathway Database to determine the coverage of these genes in each pathway in humans. We applied common shared genes to the Orphanet database to establish a list of human diseases that contain common shared genes among the four species. As examples, the senescence-related pathways and some pathways of human age-related diseases, such as Alzheimer’s disease, Parkinson’s disease, frontotemporal dementia, nonalcoholic fatty liver disease, progeria, hepatocellular carcinoma, and renal cell carcinoma, were extracted from the curated pathway and disease list to discuss the further utility of fish models for human age-related disorders.

Hybrid CNN-LSTM and modified wild horse herd Model-based prediction of genome sequences for genetic disorders

Structural genomic rearrangements are a major source of genetic disorders. The main objective of the proposed work is the detection of changes or mutations that occur in a single gene of the Deoxyribonucleic acid (DNA) sequence in the human genome. More specifically, autosomal dominant inheritance from either parent causes Monogenetic or Single-gene disorders. The proposed work of prediction of monogenetic disorders uses two different approaches. The first approach is the clustering method that uses the Microsoft sequence clustering algorithm and Needleman–Wunsch algorithm for sequence alignment. The second one is the formulation of the hybrid Convolution Neural Network (CNN) and Long short-term memory (LSTM) architecture for accurate genetic disorder prediction. The modified wild horse herd optimization (MWHHO) technique is used to enhance the weight function of the hybrid CNN-LSTM architecture. The MWHHO algorithm is formed by integrating the operations of horse herd optimization and the wild horse algorithm. Experimental findings employing the NCBI dataset demonstrate that the proposed approach has a high-performance accuracy when compared to the previous technique in the prediction of monogenetic disorders such as Marfan syndrome, Prader-Willi syndrome, and Angelman syndromes. The proposed CNN-LSTM-based MWHHO algorithm is compared to several baseline techniques in terms of AUC and error rate, including COBRA, Deep Learning, Ensemble methodology, WHISTLE, Network-based classification, LVQ-CGA, and DNN. When evaluated with the F-Score, the proposed model obtains a value of 94.83%, 90.69%, and 91.53% for the NCBI SARS-COV-2, ENCPP, and UBE3A datasets respectively. In summary, the proposed model offers improved accuracies of 98.25%, 97.89%, and 96.53% for the SARC-CoV-2, ENCPP, and UBE3A datasets.

Comparative Analysis of Machine Learning Methods for Breast Cancer Classification in Genetic Sequences.

Breast cancer is the leading cancer in women, which accounts for millions of deaths worldwide. Early and accurate detection, prognosis, cure, and prevention of breast cancer is a major challenge to society. Hence, a precise and reliable system is vital for the classification of cancerous sequences. Machine learning classifiers contribute much to the process of early prediction and diagnosis of cancer. In this paper, a comparative study of four machine learning classifiers such as random forest, decision tree, AdaBoost, and gradient boosting is implemented for the classification of a benign and malignant tumor. To derive the most efficient machine learning model, NCBI datasets are utilized. Performance evaluation is conducted, and all four classifiers are compared based on the results. The aim of the work is to derive the most efficient machine-learning model for the diagnosis of breast cancer. It was observed that gradient boosting outperformed all other models and achieved a classification accuracy of 95.82%.

The Genetic Association Identified Between Intervertebral Disc Degeneration and Associated Risk Factors Based on a Systems Biology Approach

Genetic cross-over study of intervertebral disc degeneration (IDD) and its associated risk factors. The purpose is to identify molecular biomarkers that may be involved in the IDD process and to provide effective recommendations in combination with drug analysis via systems biology methods. The pathogenesis and genetic links of IDD are still unclear. Related research is mainly based on a single data set or gene, and the impact of related risk factors on IDD is often ignored. Identifying disease-associated biomarkers and therapeutic targets through a systems biology approach that Integrative network-based gene and multi-omics analysis. In this study, the differential expression of genes was analyzed through NCBI datasets, followed by enrichment analysis. The central protein was identified through the establishment of protein-protein interaction (PPI) network. Key genes were screened out by VENN diagram and reasonable suggestions were put forward based on gene-chemical drug analysis. The IDD database analysis revealed 669 differentially expressed genes (DEGs) which were 22, 26, 168, 5, 38, 36, and 16 common DEGs with AG, SM, DEP, NAD, CED, OB, and HFD, respectively. GO and KEGG enrichment analysis may reveal the pathway by which these DEGs were involved. PPI network identified 10 central proteins including CCNB1, RETN, HMMR, BUB1, MPO, OIP5, HP, KIF11, BUB1B, and CDC25A. Three key genes BUB1, BUB1B, and CCNB1 were screened out and their expression might be related to the pathogenesis of IDD. According to the three chemical Dexamethasone, Nicotine, and Resvera-trol obtained from the analysis of genes-chemical drugs, reasonable treatment suggestions were put forward. Genetic association between IDD and risk factors in the general population was revealed by association network. Important gene-related molecular pathways and chemical drugs closely related to IDD have been found. Further study can provide guidance for the treatment and prognosis of IDD.Level of Evidence: N/A.

GenBank

GenBank® (https://www.ncbi.nlm.nih.gov/genbank/) is a comprehensive, public database that contains 15.3 trillion base pairs from over 2.5 billion nucleotide sequences for 504 000 formally described species. Recent updates include resources for data from the SARS-CoV-2 virus, including a SARS-CoV-2 landing page, NCBI Datasets, NCBI Virus and the Submission Portal. We also discuss upcoming changes to GI identifiers, a new data management interface for BioProject, and advice for providing contextual metadata in submissions.

Efficient classification of sugarcane genomes

A Phylogenetic tree construction to know to the relationship of the ancestral association of species. The genome sequences, outlining the transmission of functional and genetic classification. Analysing the quantitative conduct of phylogenetics in the conservation of biodiversity and the successful heuristics of obtaining an accurate distribution of trees plays a predominant role. The study to know higher accuracy from efficient algorithm to deducing phylogenetic relationship among Sugarcane (Saccharum) species. A sample of 431 Saccharum genome sequences was drawn from NCBI dataset. Efficient algorithms like Maximum Likelihood Estimation (MLE) method and Unweighted Pair Group Method with Arithmetic Mean (UPGMA) method were considered to construct the phylogenetic tree. The maximum likelihood with Tamura Nei model, Kimura 2-parameter model and achieves the highest precision, while MLE with Maximum likelihood with Jukes-Cantor model achieves the least. The computational biology of statistically results is justifiable and compares the functional relationship between various models in which error percentage has been reduced. The same algorithms perform on individual species under different models such as maximum likelihood with Kimura 2-parameter model and Tamura Nei model more efficient than others to differentiate the species genomic sequences and group them to correct taxon.

Insights into association between urolithiasis and prostate cancer

Background and objective: Urolithiasis or renal stones form a major urinary tract infection with formation of calcifications in the bladder and uterus. With the lifestyle diseases burgeoning, the renal stones have become a common cause with an approximate 1 in 1000 people affected all over the world with a risk ratio of 3 : 1 in men and women. On the other hand, prostate or genitourinary cancers are well documented to be associated with urolithiasis. Methods: A gene list was prepared from the published NCBI dataset, comprising all the genes related to urolithiasis primarily with mutations (both pathogenic and likely pathogenic Single nucleotide polymorphisms, SNP's) for every particular gene screened later from the published datasets. To see the interactions among all the potential genetic factors, PPI based tools were used and an interaction map was prepared. For the characterization of mutations, we have used gnomAD for verifying all the SNPs whether they are synonymous or nonsynonymous mutations. Results: We outlined the list of genes and discussed the systems bioinformatics integrated approach associated with it. Conclusion: We found a large number of genes common to them and their association is subtly known for immunomodulatory response.